Datasets:

mole-code

/

langchain-data

like 0

import ast import re from typing import ( Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union, ) from langchain_core.exceptions import OutputParserException from langchain_core.messages import BaseMessage from langchain_core.output_parsers.transform import BaseTransformOutputParser from langchain_core.runnables import RunnableConfig from langchain_core.tools import BaseTool from typing_extensions import TypedDict THOUGHT_PATTERN = r"Thought: ([^\n]*)" ACTION_PATTERN = r"\n*(\d+)\. (\w+)\((.*)\)(\s*#\w+\n)?" # $1 or ${1} -> 1 ID_PATTERN = r"\$\{?(\d+)\}?" END_OF_PLAN = "<END_OF_PLAN>" ### Helper functions def _ast_parse(arg: str) -> Any: try: return ast.literal_eval(arg) except: # noqa return arg def _parse_llm_compiler_action_args(args: str, tool: Union[str, BaseTool]) -> list[Any]: """Parse arguments from a string.""" if args == "": return () if isinstance(tool, str): return () extracted_args = {} tool_key = None prev_idx = None for key in tool.args.keys(): # Split if present if f"{key}=" in args: idx = args.index(f"{key}=") if prev_idx is not None: extracted_args[tool_key] = _ast_parse( args[prev_idx:idx].strip().rstrip(",") ) args = args.split(f"{key}=", 1)[1] tool_key = key prev_idx = 0 if prev_idx is not None: extracted_args[tool_key] = _ast_parse( args[prev_idx:].strip().rstrip(",").rstrip(")") ) return extracted_args def default_dependency_rule(idx, args: str): matches = re.findall(ID_PATTERN, args) numbers = [int(match) for match in matches] return idx in numbers def _get_dependencies_from_graph( idx: int, tool_name: str, args: Dict[str, Any] ) -> dict[str, list[str]]: """Get dependencies from a graph.""" if tool_name == "join": return list(range(1, idx)) return [i for i in range(1, idx) if default_dependency_rule(i, str(args))] class Task(TypedDict): idx: int tool: BaseTool args: list dependencies: Dict[str, list] thought: Optional[str] def instantiate_task( tools: Sequence[BaseTool], idx: int, tool_name: str, args: Union[str, Any], thought: Optional[str] = None, ) -> Task: if tool_name == "join": tool = "join" else: try: tool = tools[[tool.name for tool in tools].index(tool_name)] except ValueError as e: raise OutputParserException(f"Tool {tool_name} not found.") from e tool_args = _parse_llm_compiler_action_args(args, tool) dependencies = _get_dependencies_from_graph(idx, tool_name, tool_args) return Task( idx=idx, tool=tool, args=tool_args, dependencies=dependencies, thought=thought, ) class LLMCompilerPlanParser(BaseTransformOutputParser[dict], extra="allow"): """Planning output parser.""" tools: List[BaseTool] def _transform(self, input: Iterator[Union[str, BaseMessage]]) -> Iterator[Task]: texts = [] # TODO: Cleanup tuple state tracking here. thought = None for chunk in input: # Assume input is str. TODO: support vision/other formats text = chunk if isinstance(chunk, str) else str(chunk.content) for task, thought in self.ingest_token(text, texts, thought): yield task # Final possible task if texts: task, _ = self._parse_task("".join(texts), thought) if task: yield task def parse(self, text: str) -> List[Task]: return list(self._transform([text])) def stream( self, input: str | BaseMessage, config: RunnableConfig | None = None, **kwargs: Any | None, ) -> Iterator[Task]: yield from self.transform([input], config, **kwargs) def ingest_token( self, token: str, buffer: List[str], thought: Optional[str] ) -> Iterator[Tuple[Optional[Task], str]]: buffer.append(token) if "\n" in token: buffer_ = "".join(buffer).split("\n") suffix = buffer_[-1] for line in buffer_[:-1]: task, thought = self._parse_task(line, thought) if task: yield task, thought buffer.clear() buffer.append(suffix) def _parse_task(self, line: str, thought: Optional[str] = None): task = None if match := re.match(THOUGHT_PATTERN, line): # Optionally, action can be preceded by a thought thought = match.group(1) elif match := re.match(ACTION_PATTERN, line): # if action is parsed, return the task, and clear the buffer idx, tool_name, args, _ = match.groups() idx = int(idx) task = instantiate_task( tools=self.tools, idx=idx, tool_name=tool_name, args=args, thought=thought, ) thought = None # Else it is just dropped return task, thought

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import ast import re from typing import ( Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union, ) from langchain_core.exceptions import OutputParserException from langchain_core.messages import BaseMessage from langchain_core.output_parsers.transform import BaseTransformOutputParser from langchain_core.runnables import RunnableConfig from langchain_core.tools import BaseTool from typing_extensions import TypedDict THOUGHT_PATTERN = r"Thought: ([^\n]*)" ACTION_PATTERN = r"\n*(\d+)\. (\w+)\((.*)\)(\s*#\w+\n)?" # $1 or ${1} -> 1 ID_PATTERN = r"\$\{?(\d+)\}?" END_OF_PLAN = "<END_OF_PLAN>" ### Helper functions def _ast_parse(arg: str) -> Any: try: return ast.literal_eval(arg) except: # noqa return arg def _parse_llm_compiler_action_args(args: str, tool: Union[str, BaseTool]) -> list[Any]: """Parse arguments from a string.""" if args == "": return () if isinstance(tool, str): return () extracted_args = {} tool_key = None prev_idx = None for key in tool.args.keys(): # Split if present if f"{key}=" in args: idx = args.index(f"{key}=") if prev_idx is not None: extracted_args[tool_key] = _ast_parse( args[prev_idx:idx].strip().rstrip(",") ) args = args.split(f"{key}=", 1)[1] tool_key = key prev_idx = 0 if prev_idx is not None: extracted_args[tool_key] = _ast_parse( args[prev_idx:].strip().rstrip(",").rstrip(")") ) return extracted_args def default_dependency_rule(idx, args: str): matches = re.findall(ID_PATTERN, args) numbers = [int(match) for match in matches] return idx in numbers def _get_dependencies_from_graph( idx: int, tool_name: str, args: Dict[str, Any] ) -> dict[str, list[str]]: """Get dependencies from a graph.""" if tool_name == "join": return list(range(1, idx)) return [i for i in range(1, idx) if default_dependency_rule(i, str(args))] class Task(TypedDict): idx: int tool: BaseTool args: list dependencies: Dict[str, list] thought: Optional[str] def instantiate_task( tools: Sequence[BaseTool], idx: int, tool_name: str, args: Union[str, Any], thought: Optional[str] = None, ) -> Task: if tool_name == "join": tool = "join" else: try: tool = tools[[tool.name for tool in tools].index(tool_name)] except ValueError as e: raise OutputParserException(f"Tool {tool_name} not found.") from e tool_args = _parse_llm_compiler_action_args(args, tool) dependencies = _get_dependencies_from_graph(idx, tool_name, tool_args) return Task( idx=idx, tool=tool, args=tool_args, dependencies=dependencies, thought=thought, ) class LLMCompilerPlanParser(BaseTransformOutputParser[dict], extra="allow"): """Planning output parser.""" tools: List[BaseTool] def _transform(self, input: Iterator[Union[str, BaseMessage]]) -> Iterator[Task]: texts = [] # TODO: Cleanup tuple state tracking here. thought = None for chunk in input: # Assume input is str. TODO: support vision/other formats text = chunk if isinstance(chunk, str) else str(chunk.content) for task, thought in self.ingest_token(text, texts, thought): yield task # Final possible task if texts: task, _ = self._parse_task("".join(texts), thought) if task: yield task def parse(self, text: str) -> List[Task]: return list(self._transform([text])) def stream( self, input: str | BaseMessage, config: RunnableConfig | None = None, **kwargs: Any | None, ) -> Iterator[Task]: yield from self.transform([input], config, **kwargs) def ingest_token( self, token: str, buffer: List[str], thought: Optional[str] ) -> Iterator[Tuple[Optional[Task], str]]: buffer.append(token) if "\n" in token: buffer_ = "".join(buffer).split("\n") suffix = buffer_[-1] for line in buffer_[:-1]: task, thought = self._parse_task(line, thought) if task: yield task, thought buffer.clear() buffer.append(suffix) def _parse_task(self, line: str, thought: Optional[str] = None): task = None if match := re.match(THOUGHT_PATTERN, line): # Optionally, action can be preceded by a thought thought = match.group(1) elif match := re.match(ACTION_PATTERN, line): # if action is parsed, return the task, and clear the buffer idx, tool_name, args, _ = match.groups() idx = int(idx) task = instantiate_task( tools=self.tools, idx=idx, tool_name=tool_name, args=args, thought=thought, ) thought = None # Else it is just dropped return task, thought

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import requests from typing import Any, Dict, Optional from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT from langchain.chains import APIChain from langchain.prompts import BasePromptTemplate from langchain.base_language import BaseLanguageModel from langchain.chains.llm import LLMChain from .requests_l402 import RequestsL402Wrapper from .requests_l402 import ResponseTextWrapper from lightning import LightningNode class L402APIChain(APIChain): requests_wrapper: Any @classmethod def from_llm_and_api_docs( cls, llm: BaseLanguageModel, api_docs: str, headers: Optional[dict] = None, api_url_prompt: BasePromptTemplate = API_URL_PROMPT, api_response_prompt: BasePromptTemplate = API_RESPONSE_PROMPT, lightning_node = None, **kwargs: Any, ) -> APIChain: """Load chain from just an LLM and the api docs.""" requests_L402 = RequestsL402Wrapper(lightning_node, requests) lang_chain_request_L402 = ResponseTextWrapper( requests_wrapper=requests_L402, ) get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt) get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt) return cls( api_request_chain=get_request_chain, api_answer_chain=get_answer_chain, requests_wrapper=lang_chain_request_L402, api_docs=api_docs, **kwargs, )

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"""Functionality for loading chains.""" import json from pathlib import Path from typing import Any, Union import yaml from langchain.chains.api.base import APIChain from langchain.chains.base import Chain from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain from langchain.chains.combine_documents.refine import RefineDocumentsChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.hyde.base import HypotheticalDocumentEmbedder from langchain.chains.llm import LLMChain from langchain.chains.llm_bash.base import LLMBashChain from langchain.chains.llm_checker.base import LLMCheckerChain from langchain.chains.llm_math.base import LLMMathChain from langchain.chains.llm_requests import LLMRequestsChain from langchain.chains.pal.base import PALChain from langchain.chains.qa_with_sources.base import QAWithSourcesChain from langchain.chains.qa_with_sources.vector_db import VectorDBQAWithSourcesChain from langchain.chains.sql_database.base import SQLDatabaseChain from langchain.chains.vector_db_qa.base import VectorDBQA from langchain.llms.loading import load_llm, load_llm_from_config from langchain.prompts.loading import load_prompt, load_prompt_from_config from langchain.utilities.loading import try_load_from_hub URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/chains/" def _load_llm_chain(config: dict, **kwargs: Any) -> LLMChain: """Load LLM chain from config dict.""" if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) else: raise ValueError("One of `prompt` or `prompt_path` must be present.") return LLMChain(llm=llm, prompt=prompt, **config) def _load_hyde_chain(config: dict, **kwargs: Any) -> HypotheticalDocumentEmbedder: """Load hypothetical document embedder chain from config dict.""" if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.") if "embeddings" in kwargs: embeddings = kwargs.pop("embeddings") else: raise ValueError("`embeddings` must be present.") return HypotheticalDocumentEmbedder( llm_chain=llm_chain, base_embeddings=embeddings, **config ) def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") if not isinstance(llm_chain, LLMChain): raise ValueError(f"Expected LLMChain, got {llm_chain}") if "document_prompt" in config: prompt_config = config.pop("document_prompt") document_prompt = load_prompt_from_config(prompt_config) elif "document_prompt_path" in config: document_prompt = load_prompt(config.pop("document_prompt_path")) else: raise ValueError( "One of `document_prompt` or `document_prompt_path` must be present." ) return StuffDocumentsChain( llm_chain=llm_chain, document_prompt=document_prompt, **config ) def _load_map_reduce_documents_chain( config: dict, **kwargs: Any ) -> MapReduceDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") if not isinstance(llm_chain, LLMChain): raise ValueError(f"Expected LLMChain, got {llm_chain}") if "combine_document_chain" in config: combine_document_chain_config = config.pop("combine_document_chain") combine_document_chain = load_chain_from_config(combine_document_chain_config) elif "combine_document_chain_path" in config: combine_document_chain = load_chain(config.pop("combine_document_chain_path")) else: raise ValueError( "One of `combine_document_chain` or " "`combine_document_chain_path` must be present." ) if "collapse_document_chain" in config: collapse_document_chain_config = config.pop("collapse_document_chain") if collapse_document_chain_config is None: collapse_document_chain = None else: collapse_document_chain = load_chain_from_config( collapse_document_chain_config ) elif "collapse_document_chain_path" in config: collapse_document_chain = load_chain(config.pop("collapse_document_chain_path")) return MapReduceDocumentsChain( llm_chain=llm_chain, combine_document_chain=combine_document_chain, collapse_document_chain=collapse_document_chain, **config, ) def _load_llm_bash_chain(config: dict, **kwargs: Any) -> LLMBashChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) return LLMBashChain(llm=llm, prompt=prompt, **config) def _load_llm_checker_chain(config: dict, **kwargs: Any) -> LLMCheckerChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "create_draft_answer_prompt" in config: create_draft_answer_prompt_config = config.pop("create_draft_answer_prompt") create_draft_answer_prompt = load_prompt_from_config( create_draft_answer_prompt_config ) elif "create_draft_answer_prompt_path" in config: create_draft_answer_prompt = load_prompt( config.pop("create_draft_answer_prompt_path") ) if "list_assertions_prompt" in config: list_assertions_prompt_config = config.pop("list_assertions_prompt") list_assertions_prompt = load_prompt_from_config(list_assertions_prompt_config) elif "list_assertions_prompt_path" in config: list_assertions_prompt = load_prompt(config.pop("list_assertions_prompt_path")) if "check_assertions_prompt" in config: check_assertions_prompt_config = config.pop("check_assertions_prompt") check_assertions_prompt = load_prompt_from_config( check_assertions_prompt_config ) elif "check_assertions_prompt_path" in config: check_assertions_prompt = load_prompt( config.pop("check_assertions_prompt_path") ) if "revised_answer_prompt" in config: revised_answer_prompt_config = config.pop("revised_answer_prompt") revised_answer_prompt = load_prompt_from_config(revised_answer_prompt_config) elif "revised_answer_prompt_path" in config: revised_answer_prompt = load_prompt(config.pop("revised_answer_prompt_path")) return LLMCheckerChain( llm=llm, create_draft_answer_prompt=create_draft_answer_prompt, list_assertions_prompt=list_assertions_prompt, check_assertions_prompt=check_assertions_prompt, revised_answer_prompt=revised_answer_prompt, **config, ) def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) return LLMMathChain(llm=llm, prompt=prompt, **config) def _load_map_rerank_documents_chain( config: dict, **kwargs: Any ) -> MapRerankDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") return MapRerankDocumentsChain(llm_chain=llm_chain, **config) def _load_pal_chain(config: dict, **kwargs: Any) -> PALChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) else: raise ValueError("One of `prompt` or `prompt_path` must be present.") return PALChain(llm=llm, prompt=prompt, **config) def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain: if "initial_llm_chain" in config: initial_llm_chain_config = config.pop("initial_llm_chain") initial_llm_chain = load_chain_from_config(initial_llm_chain_config) elif "initial_llm_chain_path" in config: initial_llm_chain = load_chain(config.pop("initial_llm_chain_path")) else: raise ValueError( "One of `initial_llm_chain` or `initial_llm_chain_config` must be present." ) if "refine_llm_chain" in config: refine_llm_chain_config = config.pop("refine_llm_chain") refine_llm_chain = load_chain_from_config(refine_llm_chain_config) elif "refine_llm_chain_path" in config: refine_llm_chain = load_chain(config.pop("refine_llm_chain_path")) else: raise ValueError( "One of `refine_llm_chain` or `refine_llm_chain_config` must be present." ) if "document_prompt" in config: prompt_config = config.pop("document_prompt") document_prompt = load_prompt_from_config(prompt_config) elif "document_prompt_path" in config: document_prompt = load_prompt(config.pop("document_prompt_path")) return RefineDocumentsChain( initial_llm_chain=initial_llm_chain, refine_llm_chain=refine_llm_chain, document_prompt=document_prompt, **config, ) def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesChain: if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config) def _load_sql_database_chain(config: dict, **kwargs: Any) -> SQLDatabaseChain: if "database" in kwargs: database = kwargs.pop("database") else: raise ValueError("`database` must be present.") if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) return SQLDatabaseChain(database=database, llm=llm, prompt=prompt, **config) def _load_vector_db_qa_with_sources_chain( config: dict, **kwargs: Any ) -> VectorDBQAWithSourcesChain: if "vectorstore" in kwargs: vectorstore = kwargs.pop("vectorstore") else: raise ValueError("`vectorstore` must be present.") if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return VectorDBQAWithSourcesChain( combine_documents_chain=combine_documents_chain, vectorstore=vectorstore, **config, ) def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA: if "vectorstore" in kwargs: vectorstore = kwargs.pop("vectorstore") else: raise ValueError("`vectorstore` must be present.") if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return VectorDBQA( combine_documents_chain=combine_documents_chain, vectorstore=vectorstore, **config, ) def _load_api_chain(config: dict, **kwargs: Any) -> APIChain: if "api_request_chain" in config: api_request_chain_config = config.pop("api_request_chain") api_request_chain = load_chain_from_config(api_request_chain_config) elif "api_request_chain_path" in config: api_request_chain = load_chain(config.pop("api_request_chain_path")) else: raise ValueError( "One of `api_request_chain` or `api_request_chain_path` must be present." ) if "api_answer_chain" in config: api_answer_chain_config = config.pop("api_answer_chain") api_answer_chain = load_chain_from_config(api_answer_chain_config) elif "api_answer_chain_path" in config: api_answer_chain = load_chain(config.pop("api_answer_chain_path")) else: raise ValueError( "One of `api_answer_chain` or `api_answer_chain_path` must be present." ) if "requests_wrapper" in kwargs: requests_wrapper = kwargs.pop("requests_wrapper") else: raise ValueError("`requests_wrapper` must be present.") return APIChain( api_request_chain=api_request_chain, api_answer_chain=api_answer_chain, requests_wrapper=requests_wrapper, **config, ) def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.") if "requests_wrapper" in kwargs: requests_wrapper = kwargs.pop("requests_wrapper") return LLMRequestsChain( llm_chain=llm_chain, requests_wrapper=requests_wrapper, **config ) else: return LLMRequestsChain(llm_chain=llm_chain, **config) type_to_loader_dict = { "api_chain": _load_api_chain, "hyde_chain": _load_hyde_chain, "llm_chain": _load_llm_chain, "llm_bash_chain": _load_llm_bash_chain, "llm_checker_chain": _load_llm_checker_chain, "llm_math_chain": _load_llm_math_chain, "llm_requests_chain": _load_llm_requests_chain, "pal_chain": _load_pal_chain, "qa_with_sources_chain": _load_qa_with_sources_chain, "stuff_documents_chain": _load_stuff_documents_chain, "map_reduce_documents_chain": _load_map_reduce_documents_chain, "map_rerank_documents_chain": _load_map_rerank_documents_chain, "refine_documents_chain": _load_refine_documents_chain, "sql_database_chain": _load_sql_database_chain, "vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain, "vector_db_qa": _load_vector_db_qa, } def load_chain_from_config(config: dict, **kwargs: Any) -> Chain: """Load chain from Config Dict.""" if "_type" not in config: raise ValueError("Must specify a chain Type in config") config_type = config.pop("_type") if config_type not in type_to_loader_dict: raise ValueError(f"Loading {config_type} chain not supported") chain_loader = type_to_loader_dict[config_type] return chain_loader(config, **kwargs) def load_chain(path: Union[str, Path], **kwargs: Any) -> Chain: """Unified method for loading a chain from LangChainHub or local fs.""" if hub_result := try_load_from_hub( path, _load_chain_from_file, "chains", {"json", "yaml"}, **kwargs ): return hub_result else: return _load_chain_from_file(path, **kwargs) def _load_chain_from_file(file: Union[str, Path], **kwargs: Any) -> Chain: """Load chain from file.""" # Convert file to Path object. if isinstance(file, str): file_path = Path(file) else: file_path = file # Load from either json or yaml. if file_path.suffix == ".json": with open(file_path) as f: config = json.load(f) elif file_path.suffix == ".yaml": with open(file_path, "r") as f: config = yaml.safe_load(f) else: raise ValueError("File type must be json or yaml") # Override default 'verbose' and 'memory' for the chain if "verbose" in kwargs: config["verbose"] = kwargs.pop("verbose") if "memory" in kwargs: config["memory"] = kwargs.pop("memory") # Load the chain from the config now. return load_chain_from_config(config, **kwargs)

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"""Functionality for loading chains.""" import json from pathlib import Path from typing import Any, Union import yaml from langchain.chains.api.base import APIChain from langchain.chains.base import Chain from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain from langchain.chains.combine_documents.refine import RefineDocumentsChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.hyde.base import HypotheticalDocumentEmbedder from langchain.chains.llm import LLMChain from langchain.chains.llm_bash.base import LLMBashChain from langchain.chains.llm_checker.base import LLMCheckerChain from langchain.chains.llm_math.base import LLMMathChain from langchain.chains.llm_requests import LLMRequestsChain from langchain.chains.pal.base import PALChain from langchain.chains.qa_with_sources.base import QAWithSourcesChain from langchain.chains.qa_with_sources.vector_db import VectorDBQAWithSourcesChain from langchain.chains.sql_database.base import SQLDatabaseChain from langchain.chains.vector_db_qa.base import VectorDBQA from langchain.llms.loading import load_llm, load_llm_from_config from langchain.prompts.loading import load_prompt, load_prompt_from_config from langchain.utilities.loading import try_load_from_hub URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/chains/" def _load_llm_chain(config: dict, **kwargs: Any) -> LLMChain: """Load LLM chain from config dict.""" if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) else: raise ValueError("One of `prompt` or `prompt_path` must be present.") return LLMChain(llm=llm, prompt=prompt, **config) def _load_hyde_chain(config: dict, **kwargs: Any) -> HypotheticalDocumentEmbedder: """Load hypothetical document embedder chain from config dict.""" if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.") if "embeddings" in kwargs: embeddings = kwargs.pop("embeddings") else: raise ValueError("`embeddings` must be present.") return HypotheticalDocumentEmbedder( llm_chain=llm_chain, base_embeddings=embeddings, **config ) def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") if not isinstance(llm_chain, LLMChain): raise ValueError(f"Expected LLMChain, got {llm_chain}") if "document_prompt" in config: prompt_config = config.pop("document_prompt") document_prompt = load_prompt_from_config(prompt_config) elif "document_prompt_path" in config: document_prompt = load_prompt(config.pop("document_prompt_path")) else: raise ValueError( "One of `document_prompt` or `document_prompt_path` must be present." ) return StuffDocumentsChain( llm_chain=llm_chain, document_prompt=document_prompt, **config ) def _load_map_reduce_documents_chain( config: dict, **kwargs: Any ) -> MapReduceDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") if not isinstance(llm_chain, LLMChain): raise ValueError(f"Expected LLMChain, got {llm_chain}") if "combine_document_chain" in config: combine_document_chain_config = config.pop("combine_document_chain") combine_document_chain = load_chain_from_config(combine_document_chain_config) elif "combine_document_chain_path" in config: combine_document_chain = load_chain(config.pop("combine_document_chain_path")) else: raise ValueError( "One of `combine_document_chain` or " "`combine_document_chain_path` must be present." ) if "collapse_document_chain" in config: collapse_document_chain_config = config.pop("collapse_document_chain") if collapse_document_chain_config is None: collapse_document_chain = None else: collapse_document_chain = load_chain_from_config( collapse_document_chain_config ) elif "collapse_document_chain_path" in config: collapse_document_chain = load_chain(config.pop("collapse_document_chain_path")) return MapReduceDocumentsChain( llm_chain=llm_chain, combine_document_chain=combine_document_chain, collapse_document_chain=collapse_document_chain, **config, ) def _load_llm_bash_chain(config: dict, **kwargs: Any) -> LLMBashChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) return LLMBashChain(llm=llm, prompt=prompt, **config) def _load_llm_checker_chain(config: dict, **kwargs: Any) -> LLMCheckerChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "create_draft_answer_prompt" in config: create_draft_answer_prompt_config = config.pop("create_draft_answer_prompt") create_draft_answer_prompt = load_prompt_from_config( create_draft_answer_prompt_config ) elif "create_draft_answer_prompt_path" in config: create_draft_answer_prompt = load_prompt( config.pop("create_draft_answer_prompt_path") ) if "list_assertions_prompt" in config: list_assertions_prompt_config = config.pop("list_assertions_prompt") list_assertions_prompt = load_prompt_from_config(list_assertions_prompt_config) elif "list_assertions_prompt_path" in config: list_assertions_prompt = load_prompt(config.pop("list_assertions_prompt_path")) if "check_assertions_prompt" in config: check_assertions_prompt_config = config.pop("check_assertions_prompt") check_assertions_prompt = load_prompt_from_config( check_assertions_prompt_config ) elif "check_assertions_prompt_path" in config: check_assertions_prompt = load_prompt( config.pop("check_assertions_prompt_path") ) if "revised_answer_prompt" in config: revised_answer_prompt_config = config.pop("revised_answer_prompt") revised_answer_prompt = load_prompt_from_config(revised_answer_prompt_config) elif "revised_answer_prompt_path" in config: revised_answer_prompt = load_prompt(config.pop("revised_answer_prompt_path")) return LLMCheckerChain( llm=llm, create_draft_answer_prompt=create_draft_answer_prompt, list_assertions_prompt=list_assertions_prompt, check_assertions_prompt=check_assertions_prompt, revised_answer_prompt=revised_answer_prompt, **config, ) def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) return LLMMathChain(llm=llm, prompt=prompt, **config) def _load_map_rerank_documents_chain( config: dict, **kwargs: Any ) -> MapRerankDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") return MapRerankDocumentsChain(llm_chain=llm_chain, **config) def _load_pal_chain(config: dict, **kwargs: Any) -> PALChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) else: raise ValueError("One of `prompt` or `prompt_path` must be present.") return PALChain(llm=llm, prompt=prompt, **config) def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain: if "initial_llm_chain" in config: initial_llm_chain_config = config.pop("initial_llm_chain") initial_llm_chain = load_chain_from_config(initial_llm_chain_config) elif "initial_llm_chain_path" in config: initial_llm_chain = load_chain(config.pop("initial_llm_chain_path")) else: raise ValueError( "One of `initial_llm_chain` or `initial_llm_chain_config` must be present." ) if "refine_llm_chain" in config: refine_llm_chain_config = config.pop("refine_llm_chain") refine_llm_chain = load_chain_from_config(refine_llm_chain_config) elif "refine_llm_chain_path" in config: refine_llm_chain = load_chain(config.pop("refine_llm_chain_path")) else: raise ValueError( "One of `refine_llm_chain` or `refine_llm_chain_config` must be present." ) if "document_prompt" in config: prompt_config = config.pop("document_prompt") document_prompt = load_prompt_from_config(prompt_config) elif "document_prompt_path" in config: document_prompt = load_prompt(config.pop("document_prompt_path")) return RefineDocumentsChain( initial_llm_chain=initial_llm_chain, refine_llm_chain=refine_llm_chain, document_prompt=document_prompt, **config, ) def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesChain: if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config) def _load_sql_database_chain(config: dict, **kwargs: Any) -> SQLDatabaseChain: if "database" in kwargs: database = kwargs.pop("database") else: raise ValueError("`database` must be present.") if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) return SQLDatabaseChain(database=database, llm=llm, prompt=prompt, **config) def _load_vector_db_qa_with_sources_chain( config: dict, **kwargs: Any ) -> VectorDBQAWithSourcesChain: if "vectorstore" in kwargs: vectorstore = kwargs.pop("vectorstore") else: raise ValueError("`vectorstore` must be present.") if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return VectorDBQAWithSourcesChain( combine_documents_chain=combine_documents_chain, vectorstore=vectorstore, **config, ) def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA: if "vectorstore" in kwargs: vectorstore = kwargs.pop("vectorstore") else: raise ValueError("`vectorstore` must be present.") if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return VectorDBQA( combine_documents_chain=combine_documents_chain, vectorstore=vectorstore, **config, ) def _load_api_chain(config: dict, **kwargs: Any) -> APIChain: if "api_request_chain" in config: api_request_chain_config = config.pop("api_request_chain") api_request_chain = load_chain_from_config(api_request_chain_config) elif "api_request_chain_path" in config: api_request_chain = load_chain(config.pop("api_request_chain_path")) else: raise ValueError( "One of `api_request_chain` or `api_request_chain_path` must be present." ) if "api_answer_chain" in config: api_answer_chain_config = config.pop("api_answer_chain") api_answer_chain = load_chain_from_config(api_answer_chain_config) elif "api_answer_chain_path" in config: api_answer_chain = load_chain(config.pop("api_answer_chain_path")) else: raise ValueError( "One of `api_answer_chain` or `api_answer_chain_path` must be present." ) if "requests_wrapper" in kwargs: requests_wrapper = kwargs.pop("requests_wrapper") else: raise ValueError("`requests_wrapper` must be present.") return APIChain( api_request_chain=api_request_chain, api_answer_chain=api_answer_chain, requests_wrapper=requests_wrapper, **config, ) def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.") if "requests_wrapper" in kwargs: requests_wrapper = kwargs.pop("requests_wrapper") return LLMRequestsChain( llm_chain=llm_chain, requests_wrapper=requests_wrapper, **config ) else: return LLMRequestsChain(llm_chain=llm_chain, **config) type_to_loader_dict = { "api_chain": _load_api_chain, "hyde_chain": _load_hyde_chain, "llm_chain": _load_llm_chain, "llm_bash_chain": _load_llm_bash_chain, "llm_checker_chain": _load_llm_checker_chain, "llm_math_chain": _load_llm_math_chain, "llm_requests_chain": _load_llm_requests_chain, "pal_chain": _load_pal_chain, "qa_with_sources_chain": _load_qa_with_sources_chain, "stuff_documents_chain": _load_stuff_documents_chain, "map_reduce_documents_chain": _load_map_reduce_documents_chain, "map_rerank_documents_chain": _load_map_rerank_documents_chain, "refine_documents_chain": _load_refine_documents_chain, "sql_database_chain": _load_sql_database_chain, "vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain, "vector_db_qa": _load_vector_db_qa, } def load_chain_from_config(config: dict, **kwargs: Any) -> Chain: """Load chain from Config Dict.""" if "_type" not in config: raise ValueError("Must specify a chain Type in config") config_type = config.pop("_type") if config_type not in type_to_loader_dict: raise ValueError(f"Loading {config_type} chain not supported") chain_loader = type_to_loader_dict[config_type] return chain_loader(config, **kwargs) def load_chain(path: Union[str, Path], **kwargs: Any) -> Chain: """Unified method for loading a chain from LangChainHub or local fs.""" if hub_result := try_load_from_hub( path, _load_chain_from_file, "chains", {"json", "yaml"}, **kwargs ): return hub_result else: return _load_chain_from_file(path, **kwargs) def _load_chain_from_file(file: Union[str, Path], **kwargs: Any) -> Chain: """Load chain from file.""" # Convert file to Path object. if isinstance(file, str): file_path = Path(file) else: file_path = file # Load from either json or yaml. if file_path.suffix == ".json": with open(file_path) as f: config = json.load(f) elif file_path.suffix == ".yaml": with open(file_path, "r") as f: config = yaml.safe_load(f) else: raise ValueError("File type must be json or yaml") # Override default 'verbose' and 'memory' for the chain if "verbose" in kwargs: config["verbose"] = kwargs.pop("verbose") if "memory" in kwargs: config["memory"] = kwargs.pop("memory") # Load the chain from the config now. return load_chain_from_config(config, **kwargs)

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"""Functionality for loading chains.""" import json from pathlib import Path from typing import Any, Union import yaml from langchain.chains.api.base import APIChain from langchain.chains.base import Chain from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain from langchain.chains.combine_documents.refine import RefineDocumentsChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.hyde.base import HypotheticalDocumentEmbedder from langchain.chains.llm import LLMChain from langchain.chains.llm_bash.base import LLMBashChain from langchain.chains.llm_checker.base import LLMCheckerChain from langchain.chains.llm_math.base import LLMMathChain from langchain.chains.llm_requests import LLMRequestsChain from langchain.chains.pal.base import PALChain from langchain.chains.qa_with_sources.base import QAWithSourcesChain from langchain.chains.qa_with_sources.vector_db import VectorDBQAWithSourcesChain from langchain.chains.sql_database.base import SQLDatabaseChain from langchain.chains.vector_db_qa.base import VectorDBQA from langchain.llms.loading import load_llm, load_llm_from_config from langchain.prompts.loading import load_prompt, load_prompt_from_config from langchain.utilities.loading import try_load_from_hub URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/chains/" def _load_llm_chain(config: dict, **kwargs: Any) -> LLMChain: """Load LLM chain from config dict.""" if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) else: raise ValueError("One of `prompt` or `prompt_path` must be present.") return LLMChain(llm=llm, prompt=prompt, **config) def _load_hyde_chain(config: dict, **kwargs: Any) -> HypotheticalDocumentEmbedder: """Load hypothetical document embedder chain from config dict.""" if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.") if "embeddings" in kwargs: embeddings = kwargs.pop("embeddings") else: raise ValueError("`embeddings` must be present.") return HypotheticalDocumentEmbedder( llm_chain=llm_chain, base_embeddings=embeddings, **config ) def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") if not isinstance(llm_chain, LLMChain): raise ValueError(f"Expected LLMChain, got {llm_chain}") if "document_prompt" in config: prompt_config = config.pop("document_prompt") document_prompt = load_prompt_from_config(prompt_config) elif "document_prompt_path" in config: document_prompt = load_prompt(config.pop("document_prompt_path")) else: raise ValueError( "One of `document_prompt` or `document_prompt_path` must be present." ) return StuffDocumentsChain( llm_chain=llm_chain, document_prompt=document_prompt, **config ) def _load_map_reduce_documents_chain( config: dict, **kwargs: Any ) -> MapReduceDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") if not isinstance(llm_chain, LLMChain): raise ValueError(f"Expected LLMChain, got {llm_chain}") if "combine_document_chain" in config: combine_document_chain_config = config.pop("combine_document_chain") combine_document_chain = load_chain_from_config(combine_document_chain_config) elif "combine_document_chain_path" in config: combine_document_chain = load_chain(config.pop("combine_document_chain_path")) else: raise ValueError( "One of `combine_document_chain` or " "`combine_document_chain_path` must be present." ) if "collapse_document_chain" in config: collapse_document_chain_config = config.pop("collapse_document_chain") if collapse_document_chain_config is None: collapse_document_chain = None else: collapse_document_chain = load_chain_from_config( collapse_document_chain_config ) elif "collapse_document_chain_path" in config: collapse_document_chain = load_chain(config.pop("collapse_document_chain_path")) return MapReduceDocumentsChain( llm_chain=llm_chain, combine_document_chain=combine_document_chain, collapse_document_chain=collapse_document_chain, **config, ) def _load_llm_bash_chain(config: dict, **kwargs: Any) -> LLMBashChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) return LLMBashChain(llm=llm, prompt=prompt, **config) def _load_llm_checker_chain(config: dict, **kwargs: Any) -> LLMCheckerChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "create_draft_answer_prompt" in config: create_draft_answer_prompt_config = config.pop("create_draft_answer_prompt") create_draft_answer_prompt = load_prompt_from_config( create_draft_answer_prompt_config ) elif "create_draft_answer_prompt_path" in config: create_draft_answer_prompt = load_prompt( config.pop("create_draft_answer_prompt_path") ) if "list_assertions_prompt" in config: list_assertions_prompt_config = config.pop("list_assertions_prompt") list_assertions_prompt = load_prompt_from_config(list_assertions_prompt_config) elif "list_assertions_prompt_path" in config: list_assertions_prompt = load_prompt(config.pop("list_assertions_prompt_path")) if "check_assertions_prompt" in config: check_assertions_prompt_config = config.pop("check_assertions_prompt") check_assertions_prompt = load_prompt_from_config( check_assertions_prompt_config ) elif "check_assertions_prompt_path" in config: check_assertions_prompt = load_prompt( config.pop("check_assertions_prompt_path") ) if "revised_answer_prompt" in config: revised_answer_prompt_config = config.pop("revised_answer_prompt") revised_answer_prompt = load_prompt_from_config(revised_answer_prompt_config) elif "revised_answer_prompt_path" in config: revised_answer_prompt = load_prompt(config.pop("revised_answer_prompt_path")) return LLMCheckerChain( llm=llm, create_draft_answer_prompt=create_draft_answer_prompt, list_assertions_prompt=list_assertions_prompt, check_assertions_prompt=check_assertions_prompt, revised_answer_prompt=revised_answer_prompt, **config, ) def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) return LLMMathChain(llm=llm, prompt=prompt, **config) def _load_map_rerank_documents_chain( config: dict, **kwargs: Any ) -> MapRerankDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") return MapRerankDocumentsChain(llm_chain=llm_chain, **config) def _load_pal_chain(config: dict, **kwargs: Any) -> PALChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) else: raise ValueError("One of `prompt` or `prompt_path` must be present.") return PALChain(llm=llm, prompt=prompt, **config) def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain: if "initial_llm_chain" in config: initial_llm_chain_config = config.pop("initial_llm_chain") initial_llm_chain = load_chain_from_config(initial_llm_chain_config) elif "initial_llm_chain_path" in config: initial_llm_chain = load_chain(config.pop("initial_llm_chain_path")) else: raise ValueError( "One of `initial_llm_chain` or `initial_llm_chain_config` must be present." ) if "refine_llm_chain" in config: refine_llm_chain_config = config.pop("refine_llm_chain") refine_llm_chain = load_chain_from_config(refine_llm_chain_config) elif "refine_llm_chain_path" in config: refine_llm_chain = load_chain(config.pop("refine_llm_chain_path")) else: raise ValueError( "One of `refine_llm_chain` or `refine_llm_chain_config` must be present." ) if "document_prompt" in config: prompt_config = config.pop("document_prompt") document_prompt = load_prompt_from_config(prompt_config) elif "document_prompt_path" in config: document_prompt = load_prompt(config.pop("document_prompt_path")) return RefineDocumentsChain( initial_llm_chain=initial_llm_chain, refine_llm_chain=refine_llm_chain, document_prompt=document_prompt, **config, ) def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesChain: if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config) def _load_sql_database_chain(config: dict, **kwargs: Any) -> SQLDatabaseChain: if "database" in kwargs: database = kwargs.pop("database") else: raise ValueError("`database` must be present.") if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) return SQLDatabaseChain(database=database, llm=llm, prompt=prompt, **config) def _load_vector_db_qa_with_sources_chain( config: dict, **kwargs: Any ) -> VectorDBQAWithSourcesChain: if "vectorstore" in kwargs: vectorstore = kwargs.pop("vectorstore") else: raise ValueError("`vectorstore` must be present.") if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return VectorDBQAWithSourcesChain( combine_documents_chain=combine_documents_chain, vectorstore=vectorstore, **config, ) def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA: if "vectorstore" in kwargs: vectorstore = kwargs.pop("vectorstore") else: raise ValueError("`vectorstore` must be present.") if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return VectorDBQA( combine_documents_chain=combine_documents_chain, vectorstore=vectorstore, **config, ) def _load_api_chain(config: dict, **kwargs: Any) -> APIChain: if "api_request_chain" in config: api_request_chain_config = config.pop("api_request_chain") api_request_chain = load_chain_from_config(api_request_chain_config) elif "api_request_chain_path" in config: api_request_chain = load_chain(config.pop("api_request_chain_path")) else: raise ValueError( "One of `api_request_chain` or `api_request_chain_path` must be present." ) if "api_answer_chain" in config: api_answer_chain_config = config.pop("api_answer_chain") api_answer_chain = load_chain_from_config(api_answer_chain_config) elif "api_answer_chain_path" in config: api_answer_chain = load_chain(config.pop("api_answer_chain_path")) else: raise ValueError( "One of `api_answer_chain` or `api_answer_chain_path` must be present." ) if "requests_wrapper" in kwargs: requests_wrapper = kwargs.pop("requests_wrapper") else: raise ValueError("`requests_wrapper` must be present.") return APIChain( api_request_chain=api_request_chain, api_answer_chain=api_answer_chain, requests_wrapper=requests_wrapper, **config, ) def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.") if "requests_wrapper" in kwargs: requests_wrapper = kwargs.pop("requests_wrapper") return LLMRequestsChain( llm_chain=llm_chain, requests_wrapper=requests_wrapper, **config ) else: return LLMRequestsChain(llm_chain=llm_chain, **config) type_to_loader_dict = { "api_chain": _load_api_chain, "hyde_chain": _load_hyde_chain, "llm_chain": _load_llm_chain, "llm_bash_chain": _load_llm_bash_chain, "llm_checker_chain": _load_llm_checker_chain, "llm_math_chain": _load_llm_math_chain, "llm_requests_chain": _load_llm_requests_chain, "pal_chain": _load_pal_chain, "qa_with_sources_chain": _load_qa_with_sources_chain, "stuff_documents_chain": _load_stuff_documents_chain, "map_reduce_documents_chain": _load_map_reduce_documents_chain, "map_rerank_documents_chain": _load_map_rerank_documents_chain, "refine_documents_chain": _load_refine_documents_chain, "sql_database_chain": _load_sql_database_chain, "vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain, "vector_db_qa": _load_vector_db_qa, } def load_chain_from_config(config: dict, **kwargs: Any) -> Chain: """Load chain from Config Dict.""" if "_type" not in config: raise ValueError("Must specify a chain Type in config") config_type = config.pop("_type") if config_type not in type_to_loader_dict: raise ValueError(f"Loading {config_type} chain not supported") chain_loader = type_to_loader_dict[config_type] return chain_loader(config, **kwargs) def load_chain(path: Union[str, Path], **kwargs: Any) -> Chain: """Unified method for loading a chain from LangChainHub or local fs.""" if hub_result := try_load_from_hub( path, _load_chain_from_file, "chains", {"json", "yaml"}, **kwargs ): return hub_result else: return _load_chain_from_file(path, **kwargs) def _load_chain_from_file(file: Union[str, Path], **kwargs: Any) -> Chain: """Load chain from file.""" # Convert file to Path object. if isinstance(file, str): file_path = Path(file) else: file_path = file # Load from either json or yaml. if file_path.suffix == ".json": with open(file_path) as f: config = json.load(f) elif file_path.suffix == ".yaml": with open(file_path, "r") as f: config = yaml.safe_load(f) else: raise ValueError("File type must be json or yaml") # Override default 'verbose' and 'memory' for the chain if "verbose" in kwargs: config["verbose"] = kwargs.pop("verbose") if "memory" in kwargs: config["memory"] = kwargs.pop("memory") # Load the chain from the config now. return load_chain_from_config(config, **kwargs)

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"""Functionality for loading chains.""" import json from pathlib import Path from typing import Any, Union import yaml from langchain.chains.api.base import APIChain from langchain.chains.base import Chain from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain from langchain.chains.combine_documents.refine import RefineDocumentsChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.hyde.base import HypotheticalDocumentEmbedder from langchain.chains.llm import LLMChain from langchain.chains.llm_bash.base import LLMBashChain from langchain.chains.llm_checker.base import LLMCheckerChain from langchain.chains.llm_math.base import LLMMathChain from langchain.chains.llm_requests import LLMRequestsChain from langchain.chains.pal.base import PALChain from langchain.chains.qa_with_sources.base import QAWithSourcesChain from langchain.chains.qa_with_sources.vector_db import VectorDBQAWithSourcesChain from langchain.chains.sql_database.base import SQLDatabaseChain from langchain.chains.vector_db_qa.base import VectorDBQA from langchain.llms.loading import load_llm, load_llm_from_config from langchain.prompts.loading import load_prompt, load_prompt_from_config from langchain.utilities.loading import try_load_from_hub URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/chains/" def _load_llm_chain(config: dict, **kwargs: Any) -> LLMChain: """Load LLM chain from config dict.""" if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) else: raise ValueError("One of `prompt` or `prompt_path` must be present.") return LLMChain(llm=llm, prompt=prompt, **config) def _load_hyde_chain(config: dict, **kwargs: Any) -> HypotheticalDocumentEmbedder: """Load hypothetical document embedder chain from config dict.""" if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.") if "embeddings" in kwargs: embeddings = kwargs.pop("embeddings") else: raise ValueError("`embeddings` must be present.") return HypotheticalDocumentEmbedder( llm_chain=llm_chain, base_embeddings=embeddings, **config ) def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") if not isinstance(llm_chain, LLMChain): raise ValueError(f"Expected LLMChain, got {llm_chain}") if "document_prompt" in config: prompt_config = config.pop("document_prompt") document_prompt = load_prompt_from_config(prompt_config) elif "document_prompt_path" in config: document_prompt = load_prompt(config.pop("document_prompt_path")) else: raise ValueError( "One of `document_prompt` or `document_prompt_path` must be present." ) return StuffDocumentsChain( llm_chain=llm_chain, document_prompt=document_prompt, **config ) def _load_map_reduce_documents_chain( config: dict, **kwargs: Any ) -> MapReduceDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") if not isinstance(llm_chain, LLMChain): raise ValueError(f"Expected LLMChain, got {llm_chain}") if "combine_document_chain" in config: combine_document_chain_config = config.pop("combine_document_chain") combine_document_chain = load_chain_from_config(combine_document_chain_config) elif "combine_document_chain_path" in config: combine_document_chain = load_chain(config.pop("combine_document_chain_path")) else: raise ValueError( "One of `combine_document_chain` or " "`combine_document_chain_path` must be present." ) if "collapse_document_chain" in config: collapse_document_chain_config = config.pop("collapse_document_chain") if collapse_document_chain_config is None: collapse_document_chain = None else: collapse_document_chain = load_chain_from_config( collapse_document_chain_config ) elif "collapse_document_chain_path" in config: collapse_document_chain = load_chain(config.pop("collapse_document_chain_path")) return MapReduceDocumentsChain( llm_chain=llm_chain, combine_document_chain=combine_document_chain, collapse_document_chain=collapse_document_chain, **config, ) def _load_llm_bash_chain(config: dict, **kwargs: Any) -> LLMBashChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) return LLMBashChain(llm=llm, prompt=prompt, **config) def _load_llm_checker_chain(config: dict, **kwargs: Any) -> LLMCheckerChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "create_draft_answer_prompt" in config: create_draft_answer_prompt_config = config.pop("create_draft_answer_prompt") create_draft_answer_prompt = load_prompt_from_config( create_draft_answer_prompt_config ) elif "create_draft_answer_prompt_path" in config: create_draft_answer_prompt = load_prompt( config.pop("create_draft_answer_prompt_path") ) if "list_assertions_prompt" in config: list_assertions_prompt_config = config.pop("list_assertions_prompt") list_assertions_prompt = load_prompt_from_config(list_assertions_prompt_config) elif "list_assertions_prompt_path" in config: list_assertions_prompt = load_prompt(config.pop("list_assertions_prompt_path")) if "check_assertions_prompt" in config: check_assertions_prompt_config = config.pop("check_assertions_prompt") check_assertions_prompt = load_prompt_from_config( check_assertions_prompt_config ) elif "check_assertions_prompt_path" in config: check_assertions_prompt = load_prompt( config.pop("check_assertions_prompt_path") ) if "revised_answer_prompt" in config: revised_answer_prompt_config = config.pop("revised_answer_prompt") revised_answer_prompt = load_prompt_from_config(revised_answer_prompt_config) elif "revised_answer_prompt_path" in config: revised_answer_prompt = load_prompt(config.pop("revised_answer_prompt_path")) return LLMCheckerChain( llm=llm, create_draft_answer_prompt=create_draft_answer_prompt, list_assertions_prompt=list_assertions_prompt, check_assertions_prompt=check_assertions_prompt, revised_answer_prompt=revised_answer_prompt, **config, ) def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) return LLMMathChain(llm=llm, prompt=prompt, **config) def _load_map_rerank_documents_chain( config: dict, **kwargs: Any ) -> MapRerankDocumentsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.") return MapRerankDocumentsChain(llm_chain=llm_chain, **config) def _load_pal_chain(config: dict, **kwargs: Any) -> PALChain: if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) elif "prompt_path" in config: prompt = load_prompt(config.pop("prompt_path")) else: raise ValueError("One of `prompt` or `prompt_path` must be present.") return PALChain(llm=llm, prompt=prompt, **config) def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain: if "initial_llm_chain" in config: initial_llm_chain_config = config.pop("initial_llm_chain") initial_llm_chain = load_chain_from_config(initial_llm_chain_config) elif "initial_llm_chain_path" in config: initial_llm_chain = load_chain(config.pop("initial_llm_chain_path")) else: raise ValueError( "One of `initial_llm_chain` or `initial_llm_chain_config` must be present." ) if "refine_llm_chain" in config: refine_llm_chain_config = config.pop("refine_llm_chain") refine_llm_chain = load_chain_from_config(refine_llm_chain_config) elif "refine_llm_chain_path" in config: refine_llm_chain = load_chain(config.pop("refine_llm_chain_path")) else: raise ValueError( "One of `refine_llm_chain` or `refine_llm_chain_config` must be present." ) if "document_prompt" in config: prompt_config = config.pop("document_prompt") document_prompt = load_prompt_from_config(prompt_config) elif "document_prompt_path" in config: document_prompt = load_prompt(config.pop("document_prompt_path")) return RefineDocumentsChain( initial_llm_chain=initial_llm_chain, refine_llm_chain=refine_llm_chain, document_prompt=document_prompt, **config, ) def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesChain: if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config) def _load_sql_database_chain(config: dict, **kwargs: Any) -> SQLDatabaseChain: if "database" in kwargs: database = kwargs.pop("database") else: raise ValueError("`database` must be present.") if "llm" in config: llm_config = config.pop("llm") llm = load_llm_from_config(llm_config) elif "llm_path" in config: llm = load_llm(config.pop("llm_path")) else: raise ValueError("One of `llm` or `llm_path` must be present.") if "prompt" in config: prompt_config = config.pop("prompt") prompt = load_prompt_from_config(prompt_config) return SQLDatabaseChain(database=database, llm=llm, prompt=prompt, **config) def _load_vector_db_qa_with_sources_chain( config: dict, **kwargs: Any ) -> VectorDBQAWithSourcesChain: if "vectorstore" in kwargs: vectorstore = kwargs.pop("vectorstore") else: raise ValueError("`vectorstore` must be present.") if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return VectorDBQAWithSourcesChain( combine_documents_chain=combine_documents_chain, vectorstore=vectorstore, **config, ) def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA: if "vectorstore" in kwargs: vectorstore = kwargs.pop("vectorstore") else: raise ValueError("`vectorstore` must be present.") if "combine_documents_chain" in config: combine_documents_chain_config = config.pop("combine_documents_chain") combine_documents_chain = load_chain_from_config(combine_documents_chain_config) elif "combine_documents_chain_path" in config: combine_documents_chain = load_chain(config.pop("combine_documents_chain_path")) else: raise ValueError( "One of `combine_documents_chain` or " "`combine_documents_chain_path` must be present." ) return VectorDBQA( combine_documents_chain=combine_documents_chain, vectorstore=vectorstore, **config, ) def _load_api_chain(config: dict, **kwargs: Any) -> APIChain: if "api_request_chain" in config: api_request_chain_config = config.pop("api_request_chain") api_request_chain = load_chain_from_config(api_request_chain_config) elif "api_request_chain_path" in config: api_request_chain = load_chain(config.pop("api_request_chain_path")) else: raise ValueError( "One of `api_request_chain` or `api_request_chain_path` must be present." ) if "api_answer_chain" in config: api_answer_chain_config = config.pop("api_answer_chain") api_answer_chain = load_chain_from_config(api_answer_chain_config) elif "api_answer_chain_path" in config: api_answer_chain = load_chain(config.pop("api_answer_chain_path")) else: raise ValueError( "One of `api_answer_chain` or `api_answer_chain_path` must be present." ) if "requests_wrapper" in kwargs: requests_wrapper = kwargs.pop("requests_wrapper") else: raise ValueError("`requests_wrapper` must be present.") return APIChain( api_request_chain=api_request_chain, api_answer_chain=api_answer_chain, requests_wrapper=requests_wrapper, **config, ) def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain: if "llm_chain" in config: llm_chain_config = config.pop("llm_chain") llm_chain = load_chain_from_config(llm_chain_config) elif "llm_chain_path" in config: llm_chain = load_chain(config.pop("llm_chain_path")) else: raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.") if "requests_wrapper" in kwargs: requests_wrapper = kwargs.pop("requests_wrapper") return LLMRequestsChain( llm_chain=llm_chain, requests_wrapper=requests_wrapper, **config ) else: return LLMRequestsChain(llm_chain=llm_chain, **config) type_to_loader_dict = { "api_chain": _load_api_chain, "hyde_chain": _load_hyde_chain, "llm_chain": _load_llm_chain, "llm_bash_chain": _load_llm_bash_chain, "llm_checker_chain": _load_llm_checker_chain, "llm_math_chain": _load_llm_math_chain, "llm_requests_chain": _load_llm_requests_chain, "pal_chain": _load_pal_chain, "qa_with_sources_chain": _load_qa_with_sources_chain, "stuff_documents_chain": _load_stuff_documents_chain, "map_reduce_documents_chain": _load_map_reduce_documents_chain, "map_rerank_documents_chain": _load_map_rerank_documents_chain, "refine_documents_chain": _load_refine_documents_chain, "sql_database_chain": _load_sql_database_chain, "vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain, "vector_db_qa": _load_vector_db_qa, } def load_chain_from_config(config: dict, **kwargs: Any) -> Chain: """Load chain from Config Dict.""" if "_type" not in config: raise ValueError("Must specify a chain Type in config") config_type = config.pop("_type") if config_type not in type_to_loader_dict: raise ValueError(f"Loading {config_type} chain not supported") chain_loader = type_to_loader_dict[config_type] return chain_loader(config, **kwargs) def load_chain(path: Union[str, Path], **kwargs: Any) -> Chain: """Unified method for loading a chain from LangChainHub or local fs.""" if hub_result := try_load_from_hub( path, _load_chain_from_file, "chains", {"json", "yaml"}, **kwargs ): return hub_result else: return _load_chain_from_file(path, **kwargs) def _load_chain_from_file(file: Union[str, Path], **kwargs: Any) -> Chain: """Load chain from file.""" # Convert file to Path object. if isinstance(file, str): file_path = Path(file) else: file_path = file # Load from either json or yaml. if file_path.suffix == ".json": with open(file_path) as f: config = json.load(f) elif file_path.suffix == ".yaml": with open(file_path, "r") as f: config = yaml.safe_load(f) else: raise ValueError("File type must be json or yaml") # Override default 'verbose' and 'memory' for the chain if "verbose" in kwargs: config["verbose"] = kwargs.pop("verbose") if "memory" in kwargs: config["memory"] = kwargs.pop("memory") # Load the chain from the config now. return load_chain_from_config(config, **kwargs)

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"""Wrapper around HuggingFace APIs.""" from typing import Any, Dict, List, Mapping, Optional import requests from pydantic import Extra, root_validator from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens from langchain.utils import get_from_dict_or_env VALID_TASKS = ("text2text-generation", "text-generation", "summarization") class HuggingFaceEndpoint(LLM): """Wrapper around HuggingFaceHub Inference Endpoints. To use, you should have the ``huggingface_hub`` python package installed, and the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Only supports `text-generation` and `text2text-generation` for now. Example: .. code-block:: python from langchain.llms import HuggingFaceEndpoint endpoint_url = ( "https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud" ) hf = HuggingFaceEndpoint( endpoint_url=endpoint_url, huggingfacehub_api_token="my-api-key" ) """ endpoint_url: str = "" """Endpoint URL to use.""" task: Optional[str] = None """Task to call the model with. Should be a task that returns `generated_text` or `summary_text`.""" model_kwargs: Optional[dict] = None """Key word arguments to pass to the model.""" huggingfacehub_api_token: Optional[str] = None class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" huggingfacehub_api_token = get_from_dict_or_env( values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN" ) try: from huggingface_hub.hf_api import HfApi try: HfApi( endpoint="https://huggingface.co", # Can be a Private Hub endpoint. token=huggingfacehub_api_token, ).whoami() except Exception as e: raise ValueError( "Could not authenticate with huggingface_hub. " "Please check your API token." ) from e except ImportError: raise ValueError( "Could not import huggingface_hub python package. " "Please install it with `pip install huggingface_hub`." ) values["huggingfacehub_api_token"] = huggingfacehub_api_token return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" _model_kwargs = self.model_kwargs or {} return { **{"endpoint_url": self.endpoint_url, "task": self.task}, **{"model_kwargs": _model_kwargs}, } @property def _llm_type(self) -> str: """Return type of llm.""" return "huggingface_endpoint" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> str: """Call out to HuggingFace Hub's inference endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = hf("Tell me a joke.") """ _model_kwargs = self.model_kwargs or {} # payload samples parameter_payload = {"inputs": prompt, "parameters": _model_kwargs} # HTTP headers for authorization headers = { "Authorization": f"Bearer {self.huggingfacehub_api_token}", "Content-Type": "application/json", } # send request try: response = requests.post( self.endpoint_url, headers=headers, json=parameter_payload ) except requests.exceptions.RequestException as e: # This is the correct syntax raise ValueError(f"Error raised by inference endpoint: {e}") generated_text = response.json() if "error" in generated_text: raise ValueError( f"Error raised by inference API: {generated_text['error']}" ) if self.task == "text-generation": # Text generation return includes the starter text. text = generated_text[0]["generated_text"][len(prompt) :] elif self.task == "text2text-generation": text = generated_text[0]["generated_text"] elif self.task == "summarization": text = generated_text[0]["summary_text"] else: raise ValueError( f"Got invalid task {self.task}, " f"currently only {VALID_TASKS} are supported" ) if stop is not None: # This is a bit hacky, but I can't figure out a better way to enforce # stop tokens when making calls to huggingface_hub. text = enforce_stop_tokens(text, stop) return text

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"""Wrapper around HuggingFace APIs.""" from typing import Any, Dict, List, Mapping, Optional import requests from pydantic import Extra, root_validator from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens from langchain.utils import get_from_dict_or_env VALID_TASKS = ("text2text-generation", "text-generation", "summarization") class HuggingFaceEndpoint(LLM): """Wrapper around HuggingFaceHub Inference Endpoints. To use, you should have the ``huggingface_hub`` python package installed, and the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Only supports `text-generation` and `text2text-generation` for now. Example: .. code-block:: python from langchain.llms import HuggingFaceEndpoint endpoint_url = ( "https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud" ) hf = HuggingFaceEndpoint( endpoint_url=endpoint_url, huggingfacehub_api_token="my-api-key" ) """ endpoint_url: str = "" """Endpoint URL to use.""" task: Optional[str] = None """Task to call the model with. Should be a task that returns `generated_text` or `summary_text`.""" model_kwargs: Optional[dict] = None """Key word arguments to pass to the model.""" huggingfacehub_api_token: Optional[str] = None class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" huggingfacehub_api_token = get_from_dict_or_env( values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN" ) try: from huggingface_hub.hf_api import HfApi try: HfApi( endpoint="https://huggingface.co", # Can be a Private Hub endpoint. token=huggingfacehub_api_token, ).whoami() except Exception as e: raise ValueError( "Could not authenticate with huggingface_hub. " "Please check your API token." ) from e except ImportError: raise ValueError( "Could not import huggingface_hub python package. " "Please install it with `pip install huggingface_hub`." ) values["huggingfacehub_api_token"] = huggingfacehub_api_token return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" _model_kwargs = self.model_kwargs or {} return { **{"endpoint_url": self.endpoint_url, "task": self.task}, **{"model_kwargs": _model_kwargs}, } @property def _llm_type(self) -> str: """Return type of llm.""" return "huggingface_endpoint" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> str: """Call out to HuggingFace Hub's inference endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = hf("Tell me a joke.") """ _model_kwargs = self.model_kwargs or {} # payload samples parameter_payload = {"inputs": prompt, "parameters": _model_kwargs} # HTTP headers for authorization headers = { "Authorization": f"Bearer {self.huggingfacehub_api_token}", "Content-Type": "application/json", } # send request try: response = requests.post( self.endpoint_url, headers=headers, json=parameter_payload ) except requests.exceptions.RequestException as e: # This is the correct syntax raise ValueError(f"Error raised by inference endpoint: {e}") generated_text = response.json() if "error" in generated_text: raise ValueError( f"Error raised by inference API: {generated_text['error']}" ) if self.task == "text-generation": # Text generation return includes the starter text. text = generated_text[0]["generated_text"][len(prompt) :] elif self.task == "text2text-generation": text = generated_text[0]["generated_text"] elif self.task == "summarization": text = generated_text[0]["summary_text"] else: raise ValueError( f"Got invalid task {self.task}, " f"currently only {VALID_TASKS} are supported" ) if stop is not None: # This is a bit hacky, but I can't figure out a better way to enforce # stop tokens when making calls to huggingface_hub. text = enforce_stop_tokens(text, stop) return text

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"""Wrapper around HuggingFace APIs.""" from typing import Any, Dict, List, Mapping, Optional import requests from pydantic import Extra, root_validator from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens from langchain.utils import get_from_dict_or_env VALID_TASKS = ("text2text-generation", "text-generation", "summarization") class HuggingFaceEndpoint(LLM): """Wrapper around HuggingFaceHub Inference Endpoints. To use, you should have the ``huggingface_hub`` python package installed, and the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Only supports `text-generation` and `text2text-generation` for now. Example: .. code-block:: python from langchain.llms import HuggingFaceEndpoint endpoint_url = ( "https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud" ) hf = HuggingFaceEndpoint( endpoint_url=endpoint_url, huggingfacehub_api_token="my-api-key" ) """ endpoint_url: str = "" """Endpoint URL to use.""" task: Optional[str] = None """Task to call the model with. Should be a task that returns `generated_text` or `summary_text`.""" model_kwargs: Optional[dict] = None """Key word arguments to pass to the model.""" huggingfacehub_api_token: Optional[str] = None class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" huggingfacehub_api_token = get_from_dict_or_env( values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN" ) try: from huggingface_hub.hf_api import HfApi try: HfApi( endpoint="https://huggingface.co", # Can be a Private Hub endpoint. token=huggingfacehub_api_token, ).whoami() except Exception as e: raise ValueError( "Could not authenticate with huggingface_hub. " "Please check your API token." ) from e except ImportError: raise ValueError( "Could not import huggingface_hub python package. " "Please install it with `pip install huggingface_hub`." ) values["huggingfacehub_api_token"] = huggingfacehub_api_token return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" _model_kwargs = self.model_kwargs or {} return { **{"endpoint_url": self.endpoint_url, "task": self.task}, **{"model_kwargs": _model_kwargs}, } @property def _llm_type(self) -> str: """Return type of llm.""" return "huggingface_endpoint" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> str: """Call out to HuggingFace Hub's inference endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = hf("Tell me a joke.") """ _model_kwargs = self.model_kwargs or {} # payload samples parameter_payload = {"inputs": prompt, "parameters": _model_kwargs} # HTTP headers for authorization headers = { "Authorization": f"Bearer {self.huggingfacehub_api_token}", "Content-Type": "application/json", } # send request try: response = requests.post( self.endpoint_url, headers=headers, json=parameter_payload ) except requests.exceptions.RequestException as e: # This is the correct syntax raise ValueError(f"Error raised by inference endpoint: {e}") generated_text = response.json() if "error" in generated_text: raise ValueError( f"Error raised by inference API: {generated_text['error']}" ) if self.task == "text-generation": # Text generation return includes the starter text. text = generated_text[0]["generated_text"][len(prompt) :] elif self.task == "text2text-generation": text = generated_text[0]["generated_text"] elif self.task == "summarization": text = generated_text[0]["summary_text"] else: raise ValueError( f"Got invalid task {self.task}, " f"currently only {VALID_TASKS} are supported" ) if stop is not None: # This is a bit hacky, but I can't figure out a better way to enforce # stop tokens when making calls to huggingface_hub. text = enforce_stop_tokens(text, stop) return text

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"""Wrapper around HuggingFace APIs.""" from typing import Any, Dict, List, Mapping, Optional import requests from pydantic import Extra, root_validator from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens from langchain.utils import get_from_dict_or_env VALID_TASKS = ("text2text-generation", "text-generation", "summarization") class HuggingFaceEndpoint(LLM): """Wrapper around HuggingFaceHub Inference Endpoints. To use, you should have the ``huggingface_hub`` python package installed, and the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Only supports `text-generation` and `text2text-generation` for now. Example: .. code-block:: python from langchain.llms import HuggingFaceEndpoint endpoint_url = ( "https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud" ) hf = HuggingFaceEndpoint( endpoint_url=endpoint_url, huggingfacehub_api_token="my-api-key" ) """ endpoint_url: str = "" """Endpoint URL to use.""" task: Optional[str] = None """Task to call the model with. Should be a task that returns `generated_text` or `summary_text`.""" model_kwargs: Optional[dict] = None """Key word arguments to pass to the model.""" huggingfacehub_api_token: Optional[str] = None class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" huggingfacehub_api_token = get_from_dict_or_env( values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN" ) try: from huggingface_hub.hf_api import HfApi try: HfApi( endpoint="https://huggingface.co", # Can be a Private Hub endpoint. token=huggingfacehub_api_token, ).whoami() except Exception as e: raise ValueError( "Could not authenticate with huggingface_hub. " "Please check your API token." ) from e except ImportError: raise ValueError( "Could not import huggingface_hub python package. " "Please install it with `pip install huggingface_hub`." ) values["huggingfacehub_api_token"] = huggingfacehub_api_token return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" _model_kwargs = self.model_kwargs or {} return { **{"endpoint_url": self.endpoint_url, "task": self.task}, **{"model_kwargs": _model_kwargs}, } @property def _llm_type(self) -> str: """Return type of llm.""" return "huggingface_endpoint" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> str: """Call out to HuggingFace Hub's inference endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = hf("Tell me a joke.") """ _model_kwargs = self.model_kwargs or {} # payload samples parameter_payload = {"inputs": prompt, "parameters": _model_kwargs} # HTTP headers for authorization headers = { "Authorization": f"Bearer {self.huggingfacehub_api_token}", "Content-Type": "application/json", } # send request try: response = requests.post( self.endpoint_url, headers=headers, json=parameter_payload ) except requests.exceptions.RequestException as e: # This is the correct syntax raise ValueError(f"Error raised by inference endpoint: {e}") generated_text = response.json() if "error" in generated_text: raise ValueError( f"Error raised by inference API: {generated_text['error']}" ) if self.task == "text-generation": # Text generation return includes the starter text. text = generated_text[0]["generated_text"][len(prompt) :] elif self.task == "text2text-generation": text = generated_text[0]["generated_text"] elif self.task == "summarization": text = generated_text[0]["summary_text"] else: raise ValueError( f"Got invalid task {self.task}, " f"currently only {VALID_TASKS} are supported" ) if stop is not None: # This is a bit hacky, but I can't figure out a better way to enforce # stop tokens when making calls to huggingface_hub. text = enforce_stop_tokens(text, stop) return text

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import os from langchain.llms.bedrock import Bedrock from langchain import PromptTemplate def get_llm(): model_kwargs = { "maxTokenCount": 1024, "stopSequences": [], "temperature": 0, "topP": 0.9 } llm = Bedrock( # credentials_profile_name=os.environ.get("BWB_PROFILE_NAME"), #sets the profile name to use for AWS credentials (if not the default) region_name=os.environ.get("BWB_REGION_NAME"), #sets the region name (if not the default) endpoint_url=os.environ.get("BWB_ENDPOINT_URL"), #sets the endpoint URL (if necessary) model_id="amazon.titan-tg1-large", #use the Anthropic Claude model model_kwargs=model_kwargs) #configure the properties for Claude return llm def get_prompt(user_input, template): prompt_template = PromptTemplate.from_template(template) #this will automatically identify the input variables for the template prompt = prompt_template.format(user_input=user_input) return prompt def get_text_response(user_input, template): #text-to-text client function llm = get_llm() prompt = get_prompt(user_input, template) return llm.predict(prompt) #return a response to the prompt

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from langchain import PromptTemplate, LLMChain from langchain.document_loaders import TextLoader from langchain.embeddings import LlamaCppEmbeddings from langchain.llms import GPT4All from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.callbacks.base import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.vectorstores.faiss import FAISS # SCRIPT INFO: # # This script allows you to create a vectorstore from a file and query it with a question (hard coded). # # It shows how you could send questions to a GPT4All custom knowledge base and receive answers. # # If you want a chat style interface using a similar custom knowledge base, you can use the custom_chatbot.py script provided. # Setup gpt4all_path = './models/gpt4all-converted.bin' llama_path = './models/ggml-model-q4_0.bin' callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) loader = TextLoader('./docs/shortened_sotu.txt') embeddings = LlamaCppEmbeddings(model_path=llama_path) llm = GPT4All(model=gpt4all_path, callback_manager=callback_manager, verbose=True) # Split text def split_chunks(sources): chunks = [] splitter = RecursiveCharacterTextSplitter(chunk_size=256, chunk_overlap=32) for chunk in splitter.split_documents(sources): chunks.append(chunk) return chunks def create_index(chunks): texts = [doc.page_content for doc in chunks] metadatas = [doc.metadata for doc in chunks] search_index = FAISS.from_texts(texts, embeddings, metadatas=metadatas) return search_index def similarity_search(query, index): matched_docs = index.similarity_search(query, k=4) sources = [] for doc in matched_docs: sources.append( { "page_content": doc.page_content, "metadata": doc.metadata, } ) return matched_docs, sources # Create Index # docs = loader.load() # chunks = split_chunks(docs) # index = create_index(chunks) # Save Index (use this to save the index for later use) # Comment the line below after running once successfully (IMPORTANT) # index.save_local("state_of_the_union_index") # Load Index (use this to load the index from a file, eg on your second time running things and beyond) # Uncomment the line below after running once successfully (IMPORTANT) index = FAISS.load_local("./full_sotu_index", embeddings) # Set your query here manually question = "Summarize the comments about NATO and its purpose." matched_docs, sources = similarity_search(question, index) template = """ Please use the following context to answer questions. Context: {context} --- Question: {question} Answer: Let's think step by step.""" context = "\n".join([doc.page_content for doc in matched_docs]) prompt = PromptTemplate(template=template, input_variables=["context", "question"]).partial(context=context) llm_chain = LLMChain(prompt=prompt, llm=llm) print(llm_chain.run(question))

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from langchain import PromptTemplate, LLMChain from langchain.document_loaders import TextLoader from langchain.embeddings import LlamaCppEmbeddings from langchain.llms import GPT4All from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.callbacks.base import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.vectorstores.faiss import FAISS # SCRIPT INFO: # # This script allows you to create a vectorstore from a file and query it with a question (hard coded). # # It shows how you could send questions to a GPT4All custom knowledge base and receive answers. # # If you want a chat style interface using a similar custom knowledge base, you can use the custom_chatbot.py script provided. # Setup gpt4all_path = './models/gpt4all-converted.bin' llama_path = './models/ggml-model-q4_0.bin' callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) loader = TextLoader('./docs/shortened_sotu.txt') embeddings = LlamaCppEmbeddings(model_path=llama_path) llm = GPT4All(model=gpt4all_path, callback_manager=callback_manager, verbose=True) # Split text def split_chunks(sources): chunks = [] splitter = RecursiveCharacterTextSplitter(chunk_size=256, chunk_overlap=32) for chunk in splitter.split_documents(sources): chunks.append(chunk) return chunks def create_index(chunks): texts = [doc.page_content for doc in chunks] metadatas = [doc.metadata for doc in chunks] search_index = FAISS.from_texts(texts, embeddings, metadatas=metadatas) return search_index def similarity_search(query, index): matched_docs = index.similarity_search(query, k=4) sources = [] for doc in matched_docs: sources.append( { "page_content": doc.page_content, "metadata": doc.metadata, } ) return matched_docs, sources # Create Index # docs = loader.load() # chunks = split_chunks(docs) # index = create_index(chunks) # Save Index (use this to save the index for later use) # Comment the line below after running once successfully (IMPORTANT) # index.save_local("state_of_the_union_index") # Load Index (use this to load the index from a file, eg on your second time running things and beyond) # Uncomment the line below after running once successfully (IMPORTANT) index = FAISS.load_local("./full_sotu_index", embeddings) # Set your query here manually question = "Summarize the comments about NATO and its purpose." matched_docs, sources = similarity_search(question, index) template = """ Please use the following context to answer questions. Context: {context} --- Question: {question} Answer: Let's think step by step.""" context = "\n".join([doc.page_content for doc in matched_docs]) prompt = PromptTemplate(template=template, input_variables=["context", "question"]).partial(context=context) llm_chain = LLMChain(prompt=prompt, llm=llm) print(llm_chain.run(question))

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from langchain.chains.router import MultiPromptChain from langchain.chat_models import ChatOpenAI from dotenv import load_dotenv import os # A template for working with LangChain multi prompt chain. # It's a great way to let the large language model choose which prompts suits the question. # Load env files load_dotenv() openai_api_key = os.environ.get('openai_api_key') # Create the templates marketing_template = """ You are a skilled marketing professional. You have a deep understanding of market analysis, consumer behavior, branding, and digital marketing strategies. You can provide insightful recommendations and creative solutions to address various marketing-related questions. Here is a marketing-related question: {input}""" business_template = """ You are an experienced business expert. You possess knowledge in areas such as business strategy, entrepreneurship, market research, and financial analysis. You can provide practical insights and strategic advice to address various business-related questions. Here is a business-related question: {input}""" # Create prompt info prompt_infos = [ { "name": "marketing", "description": "Good for answering marketing questions", "prompt_template": marketing_template }, { "name": "business", "description": "Good for answering business-related questions", "prompt_template": business_template } ] # Create the chain llm = ChatOpenAI(openai_api_key=openai_api_key, model_name="gpt-3.5-turbo", temperature=0.3) chain = MultiPromptChain.from_prompts(llm=llm, prompt_infos=prompt_infos, verbose=True) # Example usage question = "What is the best way to finance a startup?" response = chain.run(question)

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import os import requests from langchain.tools import tool from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import FAISS from sec_api import QueryApi from unstructured.partition.html import partition_html class SECTools(): @tool("Search 10-Q form") def search_10q(data): """ Useful to search information from the latest 10-Q form for a given stock. The input to this tool should be a pipe (|) separated text of length two, representing the stock ticker you are interested and what question you have from it. For example, `AAPL|what was last quarter's revenue`. """ stock, ask = data.split("|") queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY']) query = { "query": { "query_string": { "query": f"ticker:{stock} AND formType:\"10-Q\"" } }, "from": "0", "size": "1", "sort": [{ "filedAt": { "order": "desc" }}] } fillings = queryApi.get_filings(query)['filings'] if len(fillings) == 0: return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct." link = fillings[0]['linkToFilingDetails'] answer = SECTools.__embedding_search(link, ask) return answer @tool("Search 10-K form") def search_10k(data): """ Useful to search information from the latest 10-K form for a given stock. The input to this tool should be a pipe (|) separated text of length two, representing the stock ticker you are interested, what question you have from it. For example, `AAPL|what was last year's revenue`. """ stock, ask = data.split("|") queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY']) query = { "query": { "query_string": { "query": f"ticker:{stock} AND formType:\"10-K\"" } }, "from": "0", "size": "1", "sort": [{ "filedAt": { "order": "desc" }}] } fillings = queryApi.get_filings(query)['filings'] if len(fillings) == 0: return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct." link = fillings[0]['linkToFilingDetails'] answer = SECTools.__embedding_search(link, ask) return answer def __embedding_search(url, ask): text = SECTools.__download_form_html(url) elements = partition_html(text=text) content = "\n".join([str(el) for el in elements]) text_splitter = CharacterTextSplitter( separator = "\n", chunk_size = 1000, chunk_overlap = 150, length_function = len, is_separator_regex = False, ) docs = text_splitter.create_documents([content]) retriever = FAISS.from_documents( docs, OpenAIEmbeddings() ).as_retriever() answers = retriever.get_relevant_documents(ask, top_k=4) answers = "\n\n".join([a.page_content for a in answers]) return answers def __download_form_html(url): headers = { 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'en-US,en;q=0.9,pt-BR;q=0.8,pt;q=0.7', 'Cache-Control': 'max-age=0', 'Dnt': '1', 'Sec-Ch-Ua': '"Not_A Brand";v="8", "Chromium";v="120"', 'Sec-Ch-Ua-Mobile': '?0', 'Sec-Ch-Ua-Platform': '"macOS"', 'Sec-Fetch-Dest': 'document', 'Sec-Fetch-Mode': 'navigate', 'Sec-Fetch-Site': 'none', 'Sec-Fetch-User': '?1', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36' } response = requests.get(url, headers=headers) return response.text

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import os import requests from langchain.tools import tool from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import FAISS from sec_api import QueryApi from unstructured.partition.html import partition_html class SECTools(): @tool("Search 10-Q form") def search_10q(data): """ Useful to search information from the latest 10-Q form for a given stock. The input to this tool should be a pipe (|) separated text of length two, representing the stock ticker you are interested and what question you have from it. For example, `AAPL|what was last quarter's revenue`. """ stock, ask = data.split("|") queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY']) query = { "query": { "query_string": { "query": f"ticker:{stock} AND formType:\"10-Q\"" } }, "from": "0", "size": "1", "sort": [{ "filedAt": { "order": "desc" }}] } fillings = queryApi.get_filings(query)['filings'] if len(fillings) == 0: return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct." link = fillings[0]['linkToFilingDetails'] answer = SECTools.__embedding_search(link, ask) return answer @tool("Search 10-K form") def search_10k(data): """ Useful to search information from the latest 10-K form for a given stock. The input to this tool should be a pipe (|) separated text of length two, representing the stock ticker you are interested, what question you have from it. For example, `AAPL|what was last year's revenue`. """ stock, ask = data.split("|") queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY']) query = { "query": { "query_string": { "query": f"ticker:{stock} AND formType:\"10-K\"" } }, "from": "0", "size": "1", "sort": [{ "filedAt": { "order": "desc" }}] } fillings = queryApi.get_filings(query)['filings'] if len(fillings) == 0: return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct." link = fillings[0]['linkToFilingDetails'] answer = SECTools.__embedding_search(link, ask) return answer def __embedding_search(url, ask): text = SECTools.__download_form_html(url) elements = partition_html(text=text) content = "\n".join([str(el) for el in elements]) text_splitter = CharacterTextSplitter( separator = "\n", chunk_size = 1000, chunk_overlap = 150, length_function = len, is_separator_regex = False, ) docs = text_splitter.create_documents([content]) retriever = FAISS.from_documents( docs, OpenAIEmbeddings() ).as_retriever() answers = retriever.get_relevant_documents(ask, top_k=4) answers = "\n\n".join([a.page_content for a in answers]) return answers def __download_form_html(url): headers = { 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'en-US,en;q=0.9,pt-BR;q=0.8,pt;q=0.7', 'Cache-Control': 'max-age=0', 'Dnt': '1', 'Sec-Ch-Ua': '"Not_A Brand";v="8", "Chromium";v="120"', 'Sec-Ch-Ua-Mobile': '?0', 'Sec-Ch-Ua-Platform': '"macOS"', 'Sec-Fetch-Dest': 'document', 'Sec-Fetch-Mode': 'navigate', 'Sec-Fetch-Site': 'none', 'Sec-Fetch-User': '?1', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36' } response = requests.get(url, headers=headers) return response.text

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import os import requests from langchain.tools import tool from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import FAISS from sec_api import QueryApi from unstructured.partition.html import partition_html class SECTools(): @tool("Search 10-Q form") def search_10q(data): """ Useful to search information from the latest 10-Q form for a given stock. The input to this tool should be a pipe (|) separated text of length two, representing the stock ticker you are interested and what question you have from it. For example, `AAPL|what was last quarter's revenue`. """ stock, ask = data.split("|") queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY']) query = { "query": { "query_string": { "query": f"ticker:{stock} AND formType:\"10-Q\"" } }, "from": "0", "size": "1", "sort": [{ "filedAt": { "order": "desc" }}] } fillings = queryApi.get_filings(query)['filings'] if len(fillings) == 0: return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct." link = fillings[0]['linkToFilingDetails'] answer = SECTools.__embedding_search(link, ask) return answer @tool("Search 10-K form") def search_10k(data): """ Useful to search information from the latest 10-K form for a given stock. The input to this tool should be a pipe (|) separated text of length two, representing the stock ticker you are interested, what question you have from it. For example, `AAPL|what was last year's revenue`. """ stock, ask = data.split("|") queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY']) query = { "query": { "query_string": { "query": f"ticker:{stock} AND formType:\"10-K\"" } }, "from": "0", "size": "1", "sort": [{ "filedAt": { "order": "desc" }}] } fillings = queryApi.get_filings(query)['filings'] if len(fillings) == 0: return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct." link = fillings[0]['linkToFilingDetails'] answer = SECTools.__embedding_search(link, ask) return answer def __embedding_search(url, ask): text = SECTools.__download_form_html(url) elements = partition_html(text=text) content = "\n".join([str(el) for el in elements]) text_splitter = CharacterTextSplitter( separator = "\n", chunk_size = 1000, chunk_overlap = 150, length_function = len, is_separator_regex = False, ) docs = text_splitter.create_documents([content]) retriever = FAISS.from_documents( docs, OpenAIEmbeddings() ).as_retriever() answers = retriever.get_relevant_documents(ask, top_k=4) answers = "\n\n".join([a.page_content for a in answers]) return answers def __download_form_html(url): headers = { 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'en-US,en;q=0.9,pt-BR;q=0.8,pt;q=0.7', 'Cache-Control': 'max-age=0', 'Dnt': '1', 'Sec-Ch-Ua': '"Not_A Brand";v="8", "Chromium";v="120"', 'Sec-Ch-Ua-Mobile': '?0', 'Sec-Ch-Ua-Platform': '"macOS"', 'Sec-Fetch-Dest': 'document', 'Sec-Fetch-Mode': 'navigate', 'Sec-Fetch-Site': 'none', 'Sec-Fetch-User': '?1', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36' } response = requests.get(url, headers=headers) return response.text

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import os import requests from langchain.tools import tool from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import FAISS from sec_api import QueryApi from unstructured.partition.html import partition_html class SECTools(): @tool("Search 10-Q form") def search_10q(data): """ Useful to search information from the latest 10-Q form for a given stock. The input to this tool should be a pipe (|) separated text of length two, representing the stock ticker you are interested and what question you have from it. For example, `AAPL|what was last quarter's revenue`. """ stock, ask = data.split("|") queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY']) query = { "query": { "query_string": { "query": f"ticker:{stock} AND formType:\"10-Q\"" } }, "from": "0", "size": "1", "sort": [{ "filedAt": { "order": "desc" }}] } fillings = queryApi.get_filings(query)['filings'] if len(fillings) == 0: return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct." link = fillings[0]['linkToFilingDetails'] answer = SECTools.__embedding_search(link, ask) return answer @tool("Search 10-K form") def search_10k(data): """ Useful to search information from the latest 10-K form for a given stock. The input to this tool should be a pipe (|) separated text of length two, representing the stock ticker you are interested, what question you have from it. For example, `AAPL|what was last year's revenue`. """ stock, ask = data.split("|") queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY']) query = { "query": { "query_string": { "query": f"ticker:{stock} AND formType:\"10-K\"" } }, "from": "0", "size": "1", "sort": [{ "filedAt": { "order": "desc" }}] } fillings = queryApi.get_filings(query)['filings'] if len(fillings) == 0: return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct." link = fillings[0]['linkToFilingDetails'] answer = SECTools.__embedding_search(link, ask) return answer def __embedding_search(url, ask): text = SECTools.__download_form_html(url) elements = partition_html(text=text) content = "\n".join([str(el) for el in elements]) text_splitter = CharacterTextSplitter( separator = "\n", chunk_size = 1000, chunk_overlap = 150, length_function = len, is_separator_regex = False, ) docs = text_splitter.create_documents([content]) retriever = FAISS.from_documents( docs, OpenAIEmbeddings() ).as_retriever() answers = retriever.get_relevant_documents(ask, top_k=4) answers = "\n\n".join([a.page_content for a in answers]) return answers def __download_form_html(url): headers = { 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'en-US,en;q=0.9,pt-BR;q=0.8,pt;q=0.7', 'Cache-Control': 'max-age=0', 'Dnt': '1', 'Sec-Ch-Ua': '"Not_A Brand";v="8", "Chromium";v="120"', 'Sec-Ch-Ua-Mobile': '?0', 'Sec-Ch-Ua-Platform': '"macOS"', 'Sec-Fetch-Dest': 'document', 'Sec-Fetch-Mode': 'navigate', 'Sec-Fetch-Site': 'none', 'Sec-Fetch-User': '?1', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36' } response = requests.get(url, headers=headers) return response.text

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# flake8: noqa from langchain.prompts.prompt import PromptTemplate _PROMPT_TEMPLATE = """You are GPT-3, and you can't do math. You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to just make up highly specific, but wrong, answers. So we hooked you up to a Python 3 kernel, and now you can execute code. If anyone gives you a hard math problem, just use this format and we’ll take care of the rest: Question: ${{Question with hard calculation.}} ```python ${{Code that prints what you need to know}} ``` ```output ${{Output of your code}} ``` Answer: ${{Answer}} Otherwise, use this simpler format: Question: ${{Question without hard calculation}} Answer: ${{Answer}} Begin. Question: What is 37593 * 67? ```python print(37593 * 67) ``` ```output 2518731 ``` Answer: 2518731 Question: {question} """ PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE)

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# flake8: noqa from langchain.prompts.prompt import PromptTemplate _PROMPT_TEMPLATE = """You are GPT-3, and you can't do math. You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to just make up highly specific, but wrong, answers. So we hooked you up to a Python 3 kernel, and now you can execute code. If anyone gives you a hard math problem, just use this format and we’ll take care of the rest: Question: ${{Question with hard calculation.}} ```python ${{Code that prints what you need to know}} ``` ```output ${{Output of your code}} ``` Answer: ${{Answer}} Otherwise, use this simpler format: Question: ${{Question without hard calculation}} Answer: ${{Answer}} Begin. Question: What is 37593 * 67? ```python print(37593 * 67) ``` ```output 2518731 ``` Answer: 2518731 Question: {question} """ PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE)

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# flake8: noqa from langchain.prompts.prompt import PromptTemplate _PROMPT_TEMPLATE = """You are GPT-3, and you can't do math. You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to just make up highly specific, but wrong, answers. So we hooked you up to a Python 3 kernel, and now you can execute code. If anyone gives you a hard math problem, just use this format and we’ll take care of the rest: Question: ${{Question with hard calculation.}} ```python ${{Code that prints what you need to know}} ``` ```output ${{Output of your code}} ``` Answer: ${{Answer}} Otherwise, use this simpler format: Question: ${{Question without hard calculation}} Answer: ${{Answer}} Begin. Question: What is 37593 * 67? ```python print(37593 * 67) ``` ```output 2518731 ``` Answer: 2518731 Question: {question} """ PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE)

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# flake8: noqa from langchain.prompts.prompt import PromptTemplate _PROMPT_TEMPLATE = """You are GPT-3, and you can't do math. You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to just make up highly specific, but wrong, answers. So we hooked you up to a Python 3 kernel, and now you can execute code. If anyone gives you a hard math problem, just use this format and we’ll take care of the rest: Question: ${{Question with hard calculation.}} ```python ${{Code that prints what you need to know}} ``` ```output ${{Output of your code}} ``` Answer: ${{Answer}} Otherwise, use this simpler format: Question: ${{Question without hard calculation}} Answer: ${{Answer}} Begin. Question: What is 37593 * 67? ```python print(37593 * 67) ``` ```output 2518731 ``` Answer: 2518731 Question: {question} """ PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE)

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import streamlit as st from langchain.prompts import PromptTemplate chat_template = PromptTemplate( input_variables=['transcript','summary','chat_history','user_message', 'sentiment_report'], template=''' You are an AI chatbot intended to discuss about the user's audio transcription. \nTRANSCRIPT: "{transcript}" \nTRANSCIRPT SUMMARY: "{summary}" \nTRANSCRIPT SENTIMENT REPORT: "{sentiment_report}" \nCHAT HISTORY: {chat_history} \nUSER MESSAGE: "{user_message}" \nAI RESPONSE HERE: ''' ) sentiment_prompt = PromptTemplate( input_variables=['transcript','summary'], template=''' Return a single word sentiment of either ['Positive','Negative' or 'Neutral'] from this transcript and summary. After that single word sentiment, add a comma, then return a sentiment report, analyzing transcript sentiment. \nTRANSCRIPT: {transcript} \nTRANSCRIPT SUMMARY: {summary} \nSENTIMENT LABEL HERE ('Positive','Negative', or 'Neutral') <comma-seperated> REPORT HERE: ''' ) fact_check_prompt = ''' Fact-check this transcript for factual or logical inacurracies or inconsistencies \nWrite a report on the factuality / logic of the transcirpt \nTRANSCRIPT: {} \nTRANSCRIPT SUMMARY: {} \nAI FACT CHECK RESPONSE HERE: '''

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from langchain.chains import LLMChain from langchain.prompts import PromptTemplate from langchain.chat_models import ChatOpenAI from dotenv import load_dotenv import os from langchain.chains import SimpleSequentialChain # Create a .env file in the root of your project and add your OpenAI API key to it # Load env files load_dotenv() openai_api_key = os.environ.get('openai_api_key') # This is an LLMChain to generate company names given a company description. llm = ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo") # Create templates template_name = """You are a company name generator. Based on a company description, it is your job to create a company name. Company description: {company_description} Company name:""" prompt_template_name = PromptTemplate(input_variables=["company_description"], template=template_name) # This is an LLMChain to generate company slogans given a company name and company description. template_slogan = """You are a company slogan generator. Based on a company name, it is your job to create a company slogan. Company name: {company_name} Company slogan:""" prompt_template_slogan = PromptTemplate(input_variables=["company_name"], template=template_slogan) # Create chains name_chain = LLMChain(llm=llm, prompt=prompt_template_name) slogan_chain = LLMChain(llm=llm, prompt=prompt_template_slogan) # This is the overall chain where we run these two chains in sequence. overall_chain = SimpleSequentialChain(chains=[name_chain, slogan_chain], verbose=True) slogan = overall_chain.run("We are a company that sells shoes.")

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import os import streamlit as st from PyPDF2 import PdfReader, PdfWriter from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import FAISS from langchain.chains.question_answering import load_qa_chain from langchain.llms import OpenAI from langchain.callbacks import get_openai_callback def ChatPDF(text): # st.write(text) #split into chunks text_splitter = CharacterTextSplitter( separator="\n", chunk_size = 1000, chunk_overlap = 200, length_function=len ) chunks = text_splitter.split_text(text) # st.write(chunks) # creating embeddings OPENAI_API_KEY = st.text_input("OPENAI API KEY", type = "password") if OPENAI_API_KEY: embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY) # st.write("Embedding Created") # st.write(embeddings) knowledge_base = FAISS.from_texts(chunks, embeddings) st.write("Knowledge Base created ") #show user input def ask_question(i=0): user_question = st.text_input("Ask a question about your PDF?",key = i) if user_question: docs = knowledge_base.similarity_search(user_question) # st.write(docs) llm = OpenAI(openai_api_key=OPENAI_API_KEY) chain = load_qa_chain(llm, chain_type="stuff") with get_openai_callback() as cb: response = chain.run(input_documents=docs, question=user_question) print(cb) st.write(response) ask_question(i+1) ask_question() def main(): st.set_page_config(page_title="Ask ur PDF", page_icon="📄") hide_st_style = """ <style> #mainMenue {visibility: hidden;} footer {visibility: hidden;} #header {visibility: hidden;} </style> """ st.markdown(hide_st_style, unsafe_allow_html=True) # st.write(st.set_page_config) st.header("Ask your PDF 🤔💭") #uploading file pdf = st.file_uploader("Upload your PDF ", type="pdf") # extract the text if pdf is not None: option = st.selectbox("What you want to do with PDF📜", [ "Meta Data📂", "Extract Raw Text📄", "Extract Links🔗", "Extract Images🖼️", "Make PDF password protected🔐", "PDF Annotation📝", "ChatPDF💬" ]) pdf_reader = PdfReader(pdf) text = "" for page in pdf_reader.pages: text += page.extract_text() if option == "Meta Data📂": st.write(pdf_reader.metadata) elif option == "Make PDF password protected🔐": pswd = st.text_input("Enter yourpass word", type="password") if pswd: with st.spinner("Encrypting..."): pdf_writer = PdfWriter() for page_num in range(len(pdf_reader.pages)): pdf_writer.add_page(pdf_reader.pages[page_num]) pdf_writer.encrypt(pswd) with open(f"{pdf.name.split('.')[0]}_encrypted.pdf", "wb") as f: pdf_writer.write(f) st.success("Encryption Successful!") st.download_button( label="Download Encrypted PDF", data=open(f"{pdf.name.split('.')[0]}_encrypted.pdf", "rb").read(), file_name=f"{pdf.name.split('.')[0]}_encrypted.pdf", mime="application/octet-stream", ) try: os.remove(f"{pdf.name.split('.')[0]}_encrypted.pdf") except: pass elif option == "Extract Raw Text📄": st.write(text) elif option == "Extract Links🔗": for page in pdf_reader.pages: if "/Annots" in page: for annot in page["/Annots"]: subtype = annot.get_object()["/Subtype"] if subtype == "/Link": try: st.write(annot.get_object()["/A"]["/URI"]) except: pass elif option == "Extract Images🖼️": for page in pdf_reader.pages: try: for img in page.images: st.write(img.name) st.image(img.data) except: pass elif option == "PDF Annotation📝": for page in pdf_reader.pages: if "/Annots" in page: for annot in page["/Annots"]: obj = annot.get_object() st.write(obj) st.write("***********") annotation = {"subtype": obj["/Subtype"], "location": obj["/Rect"]} st.write(annotation) elif option == "ChatPDF💬": ChatPDF(text) if __name__ == "__main__": main()

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import os import streamlit as st from PyPDF2 import PdfReader, PdfWriter from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import FAISS from langchain.chains.question_answering import load_qa_chain from langchain.llms import OpenAI from langchain.callbacks import get_openai_callback def ChatPDF(text): # st.write(text) #split into chunks text_splitter = CharacterTextSplitter( separator="\n", chunk_size = 1000, chunk_overlap = 200, length_function=len ) chunks = text_splitter.split_text(text) # st.write(chunks) # creating embeddings OPENAI_API_KEY = st.text_input("OPENAI API KEY", type = "password") if OPENAI_API_KEY: embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY) # st.write("Embedding Created") # st.write(embeddings) knowledge_base = FAISS.from_texts(chunks, embeddings) st.write("Knowledge Base created ") #show user input def ask_question(i=0): user_question = st.text_input("Ask a question about your PDF?",key = i) if user_question: docs = knowledge_base.similarity_search(user_question) # st.write(docs) llm = OpenAI(openai_api_key=OPENAI_API_KEY) chain = load_qa_chain(llm, chain_type="stuff") with get_openai_callback() as cb: response = chain.run(input_documents=docs, question=user_question) print(cb) st.write(response) ask_question(i+1) ask_question() def main(): st.set_page_config(page_title="Ask ur PDF", page_icon="📄") hide_st_style = """ <style> #mainMenue {visibility: hidden;} footer {visibility: hidden;} #header {visibility: hidden;} </style> """ st.markdown(hide_st_style, unsafe_allow_html=True) # st.write(st.set_page_config) st.header("Ask your PDF 🤔💭") #uploading file pdf = st.file_uploader("Upload your PDF ", type="pdf") # extract the text if pdf is not None: option = st.selectbox("What you want to do with PDF📜", [ "Meta Data📂", "Extract Raw Text📄", "Extract Links🔗", "Extract Images🖼️", "Make PDF password protected🔐", "PDF Annotation📝", "ChatPDF💬" ]) pdf_reader = PdfReader(pdf) text = "" for page in pdf_reader.pages: text += page.extract_text() if option == "Meta Data📂": st.write(pdf_reader.metadata) elif option == "Make PDF password protected🔐": pswd = st.text_input("Enter yourpass word", type="password") if pswd: with st.spinner("Encrypting..."): pdf_writer = PdfWriter() for page_num in range(len(pdf_reader.pages)): pdf_writer.add_page(pdf_reader.pages[page_num]) pdf_writer.encrypt(pswd) with open(f"{pdf.name.split('.')[0]}_encrypted.pdf", "wb") as f: pdf_writer.write(f) st.success("Encryption Successful!") st.download_button( label="Download Encrypted PDF", data=open(f"{pdf.name.split('.')[0]}_encrypted.pdf", "rb").read(), file_name=f"{pdf.name.split('.')[0]}_encrypted.pdf", mime="application/octet-stream", ) try: os.remove(f"{pdf.name.split('.')[0]}_encrypted.pdf") except: pass elif option == "Extract Raw Text📄": st.write(text) elif option == "Extract Links🔗": for page in pdf_reader.pages: if "/Annots" in page: for annot in page["/Annots"]: subtype = annot.get_object()["/Subtype"] if subtype == "/Link": try: st.write(annot.get_object()["/A"]["/URI"]) except: pass elif option == "Extract Images🖼️": for page in pdf_reader.pages: try: for img in page.images: st.write(img.name) st.image(img.data) except: pass elif option == "PDF Annotation📝": for page in pdf_reader.pages: if "/Annots" in page: for annot in page["/Annots"]: obj = annot.get_object() st.write(obj) st.write("***********") annotation = {"subtype": obj["/Subtype"], "location": obj["/Rect"]} st.write(annotation) elif option == "ChatPDF💬": ChatPDF(text) if __name__ == "__main__": main()

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"""Toolkit for the Wolfram Alpha API.""" from typing import List from langchain.tools.base import BaseTool, BaseToolkit from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper class WolframAlphaToolkit(BaseToolkit): """Tool that adds the capability to interact with Wolfram Alpha.""" wolfram_alpha_appid: str def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" wrapper = WolframAlphaAPIWrapper(wolfram_alpha_appid=self.wolfram_alpha_appid) return [ WolframAlphaQueryRun( api_wrapper=wrapper, ) ]

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"""Toolkit for the Wolfram Alpha API.""" from typing import List from langchain.tools.base import BaseTool, BaseToolkit from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper class WolframAlphaToolkit(BaseToolkit): """Tool that adds the capability to interact with Wolfram Alpha.""" wolfram_alpha_appid: str def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" wrapper = WolframAlphaAPIWrapper(wolfram_alpha_appid=self.wolfram_alpha_appid) return [ WolframAlphaQueryRun( api_wrapper=wrapper, ) ]

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"""The function tools tht are actually implemented""" import json import subprocess from langchain.agents.load_tools import load_tools from langchain.tools import BaseTool from langchain.utilities.bash import BashProcess from toolemu.tools.tool_interface import ( ArgException, ArgParameter, ArgReturn, FunctionTool, FunctionToolkit, ) from toolemu.utils.my_typing import * from .register import register_toolkit __ALL__ = ["RealTerminal", "RealPythonInterpreter", "RealWikipedia", "RealHuman"] class MyBashProcess(BashProcess): def _run(self, command: str) -> Tuple[str, int]: """ Runs a command in a subprocess and returns the output. Args: command: The command to run """ # noqa: E501 try: output = ( subprocess.run( command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, ) .stdout.decode() .strip() ) except subprocess.CalledProcessError as error: if self.return_err_output: return error.stdout.decode().strip(), error.returncode return str(error).strip(), error.returncode if self.strip_newlines: output = output.strip() return output, 0 #################### Terminal Interpreter #################### class RealTerminalExecute(FunctionTool): name = "TerminalExecute" summary = "Execute a terminal command and return the output. This command should follow proper syntax and be supported by the terminal environment." parameters: List[ArgParameter] = [ { "name": "command", "type": "string", "description": "The command to execute in the terminal.", "required": True, } ] returns: List[ArgReturn] = [ { "name": "output", "type": "string", "description": "The output generated by the executed terminal command, including both standard output and standard error streams.", }, { "name": "exit_code", "type": "integer", "description": "The exit code returned by the executed command. A zero value indicates successful execution, while non-zero values indicate errors or exceptions.", }, ] exceptions: List[ArgException] = [ { "name": "InvalidRequestException", "description": "The 'command' parameter contains an invalid or malformed command, which results in a failed execution attempt.", } ] _tool: BaseTool = MyBashProcess(return_err_output=True) def parse_return(self, tool_output: Dict[str, Any]) -> str: return json.dumps({"output": tool_output[0], "exit_code": tool_output[1]}) def _runtool(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: return self._tool._run(tool_input["command"]) def _aruntool(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: return self._tool._arun(tool_input["command"]) @register_toolkit() class RealTerminal(FunctionToolkit): name_for_human = "Terminal command executor" description_for_human = "Executes commands in a terminal." name_for_model = "Terminal" description_for_model = "Executes commands in a terminal on the user's local system. Use it to run valid terminal commands for tasks such as file management, system control, and more" tool_classes = [RealTerminalExecute] #################### Python Interpreter #################### class RealPythonInterpreterExecute(FunctionTool): name = "PythonInterpreterExecute" summary = "Execute a Python script." parameters: List[ArgParameter] = [ { "name": "script", "type": "string", "description": "The python script to execute.", "required": True, } ] returns: List[ArgReturn] = [ { "name": "result", "type": "string", "description": "The printed output of the script.", } ] exceptions: List[ArgException] = [] _tool: BaseTool = load_tools(["python_repl"])[0] def parse_return(self, tool_output: str) -> str: return json.dumps({"result": tool_output}) def _runtool(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: return self._tool._run(tool_input["script"]) def _aruntool(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: return self._tool._arun(tool_input["script"]) @register_toolkit() class RealPythonInterpreter(FunctionToolkit): name_for_human = "Python interpreter" description_for_human = "A Python shell." name_for_model = "PythonInterpreter" description_for_model = "A Python shell. Use it to execute python scripts. If you want to see the output of a value, you should print it out with `print(...)`." tool_classes = [RealPythonInterpreterExecute] #################### Wikipedia #################### class RealWikipediaSearch(FunctionTool): name = "WikipediaSearch" summary = "Query the Wikipedia tool for a given query." parameters: List[ArgParameter] = [ { "name": "query", "type": "string", "description": "The query to search for.", "required": True, } ] returns: List[ArgReturn] = [ { "name": "result", "type": "string", "description": "The summary of the Wikipedia article.", } ] exceptions: List[ArgException] = [] _tool: BaseTool = load_tools(["wikipedia"])[0] def parse_return(self, tool_output: str) -> str: return json.dumps({"result": tool_output}) def _runtool(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: return self._tool._run(tool_input["query"]) def _aruntool(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: return self._tool._arun(tool_input["query"]) @register_toolkit() class RealWikipedia(FunctionToolkit): name_for_human = "Wikipedia search tool" description_for_human = "Tool for searching through Wikipedia." name_for_model = "Wikipedia" description_for_model = "Tool for searching through Wikipedia. Use it whenever you need to provide accurate responses for general questions about people, places, companies, historical events, or other subjects." tool_classes = [RealWikipediaSearch] #################### Human #################### class RealHumanAssistanceQuery(FunctionTool): name = "HumanAssistanceQuery" summary = "Ask the human a specific question" parameters: List[ArgParameter] = [ { "name": "question", "type": "string", "description": "The question to ask.", "required": True, } ] returns: List[ArgReturn] = [ { "name": "answer", "type": "string", "description": "The answer from the human.", } ] exceptions: List[ArgException] = [] def parse_return(self, tool_output: str) -> str: return json.dumps({"answer": tool_output}) def _runtool(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: print("\n" + tool_input["question"] + "\n") return input(tool_input["question"]) def _aruntool(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: return NotImplementedError("Human tool does not support async") @register_toolkit() class RealHuman(FunctionToolkit): name_for_human = "Human assistance" description_for_human = "Seek human assistance or guidance." name_for_model = "HumanAssistance" description_for_model = "Seek human assistance or guidance. Use it when expert human or user input is necessary, e.g., when you need some human knowledge, user permission, user-specific information." tool_classes = [RealHumanAssistanceQuery]

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from typing import List, Optional, Any, Dict from langchain.llms.base import LLM from langchain.utils import get_from_dict_or_env from pydantic import Extra, root_validator from sam.gpt.quora import PoeClient, PoeResponse # token = "KaEMfvDPEXoS115jzAFRRg%3D%3D" # prompt = "write a java function that prints the nth fibonacci number. provide example usage" # streaming_response = False # render_markdown = True # chat_mode = False class Poe(LLM): client: PoeClient model: Optional[str] = "gpt-3.5-turbo" custom_model: bool = False token: str @root_validator() def validate_environment(cls, values: Dict) -> Dict: token = get_from_dict_or_env( values, "token", "POE_COOKIE" ) values["client"] = PoeClient(token) return values class Config: extra = Extra.forbid @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling Cohere API.""" models = { 'sage': 'capybara', 'gpt-4': 'beaver', 'claude-v1.2': 'a2_2', 'claude-instant-v1.0': 'a2', 'gpt-3.5-turbo': 'chinchilla', } _model = models[self.model] if not self.custom_model else self.model return { "model": _model, "token": self.token, } @property def _identifying_params(self) -> Dict[str, Any]: return {**{"model": self.model}, **self._default_params} @property def _llm_type(self) -> str: return "poe" def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str: params = self._default_params for chunk in self.client.send_message(params.model, prompt): pass response = PoeResponse( { 'id': chunk['messageId'], 'object': 'text_completion', 'created': chunk['creationTime'], 'model': params.model, 'choices': [ { 'text': chunk['text'], 'index': 0, 'logprobs': None, 'finish_reason': 'stop', } ], 'usage': { 'prompt_tokens': len(prompt), 'completion_tokens': len(chunk['text']), 'total_tokens': len(prompt) + len(chunk['text']), }, } ) text = response.completion.choices[0].text return text

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from typing import List, Optional, Any, Dict from langchain.llms.base import LLM from langchain.utils import get_from_dict_or_env from pydantic import Extra, root_validator from sam.gpt.quora import PoeClient, PoeResponse # token = "KaEMfvDPEXoS115jzAFRRg%3D%3D" # prompt = "write a java function that prints the nth fibonacci number. provide example usage" # streaming_response = False # render_markdown = True # chat_mode = False class Poe(LLM): client: PoeClient model: Optional[str] = "gpt-3.5-turbo" custom_model: bool = False token: str @root_validator() def validate_environment(cls, values: Dict) -> Dict: token = get_from_dict_or_env( values, "token", "POE_COOKIE" ) values["client"] = PoeClient(token) return values class Config: extra = Extra.forbid @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling Cohere API.""" models = { 'sage': 'capybara', 'gpt-4': 'beaver', 'claude-v1.2': 'a2_2', 'claude-instant-v1.0': 'a2', 'gpt-3.5-turbo': 'chinchilla', } _model = models[self.model] if not self.custom_model else self.model return { "model": _model, "token": self.token, } @property def _identifying_params(self) -> Dict[str, Any]: return {**{"model": self.model}, **self._default_params} @property def _llm_type(self) -> str: return "poe" def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str: params = self._default_params for chunk in self.client.send_message(params.model, prompt): pass response = PoeResponse( { 'id': chunk['messageId'], 'object': 'text_completion', 'created': chunk['creationTime'], 'model': params.model, 'choices': [ { 'text': chunk['text'], 'index': 0, 'logprobs': None, 'finish_reason': 'stop', } ], 'usage': { 'prompt_tokens': len(prompt), 'completion_tokens': len(chunk['text']), 'total_tokens': len(prompt) + len(chunk['text']), }, } ) text = response.completion.choices[0].text return text

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from __future__ import annotations from typing import List, Optional from pydantic import ValidationError from langchain.chains.llm import LLMChain from langchain.chat_models.base import BaseChatModel from langchain.experimental.autonomous_agents.autogpt.output_parser import ( AutoGPTOutputParser, BaseAutoGPTOutputParser, ) from langchain.experimental.autonomous_agents.autogpt.prompt import AutoGPTPrompt from langchain.experimental.autonomous_agents.autogpt.prompt_generator import ( FINISH_NAME, ) from langchain.schema import ( AIMessage, BaseMessage, Document, HumanMessage, SystemMessage, ) from langchain.tools.base import BaseTool from langchain.tools.human.tool import HumanInputRun from langchain.vectorstores.base import VectorStoreRetriever class AutoGPT: """Agent class for interacting with Auto-GPT.""" def __init__( self, ai_name: str, memory: VectorStoreRetriever, chain: LLMChain, output_parser: BaseAutoGPTOutputParser, tools: List[BaseTool], feedback_tool: Optional[HumanInputRun] = None, ): self.ai_name = ai_name self.memory = memory self.full_message_history: List[BaseMessage] = [] self.next_action_count = 0 self.chain = chain self.output_parser = output_parser self.tools = tools self.feedback_tool = feedback_tool @classmethod def from_llm_and_tools( cls, ai_name: str, ai_role: str, memory: VectorStoreRetriever, tools: List[BaseTool], llm: BaseChatModel, human_in_the_loop: bool = False, output_parser: Optional[BaseAutoGPTOutputParser] = None, ) -> AutoGPT: prompt = AutoGPTPrompt( ai_name=ai_name, ai_role=ai_role, tools=tools, input_variables=["memory", "messages", "goals", "user_input"], token_counter=llm.get_num_tokens, ) human_feedback_tool = HumanInputRun() if human_in_the_loop else None chain = LLMChain(llm=llm, prompt=prompt) return cls( ai_name, memory, chain, output_parser or AutoGPTOutputParser(), tools, feedback_tool=human_feedback_tool, ) def run(self, goals: List[str]) -> str: user_input = ( "Determine which next command to use, " "and respond using the format specified above:" ) # Interaction Loop loop_count = 0 while True: # Discontinue if continuous limit is reached loop_count += 1 # Send message to AI, get response assistant_reply = self.chain.run( goals=goals, messages=self.full_message_history, memory=self.memory, user_input=user_input, ) # Print Assistant thoughts print(assistant_reply) self.full_message_history.append(HumanMessage(content=user_input)) self.full_message_history.append(AIMessage(content=assistant_reply)) # Get command name and arguments action = self.output_parser.parse(assistant_reply) tools = {t.name: t for t in self.tools} if action.name == FINISH_NAME: return action.args["response"] if action.name in tools: tool = tools[action.name] try: observation = tool.run(action.args) except ValidationError as e: observation = ( f"Validation Error in args: {str(e)}, args: {action.args}" ) except Exception as e: observation = ( f"Error: {str(e)}, {type(e).__name__}, args: {action.args}" ) result = f"Command {tool.name} returned: {observation}" elif action.name == "ERROR": result = f"Error: {action.args}. " else: result = ( f"Unknown command '{action.name}'. " f"Please refer to the 'COMMANDS' list for available " f"commands and only respond in the specified JSON format." ) memory_to_add = ( f"Assistant Reply: {assistant_reply} " f"\nResult: {result} " ) if self.feedback_tool is not None: feedback = f"\n{self.feedback_tool.run('Input: ')}" if feedback in {"q", "stop"}: print("EXITING") return "EXITING" memory_to_add += feedback self.memory.add_documents([Document(page_content=memory_to_add)]) self.full_message_history.append(SystemMessage(content=result))

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from __future__ import annotations from typing import List, Optional from pydantic import ValidationError from langchain.chains.llm import LLMChain from langchain.chat_models.base import BaseChatModel from langchain.experimental.autonomous_agents.autogpt.output_parser import ( AutoGPTOutputParser, BaseAutoGPTOutputParser, ) from langchain.experimental.autonomous_agents.autogpt.prompt import AutoGPTPrompt from langchain.experimental.autonomous_agents.autogpt.prompt_generator import ( FINISH_NAME, ) from langchain.schema import ( AIMessage, BaseMessage, Document, HumanMessage, SystemMessage, ) from langchain.tools.base import BaseTool from langchain.tools.human.tool import HumanInputRun from langchain.vectorstores.base import VectorStoreRetriever class AutoGPT: """Agent class for interacting with Auto-GPT.""" def __init__( self, ai_name: str, memory: VectorStoreRetriever, chain: LLMChain, output_parser: BaseAutoGPTOutputParser, tools: List[BaseTool], feedback_tool: Optional[HumanInputRun] = None, ): self.ai_name = ai_name self.memory = memory self.full_message_history: List[BaseMessage] = [] self.next_action_count = 0 self.chain = chain self.output_parser = output_parser self.tools = tools self.feedback_tool = feedback_tool @classmethod def from_llm_and_tools( cls, ai_name: str, ai_role: str, memory: VectorStoreRetriever, tools: List[BaseTool], llm: BaseChatModel, human_in_the_loop: bool = False, output_parser: Optional[BaseAutoGPTOutputParser] = None, ) -> AutoGPT: prompt = AutoGPTPrompt( ai_name=ai_name, ai_role=ai_role, tools=tools, input_variables=["memory", "messages", "goals", "user_input"], token_counter=llm.get_num_tokens, ) human_feedback_tool = HumanInputRun() if human_in_the_loop else None chain = LLMChain(llm=llm, prompt=prompt) return cls( ai_name, memory, chain, output_parser or AutoGPTOutputParser(), tools, feedback_tool=human_feedback_tool, ) def run(self, goals: List[str]) -> str: user_input = ( "Determine which next command to use, " "and respond using the format specified above:" ) # Interaction Loop loop_count = 0 while True: # Discontinue if continuous limit is reached loop_count += 1 # Send message to AI, get response assistant_reply = self.chain.run( goals=goals, messages=self.full_message_history, memory=self.memory, user_input=user_input, ) # Print Assistant thoughts print(assistant_reply) self.full_message_history.append(HumanMessage(content=user_input)) self.full_message_history.append(AIMessage(content=assistant_reply)) # Get command name and arguments action = self.output_parser.parse(assistant_reply) tools = {t.name: t for t in self.tools} if action.name == FINISH_NAME: return action.args["response"] if action.name in tools: tool = tools[action.name] try: observation = tool.run(action.args) except ValidationError as e: observation = ( f"Validation Error in args: {str(e)}, args: {action.args}" ) except Exception as e: observation = ( f"Error: {str(e)}, {type(e).__name__}, args: {action.args}" ) result = f"Command {tool.name} returned: {observation}" elif action.name == "ERROR": result = f"Error: {action.args}. " else: result = ( f"Unknown command '{action.name}'. " f"Please refer to the 'COMMANDS' list for available " f"commands and only respond in the specified JSON format." ) memory_to_add = ( f"Assistant Reply: {assistant_reply} " f"\nResult: {result} " ) if self.feedback_tool is not None: feedback = f"\n{self.feedback_tool.run('Input: ')}" if feedback in {"q", "stop"}: print("EXITING") return "EXITING" memory_to_add += feedback self.memory.add_documents([Document(page_content=memory_to_add)]) self.full_message_history.append(SystemMessage(content=result))

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from __future__ import annotations from typing import List, Optional from pydantic import ValidationError from langchain.chains.llm import LLMChain from langchain.chat_models.base import BaseChatModel from langchain.experimental.autonomous_agents.autogpt.output_parser import ( AutoGPTOutputParser, BaseAutoGPTOutputParser, ) from langchain.experimental.autonomous_agents.autogpt.prompt import AutoGPTPrompt from langchain.experimental.autonomous_agents.autogpt.prompt_generator import ( FINISH_NAME, ) from langchain.schema import ( AIMessage, BaseMessage, Document, HumanMessage, SystemMessage, ) from langchain.tools.base import BaseTool from langchain.tools.human.tool import HumanInputRun from langchain.vectorstores.base import VectorStoreRetriever class AutoGPT: """Agent class for interacting with Auto-GPT.""" def __init__( self, ai_name: str, memory: VectorStoreRetriever, chain: LLMChain, output_parser: BaseAutoGPTOutputParser, tools: List[BaseTool], feedback_tool: Optional[HumanInputRun] = None, ): self.ai_name = ai_name self.memory = memory self.full_message_history: List[BaseMessage] = [] self.next_action_count = 0 self.chain = chain self.output_parser = output_parser self.tools = tools self.feedback_tool = feedback_tool @classmethod def from_llm_and_tools( cls, ai_name: str, ai_role: str, memory: VectorStoreRetriever, tools: List[BaseTool], llm: BaseChatModel, human_in_the_loop: bool = False, output_parser: Optional[BaseAutoGPTOutputParser] = None, ) -> AutoGPT: prompt = AutoGPTPrompt( ai_name=ai_name, ai_role=ai_role, tools=tools, input_variables=["memory", "messages", "goals", "user_input"], token_counter=llm.get_num_tokens, ) human_feedback_tool = HumanInputRun() if human_in_the_loop else None chain = LLMChain(llm=llm, prompt=prompt) return cls( ai_name, memory, chain, output_parser or AutoGPTOutputParser(), tools, feedback_tool=human_feedback_tool, ) def run(self, goals: List[str]) -> str: user_input = ( "Determine which next command to use, " "and respond using the format specified above:" ) # Interaction Loop loop_count = 0 while True: # Discontinue if continuous limit is reached loop_count += 1 # Send message to AI, get response assistant_reply = self.chain.run( goals=goals, messages=self.full_message_history, memory=self.memory, user_input=user_input, ) # Print Assistant thoughts print(assistant_reply) self.full_message_history.append(HumanMessage(content=user_input)) self.full_message_history.append(AIMessage(content=assistant_reply)) # Get command name and arguments action = self.output_parser.parse(assistant_reply) tools = {t.name: t for t in self.tools} if action.name == FINISH_NAME: return action.args["response"] if action.name in tools: tool = tools[action.name] try: observation = tool.run(action.args) except ValidationError as e: observation = ( f"Validation Error in args: {str(e)}, args: {action.args}" ) except Exception as e: observation = ( f"Error: {str(e)}, {type(e).__name__}, args: {action.args}" ) result = f"Command {tool.name} returned: {observation}" elif action.name == "ERROR": result = f"Error: {action.args}. " else: result = ( f"Unknown command '{action.name}'. " f"Please refer to the 'COMMANDS' list for available " f"commands and only respond in the specified JSON format." ) memory_to_add = ( f"Assistant Reply: {assistant_reply} " f"\nResult: {result} " ) if self.feedback_tool is not None: feedback = f"\n{self.feedback_tool.run('Input: ')}" if feedback in {"q", "stop"}: print("EXITING") return "EXITING" memory_to_add += feedback self.memory.add_documents([Document(page_content=memory_to_add)]) self.full_message_history.append(SystemMessage(content=result))

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from __future__ import annotations from typing import List, Optional from pydantic import ValidationError from langchain.chains.llm import LLMChain from langchain.chat_models.base import BaseChatModel from langchain.experimental.autonomous_agents.autogpt.output_parser import ( AutoGPTOutputParser, BaseAutoGPTOutputParser, ) from langchain.experimental.autonomous_agents.autogpt.prompt import AutoGPTPrompt from langchain.experimental.autonomous_agents.autogpt.prompt_generator import ( FINISH_NAME, ) from langchain.schema import ( AIMessage, BaseMessage, Document, HumanMessage, SystemMessage, ) from langchain.tools.base import BaseTool from langchain.tools.human.tool import HumanInputRun from langchain.vectorstores.base import VectorStoreRetriever class AutoGPT: """Agent class for interacting with Auto-GPT.""" def __init__( self, ai_name: str, memory: VectorStoreRetriever, chain: LLMChain, output_parser: BaseAutoGPTOutputParser, tools: List[BaseTool], feedback_tool: Optional[HumanInputRun] = None, ): self.ai_name = ai_name self.memory = memory self.full_message_history: List[BaseMessage] = [] self.next_action_count = 0 self.chain = chain self.output_parser = output_parser self.tools = tools self.feedback_tool = feedback_tool @classmethod def from_llm_and_tools( cls, ai_name: str, ai_role: str, memory: VectorStoreRetriever, tools: List[BaseTool], llm: BaseChatModel, human_in_the_loop: bool = False, output_parser: Optional[BaseAutoGPTOutputParser] = None, ) -> AutoGPT: prompt = AutoGPTPrompt( ai_name=ai_name, ai_role=ai_role, tools=tools, input_variables=["memory", "messages", "goals", "user_input"], token_counter=llm.get_num_tokens, ) human_feedback_tool = HumanInputRun() if human_in_the_loop else None chain = LLMChain(llm=llm, prompt=prompt) return cls( ai_name, memory, chain, output_parser or AutoGPTOutputParser(), tools, feedback_tool=human_feedback_tool, ) def run(self, goals: List[str]) -> str: user_input = ( "Determine which next command to use, " "and respond using the format specified above:" ) # Interaction Loop loop_count = 0 while True: # Discontinue if continuous limit is reached loop_count += 1 # Send message to AI, get response assistant_reply = self.chain.run( goals=goals, messages=self.full_message_history, memory=self.memory, user_input=user_input, ) # Print Assistant thoughts print(assistant_reply) self.full_message_history.append(HumanMessage(content=user_input)) self.full_message_history.append(AIMessage(content=assistant_reply)) # Get command name and arguments action = self.output_parser.parse(assistant_reply) tools = {t.name: t for t in self.tools} if action.name == FINISH_NAME: return action.args["response"] if action.name in tools: tool = tools[action.name] try: observation = tool.run(action.args) except ValidationError as e: observation = ( f"Validation Error in args: {str(e)}, args: {action.args}" ) except Exception as e: observation = ( f"Error: {str(e)}, {type(e).__name__}, args: {action.args}" ) result = f"Command {tool.name} returned: {observation}" elif action.name == "ERROR": result = f"Error: {action.args}. " else: result = ( f"Unknown command '{action.name}'. " f"Please refer to the 'COMMANDS' list for available " f"commands and only respond in the specified JSON format." ) memory_to_add = ( f"Assistant Reply: {assistant_reply} " f"\nResult: {result} " ) if self.feedback_tool is not None: feedback = f"\n{self.feedback_tool.run('Input: ')}" if feedback in {"q", "stop"}: print("EXITING") return "EXITING" memory_to_add += feedback self.memory.add_documents([Document(page_content=memory_to_add)]) self.full_message_history.append(SystemMessage(content=result))

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"""Map-reduce chain. Splits up a document, sends the smaller parts to the LLM with one prompt, then combines the results with another one. """ from __future__ import annotations from typing import Any, Dict, List, Mapping, Optional from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks from langchain.chains import ReduceDocumentsChain from langchain.chains.base import Chain from langchain.chains.combine_documents.base import BaseCombineDocumentsChain from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.llm import LLMChain from langchain.docstore.document import Document from langchain.pydantic_v1 import Extra from langchain.schema import BasePromptTemplate from langchain.schema.language_model import BaseLanguageModel from langchain.text_splitter import TextSplitter class MapReduceChain(Chain): """Map-reduce chain.""" combine_documents_chain: BaseCombineDocumentsChain """Chain to use to combine documents.""" text_splitter: TextSplitter """Text splitter to use.""" input_key: str = "input_text" #: :meta private: output_key: str = "output_text" #: :meta private: @classmethod def from_params( cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, text_splitter: TextSplitter, callbacks: Callbacks = None, combine_chain_kwargs: Optional[Mapping[str, Any]] = None, reduce_chain_kwargs: Optional[Mapping[str, Any]] = None, **kwargs: Any, ) -> MapReduceChain: """Construct a map-reduce chain that uses the chain for map and reduce.""" llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks) stuff_chain = StuffDocumentsChain( llm_chain=llm_chain, callbacks=callbacks, **(reduce_chain_kwargs if reduce_chain_kwargs else {}), ) reduce_documents_chain = ReduceDocumentsChain( combine_documents_chain=stuff_chain ) combine_documents_chain = MapReduceDocumentsChain( llm_chain=llm_chain, reduce_documents_chain=reduce_documents_chain, callbacks=callbacks, **(combine_chain_kwargs if combine_chain_kwargs else {}), ) return cls( combine_documents_chain=combine_documents_chain, text_splitter=text_splitter, callbacks=callbacks, **kwargs, ) class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.input_key] @property def output_keys(self) -> List[str]: """Return output key. :meta private: """ return [self.output_key] def _call( self, inputs: Dict[str, str], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() # Split the larger text into smaller chunks. doc_text = inputs.pop(self.input_key) texts = self.text_splitter.split_text(doc_text) docs = [Document(page_content=text) for text in texts] _inputs: Dict[str, Any] = { **inputs, self.combine_documents_chain.input_key: docs, } outputs = self.combine_documents_chain.run( _inputs, callbacks=_run_manager.get_child() ) return {self.output_key: outputs}

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"""Map-reduce chain. Splits up a document, sends the smaller parts to the LLM with one prompt, then combines the results with another one. """ from __future__ import annotations from typing import Any, Dict, List, Mapping, Optional from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks from langchain.chains import ReduceDocumentsChain from langchain.chains.base import Chain from langchain.chains.combine_documents.base import BaseCombineDocumentsChain from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.llm import LLMChain from langchain.docstore.document import Document from langchain.pydantic_v1 import Extra from langchain.schema import BasePromptTemplate from langchain.schema.language_model import BaseLanguageModel from langchain.text_splitter import TextSplitter class MapReduceChain(Chain): """Map-reduce chain.""" combine_documents_chain: BaseCombineDocumentsChain """Chain to use to combine documents.""" text_splitter: TextSplitter """Text splitter to use.""" input_key: str = "input_text" #: :meta private: output_key: str = "output_text" #: :meta private: @classmethod def from_params( cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, text_splitter: TextSplitter, callbacks: Callbacks = None, combine_chain_kwargs: Optional[Mapping[str, Any]] = None, reduce_chain_kwargs: Optional[Mapping[str, Any]] = None, **kwargs: Any, ) -> MapReduceChain: """Construct a map-reduce chain that uses the chain for map and reduce.""" llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks) stuff_chain = StuffDocumentsChain( llm_chain=llm_chain, callbacks=callbacks, **(reduce_chain_kwargs if reduce_chain_kwargs else {}), ) reduce_documents_chain = ReduceDocumentsChain( combine_documents_chain=stuff_chain ) combine_documents_chain = MapReduceDocumentsChain( llm_chain=llm_chain, reduce_documents_chain=reduce_documents_chain, callbacks=callbacks, **(combine_chain_kwargs if combine_chain_kwargs else {}), ) return cls( combine_documents_chain=combine_documents_chain, text_splitter=text_splitter, callbacks=callbacks, **kwargs, ) class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.input_key] @property def output_keys(self) -> List[str]: """Return output key. :meta private: """ return [self.output_key] def _call( self, inputs: Dict[str, str], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() # Split the larger text into smaller chunks. doc_text = inputs.pop(self.input_key) texts = self.text_splitter.split_text(doc_text) docs = [Document(page_content=text) for text in texts] _inputs: Dict[str, Any] = { **inputs, self.combine_documents_chain.input_key: docs, } outputs = self.combine_documents_chain.run( _inputs, callbacks=_run_manager.get_child() ) return {self.output_key: outputs}

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"""Map-reduce chain. Splits up a document, sends the smaller parts to the LLM with one prompt, then combines the results with another one. """ from __future__ import annotations from typing import Any, Dict, List, Mapping, Optional from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks from langchain.chains import ReduceDocumentsChain from langchain.chains.base import Chain from langchain.chains.combine_documents.base import BaseCombineDocumentsChain from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.llm import LLMChain from langchain.docstore.document import Document from langchain.pydantic_v1 import Extra from langchain.schema import BasePromptTemplate from langchain.schema.language_model import BaseLanguageModel from langchain.text_splitter import TextSplitter class MapReduceChain(Chain): """Map-reduce chain.""" combine_documents_chain: BaseCombineDocumentsChain """Chain to use to combine documents.""" text_splitter: TextSplitter """Text splitter to use.""" input_key: str = "input_text" #: :meta private: output_key: str = "output_text" #: :meta private: @classmethod def from_params( cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, text_splitter: TextSplitter, callbacks: Callbacks = None, combine_chain_kwargs: Optional[Mapping[str, Any]] = None, reduce_chain_kwargs: Optional[Mapping[str, Any]] = None, **kwargs: Any, ) -> MapReduceChain: """Construct a map-reduce chain that uses the chain for map and reduce.""" llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks) stuff_chain = StuffDocumentsChain( llm_chain=llm_chain, callbacks=callbacks, **(reduce_chain_kwargs if reduce_chain_kwargs else {}), ) reduce_documents_chain = ReduceDocumentsChain( combine_documents_chain=stuff_chain ) combine_documents_chain = MapReduceDocumentsChain( llm_chain=llm_chain, reduce_documents_chain=reduce_documents_chain, callbacks=callbacks, **(combine_chain_kwargs if combine_chain_kwargs else {}), ) return cls( combine_documents_chain=combine_documents_chain, text_splitter=text_splitter, callbacks=callbacks, **kwargs, ) class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.input_key] @property def output_keys(self) -> List[str]: """Return output key. :meta private: """ return [self.output_key] def _call( self, inputs: Dict[str, str], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() # Split the larger text into smaller chunks. doc_text = inputs.pop(self.input_key) texts = self.text_splitter.split_text(doc_text) docs = [Document(page_content=text) for text in texts] _inputs: Dict[str, Any] = { **inputs, self.combine_documents_chain.input_key: docs, } outputs = self.combine_documents_chain.run( _inputs, callbacks=_run_manager.get_child() ) return {self.output_key: outputs}

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"""Map-reduce chain. Splits up a document, sends the smaller parts to the LLM with one prompt, then combines the results with another one. """ from __future__ import annotations from typing import Any, Dict, List, Mapping, Optional from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks from langchain.chains import ReduceDocumentsChain from langchain.chains.base import Chain from langchain.chains.combine_documents.base import BaseCombineDocumentsChain from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.llm import LLMChain from langchain.docstore.document import Document from langchain.pydantic_v1 import Extra from langchain.schema import BasePromptTemplate from langchain.schema.language_model import BaseLanguageModel from langchain.text_splitter import TextSplitter class MapReduceChain(Chain): """Map-reduce chain.""" combine_documents_chain: BaseCombineDocumentsChain """Chain to use to combine documents.""" text_splitter: TextSplitter """Text splitter to use.""" input_key: str = "input_text" #: :meta private: output_key: str = "output_text" #: :meta private: @classmethod def from_params( cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, text_splitter: TextSplitter, callbacks: Callbacks = None, combine_chain_kwargs: Optional[Mapping[str, Any]] = None, reduce_chain_kwargs: Optional[Mapping[str, Any]] = None, **kwargs: Any, ) -> MapReduceChain: """Construct a map-reduce chain that uses the chain for map and reduce.""" llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks) stuff_chain = StuffDocumentsChain( llm_chain=llm_chain, callbacks=callbacks, **(reduce_chain_kwargs if reduce_chain_kwargs else {}), ) reduce_documents_chain = ReduceDocumentsChain( combine_documents_chain=stuff_chain ) combine_documents_chain = MapReduceDocumentsChain( llm_chain=llm_chain, reduce_documents_chain=reduce_documents_chain, callbacks=callbacks, **(combine_chain_kwargs if combine_chain_kwargs else {}), ) return cls( combine_documents_chain=combine_documents_chain, text_splitter=text_splitter, callbacks=callbacks, **kwargs, ) class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.input_key] @property def output_keys(self) -> List[str]: """Return output key. :meta private: """ return [self.output_key] def _call( self, inputs: Dict[str, str], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() # Split the larger text into smaller chunks. doc_text = inputs.pop(self.input_key) texts = self.text_splitter.split_text(doc_text) docs = [Document(page_content=text) for text in texts] _inputs: Dict[str, Any] = { **inputs, self.combine_documents_chain.input_key: docs, } outputs = self.combine_documents_chain.run( _inputs, callbacks=_run_manager.get_child() ) return {self.output_key: outputs}

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from typing import Any, Dict, List, Optional, Sequence from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens from langchain.pydantic_v1 import Extra, root_validator from langchain.utils import get_from_dict_or_env class AlephAlpha(LLM): """Aleph Alpha large language models. To use, you should have the ``aleph_alpha_client`` python package installed, and the environment variable ``ALEPH_ALPHA_API_KEY`` set with your API key, or pass it as a named parameter to the constructor. Parameters are explained more in depth here: https://github.com/Aleph-Alpha/aleph-alpha-client/blob/c14b7dd2b4325c7da0d6a119f6e76385800e097b/aleph_alpha_client/completion.py#L10 Example: .. code-block:: python from langchain.llms import AlephAlpha aleph_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key") """ client: Any #: :meta private: model: Optional[str] = "luminous-base" """Model name to use.""" maximum_tokens: int = 64 """The maximum number of tokens to be generated.""" temperature: float = 0.0 """A non-negative float that tunes the degree of randomness in generation.""" top_k: int = 0 """Number of most likely tokens to consider at each step.""" top_p: float = 0.0 """Total probability mass of tokens to consider at each step.""" presence_penalty: float = 0.0 """Penalizes repeated tokens.""" frequency_penalty: float = 0.0 """Penalizes repeated tokens according to frequency.""" repetition_penalties_include_prompt: Optional[bool] = False """Flag deciding whether presence penalty or frequency penalty are updated from the prompt.""" use_multiplicative_presence_penalty: Optional[bool] = False """Flag deciding whether presence penalty is applied multiplicatively (True) or additively (False).""" penalty_bias: Optional[str] = None """Penalty bias for the completion.""" penalty_exceptions: Optional[List[str]] = None """List of strings that may be generated without penalty, regardless of other penalty settings""" penalty_exceptions_include_stop_sequences: Optional[bool] = None """Should stop_sequences be included in penalty_exceptions.""" best_of: Optional[int] = None """returns the one with the "best of" results (highest log probability per token) """ n: int = 1 """How many completions to generate for each prompt.""" logit_bias: Optional[Dict[int, float]] = None """The logit bias allows to influence the likelihood of generating tokens.""" log_probs: Optional[int] = None """Number of top log probabilities to be returned for each generated token.""" tokens: Optional[bool] = False """return tokens of completion.""" disable_optimizations: Optional[bool] = False minimum_tokens: Optional[int] = 0 """Generate at least this number of tokens.""" echo: bool = False """Echo the prompt in the completion.""" use_multiplicative_frequency_penalty: bool = False sequence_penalty: float = 0.0 sequence_penalty_min_length: int = 2 use_multiplicative_sequence_penalty: bool = False completion_bias_inclusion: Optional[Sequence[str]] = None completion_bias_inclusion_first_token_only: bool = False completion_bias_exclusion: Optional[Sequence[str]] = None completion_bias_exclusion_first_token_only: bool = False """Only consider the first token for the completion_bias_exclusion.""" contextual_control_threshold: Optional[float] = None """If set to None, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-None value, control parameters are also applied to similar tokens. """ control_log_additive: Optional[bool] = True """True: apply control by adding the log(control_factor) to attention scores. False: (attention_scores - - attention_scores.min(-1)) * control_factor """ repetition_penalties_include_completion: bool = True """Flag deciding whether presence penalty or frequency penalty are updated from the completion.""" raw_completion: bool = False """Force the raw completion of the model to be returned.""" stop_sequences: Optional[List[str]] = None """Stop sequences to use.""" # Client params aleph_alpha_api_key: Optional[str] = None """API key for Aleph Alpha API.""" host: str = "https://api.aleph-alpha.com" """The hostname of the API host. The default one is "https://api.aleph-alpha.com")""" hosting: Optional[str] = None """Determines in which datacenters the request may be processed. You can either set the parameter to "aleph-alpha" or omit it (defaulting to None). Not setting this value, or setting it to None, gives us maximal flexibility in processing your request in our own datacenters and on servers hosted with other providers. Choose this option for maximal availability. Setting it to "aleph-alpha" allows us to only process the request in our own datacenters. Choose this option for maximal data privacy.""" request_timeout_seconds: int = 305 """Client timeout that will be set for HTTP requests in the `requests` library's API calls. Server will close all requests after 300 seconds with an internal server error.""" total_retries: int = 8 """The number of retries made in case requests fail with certain retryable status codes. If the last retry fails a corresponding exception is raised. Note, that between retries an exponential backoff is applied, starting with 0.5 s after the first retry and doubling for each retry made. So with the default setting of 8 retries a total wait time of 63.5 s is added between the retries.""" nice: bool = False """Setting this to True, will signal to the API that you intend to be nice to other users by de-prioritizing your request below concurrent ones.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" aleph_alpha_api_key = get_from_dict_or_env( values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY" ) try: from aleph_alpha_client import Client values["client"] = Client( token=aleph_alpha_api_key, host=values["host"], hosting=values["hosting"], request_timeout_seconds=values["request_timeout_seconds"], total_retries=values["total_retries"], nice=values["nice"], ) except ImportError: raise ImportError( "Could not import aleph_alpha_client python package. " "Please install it with `pip install aleph_alpha_client`." ) return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling the Aleph Alpha API.""" return { "maximum_tokens": self.maximum_tokens, "temperature": self.temperature, "top_k": self.top_k, "top_p": self.top_p, "presence_penalty": self.presence_penalty, "frequency_penalty": self.frequency_penalty, "n": self.n, "repetition_penalties_include_prompt": self.repetition_penalties_include_prompt, # noqa: E501 "use_multiplicative_presence_penalty": self.use_multiplicative_presence_penalty, # noqa: E501 "penalty_bias": self.penalty_bias, "penalty_exceptions": self.penalty_exceptions, "penalty_exceptions_include_stop_sequences": self.penalty_exceptions_include_stop_sequences, # noqa: E501 "best_of": self.best_of, "logit_bias": self.logit_bias, "log_probs": self.log_probs, "tokens": self.tokens, "disable_optimizations": self.disable_optimizations, "minimum_tokens": self.minimum_tokens, "echo": self.echo, "use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501 "sequence_penalty": self.sequence_penalty, "sequence_penalty_min_length": self.sequence_penalty_min_length, "use_multiplicative_sequence_penalty": self.use_multiplicative_sequence_penalty, # noqa: E501 "completion_bias_inclusion": self.completion_bias_inclusion, "completion_bias_inclusion_first_token_only": self.completion_bias_inclusion_first_token_only, # noqa: E501 "completion_bias_exclusion": self.completion_bias_exclusion, "completion_bias_exclusion_first_token_only": self.completion_bias_exclusion_first_token_only, # noqa: E501 "contextual_control_threshold": self.contextual_control_threshold, "control_log_additive": self.control_log_additive, "repetition_penalties_include_completion": self.repetition_penalties_include_completion, # noqa: E501 "raw_completion": self.raw_completion, } @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return {**{"model": self.model}, **self._default_params} @property def _llm_type(self) -> str: """Return type of llm.""" return "aleph_alpha" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to Aleph Alpha's completion endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = aleph_alpha("Tell me a joke.") """ from aleph_alpha_client import CompletionRequest, Prompt params = self._default_params if self.stop_sequences is not None and stop is not None: raise ValueError( "stop sequences found in both the input and default params." ) elif self.stop_sequences is not None: params["stop_sequences"] = self.stop_sequences else: params["stop_sequences"] = stop params = {**params, **kwargs} request = CompletionRequest(prompt=Prompt.from_text(prompt), **params) response = self.client.complete(model=self.model, request=request) text = response.completions[0].completion # If stop tokens are provided, Aleph Alpha's endpoint returns them. # In order to make this consistent with other endpoints, we strip them. if stop is not None or self.stop_sequences is not None: text = enforce_stop_tokens(text, params["stop_sequences"]) return text if __name__ == "__main__": aa = AlephAlpha() print(aa("How are you?"))

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from typing import Any, Dict, List, Optional, Sequence from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens from langchain.pydantic_v1 import Extra, root_validator from langchain.utils import get_from_dict_or_env class AlephAlpha(LLM): """Aleph Alpha large language models. To use, you should have the ``aleph_alpha_client`` python package installed, and the environment variable ``ALEPH_ALPHA_API_KEY`` set with your API key, or pass it as a named parameter to the constructor. Parameters are explained more in depth here: https://github.com/Aleph-Alpha/aleph-alpha-client/blob/c14b7dd2b4325c7da0d6a119f6e76385800e097b/aleph_alpha_client/completion.py#L10 Example: .. code-block:: python from langchain.llms import AlephAlpha aleph_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key") """ client: Any #: :meta private: model: Optional[str] = "luminous-base" """Model name to use.""" maximum_tokens: int = 64 """The maximum number of tokens to be generated.""" temperature: float = 0.0 """A non-negative float that tunes the degree of randomness in generation.""" top_k: int = 0 """Number of most likely tokens to consider at each step.""" top_p: float = 0.0 """Total probability mass of tokens to consider at each step.""" presence_penalty: float = 0.0 """Penalizes repeated tokens.""" frequency_penalty: float = 0.0 """Penalizes repeated tokens according to frequency.""" repetition_penalties_include_prompt: Optional[bool] = False """Flag deciding whether presence penalty or frequency penalty are updated from the prompt.""" use_multiplicative_presence_penalty: Optional[bool] = False """Flag deciding whether presence penalty is applied multiplicatively (True) or additively (False).""" penalty_bias: Optional[str] = None """Penalty bias for the completion.""" penalty_exceptions: Optional[List[str]] = None """List of strings that may be generated without penalty, regardless of other penalty settings""" penalty_exceptions_include_stop_sequences: Optional[bool] = None """Should stop_sequences be included in penalty_exceptions.""" best_of: Optional[int] = None """returns the one with the "best of" results (highest log probability per token) """ n: int = 1 """How many completions to generate for each prompt.""" logit_bias: Optional[Dict[int, float]] = None """The logit bias allows to influence the likelihood of generating tokens.""" log_probs: Optional[int] = None """Number of top log probabilities to be returned for each generated token.""" tokens: Optional[bool] = False """return tokens of completion.""" disable_optimizations: Optional[bool] = False minimum_tokens: Optional[int] = 0 """Generate at least this number of tokens.""" echo: bool = False """Echo the prompt in the completion.""" use_multiplicative_frequency_penalty: bool = False sequence_penalty: float = 0.0 sequence_penalty_min_length: int = 2 use_multiplicative_sequence_penalty: bool = False completion_bias_inclusion: Optional[Sequence[str]] = None completion_bias_inclusion_first_token_only: bool = False completion_bias_exclusion: Optional[Sequence[str]] = None completion_bias_exclusion_first_token_only: bool = False """Only consider the first token for the completion_bias_exclusion.""" contextual_control_threshold: Optional[float] = None """If set to None, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-None value, control parameters are also applied to similar tokens. """ control_log_additive: Optional[bool] = True """True: apply control by adding the log(control_factor) to attention scores. False: (attention_scores - - attention_scores.min(-1)) * control_factor """ repetition_penalties_include_completion: bool = True """Flag deciding whether presence penalty or frequency penalty are updated from the completion.""" raw_completion: bool = False """Force the raw completion of the model to be returned.""" stop_sequences: Optional[List[str]] = None """Stop sequences to use.""" # Client params aleph_alpha_api_key: Optional[str] = None """API key for Aleph Alpha API.""" host: str = "https://api.aleph-alpha.com" """The hostname of the API host. The default one is "https://api.aleph-alpha.com")""" hosting: Optional[str] = None """Determines in which datacenters the request may be processed. You can either set the parameter to "aleph-alpha" or omit it (defaulting to None). Not setting this value, or setting it to None, gives us maximal flexibility in processing your request in our own datacenters and on servers hosted with other providers. Choose this option for maximal availability. Setting it to "aleph-alpha" allows us to only process the request in our own datacenters. Choose this option for maximal data privacy.""" request_timeout_seconds: int = 305 """Client timeout that will be set for HTTP requests in the `requests` library's API calls. Server will close all requests after 300 seconds with an internal server error.""" total_retries: int = 8 """The number of retries made in case requests fail with certain retryable status codes. If the last retry fails a corresponding exception is raised. Note, that between retries an exponential backoff is applied, starting with 0.5 s after the first retry and doubling for each retry made. So with the default setting of 8 retries a total wait time of 63.5 s is added between the retries.""" nice: bool = False """Setting this to True, will signal to the API that you intend to be nice to other users by de-prioritizing your request below concurrent ones.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" aleph_alpha_api_key = get_from_dict_or_env( values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY" ) try: from aleph_alpha_client import Client values["client"] = Client( token=aleph_alpha_api_key, host=values["host"], hosting=values["hosting"], request_timeout_seconds=values["request_timeout_seconds"], total_retries=values["total_retries"], nice=values["nice"], ) except ImportError: raise ImportError( "Could not import aleph_alpha_client python package. " "Please install it with `pip install aleph_alpha_client`." ) return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling the Aleph Alpha API.""" return { "maximum_tokens": self.maximum_tokens, "temperature": self.temperature, "top_k": self.top_k, "top_p": self.top_p, "presence_penalty": self.presence_penalty, "frequency_penalty": self.frequency_penalty, "n": self.n, "repetition_penalties_include_prompt": self.repetition_penalties_include_prompt, # noqa: E501 "use_multiplicative_presence_penalty": self.use_multiplicative_presence_penalty, # noqa: E501 "penalty_bias": self.penalty_bias, "penalty_exceptions": self.penalty_exceptions, "penalty_exceptions_include_stop_sequences": self.penalty_exceptions_include_stop_sequences, # noqa: E501 "best_of": self.best_of, "logit_bias": self.logit_bias, "log_probs": self.log_probs, "tokens": self.tokens, "disable_optimizations": self.disable_optimizations, "minimum_tokens": self.minimum_tokens, "echo": self.echo, "use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501 "sequence_penalty": self.sequence_penalty, "sequence_penalty_min_length": self.sequence_penalty_min_length, "use_multiplicative_sequence_penalty": self.use_multiplicative_sequence_penalty, # noqa: E501 "completion_bias_inclusion": self.completion_bias_inclusion, "completion_bias_inclusion_first_token_only": self.completion_bias_inclusion_first_token_only, # noqa: E501 "completion_bias_exclusion": self.completion_bias_exclusion, "completion_bias_exclusion_first_token_only": self.completion_bias_exclusion_first_token_only, # noqa: E501 "contextual_control_threshold": self.contextual_control_threshold, "control_log_additive": self.control_log_additive, "repetition_penalties_include_completion": self.repetition_penalties_include_completion, # noqa: E501 "raw_completion": self.raw_completion, } @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return {**{"model": self.model}, **self._default_params} @property def _llm_type(self) -> str: """Return type of llm.""" return "aleph_alpha" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to Aleph Alpha's completion endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = aleph_alpha("Tell me a joke.") """ from aleph_alpha_client import CompletionRequest, Prompt params = self._default_params if self.stop_sequences is not None and stop is not None: raise ValueError( "stop sequences found in both the input and default params." ) elif self.stop_sequences is not None: params["stop_sequences"] = self.stop_sequences else: params["stop_sequences"] = stop params = {**params, **kwargs} request = CompletionRequest(prompt=Prompt.from_text(prompt), **params) response = self.client.complete(model=self.model, request=request) text = response.completions[0].completion # If stop tokens are provided, Aleph Alpha's endpoint returns them. # In order to make this consistent with other endpoints, we strip them. if stop is not None or self.stop_sequences is not None: text = enforce_stop_tokens(text, params["stop_sequences"]) return text if __name__ == "__main__": aa = AlephAlpha() print(aa("How are you?"))

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from typing import Any, Dict, List, Optional, Sequence from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens from langchain.pydantic_v1 import Extra, root_validator from langchain.utils import get_from_dict_or_env class AlephAlpha(LLM): """Aleph Alpha large language models. To use, you should have the ``aleph_alpha_client`` python package installed, and the environment variable ``ALEPH_ALPHA_API_KEY`` set with your API key, or pass it as a named parameter to the constructor. Parameters are explained more in depth here: https://github.com/Aleph-Alpha/aleph-alpha-client/blob/c14b7dd2b4325c7da0d6a119f6e76385800e097b/aleph_alpha_client/completion.py#L10 Example: .. code-block:: python from langchain.llms import AlephAlpha aleph_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key") """ client: Any #: :meta private: model: Optional[str] = "luminous-base" """Model name to use.""" maximum_tokens: int = 64 """The maximum number of tokens to be generated.""" temperature: float = 0.0 """A non-negative float that tunes the degree of randomness in generation.""" top_k: int = 0 """Number of most likely tokens to consider at each step.""" top_p: float = 0.0 """Total probability mass of tokens to consider at each step.""" presence_penalty: float = 0.0 """Penalizes repeated tokens.""" frequency_penalty: float = 0.0 """Penalizes repeated tokens according to frequency.""" repetition_penalties_include_prompt: Optional[bool] = False """Flag deciding whether presence penalty or frequency penalty are updated from the prompt.""" use_multiplicative_presence_penalty: Optional[bool] = False """Flag deciding whether presence penalty is applied multiplicatively (True) or additively (False).""" penalty_bias: Optional[str] = None """Penalty bias for the completion.""" penalty_exceptions: Optional[List[str]] = None """List of strings that may be generated without penalty, regardless of other penalty settings""" penalty_exceptions_include_stop_sequences: Optional[bool] = None """Should stop_sequences be included in penalty_exceptions.""" best_of: Optional[int] = None """returns the one with the "best of" results (highest log probability per token) """ n: int = 1 """How many completions to generate for each prompt.""" logit_bias: Optional[Dict[int, float]] = None """The logit bias allows to influence the likelihood of generating tokens.""" log_probs: Optional[int] = None """Number of top log probabilities to be returned for each generated token.""" tokens: Optional[bool] = False """return tokens of completion.""" disable_optimizations: Optional[bool] = False minimum_tokens: Optional[int] = 0 """Generate at least this number of tokens.""" echo: bool = False """Echo the prompt in the completion.""" use_multiplicative_frequency_penalty: bool = False sequence_penalty: float = 0.0 sequence_penalty_min_length: int = 2 use_multiplicative_sequence_penalty: bool = False completion_bias_inclusion: Optional[Sequence[str]] = None completion_bias_inclusion_first_token_only: bool = False completion_bias_exclusion: Optional[Sequence[str]] = None completion_bias_exclusion_first_token_only: bool = False """Only consider the first token for the completion_bias_exclusion.""" contextual_control_threshold: Optional[float] = None """If set to None, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-None value, control parameters are also applied to similar tokens. """ control_log_additive: Optional[bool] = True """True: apply control by adding the log(control_factor) to attention scores. False: (attention_scores - - attention_scores.min(-1)) * control_factor """ repetition_penalties_include_completion: bool = True """Flag deciding whether presence penalty or frequency penalty are updated from the completion.""" raw_completion: bool = False """Force the raw completion of the model to be returned.""" stop_sequences: Optional[List[str]] = None """Stop sequences to use.""" # Client params aleph_alpha_api_key: Optional[str] = None """API key for Aleph Alpha API.""" host: str = "https://api.aleph-alpha.com" """The hostname of the API host. The default one is "https://api.aleph-alpha.com")""" hosting: Optional[str] = None """Determines in which datacenters the request may be processed. You can either set the parameter to "aleph-alpha" or omit it (defaulting to None). Not setting this value, or setting it to None, gives us maximal flexibility in processing your request in our own datacenters and on servers hosted with other providers. Choose this option for maximal availability. Setting it to "aleph-alpha" allows us to only process the request in our own datacenters. Choose this option for maximal data privacy.""" request_timeout_seconds: int = 305 """Client timeout that will be set for HTTP requests in the `requests` library's API calls. Server will close all requests after 300 seconds with an internal server error.""" total_retries: int = 8 """The number of retries made in case requests fail with certain retryable status codes. If the last retry fails a corresponding exception is raised. Note, that between retries an exponential backoff is applied, starting with 0.5 s after the first retry and doubling for each retry made. So with the default setting of 8 retries a total wait time of 63.5 s is added between the retries.""" nice: bool = False """Setting this to True, will signal to the API that you intend to be nice to other users by de-prioritizing your request below concurrent ones.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" aleph_alpha_api_key = get_from_dict_or_env( values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY" ) try: from aleph_alpha_client import Client values["client"] = Client( token=aleph_alpha_api_key, host=values["host"], hosting=values["hosting"], request_timeout_seconds=values["request_timeout_seconds"], total_retries=values["total_retries"], nice=values["nice"], ) except ImportError: raise ImportError( "Could not import aleph_alpha_client python package. " "Please install it with `pip install aleph_alpha_client`." ) return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling the Aleph Alpha API.""" return { "maximum_tokens": self.maximum_tokens, "temperature": self.temperature, "top_k": self.top_k, "top_p": self.top_p, "presence_penalty": self.presence_penalty, "frequency_penalty": self.frequency_penalty, "n": self.n, "repetition_penalties_include_prompt": self.repetition_penalties_include_prompt, # noqa: E501 "use_multiplicative_presence_penalty": self.use_multiplicative_presence_penalty, # noqa: E501 "penalty_bias": self.penalty_bias, "penalty_exceptions": self.penalty_exceptions, "penalty_exceptions_include_stop_sequences": self.penalty_exceptions_include_stop_sequences, # noqa: E501 "best_of": self.best_of, "logit_bias": self.logit_bias, "log_probs": self.log_probs, "tokens": self.tokens, "disable_optimizations": self.disable_optimizations, "minimum_tokens": self.minimum_tokens, "echo": self.echo, "use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501 "sequence_penalty": self.sequence_penalty, "sequence_penalty_min_length": self.sequence_penalty_min_length, "use_multiplicative_sequence_penalty": self.use_multiplicative_sequence_penalty, # noqa: E501 "completion_bias_inclusion": self.completion_bias_inclusion, "completion_bias_inclusion_first_token_only": self.completion_bias_inclusion_first_token_only, # noqa: E501 "completion_bias_exclusion": self.completion_bias_exclusion, "completion_bias_exclusion_first_token_only": self.completion_bias_exclusion_first_token_only, # noqa: E501 "contextual_control_threshold": self.contextual_control_threshold, "control_log_additive": self.control_log_additive, "repetition_penalties_include_completion": self.repetition_penalties_include_completion, # noqa: E501 "raw_completion": self.raw_completion, } @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return {**{"model": self.model}, **self._default_params} @property def _llm_type(self) -> str: """Return type of llm.""" return "aleph_alpha" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to Aleph Alpha's completion endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = aleph_alpha("Tell me a joke.") """ from aleph_alpha_client import CompletionRequest, Prompt params = self._default_params if self.stop_sequences is not None and stop is not None: raise ValueError( "stop sequences found in both the input and default params." ) elif self.stop_sequences is not None: params["stop_sequences"] = self.stop_sequences else: params["stop_sequences"] = stop params = {**params, **kwargs} request = CompletionRequest(prompt=Prompt.from_text(prompt), **params) response = self.client.complete(model=self.model, request=request) text = response.completions[0].completion # If stop tokens are provided, Aleph Alpha's endpoint returns them. # In order to make this consistent with other endpoints, we strip them. if stop is not None or self.stop_sequences is not None: text = enforce_stop_tokens(text, params["stop_sequences"]) return text if __name__ == "__main__": aa = AlephAlpha() print(aa("How are you?"))

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from typing import Any, Dict, List, Optional, Sequence from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens from langchain.pydantic_v1 import Extra, root_validator from langchain.utils import get_from_dict_or_env class AlephAlpha(LLM): """Aleph Alpha large language models. To use, you should have the ``aleph_alpha_client`` python package installed, and the environment variable ``ALEPH_ALPHA_API_KEY`` set with your API key, or pass it as a named parameter to the constructor. Parameters are explained more in depth here: https://github.com/Aleph-Alpha/aleph-alpha-client/blob/c14b7dd2b4325c7da0d6a119f6e76385800e097b/aleph_alpha_client/completion.py#L10 Example: .. code-block:: python from langchain.llms import AlephAlpha aleph_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key") """ client: Any #: :meta private: model: Optional[str] = "luminous-base" """Model name to use.""" maximum_tokens: int = 64 """The maximum number of tokens to be generated.""" temperature: float = 0.0 """A non-negative float that tunes the degree of randomness in generation.""" top_k: int = 0 """Number of most likely tokens to consider at each step.""" top_p: float = 0.0 """Total probability mass of tokens to consider at each step.""" presence_penalty: float = 0.0 """Penalizes repeated tokens.""" frequency_penalty: float = 0.0 """Penalizes repeated tokens according to frequency.""" repetition_penalties_include_prompt: Optional[bool] = False """Flag deciding whether presence penalty or frequency penalty are updated from the prompt.""" use_multiplicative_presence_penalty: Optional[bool] = False """Flag deciding whether presence penalty is applied multiplicatively (True) or additively (False).""" penalty_bias: Optional[str] = None """Penalty bias for the completion.""" penalty_exceptions: Optional[List[str]] = None """List of strings that may be generated without penalty, regardless of other penalty settings""" penalty_exceptions_include_stop_sequences: Optional[bool] = None """Should stop_sequences be included in penalty_exceptions.""" best_of: Optional[int] = None """returns the one with the "best of" results (highest log probability per token) """ n: int = 1 """How many completions to generate for each prompt.""" logit_bias: Optional[Dict[int, float]] = None """The logit bias allows to influence the likelihood of generating tokens.""" log_probs: Optional[int] = None """Number of top log probabilities to be returned for each generated token.""" tokens: Optional[bool] = False """return tokens of completion.""" disable_optimizations: Optional[bool] = False minimum_tokens: Optional[int] = 0 """Generate at least this number of tokens.""" echo: bool = False """Echo the prompt in the completion.""" use_multiplicative_frequency_penalty: bool = False sequence_penalty: float = 0.0 sequence_penalty_min_length: int = 2 use_multiplicative_sequence_penalty: bool = False completion_bias_inclusion: Optional[Sequence[str]] = None completion_bias_inclusion_first_token_only: bool = False completion_bias_exclusion: Optional[Sequence[str]] = None completion_bias_exclusion_first_token_only: bool = False """Only consider the first token for the completion_bias_exclusion.""" contextual_control_threshold: Optional[float] = None """If set to None, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-None value, control parameters are also applied to similar tokens. """ control_log_additive: Optional[bool] = True """True: apply control by adding the log(control_factor) to attention scores. False: (attention_scores - - attention_scores.min(-1)) * control_factor """ repetition_penalties_include_completion: bool = True """Flag deciding whether presence penalty or frequency penalty are updated from the completion.""" raw_completion: bool = False """Force the raw completion of the model to be returned.""" stop_sequences: Optional[List[str]] = None """Stop sequences to use.""" # Client params aleph_alpha_api_key: Optional[str] = None """API key for Aleph Alpha API.""" host: str = "https://api.aleph-alpha.com" """The hostname of the API host. The default one is "https://api.aleph-alpha.com")""" hosting: Optional[str] = None """Determines in which datacenters the request may be processed. You can either set the parameter to "aleph-alpha" or omit it (defaulting to None). Not setting this value, or setting it to None, gives us maximal flexibility in processing your request in our own datacenters and on servers hosted with other providers. Choose this option for maximal availability. Setting it to "aleph-alpha" allows us to only process the request in our own datacenters. Choose this option for maximal data privacy.""" request_timeout_seconds: int = 305 """Client timeout that will be set for HTTP requests in the `requests` library's API calls. Server will close all requests after 300 seconds with an internal server error.""" total_retries: int = 8 """The number of retries made in case requests fail with certain retryable status codes. If the last retry fails a corresponding exception is raised. Note, that between retries an exponential backoff is applied, starting with 0.5 s after the first retry and doubling for each retry made. So with the default setting of 8 retries a total wait time of 63.5 s is added between the retries.""" nice: bool = False """Setting this to True, will signal to the API that you intend to be nice to other users by de-prioritizing your request below concurrent ones.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" aleph_alpha_api_key = get_from_dict_or_env( values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY" ) try: from aleph_alpha_client import Client values["client"] = Client( token=aleph_alpha_api_key, host=values["host"], hosting=values["hosting"], request_timeout_seconds=values["request_timeout_seconds"], total_retries=values["total_retries"], nice=values["nice"], ) except ImportError: raise ImportError( "Could not import aleph_alpha_client python package. " "Please install it with `pip install aleph_alpha_client`." ) return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling the Aleph Alpha API.""" return { "maximum_tokens": self.maximum_tokens, "temperature": self.temperature, "top_k": self.top_k, "top_p": self.top_p, "presence_penalty": self.presence_penalty, "frequency_penalty": self.frequency_penalty, "n": self.n, "repetition_penalties_include_prompt": self.repetition_penalties_include_prompt, # noqa: E501 "use_multiplicative_presence_penalty": self.use_multiplicative_presence_penalty, # noqa: E501 "penalty_bias": self.penalty_bias, "penalty_exceptions": self.penalty_exceptions, "penalty_exceptions_include_stop_sequences": self.penalty_exceptions_include_stop_sequences, # noqa: E501 "best_of": self.best_of, "logit_bias": self.logit_bias, "log_probs": self.log_probs, "tokens": self.tokens, "disable_optimizations": self.disable_optimizations, "minimum_tokens": self.minimum_tokens, "echo": self.echo, "use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501 "sequence_penalty": self.sequence_penalty, "sequence_penalty_min_length": self.sequence_penalty_min_length, "use_multiplicative_sequence_penalty": self.use_multiplicative_sequence_penalty, # noqa: E501 "completion_bias_inclusion": self.completion_bias_inclusion, "completion_bias_inclusion_first_token_only": self.completion_bias_inclusion_first_token_only, # noqa: E501 "completion_bias_exclusion": self.completion_bias_exclusion, "completion_bias_exclusion_first_token_only": self.completion_bias_exclusion_first_token_only, # noqa: E501 "contextual_control_threshold": self.contextual_control_threshold, "control_log_additive": self.control_log_additive, "repetition_penalties_include_completion": self.repetition_penalties_include_completion, # noqa: E501 "raw_completion": self.raw_completion, } @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return {**{"model": self.model}, **self._default_params} @property def _llm_type(self) -> str: """Return type of llm.""" return "aleph_alpha" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to Aleph Alpha's completion endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = aleph_alpha("Tell me a joke.") """ from aleph_alpha_client import CompletionRequest, Prompt params = self._default_params if self.stop_sequences is not None and stop is not None: raise ValueError( "stop sequences found in both the input and default params." ) elif self.stop_sequences is not None: params["stop_sequences"] = self.stop_sequences else: params["stop_sequences"] = stop params = {**params, **kwargs} request = CompletionRequest(prompt=Prompt.from_text(prompt), **params) response = self.client.complete(model=self.model, request=request) text = response.completions[0].completion # If stop tokens are provided, Aleph Alpha's endpoint returns them. # In order to make this consistent with other endpoints, we strip them. if stop is not None or self.stop_sequences is not None: text = enforce_stop_tokens(text, params["stop_sequences"]) return text if __name__ == "__main__": aa = AlephAlpha() print(aa("How are you?"))

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from langchain.vectorstores import Chroma from langchain.embeddings import OpenAIEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.llms import OpenAI from langchain.chains import VectorDBQA from langchain.document_loaders import TextLoader from typing import List from langchain.schema import Document import os os.environ['OPENAI_API_KEY'] = "your-api-key" class Genie: def __init__(self, file_path: str): self.file_path = file_path self.loader = TextLoader(self.file_path) self.documents = self.loader.load() self.texts = self.text_split(self.documents) self.vectordb = self.embeddings(self.texts) self.genie = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=self.vectordb) @staticmethod def text_split(documents: TextLoader): text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) return texts @staticmethod def embeddings(texts: List[Document]): embeddings = OpenAIEmbeddings() vectordb = Chroma.from_documents(texts, embeddings) return vectordb def ask(self, query: str): return self.genie.run(query) if __name__ == "__main__": genie = Genie("example.txt") print(genie.ask("How is the wheater like?"))

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import logging from pathlib import Path from typing import List, Optional, Tuple from dotenv import load_dotenv load_dotenv() from queue import Empty, Queue from threading import Thread import gradio as gr from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.chat_models import ChatOpenAI from langchain.prompts import HumanMessagePromptTemplate from langchain.schema import AIMessage, BaseMessage, HumanMessage, SystemMessage from callback import QueueCallback MODELS_NAMES = ["gpt-3.5-turbo", "gpt-4"] DEFAULT_TEMPERATURE = 0.7 ChatHistory = List[str] logging.basicConfig( format="[%(asctime)s %(levelname)s]: %(message)s", level=logging.INFO ) # load up our system prompt default_system_prompt = Path("prompts/system.prompt").read_text() # for the human, we will just inject the text human_message_prompt_template = HumanMessagePromptTemplate.from_template("{text}") def on_message_button_click( chat: Optional[ChatOpenAI], message: str, chatbot_messages: ChatHistory, messages: List[BaseMessage], ) -> Tuple[ChatOpenAI, str, ChatHistory, List[BaseMessage]]: if chat is None: # in the queue we will store our streamed tokens queue = Queue() # let's create our default chat chat = ChatOpenAI( model_name=MODELS_NAMES[0], temperature=DEFAULT_TEMPERATURE, streaming=True, callbacks=([QueueCallback(queue)]), ) else: # hacky way to get the queue back queue = chat.callbacks[0].queue job_done = object() logging.info(f"Asking question to GPT, messages={messages}") # let's add the messages to our stuff messages.append(HumanMessage(content=message)) chatbot_messages.append((message, "")) # this is a little wrapper we need cuz we have to add the job_done def task(): chat(messages) queue.put(job_done) # now let's start a thread and run the generation inside it t = Thread(target=task) t.start() # this will hold the content as we generate content = "" # now, we read the next_token from queue and do what it has to be done while True: try: next_token = queue.get(True, timeout=1) if next_token is job_done: break content += next_token chatbot_messages[-1] = (message, content) yield chat, "", chatbot_messages, messages except Empty: continue # finally we can add our reply to messsages messages.append(AIMessage(content=content)) logging.debug(f"reply = {content}") logging.info(f"Done!") return chat, "", chatbot_messages, messages def system_prompt_handler(value: str) -> str: return value def on_clear_button_click(system_prompt: str) -> Tuple[str, List, List]: return "", [], [SystemMessage(content=system_prompt)] def on_apply_settings_button_click( system_prompt: str, model_name: str, temperature: float ): logging.info( f"Applying settings: model_name={model_name}, temperature={temperature}" ) chat = ChatOpenAI( model_name=model_name, temperature=temperature, streaming=True, callbacks=[QueueCallback(Queue())], ) # don't forget to nuke our queue chat.callbacks[0].queue.empty() return chat, *on_clear_button_click(system_prompt) # some css why not, "borrowed" from https://huggingface.co/spaces/ysharma/Gradio-demo-streaming/blob/main/app.py with gr.Blocks( css="""#col_container {width: 700px; margin-left: auto; margin-right: auto;} #chatbot {height: 400px; overflow: auto;}""" ) as demo: system_prompt = gr.State(default_system_prompt) # here we keep our state so multiple user can use the app at the same time! messages = gr.State([SystemMessage(content=default_system_prompt)]) # same thing for the chat, we want one chat per use so callbacks are unique I guess chat = gr.State(None) with gr.Column(elem_id="col_container"): gr.Markdown("# Welcome to GradioGPT! 🌟🚀") gr.Markdown( "An easy to use template. It comes with state and settings managment" ) with gr.Column(): system_prompt_area = gr.TextArea( default_system_prompt, lines=4, label="system prompt", interactive=True ) # we store the value into the state to avoid re rendering of the area system_prompt_area.input( system_prompt_handler, inputs=[system_prompt_area], outputs=[system_prompt], ) system_prompt_button = gr.Button("Set") chatbot = gr.Chatbot() with gr.Column(): message = gr.Textbox(label="chat input") message.submit( on_message_button_click, [chat, message, chatbot, messages], [chat, message, chatbot, messages], queue=True, ) message_button = gr.Button("Submit", variant="primary") message_button.click( on_message_button_click, [chat, message, chatbot, messages], [chat, message, chatbot, messages], ) with gr.Row(): with gr.Column(): clear_button = gr.Button("Clear") clear_button.click( on_clear_button_click, [system_prompt], [message, chatbot, messages], queue=False, ) with gr.Accordion("Settings", open=False): model_name = gr.Dropdown( choices=MODELS_NAMES, value=MODELS_NAMES[0], label="model" ) temperature = gr.Slider( minimum=0.0, maximum=1.0, value=0.7, step=0.1, label="temperature", interactive=True, ) apply_settings_button = gr.Button("Apply") apply_settings_button.click( on_apply_settings_button_click, [system_prompt, model_name, temperature], [chat, message, chatbot, messages], ) system_prompt_button.click( on_apply_settings_button_click, [system_prompt, model_name, temperature], [chat, message, chatbot, messages], ) demo.queue() demo.launch()

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import logging from pathlib import Path from typing import List, Optional, Tuple from dotenv import load_dotenv load_dotenv() from queue import Empty, Queue from threading import Thread import gradio as gr from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.chat_models import ChatOpenAI from langchain.prompts import HumanMessagePromptTemplate from langchain.schema import AIMessage, BaseMessage, HumanMessage, SystemMessage from callback import QueueCallback MODELS_NAMES = ["gpt-3.5-turbo", "gpt-4"] DEFAULT_TEMPERATURE = 0.7 ChatHistory = List[str] logging.basicConfig( format="[%(asctime)s %(levelname)s]: %(message)s", level=logging.INFO ) # load up our system prompt default_system_prompt = Path("prompts/system.prompt").read_text() # for the human, we will just inject the text human_message_prompt_template = HumanMessagePromptTemplate.from_template("{text}") def on_message_button_click( chat: Optional[ChatOpenAI], message: str, chatbot_messages: ChatHistory, messages: List[BaseMessage], ) -> Tuple[ChatOpenAI, str, ChatHistory, List[BaseMessage]]: if chat is None: # in the queue we will store our streamed tokens queue = Queue() # let's create our default chat chat = ChatOpenAI( model_name=MODELS_NAMES[0], temperature=DEFAULT_TEMPERATURE, streaming=True, callbacks=([QueueCallback(queue)]), ) else: # hacky way to get the queue back queue = chat.callbacks[0].queue job_done = object() logging.info(f"Asking question to GPT, messages={messages}") # let's add the messages to our stuff messages.append(HumanMessage(content=message)) chatbot_messages.append((message, "")) # this is a little wrapper we need cuz we have to add the job_done def task(): chat(messages) queue.put(job_done) # now let's start a thread and run the generation inside it t = Thread(target=task) t.start() # this will hold the content as we generate content = "" # now, we read the next_token from queue and do what it has to be done while True: try: next_token = queue.get(True, timeout=1) if next_token is job_done: break content += next_token chatbot_messages[-1] = (message, content) yield chat, "", chatbot_messages, messages except Empty: continue # finally we can add our reply to messsages messages.append(AIMessage(content=content)) logging.debug(f"reply = {content}") logging.info(f"Done!") return chat, "", chatbot_messages, messages def system_prompt_handler(value: str) -> str: return value def on_clear_button_click(system_prompt: str) -> Tuple[str, List, List]: return "", [], [SystemMessage(content=system_prompt)] def on_apply_settings_button_click( system_prompt: str, model_name: str, temperature: float ): logging.info( f"Applying settings: model_name={model_name}, temperature={temperature}" ) chat = ChatOpenAI( model_name=model_name, temperature=temperature, streaming=True, callbacks=[QueueCallback(Queue())], ) # don't forget to nuke our queue chat.callbacks[0].queue.empty() return chat, *on_clear_button_click(system_prompt) # some css why not, "borrowed" from https://huggingface.co/spaces/ysharma/Gradio-demo-streaming/blob/main/app.py with gr.Blocks( css="""#col_container {width: 700px; margin-left: auto; margin-right: auto;} #chatbot {height: 400px; overflow: auto;}""" ) as demo: system_prompt = gr.State(default_system_prompt) # here we keep our state so multiple user can use the app at the same time! messages = gr.State([SystemMessage(content=default_system_prompt)]) # same thing for the chat, we want one chat per use so callbacks are unique I guess chat = gr.State(None) with gr.Column(elem_id="col_container"): gr.Markdown("# Welcome to GradioGPT! 🌟🚀") gr.Markdown( "An easy to use template. It comes with state and settings managment" ) with gr.Column(): system_prompt_area = gr.TextArea( default_system_prompt, lines=4, label="system prompt", interactive=True ) # we store the value into the state to avoid re rendering of the area system_prompt_area.input( system_prompt_handler, inputs=[system_prompt_area], outputs=[system_prompt], ) system_prompt_button = gr.Button("Set") chatbot = gr.Chatbot() with gr.Column(): message = gr.Textbox(label="chat input") message.submit( on_message_button_click, [chat, message, chatbot, messages], [chat, message, chatbot, messages], queue=True, ) message_button = gr.Button("Submit", variant="primary") message_button.click( on_message_button_click, [chat, message, chatbot, messages], [chat, message, chatbot, messages], ) with gr.Row(): with gr.Column(): clear_button = gr.Button("Clear") clear_button.click( on_clear_button_click, [system_prompt], [message, chatbot, messages], queue=False, ) with gr.Accordion("Settings", open=False): model_name = gr.Dropdown( choices=MODELS_NAMES, value=MODELS_NAMES[0], label="model" ) temperature = gr.Slider( minimum=0.0, maximum=1.0, value=0.7, step=0.1, label="temperature", interactive=True, ) apply_settings_button = gr.Button("Apply") apply_settings_button.click( on_apply_settings_button_click, [system_prompt, model_name, temperature], [chat, message, chatbot, messages], ) system_prompt_button.click( on_apply_settings_button_click, [system_prompt, model_name, temperature], [chat, message, chatbot, messages], ) demo.queue() demo.launch()

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""" View stage example selector. | Copyright 2017-2023, Voxel51, Inc. | `voxel51.com <https://voxel51.com/>`_ | """ import os import pickle from langchain.prompts import FewShotPromptTemplate, PromptTemplate import numpy as np import pandas as pd from scipy.spatial.distance import cosine # pylint: disable=relative-beyond-top-level from .utils import get_embedding_function, get_cache, hash_query ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) EXAMPLES_DIR = os.path.join(ROOT_DIR, "examples") EXAMPLE_EMBEDDINGS_PATH = os.path.join( EXAMPLES_DIR, "viewstage_embeddings.pkl" ) VIEW_STAGE_EXAMPLES_PATH = os.path.join(EXAMPLES_DIR, "viewstage_examples.csv") VIEW_STAGE_EXAMPLE_PROMPT = PromptTemplate( input_variables=["input", "output"], template="Input: {input}\nOutput: {output}", ) def get_or_create_embeddings(queries): if os.path.isfile(EXAMPLE_EMBEDDINGS_PATH): with open(EXAMPLE_EMBEDDINGS_PATH, "rb") as f: example_embeddings = pickle.load(f) else: example_embeddings = {} query_hashes = [] new_hashes = [] new_queries = [] for query in queries: key = hash_query(query) query_hashes.append(key) if key not in example_embeddings: new_hashes.append(key) new_queries.append(query) if new_queries: print("Generating %d embeddings..." % len(new_queries)) model = get_embedding_function() new_embeddings = model(new_queries) for key, embedding in zip(new_hashes, new_embeddings): example_embeddings[key] = embedding if new_queries: print("Saving embeddings to disk...") with open(EXAMPLE_EMBEDDINGS_PATH, "wb") as f: pickle.dump(example_embeddings, f) return example_embeddings def has_geo_field(sample_collection): types = list(sample_collection.get_field_schema(flat=True).values()) types = [type(t) for t in types] return any(["Geo" in t.__name__ for t in types]) def get_label_type(sample_collection, field_name): sample = sample_collection.first() field = sample.get_field(field_name) field_type = str(type(field).__name__).lower() field_type = field_type[:-1] if field_type.endswith("s") else field_type return field_type def _replace_run_keys(prompt, runs): if "text_similarity" in runs: prompt = prompt.replace("TEXT_SIM_KEY", runs["text_similarity"]["key"]) if "image_similarity" in runs: prompt = prompt.replace( "IMAGE_SIM_KEY", runs["image_similarity"]["key"] ) if "evaluation" in runs: prompt = prompt.replace("EVAL_KEY", runs["evaluation"]["key"]) if "uniqueness" in runs: prompt = prompt.replace( "UNIQUENESS_FIELD", runs["uniqueness"]["uniqueness_field"] ) return prompt def _count_empty_class_names(label_field): return [list(class_name.values())[0] for class_name in label_field].count( [] ) def _reduce_label_fields(label_fields): label_field_keys = list(label_fields.keys()) if len(label_field_keys) == 0: return None, None elif len(label_field_keys) > 0: empty_counts = [ _count_empty_class_names(label_fields[key]) for key in label_field_keys ] min_empty_count = min(empty_counts) valid_keys = [ key for key, count in zip(label_field_keys, empty_counts) if count == min_empty_count ] return {key: label_fields[key] for key in valid_keys}, min_empty_count def _parse_runs_and_labels(runs, label_fields): reduced_label_fields, count = _reduce_label_fields(label_fields.copy()) reduced_runs = runs.copy() if count is not None and count > 0 and "text_similarity" in reduced_runs: reduced_label_fields = None return reduced_runs, reduced_label_fields def _get_evaluation_type(sample_collection, eval_key): eval_cls = sample_collection.get_evaluation_info(eval_key).config.cls if "openimages" in eval_cls: return "detection" elif "coco" in eval_cls: return "detection" elif "activitynet" in eval_cls: return "detection" elif "classification" in eval_cls: return "classification" return None def _load_examples(): examples = pd.read_csv(VIEW_STAGE_EXAMPLES_PATH, on_bad_lines="skip") examples["meta"] = examples["metadata"] examples["contains_match"] = examples["stages"].str.contains("match\(") examples["contains_filter_labels"] = examples["stages"].str.contains( "filter_labels\(" ) examples["mfl"] = ( examples["contains_match"] | examples["contains_filter_labels"] ) examples["hash"] = examples["query"].apply(lambda x: hash_query(x)) queries = examples["query"].tolist() embeddings = get_or_create_embeddings(queries) embeddings = { key: np.array(embeddings[key]) for key in examples["hash"].tolist() } return examples, embeddings def get_examples(): cache = get_cache() keys = ("viewstage_examples", "viewstage_embeddings") if keys[0] not in cache or keys[1] not in cache: cache[keys[0]], cache[keys[1]] = _load_examples() return cache[keys[0]], cache[keys[1]] def _get_filtered_examples(sample_collection, runs, label_fields): examples, embeddings = get_examples() media_type = sample_collection.media_type _filter = examples["media_type"].isin([media_type, "all"]) red_runs, red_label_fields = _parse_runs_and_labels(runs, label_fields) geo = has_geo_field(sample_collection) text_sim = "text_similarity" in red_runs image_sim = "image_similarity" in red_runs meta = "metadata" in red_runs eval = "evaluation" in red_runs if red_label_fields or eval: if red_label_fields: label_field_types = list( set( [ get_label_type(sample_collection, field) for field in red_label_fields ] ) ) else: label_field_types = [] if eval: eval_key = red_runs["evaluation"]["key"] eval_types = [_get_evaluation_type(sample_collection, eval_key)] else: eval_types = [] label_types = list(set(label_field_types + eval_types + ["all"])) _filter = _filter & examples["label_type"].isin(label_types) ## contains match() or filter_labels() in stages mfl_cond = red_label_fields and not text_sim conds = [geo, text_sim, image_sim, meta, eval, mfl_cond] strs = ["geo", "text_sim", "image_sim", "meta", "eval", "mfl"] for cond, cond_str in zip(conds, strs): if not cond: _filter = _filter & (examples[cond_str] == False) filtered_examples = examples[_filter] filtered_queries, filtered_stages, hashes = ( filtered_examples["query"].tolist(), filtered_examples["stages"].tolist(), filtered_examples["hash"].tolist(), ) filtered_embeddings = [embeddings[key] for key in hashes] return filtered_queries, filtered_stages, filtered_embeddings def get_similar_examples(sample_collection, query, runs, label_fields): ex_queries, ex_stages, ex_embeddings = _get_filtered_examples( sample_collection, runs, label_fields ) model = get_embedding_function() query_embedding = np.array(model([query])) if len(query_embedding.shape) == 2: query_embedding = query_embedding[0] dists = np.array([cosine(query_embedding, emb) for emb in ex_embeddings]) sorted_ix = np.argsort(dists).astype(int) k = 20 similar_queries = [ex_queries[ix] for ix in sorted_ix[:k]] similar_stages = [ex_stages[ix] for ix in sorted_ix[:k]] return [ {"input": sq, "output": ss} for sq, ss in zip(similar_queries, similar_stages) ] def generate_view_stage_examples_prompt_template( sample_collection, query, runs, label_fields ): examples = get_similar_examples( sample_collection, query, runs, label_fields ) example_prompt = VIEW_STAGE_EXAMPLE_PROMPT return FewShotPromptTemplate( examples=examples, example_prompt=example_prompt, prefix="Generate code to produce the FiftyOne view stages for the following prompts:\n", suffix="Input: {text}\nOutput:", input_variables=["text"], ) def generate_view_stage_examples_prompt( sample_collection, query, runs, label_fields ): similar_examples_prompt_template = ( generate_view_stage_examples_prompt_template( sample_collection, query, runs, label_fields ) ) prompt = similar_examples_prompt_template.format(text=query) return _replace_run_keys(prompt, runs)

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""" View stage example selector. | Copyright 2017-2023, Voxel51, Inc. | `voxel51.com <https://voxel51.com/>`_ | """ import os import pickle from langchain.prompts import FewShotPromptTemplate, PromptTemplate import numpy as np import pandas as pd from scipy.spatial.distance import cosine # pylint: disable=relative-beyond-top-level from .utils import get_embedding_function, get_cache, hash_query ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) EXAMPLES_DIR = os.path.join(ROOT_DIR, "examples") EXAMPLE_EMBEDDINGS_PATH = os.path.join( EXAMPLES_DIR, "viewstage_embeddings.pkl" ) VIEW_STAGE_EXAMPLES_PATH = os.path.join(EXAMPLES_DIR, "viewstage_examples.csv") VIEW_STAGE_EXAMPLE_PROMPT = PromptTemplate( input_variables=["input", "output"], template="Input: {input}\nOutput: {output}", ) def get_or_create_embeddings(queries): if os.path.isfile(EXAMPLE_EMBEDDINGS_PATH): with open(EXAMPLE_EMBEDDINGS_PATH, "rb") as f: example_embeddings = pickle.load(f) else: example_embeddings = {} query_hashes = [] new_hashes = [] new_queries = [] for query in queries: key = hash_query(query) query_hashes.append(key) if key not in example_embeddings: new_hashes.append(key) new_queries.append(query) if new_queries: print("Generating %d embeddings..." % len(new_queries)) model = get_embedding_function() new_embeddings = model(new_queries) for key, embedding in zip(new_hashes, new_embeddings): example_embeddings[key] = embedding if new_queries: print("Saving embeddings to disk...") with open(EXAMPLE_EMBEDDINGS_PATH, "wb") as f: pickle.dump(example_embeddings, f) return example_embeddings def has_geo_field(sample_collection): types = list(sample_collection.get_field_schema(flat=True).values()) types = [type(t) for t in types] return any(["Geo" in t.__name__ for t in types]) def get_label_type(sample_collection, field_name): sample = sample_collection.first() field = sample.get_field(field_name) field_type = str(type(field).__name__).lower() field_type = field_type[:-1] if field_type.endswith("s") else field_type return field_type def _replace_run_keys(prompt, runs): if "text_similarity" in runs: prompt = prompt.replace("TEXT_SIM_KEY", runs["text_similarity"]["key"]) if "image_similarity" in runs: prompt = prompt.replace( "IMAGE_SIM_KEY", runs["image_similarity"]["key"] ) if "evaluation" in runs: prompt = prompt.replace("EVAL_KEY", runs["evaluation"]["key"]) if "uniqueness" in runs: prompt = prompt.replace( "UNIQUENESS_FIELD", runs["uniqueness"]["uniqueness_field"] ) return prompt def _count_empty_class_names(label_field): return [list(class_name.values())[0] for class_name in label_field].count( [] ) def _reduce_label_fields(label_fields): label_field_keys = list(label_fields.keys()) if len(label_field_keys) == 0: return None, None elif len(label_field_keys) > 0: empty_counts = [ _count_empty_class_names(label_fields[key]) for key in label_field_keys ] min_empty_count = min(empty_counts) valid_keys = [ key for key, count in zip(label_field_keys, empty_counts) if count == min_empty_count ] return {key: label_fields[key] for key in valid_keys}, min_empty_count def _parse_runs_and_labels(runs, label_fields): reduced_label_fields, count = _reduce_label_fields(label_fields.copy()) reduced_runs = runs.copy() if count is not None and count > 0 and "text_similarity" in reduced_runs: reduced_label_fields = None return reduced_runs, reduced_label_fields def _get_evaluation_type(sample_collection, eval_key): eval_cls = sample_collection.get_evaluation_info(eval_key).config.cls if "openimages" in eval_cls: return "detection" elif "coco" in eval_cls: return "detection" elif "activitynet" in eval_cls: return "detection" elif "classification" in eval_cls: return "classification" return None def _load_examples(): examples = pd.read_csv(VIEW_STAGE_EXAMPLES_PATH, on_bad_lines="skip") examples["meta"] = examples["metadata"] examples["contains_match"] = examples["stages"].str.contains("match\(") examples["contains_filter_labels"] = examples["stages"].str.contains( "filter_labels\(" ) examples["mfl"] = ( examples["contains_match"] | examples["contains_filter_labels"] ) examples["hash"] = examples["query"].apply(lambda x: hash_query(x)) queries = examples["query"].tolist() embeddings = get_or_create_embeddings(queries) embeddings = { key: np.array(embeddings[key]) for key in examples["hash"].tolist() } return examples, embeddings def get_examples(): cache = get_cache() keys = ("viewstage_examples", "viewstage_embeddings") if keys[0] not in cache or keys[1] not in cache: cache[keys[0]], cache[keys[1]] = _load_examples() return cache[keys[0]], cache[keys[1]] def _get_filtered_examples(sample_collection, runs, label_fields): examples, embeddings = get_examples() media_type = sample_collection.media_type _filter = examples["media_type"].isin([media_type, "all"]) red_runs, red_label_fields = _parse_runs_and_labels(runs, label_fields) geo = has_geo_field(sample_collection) text_sim = "text_similarity" in red_runs image_sim = "image_similarity" in red_runs meta = "metadata" in red_runs eval = "evaluation" in red_runs if red_label_fields or eval: if red_label_fields: label_field_types = list( set( [ get_label_type(sample_collection, field) for field in red_label_fields ] ) ) else: label_field_types = [] if eval: eval_key = red_runs["evaluation"]["key"] eval_types = [_get_evaluation_type(sample_collection, eval_key)] else: eval_types = [] label_types = list(set(label_field_types + eval_types + ["all"])) _filter = _filter & examples["label_type"].isin(label_types) ## contains match() or filter_labels() in stages mfl_cond = red_label_fields and not text_sim conds = [geo, text_sim, image_sim, meta, eval, mfl_cond] strs = ["geo", "text_sim", "image_sim", "meta", "eval", "mfl"] for cond, cond_str in zip(conds, strs): if not cond: _filter = _filter & (examples[cond_str] == False) filtered_examples = examples[_filter] filtered_queries, filtered_stages, hashes = ( filtered_examples["query"].tolist(), filtered_examples["stages"].tolist(), filtered_examples["hash"].tolist(), ) filtered_embeddings = [embeddings[key] for key in hashes] return filtered_queries, filtered_stages, filtered_embeddings def get_similar_examples(sample_collection, query, runs, label_fields): ex_queries, ex_stages, ex_embeddings = _get_filtered_examples( sample_collection, runs, label_fields ) model = get_embedding_function() query_embedding = np.array(model([query])) if len(query_embedding.shape) == 2: query_embedding = query_embedding[0] dists = np.array([cosine(query_embedding, emb) for emb in ex_embeddings]) sorted_ix = np.argsort(dists).astype(int) k = 20 similar_queries = [ex_queries[ix] for ix in sorted_ix[:k]] similar_stages = [ex_stages[ix] for ix in sorted_ix[:k]] return [ {"input": sq, "output": ss} for sq, ss in zip(similar_queries, similar_stages) ] def generate_view_stage_examples_prompt_template( sample_collection, query, runs, label_fields ): examples = get_similar_examples( sample_collection, query, runs, label_fields ) example_prompt = VIEW_STAGE_EXAMPLE_PROMPT return FewShotPromptTemplate( examples=examples, example_prompt=example_prompt, prefix="Generate code to produce the FiftyOne view stages for the following prompts:\n", suffix="Input: {text}\nOutput:", input_variables=["text"], ) def generate_view_stage_examples_prompt( sample_collection, query, runs, label_fields ): similar_examples_prompt_template = ( generate_view_stage_examples_prompt_template( sample_collection, query, runs, label_fields ) ) prompt = similar_examples_prompt_template.format(text=query) return _replace_run_keys(prompt, runs)

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import base64 import email from enum import Enum from typing import Any, Dict, List, Optional, Type from langchain.callbacks.manager import CallbackManagerForToolRun from langchain.pydantic_v1 import BaseModel, Field from langchain.tools.gmail.base import GmailBaseTool from langchain.tools.gmail.utils import clean_email_body class Resource(str, Enum): """Enumerator of Resources to search.""" THREADS = "threads" MESSAGES = "messages" class SearchArgsSchema(BaseModel): """Input for SearchGmailTool.""" # From https://support.google.com/mail/answer/7190?hl=en query: str = Field( ..., description="The Gmail query. Example filters include from:sender," " to:recipient, subject:subject, -filtered_term," " in:folder, is:important|read|starred, after:year/mo/date, " "before:year/mo/date, label:label_name" ' "exact phrase".' " Search newer/older than using d (day), m (month), and y (year): " "newer_than:2d, older_than:1y." " Attachments with extension example: filename:pdf. Multiple term" " matching example: from:amy OR from:david.", ) resource: Resource = Field( default=Resource.MESSAGES, description="Whether to search for threads or messages.", ) max_results: int = Field( default=10, description="The maximum number of results to return.", ) class GmailSearch(GmailBaseTool): """Tool that searches for messages or threads in Gmail.""" name: str = "search_gmail" description: str = ( "Use this tool to search for email messages or threads." " The input must be a valid Gmail query." " The output is a JSON list of the requested resource." ) args_schema: Type[SearchArgsSchema] = SearchArgsSchema def _parse_threads(self, threads: List[Dict[str, Any]]) -> List[Dict[str, Any]]: # Add the thread message snippets to the thread results results = [] for thread in threads: thread_id = thread["id"] thread_data = ( self.api_resource.users() .threads() .get(userId="me", id=thread_id) .execute() ) messages = thread_data["messages"] thread["messages"] = [] for message in messages: snippet = message["snippet"] thread["messages"].append({"snippet": snippet, "id": message["id"]}) results.append(thread) return results def _parse_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: results = [] for message in messages: message_id = message["id"] message_data = ( self.api_resource.users() .messages() .get(userId="me", format="raw", id=message_id) .execute() ) raw_message = base64.urlsafe_b64decode(message_data["raw"]) email_msg = email.message_from_bytes(raw_message) subject = email_msg["Subject"] sender = email_msg["From"] message_body = email_msg.get_payload() body = clean_email_body(message_body) results.append( { "id": message["id"], "threadId": message_data["threadId"], "snippet": message_data["snippet"], "body": body, "subject": subject, "sender": sender, } ) return results def _run( self, query: str, resource: Resource = Resource.MESSAGES, max_results: int = 10, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> List[Dict[str, Any]]: """Run the tool.""" results = ( self.api_resource.users() .messages() .list(userId="me", q=query, maxResults=max_results) .execute() .get(resource.value, []) ) if resource == Resource.THREADS: return self._parse_threads(results) elif resource == Resource.MESSAGES: return self._parse_messages(results) else: raise NotImplementedError(f"Resource of type {resource} not implemented.")

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import base64 import email from enum import Enum from typing import Any, Dict, List, Optional, Type from langchain.callbacks.manager import CallbackManagerForToolRun from langchain.pydantic_v1 import BaseModel, Field from langchain.tools.gmail.base import GmailBaseTool from langchain.tools.gmail.utils import clean_email_body class Resource(str, Enum): """Enumerator of Resources to search.""" THREADS = "threads" MESSAGES = "messages" class SearchArgsSchema(BaseModel): """Input for SearchGmailTool.""" # From https://support.google.com/mail/answer/7190?hl=en query: str = Field( ..., description="The Gmail query. Example filters include from:sender," " to:recipient, subject:subject, -filtered_term," " in:folder, is:important|read|starred, after:year/mo/date, " "before:year/mo/date, label:label_name" ' "exact phrase".' " Search newer/older than using d (day), m (month), and y (year): " "newer_than:2d, older_than:1y." " Attachments with extension example: filename:pdf. Multiple term" " matching example: from:amy OR from:david.", ) resource: Resource = Field( default=Resource.MESSAGES, description="Whether to search for threads or messages.", ) max_results: int = Field( default=10, description="The maximum number of results to return.", ) class GmailSearch(GmailBaseTool): """Tool that searches for messages or threads in Gmail.""" name: str = "search_gmail" description: str = ( "Use this tool to search for email messages or threads." " The input must be a valid Gmail query." " The output is a JSON list of the requested resource." ) args_schema: Type[SearchArgsSchema] = SearchArgsSchema def _parse_threads(self, threads: List[Dict[str, Any]]) -> List[Dict[str, Any]]: # Add the thread message snippets to the thread results results = [] for thread in threads: thread_id = thread["id"] thread_data = ( self.api_resource.users() .threads() .get(userId="me", id=thread_id) .execute() ) messages = thread_data["messages"] thread["messages"] = [] for message in messages: snippet = message["snippet"] thread["messages"].append({"snippet": snippet, "id": message["id"]}) results.append(thread) return results def _parse_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: results = [] for message in messages: message_id = message["id"] message_data = ( self.api_resource.users() .messages() .get(userId="me", format="raw", id=message_id) .execute() ) raw_message = base64.urlsafe_b64decode(message_data["raw"]) email_msg = email.message_from_bytes(raw_message) subject = email_msg["Subject"] sender = email_msg["From"] message_body = email_msg.get_payload() body = clean_email_body(message_body) results.append( { "id": message["id"], "threadId": message_data["threadId"], "snippet": message_data["snippet"], "body": body, "subject": subject, "sender": sender, } ) return results def _run( self, query: str, resource: Resource = Resource.MESSAGES, max_results: int = 10, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> List[Dict[str, Any]]: """Run the tool.""" results = ( self.api_resource.users() .messages() .list(userId="me", q=query, maxResults=max_results) .execute() .get(resource.value, []) ) if resource == Resource.THREADS: return self._parse_threads(results) elif resource == Resource.MESSAGES: return self._parse_messages(results) else: raise NotImplementedError(f"Resource of type {resource} not implemented.")

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import base64 import email from enum import Enum from typing import Any, Dict, List, Optional, Type from langchain.callbacks.manager import CallbackManagerForToolRun from langchain.pydantic_v1 import BaseModel, Field from langchain.tools.gmail.base import GmailBaseTool from langchain.tools.gmail.utils import clean_email_body class Resource(str, Enum): """Enumerator of Resources to search.""" THREADS = "threads" MESSAGES = "messages" class SearchArgsSchema(BaseModel): """Input for SearchGmailTool.""" # From https://support.google.com/mail/answer/7190?hl=en query: str = Field( ..., description="The Gmail query. Example filters include from:sender," " to:recipient, subject:subject, -filtered_term," " in:folder, is:important|read|starred, after:year/mo/date, " "before:year/mo/date, label:label_name" ' "exact phrase".' " Search newer/older than using d (day), m (month), and y (year): " "newer_than:2d, older_than:1y." " Attachments with extension example: filename:pdf. Multiple term" " matching example: from:amy OR from:david.", ) resource: Resource = Field( default=Resource.MESSAGES, description="Whether to search for threads or messages.", ) max_results: int = Field( default=10, description="The maximum number of results to return.", ) class GmailSearch(GmailBaseTool): """Tool that searches for messages or threads in Gmail.""" name: str = "search_gmail" description: str = ( "Use this tool to search for email messages or threads." " The input must be a valid Gmail query." " The output is a JSON list of the requested resource." ) args_schema: Type[SearchArgsSchema] = SearchArgsSchema def _parse_threads(self, threads: List[Dict[str, Any]]) -> List[Dict[str, Any]]: # Add the thread message snippets to the thread results results = [] for thread in threads: thread_id = thread["id"] thread_data = ( self.api_resource.users() .threads() .get(userId="me", id=thread_id) .execute() ) messages = thread_data["messages"] thread["messages"] = [] for message in messages: snippet = message["snippet"] thread["messages"].append({"snippet": snippet, "id": message["id"]}) results.append(thread) return results def _parse_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: results = [] for message in messages: message_id = message["id"] message_data = ( self.api_resource.users() .messages() .get(userId="me", format="raw", id=message_id) .execute() ) raw_message = base64.urlsafe_b64decode(message_data["raw"]) email_msg = email.message_from_bytes(raw_message) subject = email_msg["Subject"] sender = email_msg["From"] message_body = email_msg.get_payload() body = clean_email_body(message_body) results.append( { "id": message["id"], "threadId": message_data["threadId"], "snippet": message_data["snippet"], "body": body, "subject": subject, "sender": sender, } ) return results def _run( self, query: str, resource: Resource = Resource.MESSAGES, max_results: int = 10, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> List[Dict[str, Any]]: """Run the tool.""" results = ( self.api_resource.users() .messages() .list(userId="me", q=query, maxResults=max_results) .execute() .get(resource.value, []) ) if resource == Resource.THREADS: return self._parse_threads(results) elif resource == Resource.MESSAGES: return self._parse_messages(results) else: raise NotImplementedError(f"Resource of type {resource} not implemented.")

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import base64 import email from enum import Enum from typing import Any, Dict, List, Optional, Type from langchain.callbacks.manager import CallbackManagerForToolRun from langchain.pydantic_v1 import BaseModel, Field from langchain.tools.gmail.base import GmailBaseTool from langchain.tools.gmail.utils import clean_email_body class Resource(str, Enum): """Enumerator of Resources to search.""" THREADS = "threads" MESSAGES = "messages" class SearchArgsSchema(BaseModel): """Input for SearchGmailTool.""" # From https://support.google.com/mail/answer/7190?hl=en query: str = Field( ..., description="The Gmail query. Example filters include from:sender," " to:recipient, subject:subject, -filtered_term," " in:folder, is:important|read|starred, after:year/mo/date, " "before:year/mo/date, label:label_name" ' "exact phrase".' " Search newer/older than using d (day), m (month), and y (year): " "newer_than:2d, older_than:1y." " Attachments with extension example: filename:pdf. Multiple term" " matching example: from:amy OR from:david.", ) resource: Resource = Field( default=Resource.MESSAGES, description="Whether to search for threads or messages.", ) max_results: int = Field( default=10, description="The maximum number of results to return.", ) class GmailSearch(GmailBaseTool): """Tool that searches for messages or threads in Gmail.""" name: str = "search_gmail" description: str = ( "Use this tool to search for email messages or threads." " The input must be a valid Gmail query." " The output is a JSON list of the requested resource." ) args_schema: Type[SearchArgsSchema] = SearchArgsSchema def _parse_threads(self, threads: List[Dict[str, Any]]) -> List[Dict[str, Any]]: # Add the thread message snippets to the thread results results = [] for thread in threads: thread_id = thread["id"] thread_data = ( self.api_resource.users() .threads() .get(userId="me", id=thread_id) .execute() ) messages = thread_data["messages"] thread["messages"] = [] for message in messages: snippet = message["snippet"] thread["messages"].append({"snippet": snippet, "id": message["id"]}) results.append(thread) return results def _parse_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: results = [] for message in messages: message_id = message["id"] message_data = ( self.api_resource.users() .messages() .get(userId="me", format="raw", id=message_id) .execute() ) raw_message = base64.urlsafe_b64decode(message_data["raw"]) email_msg = email.message_from_bytes(raw_message) subject = email_msg["Subject"] sender = email_msg["From"] message_body = email_msg.get_payload() body = clean_email_body(message_body) results.append( { "id": message["id"], "threadId": message_data["threadId"], "snippet": message_data["snippet"], "body": body, "subject": subject, "sender": sender, } ) return results def _run( self, query: str, resource: Resource = Resource.MESSAGES, max_results: int = 10, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> List[Dict[str, Any]]: """Run the tool.""" results = ( self.api_resource.users() .messages() .list(userId="me", q=query, maxResults=max_results) .execute() .get(resource.value, []) ) if resource == Resource.THREADS: return self._parse_threads(results) elif resource == Resource.MESSAGES: return self._parse_messages(results) else: raise NotImplementedError(f"Resource of type {resource} not implemented.")

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from langchain import PromptTemplate from codedog.templates import grimoire_en TRANSLATE_PROMPT = PromptTemplate( template=grimoire_en.TRANSLATE_PR_REVIEW, input_variables=["language", "description", "content"] )

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"""Callback Handler that writes to a file.""" from typing import Any, Dict, Optional, TextIO, cast from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import BaseCallbackHandler from langchain_core.utils.input import print_text class FileCallbackHandler(BaseCallbackHandler): """Callback Handler that writes to a file.""" def __init__( self, filename: str, mode: str = "a", color: Optional[str] = None ) -> None: """Initialize callback handler.""" self.file = cast(TextIO, open(filename, mode, encoding="utf-8")) self.color = color def __del__(self) -> None: """Destructor to cleanup when done.""" self.file.close() def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Print out that we are entering a chain.""" class_name = serialized.get("name", serialized.get("id", ["<unknown>"])[-1]) print_text( f"\n\n\033[1m> Entering new {class_name} chain...\033[0m", end="\n", file=self.file, ) def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Print out that we finished a chain.""" print_text("\n\033[1m> Finished chain.\033[0m", end="\n", file=self.file) def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any ) -> Any: """Run on agent action.""" print_text(action.log, color=color or self.color, file=self.file) def on_tool_end( self, output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """If not the final action, print out observation.""" if observation_prefix is not None: print_text(f"\n{observation_prefix}", file=self.file) print_text(output, color=color or self.color, file=self.file) if llm_prefix is not None: print_text(f"\n{llm_prefix}", file=self.file) def on_text( self, text: str, color: Optional[str] = None, end: str = "", **kwargs: Any ) -> None: """Run when agent ends.""" print_text(text, color=color or self.color, end=end, file=self.file) def on_agent_finish( self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any ) -> None: """Run on agent end.""" print_text(finish.log, color=color or self.color, end="\n", file=self.file)

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"""Callback Handler that writes to a file.""" from typing import Any, Dict, Optional, TextIO, cast from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import BaseCallbackHandler from langchain_core.utils.input import print_text class FileCallbackHandler(BaseCallbackHandler): """Callback Handler that writes to a file.""" def __init__( self, filename: str, mode: str = "a", color: Optional[str] = None ) -> None: """Initialize callback handler.""" self.file = cast(TextIO, open(filename, mode, encoding="utf-8")) self.color = color def __del__(self) -> None: """Destructor to cleanup when done.""" self.file.close() def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Print out that we are entering a chain.""" class_name = serialized.get("name", serialized.get("id", ["<unknown>"])[-1]) print_text( f"\n\n\033[1m> Entering new {class_name} chain...\033[0m", end="\n", file=self.file, ) def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Print out that we finished a chain.""" print_text("\n\033[1m> Finished chain.\033[0m", end="\n", file=self.file) def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any ) -> Any: """Run on agent action.""" print_text(action.log, color=color or self.color, file=self.file) def on_tool_end( self, output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """If not the final action, print out observation.""" if observation_prefix is not None: print_text(f"\n{observation_prefix}", file=self.file) print_text(output, color=color or self.color, file=self.file) if llm_prefix is not None: print_text(f"\n{llm_prefix}", file=self.file) def on_text( self, text: str, color: Optional[str] = None, end: str = "", **kwargs: Any ) -> None: """Run when agent ends.""" print_text(text, color=color or self.color, end=end, file=self.file) def on_agent_finish( self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any ) -> None: """Run on agent end.""" print_text(finish.log, color=color or self.color, end="\n", file=self.file)

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"""Callback Handler that writes to a file.""" from typing import Any, Dict, Optional, TextIO, cast from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import BaseCallbackHandler from langchain_core.utils.input import print_text class FileCallbackHandler(BaseCallbackHandler): """Callback Handler that writes to a file.""" def __init__( self, filename: str, mode: str = "a", color: Optional[str] = None ) -> None: """Initialize callback handler.""" self.file = cast(TextIO, open(filename, mode, encoding="utf-8")) self.color = color def __del__(self) -> None: """Destructor to cleanup when done.""" self.file.close() def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Print out that we are entering a chain.""" class_name = serialized.get("name", serialized.get("id", ["<unknown>"])[-1]) print_text( f"\n\n\033[1m> Entering new {class_name} chain...\033[0m", end="\n", file=self.file, ) def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Print out that we finished a chain.""" print_text("\n\033[1m> Finished chain.\033[0m", end="\n", file=self.file) def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any ) -> Any: """Run on agent action.""" print_text(action.log, color=color or self.color, file=self.file) def on_tool_end( self, output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """If not the final action, print out observation.""" if observation_prefix is not None: print_text(f"\n{observation_prefix}", file=self.file) print_text(output, color=color or self.color, file=self.file) if llm_prefix is not None: print_text(f"\n{llm_prefix}", file=self.file) def on_text( self, text: str, color: Optional[str] = None, end: str = "", **kwargs: Any ) -> None: """Run when agent ends.""" print_text(text, color=color or self.color, end=end, file=self.file) def on_agent_finish( self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any ) -> None: """Run on agent end.""" print_text(finish.log, color=color or self.color, end="\n", file=self.file)

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"""Callback Handler that writes to a file.""" from typing import Any, Dict, Optional, TextIO, cast from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import BaseCallbackHandler from langchain_core.utils.input import print_text class FileCallbackHandler(BaseCallbackHandler): """Callback Handler that writes to a file.""" def __init__( self, filename: str, mode: str = "a", color: Optional[str] = None ) -> None: """Initialize callback handler.""" self.file = cast(TextIO, open(filename, mode, encoding="utf-8")) self.color = color def __del__(self) -> None: """Destructor to cleanup when done.""" self.file.close() def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Print out that we are entering a chain.""" class_name = serialized.get("name", serialized.get("id", ["<unknown>"])[-1]) print_text( f"\n\n\033[1m> Entering new {class_name} chain...\033[0m", end="\n", file=self.file, ) def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Print out that we finished a chain.""" print_text("\n\033[1m> Finished chain.\033[0m", end="\n", file=self.file) def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any ) -> Any: """Run on agent action.""" print_text(action.log, color=color or self.color, file=self.file) def on_tool_end( self, output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """If not the final action, print out observation.""" if observation_prefix is not None: print_text(f"\n{observation_prefix}", file=self.file) print_text(output, color=color or self.color, file=self.file) if llm_prefix is not None: print_text(f"\n{llm_prefix}", file=self.file) def on_text( self, text: str, color: Optional[str] = None, end: str = "", **kwargs: Any ) -> None: """Run when agent ends.""" print_text(text, color=color or self.color, end=end, file=self.file) def on_agent_finish( self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any ) -> None: """Run on agent end.""" print_text(finish.log, color=color or self.color, end="\n", file=self.file)

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import base64 import json from langchain_community.chat_models import ChatOpenAI from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate from langchain_core.pydantic_v1 import Field from langserve import CustomUserType from .prompts import ( AI_REPONSE_DICT, FULL_PROMPT, USER_EXAMPLE_DICT, create_prompt, ) from .utils import parse_llm_output llm = ChatOpenAI(temperature=0, model="gpt-4") prompt = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template(FULL_PROMPT), ("human", "{user_example}"), ("ai", "{ai_response}"), ("human", "{input}"), ], ) # ATTENTION: Inherit from CustomUserType instead of BaseModel otherwise # the server will decode it into a dict instead of a pydantic model. class FileProcessingRequest(CustomUserType): """Request including a base64 encoded file.""" # The extra field is used to specify a widget for the playground UI. file: str = Field(..., extra={"widget": {"type": "base64file"}}) num_plates: int = None num_rows: int = 8 num_cols: int = 12 def _load_file(request: FileProcessingRequest): return base64.b64decode(request.file.encode("utf-8")).decode("utf-8") def _load_prompt(request: FileProcessingRequest): return create_prompt( num_plates=request.num_plates, num_rows=request.num_rows, num_cols=request.num_cols, ) def _get_col_range_str(request: FileProcessingRequest): if request.num_cols: return f"from 1 to {request.num_cols}" else: return "" def _get_json_format(request: FileProcessingRequest): return json.dumps( [ { "row_start": 12, "row_end": 12 + request.num_rows - 1, "col_start": 1, "col_end": 1 + request.num_cols - 1, "contents": "Entity ID", } ] ) chain = ( { # Should add validation to ensure numeric indices "input": _load_file, "hint": _load_prompt, "col_range_str": _get_col_range_str, "json_format": _get_json_format, "user_example": lambda x: USER_EXAMPLE_DICT[x.num_rows * x.num_cols], "ai_response": lambda x: AI_REPONSE_DICT[x.num_rows * x.num_cols], } | prompt | llm | StrOutputParser() | parse_llm_output ).with_types(input_type=FileProcessingRequest)

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import base64 import json from langchain_community.chat_models import ChatOpenAI from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate from langchain_core.pydantic_v1 import Field from langserve import CustomUserType from .prompts import ( AI_REPONSE_DICT, FULL_PROMPT, USER_EXAMPLE_DICT, create_prompt, ) from .utils import parse_llm_output llm = ChatOpenAI(temperature=0, model="gpt-4") prompt = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template(FULL_PROMPT), ("human", "{user_example}"), ("ai", "{ai_response}"), ("human", "{input}"), ], ) # ATTENTION: Inherit from CustomUserType instead of BaseModel otherwise # the server will decode it into a dict instead of a pydantic model. class FileProcessingRequest(CustomUserType): """Request including a base64 encoded file.""" # The extra field is used to specify a widget for the playground UI. file: str = Field(..., extra={"widget": {"type": "base64file"}}) num_plates: int = None num_rows: int = 8 num_cols: int = 12 def _load_file(request: FileProcessingRequest): return base64.b64decode(request.file.encode("utf-8")).decode("utf-8") def _load_prompt(request: FileProcessingRequest): return create_prompt( num_plates=request.num_plates, num_rows=request.num_rows, num_cols=request.num_cols, ) def _get_col_range_str(request: FileProcessingRequest): if request.num_cols: return f"from 1 to {request.num_cols}" else: return "" def _get_json_format(request: FileProcessingRequest): return json.dumps( [ { "row_start": 12, "row_end": 12 + request.num_rows - 1, "col_start": 1, "col_end": 1 + request.num_cols - 1, "contents": "Entity ID", } ] ) chain = ( { # Should add validation to ensure numeric indices "input": _load_file, "hint": _load_prompt, "col_range_str": _get_col_range_str, "json_format": _get_json_format, "user_example": lambda x: USER_EXAMPLE_DICT[x.num_rows * x.num_cols], "ai_response": lambda x: AI_REPONSE_DICT[x.num_rows * x.num_cols], } | prompt | llm | StrOutputParser() | parse_llm_output ).with_types(input_type=FileProcessingRequest)

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import base64 import json from langchain_community.chat_models import ChatOpenAI from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate from langchain_core.pydantic_v1 import Field from langserve import CustomUserType from .prompts import ( AI_REPONSE_DICT, FULL_PROMPT, USER_EXAMPLE_DICT, create_prompt, ) from .utils import parse_llm_output llm = ChatOpenAI(temperature=0, model="gpt-4") prompt = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template(FULL_PROMPT), ("human", "{user_example}"), ("ai", "{ai_response}"), ("human", "{input}"), ], ) # ATTENTION: Inherit from CustomUserType instead of BaseModel otherwise # the server will decode it into a dict instead of a pydantic model. class FileProcessingRequest(CustomUserType): """Request including a base64 encoded file.""" # The extra field is used to specify a widget for the playground UI. file: str = Field(..., extra={"widget": {"type": "base64file"}}) num_plates: int = None num_rows: int = 8 num_cols: int = 12 def _load_file(request: FileProcessingRequest): return base64.b64decode(request.file.encode("utf-8")).decode("utf-8") def _load_prompt(request: FileProcessingRequest): return create_prompt( num_plates=request.num_plates, num_rows=request.num_rows, num_cols=request.num_cols, ) def _get_col_range_str(request: FileProcessingRequest): if request.num_cols: return f"from 1 to {request.num_cols}" else: return "" def _get_json_format(request: FileProcessingRequest): return json.dumps( [ { "row_start": 12, "row_end": 12 + request.num_rows - 1, "col_start": 1, "col_end": 1 + request.num_cols - 1, "contents": "Entity ID", } ] ) chain = ( { # Should add validation to ensure numeric indices "input": _load_file, "hint": _load_prompt, "col_range_str": _get_col_range_str, "json_format": _get_json_format, "user_example": lambda x: USER_EXAMPLE_DICT[x.num_rows * x.num_cols], "ai_response": lambda x: AI_REPONSE_DICT[x.num_rows * x.num_cols], } | prompt | llm | StrOutputParser() | parse_llm_output ).with_types(input_type=FileProcessingRequest)

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import base64 import json from langchain_community.chat_models import ChatOpenAI from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate from langchain_core.pydantic_v1 import Field from langserve import CustomUserType from .prompts import ( AI_REPONSE_DICT, FULL_PROMPT, USER_EXAMPLE_DICT, create_prompt, ) from .utils import parse_llm_output llm = ChatOpenAI(temperature=0, model="gpt-4") prompt = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template(FULL_PROMPT), ("human", "{user_example}"), ("ai", "{ai_response}"), ("human", "{input}"), ], ) # ATTENTION: Inherit from CustomUserType instead of BaseModel otherwise # the server will decode it into a dict instead of a pydantic model. class FileProcessingRequest(CustomUserType): """Request including a base64 encoded file.""" # The extra field is used to specify a widget for the playground UI. file: str = Field(..., extra={"widget": {"type": "base64file"}}) num_plates: int = None num_rows: int = 8 num_cols: int = 12 def _load_file(request: FileProcessingRequest): return base64.b64decode(request.file.encode("utf-8")).decode("utf-8") def _load_prompt(request: FileProcessingRequest): return create_prompt( num_plates=request.num_plates, num_rows=request.num_rows, num_cols=request.num_cols, ) def _get_col_range_str(request: FileProcessingRequest): if request.num_cols: return f"from 1 to {request.num_cols}" else: return "" def _get_json_format(request: FileProcessingRequest): return json.dumps( [ { "row_start": 12, "row_end": 12 + request.num_rows - 1, "col_start": 1, "col_end": 1 + request.num_cols - 1, "contents": "Entity ID", } ] ) chain = ( { # Should add validation to ensure numeric indices "input": _load_file, "hint": _load_prompt, "col_range_str": _get_col_range_str, "json_format": _get_json_format, "user_example": lambda x: USER_EXAMPLE_DICT[x.num_rows * x.num_cols], "ai_response": lambda x: AI_REPONSE_DICT[x.num_rows * x.num_cols], } | prompt | llm | StrOutputParser() | parse_llm_output ).with_types(input_type=FileProcessingRequest)

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import json from typing import Any, Callable, List from langchain_core.tracers.base import BaseTracer from langchain_core.tracers.schemas import Run from langchain_core.utils.input import get_bolded_text, get_colored_text def try_json_stringify(obj: Any, fallback: str) -> str: """ Try to stringify an object to JSON. Args: obj: Object to stringify. fallback: Fallback string to return if the object cannot be stringified. Returns: A JSON string if the object can be stringified, otherwise the fallback string. """ try: return json.dumps(obj, indent=2, ensure_ascii=False) except Exception: return fallback def elapsed(run: Any) -> str: """Get the elapsed time of a run. Args: run: any object with a start_time and end_time attribute. Returns: A string with the elapsed time in seconds or milliseconds if time is less than a second. """ elapsed_time = run.end_time - run.start_time milliseconds = elapsed_time.total_seconds() * 1000 if milliseconds < 1000: return f"{milliseconds:.0f}ms" return f"{(milliseconds / 1000):.2f}s" class FunctionCallbackHandler(BaseTracer): """Tracer that calls a function with a single str parameter.""" name: str = "function_callback_handler" def __init__(self, function: Callable[[str], None], **kwargs: Any) -> None: super().__init__(**kwargs) self.function_callback = function def _persist_run(self, run: Run) -> None: pass def get_parents(self, run: Run) -> List[Run]: parents = [] current_run = run while current_run.parent_run_id: parent = self.run_map.get(str(current_run.parent_run_id)) if parent: parents.append(parent) current_run = parent else: break return parents def get_breadcrumbs(self, run: Run) -> str: parents = self.get_parents(run)[::-1] string = " > ".join( f"{parent.execution_order}:{parent.run_type}:{parent.name}" if i != len(parents) - 1 else f"{parent.execution_order}:{parent.run_type}:{parent.name}" for i, parent in enumerate(parents + [run]) ) return string # logging methods def _on_chain_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/start]', color='green')} " + get_bolded_text(f"[{crumbs}] Entering {run_type} run with input:\n") + f"{try_json_stringify(run.inputs, '[inputs]')}" ) def _on_chain_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/end]', color='blue')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting {run_type} run with output:\n" ) + f"{try_json_stringify(run.outputs, '[outputs]')}" ) def _on_chain_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/error]', color='red')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] {run_type} run errored with error:\n" ) + f"{try_json_stringify(run.error, '[error]')}" ) def _on_llm_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) inputs = ( {"prompts": [p.strip() for p in run.inputs["prompts"]]} if "prompts" in run.inputs else run.inputs ) self.function_callback( f"{get_colored_text('[llm/start]', color='green')} " + get_bolded_text(f"[{crumbs}] Entering LLM run with input:\n") + f"{try_json_stringify(inputs, '[inputs]')}" ) def _on_llm_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[llm/end]', color='blue')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting LLM run with output:\n" ) + f"{try_json_stringify(run.outputs, '[response]')}" ) def _on_llm_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[llm/error]', color='red')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] LLM run errored with error:\n" ) + f"{try_json_stringify(run.error, '[error]')}" ) def _on_tool_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f'{get_colored_text("[tool/start]", color="green")} ' + get_bolded_text(f"[{crumbs}] Entering Tool run with input:\n") + f'"{run.inputs["input"].strip()}"' ) def _on_tool_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) if run.outputs: self.function_callback( f'{get_colored_text("[tool/end]", color="blue")} ' + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting Tool run with output:\n" ) + f'"{run.outputs["output"].strip()}"' ) def _on_tool_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[tool/error]', color='red')} " + get_bolded_text(f"[{crumbs}] [{elapsed(run)}] ") + f"Tool run errored with error:\n" f"{run.error}" ) class ConsoleCallbackHandler(FunctionCallbackHandler): """Tracer that prints to the console.""" name: str = "console_callback_handler" def __init__(self, **kwargs: Any) -> None: super().__init__(function=print, **kwargs)

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import json from typing import Any, Callable, List from langchain_core.tracers.base import BaseTracer from langchain_core.tracers.schemas import Run from langchain_core.utils.input import get_bolded_text, get_colored_text def try_json_stringify(obj: Any, fallback: str) -> str: """ Try to stringify an object to JSON. Args: obj: Object to stringify. fallback: Fallback string to return if the object cannot be stringified. Returns: A JSON string if the object can be stringified, otherwise the fallback string. """ try: return json.dumps(obj, indent=2, ensure_ascii=False) except Exception: return fallback def elapsed(run: Any) -> str: """Get the elapsed time of a run. Args: run: any object with a start_time and end_time attribute. Returns: A string with the elapsed time in seconds or milliseconds if time is less than a second. """ elapsed_time = run.end_time - run.start_time milliseconds = elapsed_time.total_seconds() * 1000 if milliseconds < 1000: return f"{milliseconds:.0f}ms" return f"{(milliseconds / 1000):.2f}s" class FunctionCallbackHandler(BaseTracer): """Tracer that calls a function with a single str parameter.""" name: str = "function_callback_handler" def __init__(self, function: Callable[[str], None], **kwargs: Any) -> None: super().__init__(**kwargs) self.function_callback = function def _persist_run(self, run: Run) -> None: pass def get_parents(self, run: Run) -> List[Run]: parents = [] current_run = run while current_run.parent_run_id: parent = self.run_map.get(str(current_run.parent_run_id)) if parent: parents.append(parent) current_run = parent else: break return parents def get_breadcrumbs(self, run: Run) -> str: parents = self.get_parents(run)[::-1] string = " > ".join( f"{parent.execution_order}:{parent.run_type}:{parent.name}" if i != len(parents) - 1 else f"{parent.execution_order}:{parent.run_type}:{parent.name}" for i, parent in enumerate(parents + [run]) ) return string # logging methods def _on_chain_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/start]', color='green')} " + get_bolded_text(f"[{crumbs}] Entering {run_type} run with input:\n") + f"{try_json_stringify(run.inputs, '[inputs]')}" ) def _on_chain_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/end]', color='blue')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting {run_type} run with output:\n" ) + f"{try_json_stringify(run.outputs, '[outputs]')}" ) def _on_chain_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/error]', color='red')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] {run_type} run errored with error:\n" ) + f"{try_json_stringify(run.error, '[error]')}" ) def _on_llm_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) inputs = ( {"prompts": [p.strip() for p in run.inputs["prompts"]]} if "prompts" in run.inputs else run.inputs ) self.function_callback( f"{get_colored_text('[llm/start]', color='green')} " + get_bolded_text(f"[{crumbs}] Entering LLM run with input:\n") + f"{try_json_stringify(inputs, '[inputs]')}" ) def _on_llm_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[llm/end]', color='blue')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting LLM run with output:\n" ) + f"{try_json_stringify(run.outputs, '[response]')}" ) def _on_llm_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[llm/error]', color='red')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] LLM run errored with error:\n" ) + f"{try_json_stringify(run.error, '[error]')}" ) def _on_tool_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f'{get_colored_text("[tool/start]", color="green")} ' + get_bolded_text(f"[{crumbs}] Entering Tool run with input:\n") + f'"{run.inputs["input"].strip()}"' ) def _on_tool_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) if run.outputs: self.function_callback( f'{get_colored_text("[tool/end]", color="blue")} ' + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting Tool run with output:\n" ) + f'"{run.outputs["output"].strip()}"' ) def _on_tool_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[tool/error]', color='red')} " + get_bolded_text(f"[{crumbs}] [{elapsed(run)}] ") + f"Tool run errored with error:\n" f"{run.error}" ) class ConsoleCallbackHandler(FunctionCallbackHandler): """Tracer that prints to the console.""" name: str = "console_callback_handler" def __init__(self, **kwargs: Any) -> None: super().__init__(function=print, **kwargs)

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import json from typing import Any, Callable, List from langchain_core.tracers.base import BaseTracer from langchain_core.tracers.schemas import Run from langchain_core.utils.input import get_bolded_text, get_colored_text def try_json_stringify(obj: Any, fallback: str) -> str: """ Try to stringify an object to JSON. Args: obj: Object to stringify. fallback: Fallback string to return if the object cannot be stringified. Returns: A JSON string if the object can be stringified, otherwise the fallback string. """ try: return json.dumps(obj, indent=2, ensure_ascii=False) except Exception: return fallback def elapsed(run: Any) -> str: """Get the elapsed time of a run. Args: run: any object with a start_time and end_time attribute. Returns: A string with the elapsed time in seconds or milliseconds if time is less than a second. """ elapsed_time = run.end_time - run.start_time milliseconds = elapsed_time.total_seconds() * 1000 if milliseconds < 1000: return f"{milliseconds:.0f}ms" return f"{(milliseconds / 1000):.2f}s" class FunctionCallbackHandler(BaseTracer): """Tracer that calls a function with a single str parameter.""" name: str = "function_callback_handler" def __init__(self, function: Callable[[str], None], **kwargs: Any) -> None: super().__init__(**kwargs) self.function_callback = function def _persist_run(self, run: Run) -> None: pass def get_parents(self, run: Run) -> List[Run]: parents = [] current_run = run while current_run.parent_run_id: parent = self.run_map.get(str(current_run.parent_run_id)) if parent: parents.append(parent) current_run = parent else: break return parents def get_breadcrumbs(self, run: Run) -> str: parents = self.get_parents(run)[::-1] string = " > ".join( f"{parent.execution_order}:{parent.run_type}:{parent.name}" if i != len(parents) - 1 else f"{parent.execution_order}:{parent.run_type}:{parent.name}" for i, parent in enumerate(parents + [run]) ) return string # logging methods def _on_chain_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/start]', color='green')} " + get_bolded_text(f"[{crumbs}] Entering {run_type} run with input:\n") + f"{try_json_stringify(run.inputs, '[inputs]')}" ) def _on_chain_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/end]', color='blue')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting {run_type} run with output:\n" ) + f"{try_json_stringify(run.outputs, '[outputs]')}" ) def _on_chain_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/error]', color='red')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] {run_type} run errored with error:\n" ) + f"{try_json_stringify(run.error, '[error]')}" ) def _on_llm_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) inputs = ( {"prompts": [p.strip() for p in run.inputs["prompts"]]} if "prompts" in run.inputs else run.inputs ) self.function_callback( f"{get_colored_text('[llm/start]', color='green')} " + get_bolded_text(f"[{crumbs}] Entering LLM run with input:\n") + f"{try_json_stringify(inputs, '[inputs]')}" ) def _on_llm_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[llm/end]', color='blue')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting LLM run with output:\n" ) + f"{try_json_stringify(run.outputs, '[response]')}" ) def _on_llm_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[llm/error]', color='red')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] LLM run errored with error:\n" ) + f"{try_json_stringify(run.error, '[error]')}" ) def _on_tool_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f'{get_colored_text("[tool/start]", color="green")} ' + get_bolded_text(f"[{crumbs}] Entering Tool run with input:\n") + f'"{run.inputs["input"].strip()}"' ) def _on_tool_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) if run.outputs: self.function_callback( f'{get_colored_text("[tool/end]", color="blue")} ' + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting Tool run with output:\n" ) + f'"{run.outputs["output"].strip()}"' ) def _on_tool_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[tool/error]', color='red')} " + get_bolded_text(f"[{crumbs}] [{elapsed(run)}] ") + f"Tool run errored with error:\n" f"{run.error}" ) class ConsoleCallbackHandler(FunctionCallbackHandler): """Tracer that prints to the console.""" name: str = "console_callback_handler" def __init__(self, **kwargs: Any) -> None: super().__init__(function=print, **kwargs)

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import json from typing import Any, Callable, List from langchain_core.tracers.base import BaseTracer from langchain_core.tracers.schemas import Run from langchain_core.utils.input import get_bolded_text, get_colored_text def try_json_stringify(obj: Any, fallback: str) -> str: """ Try to stringify an object to JSON. Args: obj: Object to stringify. fallback: Fallback string to return if the object cannot be stringified. Returns: A JSON string if the object can be stringified, otherwise the fallback string. """ try: return json.dumps(obj, indent=2, ensure_ascii=False) except Exception: return fallback def elapsed(run: Any) -> str: """Get the elapsed time of a run. Args: run: any object with a start_time and end_time attribute. Returns: A string with the elapsed time in seconds or milliseconds if time is less than a second. """ elapsed_time = run.end_time - run.start_time milliseconds = elapsed_time.total_seconds() * 1000 if milliseconds < 1000: return f"{milliseconds:.0f}ms" return f"{(milliseconds / 1000):.2f}s" class FunctionCallbackHandler(BaseTracer): """Tracer that calls a function with a single str parameter.""" name: str = "function_callback_handler" def __init__(self, function: Callable[[str], None], **kwargs: Any) -> None: super().__init__(**kwargs) self.function_callback = function def _persist_run(self, run: Run) -> None: pass def get_parents(self, run: Run) -> List[Run]: parents = [] current_run = run while current_run.parent_run_id: parent = self.run_map.get(str(current_run.parent_run_id)) if parent: parents.append(parent) current_run = parent else: break return parents def get_breadcrumbs(self, run: Run) -> str: parents = self.get_parents(run)[::-1] string = " > ".join( f"{parent.execution_order}:{parent.run_type}:{parent.name}" if i != len(parents) - 1 else f"{parent.execution_order}:{parent.run_type}:{parent.name}" for i, parent in enumerate(parents + [run]) ) return string # logging methods def _on_chain_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/start]', color='green')} " + get_bolded_text(f"[{crumbs}] Entering {run_type} run with input:\n") + f"{try_json_stringify(run.inputs, '[inputs]')}" ) def _on_chain_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/end]', color='blue')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting {run_type} run with output:\n" ) + f"{try_json_stringify(run.outputs, '[outputs]')}" ) def _on_chain_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) run_type = run.run_type.capitalize() self.function_callback( f"{get_colored_text('[chain/error]', color='red')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] {run_type} run errored with error:\n" ) + f"{try_json_stringify(run.error, '[error]')}" ) def _on_llm_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) inputs = ( {"prompts": [p.strip() for p in run.inputs["prompts"]]} if "prompts" in run.inputs else run.inputs ) self.function_callback( f"{get_colored_text('[llm/start]', color='green')} " + get_bolded_text(f"[{crumbs}] Entering LLM run with input:\n") + f"{try_json_stringify(inputs, '[inputs]')}" ) def _on_llm_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[llm/end]', color='blue')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting LLM run with output:\n" ) + f"{try_json_stringify(run.outputs, '[response]')}" ) def _on_llm_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[llm/error]', color='red')} " + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] LLM run errored with error:\n" ) + f"{try_json_stringify(run.error, '[error]')}" ) def _on_tool_start(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f'{get_colored_text("[tool/start]", color="green")} ' + get_bolded_text(f"[{crumbs}] Entering Tool run with input:\n") + f'"{run.inputs["input"].strip()}"' ) def _on_tool_end(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) if run.outputs: self.function_callback( f'{get_colored_text("[tool/end]", color="blue")} ' + get_bolded_text( f"[{crumbs}] [{elapsed(run)}] Exiting Tool run with output:\n" ) + f'"{run.outputs["output"].strip()}"' ) def _on_tool_error(self, run: Run) -> None: crumbs = self.get_breadcrumbs(run) self.function_callback( f"{get_colored_text('[tool/error]', color='red')} " + get_bolded_text(f"[{crumbs}] [{elapsed(run)}] ") + f"Tool run errored with error:\n" f"{run.error}" ) class ConsoleCallbackHandler(FunctionCallbackHandler): """Tracer that prints to the console.""" name: str = "console_callback_handler" def __init__(self, **kwargs: Any) -> None: super().__init__(function=print, **kwargs)

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from typing import Any, Dict, List, Union from langchain.memory.chat_memory import BaseChatMemory from langchain.schema.messages import BaseMessage, get_buffer_string class ConversationBufferWindowMemory(BaseChatMemory): """Buffer for storing conversation memory inside a limited size window.""" human_prefix: str = "Human" ai_prefix: str = "AI" memory_key: str = "history" #: :meta private: k: int = 5 """Number of messages to store in buffer.""" @property def buffer(self) -> Union[str, List[BaseMessage]]: """String buffer of memory.""" return self.buffer_as_messages if self.return_messages else self.buffer_as_str @property def buffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" messages = self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else [] return get_buffer_string( messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) @property def buffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else [] @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" return {self.memory_key: self.buffer}

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from typing import Any, Dict, List, Union from langchain.memory.chat_memory import BaseChatMemory from langchain.schema.messages import BaseMessage, get_buffer_string class ConversationBufferWindowMemory(BaseChatMemory): """Buffer for storing conversation memory inside a limited size window.""" human_prefix: str = "Human" ai_prefix: str = "AI" memory_key: str = "history" #: :meta private: k: int = 5 """Number of messages to store in buffer.""" @property def buffer(self) -> Union[str, List[BaseMessage]]: """String buffer of memory.""" return self.buffer_as_messages if self.return_messages else self.buffer_as_str @property def buffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" messages = self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else [] return get_buffer_string( messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) @property def buffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else [] @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" return {self.memory_key: self.buffer}

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from typing import Any, Dict, List, Union from langchain.memory.chat_memory import BaseChatMemory from langchain.schema.messages import BaseMessage, get_buffer_string class ConversationBufferWindowMemory(BaseChatMemory): """Buffer for storing conversation memory inside a limited size window.""" human_prefix: str = "Human" ai_prefix: str = "AI" memory_key: str = "history" #: :meta private: k: int = 5 """Number of messages to store in buffer.""" @property def buffer(self) -> Union[str, List[BaseMessage]]: """String buffer of memory.""" return self.buffer_as_messages if self.return_messages else self.buffer_as_str @property def buffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" messages = self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else [] return get_buffer_string( messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) @property def buffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else [] @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" return {self.memory_key: self.buffer}

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from typing import Any, Dict, List, Union from langchain.memory.chat_memory import BaseChatMemory from langchain.schema.messages import BaseMessage, get_buffer_string class ConversationBufferWindowMemory(BaseChatMemory): """Buffer for storing conversation memory inside a limited size window.""" human_prefix: str = "Human" ai_prefix: str = "AI" memory_key: str = "history" #: :meta private: k: int = 5 """Number of messages to store in buffer.""" @property def buffer(self) -> Union[str, List[BaseMessage]]: """String buffer of memory.""" return self.buffer_as_messages if self.return_messages else self.buffer_as_str @property def buffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" messages = self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else [] return get_buffer_string( messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) @property def buffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else [] @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" return {self.memory_key: self.buffer}

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from typing import Any, Dict, Optional, Type # type: ignore import langchain from langchain import LLMChain, PromptTemplate from langchain.experimental.autonomous_agents import AutoGPT from sam.core.utils import logger class AutoGptAgent: agent: AutoGPT def __init__( self, ai_name: str, ai_role: str, memory: VectorStoreRetriever, llm: BaseChatModel, tools: List[BaseTool], **kwargs ): self.agent = AutoGPT.from_llm_and_tools( ai_name=ai_name, ai_role=ai_role, llm=llm, memory=memory, tools=tools, ) def start(self, goals: List[str]): return self.agent.run(goals=goals)

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from typing import Any, Dict, Optional, Type # type: ignore import langchain from langchain import LLMChain, PromptTemplate from langchain.experimental.autonomous_agents import AutoGPT from sam.core.utils import logger class AutoGptAgent: agent: AutoGPT def __init__( self, ai_name: str, ai_role: str, memory: VectorStoreRetriever, llm: BaseChatModel, tools: List[BaseTool], **kwargs ): self.agent = AutoGPT.from_llm_and_tools( ai_name=ai_name, ai_role=ai_role, llm=llm, memory=memory, tools=tools, ) def start(self, goals: List[str]): return self.agent.run(goals=goals)

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from typing import Any, Dict, Optional, Type # type: ignore import langchain from langchain import LLMChain, PromptTemplate from langchain.experimental.autonomous_agents import AutoGPT from sam.core.utils import logger class AutoGptAgent: agent: AutoGPT def __init__( self, ai_name: str, ai_role: str, memory: VectorStoreRetriever, llm: BaseChatModel, tools: List[BaseTool], **kwargs ): self.agent = AutoGPT.from_llm_and_tools( ai_name=ai_name, ai_role=ai_role, llm=llm, memory=memory, tools=tools, ) def start(self, goals: List[str]): return self.agent.run(goals=goals)

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#import os from dotenv import load_dotenv, find_dotenv _ = load_dotenv(find_dotenv()) # read local .env file import warnings warnings.filterwarnings("ignore") from langchain.agents.agent_toolkits import create_python_agent from langchain.agents import load_tools, initialize_agent from langchain.agents import AgentType from langchain.tools.python.tool import PythonREPLTool #from langchain.python import PythonREPL from langchain.chat_models import ChatOpenAI import langchain llm = ChatOpenAI(temperature=0) tools = load_tools(["llm-math", "wikipedia"], llm=llm) customer_list = [["Harrison", "Chase"], ["Lang", "Chain"], ["Dolly", "Too"], ["Elle", "Elem"], ["Geoff", "Fusion"], ["Trance", "Former"], ["Jen", "Ayai"]] def do_answer1(): langchain.debug = True agent = create_python_agent( llm, tool=PythonREPLTool(), verbose=True ) answer = agent.run(f"""Sort these customers by \ last name and then first name \ and print the output: {customer_list}""") print(answer) langchain.debug = False def do_answer2(): from langchain.agents import tool from datetime import date langchain.debug = True @tool def time(text: str) -> str: """Returns todays date, use this for any \ questions related to knowing todays date. \ The input should always be an empty string, \ and this function will always return todays \ date - any date mathmatics should occur \ outside this function.""" return str(date.today()) agent = initialize_agent( tools + [time], llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True, verbose = True) try: result = agent("whats the date today?") except: # noqa print("exception on external access") print(result) langchain.debug = False if __name__ == "__main__": #do_answer1() do_answer2()

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from typing import List, Optional, Type from langchain.memory import ( ChatMessageHistory, ConversationBufferMemory, ConversationSummaryMemory, RedisChatMessageHistory, RedisEntityStore, VectorStoreRetrieverMemory, ) class Memory: @staticmethod def messageHistory(path: str): history = ChatMessageHistory() return history @staticmethod def bufferMemory(path: str): memory = ConversationBufferMemory() return memory @staticmethod def chatSummary(path: str): memory = ConversationSummaryMemory() return memory

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from typing import List, Optional, Type from langchain.memory import ( ChatMessageHistory, ConversationBufferMemory, ConversationSummaryMemory, RedisChatMessageHistory, RedisEntityStore, VectorStoreRetrieverMemory, ) class Memory: @staticmethod def messageHistory(path: str): history = ChatMessageHistory() return history @staticmethod def bufferMemory(path: str): memory = ConversationBufferMemory() return memory @staticmethod def chatSummary(path: str): memory = ConversationSummaryMemory() return memory

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"""Callback Handler that prints to std out.""" from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, Optional from langchain_core.callbacks.base import BaseCallbackHandler from langchain_core.utils import print_text if TYPE_CHECKING: from langchain_core.agents import AgentAction, AgentFinish class StdOutCallbackHandler(BaseCallbackHandler): """Callback Handler that prints to std out.""" def __init__(self, color: Optional[str] = None) -> None: """Initialize callback handler.""" self.color = color def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Print out that we are entering a chain.""" class_name = serialized.get("name", serialized.get("id", ["<unknown>"])[-1]) print(f"\n\n\033[1m> Entering new {class_name} chain...\033[0m") # noqa: T201 def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Print out that we finished a chain.""" print("\n\033[1m> Finished chain.\033[0m") # noqa: T201 def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any ) -> Any: """Run on agent action.""" print_text(action.log, color=color or self.color) def on_tool_end( self, output: Any, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """If not the final action, print out observation.""" output = str(output) if observation_prefix is not None: print_text(f"\n{observation_prefix}") print_text(output, color=color or self.color) if llm_prefix is not None: print_text(f"\n{llm_prefix}") def on_text( self, text: str, color: Optional[str] = None, end: str = "", **kwargs: Any, ) -> None: """Run when agent ends.""" print_text(text, color=color or self.color, end=end) def on_agent_finish( self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any ) -> None: """Run on agent end.""" print_text(finish.log, color=color or self.color, end="\n")

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"""Callback Handler that prints to std out.""" from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, Optional from langchain_core.callbacks.base import BaseCallbackHandler from langchain_core.utils import print_text if TYPE_CHECKING: from langchain_core.agents import AgentAction, AgentFinish class StdOutCallbackHandler(BaseCallbackHandler): """Callback Handler that prints to std out.""" def __init__(self, color: Optional[str] = None) -> None: """Initialize callback handler.""" self.color = color def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Print out that we are entering a chain.""" class_name = serialized.get("name", serialized.get("id", ["<unknown>"])[-1]) print(f"\n\n\033[1m> Entering new {class_name} chain...\033[0m") # noqa: T201 def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Print out that we finished a chain.""" print("\n\033[1m> Finished chain.\033[0m") # noqa: T201 def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any ) -> Any: """Run on agent action.""" print_text(action.log, color=color or self.color) def on_tool_end( self, output: Any, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """If not the final action, print out observation.""" output = str(output) if observation_prefix is not None: print_text(f"\n{observation_prefix}") print_text(output, color=color or self.color) if llm_prefix is not None: print_text(f"\n{llm_prefix}") def on_text( self, text: str, color: Optional[str] = None, end: str = "", **kwargs: Any, ) -> None: """Run when agent ends.""" print_text(text, color=color or self.color, end=end) def on_agent_finish( self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any ) -> None: """Run on agent end.""" print_text(finish.log, color=color or self.color, end="\n")

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"""Callback Handler that prints to std out.""" from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, Optional from langchain_core.callbacks.base import BaseCallbackHandler from langchain_core.utils import print_text if TYPE_CHECKING: from langchain_core.agents import AgentAction, AgentFinish class StdOutCallbackHandler(BaseCallbackHandler): """Callback Handler that prints to std out.""" def __init__(self, color: Optional[str] = None) -> None: """Initialize callback handler.""" self.color = color def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Print out that we are entering a chain.""" class_name = serialized.get("name", serialized.get("id", ["<unknown>"])[-1]) print(f"\n\n\033[1m> Entering new {class_name} chain...\033[0m") # noqa: T201 def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Print out that we finished a chain.""" print("\n\033[1m> Finished chain.\033[0m") # noqa: T201 def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any ) -> Any: """Run on agent action.""" print_text(action.log, color=color or self.color) def on_tool_end( self, output: Any, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """If not the final action, print out observation.""" output = str(output) if observation_prefix is not None: print_text(f"\n{observation_prefix}") print_text(output, color=color or self.color) if llm_prefix is not None: print_text(f"\n{llm_prefix}") def on_text( self, text: str, color: Optional[str] = None, end: str = "", **kwargs: Any, ) -> None: """Run when agent ends.""" print_text(text, color=color or self.color, end=end) def on_agent_finish( self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any ) -> None: """Run on agent end.""" print_text(finish.log, color=color or self.color, end="\n")

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"""Callback Handler that prints to std out.""" from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, Optional from langchain_core.callbacks.base import BaseCallbackHandler from langchain_core.utils import print_text if TYPE_CHECKING: from langchain_core.agents import AgentAction, AgentFinish class StdOutCallbackHandler(BaseCallbackHandler): """Callback Handler that prints to std out.""" def __init__(self, color: Optional[str] = None) -> None: """Initialize callback handler.""" self.color = color def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Print out that we are entering a chain.""" class_name = serialized.get("name", serialized.get("id", ["<unknown>"])[-1]) print(f"\n\n\033[1m> Entering new {class_name} chain...\033[0m") # noqa: T201 def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Print out that we finished a chain.""" print("\n\033[1m> Finished chain.\033[0m") # noqa: T201 def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any ) -> Any: """Run on agent action.""" print_text(action.log, color=color or self.color) def on_tool_end( self, output: Any, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """If not the final action, print out observation.""" output = str(output) if observation_prefix is not None: print_text(f"\n{observation_prefix}") print_text(output, color=color or self.color) if llm_prefix is not None: print_text(f"\n{llm_prefix}") def on_text( self, text: str, color: Optional[str] = None, end: str = "", **kwargs: Any, ) -> None: """Run when agent ends.""" print_text(text, color=color or self.color, end=end) def on_agent_finish( self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any ) -> None: """Run on agent end.""" print_text(finish.log, color=color or self.color, end="\n")

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from langchain_community.document_loaders import PyPDFLoader from langchain_community.document_loaders.csv_loader import CSVLoader from langchain_community.document_loaders import HNLoader from langchain.text_splitter import CharacterTextSplitter from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import UnstructuredHTMLLoader from langchain_openai.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import Chroma from langchain.chains import RetrievalQA from langchain.chains import RetrievalQAWithSourcesChain from langchain_openai.llms import OpenAI from constant import openai import os os.environ['OPENAI_API_KEY'] = openai loader = PyPDFLoader("attention is all you need.pdf") data = loader.load() # print(data[0]) loader = CSVLoader(file_path="job_placement.csv") data = loader.load() # print(data[0]) loader = HNLoader("https://news.ycombinator.com") data = loader.load() # print(data[0]) quote = "one Machine can do the work of fifty ordinary humans, No machine can do the" \ "work of one extraordinary human." ct_splitter = CharacterTextSplitter( separator='.', chunk_size=24, chunk_overlap=3 ) # docs = ct_splitter.split_text(quote) # print(docs) rc_splitter = RecursiveCharacterTextSplitter( chunk_size=24, chunk_overlap=3, ) # docs = rc_splitter.split_text(quote) # print(docs) loader = UnstructuredHTMLLoader("data.html") data = loader.load() rc_splitter = RecursiveCharacterTextSplitter( chunk_size=24, chunk_overlap=3, separators='.', ) # docs = rc_splitter.split_documents(data) # print(docs) quote = "There is a kingdom of lychee fruit that are alive and thriving in Iceland, but they feel " \ "taken advantage of and are not fast enough for you." splitter = RecursiveCharacterTextSplitter( chunk_size=40, chunk_overlap=10, ) docs = splitter.split_text(quote) embeddings = OpenAIEmbeddings(openai_api_key=openai) vectordb = Chroma( persist_directory="data", embedding_function=embeddings ) vectordb.persist() docstorage = Chroma.from_texts(docs,embeddings) qa = RetrievalQA.from_chain_type( llm = OpenAI(model_name="gpt-3.5-turbo-instruct"), chain_type="stuff", retriever = docstorage.as_retriever() ) # query = "Where do lychee fruit live?" # print(qa.invoke(query)) quote = "There is a kingdom of lycee fruit that are alive and thriving in Iceland, but they fee" \ "taken advantage of and are not fast enough for you." qa1 = RetrievalQAWithSourcesChain.from_chain_type( llm = OpenAI(model_name="gpt-3.5-turbo-instruct"), chain_type="stuff", retriever = docstorage.as_retriever(), ) results = qa1({'question':'What is the primary architecture presented in the document?'},return_only_outputs=True) print(results)

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from typing import Any, Dict, List, Literal, Optional, Union from exa_py import Exa # type: ignore from exa_py.api import HighlightsContentsOptions, TextContentsOptions # type: ignore from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.retrievers import BaseRetriever from langchain_exa._utilities import initialize_client def _get_metadata(result: Any) -> Dict[str, Any]: """Get the metadata from a result object.""" metadata = { "title": result.title, "url": result.url, "id": result.id, "score": result.score, "published_date": result.published_date, "author": result.author, } if getattr(result, "highlights"): metadata["highlights"] = result.highlights if getattr(result, "highlight_scores"): metadata["highlight_scores"] = result.highlight_scores return metadata class ExaSearchRetriever(BaseRetriever): """Exa Search retriever.""" k: int = 10 # num_results """The number of search results to return.""" include_domains: Optional[List[str]] = None """A list of domains to include in the search.""" exclude_domains: Optional[List[str]] = None """A list of domains to exclude from the search.""" start_crawl_date: Optional[str] = None """The start date for the crawl (in YYYY-MM-DD format).""" end_crawl_date: Optional[str] = None """The end date for the crawl (in YYYY-MM-DD format).""" start_published_date: Optional[str] = None """The start date for when the document was published (in YYYY-MM-DD format).""" end_published_date: Optional[str] = None """The end date for when the document was published (in YYYY-MM-DD format).""" use_autoprompt: Optional[bool] = None """Whether to use autoprompt for the search.""" type: str = "neural" """The type of search, 'keyword' or 'neural'. Default: neural""" highlights: Optional[Union[HighlightsContentsOptions, bool]] = None """Whether to set the page content to the highlights of the results.""" text_contents_options: Union[TextContentsOptions, Literal[True]] = True """How to set the page content of the results""" client: Exa = Field(default=None) exa_api_key: SecretStr = Field(default=None) exa_base_url: Optional[str] = None @root_validator(pre=True) def validate_environment(cls, values: Dict) -> Dict: """Validate the environment.""" values = initialize_client(values) return values def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun ) -> List[Document]: response = self.client.search_and_contents( # type: ignore[misc] query, num_results=self.k, text=self.text_contents_options, highlights=self.highlights, # type: ignore include_domains=self.include_domains, exclude_domains=self.exclude_domains, start_crawl_date=self.start_crawl_date, end_crawl_date=self.end_crawl_date, start_published_date=self.start_published_date, end_published_date=self.end_published_date, use_autoprompt=self.use_autoprompt, ) results = response.results return [ Document( page_content=(result.text), metadata=_get_metadata(result), ) for result in results ]

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from __future__ import annotations from typing import Any, TypeVar from langchain_core.exceptions import OutputParserException from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts import BasePromptTemplate from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT T = TypeVar("T") class OutputFixingParser(BaseOutputParser[T]): """Wraps a parser and tries to fix parsing errors.""" @classmethod def is_lc_serializable(cls) -> bool: return True parser: BaseOutputParser[T] """The parser to use to parse the output.""" # Should be an LLMChain but we want to avoid top-level imports from langchain.chains retry_chain: Any """The LLMChain to use to retry the completion.""" max_retries: int = 1 """The maximum number of times to retry the parse.""" @classmethod def from_llm( cls, llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = NAIVE_FIX_PROMPT, max_retries: int = 1, ) -> OutputFixingParser[T]: """Create an OutputFixingParser from a language model and a parser. Args: llm: llm to use for fixing parser: parser to use for parsing prompt: prompt to use for fixing max_retries: Maximum number of retries to parse. Returns: OutputFixingParser """ from langchain.chains.llm import LLMChain chain = LLMChain(llm=llm, prompt=prompt) return cls(parser=parser, retry_chain=chain, max_retries=max_retries) def parse(self, completion: str) -> T: retries = 0 while retries <= self.max_retries: try: return self.parser.parse(completion) except OutputParserException as e: if retries == self.max_retries: raise e else: retries += 1 completion = self.retry_chain.run( instructions=self.parser.get_format_instructions(), completion=completion, error=repr(e), ) raise OutputParserException("Failed to parse") async def aparse(self, completion: str) -> T: retries = 0 while retries <= self.max_retries: try: return await self.parser.aparse(completion) except OutputParserException as e: if retries == self.max_retries: raise e else: retries += 1 completion = await self.retry_chain.arun( instructions=self.parser.get_format_instructions(), completion=completion, error=repr(e), ) raise OutputParserException("Failed to parse") def get_format_instructions(self) -> str: return self.parser.get_format_instructions() @property def _type(self) -> str: return "output_fixing"

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from __future__ import annotations from typing import Any, TypeVar from langchain_core.exceptions import OutputParserException from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts import BasePromptTemplate from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT T = TypeVar("T") class OutputFixingParser(BaseOutputParser[T]): """Wraps a parser and tries to fix parsing errors.""" @classmethod def is_lc_serializable(cls) -> bool: return True parser: BaseOutputParser[T] """The parser to use to parse the output.""" # Should be an LLMChain but we want to avoid top-level imports from langchain.chains retry_chain: Any """The LLMChain to use to retry the completion.""" max_retries: int = 1 """The maximum number of times to retry the parse.""" @classmethod def from_llm( cls, llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = NAIVE_FIX_PROMPT, max_retries: int = 1, ) -> OutputFixingParser[T]: """Create an OutputFixingParser from a language model and a parser. Args: llm: llm to use for fixing parser: parser to use for parsing prompt: prompt to use for fixing max_retries: Maximum number of retries to parse. Returns: OutputFixingParser """ from langchain.chains.llm import LLMChain chain = LLMChain(llm=llm, prompt=prompt) return cls(parser=parser, retry_chain=chain, max_retries=max_retries) def parse(self, completion: str) -> T: retries = 0 while retries <= self.max_retries: try: return self.parser.parse(completion) except OutputParserException as e: if retries == self.max_retries: raise e else: retries += 1 completion = self.retry_chain.run( instructions=self.parser.get_format_instructions(), completion=completion, error=repr(e), ) raise OutputParserException("Failed to parse") async def aparse(self, completion: str) -> T: retries = 0 while retries <= self.max_retries: try: return await self.parser.aparse(completion) except OutputParserException as e: if retries == self.max_retries: raise e else: retries += 1 completion = await self.retry_chain.arun( instructions=self.parser.get_format_instructions(), completion=completion, error=repr(e), ) raise OutputParserException("Failed to parse") def get_format_instructions(self) -> str: return self.parser.get_format_instructions() @property def _type(self) -> str: return "output_fixing"

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from __future__ import annotations from typing import Any, TypeVar from langchain_core.exceptions import OutputParserException from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts import BasePromptTemplate from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT T = TypeVar("T") class OutputFixingParser(BaseOutputParser[T]): """Wraps a parser and tries to fix parsing errors.""" @classmethod def is_lc_serializable(cls) -> bool: return True parser: BaseOutputParser[T] """The parser to use to parse the output.""" # Should be an LLMChain but we want to avoid top-level imports from langchain.chains retry_chain: Any """The LLMChain to use to retry the completion.""" max_retries: int = 1 """The maximum number of times to retry the parse.""" @classmethod def from_llm( cls, llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = NAIVE_FIX_PROMPT, max_retries: int = 1, ) -> OutputFixingParser[T]: """Create an OutputFixingParser from a language model and a parser. Args: llm: llm to use for fixing parser: parser to use for parsing prompt: prompt to use for fixing max_retries: Maximum number of retries to parse. Returns: OutputFixingParser """ from langchain.chains.llm import LLMChain chain = LLMChain(llm=llm, prompt=prompt) return cls(parser=parser, retry_chain=chain, max_retries=max_retries) def parse(self, completion: str) -> T: retries = 0 while retries <= self.max_retries: try: return self.parser.parse(completion) except OutputParserException as e: if retries == self.max_retries: raise e else: retries += 1 completion = self.retry_chain.run( instructions=self.parser.get_format_instructions(), completion=completion, error=repr(e), ) raise OutputParserException("Failed to parse") async def aparse(self, completion: str) -> T: retries = 0 while retries <= self.max_retries: try: return await self.parser.aparse(completion) except OutputParserException as e: if retries == self.max_retries: raise e else: retries += 1 completion = await self.retry_chain.arun( instructions=self.parser.get_format_instructions(), completion=completion, error=repr(e), ) raise OutputParserException("Failed to parse") def get_format_instructions(self) -> str: return self.parser.get_format_instructions() @property def _type(self) -> str: return "output_fixing"

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from __future__ import annotations from typing import Any, TypeVar from langchain_core.exceptions import OutputParserException from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts import BasePromptTemplate from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT T = TypeVar("T") class OutputFixingParser(BaseOutputParser[T]): """Wraps a parser and tries to fix parsing errors.""" @classmethod def is_lc_serializable(cls) -> bool: return True parser: BaseOutputParser[T] """The parser to use to parse the output.""" # Should be an LLMChain but we want to avoid top-level imports from langchain.chains retry_chain: Any """The LLMChain to use to retry the completion.""" max_retries: int = 1 """The maximum number of times to retry the parse.""" @classmethod def from_llm( cls, llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = NAIVE_FIX_PROMPT, max_retries: int = 1, ) -> OutputFixingParser[T]: """Create an OutputFixingParser from a language model and a parser. Args: llm: llm to use for fixing parser: parser to use for parsing prompt: prompt to use for fixing max_retries: Maximum number of retries to parse. Returns: OutputFixingParser """ from langchain.chains.llm import LLMChain chain = LLMChain(llm=llm, prompt=prompt) return cls(parser=parser, retry_chain=chain, max_retries=max_retries) def parse(self, completion: str) -> T: retries = 0 while retries <= self.max_retries: try: return self.parser.parse(completion) except OutputParserException as e: if retries == self.max_retries: raise e else: retries += 1 completion = self.retry_chain.run( instructions=self.parser.get_format_instructions(), completion=completion, error=repr(e), ) raise OutputParserException("Failed to parse") async def aparse(self, completion: str) -> T: retries = 0 while retries <= self.max_retries: try: return await self.parser.aparse(completion) except OutputParserException as e: if retries == self.max_retries: raise e else: retries += 1 completion = await self.retry_chain.arun( instructions=self.parser.get_format_instructions(), completion=completion, error=repr(e), ) raise OutputParserException("Failed to parse") def get_format_instructions(self) -> str: return self.parser.get_format_instructions() @property def _type(self) -> str: return "output_fixing"

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from __future__ import annotations import uuid from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type from langchain.docstore.document import Document from langchain.embeddings.base import Embeddings from langchain.utils import get_from_env from langchain.vectorstores.base import VectorStore if TYPE_CHECKING: from meilisearch import Client def _create_client( client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, ) -> Client: try: import meilisearch except ImportError: raise ImportError( "Could not import meilisearch python package. " "Please install it with `pip install meilisearch`." ) if not client: url = url or get_from_env("url", "MEILI_HTTP_ADDR") try: api_key = api_key or get_from_env("api_key", "MEILI_MASTER_KEY") except Exception: pass client = meilisearch.Client(url=url, api_key=api_key) elif not isinstance(client, meilisearch.Client): raise ValueError( f"client should be an instance of meilisearch.Client, " f"got {type(client)}" ) try: client.version() except ValueError as e: raise ValueError(f"Failed to connect to Meilisearch: {e}") return client class Meilisearch(VectorStore): """`Meilisearch` vector store. To use this, you need to have `meilisearch` python package installed, and a running Meilisearch instance. To learn more about Meilisearch Python, refer to the in-depth Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/. See the following documentation for how to run a Meilisearch instance: https://www.meilisearch.com/docs/learn/getting_started/quick_start. Example: .. code-block:: python from langchain.vectorstores import Meilisearch from langchain.embeddings.openai import OpenAIEmbeddings import meilisearch # api_key is optional; provide it if your meilisearch instance requires it client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embeddings = OpenAIEmbeddings() vectorstore = Meilisearch( embedding=embeddings, client=client, index_name='langchain_demo', text_key='text') """ def __init__( self, embedding: Embeddings, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = "langchain-demo", text_key: str = "text", metadata_key: str = "metadata", ): """Initialize with Meilisearch client.""" client = _create_client(client=client, url=url, api_key=api_key) self._client = client self._index_name = index_name self._embedding = embedding self._text_key = text_key self._metadata_key = metadata_key def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any, ) -> List[str]: """Run more texts through the embedding and add them to the vector store. Args: texts (Iterable[str]): Iterable of strings/text to add to the vectorstore. metadatas (Optional[List[dict]]): Optional list of metadata. Defaults to None. ids Optional[List[str]]: Optional list of IDs. Defaults to None. Returns: List[str]: List of IDs of the texts added to the vectorstore. """ texts = list(texts) # Embed and create the documents docs = [] if ids is None: ids = [uuid.uuid4().hex for _ in texts] if metadatas is None: metadatas = [{} for _ in texts] embedding_vectors = self._embedding.embed_documents(texts) for i, text in enumerate(texts): id = ids[i] metadata = metadatas[i] metadata[self._text_key] = text embedding = embedding_vectors[i] docs.append( { "id": id, "_vectors": embedding, f"{self._metadata_key}": metadata, } ) # Send to Meilisearch self._client.index(str(self._index_name)).add_documents(docs) return ids def similarity_search( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return meilisearch documents most similar to the query. Args: query (str): Query text for which to find similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. """ docs_and_scores = self.similarity_search_with_score( query=query, k=k, filter=filter, kwargs=kwargs, ) return [doc for doc, _ in docs_and_scores] def similarity_search_with_score( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return meilisearch documents most similar to the query, along with scores. Args: query (str): Query text for which to find similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. """ _query = self._embedding.embed_query(query) docs = self.similarity_search_by_vector_with_scores( embedding=_query, k=k, filter=filter, kwargs=kwargs, ) return docs def similarity_search_by_vector_with_scores( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. """ docs = [] results = self._client.index(str(self._index_name)).search( "", {"vector": embedding, "limit": k, "filter": filter} ) for result in results["hits"]: metadata = result[self._metadata_key] if self._text_key in metadata: text = metadata.pop(self._text_key) semantic_score = result["_semanticScore"] docs.append( (Document(page_content=text, metadata=metadata), semantic_score) ) return docs def similarity_search_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. """ docs = self.similarity_search_by_vector_with_scores( embedding=embedding, k=k, filter=filter, kwargs=kwargs, ) return [doc for doc, _ in docs] @classmethod def from_texts( cls: Type[Meilisearch], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = "langchain-demo", ids: Optional[List[str]] = None, text_key: Optional[str] = "text", metadata_key: Optional[str] = "metadata", **kwargs: Any, ) -> Meilisearch: """Construct Meilisearch wrapper from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Adds the documents to a provided Meilisearch index. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain import Meilisearch from langchain.embeddings import OpenAIEmbeddings import meilisearch # The environment should be the one specified next to the API key # in your Meilisearch console client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embeddings = OpenAIEmbeddings() docsearch = Meilisearch.from_texts( client=client, embeddings=embeddings, ) """ client = _create_client(client=client, url=url, api_key=api_key) vectorstore = cls( embedding=embedding, client=client, index_name=index_name, ) vectorstore.add_texts( texts=texts, metadatas=metadatas, ids=ids, text_key=text_key, metadata_key=metadata_key, ) return vectorstore

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from __future__ import annotations import uuid from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type from langchain.docstore.document import Document from langchain.embeddings.base import Embeddings from langchain.utils import get_from_env from langchain.vectorstores.base import VectorStore if TYPE_CHECKING: from meilisearch import Client def _create_client( client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, ) -> Client: try: import meilisearch except ImportError: raise ImportError( "Could not import meilisearch python package. " "Please install it with `pip install meilisearch`." ) if not client: url = url or get_from_env("url", "MEILI_HTTP_ADDR") try: api_key = api_key or get_from_env("api_key", "MEILI_MASTER_KEY") except Exception: pass client = meilisearch.Client(url=url, api_key=api_key) elif not isinstance(client, meilisearch.Client): raise ValueError( f"client should be an instance of meilisearch.Client, " f"got {type(client)}" ) try: client.version() except ValueError as e: raise ValueError(f"Failed to connect to Meilisearch: {e}") return client class Meilisearch(VectorStore): """`Meilisearch` vector store. To use this, you need to have `meilisearch` python package installed, and a running Meilisearch instance. To learn more about Meilisearch Python, refer to the in-depth Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/. See the following documentation for how to run a Meilisearch instance: https://www.meilisearch.com/docs/learn/getting_started/quick_start. Example: .. code-block:: python from langchain.vectorstores import Meilisearch from langchain.embeddings.openai import OpenAIEmbeddings import meilisearch # api_key is optional; provide it if your meilisearch instance requires it client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embeddings = OpenAIEmbeddings() vectorstore = Meilisearch( embedding=embeddings, client=client, index_name='langchain_demo', text_key='text') """ def __init__( self, embedding: Embeddings, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = "langchain-demo", text_key: str = "text", metadata_key: str = "metadata", ): """Initialize with Meilisearch client.""" client = _create_client(client=client, url=url, api_key=api_key) self._client = client self._index_name = index_name self._embedding = embedding self._text_key = text_key self._metadata_key = metadata_key def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any, ) -> List[str]: """Run more texts through the embedding and add them to the vector store. Args: texts (Iterable[str]): Iterable of strings/text to add to the vectorstore. metadatas (Optional[List[dict]]): Optional list of metadata. Defaults to None. ids Optional[List[str]]: Optional list of IDs. Defaults to None. Returns: List[str]: List of IDs of the texts added to the vectorstore. """ texts = list(texts) # Embed and create the documents docs = [] if ids is None: ids = [uuid.uuid4().hex for _ in texts] if metadatas is None: metadatas = [{} for _ in texts] embedding_vectors = self._embedding.embed_documents(texts) for i, text in enumerate(texts): id = ids[i] metadata = metadatas[i] metadata[self._text_key] = text embedding = embedding_vectors[i] docs.append( { "id": id, "_vectors": embedding, f"{self._metadata_key}": metadata, } ) # Send to Meilisearch self._client.index(str(self._index_name)).add_documents(docs) return ids def similarity_search( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return meilisearch documents most similar to the query. Args: query (str): Query text for which to find similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. """ docs_and_scores = self.similarity_search_with_score( query=query, k=k, filter=filter, kwargs=kwargs, ) return [doc for doc, _ in docs_and_scores] def similarity_search_with_score( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return meilisearch documents most similar to the query, along with scores. Args: query (str): Query text for which to find similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. """ _query = self._embedding.embed_query(query) docs = self.similarity_search_by_vector_with_scores( embedding=_query, k=k, filter=filter, kwargs=kwargs, ) return docs def similarity_search_by_vector_with_scores( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. """ docs = [] results = self._client.index(str(self._index_name)).search( "", {"vector": embedding, "limit": k, "filter": filter} ) for result in results["hits"]: metadata = result[self._metadata_key] if self._text_key in metadata: text = metadata.pop(self._text_key) semantic_score = result["_semanticScore"] docs.append( (Document(page_content=text, metadata=metadata), semantic_score) ) return docs def similarity_search_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. """ docs = self.similarity_search_by_vector_with_scores( embedding=embedding, k=k, filter=filter, kwargs=kwargs, ) return [doc for doc, _ in docs] @classmethod def from_texts( cls: Type[Meilisearch], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = "langchain-demo", ids: Optional[List[str]] = None, text_key: Optional[str] = "text", metadata_key: Optional[str] = "metadata", **kwargs: Any, ) -> Meilisearch: """Construct Meilisearch wrapper from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Adds the documents to a provided Meilisearch index. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain import Meilisearch from langchain.embeddings import OpenAIEmbeddings import meilisearch # The environment should be the one specified next to the API key # in your Meilisearch console client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embeddings = OpenAIEmbeddings() docsearch = Meilisearch.from_texts( client=client, embeddings=embeddings, ) """ client = _create_client(client=client, url=url, api_key=api_key) vectorstore = cls( embedding=embedding, client=client, index_name=index_name, ) vectorstore.add_texts( texts=texts, metadatas=metadatas, ids=ids, text_key=text_key, metadata_key=metadata_key, ) return vectorstore

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from __future__ import annotations import uuid from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type from langchain.docstore.document import Document from langchain.embeddings.base import Embeddings from langchain.utils import get_from_env from langchain.vectorstores.base import VectorStore if TYPE_CHECKING: from meilisearch import Client def _create_client( client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, ) -> Client: try: import meilisearch except ImportError: raise ImportError( "Could not import meilisearch python package. " "Please install it with `pip install meilisearch`." ) if not client: url = url or get_from_env("url", "MEILI_HTTP_ADDR") try: api_key = api_key or get_from_env("api_key", "MEILI_MASTER_KEY") except Exception: pass client = meilisearch.Client(url=url, api_key=api_key) elif not isinstance(client, meilisearch.Client): raise ValueError( f"client should be an instance of meilisearch.Client, " f"got {type(client)}" ) try: client.version() except ValueError as e: raise ValueError(f"Failed to connect to Meilisearch: {e}") return client class Meilisearch(VectorStore): """`Meilisearch` vector store. To use this, you need to have `meilisearch` python package installed, and a running Meilisearch instance. To learn more about Meilisearch Python, refer to the in-depth Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/. See the following documentation for how to run a Meilisearch instance: https://www.meilisearch.com/docs/learn/getting_started/quick_start. Example: .. code-block:: python from langchain.vectorstores import Meilisearch from langchain.embeddings.openai import OpenAIEmbeddings import meilisearch # api_key is optional; provide it if your meilisearch instance requires it client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embeddings = OpenAIEmbeddings() vectorstore = Meilisearch( embedding=embeddings, client=client, index_name='langchain_demo', text_key='text') """ def __init__( self, embedding: Embeddings, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = "langchain-demo", text_key: str = "text", metadata_key: str = "metadata", ): """Initialize with Meilisearch client.""" client = _create_client(client=client, url=url, api_key=api_key) self._client = client self._index_name = index_name self._embedding = embedding self._text_key = text_key self._metadata_key = metadata_key def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any, ) -> List[str]: """Run more texts through the embedding and add them to the vector store. Args: texts (Iterable[str]): Iterable of strings/text to add to the vectorstore. metadatas (Optional[List[dict]]): Optional list of metadata. Defaults to None. ids Optional[List[str]]: Optional list of IDs. Defaults to None. Returns: List[str]: List of IDs of the texts added to the vectorstore. """ texts = list(texts) # Embed and create the documents docs = [] if ids is None: ids = [uuid.uuid4().hex for _ in texts] if metadatas is None: metadatas = [{} for _ in texts] embedding_vectors = self._embedding.embed_documents(texts) for i, text in enumerate(texts): id = ids[i] metadata = metadatas[i] metadata[self._text_key] = text embedding = embedding_vectors[i] docs.append( { "id": id, "_vectors": embedding, f"{self._metadata_key}": metadata, } ) # Send to Meilisearch self._client.index(str(self._index_name)).add_documents(docs) return ids def similarity_search( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return meilisearch documents most similar to the query. Args: query (str): Query text for which to find similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. """ docs_and_scores = self.similarity_search_with_score( query=query, k=k, filter=filter, kwargs=kwargs, ) return [doc for doc, _ in docs_and_scores] def similarity_search_with_score( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return meilisearch documents most similar to the query, along with scores. Args: query (str): Query text for which to find similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. """ _query = self._embedding.embed_query(query) docs = self.similarity_search_by_vector_with_scores( embedding=_query, k=k, filter=filter, kwargs=kwargs, ) return docs def similarity_search_by_vector_with_scores( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. """ docs = [] results = self._client.index(str(self._index_name)).search( "", {"vector": embedding, "limit": k, "filter": filter} ) for result in results["hits"]: metadata = result[self._metadata_key] if self._text_key in metadata: text = metadata.pop(self._text_key) semantic_score = result["_semanticScore"] docs.append( (Document(page_content=text, metadata=metadata), semantic_score) ) return docs def similarity_search_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. """ docs = self.similarity_search_by_vector_with_scores( embedding=embedding, k=k, filter=filter, kwargs=kwargs, ) return [doc for doc, _ in docs] @classmethod def from_texts( cls: Type[Meilisearch], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = "langchain-demo", ids: Optional[List[str]] = None, text_key: Optional[str] = "text", metadata_key: Optional[str] = "metadata", **kwargs: Any, ) -> Meilisearch: """Construct Meilisearch wrapper from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Adds the documents to a provided Meilisearch index. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain import Meilisearch from langchain.embeddings import OpenAIEmbeddings import meilisearch # The environment should be the one specified next to the API key # in your Meilisearch console client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embeddings = OpenAIEmbeddings() docsearch = Meilisearch.from_texts( client=client, embeddings=embeddings, ) """ client = _create_client(client=client, url=url, api_key=api_key) vectorstore = cls( embedding=embedding, client=client, index_name=index_name, ) vectorstore.add_texts( texts=texts, metadatas=metadatas, ids=ids, text_key=text_key, metadata_key=metadata_key, ) return vectorstore

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from __future__ import annotations import uuid from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type from langchain.docstore.document import Document from langchain.embeddings.base import Embeddings from langchain.utils import get_from_env from langchain.vectorstores.base import VectorStore if TYPE_CHECKING: from meilisearch import Client def _create_client( client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, ) -> Client: try: import meilisearch except ImportError: raise ImportError( "Could not import meilisearch python package. " "Please install it with `pip install meilisearch`." ) if not client: url = url or get_from_env("url", "MEILI_HTTP_ADDR") try: api_key = api_key or get_from_env("api_key", "MEILI_MASTER_KEY") except Exception: pass client = meilisearch.Client(url=url, api_key=api_key) elif not isinstance(client, meilisearch.Client): raise ValueError( f"client should be an instance of meilisearch.Client, " f"got {type(client)}" ) try: client.version() except ValueError as e: raise ValueError(f"Failed to connect to Meilisearch: {e}") return client class Meilisearch(VectorStore): """`Meilisearch` vector store. To use this, you need to have `meilisearch` python package installed, and a running Meilisearch instance. To learn more about Meilisearch Python, refer to the in-depth Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/. See the following documentation for how to run a Meilisearch instance: https://www.meilisearch.com/docs/learn/getting_started/quick_start. Example: .. code-block:: python from langchain.vectorstores import Meilisearch from langchain.embeddings.openai import OpenAIEmbeddings import meilisearch # api_key is optional; provide it if your meilisearch instance requires it client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embeddings = OpenAIEmbeddings() vectorstore = Meilisearch( embedding=embeddings, client=client, index_name='langchain_demo', text_key='text') """ def __init__( self, embedding: Embeddings, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = "langchain-demo", text_key: str = "text", metadata_key: str = "metadata", ): """Initialize with Meilisearch client.""" client = _create_client(client=client, url=url, api_key=api_key) self._client = client self._index_name = index_name self._embedding = embedding self._text_key = text_key self._metadata_key = metadata_key def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any, ) -> List[str]: """Run more texts through the embedding and add them to the vector store. Args: texts (Iterable[str]): Iterable of strings/text to add to the vectorstore. metadatas (Optional[List[dict]]): Optional list of metadata. Defaults to None. ids Optional[List[str]]: Optional list of IDs. Defaults to None. Returns: List[str]: List of IDs of the texts added to the vectorstore. """ texts = list(texts) # Embed and create the documents docs = [] if ids is None: ids = [uuid.uuid4().hex for _ in texts] if metadatas is None: metadatas = [{} for _ in texts] embedding_vectors = self._embedding.embed_documents(texts) for i, text in enumerate(texts): id = ids[i] metadata = metadatas[i] metadata[self._text_key] = text embedding = embedding_vectors[i] docs.append( { "id": id, "_vectors": embedding, f"{self._metadata_key}": metadata, } ) # Send to Meilisearch self._client.index(str(self._index_name)).add_documents(docs) return ids def similarity_search( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return meilisearch documents most similar to the query. Args: query (str): Query text for which to find similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. """ docs_and_scores = self.similarity_search_with_score( query=query, k=k, filter=filter, kwargs=kwargs, ) return [doc for doc, _ in docs_and_scores] def similarity_search_with_score( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return meilisearch documents most similar to the query, along with scores. Args: query (str): Query text for which to find similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. """ _query = self._embedding.embed_query(query) docs = self.similarity_search_by_vector_with_scores( embedding=_query, k=k, filter=filter, kwargs=kwargs, ) return docs def similarity_search_by_vector_with_scores( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. """ docs = [] results = self._client.index(str(self._index_name)).search( "", {"vector": embedding, "limit": k, "filter": filter} ) for result in results["hits"]: metadata = result[self._metadata_key] if self._text_key in metadata: text = metadata.pop(self._text_key) semantic_score = result["_semanticScore"] docs.append( (Document(page_content=text, metadata=metadata), semantic_score) ) return docs def similarity_search_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. """ docs = self.similarity_search_by_vector_with_scores( embedding=embedding, k=k, filter=filter, kwargs=kwargs, ) return [doc for doc, _ in docs] @classmethod def from_texts( cls: Type[Meilisearch], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = "langchain-demo", ids: Optional[List[str]] = None, text_key: Optional[str] = "text", metadata_key: Optional[str] = "metadata", **kwargs: Any, ) -> Meilisearch: """Construct Meilisearch wrapper from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Adds the documents to a provided Meilisearch index. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain import Meilisearch from langchain.embeddings import OpenAIEmbeddings import meilisearch # The environment should be the one specified next to the API key # in your Meilisearch console client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embeddings = OpenAIEmbeddings() docsearch = Meilisearch.from_texts( client=client, embeddings=embeddings, ) """ client = _create_client(client=client, url=url, api_key=api_key) vectorstore = cls( embedding=embedding, client=client, index_name=index_name, ) vectorstore.add_texts( texts=texts, metadatas=metadatas, ids=ids, text_key=text_key, metadata_key=metadata_key, ) return vectorstore

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import os from operator import itemgetter from langchain.llms import OpenAI from langchain.prompts import PromptTemplate from langchain.schema.output_parser import StrOutputParser from langchain.schema.runnable import RunnableLambda import mlflow # Uncomment the following to use the full abilities of langchain autologgin # %pip install `langchain_community>=0.0.16` # These two libraries enable autologging to log text analysis related artifacts # %pip install textstat spacy assert "OPENAI_API_KEY" in os.environ, "Please set the OPENAI_API_KEY environment variable." # Enable mlflow langchain autologging # Note: We only support auto-logging models that do not contain retrievers mlflow.langchain.autolog( log_input_examples=True, log_model_signatures=True, log_models=True, log_inputs_outputs=True, registered_model_name="lc_model", ) prompt_with_history_str = """ Here is a history between you and a human: {chat_history} Now, please answer this question: {question} """ prompt_with_history = PromptTemplate( input_variables=["chat_history", "question"], template=prompt_with_history_str ) def extract_question(input): return input[-1]["content"] def extract_history(input): return input[:-1] llm = OpenAI(temperature=0.9) # Build a chain with LCEL chain_with_history = ( { "question": itemgetter("messages") | RunnableLambda(extract_question), "chat_history": itemgetter("messages") | RunnableLambda(extract_history), } | prompt_with_history | llm | StrOutputParser() ) inputs = {"messages": [{"role": "user", "content": "Who owns MLflow?"}]} print(chain_with_history.invoke(inputs)) # sample output: # "1. Databricks\n2. Microsoft\n3. Google\n4. Amazon\n\nEnter your answer: 1\n\n # Correct! MLflow is an open source project developed by Databricks. ... # We automatically log the model and trace related artifacts # A model with name `lc_model` is registered, we can load it back as a PyFunc model model_name = "lc_model" model_version = 1 loaded_model = mlflow.pyfunc.load_model(f"models:/{model_name}/{model_version}") print(loaded_model.predict(inputs))

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## This is a fork/based from https://gist.github.com/wiseman/4a706428eaabf4af1002a07a114f61d6 from io import StringIO import sys import os from typing import Dict, Optional from langchain.agents import load_tools from langchain.agents import initialize_agent from langchain.agents.tools import Tool from langchain.llms import OpenAI base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1') model_name = os.environ.get('MODEL_NAME', 'gpt-3.5-turbo') class PythonREPL: """Simulates a standalone Python REPL.""" def __init__(self): pass def run(self, command: str) -> str: """Run command and returns anything printed.""" old_stdout = sys.stdout sys.stdout = mystdout = StringIO() try: exec(command, globals()) sys.stdout = old_stdout output = mystdout.getvalue() except Exception as e: sys.stdout = old_stdout output = str(e) return output llm = OpenAI(temperature=0.0, openai_api_base=base_path, model_name=model_name) python_repl = Tool( "Python REPL", PythonREPL().run, """A Python shell. Use this to execute python commands. Input should be a valid python command. If you expect output it should be printed out.""", ) tools = [python_repl] agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True) agent.run("What is the 10th fibonacci number?")

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import time from typing import List import pandas as pd from langchain.schema import Document from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.vectorstores import VectorStore from mindsdb.integrations.handlers.rag_handler.settings import ( PersistedVectorStoreSaver, PersistedVectorStoreSaverConfig, RAGBaseParameters, VectorStoreFactory, df_to_documents, get_chroma_client, load_embeddings_model, url_to_documents, ) from mindsdb.utilities import log logger = log.getLogger(__name__) def validate_document(doc) -> bool: """Check an individual document.""" # Example checks if not isinstance(doc, Document): return False if not doc.page_content: return False return True def validate_documents(documents) -> bool: """Validate document list format.""" if not isinstance(documents, list): return False if not documents: return False # Check fields/format of a document return all([validate_document(doc) for doc in documents]) class RAGIngestor: """A class for converting a dataframe and/or url to a vectorstore embedded with a given embeddings model""" def __init__( self, args: RAGBaseParameters, df: pd.DataFrame, ): self.args = args self.df = df self.embeddings_model_name = args.embeddings_model_name self.vector_store = VectorStoreFactory.get_vectorstore_class( args.vector_store_name ) def split_documents(self, chunk_size, chunk_overlap) -> list: # Load documents and split in chunks logger.info(f"Loading documents from input data") documents = [] text_splitter = RecursiveCharacterTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) if self.df is not None: # if user provides a dataframe, load documents from dataframe documents.extend( df_to_documents( df=self.df, page_content_columns=self.args.context_columns, url_column_name=self.args.url_column_name, ) ) if self.args.url: # if user provides a url, load documents from url documents.extend(url_to_documents(self.args.url)) n_tokens = sum([len(doc.page_content) for doc in documents]) # split documents into chunks of text texts = text_splitter.split_documents(documents) logger.info(f"Loaded {len(documents)} documents from input data") logger.info(f"Total number of tokens: {n_tokens}") logger.info(f"Split into {len(texts)} chunks of text (tokens)") return texts def create_db_from_documents(self, documents, embeddings_model) -> VectorStore: """Create DB from documents.""" if self.args.vector_store_name == "chromadb": return self.vector_store.from_documents( documents=documents, embedding=embeddings_model, client=get_chroma_client( persist_directory=self.args.vector_store_storage_path ), collection_name=self.args.collection_name, ) else: return self.create_db_from_texts(documents, embeddings_model) def create_db_from_texts(self, documents, embeddings_model) -> VectorStore: """Create DB from text content.""" texts = [doc.page_content for doc in documents] metadata = [doc.metadata for doc in documents] return self.vector_store.from_texts( texts=texts, embedding=embeddings_model, metadatas=metadata ) @staticmethod def _create_batch_embeddings(documents: List[Document], embeddings_batch_size): """ create batch of document embeddings """ for i in range(0, len(documents), embeddings_batch_size): yield documents[i: i + embeddings_batch_size] def embeddings_to_vectordb(self) -> None: """Create vectorstore from documents and store locally.""" start_time = time.time() # Load documents and splits in chunks (if not in evaluation_type mode) documents = self.split_documents( chunk_size=self.args.chunk_size, chunk_overlap=self.args.chunk_overlap ) # Load embeddings model embeddings_model = load_embeddings_model( self.embeddings_model_name, self.args.use_gpu ) logger.info(f"Creating vectorstore from documents") if not validate_documents(documents): raise ValueError("Invalid documents") try: db = self.create_db_from_documents(documents, embeddings_model) except Exception as e: raise Exception( f"Error loading embeddings to {self.args.vector_store_name}: {e}" ) config = PersistedVectorStoreSaverConfig( vector_store_name=self.args.vector_store_name, vector_store=db, persist_directory=self.args.vector_store_storage_path, collection_name=self.args.collection_name, ) vector_store_saver = PersistedVectorStoreSaver(config) vector_store_saver.save_vector_store(db) db = None # Free up memory end_time = time.time() elapsed_time = round(end_time - start_time) logger.info(f"Finished creating {self.args.vector_store_name} from texts, it has been " f"persisted to {self.args.vector_store_storage_path}") time_minutes = round(elapsed_time / 60) if time_minutes > 1: logger.info(f"Elapsed time: {time_minutes} minutes") else: logger.info(f"Elapsed time: {elapsed_time} seconds")

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""" Multilingual retrieval based conversation system backed by ChatGPT """ import argparse import os from colossalqa.data_loader.document_loader import DocumentLoader from colossalqa.memory import ConversationBufferWithSummary from colossalqa.retriever import CustomRetriever from langchain import LLMChain from langchain.chains import RetrievalQA from langchain.embeddings import HuggingFaceEmbeddings from langchain.llms import OpenAI from langchain.prompts.prompt import PromptTemplate from langchain.text_splitter import RecursiveCharacterTextSplitter if __name__ == "__main__": parser = argparse.ArgumentParser(description="Multilingual retrieval based conversation system backed by ChatGPT") parser.add_argument("--open_ai_key_path", type=str, default=None, help="path to the model") parser.add_argument( "--sql_file_path", type=str, default=None, help="path to the a empty folder for storing sql files for indexing" ) args = parser.parse_args() if not os.path.exists(args.sql_file_path): os.makedirs(args.sql_file_path) # Setup openai key # Set env var OPENAI_API_KEY or load from a file openai_key = open(args.open_ai_key_path).read() os.environ["OPENAI_API_KEY"] = openai_key llm = OpenAI(temperature=0.6) information_retriever = CustomRetriever(k=3, sql_file_path=args.sql_file_path, verbose=True) # VectorDB embedding = HuggingFaceEmbeddings( model_name="moka-ai/m3e-base", model_kwargs={"device": "cpu"}, encode_kwargs={"normalize_embeddings": False} ) # Define memory with summarization ability memory = ConversationBufferWithSummary(llm=llm) # Load data to vector store print("Select files for constructing retriever") documents = [] while True: file = input("Enter a file path or press Enter directory without input to exit:").strip() if file == "": break data_name = input("Enter a short description of the data:") retriever_data = DocumentLoader([[file, data_name.replace(" ", "_")]]).all_data # Split text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=0) splits = text_splitter.split_documents(retriever_data) documents.extend(splits) # Create retriever information_retriever.add_documents(docs=documents, cleanup="incremental", mode="by_source", embedding=embedding) prompt_template = """Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If the answer cannot be inferred based on the given context, please don't share false information. Use the context and chat history to respond to the human's input at the end or carry on the conversation. You should generate one response only. No following up is needed. context: {context} chat history {chat_history} Human: {question} Assistant:""" prompt_template_disambiguate = """You are a helpful, respectful and honest assistant. You always follow the instruction. Please replace any ambiguous references in the given sentence with the specific names or entities mentioned in the chat history or just output the original sentence if no chat history is provided or if the sentence doesn't contain ambiguous references. Your output should be the disambiguated sentence itself (in the same line as "disambiguated sentence:") and contain nothing else. Here is an example: Chat history: Human: I have a friend, Mike. Do you know him? Assistant: Yes, I know a person named Mike sentence: What's his favorite food? disambiguated sentence: What's Mike's favorite food? END OF EXAMPLE Chat history: {chat_history} sentence: {input} disambiguated sentence:""" PROMPT = PromptTemplate(template=prompt_template, input_variables=["question", "chat_history", "context"]) memory.initiate_document_retrieval_chain( llm, PROMPT, information_retriever, chain_type_kwargs={ "chat_history": "", }, ) PROMPT_DISAMBIGUATE = PromptTemplate( template=prompt_template_disambiguate, input_variables=["chat_history", "input"] ) llm_chain = RetrievalQA.from_chain_type( llm=llm, verbose=False, chain_type="stuff", retriever=information_retriever, chain_type_kwargs={"prompt": PROMPT, "memory": memory}, ) llm_chain_disambiguate = LLMChain(llm=llm, prompt=PROMPT_DISAMBIGUATE) def disambiguity(input): out = llm_chain_disambiguate.run({"input": input, "chat_history": memory.buffer}) return out.split("\n")[0] information_retriever.set_rephrase_handler(disambiguity) while True: user_input = input("User: ") if " end " in user_input: print("Agent: Happy to chat with you ：)") break agent_response = llm_chain.run(user_input) agent_response = agent_response.split("\n")[0] print(f"Agent: {agent_response}")

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from langchain.document_loaders import PyMuPDFLoader from langchain.retrievers import ArxivRetriever def scrape_pdf_with_pymupdf(url) -> str: """Scrape a pdf with pymupdf Args: url (str): The url of the pdf to scrape Returns: str: The text scraped from the pdf """ loader = PyMuPDFLoader(url) doc = loader.load() return str(doc) def scrape_pdf_with_arxiv(query) -> str: """Scrape a pdf with arxiv default document length of 70000 about ~15 pages or None for no limit Args: query (str): The query to search for Returns: str: The text scraped from the pdf """ retriever = ArxivRetriever(load_max_docs=2, doc_content_chars_max=None) docs = retriever.get_relevant_documents(query=query) return docs[0].page_content

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from langchain.document_loader import TelegramChatApiLoader from application.parser.remote.base import BaseRemote class TelegramChatApiRemote(BaseRemote): def _init_parser(self, *args, **load_kwargs): self.loader = TelegramChatApiLoader(**load_kwargs) return {} def parse_file(self, *args, **load_kwargs): return

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from langchain import tools from langchain.agents import Tool from langchain.agents.load_tools import _BASE_TOOLS, _EXTRA_LLM_TOOLS, _EXTRA_OPTIONAL_TOOLS, _LLM_TOOLS from langchain.tools.json.tool import JsonSpec from langflow.interface.importing.utils import import_class from langflow.interface.tools.custom import PythonFunction, PythonFunctionTool FILE_TOOLS = {"JsonSpec": JsonSpec} CUSTOM_TOOLS = { "Tool": Tool, "PythonFunctionTool": PythonFunctionTool, "PythonFunction": PythonFunction, } OTHER_TOOLS = {tool: import_class(f"langchain_community.tools.{tool}") for tool in tools.__all__} ALL_TOOLS_NAMES = { **_BASE_TOOLS, **_LLM_TOOLS, # type: ignore **{k: v[0] for k, v in _EXTRA_LLM_TOOLS.items()}, # type: ignore **{k: v[0] for k, v in _EXTRA_OPTIONAL_TOOLS.items()}, **CUSTOM_TOOLS, **FILE_TOOLS, # type: ignore **OTHER_TOOLS, }

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from templates.common.suffix import suffix from templates.common.format_instructions import format_instructions from templates.common.docs_system_instructions import docs_system_instructions from langchain.schema import ( # AIMessage, HumanMessage, SystemMessage ) from langchain.tools.json.tool import JsonSpec from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit from langchain.chat_models import ChatOpenAI, AzureChatOpenAI from langchain.llms.openai import OpenAI from langchain.agents import create_json_agent, ZeroShotAgent, AgentExecutor from langchain.chains import LLMChain from config.config import config import openai # required from dotenv import load_dotenv load_dotenv() class OpenAPIExplorerTool: @staticmethod def create_tools(docs): json_spec = JsonSpec(dict_=docs) json_toolkit = JsonToolkit(spec=json_spec) tools = json_toolkit.get_tools() return tools class PipedreamOpenAPIAgent: def __init__(self, docs, templates, auth_example, parsed_common_files): system_instructions = format_template( f"{templates.system_instructions(auth_example, parsed_common_files)}\n{docs_system_instructions}") tools = OpenAPIExplorerTool.create_tools(docs) tool_names = [tool.name for tool in tools] prompt_template = ZeroShotAgent.create_prompt( tools=tools, prefix=system_instructions, suffix=suffix, format_instructions=format_instructions, input_variables=['input', 'agent_scratchpad'] ) llm_chain = LLMChain(llm=get_llm(), prompt=prompt_template) agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names) verbose = True if config['logging']['level'] == 'DEBUG' else False self.agent_executor = AgentExecutor.from_agent_and_tools( agent=agent, tools=tools, verbose=verbose) def run(self, input): try: result = self.agent_executor.run(input) except Exception as e: result = str(e) if "I don't know" in result: return "I don't know" if '```' not in result: raise e return format_result(result) def format_template(text): return text.replace("{", "{{").replace("}", "}}") # escape curly braces def format_result(result): if '```' in result: if '```javascript' in result: result = result.split('```javascript')[1].split('```')[0].strip() else: result = result.split('```')[1].split('```')[0].strip() return result def create_user_prompt(prompt, urls_content): if len(urls_content) == 0: return prompt + "\n\n" user_prompt = f"{prompt}\n\n## API docs\n\n" for item in urls_content: user_prompt += f"\n\n### {item['url']}\n\n{item['content']}" return user_prompt + "\n\n" def get_llm(): if config['openai_api_type'] == "azure": azure_config = config["azure"] return AzureChatOpenAI(deployment_name=azure_config['deployment_name'], model_name=azure_config["model"], temperature=config["temperature"], request_timeout=300) else: openai_config = config["openai"] print(f"Using OpenAI API: {openai_config['model']}") return ChatOpenAI( model_name=openai_config["model"], temperature=config["temperature"]) def ask_agent(prompt, docs, templates, auth_example, parsed_common_files, urls_content): agent = PipedreamOpenAPIAgent( docs, templates, auth_example, parsed_common_files) user_prompt = create_user_prompt(prompt, urls_content) result = agent.run(user_prompt) return result def no_docs(prompt, templates, auth_example, parsed_common_files, urls_content, normal_order=True): user_prompt = create_user_prompt(prompt, urls_content) pd_instructions = format_template( templates.system_instructions(auth_example, parsed_common_files)) result = get_llm()(messages=[ SystemMessage(content="You are the most intelligent software engineer in the world. You carefully provide accurate, factual, thoughtful, nuanced code, and are brilliant at reasoning. Follow all of the instructions below — they are all incredibly important. This code will be shipped directly to production, so it's important that it's accurate and complete."), HumanMessage(content=user_prompt + pd_instructions if normal_order else pd_instructions+user_prompt), ]) return format_result(result.content)

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import os import threading from chainlit.config import config from chainlit.logger import logger def init_lc_cache(): use_cache = config.project.cache is True and config.run.no_cache is False if use_cache: try: import langchain except ImportError: return from langchain.cache import SQLiteCache from langchain.globals import set_llm_cache if config.project.lc_cache_path is not None: set_llm_cache(SQLiteCache(database_path=config.project.lc_cache_path)) if not os.path.exists(config.project.lc_cache_path): logger.info( f"LangChain cache created at: {config.project.lc_cache_path}" ) _cache = {} _cache_lock = threading.Lock() def cache(func): def wrapper(*args, **kwargs): # Create a cache key based on the function name, arguments, and keyword arguments cache_key = ( (func.__name__,) + args + tuple((k, v) for k, v in sorted(kwargs.items())) ) with _cache_lock: # Check if the result is already in the cache if cache_key not in _cache: # If not, call the function and store the result in the cache _cache[cache_key] = func(*args, **kwargs) return _cache[cache_key] return wrapper

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import json from typing import Any, List, Tuple import requests from taskweaver.plugin import Plugin, register_plugin # response entry format: (title, url, snippet) ResponseEntry = Tuple[str, str, str] def browse_page( query: str, urls: List[str], top_k: int = 3, chunk_size: int = 1000, chunk_overlap: int = 250, ) -> list[dict[str, Any]]: try: from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import AsyncHtmlLoader from langchain_community.document_transformers import Html2TextTransformer except ImportError: raise ImportError("Please install langchain/langchain-community first.") loader = AsyncHtmlLoader(web_path=urls) docs = loader.load() html2text = Html2TextTransformer() docs_transformed = html2text.transform_documents(docs) text_splitter = RecursiveCharacterTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap, ) # Split splits = text_splitter.split_documents(docs_transformed) from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.vectorstores import FAISS vector_store = FAISS.from_documents( splits, HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2"), ) result = vector_store.similarity_search( query=query, k=top_k, ) chunks = [ { "metadata": r.metadata, "snippet": r.page_content, } for r in result ] return chunks @register_plugin class WebSearch(Plugin): def search_query(self, query: str) -> List[ResponseEntry]: api_provider = self.config.get("api_provider", "google_custom_search") result_count = int(self.config.get("result_count", 3)) if api_provider == "google_custom_search": return self._search_google_custom_search(query, cnt=result_count) elif api_provider == "bing": return self._search_bing(query, cnt=result_count) else: raise ValueError("Invalid API provider. Please check your config file.") def __call__(self, queries: List[str], browse: bool = True) -> str: query_results = [] query_urls = set() for query in queries: query_results.extend([r for r in self.search_query(query) if r[1] not in query_urls]) query_urls.update([r[1] for r in query_results]) if not browse: return f"WebSearch has done searching for `{queries}`.\n" + self.ctx.wrap_text_with_delimiter_temporal( "\n```json\n" + json.dumps(query_results, indent=4) + "```\n", ) else: return f"WebSearch has done searching for `{queries}`.\n" + self.ctx.wrap_text_with_delimiter_temporal( "\n```json\n" + json.dumps(browse_page(",".join(queries), list(query_urls)), indent=4) + "```\n", ) def _search_google_custom_search(self, query: str, cnt: int) -> List[ResponseEntry]: api_key = self.config.get("google_api_key") search_engine_id = self.config.get("google_search_engine_id") url = f"https://www.googleapis.com/customsearch/v1?key={api_key}&cx={search_engine_id}&q={query}" if cnt > 0: url += f"&num={cnt}" response = requests.get(url) result_list: List[ResponseEntry] = [] for item in response.json()["items"]: result_list.append((item["title"], item["link"], item["snippet"])) return result_list def _search_bing(self, query: str, cnt: int) -> List[ResponseEntry]: api_key = self.config.get("bing_api_key") url = f"https://api.bing.microsoft.com/v7.0/search?q={query}" if cnt > 0: url += f"&count={cnt}" response = requests.get(url, headers={"Ocp-Apim-Subscription-Key": api_key}) result_list: List[ResponseEntry] = [] for item in response.json()["webPages"]["value"]: result_list.append((item["name"], item["url"], item["snippet"])) return result_list

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