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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
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+ENV/
+env.bak/
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+
+# Rope project settings
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+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
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+
+# Cython debug symbols
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+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

.streamlit/config.toml

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+[theme]
+primaryColor="#F55036"
+backgroundColor="#FFFFFF"
+textColor="#000000"
+base="light"
+
+[server]
+enableStaticServing = true

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt ./
+COPY . /app/
+
+RUN pip install -r requirements.txt
+
+CMD ["streamlit", "run", "app.py", "--server.port", "8080"]

LICENSE

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moa/agent/__init__.py

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+from .moa import MOAgent

moa/agent/moa.py

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+"""
+Langchain agent
+"""
+from typing import Generator, Dict, Optional, Literal, TypedDict, List, Any
+from dotenv import load_dotenv
+
+from langchain_groq import ChatGroq
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import BaseMessage
+from langchain_core.runnables import RunnablePassthrough, RunnableLambda, RunnableSerializable
+from langchain_core.output_parsers import StrOutputParser
+
+from .prompts import SYSTEM_PROMPT, REFERENCE_SYSTEM_PROMPT
+
+load_dotenv()
+valid_model_names = Literal[
+    'llama3-70b-8192',
+    'llama3-8b-8192',
+    'gemma-7b-it',
+    'gemma2-9b-it',
+    'mixtral-8x7b-32768',
+    'llama-3.1-8b-instant',
+    'llama-3.1-70b-versatile'
+]
+
+class ResponseChunk(TypedDict):
+    delta: str
+    response_type: Literal['intermediate', 'output']
+    metadata: Dict[str, Any]
+
+
+class MOAgent:
+    def __init__(
+        self,
+        main_agent: RunnableSerializable[Dict, str],
+        layer_agent: RunnableSerializable[Dict, Dict],
+        reference_system_prompt: Optional[str] = None,
+        cycles: Optional[int] = None,
+        chat_memory: Optional[ConversationBufferMemory] = None
+    ) -> None:
+        self.reference_system_prompt = reference_system_prompt or REFERENCE_SYSTEM_PROMPT
+        self.main_agent = main_agent
+        self.layer_agent = layer_agent
+        self.cycles = cycles or 1
+        self.chat_memory = chat_memory or ConversationBufferMemory(
+            memory_key="messages",
+            return_messages=True
+        )
+
+    @staticmethod
+    def concat_response(
+        inputs: Dict[str, str],
+        reference_system_prompt: Optional[str] = None
+    ):
+        reference_system_prompt = reference_system_prompt or REFERENCE_SYSTEM_PROMPT
+
+        responses = ""
+        res_list = []
+        for i, out in enumerate(inputs.values()):
+            responses += f"{i}. {out}\n"
+            res_list.append(out)
+
+        formatted_prompt = reference_system_prompt.format(responses=responses)
+        return {
+            'formatted_response': formatted_prompt,
+            'responses': res_list
+        }
+
+    @classmethod
+    def from_config(
+        cls,
+        main_model: Optional[valid_model_names] = 'llama3-70b-8192',
+        system_prompt: Optional[str] = None,
+        cycles: int = 1,
+        layer_agent_config: Optional[Dict] = None,
+        reference_system_prompt: Optional[str] = None,
+        **main_model_kwargs
+    ):
+        reference_system_prompt = reference_system_prompt or REFERENCE_SYSTEM_PROMPT
+        system_prompt = system_prompt or SYSTEM_PROMPT
+        layer_agent = MOAgent._configure_layer_agent(layer_agent_config)
+        main_agent = MOAgent._create_agent_from_system_prompt(
+            system_prompt=system_prompt,
+            model_name=main_model,
+            **main_model_kwargs
+        )
+        return cls(
+            main_agent=main_agent,
+            layer_agent=layer_agent,
+            reference_system_prompt=reference_system_prompt,
+            cycles=cycles
+        )
+
+    @staticmethod
+    def _configure_layer_agent(
+        layer_agent_config: Optional[Dict] = None
+    ) -> RunnableSerializable[Dict, Dict]:
+        if not layer_agent_config:
+            layer_agent_config = {
+                'layer_agent_1' : {'system_prompt': SYSTEM_PROMPT, 'model_name': 'llama3-8b-8192'},
+                'layer_agent_2' : {'system_prompt': SYSTEM_PROMPT, 'model_name': 'gemma-7b-it'},
+                'layer_agent_3' : {'system_prompt': SYSTEM_PROMPT, 'model_name': 'mixtral-8x7b-32768'}
+            }
+
+        parallel_chain_map = dict()
+        for key, value in layer_agent_config.items():
+            chain = MOAgent._create_agent_from_system_prompt(
+                system_prompt=value.pop("system_prompt", SYSTEM_PROMPT), 
+                model_name=value.pop("model_name", 'llama3-8b-8192'),
+                **value
+            )
+            parallel_chain_map[key] = RunnablePassthrough() | chain
+        
+        chain = parallel_chain_map | RunnableLambda(MOAgent.concat_response)
+        return chain
+
+    @staticmethod
+    def _create_agent_from_system_prompt(
+        system_prompt: str = SYSTEM_PROMPT,
+        model_name: str = "llama3-8b-8192",
+        **llm_kwargs
+    ) -> RunnableSerializable[Dict, str]:
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", system_prompt),
+            MessagesPlaceholder(variable_name="messages", optional=True),
+            ("human", "{input}")
+        ])
+
+        assert 'helper_response' in prompt.input_variables
+        llm = ChatGroq(model=model_name, **llm_kwargs)
+        
+        chain = prompt | llm | StrOutputParser()
+        return chain
+
+    def chat(
+        self, 
+        input: str,
+        messages: Optional[List[BaseMessage]] = None,
+        cycles: Optional[int] = None,
+        save: bool = True,
+        output_format: Literal['string', 'json'] = 'string'
+    ) -> Generator[str | ResponseChunk, None, None]:
+        cycles = cycles or self.cycles
+        llm_inp = {
+            'input': input,
+            'messages': messages or self.chat_memory.load_memory_variables({})['messages'],
+            'helper_response': ""
+        }
+        for cyc in range(cycles):
+            layer_output = self.layer_agent.invoke(llm_inp)
+            l_frm_resp = layer_output['formatted_response']
+            l_resps = layer_output['responses']
+            
+            llm_inp = {
+                'input': input,
+                'messages': self.chat_memory.load_memory_variables({})['messages'],
+                'helper_response': l_frm_resp
+            }
+
+            if output_format == 'json':
+                for l_out in l_resps:
+                    yield ResponseChunk(
+                        delta=l_out,
+                        response_type='intermediate',
+                        metadata={'layer': cyc + 1}
+                    )
+
+        stream = self.main_agent.stream(llm_inp)
+        response = ""
+        for chunk in stream:
+            if output_format == 'json':
+                    yield ResponseChunk(
+                        delta=chunk,
+                        response_type='output',
+                        metadata={}
+                    )
+            else:
+                yield chunk
+            response += chunk
+
+        if save:
+            self.chat_memory.save_context({'input': input}, {'output': response})

moa/agent/prompts.py

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+SYSTEM_PROMPT = """\
+You are a personal assistant that is helpful.
+
+{helper_response}\
+"""
+
+REFERENCE_SYSTEM_PROMPT = """\
+You have been provided with a set of responses from various open-source models to the latest user query. 
+Your task is to synthesize these responses into a single, high-quality response. 
+It is crucial to critically evaluate the information provided in these responses, recognizing that some of it may be biased or incorrect. 
+Your response should not simply replicate the given answers but should offer a refined, accurate, and comprehensive reply to the instruction. 
+Ensure your response is well-structured, coherent, and adheres to the highest standards of accuracy and reliability.
+Responses from models:
+{responses}
+"""

moa/main.py

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+from agent import MOAgent
+
+# Configure agent
+layer_agent_config = {
+    'layer_agent_1' : {'system_prompt': "Think through your response with step by step {helper_response}", 'model_name': 'llama3-8b-8192'},
+    'layer_agent_2' : {'system_prompt': "Respond with a thought and then your response to the question {helper_response}", 'model_name': 'gemma-7b-it'},
+    'layer_agent_3' : {'model_name': 'llama3-8b-8192'},
+    'layer_agent_4' : {'model_name': 'gemma-7b-it'},
+    'layer_agent_5' : {'model_name': 'llama3-8b-8192'},
+}
+agent = MOAgent.from_config(
+    main_model='mixtral-8x7b-32768',
+    layer_agent_config=layer_agent_config
+)
+
+while True:
+    inp = input("\nAsk a question: ")
+    stream = agent.chat(inp, output_format='json')
+    for chunk in stream:
+        print(chunk)

requirements.txt

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+langchain>=0.2.5
+langchain_groq>=0.1.5
+streamlit>=1.36.0
+watchdog>=4.0.1
+python-dotenv>=1.0.1
+streamlit-ace>=0.1.1