utils.py

import os
import pickle
import re
import time
from typing import List, Union
from urllib.parse import urlparse, urljoin

import faiss
import requests
from PyPDF2 import PdfReader
from bs4 import BeautifulSoup
from langchain import OpenAI, LLMChain
from langchain.agents import ConversationalAgent
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import BaseChatPromptTemplate
from langchain.chains import ConversationalRetrievalChain
from langchain.docstore.document import Document
from langchain.embeddings import OpenAIEmbeddings
from langchain.memory import ConversationBufferWindowMemory
from langchain.schema import AgentAction, AgentFinish, HumanMessage
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.faiss import FAISS

book_url = 'https://www.linkedin.com/in/shreyasachdev/'
book_file = "book.pdf"
url = 'http://shreyasachdev.com/'
pickle_file = "open_ai.pkl"
index_file = "open_ai.index"


gpt_3_5 = OpenAI(model_name='gpt-3.5-turbo',temperature=0)

embeddings = OpenAIEmbeddings()

chat_history = []

memory = ConversationBufferWindowMemory(memory_key="chat_history")

gpt_3_5_index = None

class CustomOutputParser(AgentOutputParser):

    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
        # Check if agent replied without using tools
        if "AI:" in llm_output:
            return AgentFinish(return_values={"output": llm_output.split("AI:")[-1].strip()},
                               log=llm_output)
        # Check if agent should finish
        if "Final Answer:" in llm_output:
            return AgentFinish(
                # Return values is generally always a dictionary with a single `output` key
                # It is not recommended to try anything else at the moment :)
                return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
                log=llm_output,
            )
        # Parse out the action and action input
        regex = r"Action: (.*?)[\n]*Action Input:[\s]*(.*)"
        match = re.search(regex, llm_output, re.DOTALL)
        if not match:
            raise ValueError(f"Could not parse LLM output: `{llm_output}`")
        action = match.group(1).strip()
        action_input = match.group(2)
        # Return the action and action input
        return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)

# Set up a prompt template
class CustomPromptTemplate(BaseChatPromptTemplate):
    # The template to use
    template: str
    # The list of tools available
    tools: List[Tool]

    def format_messages(self, **kwargs) -> str:
        # Get the intermediate steps (AgentAction, Observation tuples)
        # Format them in a particular way
        intermediate_steps = kwargs.pop("intermediate_steps")
        thoughts = ""
        for action, observation in intermediate_steps:
            thoughts += action.log
            thoughts += f"\nObservation: {observation}\nThought: "
        # Set the agent_scratchpad variable to that value
        kwargs["agent_scratchpad"] = thoughts
        # Create a tools variable from the list of tools provided
        kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
        # Create a list of tool names for the tools provided
        kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
        formatted = self.template.format(**kwargs)
        return [HumanMessage(content=formatted)]

def get_search_index():
    global gpt_3_5_index
    if os.path.isfile(pickle_file) and os.path.isfile(index_file) and os.path.getsize(pickle_file) > 0:
        # Load index from pickle file
        with open(pickle_file, "rb") as f:
            search_index = pickle.load(f)
    else:
        search_index = create_index()

    gpt_3_5_index = search_index


def create_index():
    source_chunks = create_chunk_documents()
    search_index = search_index_from_docs(source_chunks)
    faiss.write_index(search_index.index, index_file)
    # Save index to pickle file
    with open(pickle_file, "wb") as f:
        pickle.dump(search_index, f)
    return search_index


def create_chunk_documents():
    sources = fetch_data_for_embeddings(url, book_file, book_url)
    # print("sources" + str(len(sources)))

    splitter = CharacterTextSplitter(separator=" ", chunk_size=800, chunk_overlap=0)

    source_chunks = splitter.split_documents(sources)
    return source_chunks


   

def fetch_data_for_embeddings(url, book_file, book_url):
    sources = get_website_data(url)
    sources.extend(get_document_data(book_file, book_url))
    return sources

def get_website_data(index_url):
    # Get all page paths from index
    paths = get_paths(index_url)

    # Filter out invalid links and join them with the base URL
    links = get_links(index_url, paths)

    return get_content_from_links(links, index_url)


def get_content_from_links(links, index_url):
    content_list = []
    for link in set(links):
        if link.startswith(index_url):
            page_data = requests.get(link).content
            soup = BeautifulSoup(page_data, "html.parser")

            # Get page content
            content = soup.get_text(separator="\n")
            # print(link)

            # Get page metadata
            metadata = {"source": link}

            content_list.append(Document(page_content=content, metadata=metadata))
    time.sleep(1)
    # print("content list" + str(len(content_list)))
    return content_list


def get_paths(index_url):
    index_data = requests.get(index_url).content
    soup = BeautifulSoup(index_data, "html.parser")
    paths = set([a.get('href') for a in soup.find_all('a', href=True)])
    return paths


def get_links(index_url, paths):
    links = []
    for path in paths:
        url = urljoin(index_url, path)
        parsed_url = urlparse(url)
        if parsed_url.scheme in ["http", "https"] and "shreyasachdev" in parsed_url.netloc:
            links.append(url)
    return links


def get_document_data(book_file, book_url):
    document_list = []
    with open(book_file, 'rb') as f:
        pdf_reader = PdfReader(f)
        for i in range(len(pdf_reader.pages)):
            page_text = pdf_reader.pages[i].extract_text()
            metadata = {"source": book_url}
            document_list.append(Document(page_content=page_text, metadata=metadata))

    # print("document list" + str(len(document_list)))
    return document_list

def search_index_from_docs(source_chunks):
    # Create index from chunk documents
    # print("Size of chunk" + str(len(source_chunks)))
    search_index = FAISS.from_texts([doc.page_content for doc in source_chunks], embeddings, metadatas=[doc.metadata for doc in source_chunks])
    return search_index


def get_qa_chain(gpt_3_5_index):
    global gpt_3_5
    print("index: " + str(gpt_3_5_index))
    return ConversationalRetrievalChain.from_llm(gpt_3_5, chain_type="stuff", get_chat_history=get_chat_history,
                                                 retriever=gpt_3_5_index.as_retriever(), return_source_documents=True, verbose=True)

def get_chat_history(inputs) -> str:
    res = []
    for human, ai in inputs:
        res.append(f"Human:{human}\nAI:{ai}")
    return "\n".join(res)


def generate_answer(question) -> str:
    global chat_history, gpt_3_5_index
    gpt_3_5_chain = get_qa_chain(gpt_3_5_index)
    result = gpt_3_5_chain(
        {"question": question, "chat_history": chat_history,"vectordbkwargs": {"search_distance": 0.4}})
    print("REsult: " + str(result))
    chat_history = [(question, result["answer"])]
    sources = []

    for document in result['source_documents']:
        source = document.metadata['source']
        sources.append(source)

    source = ',\n'.join(set(sources))
    return result['answer'] + '\nSOURCES: ' + source


def get_agent_chain(prompt, tools):
    global gpt_3_5
    # output_parser = CustomOutputParser()
    llm_chain = LLMChain(llm=gpt_3_5, prompt=prompt)
    agent = ConversationalAgent(llm_chain=llm_chain, tools=tools, verbose=True)
    agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory,
                                                     intermediate_steps=True)
    return agent_chain


def get_prompt_and_tools():
    tools = get_tools()

    prefix = """Have a conversation with a human, answering the following questions as best you can. 
    Always try to use Vectorstore first. 
    You are a bot that is trained on my writing and on my resume. You will answer questions from a recruiter looking to hire me for my skills. You have access to the following tools:"""
    suffix = """Begin! If you use any tool, ALWAYS return a "SOURCES" part in your answer"
    
    {chat_history}
    Question: {input}
    {agent_scratchpad}
    SOURCES:"""
    prompt = ConversationalAgent.create_prompt(
        tools,
        prefix=prefix,
        suffix=suffix,
        input_variables=["input", "chat_history", "agent_scratchpad"]
    )
    # print("Template: " + prompt.template)
    return prompt, tools


def get_tools():
    tools = [
        Tool(
            name="Vectorstore",
            func=generate_answer,
            description="useful for when you need to answer questions about my work experience.",
            return_direct=True
        )]
    return tools

def get_custom_agent(prompt, tools):

    llm_chain = LLMChain(llm=gpt_3_5, prompt=prompt)

    output_parser = CustomOutputParser()
    tool_names = [tool.name for tool in tools]
    agent = LLMSingleActionAgent(
        llm_chain=llm_chain,
        output_parser=output_parser,
        stop=["\nObservation:"],
        allowed_tools=tool_names
    )
    agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory,
                                                        intermediate_steps=True)
    return agent_executor

def get_prompt_and_tools_for_custom_agent():
    template = """
    You are a polite applicant applying for the job description below. Answer these interview questions in elaborative manner and sharing maximum information possible
    You are a bot that is trained on my writing on a blog for the last 10 years and on my resume. Find the most relevant sources from linked in, resume and/or blog posts to answer the questions about me and how it aligns with the job requirements below 

    The job description is given below within quotes
    "The successful candidate(s) will work directly with a highly interdisciplinary team from Siebel Center for Design and collaborate with MMLI’s science as well as technology experts on the design, implementation, streamlining, and evaluation of science education activities, tools, and curricula conceptualized to help people of all ages engage with diverse topics in STEM/chemistry and artificial intelligence. You may also be particularly asked to help design science learning experiences for primary/elementary school kids. In addition, you will help train teachers in the implementation of the developed tools and activities. You may also get an opportunity to join a new project on science education game/escape room design and development. 
    Knowledge Requirements:
    A background in one or more of the following areas is desirable: chemistry, chemistry education, chemical engineering, engineering education, data analytics/science in education, computer science education, machine learning, artificial intelligence education
    Familiarity with US K-12 (science) curricula and school education systems
    Passion for designing and implementing science educational activities/curricula
    Interest in educational technology tool design and implementation
    Preferred: experience in science/chemistry education and/or computer science education will be preferred
    Preferred science teaching experience at any level "
    You will answer questions from a recruiter looking to hire me for my skills
    You will respond in the first person as me. My name is Shreya Sachdev
    Your objective is to get a job with the Molecule Makerlab team at the Siebel Center for Design. 
    
    Always try to use Vectorstore first. 
    
    
    {tools}

    To answer for the new input, use the following format:
    
    New Input: the input question you must answer
    Thought: Do I need to use a tool? Yes
    Action: the action to take, should be one of [{tool_names}]
    Action Input: the input to the action
    Observation: the result of the action
    ... (this Thought/Action/Action Input/Observation can repeat N times)
    Thought: I now know the final answer
    Final Answer: the final answer to the original input question. SOURCES: the sources referred to find the final answer

    
    When you have a response to say to the Human and DO NOT need to use a tool:
    1. DO NOT return "SOURCES" if you did not use any tool.
    2. You MUST use this format:
    ```
    Thought: Do I need to use a tool? No
    AI: [your response here]
    ```

    Begin! Remember to speak as a personal assistant when giving your final answer.
    ALWAYS return a "SOURCES" part in your answer, if you used any tool. 
    
    Previous conversation history:
    {chat_history}
    New input: {input}
    {agent_scratchpad}
    SOURCES:"""
    tools = get_tools()
    prompt = CustomPromptTemplate(
        template=template,
        tools=tools,
        # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
        # This includes the `intermediate_steps` variable because that is needed
        input_variables=["input", "intermediate_steps", "chat_history"]
    )
    return prompt, tools