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://g.co/kgs/2VFC7u'
book_file = "Book.pdf"
url = 'https://makerlab.illinois.edu/'

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)

    for chunk in source_chunks:
        print("Size of chunk: " + str(len(chunk.page_content) + len(chunk.metadata)))
        if chunk.page_content is None or chunk.page_content == '':
            print("removing chunk: "+ chunk.page_content)
            source_chunks.remove(chunk)
        elif len(chunk.page_content) >=1000:
            print("splitting document")
            source_chunks.extend(splitter.split_documents([chunk]))
    # print("Chunks: " + str(len(source_chunks)) + "and type " + str(type(source_chunks)))
    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 "squarespace" not 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": 0.8}})
    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. 
    Your name is Makerlab Bot because you are a personal assistant of Makerlab. 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 the Makerlab or 3D Printing.",
            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 = """
    Have a conversation with a human, answering the following questions as best you can. 
    Always try to use Vectorstore first. 
    Your name is Makerlab Bot because you are a personal assistant of Makerlab. You have access to the following tools:
    
    {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