tools.py

import os
import openai
from langchain.chains.question_answering import load_qa_chain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import UnstructuredPDFLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.tools import Tool
from langchain.vectorstores import Chroma

import chainlit as cl

# OpenAI API Key Setup
openai.api_key = os.environ["OPENAI_API_KEY"]

# Define our RAG tool function
def rag(query):
    # Load The Goal PDF
    loader = UnstructuredPDFLoader("data/The Goal - A Process of Ongoing Improvement (Third Revised Edition).pdf") # , mode="elements"
    docs = loader.load()
    
    # Split Text Chunks
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    splits = text_splitter.split_documents(docs)
    
    # Embed Chunks into Chroma Vector Store
    vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
    retriever = vectorstore.as_retriever()
    
    # Use RAG Prompt Template
    prompt = hub.pull("rlm/rag-prompt")
    llm = ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0) # or gpt-3.5-turbo
    
    
    def format_docs(docs):
        return "\n\n".join(doc.page_content for doc in docs)
    
    
    rag_chain = (
        {"context": retriever | format_docs, "question": RunnablePassthrough()}
        | prompt
        | llm
        | StrOutputParser()
    )

    response = ""
    for chunk in rag_chain.stream(query): #e.g. "What is a Bottleneck Constraint?"
        cl.user_session(chunk, end="", flush=True)
        response += f"\n{chunk}"
    
    # rag_chain.invoke("What is a Bottleneck Constraint?")

    return response


# this is our tool - which is what allows our agent to access RAG agent
# the `description` field is of utmost imporance as it is what the LLM "brain" uses to determine
# which tool to use for a given input.
rag_format = '{{"prompt": "prompt"}}'
rag_tool = Tool.from_function(
    func=rag,
    name="RAG",
    description=f"Useful for retrieving contextual information about the PDF to answer user questions. Input should be a single string strictly in the following JSON format: {rag_format}",
    return_direct=True,
)