rag.py

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

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

# 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()
)

for chunk in rag_chain.stream("What is a Bottleneck Constraint?"):
    print(chunk, end="", flush=True)

rag_chain.invoke("What is a Bottleneck Constraint?")