Delete rag.py
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rag.py
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import os
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import openai
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.vectorstores import Chroma
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from langchain.chains.question_answering import load_qa_chain
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.document_loaders import UnstructuredPDFLoader
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# OpenAI API Key Setup
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openai.api_key = os.environ["OPENAI_API_KEY"]
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# Load The Goal PDF
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loader = UnstructuredPDFLoader("data/The Goal - A Process of Ongoing Improvement (Third Revised Edition).pdf") # , mode="elements"
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docs = loader.load()
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# Split Text Chunks
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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splits = text_splitter.split_documents(docs)
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# Embed Chunks into Chroma Vector Store
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vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
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retriever = vectorstore.as_retriever()
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# Use RAG Prompt Template
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prompt = hub.pull("rlm/rag-prompt")
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llm = ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0) # or gpt-3.5-turbo
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def format_docs(docs):
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return "\n\n".join(doc.page_content for doc in docs)
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rag_chain = (
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{"context": retriever | format_docs, "question": RunnablePassthrough()}
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| prompt
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| llm
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| StrOutputParser()
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)
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for chunk in rag_chain.stream("What is a Bottleneck Constraint?"):
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print(chunk, end="", flush=True)
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rag_chain.invoke("What is a Bottleneck Constraint?")
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