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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 = os.environ["OPENAI_API_KEY"] |
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loader = UnstructuredPDFLoader("data/The Goal - A Process of Ongoing Improvement (Third Revised Edition).pdf") |
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docs = loader.load() |
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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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vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings()) |
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retriever = vectorstore.as_retriever() |
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prompt = hub.pull("rlm/rag-prompt") |
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llm = ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0) |
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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?") |