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Example Use Case: Building a Q&A system for a PDF research paper.

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Ingestion: Preparing the Document

1. Loading the Document
   Load a PDF research paper into the system.

   from langchain.document_loaders import PyPDFLoader
   
   # Load the PDF
   loader = PyPDFLoader("research_paper.pdf")
   documents = loader.load()
   print(f"Loaded {len(documents)} pages from the document.")

2. Splitting the Document
   Divide the document into smaller chunks for processing.

   from langchain.text_splitter import RecursiveCharacterTextSplitter
   
   # Split the document into chunks
   text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
   chunks = text_splitter.split_documents(documents)
   print(f"Split document into {len(chunks)} chunks.")

3. Creating Embeddings
   Convert the chunks into vector embeddings.

   from langchain.embeddings import OpenAIEmbeddings
   
   # Generate embeddings
   embeddings = OpenAIEmbeddings()
   chunk_embeddings = [embeddings.embed_document(chunk) for chunk in chunks]
   print("Embeddings created for all chunks.")

4. Storing in a Vector Store
   Store these embeddings in a vector database for retrieval.

   from langchain.vectorstores import FAISS
   
   # Save embeddings in FAISS
   vector_store = FAISS.from_documents(chunks, embeddings)
   print("Embeddings stored in FAISS vector database.")

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Generation: Answering the Question

1. Accepts a User's Question
   Take a question from the user.

   user_query = "What are the main findings of the research?"

2. Finds Relevant Content
   Use the vector database to retrieve the most relevant chunks for the query.

   from langchain.chains import RetrievalQAChain
   from langchain.llms import OpenAI
   
   # Set up retriever
   retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k":3})
   
   # Use a QA chain to retrieve relevant content
   llm = OpenAI(model="text-davinci-003")
   qa_chain = RetrievalQAChain.from_chain_type(llm=llm, retriever=retriever)

3. Generates an Answer
   Generate the answer based on the retrieved content.

   answer = qa_chain.run(user_query)
   print(f"Answer: {answer}")

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Output Walkthrough

• Loading: The PDF is loaded into the system as documents.
• Splitting: The PDF is split into manageable chunks with overlapping sections for context.
• Embedding: The chunks are embedded into high-dimensional vectors.
• Storage: The embeddings are stored in FAISS, making them searchable.
• Retrieval: When a user asks a question, the system retrieves the most relevant chunks.
• Answer Generation: An LLM generates an answer based on the retrieved content.

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Illustrative Result

User Question: "What are the main findings of the research?"
System Answer: "The research highlights that X intervention improves Y outcomes by Z% according to the data in Table 2."

This demonstrates how LangChain simplifies ingestion and generation phases for creating a scalable, efficient Q&A system.