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Build a Custom AI Document Q&A System with LangChain and OpenAI

1. Setting Up Your Development Environment

• Install Python 3.9+, create a virtual environment, and install core dependencies: langchain, openai, chromadb, and pypdf.
• Obtain your OpenAI API key and set it as an environment variable for secure access throughout the project.
• Verify the setup by running a simple LangChain LLM call to confirm the API key and dependencies work correctly.

2. Loading and Splitting Your Documents

• Use LangChain’s PyPDFLoader or TextLoader to ingest PDFs, Word files, or plain text documents from a local folder.
• Implement a recursive character text splitter with chunk size of 500–1000 tokens and 10% overlap to preserve context across chunks.
• Test the splitting logic by printing the first three chunks to ensure the content is coherent and not cut mid-sentence.

3. Creating Embeddings and Building a Vector Store

• Use OpenAI’s text-embedding-ada-002 model via LangChain’s OpenAIEmbeddings to convert each chunk into a 1536-dimensional vector.
• Store the embeddings in ChromaDB (a local, persistent vector database) with Chroma.from_documents() for fast similarity search.
• Add a metadata field (e.g., source filename and page number) to each chunk so you can trace answers back to the original document.

4. Implementing the Retrieval-Augmented Generation (RAG) Chain

• Configure a LangChain RetrievalQA chain that uses ChromaDB as the retriever (top‑3 most similar chunks) and gpt-3.5-turbo as the LLM.
• Customize the prompt template to
  

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