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Last updated: August 21, 2026
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, andpypdf. - 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
PyPDFLoaderorTextLoaderto 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-002model via LangChain’sOpenAIEmbeddingsto 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
RetrievalQAchain that uses ChromaDB as the retriever (top‑3 most similar chunks) andgpt-3.5-turboas the LLM. - Customize the prompt template to
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