What is the best way to handle long documents in LangChain using RAG pipelines?

All answers to this question.

For legal documents, "RecursiveCharacterTextSplitter" is usually your best bet. It tries to keep paragraphs and sentences together, which preserves the semantic meaning of legal clauses. For the database, Pinecone or Milvus work great. The step-by-step logic should be: Load -> Split -> Embed (using OpenAI or HuggingFace) -> Store. When the user asks a question, use a "Self-Query Retriever" if your documents have metadata like "date" or "case type." This allows the agent to filter the search space before doing semantic similarity, which drastically improves accuracy. We implemented this for a contract analysis project in late 2023, and it cut down hallucination rates by nearly 40%.

   Answered 2025-11-05 by Olivia Marshall

Does using a "Parent Document Retriever" help in this case? I've heard it allows the agent to find small chunks but return the full context of the surrounding paragraph to the LLM.

   Answered 2025-11-10 by Zachary Taylor

• Zachary, that is actually the "pro move" for legal docs! When you build AI agents using LangChain for complex text, the agent often needs the context around a specific sentence to understand the legal intent. The Parent Document Retriever stores small chunks for the vector search but maps them to larger "parent" chunks. This gives the LLM the full context it needs to answer accurately without overflowing the context window with irrelevant parts of the 200-page file.

     Commented 2025-11-12 by Nathan Brooks

Don't forget to use a "Multi-Query Retriever." It generates multiple versions of the user's question to capture different nuances during the vector search phase.

   Answered 2025-11-15 by Haley Crawford

• Great point, Haley. Sometimes users don't use the exact legal terminology, so generating variations helps the retriever find the right sections regardless of the phrasing.

     Commented 2025-11-16 by Joshua Graham