LangChain chunking
LangChain chunking is the process of slicing long documents into smaller, overlapping pieces so that an embedding model can turn them into vectors, a vector store can index and search them, and a large language model (LLM) receives only the fragments relevant to a given question. In the LangChain framework you create a Textsplitter, set parameters such as chunk_size (token or character count) and chunk_overlap (to preserve context), then pass raw text, PDFs, or HTML through it. Each chunk is independently vector-embedded and stored in a database such as Chroma or Pinecone. During a Retrieval-Augmented Generation (RAG) query, LangChain computes an embedding for the user prompt, performs similarity search against the stored chunks, and feeds only the top-k snippets to the LLM—dramatically cutting token cost and hallucination risk. Tuning chunk size optimizes the trade-off between semantic completeness and recall; typical starting points are a few hundred tokens with an overlap of roughly a tenth to a fifth of the chunk, but the working values are found experimentally for a given corpus, embedding model, and context window. Because chunking runs offline at ingestion time, you can re-index quickly when docs change, enabling near-real-time knowledge updates for chatbots, copilots, and analytics agents.
Related terms
Related services: LangChain development company, Agentic AI consulting.
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