Principle:Langchain ai Langchain Document Indexing
| Knowledge Sources | |
|---|---|
| Domains | Vector_Search, Data_Indexing |
| Last Updated | 2026-02-11 00:00 GMT |
Overview
The process of embedding document chunks and storing them with their vectors and metadata in a vector store for later retrieval.
Description
Document indexing is the write path of the vector store pipeline. It takes prepared documents, generates embeddings via the configured embedding model, and stores the text, vectors, and metadata in the vector store backend. The process is typically batched for efficiency.
Usage
Index documents after preparation (splitting) and before performing searches. Re-index when documents are updated.
Theoretical Basis
# Abstract algorithm (not real code)
for batch in batches(documents, batch_size):
texts = [doc.page_content for doc in batch]
vectors = embedding_model.embed_documents(texts)
vector_store.upsert(ids, vectors, texts, metadatas)
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