ModelRefs / Indexing Pipeline — AI Glossary
Indexing Pipeline — AI Glossary
The offline process of loading, cleaning, chunking, embedding, and storing documents into a vector index for later retrieval.
Overview
Indexing pipelines: load documents (document loaders for PDF, HTML, Notion, Google Drive), clean/normalize, split into chunks (text splitter with overlap), encode with embedding model, and upsert into vector store with metadata. Run on schedule or event-trigger (new document). Tools: LlamaIndex Ingestion Pipeline, LangChain document loaders, Unstructured.io.
Reference details
| Topic | rag |
|---|---|
| Last reviewed | 2026-06-24 |
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Frequently asked questions
What is Indexing Pipeline?
The offline process of loading, cleaning, chunking, embedding, and storing documents into a vector index for later retrieval.
What concepts are related to Indexing Pipeline?
Closely related concepts include retrieval pipeline, document loader, text splitter.