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Embedding pipeline

An embedding pipeline is an automated workflow that chains together collecting documents, splitting them into small pieces, converting them into embeddings (lists of numbers that capture meaning) and loading them into a vector DB. This preparation has to be in place before RAG or semantic search can work properly.

About this term
Level
Advanced · Specialist terms that need the inner workings
Added
2026-10-08
In plain words It is similar to the work a library does when new books arrive: classifying them, making index cards and shelving them. The difference is that a librarian writes subject terms by hand, while this pipeline has an AI model record the meaning as numbers.

How it works

First, source documents such as internal wikis, PDFs and web pages are collected. Next, clutter such as tables and headers is cleaned up, and the documents are split into pieces of a size suited to search (chunking). For each piece, an embedding model converts its meaning into a list of numbers (a vector), which is stored in the vector DB together with metadata such as source and date. When a user asks a question, the question is also converted into a vector with the same model, and the nearest pieces are found and passed to the LLM.

Flow of an embedding pipelineSource documents → Cleanup · chunking; Cleanup · chunking → Embedding model; Embedding model → Vector DB(Store); User question → Question embedding; Question embedding → Search nearest pieces; Vector DB → Search nearest pieces(Look up); Search nearest pieces → LLM answer① In advance · indexing② At question time · searchSource documentsWiki · PDF · webCleanup · chunkingRemove clutterSplit into piecesEmbedding modelMeaning to vectorsVector DBVectors + metadataUser questionQuestion embeddingSame model usedSearch nearestpiecesLLM answerStoreLook up
Flow of an embedding pipeline The top row is the indexing work done in advance; the bottom row is the flow that finds and uses stored vectors when a question comes in.

Why it matters

The quality of RAG answers ultimately depends on 'what gets retrieved,' and the material to retrieve is prepared in advance by this pipeline. If pieces are too large, unrelated content gets mixed in; if they are too small, the surrounding context is lost. At a company whose documents keep changing, the work is not done once and finished: reprocessing only the changed documents has to keep running. That is why it is managed as a 'pipeline' rather than a one-off script.

Things to know

If you change the embedding model, old and new vectors cannot be compared with each other, so every stored document has to be re-embedded. If the same document is added several times, search results become skewed toward it, so deduplication is needed. Documents containing personal information should be de-identified before storage or stored with access permission information attached. Without regular evaluation of whether search works well, quality can quietly decline without anyone noticing.

Pre-launch checklist

Data

  • Are duplicate documents kept out?
  • Is personal information filtered out before storage?
  • Is access permission information stored alongside?

Operations

  • Are only changed documents reprocessed?
  • Is there a plan for full re-embedding when the model changes?
  • Is search quality evaluated regularly?
Things to check before actually running the pipeline.

Example

Suppose you are building an internal policy chatbot. Every night, the HR and security policy documents are checked, only newly changed documents are picked out, split into paragraphs, embedded and updated in the vector DB. When an employee asks, "How do I apply for remote work?", the chatbot finds the closest of the pre-stored pieces and uses it as the basis for its answer. If document updates fall behind, the chatbot ends up answering with old policies, so running the pipeline reliably is itself a way of managing answer quality.

Related terms

Embedding Chunking Vector DB RAG Data pipeline Deduplication