Principle:Hpcaitech ColossalAI Vector Embedding Retrieval
| Knowledge Sources | |
|---|---|
| Domains | RAG, Information_Retrieval |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
A vector-based document retrieval pattern that embeds text chunks into dense vectors and retrieves the most relevant chunks for a given query using similarity search.
Description
Vector Embedding and Retrieval converts text chunks into dense vector representations using an embedding model, stores them in a vector database (Chroma), and retrieves the most similar chunks for user queries. ColossalQA's CustomRetriever manages multiple vector stores (one per document source) with incremental indexing via SQL record managers.
Usage
Use after text splitting to build the retrieval index. This is the core retrieval component of the RAG pipeline.
Theoretical Basis
The retrieval process:
- Embedding: Each text chunk is converted to a dense vector using a sentence embedding model
- Indexing: Vectors are stored in a Chroma vector database with metadata
- Query: The user query is embedded using the same model
- Similarity Search: Top-k vectors closest to the query (by cosine similarity) are retrieved
- Deduplication: Results across multiple vector stores are merged and deduplicated
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