Principle:Ggml org Llama cpp Text Retrieval
| Knowledge Sources | |
|---|---|
| Domains | Embedding, Retrieval |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Text Retrieval is the principle of using embedding models to find semantically similar documents from a corpus given a query.
Description
This principle covers the retrieval pipeline that uses llama.cpp's embedding capabilities to perform semantic search. A corpus of documents is encoded into embedding vectors, and a query is similarly encoded. The system then finds the most similar documents by computing cosine similarity between the query embedding and all document embeddings. This enables retrieval-augmented generation (RAG) and semantic search applications.
Usage
Apply this principle when building RAG systems, semantic search engines, or any application that needs to find documents relevant to a query using embedding-based similarity rather than keyword matching.
Theoretical Basis
Embedding-based retrieval maps text into a dense vector space where semantic similarity corresponds to geometric proximity. The retrieval process involves encoding the document corpus into a matrix of embedding vectors (typically done once offline), encoding the query into a vector, and computing similarity scores (usually cosine similarity) between the query vector and all document vectors. The top-k most similar documents are returned as retrieval results. The quality of retrieval depends on the embedding model's ability to capture semantic meaning and the normalization of embedding vectors.