Principle:Cohere ai Cohere python Vector Similarity Search
| Metadata | Value |
|---|---|
| Source | Cohere Semantic Search |
| Source | Efficient and Robust Approximate Nearest Neighbor Search |
| Domains | Vector_Search, Information_Retrieval, Semantic_Search |
| Last Updated | 2026-02-15 14:00 GMT |
| Implemented By | Implementation:Cohere_ai_Cohere_python_Vector_Retrieval_Pattern |
Overview
A retrieval technique for finding semantically similar documents using vector distance metrics on embedding representations.
Description
Vector Similarity Search is the process of finding the most similar documents to a query by comparing their embedding vectors. After generating embeddings with the Cohere embed API, documents are indexed in a vector database. At query time, the query embedding is compared against all document embeddings using a distance metric (cosine similarity, dot product, or Euclidean distance). Approximate Nearest Neighbor (ANN) algorithms enable sub-linear search time over millions of vectors. This is the first stage in a typical two-stage retrieval pipeline (vector search followed by rerank).
Usage
Use after embedding documents with input_type="search_document" and queries with input_type="search_query". Store embeddings in a vector database (FAISS, Pinecone, Weaviate, Qdrant, etc.). Retrieve top-K candidates for optional reranking.
Theoretical Basis
Cosine similarity measures the angle between vectors:
sim(a, b) = (a . b) / (||a|| * ||b||)
Values range from -1 to 1, with 1 being identical. ANN algorithms (HNSW, IVF, ScaNN) build graph or tree indices for O(log N) approximate search instead of O(N) brute-force comparison.
Practical Guide
- Use the same embedding model for documents and queries
- Always use input_type="search_document" for indexing, "search_query" for queries
- Start with cosine similarity as the default metric
- Retrieve more candidates than needed (e.g., top-100) and rerank to top-10
- Consider quantized embeddings (int8, binary) for storage efficiency