Vector Search Patterns

What it does

Conveys how to perform vector (semantic) search using embeddings, covering concepts like embeddings, vector databases, distance metrics, indexing, and evaluation for a range of backends. It also provides practical code sketches for common tasks such as embedding generation, upserting vectors, and querying across several vector stores.

How it works

The skill explains a four-step flow: Embedding (convert text/images to vectors via ML models), Indexing (store vectors in a vector database with an efficient index), Querying (convert the user query to a vector and find nearest neighbors), and Ranking (return results by similarity). It then demonstrates concrete setup and usage patterns for multiple backends: Pinecone, Weaviate, Qdrant, Milvus, Chroma, pgvector (PostgreSQL), and Redis Vector Search. For each backend, it shows typical operations: initializing the client, creating a collection/index, upserting vectors with metadata, and performing a semantic search with optional filters. It also covers distance metrics like Cosine, Euclidean, and Dot Product, and lists common embedding models and example code blocks.

When to use it

The skill outlines prerequisites and triggers across contexts where semantic similarity is needed, such as locating items by meaning, enabling hybrid search, or utilizing RAG approaches. It implies use when your task involves converting data to vectors, storing them efficiently, and retrieving by similarity with optional constraints.

What it can touch

The skill references tools and libraries for various backends: Pinecone, Weaviate, Qdrant, Milvus, Chroma, pgvector, and Redis Vector Search. It includes code snippets that demonstrate how to interact with these backends, upsert vectors, query embeddings, and apply filters (e.g., category and price). It also shows embedding generation using text models like all-MiniLM-L6-v2 and text-embedding-ada-002, and includes snippets for Python and SQL.

Caveats

It notes several prerequisites and limitations, such as understanding vector mathematics, embeddings, and database concepts, plus familiarity with cloud services. It lists potential model and vector dimension details (e.g., 1536-dimensional embeddings, 384-1536 dimensions depending on model) and common filtering capabilities in queries. No outcomes are promised beyond the described steps, and no subjective judgments are made about the method quality beyond factual descriptions.