Results for “spectral-vector-search”

Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.

Guides selection of Qdrant search strategies including hybrid search, reranking, relevance feedback, MMR, and discovery APIs to improve retrieval quality.

Builds production RAG and semantic search systems with Qdrant, covering collection setup, vector indexing, filtered and hybrid search, and integration with LangChain and LlamaIndex.

Enables semantic file search across a codebase using a vector-powered CLI, with integration for Claude and Codex agents.

Manage vector database operations across Pinecone, Weaviate, Qdrant, and ChromaDB, including embedding generation, index creation, metadata filtering, hybrid search, and production deployment for RAG and similarity search.

Generates and manages vector embeddings for semantic search and RAG retrieval across knowledge bases, with self-learning capabilities.

Explains dense vector embeddings, their key concepts, common use cases, and best practices for semantic search and RAG applications.

Enables fast similarity search and clustering of dense vectors using FAISS, supporting billions of vectors, GPU acceleration, and various index types.