Results for “vectorization”

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

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

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

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

Embeds target and source image parquets, then mines nearest-neighbour source images for augmentation in VCN AOI workflows.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.

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

Reference for configuring and using LangChain4j vector stores, covering setup, search, filtering, and ingestion.

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

Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.