Redis LangCache

Vector search plugin for Azure Cache for Redis via RediSearch module, supporting HNSW/Flat indexes, hybrid lexical+vector with BM25 fusion, metadata filtering. Suited for enterprise semantic caching, real-time RAG, and recommendations. Integrated caching layer provides sub-ms latency vs standalone vector DBs like Weaviate.

Redis Vector Search (part of Redis Stack) enables vector similarity search on Redis with HNSW indexing, hybrid BM25+vector, and metadata filtering. It leverages Redis caching for low-latency real-time apps like semantic search. Vs dedicated DBs like Pinecone, Redis offers multi-model (JSON/KV + vectors) but requires more config for scale.

Lucene KNN vector plugin for Elasticsearch search engine, enabling hybrid lexical+vector search, BM25 fusion, HNSW/IVF indexes for ANN. Used for enterprise search, RAG, multimodal apps. Integrated vs standalone like Weaviate: superior hybrid text handling but higher resource footprint.

k-NN vector plugin for OpenSearch (Lucene-based), supporting hybrid lexical+vector, BM25 fusion, HNSW/IVF indexes, multimodal. For enterprise RAG, semantic search. Integrated vs standalone like Weaviate: excels in hybrid text+vector but heavier footprint.

The process of determining optimal text segment sizes for embedding and retrieval in vector databases. Chunk size significantly impacts RAG quality, balancing between capturing complete context (larger chunks) and retrieval precision (smaller chunks), typically ranging from 256 to 1024 tokens.

Techniques for managing limited LLM context windows in RAG systems, including chunk selection, summarization, and iterative retrieval. As context windows fill with retrieved documents, strategies ensure the most relevant information reaches the model while respecting token limits.