Semantic search and retrieval system for local documents using vector embeddings, enabling AI-powered search across your document collections with support for multiple embedding providers.
Provides persistent memory and semantic code understanding for AI assistants using MongoDB Atlas Vector Search. Enables intelligent code search, memory management, and pattern detection across codebases with complete semantic context preservation.
A Retrieval-Augmented Generation system that enables uploading, processing, and semantic search of PDF documents using vector embeddings and FAISS indexing for context-aware question answering.
Enables semantic code search across codebases using Qdrant vector database and OpenAI embeddings, allowing users to find code by meaning rather than just keywords through natural language queries.
Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.