FoxNose

Building AI apps usually means stitching together a vector DB, a search engine, an embedding pipeline, and a ton of glue code to keep them in sync. FoxNose replaces all of that with a single database. Store structured data, get auto-generated embeddings, and run hybrid search — all through one API. Use cases: 🔍 RAG — managed retrieval pipeline out of the box. No vector DB setup, no ETL. Store content and query it with semantic + keyword search. 🧠 AI Agent Memory — persistent, structured read-write memory for agents. Your chatbot remembers context across sessions without fine-tuning. 📦 AI-powered content management — store, version, and search content with built-in embeddings. One platform instead of Contentful + Algolia + Pinecone. ⚡ Instant AI backend — define a schema, get a production API with vector search in 10 minutes. No infra to manage. Under the hood: ✓ Auto-embeddings — store data, vectors are generated automatically. Zero pipeline config. ✓ Hybrid search — semantic + full-text + structured filters in one query ✓ Schema-first API — define your model, get a strict REST API. No document soup. ✓ Data governance — versioning, audit trails, RBAC built in ✓ LangChain integration — native support, Python & TypeScript SDKs If you're juggling Pinecone + Postgres + Elasticsearch + an embedding service — FoxNose is the single replacement.