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What is Ahnlich?

Ahnlich is an in-memory vector database with a built-in AI proxy that embeds your text, images, and audio for you. Point it at raw content, search by meaning, and get ranked results — all from a single binary with no external services.

docker run -d -p 1370:1370 ghcr.io/deven96/ahnlich-ai:latest

Ready to try it? Jump straight to the Quickstart.

Why Ahnlich?​

  • No embedding pipeline to build. Send raw text, images, or audio; the AI proxy embeds and stores them automatically. No separate model server to run.
  • Runs anywhere, instantly. A self-contained binary — no cluster, no managed service, no cloud dependency. Great for local dev, prototypes, and the edge.
  • Fast semantic search. RAM-resident vectors with Cosine, Euclidean (L2), or Dot Product similarity.
  • Filter while you search. Attach metadata (author, genre, timestamps…) and combine similarity with metadata conditions in one query.
  • Update in place. Add, change, or delete vectors on the fly — no full index rebuilds.
  • Scales when you need it. Approximate search via HNSW indexes for large datasets.
  • Use your language. Native clients for Python, Rust, Node, and Go, plus an interactive CLI.

What can I build with it?​

  • Semantic document & FAQ search — find content by meaning, not keywords.
  • RAG chat memory — fetch the most relevant context to enrich LLM prompts.
  • Recommendations — turn users, products, or docs into vectors and rank by similarity plus metadata.
  • Code & log search — surface meaningfully similar snippets, not exact matches.

How it fits together​

Ahnlich ships two services and a CLI:

ComponentWhat it does
ahnlich-dbThe in-memory vector store — holds vectors and metadata, runs similarity search.
ahnlich-aiThe AI proxy — turns raw text, images, or audio into embeddings, then talks to the DB for you.
ahnlich-cliAn interactive shell for creating stores, inserting data, and querying.

Use ahnlich-ai when you want automatic embeddings, or talk to ahnlich-db directly if you already have your own vectors.

Next steps​