Generate a docker-compose stack for a local RAG setup.
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The Local RAG Stack Generator is a free page for planning a self-hosted retrieval-augmented generation stack. It frames the four components you need to choose: an embedding model, a vector database, a generation model and an API layer. It suits teams that want RAG running on their own hardware or private cloud, whether for privacy, cost or control. No sign-up is required. Use it as a configuration starting point, then benchmark each component on your own data.
You need a way to embed documents, a vector store for similarity search, a generation model that answers from retrieved context, and an API layer to connect them. Optionally add a reranker for better ordering. Each choice affects memory, latency and answer quality.
For a prototype, an embedded database such as Chroma or FAISS keeps setup simple. For growing production workloads, consider Qdrant, Weaviate, Milvus or pgvector depending on scale, filtering needs and operational preferences. Measure recall and latency on your own corpus.
Yes. A common pattern keeps embeddings and the vector store local while a hosted model handles generation, or the reverse. Plugsky's OpenAI-compatible API covers 30+ models and can serve as the generation layer while you keep retrieval in-house.
Canonical pricing and plans: plugsky.com/#sec-pricing · Terms · SLA · Docs