serviette

serviette

Looking for llm-app templates? All templates, cookbooks and examples are still available in full in the Pathway repository under `examples/llm-app`.

A no-code, always up-to-date RAG server for any vector database — built on the [Pathway Live Data Framework](https://github.com/pathwaycom/pathway).

Point serviette at a folder of documents, pick a vector database and an embedder in one YAML file, and run two commands: you get a chat and a REST API that answer from those documents and cite their sources. From then on, any change to the documents — an edit, a new file, a deletion — is reflected in the answers within seconds.

serviette runs on Pathway, which needs a free license key — one click at <https://pathway.com/framework/get-license> (GitHub or LinkedIn sign-in). Every command below expects it in PATHWAY_LICENSE_KEY.

Try it in four commands:

pip install serviette
export PATHWAY_LICENSE_KEY=...        # free: https://pathway.com/framework/get-license
export OPENAI_API_KEY=sk-...
serviette demo --embedder openai      # → chat at http://localhost:8989

Use pip 23.1 or newer (python -m pip install -U pip first): older pip lacks the resolver improvements this dependency tree needs and can take hours.

This indexes a small bundled corpus (a fictional coffee-machine company) into ./serviette-demo/docs and opens a chat over it. Edit or drop files into that folder while it runs — the answers follow within seconds.

<em>One command to a live RAG stack — then edit a document and watch the answer change.</em>

Who is it for

What you get. A question-answering assistant over your organisation's documents, with sources cited and answers that follow the documents: an edit, a new file or a deletion is reflected within seconds, with no re-upload or re-indexing step for anyone to run or forget.

What it costs to run. Two processes (an indexer and a server) and one YAML file. Embeddings can be computed locally for free; the Pathway license is free; the only metered component is the optional model API that writes the answer text, billed per question rather than per document.

Where your data goes. Nowhere by default. With the default local embedder, documents, their embeddings and the index stay on the machine or in the database you choose; the only thing that leaves is the handful of passages retrieved for a question, sent to the model API that writes the answer — and only if you configure one. (Choosing an API embedder instead sends the document text through that API as well.)