README

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.

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
 (GitHub or LinkedIn sign-in). Every
command below expects it in PATHWAY_LICENSE_KEY.

Try it in four commands:

bash
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.


  

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

 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.)

How mature it is. serviette is a thin layer over the
Pathway Live Data Framework — the
open-source engine that does the watching, parsing, embedding and writing: in
development since 2022, 60 000+ GitHub stars, in production use at
organisations such as
DB Schenker,
Intel,
NATO,
La Poste,
Transdev and
CMA CGM.
serviette's benchmarks cover indexing up to 50 GB of text (26 million pages,
17 million files), with indexer memory measured throughout the run, and
retrieval accuracy on a public benchmark (FRAMES). Eight vector databases
are under 

Top issues

Discussions

Latest release

llm-app is now serviette. This repository used to host Pathway's LLM app templates. They are all still available, unchanged, in pathway/examples/llm-app. The repository itself now hosts serviette, a no-code RAG server built from what we learned from those templates.

serviette is a no-code RAG server that keeps its answers in sync with your documents. Point it at a folder, pick a vector database and an embedder in one YAML file, and you get a chat UI and a REST API that answer from those documents and cite their sources. Edits, new files and deletions show up in the answers within seconds, w