33rd of 12 in RAG and search
llm-app
Pathway RAG pipeline templates that re-index live data sources
Live demo ↗
(opens in a new tab)Documentation ↗
(opens in a new tab)Website ↗
(opens in a new tab)Repository on GitHub ↗
(opens in a new tab)
- Stars
- 58.8k
- License
- MIT
- Last commit
- Jul 2026
- Language
- Jupyter Notebook
Overview
Eight Dockerized Python pipelines on the Pathway framework: question-answering RAG, a live document indexer, multimodal RAG with GPT-4o, unstructured-to-SQL, adaptive RAG, a private Mistral plus Ollama variant, slide search and video RAG. Each watches a source (file system, Google Drive, SharePoint, S3, Kafka, Postgres), keeps an in-memory vector and full-text index current and serves an HTTP API. For teams whose documents change constantly.
Who it is for: Teams running RAG over documents that change constantly
Strengths
- No separate vector DB, cache or API framework; indexing is in-process (usearch, Tantivy)
- Connectors for file system, Google Drive, SharePoint, S3, Kafka and Postgres with live sync
- Private variant runs fully local with Mistral and Ollama
- Docker images and tests included
Weaknesses
- Pathway is the real dependency; its Rust engine is opaque to most Python teams
- Pipelines are backends; the UI is an optional Streamlit demo
- Root README has no setup; each template README is required reading
- Index lives in memory; sizing for millions of pages is on you
What it needs
- Jupyter Notebook, pathway, openai, mistral, ollama
- Needs docker, openai-api-key, data-source-credentials
Also in RAG and search
See all 12| Rank | Project | Score |
|---|---|---|
| 1 | azure-search-openai-demoAzure RAG chat reference on AI Search and Azure OpenAI | 56 out of 100 |
| 2 | rag-postgres-openai-pythonRAG over Postgres table rows with hybrid search and SQL filters | 51 out of 100 |
| #4 | chat-langchainLangChain docs assistant as a Managed Deep Agent with Next.js UI | 45 out of 100 |
| #5 | chat-with-your-data-solution-acceleratorAzure RAG chat app that answers from your documents with citations | 44 out of 100 |
| #6 | llm-answer-enginePerplexity-style Next.js answer engine over Brave search results | 41 out of 100 |