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llm-app is by pathwaycom — not by us.

We indexed this project so people can find it. We are not selling it, we host no copy of the code, and we are not affiliated with or endorsed by its authors — get it from them.

Indexed Oct 5, 2026 · 58,842 stars at index time. Maintainers: claiming verifies your identity and unlocks a higher assurance tier. Removal requests are honored.

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llm-app

unclaimed listingactively maintainedFreeMIT

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in s

by Open Source Community · New publisher

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pathway.com/developers/templates/

pathwaycom/llm-app is an open-source project by pathwaycom: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.. Indexed here so it can be found — not resold.

It is free. Get it from the upstream repository: https://github.com/pathwaycom/llm-app

From the project's own README (excerpt, reproduced for discovery under its MIT license):

Pathway Live Data Framework AI Pipelines

The Pathway Live Data Framework's AI Pipelines allow you to quickly put in production AI applications that offer high-accuracy RAG and AI enterprise search at scale using the most up-to-date knowledge available in your data sources. It provides you ready-to-deploy LLM (Large Language Model) App Templates. You can test them on your own machine and deploy on-cloud (GCP, AWS, Azure, Render,...) or on-premises.

The apps connect and sync (all new data additions, deletions, updates) with data sources on your file system, Google Drive, Sharepoint, S3, Kafka, PostgreSQL, real-time data APIs. They come with no infrastructure dependencies that would need a separate setup. They include built-in data indexing enabling vector search, hybrid search, and full-text search - all done in-memory, with cache.

Application Templates

The application templates provided in this repo scale up to millions of pages of documents. Some of them are optimized for simplicity, some are optimized for amazing accuracy. Pick the one that suits you best. You can use it out of the box, or change some steps of the pipeline - for example, if you would like to add a new data source, or change a Vector Index into a Hybrid Index, it's just a one-line change.

Application (template) Description --------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Question-Answering RAG…

Preview

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01Capabilities

Does

  • No capabilities recorded

Doesn’t

  • No exclusions declared

02Requirements & stack

Depends on

No declared dependencies

Credentials needed

None declared

Stack

jupyter notebook

03Community

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This listing is unclaimed, so publisher identity cannot be verified and it stays below the “verified” tier by design — that is a statement about the listing, not about the project’s quality. Our automated scans still ran; a maintainer who claims it unlocks identity verification.

VersionChannelReleasedNotes
0.0.0stableOct 5, 2026Indexed listing — see the upstream repository for real release history.