Verified project record

pathwaycom/llm-app

Ready-to-deploy templates for production RAG and enterprise search that natively synchronize live data sources. Built-in indexing handles vector, hybrid, and full-text search without requiring external vector databases.

Project overview

Project type: RAG · Workflow · Vector Database
Use cases: Knowledge Q&A · Data Analysis
Deployment: Refer to project documentation
License: MIT

Best for

Developers and enterprise teams needing ready-to-deploy templates for production RAG applications that synchronize continuously with live data sources.
AI engineers looking to run high-accuracy retrieval with built-in indexing and search without maintaining separate vector database or cache infrastructure.

Key capabilities

Templates automatically synchronize all new data additions, deletions, and updates from connected live data sources like Sharepoint, Google Drive, S3, and Kafka.
Provides in-memory built-in data indexing enabling vector search, hybrid search, and full-text search with cache, removing the need for external vector databases.
Uses GPT-4o during parsing to index PDFs and other documents, designed for extracting information including charts and tables from unstructured folders.
Provides a fully private, local RAG pipeline using Mistral and Ollama without external API dependencies.

Limitations and risks

Using templates with OpenAI GPT models incurs API costs and requires external network access.

Getting started

Pick a template and run it out of the box via Docker or Python, with the option to connect via an HTTP API or Streamlit UI.

Evidence and sources

GitHub project description: 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, re…
README: 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…
README: The apps can be run as **Docker containers**, and expose an **HTTP API** to connect the frontend. To allow quick testing and demos, some app templates also include an optional Str…
README: Basic end-to-end RAG app. A question-answering pipeline that uses the GPT model of choice to provide answers to queries to your documents (PDF, DOCX,...) on a live connected data…
README: [`Multimodal RAG pipeline with GPT4o`](templates/multimodal_rag/) | Multimodal RAG using GPT-4o in the parsing stage to index PDFs and other documents from a connected data source…

Reviewed at: 2026-07-17T06:41:43Z