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StreamRAG

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Basic Information

StreamRAG is a GPT-powered video retrieval and streaming agent intended to let developers create searchable video collections and expose them through a ChatGPT integration. The project enables uploading multiple videos to a VideoDB-powered library, indexing their content for retrieval-augmented generation, and serving real-time video responses or compiled clips. The README documents how to configure an API key for VideoDB, add video links via upload.py, install dependencies with pip, and run a local Flask server with app.py. It also includes guidance for publishing the service as a ChatGPT-compatible GPT by editing openapi.yaml and copying prompts from prompts.txt. The repo targets developers who want to combine video search, summarization, and streaming within conversational workflows.

Links

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479
App URL
https://github.com/video-db/StreamRAG
Github Repository
https://github.com/video-db/StreamRAG/blob/main/README.md

App Details

Features
The repository provides core features for video-centric RAG workflows including multi-video upload and library creation, semantic search across videos, and generation of summarized text answers based on retrieved video segments. It supports producing real-time video responses or curated compilations and extracting key insights such as episode highlights. Implementation artifacts referenced in the README include upload.py for adding sources, app.py to run a Flask server, openapi.yaml for ChatGPT integration, and prompts.txt for instruction content. The README also points to demo videos and a VideoDB console for obtaining an API key. Roadmap items and contributor guidelines are included to support future integrations and deployments.
Use Cases
StreamRAG helps developers and teams turn video archives into interactive, conversational resources by combining video indexing with GPT-style retrieval and summarization. It reduces manual effort required to locate relevant moments in long recordings by enabling semantic search and returning video snippets or summaries in response to natural language queries. The Flask server and openapi configuration make it straightforward to expose the functionality as a ChatGPT action or GPT for end users. The project also documents simple deployment and publishing steps, and lists future plans to integrate cloud deployments, external storage providers, and meeting recorder APIs to broaden data sources and streamline ingestion.

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