integration source-backed
GPT Researcher: Jev context selection
GPT Researcher can use Jev to score scraped passages for usefulness to a research question. Its compressor selects context from the same chunk pool and output budget used by the alternative retrieval pipeline.
Notes
The implementation applies a four-level relevance rubric to individual chunks before assembling research context.
source-backed — Public repository, docs or live artifact. About this label
Destinations
How to run
- Stack
- Python / FastAPI
- Requirements
- Python 3.12+ and pip
- Keys / accounts
- The default setup uses OpenAI and Tavily keys; Jev context filtering also uses TYPESAFE_API_KEY.
Clone the repository, configure provider keys privately in the environment or .env, install requirements and start the development server. Open localhost:8000. Without a TypeSafe key the documented context-filter fallback is keyword ranking.
git clone https://github.com/assafelovic/gpt-researcher.git
cd gpt-researcher
pip install -r requirements.txt
python -m uvicorn main:app --reloadREADME review · · Documentation reviewed; project installation and live API not run.
Next: follow the practical guide → · compare projects for this task →
Try a walkthrough
- Select relevant context with Jev →
Score a small shortlist of passages, preserve their source IDs and pass selected evidence to your answering model.
Verification levels
- HTTP availability
- Link reachable ·
Method: bounded HTTP request with redirects. HTTP 200 · HTTP response only. Checked destination ↗ · Last reachable - Browser demo scenario
- Not checked
- Build
- Not checked
- Live API test
- Not checked