semantic-coverage
Automated detection of knowledge gaps and blind spots in RAG vector stores
GraphCanon updated 2w · GitHub synced 2w
Decision brief
Semantic-Coverage focuses on identifying knowledge gaps within RAG vector stores, providing unique insights into its performance and coverage. Key insights are drawn from specific functions in the evaluation toolkit.
Good fit when
- When you need to pinpoint areas where a Retriever-Aggregator-Generator (RAG) system lacks sufficient data or has blind spots.
- For deep analysis of vector databases used within RAG systems, especially when looking to improve coverage and accuracy.
Avoid when
- If your focus is on integrating RAG models without the need for advanced evaluation metrics.
- When only concerned with deploying basic vector store setups that do not require extensive post-deployment analysis or fine-tuning.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (221d since push)
- As of 2w
- Provenance
- Not a fork · Personal account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install semantic-coverage PyPISimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A tool for identifying areas where a Retriever-Aggregator-Generator (RAG) system may not have sufficient data or coverage, likely focusing on the analysis and evaluation of vector databases used in RAG systems.
Capability facts
- Languages
- python
Source: github.language · Aug 2, 2026
Categories
Tags
README
1. Installation
git clone https://github.com/aashirpersonal/semantic-coverage.git
cd semantic-coverage
For agents
This page has a .md twin and JSON over the API.