semantic-coverage logo

semantic-coverage

aashirpersonal/semantic-coverage

Automated detection of knowledge gaps and blind spots in RAG vector stores

GraphCanon updated 2w · GitHub synced 2w

12 stars0 forksLast push 8mo Python

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

Verify the decision

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
PyPI

Similar 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.

Was this helpful?

Anonymous feedback helps us improve pages and translations.