semantic-coverage
Automated detection of knowledge gaps and blind spots in RAG vector stores
GraphCanon updated Sep 30, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Semantic-coverage is a Python tool designed to automate the detection of knowledge gaps and blind spots in Retrieval-Augmented Generation (RAG) vector stores, aiding in the evaluation of knowledge completeness.
Good fit when
- When you need to evaluate the completeness of knowledge in RAG vector stores and identify specific areas where the data is lacking.
- If your project involves continuous updates to a RAG vector store and you require regular checks for new blind spots or gaps.
Avoid when
- If your application does not involve RAG vector stores or if you are not concerned with the completeness of the knowledge within these stores.
- When you are working with small datasets where manual inspection of knowledge gaps is feasible and more efficient than automated detection.
- Requirements:
- The tool requires Python and relevant dependencies to be installed.
Observed Sep 18, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (267d since push)
- As of Sep 18, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 18, 2026
- Security (OSV)
- No lockfile
- As of Sep 18, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install semantic-coverage PyPISimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A tool for identifying gaps and blind spots in Retrieval-Augmented Generation (RAG) vector stores, aiding in the evaluation of knowledge completeness.
Capability facts
- Languages
- python
Source: github.language · Sep 18, 2026
Categories
Tags
README
1. Installation ```bash 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.