corpuslint 0.2.0

A linter for your RAG knowledge base

corpuslint

A linter for your RAG knowledge base. RAGAS & co. evaluate the answer. corpuslint evaluates the data that feeds it — before it reaches your users.

Why

Most bad RAG answers are a corpus problem, not a model problem: duplicates, near-duplicates, low-information chunks, size anomalies, embedding outliers, and contradictions. corpuslint scores your corpus and shows you exactly what to fix.

Install

pip install corpuslint            # core, runs offline and free
pip install "corpuslint[local]"   # + local embeddings (near-dupes, outliers)
pip install "corpuslint[llm]"     # + LLM contradiction check (OpenAI / Azure OpenAI)
pip install "corpuslint[azure]"   # + Azure AI Search source connector

Use

corpuslint ./docs                       # terminal report
corpuslint ./docs --html report.html    # shareable HTML
corpuslint ./docs --fail-under 70       # CI gate (exit 1 if score < 70)
corpuslint ./chunks.jsonl               # pre-chunked input

# LLM contradiction check (needs the [llm] extra + an API key):
export OPENAI_API_KEY=sk-...
corpuslint ./docs --llm                             # OpenAI, default gpt-4o-mini
corpuslint ./docs --llm --llm-model gpt-4o          # pick a model
corpuslint ./docs --llm --llm-max-pairs 50          # cap paid calls (default 200)

# Azure OpenAI — reads AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT and
# AZURE_OPENAI_API_VERSION (default 2024-10-21) from the environment.
# --llm-model is the Azure *deployment* name.
export AZURE_OPENAI_API_KEY=...  AZURE_OPENAI_ENDPOINT=https://<res>.openai.azure.com
corpuslint ./docs --llm --llm-provider azure --llm-model my-deployment

The contradiction check is O(n²): it prefilters candidate pairs by embedding similarity, then asks the LLM about each. --llm-max-pairs bounds how many pairs reach the LLM (highest-similarity first) so cost stays predictable; skipped pairs are reported (--llm-max-pairs 0 skips the LLM entirely).

Sources

By default corpuslint reads files and directories. It can also pull the corpus straight from a vector store and run the same checks on it.

Azure AI Search

Needs the [azure] extra. Reads the endpoint and admin/query key from the environment; the index is passed with --index:

pip install "corpuslint[azure]"
export AZURE_SEARCH_ENDPOINT=https://<service>.search.windows.net
export AZURE_SEARCH_API_KEY=<key>

corpuslint --source azure-search --index my-index
corpuslint --source azure-search --index my-index --content-field body --id-field key

It pages through every document in the index (no silent cap), maps each to a document whose source is azure-search://<index>/<id>, and feeds them through the normal chunking + check pipeline. --content-field (default content) selects the field holding the text; --id-field (default id) selects the id field. Documents missing the content field are skipped with a warning.

Checks

exact duplicates · near duplicates · low-information chunks · chunk-size anomalies · embedding outliers · contradictions (opt-in).

Config

Optional .corpuslint.yml overrides thresholds and check selection.

Architecture

Library-first: corpuslint.analyze(paths, config) -> Report. The CLI is a thin wrapper; an MCP server, Azure AI Search connector, eval-set generation, and drift monitoring are on the roadmap.

MIT licensed.