Document QA With Citations and LangChain

A citation receipt that separates exact source support from relevance to the question.

Python 3.10+; pathlib, strings, offsets, dictionaries and callback functions. Optional LangChain dependencies are pinned separately. The core uses standard libraries. The grader checks your selected workspace; it never fills in missing behavior from the reference.

Build and run your version

From the repository root, initialize once. A fresh starter fails intentionally.

python3 scripts/project_test.py doc-qa-with-citations --init learning-artifacts/doc-qa-with-citations
python3 scripts/project_test.py doc-qa-with-citations --stage 1 --path learning-artifacts/doc-qa-with-citations --strict

Implement each stage, then run the cumulative grader and the supplied input driver:

python3 scripts/project_test.py doc-qa-with-citations --all --path learning-artifacts/doc-qa-with-citations --strict
cd learning-artifacts/doc-qa-with-citations
python3 cli.py samples/docs "When does cache expire?" --output answer.json --html answer.html

The driver and offline samples are provided scaffolding. Its imports resolve to your implementation. Public input types and function signatures live in the starter and API contract.

Inspect the reference separately

From the repository root:

python3 scripts/project_test.py doc-qa-with-citations --all --solution --strict
cd projects/doc-qa-with-citations/solution
python3 cli.py samples/docs "When does cache expire?" --output answer.json --html answer.html

Observe the change

A cache question selects the cache sentence and returns its exact source span, line and SHA-256. A query for zebras abstains; a verbatim banana quote for a cache question becomes needs_review.

Edit a copy of the sample and rerun the command. Keep the input beside the output so someone else can reproduce the result; the supplied samples are authored teaching data.

Integration and limits

Import query(directory, question, model). A model callback returns JSON {source:chunk_id,quote:exact_text}. --response accepts a recorded response and applies the same gate.

The default answerer is local and extractive. Lexical overlap cannot establish semantic entailment. A valid citation can still be irrelevant or outdated; the content hash lets a reader detect source changes.

Stages

1. Load local documents with stable provenance
2. Rank chunks with an inspectable keyword score
3. Accept only answers grounded in retrieved spans
4. Use a framework splitter without losing offsets

Optional framework integration

The baseline stages use only the standard library. The framework adapter is implemented and has a separate smoke test that uses the real installed SDK with a local fake model. It never contacts a cloud service.

python3 -m venv .venv
.venv/bin/python -m pip install -r projects/doc-qa-with-citations/requirements-framework.txt
.venv/bin/python scripts/project_test.py doc-qa-with-citations --solution --optional --strict
.venv/bin/python projects/doc-qa-with-citations/solution/framework_demo.py

The integration was verified against langchain-text-splitters==1.1.2. The cloud-provider path, where present, remains opt-in and requires your own environment credentials; no cloud deployment is performed.

The default grader covers the offline core and input integration. --optional adds 5 tests that use the real SDK and a deterministic local model. A missing SDK is reported as SKIP with an install hint; --optional --strict fails when the dependency is missing. Passing only the default tests does not claim framework verification.

Primary references