handovergap 0.1.5

Tacit Context Gap Detection for Handover-oriented RAG

HandoverGap RAG

日本語 | Repository version: README.ja.md

HandoverGap RAG detects tacit context that is missing from otherwise correct organizational memories.

Correct memories are not always transferable.

PyPI: https://pypi.org/project/handovergap/

Latest tested release: handovergap==0.1.5

Usage guide: https://masanori0209.github.io/handovergap/

A normal RAG system may retrieve:

For Company A, use CSV for this release. The API will come in the next phase.

The statement can be correct while still being unsafe for a successor who must answer customers. They may not know:

• whether the customer was informed;
• what “this release” covers;
• what support is authorized to promise;
• what fallback or escalation path to use.

HandoverGap performs successor-profile-conditioned slot checks, blocks unsafe transfer, and generates clarification questions. The packaged dataset uses Support, Engineering, and Sales handover profiles as examples; the thesis is not limited to those business functions.

What HandoverGap Adds

Approach	What it optimizes	What it misses
Naive RAG	Return a relevant memory	Whether a successor can safely act on it
Hybrid RAG	Add related evidence or risk warnings	Profile-specific missing context
General context engineering	Better prompts and context packaging	A durable audit trail for why an answer was withheld
HandoverGap RAG	Check profile-required slots before answering	Production tuning still needs real organizational annotation

The practical pattern is:

1. Treat correctness and transferability as separate checks.
2. Define required slots per successor responsibility profile.
3. Do not invent missing context; turn missing slots into questions.
4. Store the slot attempts, gaps, questions, and transfer decision so the stop reason is explainable.

Quickstart

pip install handovergap

handovergap demo
handovergap detect --scenario S001 --role CS
handovergap evaluate --compare

No TiDB account, OpenAI key, or external dataset is required.

Demo

Install the optional Streamlit UI:

pip install "handovergap[demo]"
handovergap serve

The demo defaults to Japanese and includes an English language switch. The default local-sample mode runs the real deterministic HandoverGap detector against bundled fictional handover cases. It compares:

• naive_rag: answers directly;
• hybrid_rag: adds related evidence;
• handovergap: withholds unsafe answers and asks questions.

For a live semantic slot-filling demo with OpenAI and TiDB audit persistence:

pip install "handovergap[live]"
handovergap serve

Set OPENAI_API_KEY plus either HANDOVERGAP_TIDB_URL or the TIDB_HOST / TIDB_USER / TIDB_PASSWORD environment variables. In Live OpenAI + TiDB mode, the app asks the selected model to fill profile-required slots, runs HandoverGap on those filled slots, and persists slot-fill attempts, context gaps, and transfer assessments to TiDB.

In the current MVP, CS, Engineer, and Sales are built-in role profiles used to demonstrate different successor responsibilities. They are not meant to imply that HandoverGap only works for those departments; custom role/slot taxonomies are the natural extension point beyond the packaged benchmark.

Evaluation

handovergap evaluate --compare runs the bundled synthetic HandoverGapBench mini dataset.

Method	Tacit Gap Recall	Unsafe Transfer Prevention	Question Coverage	Safe Transfer Allowance	Blocked Precision	False Clarification Rate
naive_rag	0.00	0.00	0.00	1.00	0.00	0.00
hybrid_rag	0.21	0.59	0.21	0.67	0.91	1.00
handovergap	1.00	1.00	1.00	1.00	1.00	0.00

These are deterministic results from the bundled 20-scenario dataset. The benchmark is synthetic and intentionally small; it demonstrates reproducible behavior rather than production accuracy.

For a small unknown holdout set with adjudicated synthetic reviewer labels and slot-filling stress profiles:

handovergap evaluate --dataset holdout --stress-filling

Method	Tacit Gap Recall	Unsafe Transfer Prevention	Question Coverage	Safe Transfer Allowance	Blocked Precision	False Clarification Rate
handovergap/provided	1.00	0.67	1.00	1.00	1.00	0.00
handovergap/conservative	1.00	0.67	1.00	0.67	0.67	1.00
handovergap/optimistic	0.64	0.67	0.64	1.00	1.00	0.00

The optimistic profile simulates an LLM over-filling ambiguous slots. It shows a real failure mode: recall drops, while unsafe-transfer prevention stays incomplete at 0.67.

The adversarial split breaks the structural alignment between provided_slots and gold_gaps:

handovergap evaluate --dataset adversarial --compare

Method	Tacit Gap Recall	Unsafe Transfer Prevention	Question Coverage	Safe Transfer Allowance	Blocked Precision	False Clarification Rate
naive_rag	0.00	0.00	0.00	1.00	0.00	0.00
hybrid_rag	0.25	0.67	0.25	1.00	1.00	0.00
handovergap	0.38	0.67	0.38	1.00	1.00	0.00

This is intentionally harder. It shows that simply increasing scenario count is not enough when labels and detector inputs share the same structure. In 0.1.5, HandoverGap also treats explicit evidence slots as filled, which reduces the adversarial false clarification rate from 0.67 to 0.00 without pretending recall is solved.

For a field-realistic but still non-sensitive dataset, use the sanitized split:

handovergap evaluate --dataset sanitized --compare

Method	Tacit Gap Recall	Unsafe Transfer Prevention	Question Coverage	Safe Transfer Allowance	Blocked Precision	False Clarification Rate
naive_rag	0.00	0.00	0.00	1.00	0.00	0.00
hybrid_rag	0.71	0.20	0.71	1.00	1.00	0.00
handovergap	1.00	1.00	1.00	1.00	1.00	0.00

The sanitized split is synthetic, but it is written like anonymized CRM notes, incident timelines, runbooks, release checklists, and deal reviews. It does not include real company, employee, customer, ticket, or account data.

With optional live OpenAI semantic slot filling:

python harness/validation/openai_slot_filling_check.py --dataset holdout --persist-tidb

Observed with gpt-4.1-mini: tacit gap recall 0.91, unsafe transfer prevention 0.33, safe transfer allowance 0.67, blocked precision 0.50. The detailed per-scenario output is saved to article/openai_slot_filling_results.json.

Observed with gpt-5-mini: tacit gap recall 0.45, unsafe transfer prevention 0.33, safe transfer allowance 0.67, blocked precision 0.50. The run used 1,901 input tokens and 8,136 output tokens, including 5,184 reasoning tokens, for an estimated cost of about $0.0167. This lower recall is intentional evidence in the repository: semantic slot filling is model- and prompt-sensitive, so HandoverGap should report the sensitivity instead of hiding it.

With the tuned gpt5_strict prompt profile for gpt-5-mini: tacit gap recall 1.00, unsafe transfer prevention 0.67, safe transfer allowance 1.00, blocked precision 1.00. This prompt is calibrated to the holdout evidence-summary protocol, so it is useful model-specific evidence rather than a production accuracy claim.

Optional TiDB Store

pip install "handovergap[tidb]"
handovergap schema --dialect tidb

from handovergap import TiDBStore

store = TiDBStore("mysql+pymysql://user:password@host:4000/handovergap")
store.create_schema()

The packaged schema models source evidence, memories, role requirements, slot-fill attempts, context gaps, clarification questions, transfer assessments, and evaluation runs. Live persistence methods are available for slot-fill attempts, context gaps, transfer assessments, and evaluation runs.

The TiDB-specific value is not only vector search. HandoverGap stores the whole decision path so a blocked answer can be traced with SQL:

handovergap audit-sql
handovergap audit-example
handovergap audit-benchmark --dataset all --iterations 100

That query joins transfer_assessments, memory_items, context_gaps, slot_fill_attempts, source_events, and clarification_questions. It answers the operational question: “this memory was retrieved, so exactly which required slot was missing, what evidence was checked, and what should we ask before handing it over?”

audit-example prints a compact blocked-transfer result table so the audit path can be reviewed without a live TiDB connection.

audit-benchmark measures local audit-row materialization for the bundled scenarios and reports row counts, blocked-transfer counts, top missing slots, and p50/p95 local runtime. It is not a TiDB latency claim; it sizes the audit workload that TiDB stores and queries.

Live TiDB Validation

After creating a TiDB Cloud cluster, open Connect, choose a public Python/SQLAlchemy-compatible connection, generate or reset the password, and export the connection values locally:

export TIDB_HOST="..."
export TIDB_PORT="4000"
export TIDB_USER="..."
export TIDB_PASSWORD="..."
export TIDB_DB_NAME="test"
export TIDB_CA_PATH="/path/to/ca-certificates.crt"

Then run:

python harness/validation/tidb_live_check.py --create-schema

The check creates the packaged schema if needed, writes one synthetic memory, persists a slot-fill attempt, a context gap, a transfer assessment, and the holdout stress evaluation runs, then prints row counts as JSON. Do not commit .env files or TiDB credentials.

Python API

from handovergap import HandoverGapDetector, InMemoryStore

store = InMemoryStore.from_builtin_dataset()
detector = HandoverGapDetector(store)
result = detector.detect(scenario_id="S001", successor_role="CS")

print(result.transferability_status)
print(result.gaps)
print(result.questions)

Development

python3 -m venv .venv
.venv/bin/python -m pip install -e ".[dev,demo]"
.venv/bin/pytest

Limitations

• The bundled detector and baselines are deterministic rules, not learned models.
• HandoverGapBench mini and holdout contain synthetic scenarios; the sanitized split is field-realistic but still synthetic and non-sensitive.
• Adding more scenarios alone does not prove production accuracy if required slots and gold gaps are structurally aligned; independent annotation is the next step.
• Slot-filling stress profiles simulate LLM variance; they are not a replacement for a live LLM evaluation.
• Live OpenAI slot filling is optional and not required for first-run usage.
• Live OpenAI slot filling is model-sensitive; current holdout results differ materially between gpt-4.1-mini and gpt-5-mini.
• The Streamlit demo uses fictional handover cases. Live OpenAI + TiDB mode exercises OpenAI slot filling and TiDB audit persistence, but it is still a local demo rather than a production retrieval service.
• Semantic equivalence scoring for generated questions is not implemented in the MVP.
• Live TiDB integration requires the optional tidb extra and a configured database.

License

MIT

日本語

HandoverGap RAGは、正しい業務記憶に不足している暗黙前提を、引き継ぎ先の責任範囲ごとに検出します。

正しい記憶でも、引き継げるとは限らない。

pip install handovergap
handovergap demo
handovergap detect --scenario S001 --role CS
handovergap evaluate --compare

Streamlitデモは日本語がデフォルトで、英語へ切り替えられます。

pip install "handovergap[demo]"
handovergap serve

詳細な日本語ドキュメントはREADME.ja.mdを参照してください。