import json from urllib.parse import parse_qs import httpx from yuqei_sdk import ( AiPlatformClient, AiQualityImprovementActionCreate, AiQualityImprovementActionUpdate, AiQualityLabelReviewUpdate, AiQualityRuleConfigUpdate, ContractReviewRequest, DocumentExtractionRequest, KnowledgeDocumentCreate, KnowledgeImportBatchCreate, KnowledgeReindexRequest, LegalQaRequest, ModelJobQualityReviewRequest, PromptTemplateCreate, PromptTemplatePublishRequest, PromptTemplateRollbackRequest, ProviderConfigCreate, ProviderConfigUpsertRequest, RequestContext, WorkerRunOnceRequest, ) def test_ai_platform_client_ignores_ambient_proxy_by_default() -> None: client = AiPlatformClient( "http://ai-platform.test", transport=httpx.MockTransport(lambda _request: httpx.Response(200, json={})), ) assert client._client._trust_env is False # noqa: SLF001 client.close() def test_ai_platform_client_posts_legal_qa_with_context_headers() -> None: captured_request: httpx.Request | None = None def handler(request: httpx.Request) -> httpx.Response: nonlocal captured_request captured_request = request return httpx.Response( 200, json={ "answer": "Use Article 19 and verify the contract term.", "citations": [ { "source_type": "law", "title": "Labor Contract Law", "reference": "Article 19 chunk-1", "quote": "Probation period must stay within the statutory limit.", "score": 0.82, "locator": "chunk-1", "document_id": "doc-1", "chunk_id": "chunk-1", "knowledge_base_id": "laws-cn", "keyword_score": 0.78, "vector_score": 0.87, "hybrid_score": 0.82, "retrieval_strategy": "hybrid", "matched_terms": ["probation"], } ], "primary_sources": [ { "source_type": "law", "title": "Labor Contract Law", "reference": "Article 19 chunk-1", "quote": "Probation period must stay within the statutory limit.", "score": 0.82, "locator": "chunk-1", "document_id": "doc-1", "chunk_id": "chunk-1", "knowledge_base_id": "laws-cn", "keyword_score": 0.78, "vector_score": 0.87, "hybrid_score": 0.82, "retrieval_strategy": "hybrid", "matched_terms": ["probation"], } ], "confidence": 0.72, }, ) client = AiPlatformClient( "http://ai-platform.test", transport=httpx.MockTransport(handler), service_key="ai-platform-service-key", # pragma: allowlist secret ) response = client.run_legal_qa( LegalQaRequest(question="How should probation period be set?"), context=RequestContext(trace_id="trace-1", tenant_id="tenant-1", user_id="user-1"), ) assert response.citations[0].reference == "Article 19 chunk-1" assert response.primary_sources[0].score == 0.82 assert response.primary_sources[0].hybrid_score == 0.82 assert response.primary_sources[0].retrieval_strategy == "hybrid" assert captured_request is not None assert captured_request.url.path == "/api/v1/legal/qa" assert captured_request.headers["X-Trace-Id"] == "trace-1" assert captured_request.headers["X-Tenant-Id"] == "tenant-1" assert captured_request.headers["X-User-Id"] == "user-1" assert captured_request.headers["X-AI-Platform-Service-Key"] == "ai-platform-service-key" def test_ai_platform_client_supports_model_job_operations() -> None: seen_requests: list[httpx.Request] = [] def job_payload(status: str = "queued") -> dict[str, object]: return { "id": "model-job-1", "tenant_id": "tenant-1", "operation": "legal_qa", "created_by": "user-1", "display_title": "付款期限如何约定？", "status": status, "stage": "queued" if status == "queued" else "cancelled_by_user", "progress_percent": 0 if status == "queued" else 100, "result": None, "attempt_count": 0, "max_attempts": 3, "retryable": status == "queued", "fallback_used": False, "quality_status": "pending", "quality_score": 0, "quality_labels": [], "queue_latency_ms": 0, "execution_latency_ms": 0, "total_latency_ms": 0, "recommended_poll_after_ms": 2000 if status == "queued" else 0, "created_at": "2026-08-26T08:00:00Z", "updated_at": "2026-08-26T08:00:00Z", } def handler(request: httpx.Request) -> httpx.Response: seen_requests.append(request) if request.url.path.endswith("/legal/qa/jobs"): return httpx.Response(202, json=job_payload()) if request.url.path.endswith("/model-jobs/history"): return httpx.Response(200, json={"items": [job_payload()], "next_cursor": None, "has_more": False}) if request.url.path.endswith("/model-jobs/review-queue"): return httpx.Response(200, json=[job_payload("awaiting_human")]) if request.url.path.endswith("/quality-reviews") and request.method == "POST": return httpx.Response(200, json={ "id": "model-review-1", "tenant_id": "tenant-1", "job_id": "model-job-1", "review_trigger": "high_risk", "reviewer_id": "user-1", "legal_fact_consistency": "pass", "citation_verifiability": "uncertain", "high_risk_omission": "not_detected", "disposition": "accepted", "feedback_text": "引用待正式法源库复核。", "quality_gate_version": "model-job-v1", "created_at": "2026-08-26T08:10:00Z", }) if request.url.path.endswith("/quality-reviews") and request.method == "GET": return httpx.Response(200, json=[]) if request.url.path.endswith("/cancel"): return httpx.Response(200, json=job_payload("cancelled")) if request.url.path.endswith("/model-jobs/operations"): return httpx.Response(200, json={ "metrics": { "window_hours": 168, "total": 1, "queued": 1, "running": 0, "succeeded": 0, "fallback": 0, "failed": 0, "awaiting_human": 0, "resolved": 0, "fallback_rate": 0, "failure_rate": 0, "manual_handoff_rate": 0, "retry_rate": 0, "queue_latency_p50_ms": 0, "queue_latency_p95_ms": 0, "execution_latency_p50_ms": 0, "execution_latency_p95_ms": 0, "total_latency_p50_ms": 0, "total_latency_p95_ms": 0, }, "alerts": [], "completed_sample_count": 0, "target_sample_count": 30, "remaining_sample_count": 30, "baseline_ready": False, "quality_passed": 0, "quality_needs_review": 0, "quality_failed": 0, "quality_pass_rate": 0, "review_required": 0, "review_completed": 0, "evaluation_run_id": "stratified-v1", "admission": { "running": 0, "queued": 1, "tenant_active_limit": 10, "user_active_limit": 3, "tenant_queue_limit": 25, "active_saturation_rate": 0.1, "queue_saturation_rate": 0.04, "accepting_submissions": True, }, "quality_segments": [], }) return httpx.Response(404) client = AiPlatformClient("http://ai-platform.test", transport=httpx.MockTransport(handler)) context = RequestContext(tenant_id="tenant-1", user_id="user-1") created = client.create_legal_qa_job( LegalQaRequest(question="付款期限如何约定？"), idempotency_key="submit-1", evaluation_run_id="stratified-v1", evaluation_case_id="purchase-01", evaluation_contract_type="purchase", evaluation_risk_level="high", evaluation_industry="manufacturing", context=context, ) history = client.list_model_jobs(operation="legal_qa", context=context) cancelled = client.cancel_model_job(created.id, reason="不再需要", context=context) operations = client.get_model_job_operations(evaluation_run_id="stratified-v1", context=context) review_queue = client.list_model_job_review_queue(context=context) review = client.create_model_job_quality_review( created.id, ModelJobQualityReviewRequest( legal_fact_consistency="pass", citation_verifiability="uncertain", high_risk_omission="not_detected", disposition="accepted", feedback_text="引用待正式法源库复核。", ), context=context, ) assert created.id == "model-job-1" assert history.items[0].display_title == "付款期限如何约定？" assert cancelled.status == "cancelled" assert operations.remaining_sample_count == 30 assert review_queue[0].status == "awaiting_human" assert review.reviewer_id == "user-1" assert seen_requests[0].headers["Idempotency-Key"] == "submit-1" assert seen_requests[0].headers["X-Evaluation-Contract-Type"] == "purchase" history_query = parse_qs(seen_requests[1].url.query.decode()) assert history_query["operation"] == ["legal_qa"] assert "status" not in history_query assert "cursor" not in history_query def test_ai_platform_client_posts_semantic_review_job_to_dedicated_path() -> None: captured: list[httpx.Request] = [] def handler(request: httpx.Request) -> httpx.Response: captured.append(request) return httpx.Response(202, json={ "id": "semantic-job-1", "tenant_id": "tenant-1", "operation": "contract_draft_semantic_review", "created_by": "user-1", "status": "queued", "stage": "queued", "progress_percent": 0, "result": None, "attempt_count": 0, "max_attempts": 2, "retryable": True, "fallback_used": False, "quality_status": "pending", "quality_score": 0, "quality_labels": [], "queue_latency_ms": 0, "execution_latency_ms": 0, "total_latency_ms": 0, "recommended_poll_after_ms": 2000, "created_at": "2026-09-10T00:00:00Z", "updated_at": "2026-09-10T00:00:00Z"}) class RequestStub: def model_dump(self, **_kwargs): return {"schema_version": "draft-semantic-review-request.v1", "candidate_id": "candidate-1"} client = AiPlatformClient("http://ai-platform.test", transport=httpx.MockTransport(handler)) job = client.create_contract_draft_semantic_review_job( RequestStub(), idempotency_key="semantic-submit-1", context=RequestContext(tenant_id="tenant-1", user_id="user-1")) assert job.operation == "contract_draft_semantic_review" and job.max_attempts == 2 assert captured[0].url.path == "/api/v1/legal/contract-draft-semantic-review/jobs" assert captured[0].headers["Idempotency-Key"] == "semantic-submit-1" def test_ai_platform_client_posts_contract_review_with_context_headers() -> None: captured_request: httpx.Request | None = None def handler(request: httpx.Request) -> httpx.Response: nonlocal captured_request captured_request = request return httpx.Response( 200, json={ "summary": "The contract has payment and acceptance risks.", "risk_level": "high", "risk_items": [ { "id": "risk-payment-acceptance", "title": "Payment and acceptance should be clarified.", "severity": "high", "suggestion": "Clarify delivery, acceptance period and payment trigger.", "related_clause": "Payment", "citation_refs": ["Civil Code Article 509"], } ], "citations": [ { "source_type": "law", "title": "Civil Code", "reference": "Article 509", "quote": "Parties shall fully perform their obligations.", } ], "primary_sources": [ { "source_type": "law", "title": "Civil Code", "reference": "Article 509", "quote": "Parties shall fully perform their obligations.", } ], "confidence": 0.78, "audit_run_id": "run-review-1", }, ) client = AiPlatformClient( "http://ai-platform.test", transport=httpx.MockTransport(handler), ) response = client.run_contract_review( ContractReviewRequest( contract_title="Technical Service Contract", contract_text="Payment is due after acceptance.", review_focus="payment and acceptance", ), context=RequestContext(trace_id="trace-review", tenant_id="tenant-1", user_id="contract-api"), ) assert response.risk_level == "high" assert response.risk_items[0].id == "risk-payment-acceptance" assert response.citations[0].reference == "Article 509" assert response.audit_run_id == "run-review-1" assert captured_request is not None assert captured_request.url.path == "/api/v1/legal/contract-review" assert captured_request.headers["X-Trace-Id"] == "trace-review" assert captured_request.headers["X-Tenant-Id"] == "tenant-1" assert captured_request.headers["X-User-Id"] == "contract-api" def test_ai_platform_client_extracts_document_text_with_context_headers() -> None: captured_request: httpx.Request | None = None def handler(request: httpx.Request) -> httpx.Response: nonlocal captured_request captured_request = request return httpx.Response( 200, json={ "status": "parsed", "progress": 100, "text": "[第 1 页]\n付款期限十日", "pages": [{"page_number": 1, "text": "付款期限十日"}], "page_count": 1, "ocr_status": "not_required", "message": "Document parsing completed.", }, ) client = AiPlatformClient("http://ai-platform.test", transport=httpx.MockTransport(handler)) response = client.extract_document_text( DocumentExtractionRequest( file_name="contract.txt", content_base64="dGVzdA==", content_type="text/plain", ), context=RequestContext(trace_id="trace-parse", tenant_id="tenant-1", user_id="contract-api"), ) assert response.status == "parsed" assert response.pages[0].page_number == 1 assert captured_request is not None assert captured_request.url.path == "/api/v1/document-intelligence/extract-text" assert captured_request.headers["X-Trace-Id"] == "trace-parse" assert captured_request.headers["X-Tenant-Id"] == "tenant-1" def test_ai_platform_client_covers_admin_data_endpoints() -> None: seen_paths: list[str] = [] def quality_action_payload( *, status: str = "open", resolution_note: str | None = None, resolved_at: str | None = None, ) -> dict[str, object]: return { "id": "qa-action-1", "dimension": "model", "key": "deepseek / deepseek-chat", "label": "deepseek / deepseek-chat", "action_type": "tune_model", "title": "Tune model config: deepseek / deepseek-chat", "description": "Check provider timeout and model configuration.", "owner": "admin", "priority": "high", "status": status, "source_label_codes": ["source_limited"], "source_run_ids": ["run-1"], "root_cause_hint": "Model quality issues are concentrated.", "linked_prompt_template_id": "prompt-1", "linked_prompt_template_version": "v2", "linked_knowledge_base_id": "laws-cn", "linked_import_batch_id": "kb-batch-1", "evaluation_days": 7, "baseline_total_runs": 1, "baseline_affected_runs": 1, "baseline_pending_labels": 1, "baseline_quality_score": 90.0, "current_total_runs": 1, "current_affected_runs": 1, "current_pending_labels": 1, "current_quality_score": 90.0, "post_change_total_runs": 2, "post_change_affected_runs": 0, "post_change_pending_labels": 0, "post_change_quality_score": 98.0, "post_change_affected_rate": 0.0, "post_change_observation_days": 3, "affected_rate_delta": 0.0, "pending_labels_delta": 0, "quality_score_delta": 0.0, "post_change_affected_rate_delta": -1.0, "post_change_pending_labels_delta": -1, "post_change_quality_score_delta": 8.0, "related_change_actions_count": 2, "effect_status": "unchanged", "effect_summary": "Baseline affected rate 100%, post-change 0% over 2 run(s); pending labels delta -1; quality score delta +8.0.", "resolution_note": resolution_note, "created_at": "2026-06-22T00:00:00Z", "updated_at": "2026-06-22T00:02:00Z", "resolved_at": resolved_at, } def handler(request: httpx.Request) -> httpx.Response: seen_paths.append(request.url.path) if request.url.path == "/api/v1/model-configs" and request.method == "POST": payload = json.loads(request.read().decode("utf-8")) assert payload["change_note"] == "Use production legal QA model." return httpx.Response( 200, json={ "id": "provider-1", "provider": "deepseek", "model": "deepseek-chat", "display_name": "DeepSeek Chat", "base_url": None, "temperature": 0.1, "max_tokens": 4096, "enabled": True, "is_default": True, "updated_at": "2026-06-22T00:00:00Z", }, ) if request.url.path == "/api/v1/model-configs/changes": return httpx.Response( 200, json=[ { "id": "provider-change-1", "provider_config_id": "provider-1", "provider": "deepseek", "model": "deepseek-chat", "display_name": "DeepSeek Chat", "before_config": None, "after_config": { "id": "provider-1", "provider": "deepseek", "model": "deepseek-chat", "display_name": "DeepSeek Chat", "base_url": None, "temperature": 0.1, "max_tokens": 4096, "enabled": True, "is_default": True, "updated_at": "2026-06-22T00:00:00Z", }, "changed_by": "admin", "change_note": "Use production legal QA model.", "created_at": "2026-06-22T00:00:00Z", } ], ) if request.url.path == "/api/v1/model-configs": return httpx.Response( 200, json=[ { "id": "provider-1", "provider": "deepseek", "model": "deepseek-chat", "display_name": "DeepSeek Chat", "base_url": None, "temperature": 0.1, "max_tokens": 4096, "enabled": True, "is_default": True, "updated_at": "2026-06-22T00:00:00Z", } ], ) if request.url.path == "/api/v1/prompt-templates" and request.method == "POST": return httpx.Response( 200, json={ "id": "prompt-1", "name": "legal_qa.default", "version": "v2", "content": "Search evidence first, then answer.", "enabled": True, "status": "draft", "rollback_from_version": None, "published_at": None, "updated_at": "2026-06-22T00:00:00Z", }, ) if request.url.path == "/api/v1/prompt-templates/legal_qa.default/publish": payload = json.loads(request.read().decode("utf-8")) assert payload["version"] == "v2" assert payload["changed_by"] == "admin" return httpx.Response( 200, json={ "id": "prompt-1", "name": "legal_qa.default", "version": "v2", "content": "Search evidence first, then answer.", "enabled": True, "status": "published", "rollback_from_version": None, "published_at": "2026-06-22T00:01:00Z", "updated_at": "2026-06-22T00:01:00Z", }, ) if request.url.path == "/api/v1/prompt-templates/legal_qa.default/rollback": payload = json.loads(request.read().decode("utf-8")) assert payload["from_version"] == "v1" return httpx.Response( 200, json={ "id": "prompt-rollback-1", "name": "legal_qa.default", "version": "v1-rollback", "content": "Use the stable prompt.", "enabled": True, "status": "published", "rollback_from_version": "v1", "published_at": "2026-06-22T00:02:00Z", "updated_at": "2026-06-22T00:02:00Z", }, ) if request.url.path == "/api/v1/prompt-templates/changes": assert request.url.params["prompt_template_id"] == "prompt-1" return httpx.Response( 200, json=[ { "id": "prompt-change-1", "prompt_template_id": "prompt-1", "name": "legal_qa.default", "version": "v2", "operation": "publish", "before_template": None, "after_template": { "id": "prompt-1", "name": "legal_qa.default", "version": "v2", "content": "Search evidence first, then answer.", "enabled": True, "status": "published", "rollback_from_version": None, "published_at": "2026-06-22T00:01:00Z", "updated_at": "2026-06-22T00:01:00Z", }, "changed_by": "admin", "change_note": "Publish prompt for quality action.", "created_at": "2026-06-22T00:01:00Z", } ], ) if request.url.path == "/api/v1/prompt-templates": return httpx.Response( 200, json=[ { "id": "prompt-1", "name": "legal_qa.default", "version": "v2", "content": "Search evidence first, then answer.", "enabled": True, "status": "published", "rollback_from_version": None, "published_at": "2026-06-22T00:01:00Z", "updated_at": "2026-06-22T00:00:00Z", } ], ) if request.url.path == "/api/v1/quality-rules/default" and request.method == "PUT": payload = json.loads(request.read().decode("utf-8")) assert payload["min_source_count"] == 1 return httpx.Response( 200, json={ "id": "default", "min_citation_count": 1, "min_source_count": 1, "high_token_threshold": 9000, "high_latency_ms": 12000, "critical_latency_ms": 24000, "warning_penalty": 7, "error_penalty": 21, "updated_at": "2026-06-22T00:00:00Z", }, ) if request.url.path == "/api/v1/quality-rules/default": return httpx.Response( 200, json={ "id": "default", "min_citation_count": 1, "min_source_count": 2, "high_token_threshold": 8000, "high_latency_ms": 15000, "critical_latency_ms": 30000, "warning_penalty": 10, "error_penalty": 30, "updated_at": "2026-06-22T00:00:00Z", }, ) if request.url.path == "/api/v1/runtime/worker-health": return httpx.Response( 200, json={ "status": "ok", "queue_counts": {"queued": 1, "running": 0, "succeeded": 2, "failed": 0}, "workers": [ { "worker_name": "knowledge-worker-1", "status": "ok", "queue_name": "yuqei-v2", "processed": 3, "succeeded": 2, "failed": 0, "error_message": None, "updated_at": "2026-06-22T00:00:00Z", } ], "stale_running_jobs": 0, "checked_at": "2026-06-22T00:00:01Z", }, ) if request.url.path == "/api/v1/runtime/worker-runs/run-once": payload = json.loads(request.read().decode("utf-8")) assert payload["limit"] == 1 assert payload["worker_name"] == "manual-sdk-worker" assert payload["triggered_by"] == "admin" return httpx.Response( 200, json={ "id": "worker-run-1", "worker_name": "manual-sdk-worker", "trigger_type": "manual", "triggered_by": "admin", "queue_name": "manual", "status": "succeeded", "limit": 1, "processed": 1, "succeeded": 1, "failed": 0, "error_message": None, "duration_ms": 15, "started_at": "2026-06-22T00:00:02Z", "completed_at": "2026-06-22T00:00:03Z", }, ) if request.url.path == "/api/v1/runtime/worker-runs": assert request.url.params["limit"] == "5" return httpx.Response( 200, json=[ { "id": "worker-run-1", "worker_name": "manual-sdk-worker", "trigger_type": "manual", "triggered_by": "admin", "queue_name": "manual", "status": "succeeded", "limit": 1, "processed": 1, "succeeded": 1, "failed": 0, "error_message": None, "duration_ms": 15, "started_at": "2026-06-22T00:00:02Z", "completed_at": "2026-06-22T00:00:03Z", } ], ) if request.url.path == "/api/v1/knowledge-import-batches" and request.method == "POST": return httpx.Response( 200, json={ "id": "kb-batch-1", "knowledge_base_id": "laws-cn", "source_name": "civil-code-lease", "source_type": "law_import", "status": "indexed", "documents_count": 0, "chunks_count": 0, "created_at": "2026-06-22T00:00:00Z", "updated_at": "2026-06-22T00:00:00Z", }, ) if request.url.path == "/api/v1/knowledge-bases/laws-cn/documents" and request.method == "POST": body = request.read() assert b'filename="lease.md"' in body assert b"Deposit refund rules" in body assert request.headers["X-Trace-Id"] == "trace-upload" return httpx.Response( 202, json={ "id": "ingest-upload-1", "knowledge_base_id": "laws-cn", "source_name": "lease.md", "source_type": "file_upload", "status": "queued", "parse_status": "pending", "ocr_status": "not_required", "import_batch_id": None, "document_id": None, "title": "Lease Upload", "reference": "lease.md", "mime_type": "text/markdown", "file_size_bytes": 43, "storage_backend": "local", "storage_bucket": "ai-platform-uploads", "storage_object_key": "knowledge-ingestion/test.md", "documents_count": 0, "chunks_count": 0, "retry_count": 0, "max_retries": 3, "error_message": None, "worker_name": None, "idempotency_key": None, "created_at": "2026-06-22T00:03:00Z", "updated_at": "2026-06-22T00:03:00Z", "started_at": None, "completed_at": None, }, ) if request.url.path == "/api/v1/knowledge-ingestion-jobs" and request.method == "GET": assert request.url.params["knowledge_base_id"] == "laws-cn" assert request.url.params["status"] == "queued" return httpx.Response( 200, json=[ { "id": "ingest-upload-1", "knowledge_base_id": "laws-cn", "source_name": "lease.md", "source_type": "file_upload", "status": "queued", "parse_status": "pending", "ocr_status": "not_required", "import_batch_id": None, "document_id": None, "title": "Lease Upload", "reference": "lease.md", "mime_type": "text/markdown", "file_size_bytes": 43, "storage_backend": "local", "storage_bucket": "ai-platform-uploads", "storage_object_key": "knowledge-ingestion/test.md", "documents_count": 0, "chunks_count": 0, "retry_count": 0, "max_retries": 3, "error_message": None, "worker_name": None, "idempotency_key": None, "created_at": "2026-06-22T00:03:00Z", "updated_at": "2026-06-22T00:03:00Z", "started_at": None, "completed_at": None, } ], ) if request.url.path == "/api/v1/knowledge-ingestion-jobs/ingest-upload-1/retry": assert request.url.params["triggered_by"] == "admin" return httpx.Response( 200, json={ "id": "ingest-upload-1", "knowledge_base_id": "laws-cn", "source_name": "lease.md", "source_type": "file_upload", "status": "queued", "parse_status": "pending", "ocr_status": "not_required", "import_batch_id": None, "document_id": None, "title": "Lease Upload", "reference": "lease.md", "mime_type": "text/markdown", "file_size_bytes": 43, "storage_backend": "local", "storage_bucket": "ai-platform-uploads", "storage_object_key": "knowledge-ingestion/test.md", "documents_count": 0, "chunks_count": 0, "retry_count": 1, "max_retries": 3, "error_message": None, "worker_name": "admin", "idempotency_key": None, "created_at": "2026-06-22T00:03:00Z", "updated_at": "2026-06-22T00:04:00Z", "started_at": None, "completed_at": None, }, ) if request.url.path == "/api/v1/knowledge-import-batches": return httpx.Response( 200, json=[ { "id": "kb-batch-1", "knowledge_base_id": "laws-cn", "source_name": "civil-code-lease", "source_type": "law_import", "status": "indexed", "documents_count": 1, "chunks_count": 1, "created_at": "2026-06-22T00:00:00Z", "updated_at": "2026-06-22T00:00:00Z", } ], ) if request.url.path == "/api/v1/knowledge-import-batches/kb-batch-1": return httpx.Response( 200, json={ "batch": { "id": "kb-batch-1", "knowledge_base_id": "laws-cn", "source_name": "civil-code-lease", "source_type": "law_import", "status": "indexed", "documents_count": 1, "chunks_count": 1, "created_at": "2026-06-22T00:00:00Z", "updated_at": "2026-06-22T00:00:00Z", }, "documents": [ { "id": "doc-1", "import_batch_id": "kb-batch-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "content": "A lease contract defines rent and use of leased property.", "index_status": "indexed", "created_at": "2026-06-22T00:00:00Z", } ], "chunks": [ { "id": "chunk-1", "document_id": "doc-1", "import_batch_id": "kb-batch-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "content": "A lease contract defines rent and use of leased property.", "chunk_index": 1, "locator": "chunk-1", "embedding_provider": "local-hash", "embedding_model": "local-hash-v1", "embedding_dimension": 64, "created_at": "2026-06-22T00:00:00Z", } ], }, ) if request.url.path == "/api/v1/knowledge-import-batches/kb-batch-1/documents": return httpx.Response( 200, json=[ { "id": "doc-1", "import_batch_id": "kb-batch-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "content": "A lease contract defines rent and use of leased property.", "index_status": "indexed", "created_at": "2026-06-22T00:00:00Z", } ], ) if request.url.path == "/api/v1/knowledge-import-batches/kb-batch-1/chunks": return httpx.Response( 200, json=[ { "id": "chunk-1", "document_id": "doc-1", "import_batch_id": "kb-batch-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "content": "A lease contract defines rent and use of leased property.", "chunk_index": 1, "locator": "chunk-1", "embedding_provider": "local-hash", "embedding_model": "local-hash-v1", "embedding_dimension": 64, "created_at": "2026-06-22T00:00:00Z", } ], ) if request.url.path == "/api/v1/knowledge-import-batches/kb-batch-1/reindex": return httpx.Response( 200, json={ "documents_count": 1, "chunks_count": 1, "embedding_provider": "openai-compatible", "embedding_model": "text-embedding-prod", "embedding_dimension": 1536, "document_ids": ["doc-1"], }, ) if request.url.path == "/api/v1/knowledge-documents" and request.method == "POST": return httpx.Response( 200, json={ "id": "doc-1", "import_batch_id": "kb-batch-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "content": "A lease contract defines rent and use of leased property.", "index_status": "indexed", "created_at": "2026-06-22T00:00:00Z", }, ) if request.url.path == "/api/v1/knowledge-documents/doc-1/chunks": return httpx.Response( 200, json=[ { "id": "chunk-1", "document_id": "doc-1", "import_batch_id": "kb-batch-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "content": "A lease contract defines rent and use of leased property.", "chunk_index": 1, "locator": "chunk-1", "embedding_provider": "local-hash", "embedding_model": "local-hash-v1", "embedding_dimension": 64, "created_at": "2026-06-22T00:00:00Z", } ], ) if request.url.path == "/api/v1/knowledge-documents/search": query = parse_qs(request.url.query.decode()) assert query["keyword"] == ["lease"] assert query["limit"] == ["3"] return httpx.Response( 200, json=[ { "id": "doc-1", "import_batch_id": "kb-batch-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "content": "A lease contract defines rent and use of leased property.", "index_status": "indexed", "created_at": "2026-06-22T00:00:00Z", } ], ) if request.url.path == "/api/v1/knowledge-documents/reindex": payload = json.loads(request.read().decode("utf-8")) assert payload["knowledge_base_id"] == "laws-cn" assert payload["triggered_by"] == "admin" return httpx.Response( 200, json={ "documents_count": 1, "chunks_count": 1, "embedding_provider": "openai-compatible", "embedding_model": "text-embedding-prod", "embedding_dimension": 1536, "document_ids": ["doc-1"], }, ) if request.url.path == "/api/v1/knowledge-chunks/search": return httpx.Response( 200, json=[ { "chunk": { "id": "chunk-1", "document_id": "doc-1", "import_batch_id": "kb-batch-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "content": "A lease contract defines rent and use of leased property.", "chunk_index": 1, "locator": "chunk-1", "embedding_provider": "local-hash", "embedding_model": "local-hash-v1", "embedding_dimension": 64, "created_at": "2026-06-22T00:00:00Z", }, "score": 0.82, "keyword_score": 0.76, "vector_score": 0.89, "hybrid_score": 0.82, "retrieval_strategy": "hybrid", "matched_terms": ["lease"], } ], ) if request.url.path == "/api/v1/knowledge-chunks/search/explain": query = parse_qs(request.url.query.decode()) assert query["keyword"] == ["lease"] assert query["limit"] == ["3"] assert query["candidate_limit"] == ["9"] assert query["strategy"] == ["hybrid"] return httpx.Response( 200, json={ "keyword": "lease", "normalized_terms": ["lease"], "retrieval_strategy": "hybrid", "knowledge_base_ids": [], "limit": 3, "candidate_limit": 9, "candidate_count": 1, "included_count": 1, "retrieval_backend": "json-explain", "embedding_provider": "local-hash", "embedding_model": "local-hash-v1", "query_embedding_dimension": 64, "keyword_threshold": 0.0, "vector_threshold": 0.35, "hybrid_keyword_weight": 0.55, "hybrid_vector_weight": 0.45, "results": [ { "chunk": { "id": "chunk-1", "document_id": "doc-1", "import_batch_id": "kb-batch-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "content": "A lease contract defines rent and use of leased property.", "chunk_index": 1, "locator": "chunk-1", "embedding_provider": "local-hash", "embedding_model": "local-hash-v1", "embedding_dimension": 64, "created_at": "2026-06-22T00:00:00Z", }, "score": 0.82, "keyword_score": 0.76, "vector_score": 0.89, "hybrid_score": 0.82, "retrieval_strategy": "hybrid", "matched_terms": ["lease"], "included": True, "rank": 1, "filter_reason": None, } ], }, ) if request.url.path == "/api/v1/audit/ai-runs": assert request.url.params.get("archive_status") == "archived" return httpx.Response( 200, json=[ { "id": "run-1", "operation": "legal_qa", "provider_config_id": "provider-1", "prompt_template_id": "prompt-1", "provider": "deepseek", "model": "deepseek-chat", "provider_status": "succeeded", "provider_error": None, "prompt_preview": "Search evidence first, then answer. Question: How should lease deposit refund be handled?", "question": "How should lease deposit refund be handled?", "answer": "The contract should clearly state deposit refund conditions.", "citations_count": 1, "retrieval_query": "How should lease deposit refund be handled?", "retrieval_strategy": "hybrid", "retrieved_chunk_ids": ["chunk-1"], "retrieved_sources": [ { "chunk_id": "chunk-1", "document_id": "doc-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "locator": "chunk-1", "score": 0.82, "keyword_score": 0.76, "vector_score": 0.89, "hybrid_score": 0.82, "retrieval_strategy": "hybrid", "matched_terms": ["lease"], } ], "prompt_tokens": 12, "completion_tokens": 8, "total_tokens": 20, "estimated_cost": 0.00028, "latency_ms": 12, "quality_status": "passed", "quality_score": 100, "quality_labels": [ { "code": "citation_present", "severity": "info", "title": "Citation present", "detail": "The answer returned 1 citation.", "review_status": "pending", "review_note": None, "reviewed_by": None, "reviewed_at": None, } ], "quality_summary": "passed: 0 error(s), 0 warning(s), score 100.", "redaction_status": "redacted", "redaction_policy": "default", "retention_until": "2026-06-30T00:00:00Z", "archive_status": "archived", "archived_at": "2026-07-01T00:00:00Z", "status": "succeeded", "created_at": "2026-06-22T00:00:00Z", } ], ) if request.url.path == "/api/v1/audit/ai-runs/run-1": return httpx.Response( 200, json={ "id": "run-1", "operation": "legal_qa", "provider_config_id": "provider-1", "prompt_template_id": "prompt-1", "provider": "deepseek", "model": "deepseek-chat", "provider_status": "succeeded", "provider_error": None, "prompt_preview": "Search evidence first, then answer. Question: How should lease deposit refund be handled?", "question": "How should lease deposit refund be handled?", "answer": "The contract should clearly state deposit refund conditions.", "citations_count": 1, "retrieval_query": "How should lease deposit refund be handled?", "retrieval_strategy": "hybrid", "retrieved_chunk_ids": ["chunk-1"], "retrieved_sources": [ { "chunk_id": "chunk-1", "document_id": "doc-1", "knowledge_base_id": "laws-cn", "title": "Civil Code", "source_type": "law", "reference": "Article 703", "locator": "chunk-1", "score": 0.82, "keyword_score": 0.76, "vector_score": 0.89, "hybrid_score": 0.82, "retrieval_strategy": "hybrid", "matched_terms": ["lease"], } ], "prompt_tokens": 12, "completion_tokens": 8, "total_tokens": 20, "estimated_cost": 0.00028, "latency_ms": 12, "quality_status": "passed", "quality_score": 100, "quality_labels": [ { "code": "citation_present", "severity": "info", "title": "Citation present", "detail": "The answer returned 1 citation.", "review_status": "pending", "review_note": None, "reviewed_by": None, "reviewed_at": None, } ], "quality_summary": "passed: 0 error(s), 0 warning(s), score 100.", "status": "succeeded", "created_at": "2026-06-22T00:00:00Z", }, ) if request.url.path == "/api/v1/audit/quality-dashboard": query = parse_qs(request.url.query.decode()) assert query["days"] == ["7"] assert query["pending_limit"] == ["5"] return httpx.Response( 200, json={ "summary": { "days": 7, "total_runs": 1, "passed_runs": 1, "needs_review_runs": 0, "failed_quality_runs": 0, "fallback_runs": 0, "source_issue_runs": 0, "token_issue_runs": 0, "latency_issue_runs": 0, "pending_labels": 1, "pass_rate": 1.0, "fallback_rate": 0.0, "source_issue_rate": 0.0, "latency_issue_rate": 0.0, }, "daily_metrics": [ { "date": "2026-06-22", "total_runs": 1, "passed_runs": 1, "needs_review_runs": 0, "failed_quality_runs": 0, "fallback_runs": 0, "source_issue_runs": 0, "token_issue_runs": 0, "latency_issue_runs": 0, "pending_labels": 1, "pass_rate": 1.0, "fallback_rate": 0.0, "source_issue_rate": 0.0, "latency_issue_rate": 0.0, } ], "pending_labels": [ { "run_id": "run-1", "label_code": "citation_present", "severity": "info", "title": "Citation present", "detail": "The answer returned 1 citation.", "review_status": "pending", "created_at": "2026-06-22T00:00:00Z", "question": "How should lease deposit refund be handled?", "provider": "deepseek", "model": "deepseek-chat", "quality_status": "passed", "quality_score": 100, } ], "attribution": { "by_model": [ { "dimension": "model", "key": "deepseek / deepseek-chat", "label": "deepseek / deepseek-chat", "total_runs": 1, "affected_runs": 1, "warning_labels": 1, "error_labels": 0, "pending_labels": 1, "fallback_runs": 0, "average_quality_score": 90.0, "top_label_codes": ["source_limited"], "root_cause_hint": "该模型相关质量异常较集中，优先对比其他模型的回答质量和成本。", } ], "by_prompt_template": [], "by_knowledge_base": [], "by_label_code": [ { "dimension": "label_code", "key": "source_limited", "label": "Source limited", "total_runs": 1, "affected_runs": 1, "warning_labels": 1, "error_labels": 0, "pending_labels": 1, "fallback_runs": 0, "average_quality_score": 90.0, "top_label_codes": ["source_limited"], "root_cause_hint": "来源数量偏少，优先补充文档或放宽检索召回策略。", } ], }, "improvement_actions": [quality_action_payload()], }, ) if request.url.path == "/api/v1/audit/quality-improvement-actions" and request.method == "POST": payload = json.loads(request.read().decode("utf-8")) assert payload["dimension"] == "model" assert payload["action_type"] == "tune_model" assert payload["source_label_codes"] == ["source_limited"] assert payload["linked_prompt_template_id"] == "prompt-1" return httpx.Response(200, json=quality_action_payload()) if request.url.path == "/api/v1/audit/quality-improvement-actions": query = parse_qs(request.url.query.decode()) assert query["status"] == ["open"] assert query["owner"] == ["admin"] assert query["priority"] == ["high"] assert query["action_type"] == ["tune_model"] assert query["dimension"] == ["model"] assert query["limit"] == ["5"] return httpx.Response(200, json=[quality_action_payload()]) if request.url.path == "/api/v1/audit/quality-improvement-actions/qa-action-1" and request.method == "GET": return httpx.Response(200, json=quality_action_payload()) if request.url.path == "/api/v1/audit/quality-improvement-actions/qa-action-1/executions": return httpx.Response( 200, json={ "action_id": "qa-action-1", "model_config_changes": [ { "id": "provider-change-1", "provider_config_id": "provider-1", "provider": "deepseek", "model": "deepseek-chat", "display_name": "DeepSeek Chat", "before_config": None, "after_config": { "id": "provider-1", "provider": "deepseek", "model": "deepseek-chat", "display_name": "DeepSeek Chat", "base_url": None, "temperature": 0.1, "max_tokens": 4096, "enabled": True, "is_default": True, "updated_at": "2026-06-22T00:00:00Z", }, "changed_by": "admin", "change_note": "Use production legal QA model.", "created_at": "2026-06-22T00:00:00Z", } ], "prompt_template_changes": [ { "id": "prompt-change-1", "prompt_template_id": "prompt-1", "name": "legal_qa.default", "version": "v2", "operation": "publish", "before_template": None, "after_template": { "id": "prompt-1", "name": "legal_qa.default", "version": "v2", "content": "Search evidence first, then answer.", "enabled": True, "status": "published", "rollback_from_version": None, "published_at": "2026-06-22T00:01:00Z", "updated_at": "2026-06-22T00:01:00Z", }, "changed_by": "admin", "change_note": "Publish prompt for quality action.", "created_at": "2026-06-22T00:01:00Z", } ], "knowledge_ingestion_jobs": [ { "id": "ingest-upload-1", "knowledge_base_id": "laws-cn", "source_name": "lease.md", "source_type": "file_upload", "status": "succeeded", "parse_status": "parsed", "ocr_status": "not_required", "import_batch_id": "kb-batch-1", "document_id": "doc-1", "documents_count": 1, "chunks_count": 1, "retry_count": 0, "max_retries": 3, "error_message": None, "worker_name": "api-sync-ingest", "idempotency_key": None, "created_at": "2026-06-22T00:02:00Z", "updated_at": "2026-06-22T00:02:00Z", "started_at": "2026-06-22T00:02:00Z", "completed_at": "2026-06-22T00:02:00Z", } ], "knowledge_reindex_executions": [ { "id": "reindex-1", "document_id": None, "knowledge_base_id": "laws-cn", "import_batch_id": "kb-batch-1", "documents_count": 1, "chunks_count": 1, "embedding_provider": "openai-compatible", "embedding_model": "text-embedding-prod", "embedding_dimension": 1536, "document_ids": ["doc-1"], "status": "succeeded", "triggered_by": "admin", "execution_note": "Rebuild index for quality action.", "created_at": "2026-06-22T00:03:00Z", } ], "related_trace_runs": [ { "id": "run-1", "operation": "legal_qa", "provider_config_id": "provider-1", "prompt_template_id": "prompt-1", "provider": "deepseek", "model": "deepseek-chat", "provider_status": "succeeded", "provider_error": None, "prompt_preview": "Search evidence first, then answer. Question: How should lease deposit refund be handled?", "question": "How should lease deposit refund be handled?", "answer": "The contract should clearly state deposit refund conditions.", "citations_count": 1, "retrieval_query": "How should lease deposit refund be handled?", "retrieval_strategy": "hybrid", "retrieved_chunk_ids": ["chunk-1"], "retrieved_sources": [], "prompt_tokens": 12, "completion_tokens": 8, "total_tokens": 20, "estimated_cost": 0.00028, "latency_ms": 12, "quality_status": "passed", "quality_score": 100, "quality_labels": [], "quality_summary": "passed: 0 error(s), 0 warning(s), score 100.", "status": "succeeded", "created_at": "2026-06-22T00:00:00Z", } ], "impacted_trace_runs": [ { "run_id": "run-2", "operation": "legal_qa", "provider": "deepseek", "model": "deepseek-chat", "quality_status": "passed", "quality_score": 96, "matched_label_codes": [], "issue_label_codes": [], "pending_label_codes": [], "created_at": "2026-06-22T00:06:00Z", } ], "effect_breakdown": [ { "label_code": "missing_citation", "current_total_runs": 3, "current_affected_runs": 1, "current_pending_labels": 1, "current_affected_rate": 0.3333, "post_change_total_runs": 1, "post_change_affected_runs": 0, "post_change_pending_labels": 0, "post_change_affected_rate": 0, "affected_rate_delta": -0.3333, "pending_labels_delta": -1, } ], }, ) if request.url.path == "/api/v1/audit/quality-improvement-actions/qa-action-1" and request.method == "PATCH": payload = json.loads(request.read().decode("utf-8")) assert payload["status"] == "resolved" assert payload["resolution_note"] == "Provider config was tuned." assert payload["linked_import_batch_id"] == "kb-batch-2" return httpx.Response( 200, json=quality_action_payload( status="resolved", resolution_note="Provider config was tuned.", resolved_at="2026-06-22T00:05:00Z", ), ) if request.url.path == "/api/v1/audit/ai-runs/run-1/quality-labels/citation_present": payload = json.loads(request.read().decode("utf-8")) assert request.method == "PATCH" assert payload["review_status"] == "resolved" return httpx.Response( 200, json={ "id": "run-1", "operation": "legal_qa", "provider_config_id": "provider-1", "prompt_template_id": "prompt-1", "provider": "deepseek", "model": "deepseek-chat", "provider_status": "succeeded", "provider_error": None, "prompt_preview": "Search evidence first, then answer. Question: How should lease deposit refund be handled?", "question": "How should lease deposit refund be handled?", "answer": "The contract should clearly state deposit refund conditions.", "citations_count": 1, "retrieval_query": "How should lease deposit refund be handled?", "retrieval_strategy": "hybrid", "retrieved_chunk_ids": ["chunk-1"], "retrieved_sources": [], "prompt_tokens": 12, "completion_tokens": 8, "total_tokens": 20, "estimated_cost": 0.00028, "latency_ms": 12, "quality_status": "passed", "quality_score": 100, "quality_labels": [ { "code": "citation_present", "severity": "info", "title": "Citation present", "detail": "The answer returned 1 citation.", "review_status": "resolved", "review_note": "已确认依据有效。", "reviewed_by": "admin", "reviewed_at": "2026-06-22T00:01:00Z", } ], "quality_summary": "passed: 0 error(s), 0 warning(s), score 100.", "status": "succeeded", "created_at": "2026-06-22T00:00:00Z", }, ) return httpx.Response(404) client = AiPlatformClient("http://ai-platform.test", transport=httpx.MockTransport(handler)) assert client.upsert_model_config( ProviderConfigUpsertRequest( provider="deepseek", model="deepseek-chat", display_name="DeepSeek Chat", temperature=0.1, is_default=True, changed_by="admin", change_note="Use production legal QA model.", ) ).model == "deepseek-chat" assert client.list_model_configs()[0].provider == "deepseek" model_changes = client.list_model_config_changes(provider_config_id="provider-1") assert model_changes[0].after_config.model == "deepseek-chat" assert model_changes[0].change_note == "Use production legal QA model." assert client.upsert_prompt_template( PromptTemplateCreate(name="legal_qa.default", version="v2", content="Search evidence first.") ).version == "v2" assert client.list_prompt_templates()[0].name == "legal_qa.default" assert client.publish_prompt_template( "legal_qa.default", PromptTemplatePublishRequest( version="v2", changed_by="admin", change_note="Publish prompt for quality action.", ), ).status == "published" prompt_changes = client.list_prompt_template_changes(prompt_template_id="prompt-1") assert prompt_changes[0].id == "prompt-change-1" assert prompt_changes[0].after_template.status == "published" rolled_back_prompt = client.rollback_prompt_template( "legal_qa.default", PromptTemplateRollbackRequest(from_version="v1", target_version="v1-rollback"), ) assert rolled_back_prompt.version == "v1-rollback" assert rolled_back_prompt.rollback_from_version == "v1" assert client.get_quality_rule_config().min_source_count == 2 assert client.upsert_quality_rule_config( AiQualityRuleConfigUpdate( min_citation_count=1, min_source_count=1, high_token_threshold=9000, high_latency_ms=12000, critical_latency_ms=24000, warning_penalty=7, error_penalty=21, ) ).warning_penalty == 7 runtime_health = client.get_worker_runtime_health() assert runtime_health.status == "ok" assert runtime_health.queue_counts["queued"] == 1 assert runtime_health.workers[0].worker_name == "knowledge-worker-1" manual_run = client.run_worker_once( WorkerRunOnceRequest(limit=1, worker_name="manual-sdk-worker", triggered_by="admin") ) assert manual_run.id == "worker-run-1" assert manual_run.processed == 1 assert manual_run.succeeded == 1 assert client.list_worker_run_executions(limit=5)[0].worker_name == "manual-sdk-worker" assert client.create_knowledge_import_batch( KnowledgeImportBatchCreate( knowledge_base_id="laws-cn", source_name="civil-code-lease", source_type="law_import", ) ).id == "kb-batch-1" upload_result = client.upload_knowledge_document( knowledge_base_id="laws-cn", filename="lease.md", content=b"Deposit refund rules must be written clearly.", title="Lease Upload", reference="lease.md", context=RequestContext(trace_id="trace-upload"), ) assert upload_result.id == "ingest-upload-1" assert upload_result.status == "queued" assert upload_result.parse_status == "pending" assert upload_result.title == "Lease Upload" assert upload_result.storage_object_key == "knowledge-ingestion/test.md" ingestion_jobs = client.list_knowledge_ingestion_jobs(knowledge_base_id="laws-cn", status="queued") assert ingestion_jobs[0].id == "ingest-upload-1" assert ingestion_jobs[0].storage_bucket == "ai-platform-uploads" assert client.retry_knowledge_ingestion_job("ingest-upload-1", triggered_by="admin").retry_count == 1 assert client.list_knowledge_import_batches(knowledge_base_id="laws-cn")[0].chunks_count == 1 batch_detail = client.get_knowledge_import_batch_detail("kb-batch-1") assert batch_detail.batch.id == "kb-batch-1" assert batch_detail.documents[0].id == "doc-1" assert batch_detail.chunks[0].id == "chunk-1" assert client.list_knowledge_import_batch_documents("kb-batch-1")[0].title == "Civil Code" assert client.list_knowledge_import_batch_chunks("kb-batch-1")[0].locator == "chunk-1" assert client.add_knowledge_document( KnowledgeDocumentCreate( import_batch_id="kb-batch-1", knowledge_base_id="laws-cn", title="Civil Code", source_type="law", reference="Article 703", content="A lease contract defines rent and use of leased property.", ) ).reference == "Article 703" assert client.list_knowledge_chunks("doc-1")[0].locator == "chunk-1" assert client.list_knowledge_chunks("doc-1")[0].embedding_provider == "local-hash" assert client.list_knowledge_chunks("doc-1")[0].embedding_model == "local-hash-v1" assert client.search_knowledge_documents("lease", limit=3)[0].title == "Civil Code" reindex_result = client.reindex_knowledge_documents( KnowledgeReindexRequest( knowledge_base_id="laws-cn", triggered_by="admin", execution_note="Rebuild index for quality action.", ) ) assert reindex_result.embedding_provider == "openai-compatible" assert reindex_result.embedding_dimension == 1536 assert reindex_result.document_ids == ["doc-1"] batch_reindex_result = client.reindex_knowledge_import_batch("kb-batch-1") assert batch_reindex_result.document_ids == ["doc-1"] chunk_result = client.search_knowledge_chunks("lease", limit=3) assert chunk_result[0].score == 0.82 assert chunk_result[0].hybrid_score == 0.82 explain = client.explain_knowledge_search("lease", limit=3, candidate_limit=9) assert explain.embedding_provider == "local-hash" assert explain.results[0].included is True assert explain.results[0].rank == 1 audit = client.list_ai_run_audits(limit=5, archive_status="archived")[0] assert audit.operation == "legal_qa" assert audit.provider == "deepseek" assert audit.model == "deepseek-chat" assert audit.provider_status == "succeeded" assert audit.provider_error is None assert audit.prompt_preview is not None assert "lease deposit" in audit.prompt_preview assert audit.prompt_tokens == 12 assert audit.completion_tokens == 8 assert audit.total_tokens == 20 assert audit.estimated_cost == 0.00028 assert audit.retrieved_chunk_ids == ["chunk-1"] assert audit.retrieved_sources[0].reference == "Article 703" assert audit.quality_status == "passed" assert audit.quality_score == 100 assert audit.quality_labels[0].code == "citation_present" assert audit.quality_labels[0].review_status == "pending" assert audit.quality_summary == "passed: 0 error(s), 0 warning(s), score 100." assert audit.redaction_status == "redacted" assert audit.redaction_policy == "default" assert audit.archive_status == "archived" assert audit.archived_at == "2026-07-01T00:00:00Z" audit_detail = client.get_ai_run_audit("run-1") assert audit_detail.id == "run-1" assert audit_detail.retrieved_sources[0].hybrid_score == 0.82 assert audit_detail.quality_labels[0].code == "citation_present" dashboard = client.get_quality_dashboard(days=7, pending_limit=5) assert dashboard.summary.pass_rate == 1.0 assert dashboard.daily_metrics[0].date == "2026-06-22" assert dashboard.pending_labels[0].run_id == "run-1" assert dashboard.attribution.by_model[0].key == "deepseek / deepseek-chat" assert dashboard.attribution.by_label_code[0].top_label_codes == ["source_limited"] assert dashboard.attribution.by_label_code[0].root_cause_hint is not None assert dashboard.improvement_actions[0].id == "qa-action-1" created_action = client.create_quality_improvement_action( AiQualityImprovementActionCreate( dimension="model", key="deepseek / deepseek-chat", label="deepseek / deepseek-chat", action_type="tune_model", description="Check provider timeout and model configuration.", owner="admin", priority="high", source_label_codes=["source_limited"], source_run_ids=["run-1"], linked_prompt_template_id="prompt-1", evaluation_days=7, ) ) assert created_action.status == "open" assert created_action.linked_prompt_template_id == "prompt-1" assert created_action.linked_import_batch_id == "kb-batch-1" assert created_action.baseline_affected_runs == 1 assert created_action.effect_status == "unchanged" assert created_action.post_change_total_runs == 2 assert created_action.related_change_actions_count == 2 listed_actions = client.list_quality_improvement_actions( status="open", owner="admin", priority="high", action_type="tune_model", dimension="model", limit=5, ) assert listed_actions[0].id == "qa-action-1" assert listed_actions[0].effect_summary is not None action_detail = client.get_quality_improvement_action("qa-action-1") assert action_detail.id == "qa-action-1" assert action_detail.source_run_ids == ["run-1"] assert action_detail.effect_summary is not None action_executions = client.get_quality_improvement_action_executions("qa-action-1") assert action_executions.model_config_changes[0].after_config.model == "deepseek-chat" assert action_executions.prompt_template_changes[0].version == "v2" assert action_executions.knowledge_ingestion_jobs[0].id == "ingest-upload-1" assert action_executions.knowledge_reindex_executions[0].import_batch_id == "kb-batch-1" assert action_executions.related_trace_runs[0].id == "run-1" assert action_executions.impacted_trace_runs[0].run_id == "run-2" assert action_executions.effect_breakdown[0].label_code == "missing_citation" assert action_executions.effect_breakdown[0].affected_rate_delta < 0 updated_action = client.update_quality_improvement_action( "qa-action-1", AiQualityImprovementActionUpdate( status="resolved", resolution_note="Provider config was tuned.", linked_import_batch_id="kb-batch-2", ), ) assert updated_action.status == "resolved" assert updated_action.resolved_at == "2026-06-22T00:05:00Z" assert updated_action.post_change_total_runs == 2 assert updated_action.effect_summary and "post-change" in updated_action.effect_summary updated_audit = client.update_ai_run_quality_label( "run-1", "citation_present", AiQualityLabelReviewUpdate( review_status="resolved", review_note="已确认依据有效。", reviewed_by="admin", ), ) assert updated_audit.quality_labels[0].review_status == "resolved" assert updated_audit.quality_labels[0].reviewed_at == "2026-06-22T00:01:00Z" assert "/api/v1/audit/ai-runs" in seen_paths assert "/api/v1/audit/ai-runs/run-1" in seen_paths assert "/api/v1/audit/quality-dashboard" in seen_paths assert "/api/v1/audit/quality-improvement-actions" in seen_paths assert "/api/v1/audit/quality-improvement-actions/qa-action-1" in seen_paths assert "/api/v1/audit/quality-improvement-actions/qa-action-1/executions" in seen_paths assert "/api/v1/quality-rules/default" in seen_paths