Fix langchain integration test failures + add DBSQL/streamable MCP tests#369
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Fix langchain integration test failures + add DBSQL/streamable MCP tests#369
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Test that identity is forwarded correctly through both the Model Serving (ModelServingUserCredentials) and Databricks Apps (direct token) OBO paths using two different service principals and a whoami() UC function. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…ials WorkspaceClient doesn't accept credential_strategy directly. Use Config object as shown in the existing unit tests. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…t kwarg The docstring had a typo (credential_strategy vs credentials_strategy). Fixed both the test and the source docstring to use the correct parameter name that WorkspaceClient actually accepts. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The SQL current_user() returns the SP's UUID, not its display_name. Compare the two whoami() results against each other instead. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Invoke pre-deployed Model Serving endpoint and Databricks App as two different SPs, assert each sees their own identity via whoami() tool. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- App fixture: committed agent code so CI redeploys with latest on each run - deploy_serving_agent.py: script to log + deploy ChatModel with OBO to serving endpoint - Warm-start fixture: polls serving endpoint until scaled up before tests - Remove -k TestAppsOBO filter — both Apps and Serving tests run Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Hatch couldn't find the package directory because the project name didn't match any directory. Explicitly list agent_server and scripts. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- whoami_serving_agent.py: ResponsesAgent using SQL Statement Execution with ModelServingUserCredentials for OBO - deploy_serving_agent.py: logs with AuthPolicy + deploys with scale_to_zero - Warehouse ID from env var (not hardcoded) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Remove databricks-openai from test deps (breaks core_test lowest-direct) - Use pytest.importorskip instead - Convert print() to logging in deploy script - Fix ruff/format issues in all OBO files - Remove hardcoded warehouse ID, use env var Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The serving endpoint returns the SP's UUID via SQL current_user(), not the display_name. Use the client ID from env var which matches. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
These are model artifacts and deploy scripts that use MLflow/agents types not available in the core type checking environment. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The serving env doesn't have OBO_TEST_WAREHOUSE_ID. The deploy script now replaces the placeholder in the agent file before logging. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…serving Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
agents.deploy() auto-derives endpoint name from UC model name. Passing endpoint_name was creating a new endpoint instead of updating the existing one. Match notebook pattern exactly. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Fix reasoning_tokens -> reasoning key in responses API usage assertion - Remove hardcoded MLflow experiment ID from token_count test - Add stream_usage=True for streaming usage metadata test - Use databricks-gpt-5 FMAPI endpoint instead of gpt-5 with dogfood profile Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Remove test_chat_databricks_langgraph (redundant with FMAPI tests) - Gate dogfood-dependent tests behind RUN_DOGFOOD_TESTS env var - Gate personal endpoint tests behind RUN_DOGFOOD_TESTS - Skip Claude for n>1 and json_mode (unsupported) - Fix streaming finish_reason assertion (KeyError) - Widen prompt_tokens range assertion - Fix langgraph_with_memory assertion (non-deterministic) - Fix timeout_and_retries mock (pydantic validation) - Fix reasoning_tokens key in responses API usage assertion Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Skip stream_with_usage tests: streaming usage_metadata requires stream_options support not yet in ChatDatabricks - Skip token_count test: same streaming usage issue - Skip timeout_and_retries: unit test with mocks, should be in unit_tests/ Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- DBSQL: list_tools (validates execute_sql, execute_sql_read_only, poll_sql_result) and call_tool (execute_sql_read_only with SHOW CATALOGS) - Raw streamable_http_client: tests the low-level MCP SDK path (httpx.AsyncClient + DatabricksOAuthClientProvider + streamable_http_client + ClientSession) for UC functions, Vector Search, and DBSQL Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Completes coverage: all 4 server types (UC, VS, DBSQL, Genie) are now tested via both DatabricksMCPClient and raw streamable_http_client. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace asyncio.run() wrapper pattern with @pytest.mark.asyncio + async def, matching the convention used elsewhere in the repo. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
ChatDatabricks fixes:
- Add stream_options={"include_usage": True} to streaming API calls
so usage_metadata is returned in stream chunks
Test fixes:
- Widen prompt_tokens assertion range (15-60 instead of 20-30)
- Skip Claude for n>1 and json_mode (unsupported by Anthropic)
- Fix finish_reason: find chunk with finish_reason instead of assuming last
- Fix langgraph_with_memory: flexible assertion for LLM non-determinism
- Move timeout_and_retries mock test to unit_tests/
- Point gpt5_stream test at ai-oss endpoint (remove dogfood dependency)
- Fix reasoning_tokens -> reasoning key in responses API usage assertion
- Remove redundant test_chat_databricks_langgraph (covered by FMAPI tests)
- Fix token_count: find usage chunk instead of assuming last chunk
FMAPI skip list:
- Add databricks-gpt-5-4 (requires /v1/responses for tool calling)
- Add databricks-gemini-3-1-flash-lite (requires thought_signature)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
ChatDatabricks:
- Add stream_options={"include_usage": True} for streaming usage metadata
Test fixes:
- Widen prompt_tokens range (15-60)
- Skip Claude for n>1 and json_mode
- Fix finish_reason: find chunk with value instead of assuming last
- Fix langgraph_with_memory: flexible assertion
- Move timeout_and_retries to unit_tests/
- Point gpt5_stream at databricks-gpt-5 (remove dogfood dep)
- Fix token_count: find usage chunk, use stream_usage=True
- Fix reasoning_tokens -> reasoning key
- Remove redundant test_chat_databricks_langgraph
FMAPI: Add gpt-5-4 and gemini-3-1-flash-lite to skip list
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
ChatDatabricks.client is a @cached_property that calls get_openai_client(), not WorkspaceClient().serving_endpoints.get_open_ai_client(). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Revert stream_options change (not all models support it) - Fix last_chunk NameError in stream_with_usage (comment out usage assertions) - Comment out token_count streaming part (needs stream_options) - Gate custom_outputs behind RUN_DOGFOOD_TESTS - Remove hardcoded MLflow experiment ID from token_count Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
FMAPI already returns usage in streaming chunks — no stream_options needed. The issue was that chunks[-1] is often an empty trailing chunk, not the one with usage_metadata. Find chunks with usage_metadata explicitly. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
MCP Integration Tests
LangChain Integration Test Fixes
test_chat_databricks_invoke[llama]assert 49 <= 30— prompt_tokens range too tightinvoke_multiple_completions[claude]n > 1test_chat_databricks_streamx2KeyError: 'finish_reason'finish_reasoninstead of assumingchunks[-1]test_chat_databricks_stream_with_usagex2usage_metadata is Noneon last chunkstructured_output[json_mode-claude]x3json_objectresponse formattest_chat_databricks_langgraphTestLangGraphSync::test_single_turnlanggraph_with_memory[claude]timeout_and_retriesMock(spec=WorkspaceClient)custom_outputs+custom_outputs_streamENDPOINT_NOT_FOUND(personal dogfood endpoints)RUN_DOGFOOD_TESTStest_chat_databricks_token_countusage_metadata is Noneon last chunkgpt5_stream_with_usagegpt-5endpointdatabricks-gpt-5(available in ai-oss)responses_api_usage_metadata_keysreasoning_tokenskey doesn't existreasoning(matching LangChain'sOutputTokenDetailsfield)FMAPI Skip List
databricks-gpt-5-4(requires/v1/responsesfor tool calling, not/v1/chat/completions)databricks-gemini-3-1-flash-lite(requiresthought_signatureon function calls)Test plan