Release candidate v1.10.0: bug-bash fixes on clean lineage (no V2)#167
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forrestmurray-db wants to merge 112 commits into
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Release candidate v1.10.0: bug-bash fixes on clean lineage (no V2)#167forrestmurray-db wants to merge 112 commits into
forrestmurray-db wants to merge 112 commits into
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Replace the hardcoded MODEL_MAPPING with a live API call to Databricks serving-endpoints. The backend uses async httpx to avoid blocking the event loop, and the frontend fetches models via useAvailableModels and builds options dynamically with buildModelOptions. All components now store and pass endpoint names directly instead of translating between display names and backend names. Also switches model prefetching from an eager useEffect in WorkflowContext to intent-based prefetchQuery on hover/focus of navigation buttons, and clears Databricks auth env vars that can override token auth in the MLflow intake service. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace stale hasMlflowConfig references in DiscoveryAnalysisTab with modelOptions.length checks to match the switch to dynamic model listing. Fix discovery-complete endpoint returning 404 for facilitators whose workshop_id is NULL by also checking against workshop.facilitator_id. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Prevent worktree contents from being tracked. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…vice init Add a public resolve_databricks_token() function that uses the Databricks SDK for auth (service principal on Apps, CLI profile locally) with a fallback to DATABRICKS_TOKEN env var. Remove the token_storage/db_service fallback chain from DatabricksService.__init__. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
MLflow uses whatever Databricks auth the SDK provides. Stop setting DATABRICKS_TOKEN in the environment — only set DATABRICKS_HOST so the SDK knows which workspace to target. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Mark databricks_token as deprecated with empty default in Python models (MLflowIntakeConfig, MLflowIntakeConfigCreate, DBSQLExportRequest, DatabricksConfig) and optional in TypeScript models. SDK auth is used instead. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…outer Replace 10+ token_storage.get_token / db_service.get_databricks_token fallback chains with resolve_databricks_token(). Remove all os.environ["DATABRICKS_TOKEN"] mutations. Update test mocks to patch resolve_databricks_token instead of token_storage. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…outers Update discovery_service (7 refs), judge_service, draft_rubric_grouping, database_service, databricks router, dbsql_export router. Remove set/get_databricks_token methods from database_service. Update test mocks to patch resolve_databricks_token. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove the token persistence infrastructure: - DatabricksTokenDB SQLAlchemy model from database.py - databricks_tokens from postgres_manager ALLOWED_TABLES and CREATE TABLE - DatabricksTokenDB import from database_service.py - test_token_storage_service.py (5 tests for deleted functionality) - Update postgres_manager test expectations token_storage_service.py is kept for Custom LLM API key storage. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…Page Users no longer need to provide Databricks tokens — the backend uses SDK auth (service principal on Apps, CLI profile locally). Remove all token state, localStorage persistence, form fields, and validation from both pages. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove os.environ["DATABRICKS_TOKEN"] and DATABRICKS_CLIENT_ID/SECRET pop() calls from alignment_service, judge_service, dbsql_export_service, and database_service. The SDK handles auth automatically — only DATABRICKS_HOST needs to be set for MLflow to know which workspace. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
AUTHENTICATION_SPEC: - Rewrite Architecture Context to describe the two-layer model accurately - Add new "Databricks API Authentication" section with token resolution contract, environment-specific behavior, MLflow auth, and what was removed - Add "Future: Per-User Auth" subsection for OBO pattern - Add 8 success criteria for Databricks API auth - Mark SDK Auth Migration as complete in implementation log BUILD_AND_DEPLOY_SPEC: - Mark DATABRICKS_TOKEN as optional (SDK auth preferred) in env vars table - Update Databricks Apps Authentication section to reference resolve_databricks_token() and link to AUTHENTICATION_SPEC JUDGE_EVALUATION_SPEC: - Fix troubleshooting note: "host, token" → "host, experiment ID + SDK auth" - Add SDK Auth Migration to implementation log README.md: - Add keyword index entries: PAT, SDK auth, resolve_databricks_token, service principal, DATABRICKS_TOKEN, DATABRICKS_CLIENT_ID, OAuth, CLI profile Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Document the Databricks resources the app's service principal needs access to: MLflow Experiment (Can edit), Model Serving Endpoints (Can query), SQL Warehouse (Can use), Unity Catalog Volume (Can read and write). Note which are required vs optional. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Lakebase (PostgreSQL) is the primary production database. Its OAuth tokens are refreshed via WorkspaceClient().config.oauth_token() every 15 minutes. Split permissions into core (Lakebase, MLflow, Serving Endpoints) vs optional (SQL Warehouse, UC Volume). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
AUTHENTICATION_SPEC: - Add "Lakebase Connection Pool" section with token lifecycle, do_connect injection pattern, required pool settings, credential API, and setup prerequisites — all with links to Databricks docs - Update Lakebase row in permissions table to reference generate_database_credential - Add 7 Lakebase connection pool success criteria - Add implementation log entry BUILD_AND_DEPLOY_SPEC: - Add Lakebase env vars (PGHOST, PGDATABASE, PGUSER, PGPORT, PGSSLMODE, PGAPPNAME, ENDPOINT_NAME, DATABASE_ENV) to environment variables table - Add implementation log section with SDK auth and Lakebase pool entries Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…ings Replace the creator-based connection factory with the recommended do_connect event pattern from Databricks docs. Key changes: - OAuthTokenManager → LakebaseCredentialManager using generate_database_credential(endpoint=ENDPOINT_NAME) API - Token injection via do_connect event (not creator callable) - pool_recycle: 300s → 3600s (was causing excessive connection churn) - pool_pre_ping: True → False (conflicts with do_connect injection) - max_overflow: 10 → 5 (caps at 20 total across 2 workers) - postgres_manager: pool created once with custom OAuthConnection class, never recreated on token refresh - database.py: _reset_connection_pool no longer calls force_refresh Reference: https://docs.databricks.com/aws/en/lakebase/connect/custom-app.html Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove databricks_token from CSV upload body type, make DatabricksConfig.token optional, update ApiService/WorkshopsService docstrings to reflect SDK auth. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When Lakebase is added as a Databricks App resource, the platform automatically creates a Postgres role for the service principal. Manual databricks_create_role() is only needed for external/additional identities outside the App resource integration. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…ndency - Add summarization_enabled, summarization_model, summarization_guidance columns to WorkshopDB - Add summary (JSON) column to TraceDB for structured milestone views - Add corresponding Pydantic model fields and DB service methods - Add pydantic-ai-slim[openai] dependency - Create TRACE_SUMMARIZATION_SPEC with success criteria - Create implementation plan Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
… with batch support Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…raceViewer Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…ingestion
- PUT /workshops/{id}/summarization-settings for facilitator config
- POST /workshops/{id}/resummarize for on-demand re-summarization
- Background summarization triggered after MLflow trace ingestion
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…odelOptions The settings agent used a function name that doesn't exist in modelMapping.ts. Fixed to follow the same pattern as other components: useAvailableModels() + buildModelOptions(). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…s fork The FastAPI lifespan bootstrap ran migrations in each worker process, requiring interprocess locks and never applying new migrations after initial deploy. Move migration execution to gunicorn's on_starting hook which runs exactly once in the master process before any workers fork. 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>
# Conflicts: # specs/BUILD_AND_DEPLOY_SPEC.md
…nd tasks - Use resolve_databricks_token() instead of stored PAT (SDK auth compat) - Create new SessionLocal() inside background tasks to avoid using the request-scoped DB session after it's closed - Add logging for summarization completion Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…mpatibility (#148) Migrations 0002, 0004, and 0010 used _is_postgres() to branch between sa.text("0")/sa.text("FALSE") for boolean server_default values. This check returned False on Lakebase (likely due to render_as_batch=True context), causing PostgreSQL to reject DEFAULT 0 for BOOLEAN columns with DatatypeMismatch. Replace with sa.false()/sa.true() — SQLAlchemy's dialect-agnostic boolean literals that render correctly on both SQLite and PostgreSQL. This matches the pattern already used by migration 0021. Strengthened the guard test to reject the _is_postgres() branching pattern and require sa.true()/sa.false() going forward. Co-authored-by: Isaac Co-authored-by: Max Fisher <max.fisher+data@databricks.com>
- Disable strict tool definitions on the trace summarization model profile so the Databricks OpenAI-compat shim accepts requests across Claude 4.6/4.7, gpt-5, gpt-5-codex, and Gemini Flash 3.5 (the shim rejects "tools.N.custom.strict" regardless of backing model). - Set litellm.drop_params=True at DSPy LM construction so hardcoded sampling params (temperature=0.2/0.3 in discovery + followup) don't 400 on gpt-5 reasoning models that require temperature=1. - Add a workspace-keyed TTL cache to DatabricksService.list_serving_endpoints with concurrent-request deduplication via per-key asyncio.Lock; the frontend's per-workshop React Query cache was triggering frequent upstream refetches of a workspace-global list. TTL configurable via DATABRICKS_ENDPOINTS_CACHE_TTL_S (default 300s). Co-authored-by: Isaac
* Add Docusaurus docs at /docs with spec coverage and local search. Serve built docs from FastAPI even when Lakebase is not configured, gate the app on setup status, and embed spec coverage in all specifications. * Fix docs/app navigation and prevent localhost redirect on Databricks Apps. Rewrite internal redirect Location headers, add cross-links between the workshop UI and Docusaurus, and keep setup redirects on the public app hostname.
Resolves the Lakebase pool-exhaustion cascade in gh#163. Three things were compounding under concurrent workshop load: 1. ENDPOINT_NAME was never bound in app.yaml, so credential generation silently fell back to a workspace OAuth token instead of using generate_database_credential(endpoint=...). Adds `valueFrom: postgres` binding and removes the silent fallback — missing ENDPOINT_NAME now fails loudly at engine creation. 2. _is_connection_error() matched the substring "connection timed out", which is also present in SQLAlchemy QueuePool TimeoutError. Pool saturation was being misclassified as a transient connection error, triggering engine.dispose() during the retry path and dropping in-flight connections held by other concurrent requests — amplifying the outage. TimeoutError is now explicitly rejected; DatabaseErrorMiddleware still returns a clean 503 for the client. 3. pool_pre_ping=True added a SELECT 1 per checkout (increasing checkout latency under contention) and pool_recycle=2700 forced unnecessary connection churn. Both realigned to AUTHENTICATION_SPEC.md:118-119 (pool_pre_ping=False, pool_recycle=3600). Co-authored-by: Isaac
Two client-side fixes for Gemini-backed Databricks serving endpoints,
plus an integration test that pins the cross-provider API matrix.
- Replace null `id` on chat completion responses with a placeholder so
OpenAI SDK 2.x's Pydantic validator doesn't reject. Installed as an
httpx response hook on the shared OpenAI client; mutates only chat
completion shapes (other JSON like endpoint listings passes through).
- Normalize `message.content` when it arrives as Gemini's array of part
dicts (`[{type:"text", text:..., thoughtSignature:...}]`) into a plain
string so `discovery_analysis_service`, `rubric_generation_service`,
and similar callers don't have to special-case Gemini.
- Add an integration test that probes every model in the workshop's
picker against both Chat Completions and Responses API. Pins the
design constraint that Databricks' Responses API passthrough is
OpenAI-only (Claude/Gemini/Llama reject by design, so they must stay
on Chat Completions). Skipped automatically when Databricks creds
aren't configured; runnable on demand via `just test-integration`.
Co-authored-by: Isaac
Trace summarization on Gemini 3.5 Flash via Databricks' OpenAI-compat shim breaks on the second turn — the OpenAI Chat Completions wire format has no slot for Gemini's ``thought_signature``, and Gemini 3+ requires it round- tripped per turn. Route Gemini through the native passthrough at ``/ai-gateway/gemini`` using pydantic-ai's ``GoogleModel``, which handles ``thought_signature`` natively. Other foundation models (Claude, gpt-5, Llama) keep going through the OpenAI shim with ``OpenAIChatModel``. - ``TraceSummarizationService`` detects Gemini-family endpoint names at construction and builds ``GoogleModel`` over a ``google.genai.Client`` pointed at the workspace's ai-gateway/gemini URL with the Databricks token in the Authorization header. - Force httpx transport via ``HttpOptions.httpx_async_client``. google- genai prefers aiohttp when both are installed, which silently bypasses our request hook (see ``_use_aiohttp`` in google.genai._api_client). - Install an httpx request hook that strips ``id`` from outgoing ``functionCall``/``functionResponse`` parts before they reach the ai-gateway. Vertex AI's ``FunctionCall`` proto has no ``id`` field, but the google-genai SDK adds one when echoing the model's previous tool call back; the ai-gateway is a pure passthrough that doesn't strip it, so multi-turn requests 400 without this. The hook rewrites ``request.stream`` (where httpx actually reads body from), not just ``_content``, otherwise the wire body still carries the original ``id`` while Content-Length reflects the new size. - Add ``pydantic-ai-slim[google]`` extra to bring in ``google-genai``. Tests: - Unit: model-routing dispatch (Gemini → GoogleModel, others → OpenAIChatModel), Gemini client's base_url points at ai-gateway, function_call/function_response id strip, no-op on simple text turns. - Integration: live multi-turn summarization against ``databricks-gemini-3-5-flash``. Exercises the full chain end-to-end and acts as a regression guard if Databricks ships changes to the ai-gateway proto. Co-authored-by: Isaac
…dation Follow-up to 53d9e70. The previous fix bound ENDPOINT_NAME via `valueFrom: postgres`, but the Apps platform exposes the Lakebase endpoint identifier under the default resource alias `database`. `valueFrom: postgres` resolved to an empty string at runtime, and db_bootstrap.py — which runs in the gunicorn on_starting hook before create_engine_for_backend() — bypassed the engine-creation guard and handed the empty value straight to the SDK, crashing app startup. - app.yaml: `valueFrom: postgres` → `valueFrom: database`. - LakebaseCredentialManager.get_password() now validates the endpoint argument itself, so all three call sites (do_connect handler, db_bootstrap, postgres_manager) surface the same actionable error on misconfiguration rather than the opaque SDK protobuf failure. Co-authored-by: Isaac
The /discovery-comments/stream and /discovery-agent-runs/{id}/stream
routes bound `db: Session = Depends(get_db)`, holding one pool
connection per subscribed EventSource for the entire stream lifetime.
Each DiscoveryTraceCard opens an EventSource for the comments stream
(always) plus the agent-run stream (while a run is active), so a
single user with ~10 visible trace cards already approaches the
pool ceiling (5+5 per worker × 2 workers = 20). Combined with any
background-worker connections, this is what was driving the
production cascade on gh#163 after pool-timeout retries were
neutered in 53d9e70.
Refactor both routes to acquire SessionLocal() per poll iteration
and release before the sleep, so connection holding time drops from
the stream lifetime to single-digit milliseconds per query.
Co-authored-by: Isaac
Production reported repeated 502s from discovery analysis on Gemini:
``server received an invalid response from an upstream server``. The
OpenAI-compat shim at ``/serving-endpoints/chat/completions`` translates
Vertex AI responses into OpenAI shape, but for some Gemini outputs
(safety blocks, certain content-part configurations) that translation
fails and the shim returns a 502 instead of a usable response.
Route Gemini chat completions through Databricks' native ai-gateway/gemini
passthrough using ``google.genai.Client.models.generate_content``. The
adapter returns the chat-completions dict shape callers already expect,
so ``discovery_analysis_service`` and ``rubric_generation_service`` work
unchanged. Other foundation models (Claude, gpt-5, Llama) stay on the
OpenAI-compat shim — they don't have the response-shape issues that
trip the translator for Gemini.
- ``DatabricksService.call_chat_completion`` detects Gemini endpoint
names and dispatches to ``_call_gemini_chat_via_ai_gateway``.
- ``_get_gemini_client`` lazily builds and caches one
``google.genai.Client`` per workspace, pointed at
``{workspace}/ai-gateway/gemini`` with the Databricks token in the
Authorization header.
- Helpers ``_messages_to_genai_contents`` and ``_genai_response_to_chat_shape``
translate between OpenAI chat messages and Gemini ``Content``
objects / ``GenerateContentResponse``. System messages collapse into
``system_instruction``; response text parts concatenate into the
chat-completions string content.
- The existing ``_normalize_shim_content`` safety net stays in place
for any non-Gemini model that ever returns array-shaped content.
Tests:
- Unit: Gemini endpoint names dispatch to the ai-gateway helper (and
must NOT touch the OpenAI client); non-Gemini endpoints stay on the
OpenAI client; helpers correctly translate messages and responses.
- Integration: live Gemini chat completion via ai-gateway returns a
plain string content (the discovery_analysis_service contract).
Co-authored-by: Isaac
The Gemini ai-gateway routing for trace summarization and discovery
analysis depends on the ``google.genai`` package, brought in via
``pydantic-ai-slim[google]`` in pyproject.toml. uv.lock was already
updated, but requirements.txt — which the Databricks app build uses
(``uv pip install -r requirements.txt``) — wasn't. The deployed app
failed at runtime with ``No module named 'google.genai'``.
Regenerated via:
uv export --format requirements-txt --no-emit-project -o requirements.txt
Co-authored-by: Isaac
Production hit 400 on discovery analysis with gpt-5.5:
"Unsupported value: 'temperature' does not support 0.3 with this
model. Only the default (1) value is supported."
OpenAI reasoning models (gpt-5 / gpt-5.1 / gpt-5.5 / gpt-5-codex and the
o1/o3/o4 series) reject any temperature != 1. LiteLLM has ``drop_params``
to handle this transparently on the DSPy path (already enabled in
``discovery_dspy._configure_litellm_drop_params``), but the OpenAI Python
SDK that ``DatabricksService.call_chat_completion`` uses has no
equivalent — we have to normalize the request ourselves.
- Add ``_is_openai_reasoning_model`` detector matching ``gpt-5``,
``o1``, ``o3``, ``o4`` endpoint names (with or without the
``databricks-`` prefix).
- Add ``_normalize_request_for_reasoning_model`` which forces
``temperature=1.0`` for detected reasoning models and logs the
override for auditability.
- Apply normalization in both ``call_chat_completion`` and
``call_serving_endpoint`` so all caller paths benefit.
Verified live against dogfood-staging: ``databricks-gpt-5`` and
``databricks-gpt-5-mini`` now return content for a discovery-analysis-
shaped request that previously 400'd.
Tests:
- Parametrized detector tests covering gpt-5, gpt-5-codex, gpt-5.1,
gpt-5.5, o1-preview, o3-mini, o4-mini.
- Negative tests confirming Claude / Llama / Gemini / gpt-4o are NOT
treated as reasoning models.
- Unit test for the normalization helper.
- End-to-end test that call_chat_completion forwards temperature=1.0
to the OpenAI client even when the caller passed 0.3.
Co-authored-by: Isaac
…162) * fix(llm): enable cross-provider interop and cache serving endpoints - Disable strict tool definitions on the trace summarization model profile so the Databricks OpenAI-compat shim accepts requests across Claude 4.6/4.7, gpt-5, gpt-5-codex, and Gemini Flash 3.5 (the shim rejects "tools.N.custom.strict" regardless of backing model). - Set litellm.drop_params=True at DSPy LM construction so hardcoded sampling params (temperature=0.2/0.3 in discovery + followup) don't 400 on gpt-5 reasoning models that require temperature=1. - Add a workspace-keyed TTL cache to DatabricksService.list_serving_endpoints with concurrent-request deduplication via per-key asyncio.Lock; the frontend's per-workshop React Query cache was triggering frequent upstream refetches of a workspace-global list. TTL configurable via DATABRICKS_ENDPOINTS_CACHE_TTL_S (default 300s). Co-authored-by: Isaac * fix(db): wire ENDPOINT_NAME binding and stop pool-timeout cascade Resolves the Lakebase pool-exhaustion cascade in gh#163. Three things were compounding under concurrent workshop load: 1. ENDPOINT_NAME was never bound in app.yaml, so credential generation silently fell back to a workspace OAuth token instead of using generate_database_credential(endpoint=...). Adds `valueFrom: postgres` binding and removes the silent fallback — missing ENDPOINT_NAME now fails loudly at engine creation. 2. _is_connection_error() matched the substring "connection timed out", which is also present in SQLAlchemy QueuePool TimeoutError. Pool saturation was being misclassified as a transient connection error, triggering engine.dispose() during the retry path and dropping in-flight connections held by other concurrent requests — amplifying the outage. TimeoutError is now explicitly rejected; DatabaseErrorMiddleware still returns a clean 503 for the client. 3. pool_pre_ping=True added a SELECT 1 per checkout (increasing checkout latency under contention) and pool_recycle=2700 forced unnecessary connection churn. Both realigned to AUTHENTICATION_SPEC.md:118-119 (pool_pre_ping=False, pool_recycle=3600). Co-authored-by: Isaac * fix(llm): patch Gemini Chat Completions shim quirks; pin API matrix Two client-side fixes for Gemini-backed Databricks serving endpoints, plus an integration test that pins the cross-provider API matrix. - Replace null `id` on chat completion responses with a placeholder so OpenAI SDK 2.x's Pydantic validator doesn't reject. Installed as an httpx response hook on the shared OpenAI client; mutates only chat completion shapes (other JSON like endpoint listings passes through). - Normalize `message.content` when it arrives as Gemini's array of part dicts (`[{type:"text", text:..., thoughtSignature:...}]`) into a plain string so `discovery_analysis_service`, `rubric_generation_service`, and similar callers don't have to special-case Gemini. - Add an integration test that probes every model in the workshop's picker against both Chat Completions and Responses API. Pins the design constraint that Databricks' Responses API passthrough is OpenAI-only (Claude/Gemini/Llama reject by design, so they must stay on Chat Completions). Skipped automatically when Databricks creds aren't configured; runnable on demand via `just test-integration`. Co-authored-by: Isaac * feat(summarization): route Gemini through ai-gateway for multi-turn Trace summarization on Gemini 3.5 Flash via Databricks' OpenAI-compat shim breaks on the second turn — the OpenAI Chat Completions wire format has no slot for Gemini's ``thought_signature``, and Gemini 3+ requires it round- tripped per turn. Route Gemini through the native passthrough at ``/ai-gateway/gemini`` using pydantic-ai's ``GoogleModel``, which handles ``thought_signature`` natively. Other foundation models (Claude, gpt-5, Llama) keep going through the OpenAI shim with ``OpenAIChatModel``. - ``TraceSummarizationService`` detects Gemini-family endpoint names at construction and builds ``GoogleModel`` over a ``google.genai.Client`` pointed at the workspace's ai-gateway/gemini URL with the Databricks token in the Authorization header. - Force httpx transport via ``HttpOptions.httpx_async_client``. google- genai prefers aiohttp when both are installed, which silently bypasses our request hook (see ``_use_aiohttp`` in google.genai._api_client). - Install an httpx request hook that strips ``id`` from outgoing ``functionCall``/``functionResponse`` parts before they reach the ai-gateway. Vertex AI's ``FunctionCall`` proto has no ``id`` field, but the google-genai SDK adds one when echoing the model's previous tool call back; the ai-gateway is a pure passthrough that doesn't strip it, so multi-turn requests 400 without this. The hook rewrites ``request.stream`` (where httpx actually reads body from), not just ``_content``, otherwise the wire body still carries the original ``id`` while Content-Length reflects the new size. - Add ``pydantic-ai-slim[google]`` extra to bring in ``google-genai``. Tests: - Unit: model-routing dispatch (Gemini → GoogleModel, others → OpenAIChatModel), Gemini client's base_url points at ai-gateway, function_call/function_response id strip, no-op on simple text turns. - Integration: live multi-turn summarization against ``databricks-gemini-3-5-flash``. Exercises the full chain end-to-end and acts as a regression guard if Databricks ships changes to the ai-gateway proto. Co-authored-by: Isaac * fix(db): correct Lakebase resource alias and centralize endpoint validation Follow-up to 53d9e70. The previous fix bound ENDPOINT_NAME via `valueFrom: postgres`, but the Apps platform exposes the Lakebase endpoint identifier under the default resource alias `database`. `valueFrom: postgres` resolved to an empty string at runtime, and db_bootstrap.py — which runs in the gunicorn on_starting hook before create_engine_for_backend() — bypassed the engine-creation guard and handed the empty value straight to the SDK, crashing app startup. - app.yaml: `valueFrom: postgres` → `valueFrom: database`. - LakebaseCredentialManager.get_password() now validates the endpoint argument itself, so all three call sites (do_connect handler, db_bootstrap, postgres_manager) surface the same actionable error on misconfiguration rather than the opaque SDK protobuf failure. Co-authored-by: Isaac * fix(discovery): release DB sessions between SSE polls The /discovery-comments/stream and /discovery-agent-runs/{id}/stream routes bound `db: Session = Depends(get_db)`, holding one pool connection per subscribed EventSource for the entire stream lifetime. Each DiscoveryTraceCard opens an EventSource for the comments stream (always) plus the agent-run stream (while a run is active), so a single user with ~10 visible trace cards already approaches the pool ceiling (5+5 per worker × 2 workers = 20). Combined with any background-worker connections, this is what was driving the production cascade on gh#163 after pool-timeout retries were neutered in 53d9e70. Refactor both routes to acquire SessionLocal() per poll iteration and release before the sleep, so connection holding time drops from the stream lifetime to single-digit milliseconds per query. Co-authored-by: Isaac * fix(discovery): route Gemini chat completions through ai-gateway Production reported repeated 502s from discovery analysis on Gemini: ``server received an invalid response from an upstream server``. The OpenAI-compat shim at ``/serving-endpoints/chat/completions`` translates Vertex AI responses into OpenAI shape, but for some Gemini outputs (safety blocks, certain content-part configurations) that translation fails and the shim returns a 502 instead of a usable response. Route Gemini chat completions through Databricks' native ai-gateway/gemini passthrough using ``google.genai.Client.models.generate_content``. The adapter returns the chat-completions dict shape callers already expect, so ``discovery_analysis_service`` and ``rubric_generation_service`` work unchanged. Other foundation models (Claude, gpt-5, Llama) stay on the OpenAI-compat shim — they don't have the response-shape issues that trip the translator for Gemini. - ``DatabricksService.call_chat_completion`` detects Gemini endpoint names and dispatches to ``_call_gemini_chat_via_ai_gateway``. - ``_get_gemini_client`` lazily builds and caches one ``google.genai.Client`` per workspace, pointed at ``{workspace}/ai-gateway/gemini`` with the Databricks token in the Authorization header. - Helpers ``_messages_to_genai_contents`` and ``_genai_response_to_chat_shape`` translate between OpenAI chat messages and Gemini ``Content`` objects / ``GenerateContentResponse``. System messages collapse into ``system_instruction``; response text parts concatenate into the chat-completions string content. - The existing ``_normalize_shim_content`` safety net stays in place for any non-Gemini model that ever returns array-shaped content. Tests: - Unit: Gemini endpoint names dispatch to the ai-gateway helper (and must NOT touch the OpenAI client); non-Gemini endpoints stay on the OpenAI client; helpers correctly translate messages and responses. - Integration: live Gemini chat completion via ai-gateway returns a plain string content (the discovery_analysis_service contract). Co-authored-by: Isaac * chore(deps): regenerate requirements.txt to include google-genai The Gemini ai-gateway routing for trace summarization and discovery analysis depends on the ``google.genai`` package, brought in via ``pydantic-ai-slim[google]`` in pyproject.toml. uv.lock was already updated, but requirements.txt — which the Databricks app build uses (``uv pip install -r requirements.txt``) — wasn't. The deployed app failed at runtime with ``No module named 'google.genai'``. Regenerated via: uv export --format requirements-txt --no-emit-project -o requirements.txt Co-authored-by: Isaac * fix(discovery): force temperature=1 for gpt-5 / o-series endpoints Production hit 400 on discovery analysis with gpt-5.5: "Unsupported value: 'temperature' does not support 0.3 with this model. Only the default (1) value is supported." OpenAI reasoning models (gpt-5 / gpt-5.1 / gpt-5.5 / gpt-5-codex and the o1/o3/o4 series) reject any temperature != 1. LiteLLM has ``drop_params`` to handle this transparently on the DSPy path (already enabled in ``discovery_dspy._configure_litellm_drop_params``), but the OpenAI Python SDK that ``DatabricksService.call_chat_completion`` uses has no equivalent — we have to normalize the request ourselves. - Add ``_is_openai_reasoning_model`` detector matching ``gpt-5``, ``o1``, ``o3``, ``o4`` endpoint names (with or without the ``databricks-`` prefix). - Add ``_normalize_request_for_reasoning_model`` which forces ``temperature=1.0`` for detected reasoning models and logs the override for auditability. - Apply normalization in both ``call_chat_completion`` and ``call_serving_endpoint`` so all caller paths benefit. Verified live against dogfood-staging: ``databricks-gpt-5`` and ``databricks-gpt-5-mini`` now return content for a discovery-analysis- shaped request that previously 400'd. Tests: - Parametrized detector tests covering gpt-5, gpt-5-codex, gpt-5.1, gpt-5.5, o1-preview, o3-mini, o4-mini. - Negative tests confirming Claude / Llama / Gemini / gpt-4o are NOT treated as reasoning models. - Unit test for the normalization helper. - End-to-end test that call_chat_completion forwards temperature=1.0 to the OpenAI client even when the caller passed 0.3. Co-authored-by: Isaac
* docs: revamp README for VibeScaler public release Rewrite for an external/OSS audience ahead of the public v1.10 release: value-first lead (what it is, who it's for, why), a How it works section (Discovery, Annotation/IRR, Alignment via MLflow align(), Evaluate at scale), a filled-in Quick Start, expanded docs index, and Built on MLflow / Contributing / Security sections. Rename product to VibeScaler and fix the LICENSE link. Co-authored-by: Isaac * docs: address README review (omit Discovery link, add last-updated note) Drop the Discovery doc link per review, add a last-updated and what-changed note at the top, and keep the SME wording from review. Co-authored-by: Isaac * docs: address review feedback on README Per Forrest's review: remove the last-updated line and the dated release-zip step, reword the tagline to lead with collaboration, generalize the alignment step to optimization techniques and tracked metrics (no specific APIs), add the Databricks Marketplace as a deploy option, and use 'project' instead of 'workshop'. Co-authored-by: Isaac --------- Co-authored-by: yulin-yang_data <yulin.yang@databricks.com>
…t-cache' into rc/v1.10.0 # Conflicts: # tests/unit/services/test_databricks_service.py # tests/unit/services/test_discovery_dspy_litellm_interop.py
Ports the reviewed bug-bash fixes from the integration line onto the release lineage (no V2 feature work). All ports verified byte-equivalent to the reviewed fixes where file lineages matched (they did, everywhere). Fixes ported: - #151: Copy Output copies the displayed representation (formatted vs raw) - #152/#154: multi-line criterion text round-trips (section-aware build/parse in rubricUtils; whitespace-pre-wrap displays) - #153: free-form criterion type removed from rubric creation UI (legacy criteria parse as likert) - #155: annotation completion shows terminal complete screen - #156: facilitator annotation stats poll every 15s - #157: hard-coded Results recommendations removed end-to-end - #158: high-disagreement finding scoped per metric (no cross-question or legacy-rating leakage past the sigma threshold) - #150: fallback follow-up questions badged for participants - #161: episodic-memory dedup on judge re-alignment (skip already-aligned traces; repair corrupted judges) - /deployment/status requires Lakebase setup only for postgres targets; sqlite deployments are fully operable Test repairs (stale on this lineage before this commit): - test_db_config.py aligned with the shipped SQLite fallback behavior - test_gunicorn_conf.py pins optimistic startup instead of fail-fast Specs: RUBRIC_SPEC drops the free-form type, JUDGE_EVALUATION_SPEC trims the MemAlign judge-type list, TRACE_DISPLAY_SPEC adds the copy-output criterion, BUILD_AND_DEPLOY_SPEC adds the setup-gate criterion; SPEC_COVERAGE_MAP regenerated. Co-authored-by: Isaac
Co-authored-by: Isaac
Working plans (.claude/plans, docs/plans, docs/superpowers/plans) and the claude tracing log are development artifacts, not release content; the log is now gitignored (matching the integration line). Co-authored-by: Isaac
- uv.lock: the hotfix-branch lock was resolved against pypi-proxy.dev.databricks.com, which is unreachable from Databricks Apps builds and external deployments. Registry-URL rewrite only (same approach as 3aba148); resolved versions and artifact hashes unchanged, all artifact URLs already files.pythonhosted.org. npm side verified clean: app build pins registry.npmjs.org and no package-lock files exist on this lineage. - docs/plans: restore the 6 design/implementation plan docs removed in 816d27b — they are roadmap content, not cruft. (.claude/plans and the tracing log stay removed.) Co-authored-by: Isaac
Co-authored-by: Isaac
Release-readiness review — RC v1.10.0Reviewed the full The release engineering itself is sound. The lineage into A few public-readiness gaps are worth clearing while the release moves across, none of them a correctness regression in the app code. The first and most important is the dead links in The scrub also left internal artifacts in the tree. On verification, the GO ran at Two smaller things round it out. |
…ful criteria Three-way audit (doc/ ship intent vs specs vs code+tests) followed by an owner-approved honesty remediation wave. Coverage bars now mean what ABOUT_THESE_DOCS promises: a green criterion has a passing test that genuinely asserts it. Analyzer (tools/spec_coverage_analyzer.py): - skipped/xfail tests no longer count as coverage (static detection, pytest + Playwright + Vitest); criteria backed only by skipped tests render as "(skipped-only)" - "### Roadmap" / "(roadmap)" criteria are excluded from denominators and listed separately - unknown spec tags reported loudly; TRACE_INGESTION_SPEC and TRACE_SUMMARIZATION_SPEC (real spec files, never registered) added to KNOWN_SPECS; retired specs removed - analyzer subprocess and all justfile uv invocations are now lock-preserving (script-interpreter --no-sync, UV_FROZEN export); new registry-portability guard test pins uv.lock to public registries Specs: - ASSISTED_FACILITATION_SPEC and DISCOVERY_TRACE_ASSIGNMENT_SPEC retired; shipped behavior folded into DISCOVERY_SPEC with tombstones in the index - stale criteria replaced with shipped contracts (ANNOTATION toasts -> saved-indicator/error-toast model; RUBRIC serialization -> real |||JUDGE_TYPE||| format; JUDGE_EVALUATION fictional routes/entities -> real API; AUTH narrative; BUILD_AND_DEPLOY startup/deploy prose; DESIGN_SYSTEM tokens -> shipped palette) - unbuilt features moved to Roadmap (DATASETS union/subtract/lineage, CUSTOM_LLM judge-evaluation half, EVAL_MODE judge execution, dark mode, social-thread LLM assistants) - new criteria for shipped-but-unspecified behavior (completion terminal state, facilitator polling, participant notes, episodic-memory persistence #161, Lakebase persistence, upsert semantics, social thread mechanics) Tests: - ~90 hollow/squatting/crossed @Req tags corrected across all specs - ~40 new genuine tests (save retry/backoff, navigation debounce, alignment job lifecycle, IRR recompute, deployment gate, server-boot smoke, SSE generators, social mechanics, scoring math, registry guard) - vacuous e2e assertions hardened (auto-evaluation, discovery-analysis, scale rendering); 3 stale-selector e2e failures fixed (jsonpath save testid, draft-rubric Add exact-match + navigation, model-selection dynamic-models mock); dead e2e suites for the retired v2 dashboard deleted; backend freeform->likert coercion completes #153 Verification (fresh, sequential): e2e 78 passed/0 failed/10 skipped; pytest 977/0; vitest green; lint clean; spec coverage 68% of 407 active criteria (honest) vs 62% of 355 (inflated) before; coverage baseline updated; uv.lock clean of internal proxy references. Known-and-annotated, pending owner decisions: alignment-service bypasses the trace display pipeline; two live discovery UI regressions (color-coding, <2-participant warning); social-mode ship policy. Co-authored-by: Isaac
- Repoint pyproject.toml project URLs from the dead databricks/human-eval-workshop repo to the live repo - Remove skill-eval workspace byproducts (.claude/skills/*-workspace/) and the tracked client/.claude/mlflow/claude_tracing.log; broaden .gitignore so nested tracing logs and eval workspaces stay untracked - Replace unenforced CODEOWNERS.txt with .github/CODEOWNERS so GitHub applies review routing Keeps .claude/CLAUDE.md, skills, agents, settings, and hooks: they are the working Claude Code setup for new contributors, not internal artifacts. Co-authored-by: Isaac
Collaborator
Author
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Addressed in pyproject.toml dead links — repointed all four Internal artifacts — removed the 63 skill-eval byproduct files ( CODEOWNERS — Build/deploy verification of the Pre-existing test failures (4 frontend-unit, 3 e2e) — leaving these red in this PR. They're documented pre-existing lineage debt with commit-level evidence in the verification report, and we'd rather fix or skip them deliberately as follow-ups than annotate them inside an RC whose application code is otherwise frozen. |
…ns, honest linkage - alignment_service now extracts evaluation inputs/outputs through the shared trace display pipeline (get_display_text), completing the 918e51e wiring from PR #147 that covered judge/discovery services but never alignment: judges now calibrate on the same span-filtered/ JSONPath view SMEs annotate. Raw fallback unchanged for unconfigured workshops; #161 dedup logic untouched. The pipeline-consistency test now includes alignment_service (closing the omission that let the criterion pass vacuously) plus behavioral tests both ways. - restore disagreement priority color tiers on trace cards (was collapsed to all-rose) and the Limited Participant Data warning for <2-participant analyses, each pinned by a regression test. - social mode ships as an experiment: comment threads/votes/live updates unchanged; the UI no longer advertises @assistant/@agent mentions (placeholder copy removed; backend mention mechanics remain, spec'd at API level). - TRACE_INGESTION_SPEC linked honestly (0% -> 52%; slug @reqs replaced with exact criteria; untestably-bundled criterion split, unbuilt half stays red). TRACE_SUMMARIZATION_SPEC 32% -> 35% (all uncovered groups verified to have shipped code — honest reds, no roadmap abuse). - TRACE_DISPLAY/DISCOVERY audit annotations resolved; draft-rubric origin-link criterion reworded to the shipped pattern. Verification (fresh, sequential): pytest 979/0, vitest green, lint clean, e2e 79 passed/0 failed/9 skipped; coverage 71% of 408 active criteria; baseline updated; uv.lock clean. Co-authored-by: Isaac
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What this is
Clean v1.10.0 release candidate, cut from
main's lineage with no V2 feature work. Contents:1d3c4da— the pre-existing release lineage (v1.10 feature work + stabilization PRs fix: address eval-mode intake and Databricks host regressions #128–docs: revamp README for VibeScaler public release #166)f3184f2— merge ofhotfix/model-interop-and-endpoint-cache(Lakebase Postgres connection pool exhausted under load (size=5, overflow=5) #163 pool-exhaustion fixes: ENDPOINT_NAME binding + loud failure, pool-timeout cascade removal, per-poll SSE sessions; Gemini ai-gateway routing; reasoning-model temperature handling)66420ab— roll-up of the v1.10 bug-bash fixes, each ported from the reviewed integration line and verified byte-equivalent where file lineages matched (they did, everywhere): Discovery follow-up questions silently fall back to static — LLM calls failing #150, "Copy Output" always copies raw JSON even when viewing formatted output #151, Binary criterion custom text truncates beyond line 1 #152, Remove free-form criterion type — not functional #153, Newline handling bugs in rubric criteria text fields #154, Annotation completion modal doesn't navigate user anywhere #155, Facilitator must refresh page to see updated annotation stats #156, Remove hard-coded Results recommendations #157, "High Disagreement" finding always appears in Results analysis #158, Judge Tuning shows 20 evaluations when annotation produced only 10 #161, plus the/deployment/statussqlite gate fix, stale-test repairs (db_config, gunicorn_conf), and spec updates (RUBRIC, JUDGE_EVALUATION, TRACE_DISPLAY, BUILD_AND_DEPLOY)0517490— regenerated OpenAPI client + coverage map from the verification run816d27b— scrub: removed committed plan files and the root tracing log184b65b— repointeduv.lockto pypi.org (was the internal Databricks proxy); restored docs/plans roadmap76189dd— docs: release-readiness doc-alignment mode for the spec-audit skilld09a29f— spec release-readiness audit: honest coverage numbers, retired specs, truthful criteria800ca8d— public-readiness review fixes: live repo URLs inpyproject.toml, removed skill-eval workspace byproducts (63 files) and the trackedclient/.claude/mlflow/claude_tracing.log(gitignore broadened to match nested copies), replacedCODEOWNERS.txtwith an enforced.github/CODEOWNERSCommits 5–9 touch only docs, specs, release metadata, and file removals — no application code. The verification report below ran at
66420ab, so its 921/0 backend result still describes the application code at HEAD; what it does not cover is theuv.lockregistry rewrite through a real build, which can only be meaningfully proven by a Databricks App deploy (see review discussion).Deliberately excluded (V2-only, live on the integration line): V2 project setup flow, social mode redesign, provider-auth refactor, custom-LLM endpoint restoration (this lineage never lost them), AG-UI session hardening.
Closed bug-bash issues covered: #150–#159, #161, #163.
Promotion plan
Once this RC is blessed: fast-forward (or reset)
release/v1.10.0to this branch.origin/release/v1.10.0currently carries the V2 integration line from an earlier push and is left untouched until then.Verification report (verbatim from the dedicated verification agent)
RC Verification Report —
rc/v1.10.0@66420abVerification performed in the RC worktree per
.claude/skills/verification-testing/SKILL.mdand CONTRIBUTING.md "Verification Commands". Gate 7 ran in affected-spec mode per coordinator scope change (not the full legacy suite). Nothing was committed, pushed, or edited by the verification agent.Gate-by-gate results
just test-serverjust ui-test-unitjust ui-typecheckjust ui-lintjust spec-coveragejust spec-validatejust e2e-spec <SPEC> headless 1×6Backend note: the RC baseline
f3184f2was 904 passed / 6 failed (5×test_db_config.py, 1×test_gunicorn_conf.py). Those 6 now pass — the roll-up's test repairs landed as advertised, and zero db_config/gunicorn failures remain (any such failure would have been port-caused).Gate 7 detail — affected-spec E2E
Detector output:
just spec-coverage --affected 1d3c4damarked all 16 specs affected, becauseserver/app.py(touched by the roll-up) is classified as a core file affecting every spec. That set is degenerate (≈ full suite), so per the fallback instruction the practical set was derived fromgit diff --name-only 1d3c4da..66420abcross-referenced with@spectags of the ported test files and edited spec files:test.skipat annotation-last-trace.spec.ts:202, pre-existing)@spec:BUILD_AND_DEPLOY_SPEC/deployment/statussqlite gate is verified at unit level only (tests/unit/test_build_deploy.py)Not exercised: 13 of 33 legacy Playwright files (assisted-facilitation ×5, custom-llm-provider, dataset-operations, design-system, discovery-invite-traces, eval-mode-workflow, example-new-infrastructure, trace-visibility, ui-components).
Failure detail and classification
Frontend unit (gate 2) — 4 failures, all pre-existing
client/src/context/UserContext.test.tsx— "isLoading starts true and transitions to false" —TypeError: Cannot read properties of undefined (reading 'clear')at line 38 (localStorage.clear()inbeforeEach). Environment-sensitive: Node v26.3.0's experimental webstorage global shadows jsdom's (ExperimentalWarning: localStorage is not available because --localstorage-file was not providedin the run log). File last changed in v1.9.0 (4858ab5); not in the roll-up footprint. Pre-existing / infra (Node-version interaction).2-4.
client/src/components/discovery/DiscoveryOverviewBar.test.tsx— "renders inline stats" (/10 traces/not found), "renders Run Analysis button", "disables Run Analysis when mlflow not configured" (no/run analysis/ibutton). The component was redesigned in1c39694(facilitator comment moderation — now renders "Discovery Workspace" header with "Add Traces"/"Pause Phase"); the test is stale. Neither file is ingit show 66420ab --stat. Pre-existing (stale test vs pre-baseline redesign).E2E — 3 failures, all pre-existing
jsonpath-trace-display.spec.ts:77— "facilitator can configure JSONPath settings and preview extraction" — Playwright strict-mode violation:getByRole('button', { name: /Save Settings/i })resolves to 2 elements (JsonPathSettings.tsx:418andSummarizationSettings.tsx:214, both mounted on the dashboards). The second button exists at baselinef3184f2(verified viagit show f3184f2:client/src/components/SummarizationSettings.tsx), introduced by9ff4dfa(feat/summarization-cancel, ancestor of the baseline). None of these files touched by66420ab. Pre-existing (ambiguous selector vs pre-baseline UI).discovery-draft-rubric-crud.spec.ts— "facilitator can manually add a draft rubric item" — strict-mode violation in test helperclient/tests/lib/actions/discovery.ts:419:.border-b+/Add/inow also matches the "Add Traces" button, present at baseline (verified viagit show f3184f2:...DiscoveryOverviewBar.tsx). Helper not in roll-up footprint. Pre-existing.discovery-model-selection.spec.ts:72— "model selector dropdown shows available options" — expects a hardcodedClaude Opusoption; model options became dynamically fetched in854cfbb("fetch available models from Databricks instead of hardcoded list", ancestor of baseline). The roll-up's onlyuseWorkshopApi.tschange is the 15s facilitator polling interval — unrelated. Pre-existing.Baseline note:
git diff f3184f2^1 f3184f2is empty (the hotfix merge introduced no content delta vs its first parent), so these "pre-existing at f3184f2" failures also pre-date the hotfix — they are release-lineage debt, not RC regressions.Infra
.test-results/api-server.logshowssqlite3.OperationalError: disk I/O error. Root cause: thejust e2ereciperm -f's only.e2e-workshop.db, leaving the previous run's-wal/-shmsidecars orphaned. Retried once after removing all three files: 7/7 passed. Infra flake (the exact failure mode the procedure anticipated). All subsequent runs got proactive sidecar cleanup; no recurrence.Gate 6 (387 untagged tests)
Sampled the untagged entries inside roll-up-touched files (
test_db_config.py,test_build_deploy.py,test_alignment_service.py,test_irr_utils.py): every sampled test name exists at baselinef3184f2— the roll-up's new tests are tagged. The 387 count is entirely pre-existing debt.Spec coverage summary (gate 5)
(Analyzer also flags 3 unknown spec references: SQLITE_CONCURRENCY, TRACE_INGESTION_SPEC, TRACE_SUMMARIZATION_SPEC.)
Recommendation: GO for promoting
rc/v1.10.066420ab, with commit-level evidence.What this verification does NOT cover
/deployment/statussqlite-gate fix is verified at unit level only.@e2e-realruns — all E2E was mocked-mode; MLflow/Databricks interactions were not exercised against real services.UserContextvitest failure is Node-version-sensitive and CI may behave differently.test.skipin ANNOTATION ("UI: 10th annotation saved when clicking Complete") remains skipped — that path is untested.This pull request and its description were written by Isaac.