feat(wren-ai-service): generate semantics for alias #976
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WalkthroughThe pull request introduces a comprehensive renaming of the "Semantics Description" functionality to "Semantics Enrichment" across multiple files in the Wren AI service. This change involves updating configuration files, service implementations, routers, tests, and other related components to reflect the new terminology. The modifications are consistent across different parts of the application, suggesting a deliberate effort to refine the semantic processing pipeline's nomenclature and potentially its underlying functionality. Changes
Sequence DiagramsequenceDiagram
participant Client
participant Router
participant Service
participant Pipeline
participant LLM
Client->>Router: POST /semantics-enrichment
Router->>Service: Generate semantics enrichment
Service->>Pipeline: Process models and columns
Pipeline->>LLM: Request semantic enrichment
LLM-->>Pipeline: Return enriched metadata
Pipeline-->>Service: Return enriched results
Service-->>Router: Return task details
Router-->>Client: Confirm task creation
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Actionable comments posted: 1
🧹 Nitpick comments (10)
wren-ai-service/src/web/v1/routers/semantics_enrichment.py (1)
95-102: Deprecation Strategy
It's good practice to keep the old endpoint (semantics-descriptions) withdeprecated=Truefor backward compatibility. Consider documenting a sunset date or version where it will be removed so that clients can plan migrations.wren-ai-service/src/pipelines/generation/__init__.py (1)
18-35: Comprehensive Public API
The consolidated list in__all__indicates a well-organized approach to your generation pipelines. Consider adding docstrings or usage examples for top-level classes for convenience and clarity.wren-ai-service/src/web/v1/services/semantics_enrichment.py (4)
58-58: Detailed Error Logging
LoggingProject IDwith the error provides valuable context for troubleshooting. Make sure sensitive user data is not accidentally leaked in logs.
68-70: Refined Model Selection
The_model_pickerclosure is a nice abstraction. Returningmodel["name"] in selected or "*" in selectedis straightforward. Consider extending the logic if partial matches, regex, or advanced selection might be needed in the future.
100-102: Improved Logging
Using a descriptive log statement (Enrich Semantics pipeline is running...) is helpful for debugging. Consider including chunk sizes or number of chunks for additional context.
122-122: Exception Handling
Catching the broadExceptionensures resilience, but can obscure specific issues. Where feasible, handle known error types individually for more precise feedback to the client.wren-ai-service/src/pipelines/generation/semantics_enrichment.py (2)
39-49: Synchronized field usage.Using
prop = model["properties"]for retrieving bothaliasanddescriptionensures consistency. However, ifmodel["properties"]might be missing or malformed, consider robust error handling.
219-220: Usage example clarity.This example is helpful for local validation. Considering adding docstrings or inline comments to illustrate sample usage scenarios for future maintainers.
wren-ai-service/tools/config/config.full.yaml (1)
127-128: Update documentation to reflect the new pipeline nameConsider updating the documentation to reflect the semantic change from "description" to "enrichment" and explain the enhanced capabilities.
Would you like me to help create a documentation PR that:
- Updates all references to the pipeline name
- Explains the enhanced semantics capabilities
- Provides migration guidelines for existing users
deployment/kustomizations/base/cm.yaml (1)
144-145: Consider adding a temporary pipeline alias for backward compatibility.Since this is a breaking change that requires client updates, consider temporarily maintaining both pipeline names to ensure a smooth transition period.
Add this entry to maintain backward compatibility:
- name: semantics_enrichment llm: litellm_llm.gpt-4o-mini-2024-07-18 + # TODO: Remove this alias after all clients have migrated to semantics_enrichment + - name: semantics_description + llm: litellm_llm.gpt-4o-mini-2024-07-18
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📒 Files selected for processing (13)
deployment/kustomizations/base/cm.yaml(1 hunks)docker/config.example.yaml(1 hunks)wren-ai-service/src/globals.py(7 hunks)wren-ai-service/src/pipelines/generation/__init__.py(1 hunks)wren-ai-service/src/pipelines/generation/semantics_enrichment.py(7 hunks)wren-ai-service/src/pipelines/retrieval/__init__.py(1 hunks)wren-ai-service/src/web/v1/routers/__init__.py(2 hunks)wren-ai-service/src/web/v1/routers/semantics_enrichment.py(5 hunks)wren-ai-service/src/web/v1/services/semantics_enrichment.py(7 hunks)wren-ai-service/tests/data/config.test.yaml(1 hunks)wren-ai-service/tests/pytest/services/test_semantics_enrichment.py(10 hunks)wren-ai-service/tools/config/config.example.yaml(1 hunks)wren-ai-service/tools/config/config.full.yaml(1 hunks)
🔇 Additional comments (45)
wren-ai-service/src/web/v1/routers/semantics_enrichment.py (9)
15-15: Import Consistency Check
Great that you're directly importing SemanticsEnrichment from src.web.v1.services.semantics_enrichment. Ensure all references to SemanticsEnrichment in this file consistently point to the proper module to avoid confusion with any pipeline-level import.
20-64: Comprehensive Documentation
The inline documentation is exceptionally thorough, providing clear instructions and detailed examples. This helps future contributors and users understand the API.
71-71: Confirm Sufficient Async Handling
The note about asynchronous processing is helpful. Ensure that any potential race conditions or concurrency edge cases (e.g., multiple concurrent requests for the same ID) are handled gracefully in the background task.
111-114: Service Container Access
Assigning service = service_container.semantics_enrichment neatly centralizes service logic. This approach avoids multiple direct references to service_container throughout the router.
136-139: Consistent GET Endpoint Coverage
The new GET route for /semantics-enrichment/{id} mirrors the old one well. Confirm that all consumer integrations are updated to fetch from the new path if needed, especially if the deprecated endpoint eventually goes away.
143-143: Proper Deprecation Flag
Marking the old GET endpoint as deprecated is precise. Make sure you maintain logs or metrics to see if it is still in wide use before removing it completely.
149-149: Potential Missing Resource Handling
Accessing service_container.semantics_enrichment[id] may raise a KeyError if the ID is not in cache. The code below handles that scenario in the __getitem__, returning a "failed" resource with RESOURCE_NOT_FOUND. This approach is consistent.
161-161: Verify Null Aliases
Using column["properties"].get("alias", "") is a safe fallback. However, verify whether an empty string vs. None might cause confusion. If you rely on an empty string, confirm that downstream rendering handles it effectively.
166-166: Fallback for Model DisplayName
Similarly, using an empty string fallback for model-level alias helps avoid KeyError. Ensure there's no scenario where an empty string collides with real data, especially in UI.
[approve]
wren-ai-service/src/pipelines/retrieval/__init__.py (1)
1-6: Public API Expansion
The newly added classes (HistoricalQuestion, PreprocessSqlData, Retrieval, SQLExecutor) in __all__ nicely clarify what the module exports. Ensure the code in these classes is adequately documented and tested, as they are now part of the module’s public API.
wren-ai-service/src/web/v1/routers/__init__.py (2)
10-10: Module Import Organization
Importing semantics_enrichment here indicates an architectural preference for distinct subrouters. This separation improves maintainability but ensure the team is notified of new endpoints if cross-module references are needed.
23-23: Router Inclusion
Including router.include_router(semantics_enrichment.router) is consistent with the renamed semantics enrichment feature. Validate that referencing the old semantics_description router is fully removed to avoid confusion in application routes.
wren-ai-service/src/pipelines/generation/__init__.py (2)
1-9: Shared Pipeline Imports
These newly imported pipelines, like ChartAdjustment and DataAssistance, clarify the generation pipeline’s scope. Continue to ensure each class’s responsibilities remain well-defined and tested.
10-17: Exported Entities
Declaring them under __all__ helps with explicit module boundaries. Verify that references from other modules are properly updated to avoid referencing classes not explicitly exported by name.
wren-ai-service/src/web/v1/services/semantics_enrichment.py (5)
17-17: Renaming for Clarity
Renaming the class to SemanticsEnrichment aligns well with the updated feature scope. This improves code discoverability and sets a clear domain concept.
43-43: TTLCache for Scalability
Using TTLCache for caching resources is efficient. Validate if the maxsize and ttl values are optimal for typical usage scenarios to avoid excessive memory usage or stale data.
77-81: Chunking Logic
Breaking large MDL data into chunks is a solid approach to controlling payloads. Confirm that partial chunk failures are handled gracefully and that combined results preserve the correct order of columns.
97-102: Async Generation Flow
The asynchronous gather approach is well-structured. Confirm that partial failures in one of the tasks don’t mask or disrupt final status updates (e.g., a single chunk failing after all tasks are initiated).
131-131: Graceful Resource Lookup
When a resource is not found, returning a “failed” resource with RESOURCE_NOT_FOUND code is user-friendly. This approach avoids surprising 500 errors and helps the client handle the missing resource scenario gracefully.
wren-ai-service/src/pipelines/generation/semantics_enrichment.py (6)
Line range hint 20-29: Ensure alignment with broader logic changes.
The picked_models function now extracts all models without filtering. Confirm that services depending on model filtration logic are updated accordingly to avoid unexpected results.
Line range hint 30-36: Nicely structured property addition.
The addition of "alias": column["properties"].get("displayName", "") is consistent with the rest of the code and enhances clarity.
Line range hint 92-96: Well-defined Pydantic model.
ModelProperties properly captures new fields. This strongly enforces typed usage and helps keep the enrichment data structured.
111-120: Naming continuity check.
semantics_enrichment_KWARGS references the updated schema. Confirm that any references from previous naming (SEMANTICS_DESCRIPTION_MODEL_KWARGS) are fully replaced throughout the codebase.
178-181: Appropriate usage of template.
The user_prompt_template incorporates relevant placeholders for user prompt and picked models. Ensure the placeholders match the Jinja-like logic used by PromptBuilder.
188-190: Confirm generation kwargs usage.
Passing generation_kwargs=semantics_enrichment_KWARGS aligns well with the pipeline approach. Verify the desired openAI or language model parameters are honored.
wren-ai-service/src/globals.py (4)
9-9: Modular import improvement.
Aggregating imports into generation, indexing, and retrieval can reduce clutter. Confirm that there's no performance overhead or unused imports.
16-16: Renaming for clarity.
Using SemanticsEnrichment improves the descriptive nature compared to SemanticsDescription. This fosters better domain understanding.
32-32: Correctly updated service container field.
semantics_enrichment: SemanticsEnrichment is consistent with the new service naming. This ensures code consistency and maintainability.
57-60: Instantiation logic.
Wrapping generation.SemanticsEnrichment(**pipe_components["semantics_enrichment"]) inside your own service class is a clean approach. Confirm if there's any legacy references to the old name.
wren-ai-service/tests/pytest/services/test_semantics_enrichment.py (11)
7-7: Renamed import to match new module.
from src.web.v1.services.semantics_enrichment import SemanticsEnrichment keeps unit tests aligned with the rename.
16-26: Mock pipeline's structure alignment.
Mock returns columns and properties with alias fields. This matches the new data structure. Good thoroughness in test coverage.
Line range hint 40-72: Coverage for valid generation flow.
The test verifies the id, status, and the enriched model structure thoroughly. This ensures no regression in the normal usage path.
Line range hint 79-95: Validation for invalid MDL.
The MDL_PARSE_ERROR assertion clarifies error-handling. Including checks on the error message further cements robust test coverage.
Line range hint 101-123: Exception handling scenario.
Simulating an unexpected exception with service._pipelines["semantics_enrichment"].run.side_effect ensures the service gracefully returns a failure status. Great to see negative test coverage.
Line range hint 126-139: Resource retrieval.
Accessing results via service["test_id"] is succinct. Ensures that resources store the final response for the tested scenario.
Line range hint 141-150: Non-existent resource scenario.
Ensuring RESOURCE_NOT_FOUND is returned for unknown IDs is essential for robust error handling.
Line range hint 155-172: Batch model coverage.
Testing multiple models ensures the batch logic for chunking is correct. Confirm the user prompt is effectively shared across chunks.
190-231: Custom chunk size test.
Verifies chunking logic with a chunk size of 1. This level of granularity ensures small data sets are handled consistently.
236-259: Partial failure scenario.
Asserting a failing chunk sets the overall status to failed aligns with typical batch processing semantics.
Line range hint 275-296: Concurrent execution test.
The delay approach is a good way to mimic race conditions. Ensuring the final result merges partial responses from multiple models without data clobbering preserves concurrency safety.
wren-ai-service/tests/data/config.test.yaml (1)
73-73: Renamed pipeline step.
Changing semantics_description to semantics_enrichment ensures consistency with the service rename. Verify references in other config files, if any remain, for correctness.
docker/config.example.yaml (1)
94-95: LGTM! Verify Docker deployment updates
The pipeline renaming from semantics_description to semantics_enrichment is consistent with the PR objectives. The configuration maintains the correct model reference.
Run this script to verify Docker deployment configurations:
✅ Verification successful
Pipeline renaming is consistently applied across Docker configurations
After checking all Docker and deployment configurations, the pipeline name change from semantics_description to semantics_enrichment is correctly reflected in all relevant configuration files. The new pipeline name is consistently used in:
- docker/config.example.yaml
- deployment/kustomizations/base/cm.yaml
- wren-ai-service/tools/config/config.full.yaml
No instances of the old pipeline name semantics_description were found in any Docker or deployment configurations, indicating a complete migration.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for any remaining references to the old pipeline name in Docker configurations
# and deployment files to ensure complete migration.
echo "Checking Docker and deployment configurations..."
rg -l "semantics_description" 'docker/' 'deployment/'
Length of output: 156
Script:
#!/bin/bash
# Let's try a broader search to find any references to either the old or new pipeline name
echo "Searching for both old and new pipeline names in the entire codebase..."
rg -l "semantics_(description|enrichment)"
# Also check for any Docker or deployment related files
echo -e "\nListing Docker and deployment related files:"
fd -t f -e yaml -e yml -e dockerfile -e docker
Length of output: 2182
Script:
#!/bin/bash
# Let's check the content of deployment and configuration files for the old pipeline name
echo "Checking content of deployment and configuration files..."
echo "1. Checking deployment/kustomizations/base/cm.yaml:"
cat deployment/kustomizations/base/cm.yaml
echo -e "\n2. Checking docker/docker-compose.yaml:"
cat docker/docker-compose.yaml
echo -e "\n3. Checking docker/docker-compose-dev.yaml:"
cat docker/docker-compose-dev.yaml
echo -e "\n4. Checking wren-ai-service/tools/config/config.full.yaml:"
cat wren-ai-service/tools/config/config.full.yaml
Length of output: 15653
wren-ai-service/tools/config/config.example.yaml (1)
108-109: LGTM! Verify local development setup
The pipeline renaming is consistent with the Docker configuration. The development configuration maintains the correct model reference and localhost endpoints.
Run this script to verify local development configurations:
✅ Verification successful
No references to old pipeline name found
The verification shows that there are no remaining references to semantics_description in any Python files, YAML configurations, or other development-related files within the wren-ai-service/tools and wren-ai-service/tests directories. The renaming to semantics_enrichment appears to be complete and consistent across the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for any remaining references to the old pipeline name in development
# configurations and ensure all development tools are updated.
echo "Checking development configurations..."
rg -l "semantics_description" 'wren-ai-service/tools/' 'wren-ai-service/tests/'
# Check if any Python files still reference the old name
ast-grep --pattern 'semantics_description' -l '*.py'
Length of output: 324
Script:
#!/bin/bash
# Description: Check for any remaining references to the old pipeline name in development
# configurations and ensure all development tools are updated.
echo "Checking development configurations..."
# Search in Python files
rg -l "semantics_description" --type py
# Search in YAML files
rg -l "semantics_description" --type yaml
# Search in development-related files
fd -e yaml -e yml -e json -e ini -e conf -e config . wren-ai-service/tools wren-ai-service/tests --exec rg -l "semantics_description" {}
Length of output: 307
wren-ai-service/tools/config/config.full.yaml (1)
127-128: LGTM! Verify production configuration
The pipeline renaming is consistent across all configuration files. The full configuration maintains all necessary components and production settings.
Run this script to verify production configurations and documentation:
✅ Verification successful
Pipeline name has been successfully renamed across all configuration files
Based on the search results, I can confirm that:
- The pipeline name has been consistently renamed from
semantics_descriptiontosemantics_enrichmentin all configuration files. - The change appears in the full configuration example (
config.full.yaml) and is consistent with other configuration files shown in the output. - No other references to the old name
semantics_descriptionwere found in the codebase. - The pipeline configuration maintains all necessary components and settings.
The change is isolated to just the pipeline name and does not impact any functionality or dependencies. The pipeline continues to use the same LLM model configuration (litellm_llm.gpt-4o-mini-2024-07-18).
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for any remaining references to the old pipeline name in production
# configurations and documentation.
echo "Checking production configurations and documentation..."
# Check configuration files
rg -l "semantics_description" 'deployment/' 'docs/'
# Check Python files for any remaining references
ast-grep --pattern 'semantics_description' -l '*.py'
# Check documentation files
fd -e md -e rst -e txt | xargs rg "semantics[_\s]description" -l
Length of output: 443
Script:
#!/bin/bash
# Let's try a different approach to search for any remaining references
# Search for any files containing the old name pattern
echo "Searching for files with 'semantics_description'..."
rg -l "semantics[_\s]description"
# Find Python files and search within them
echo -e "\nSearching in Python files..."
fd -e py | xargs rg "semantics[_\s]description" -l
# Find YAML files and search within them
echo -e "\nSearching in YAML files..."
fd -e yaml -e yml | xargs rg "semantics[_\s]description" -l
# Find any documentation files
echo -e "\nSearching in documentation files..."
fd -e md | xargs rg "semantics[_\s]description" -l
Length of output: 551
Script:
#!/bin/bash
# Let's try one final approach focusing on the pipeline configuration and related files
# Search for any pipeline configuration files
echo "Finding all pipeline configuration files..."
fd -e yaml -e yml | grep -i 'config\|pipeline'
# Show content of these files to check pipeline names
echo -e "\nChecking pipeline configurations..."
fd -e yaml -e yml | grep -i 'config\|pipeline' | xargs cat
# Search for Python files that might handle pipeline configurations
echo -e "\nSearching for pipeline-related Python files..."
fd -e py | grep -i 'pipeline\|config' | xargs cat
Length of output: 129260
deployment/kustomizations/base/cm.yaml (1)
144-145: LGTM! Verify dependent services.
The pipeline renaming from semantics_description to semantics_enrichment aligns with the PR objectives. The configuration structure is maintained correctly.
Let's verify that all dependent services have been updated to use the new pipeline name:
✅ Verification successful
Pipeline renaming is consistently implemented
The search results show that the new pipeline name semantics_enrichment is used consistently across the codebase in all relevant files:
- Configuration files (config.example.yaml, config.full.yaml, config.test.yaml)
- Source code files (globals.py, pipelines, web routers)
- Test files (test_semantics_enrichment.py)
- Deployment configuration (cm.yaml)
No occurrences of the old name semantics_description were found, indicating that the renaming has been properly implemented across all dependent services and configurations.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for any remaining references to the old pipeline name
rg -l 'semantics_description' --type yaml --type python
# Search for the new pipeline name to confirm updates
rg -l 'semantics_enrichment' --type yaml --type python
Length of output: 172
Script:
#!/bin/bash
# Search for any remaining references to the old pipeline name without file type restrictions
rg -l 'semantics_description'
# Search for the new pipeline name to confirm updates
rg -l 'semantics_enrichment'
Length of output: 645
Script:
#!/bin/bash
# Let's check the content of these files to ensure there are no remaining references to the old name
rg 'semantics_description' -C 2
Length of output: 31
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| async def _generate_task(self, request_id: str, chunk: dict): | ||
| resp = await self._pipelines["semantics_description"].run(**chunk) | ||
| resp = await self._pipelines["semantics_enrichment"].run(**chunk) |
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🛠️ Refactor suggestion
Pipeline Key Verification
Accessing self._pipelines["semantics_enrichment"] presumes the pipeline is correctly registered. Consider adding a guard check or fallback error if the pipeline key is missing to improve reliability.
This PR refactors the semantics description service to improve clarity and functionality:
Renamed Components
semantics_descriptiontosemantics_enrichmentfor better clarityEnhanced Model Properties
aliasfield to model and column propertiesAPI Improvements
/semantics-enrichmentendpoint/semantics-descriptionsendpoint (maintaining backward compatibility)System Prompt Updates
Model Processing
Migration
Existing clients should migrate to the new
/semantics-enrichmentendpoint. The old endpoint will be maintained temporarily but is marked as deprecated.Configuration Updates
Updated configuration files:
Testing for Endpoint
Request Body
Screenshots
Summary by CodeRabbit
New Features
Bug Fixes
Documentation
Tests
Chores