chore(wren-ai-service): Try to make evaluation work again #1085
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WalkthroughThe changes introduce significant modifications to the Changes
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🧰 Additional context used🪛 Ruff (0.8.2)wren-ai-service/eval/pipelines.py4-4: Remove unused import: (F401) 11-11: Remove unused import: (F401) 15-15: Remove unused import: (F401) 16-16: Remove unused import: (F401) 18-18: Remove unused import: (F401) 22-22: Remove unused import (F401) 23-23: Remove unused import (F401) 24-24: Remove unused import (F401) wren-ai-service/eval/prediction.py16-16: Remove unused import: (F401) 🔇 Additional comments (1)
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Actionable comments posted: 1
🧹 Nitpick comments (11)
wren-ai-service/eval/pipelines.py (9)
15-15: Unused import detected.
Thesettingsimport doesn't appear to be used anywhere in this file. Consider removing it to keep the imports clean.- from src.config import settings🧰 Tools
🪛 Ruff (0.8.2)
15-15:
src.config.settingsimported but unusedRemove unused import:
src.config.settings(F401)
16-16: Unused import detected.
Thegenerate_componentsimport is unused in this file. Consider removing it to reduce clutter.- from src.providers import generate_components🧰 Tools
🪛 Ruff (0.8.2)
16-16:
src.providers.generate_componentsimported but unusedRemove unused import:
src.providers.generate_components(F401)
17-26: Potentially redundant import.
All imported symbols here (AskRequest,AskResultRequest,AskResultResponse,AskService, etc.) are used except possiblyAskResultResponse. Since the code callsask_service_var.get_ask_result(...)(which returns an object of typeAskResultResponse), you may keep it for clarity or for type hints. Otherwise, if it's not referenced directly (e.g., in annotations), removing it can reduce confusion.🧰 Tools
🪛 Ruff (0.8.2)
24-24:
src.web.v1.services.ask.AskResultResponseimported but unusedRemove unused import:
src.web.v1.services.ask.AskResultResponse(F401)
60-64: Commented-out code identified.
This block is commented out, presumably for debugging or future reference. If no longer needed, consider removing it for clarity.
213-213: Commented-out code identified.
Similar to lines 60-64, if no future usage is planned, consider removing thedeploy_modelreference for clarity.
355-364: Helper method for dictionary visualization.
Thedict_to_stringmethod is a neat utility. Consider adding small checks for cyclical references if there's any chance of them. Otherwise, looks good.
418-434: Polling for ask result.
The loop checks for"finished"or"failed"states; consider adding a max-retry or timeout to avoid an infinite loop in case of unexpected status.while ( ask_result_response.status != "finished" and ask_result_response.status != "failed" ): + # Possibly add a counter or timeout check to prevent infinite loops. ask_result_response = self.ask_service_var.get_ask_result( ... )
435-435: Commented-out retrieval code.
If retrieval logic is not needed for theAskPipeline, consider removing it to avoid confusion.
445-445: Retrieval context commented out.
Same reasoning as above; remove if it’s no longer part of the pipeline flow.wren-ai-service/eval/prediction.py (2)
24-27: Partially unused import.
create_service_containeris imported but never used.create_service_metadatais utilized at line 189. Consider removingcreate_service_container.from src.globals import ( - create_service_container, create_service_metadata, )🧰 Tools
🪛 Ruff (0.8.2)
25-25:
src.globals.create_service_containerimported but unusedRemove unused import:
src.globals.create_service_container(F401)
91-95: Commented-out repository checks.
If these checks are no longer needed, consider removing them. If they're still useful, add a quick comment explaining why they're disabled.
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Review profile: CHILL
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📒 Files selected for processing (3)
wren-ai-service/eval/pipelines.py(7 hunks)wren-ai-service/eval/prediction.py(5 hunks)wren-ai-service/src/globals.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
wren-ai-service/eval/prediction.py
25-25: src.globals.create_service_container imported but unused
Remove unused import: src.globals.create_service_container
(F401)
145-145: Undefined name provider
(F821)
wren-ai-service/eval/pipelines.py
10-10: orjson imported but unused
Remove unused import: orjson
(F401)
15-15: src.config.settings imported but unused
Remove unused import: src.config.settings
(F401)
16-16: src.providers.generate_components imported but unused
Remove unused import: src.providers.generate_components
(F401)
24-24: src.web.v1.services.ask.AskResultResponse imported but unused
Remove unused import: src.web.v1.services.ask.AskResultResponse
(F401)
🔇 Additional comments (25)
wren-ai-service/eval/pipelines.py (16)
5-5: Import usage is valid.
Theuuidimport is used to generate a uniqueproject_idat line 383.
11-11: Import usage is valid.
Thejsonimport is used to serializemdlat line 384.
27-32: Imports are used properly.
The pipeline modules (data_assistance,intent_classification, etc.) are referenced within theask_servicemethod.
57-57: Import usage is valid.
indexingimport is used for referencingindexing.DBSchema,indexing.HistoricalQuestion, andindexing.TableDescription.
131-131: No actionable changes.
This line is a comment referencing how the subclass calls__call__.
313-313: Empty line.
No issues here.
315-330: Indexing service definition looks good.
Theindexing_servicemethod correctly initializes aSemanticsPreparationServiceusing the component definitions frompipe_components.
331-354: Ask service definition is well-structured.
Theask_servicemethod constructs anAskServiceinstance with properly mapped pipeline steps. This design cleanly separates the pipeline logic.
369-370: Constructor parameters expanded.
Acceptingservice_metadataandpipe_componentsbroadens the pipeline’s configurability.
373-374: Assignment is straightforward.
Storingservice_metadatafor later usage. No issues.
382-386: Initialization logic is correct.
Assigning pipeline components, generating IDs, and creating service instances ensures the pipeline is fully prepared.
388-388: Hash generation.
Storingmdl_hashby hashing the JSON string is useful for checks. No issues spotted.
398-407: Preparing semantics step.
The asynchronous call toprepare_semanticsuses the correctSemanticsPreparationRequestarguments. Properly referencesservice_metadata.
409-416: Ask request construction.
TheAskRequestobject is built properly. The logic to generate aquery_idis consistent with the pipeline’s flow.
485-486: Function signature extended.
initnow includesservice_metadataandpipe_components; the new approach is consistent with the constructor changes inAskPipeline.
488-488: Collecting arguments.
Packaging the arguments into a dictionary is a clean pattern for passing them to the pipeline classes.wren-ai-service/eval/prediction.py (8)
17-18: Valid usage.
Bothsettingsandgenerate_componentsare utilized later in this file.
53-56: Settings-based defaults.
Usingsettingsfor these indexing and retrieval parameters is consistent with the rest of the codebase.
147-150: Provider outputs assigned properly.
These assignments look fine, assuming the undefinedproviderissue is resolved.
182-182: Generating pipeline components.
generate_components(settings.components)is a straightforward approach for building the pipeline modules.
187-187: Keyword unpacking usage.
The code unpacks a portion ofpipe_components["db_schema_retrieval"]to pass intogenerate_meta. Ensure that the function indeed accepts those keys.
189-189: Service metadata creation.
The call tocreate_service_metadata(pipe_components)is consistent with the pipeline’s new constructor design.
194-195: Passing new arguments to pipeline init.
Forwardingservice_metadataandpipe_componentsto match the revised signatures.
198-198: Partial prediction usage.
Only the first item indataset["eval_dataset"]is predicted. Make sure this behavior is intentional if you expected a full batch run.wren-ai-service/src/globals.py (1)
70-76:getmethod inServiceMetadata.
This new method is straightforward and matches typical dictionary-like access patterns. Ifgetusage grows beyondservice_versionandpipes_metadata, consider implementing a more robust lookup or typed property approach.
wren-ai-service/eval/prediction.py
Outdated
| raise ValueError("Invalid datasource") | ||
|
|
||
| providers = provider.init_providers(engine_config=engine_config) | ||
| providers_inner = provider.init_providers(engine_config=engine_config) |
There was a problem hiding this comment.
Undefined name provider.
This call to provider.init_providers will fail unless provider is imported or defined. Fix this reference.
- providers_inner = provider.init_providers(engine_config=engine_config)
+ from src.providers import provider
+ providers_inner = provider.init_providers(engine_config=engine_config)Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff (0.8.2)
145-145: Undefined name provider
(F821)
cyyeh
changed the title
There was a problem hiding this comment.
Actionable comments posted: 5
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (3)
wren-ai-service/eval/pipelines.py(7 hunks)wren-ai-service/eval/prediction.py(4 hunks)wren-ai-service/src/pipelines/common.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
wren-ai-service/eval/pipelines.py
10-10: orjson imported but unused
Remove unused import: orjson
(F401)
15-15: src.config.settings imported but unused
Remove unused import: src.config.settings
(F401)
16-16: src.providers.generate_components imported but unused
Remove unused import: src.providers.generate_components
(F401)
24-24: src.web.v1.services.ask.AskResultResponse imported but unused
Remove unused import: src.web.v1.services.ask.AskResultResponse
(F401)
wren-ai-service/eval/prediction.py
25-25: src.globals.create_service_container imported but unused
Remove unused import: src.globals.create_service_container
(F401)
145-145: Undefined name provider
(F821)
🔇 Additional comments (1)
wren-ai-service/eval/prediction.py (1)
145-150:⚠️ Potential issueFix undefined reference to
providermodule.The code references an undefined
providermodule. This will cause a NameError at runtime.Apply this diff to fix the undefined reference:
- providers_inner = provider.init_providers(engine_config=engine_config) + from src.core.provider import init_providers + providers_inner = init_providers(engine_config=engine_config)Likely invalid or redundant comment.
🧰 Tools
🪛 Ruff (0.8.2)
145-145: Undefined name
provider(F821)
| if no_error: | ||
| status, _, addition = await self._engine.execute_sql( | ||
| quoted_sql, session, project_id=project_id | ||
| quoted_sql, session, project_id=int(project_id) |
There was a problem hiding this comment.
💡 Codebase verification
🛠️ Refactor suggestion
Unsafe type casting of project_id needs revision
The type casting of project_id to integer is problematic because:
- It's inconsistent with the codebase where
project_idis handled as an optional string - It can raise ValueError for None values, which are valid according to the function signatures
Suggested fix:
quoted_sql, session, project_id=str(project_id) if project_id is not None else None🔗 Analysis chain
Verify project_id type casting and add error handling.
The change to cast project_id to integer might fix the evaluation error, but it could raise a ValueError if project_id is None or not a valid integer string.
Add error handling to safely handle invalid project_id values:
- quoted_sql, session, project_id=int(project_id)
+ quoted_sql, session, project_id=int(project_id) if project_id is not None else None🏁 Scripts executed
The following scripts were executed for the analysis:
Error: No shell scripts found
Script:
#!/bin/bash
# Find execute_sql function definition and its usage
ast-grep --pattern 'def execute_sql($$$)'
# Search for project_id parameter in SQL execution contexts
rg "execute_sql.*project_id" -A 2
# Look for None checks on project_id
rg "project_id.*None" -A 2
rg "if.*project_id" -A 2
Length of output: 17594
wren-ai-service/eval/pipelines.py
Outdated
| def dict_to_string(self, d: dict) -> str: | ||
| if not isinstance(d, dict): | ||
| return str(d) | ||
|
|
||
| result = "{" | ||
| for key, value in d.items(): | ||
| result += f"'{key}': {self.dict_to_string(value)}, " | ||
| result = result.rstrip(", ") + "}" | ||
| return result |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for circular references and unhashable keys.
The recursive dictionary to string conversion could fail in several scenarios:
- Circular references could cause infinite recursion
- Unhashable dictionary keys could raise TypeError
Add error handling to handle these cases:
def dict_to_string(self, d: dict, seen=None) -> str:
+ if seen is None:
+ seen = set()
+
if not isinstance(d, dict):
return str(d)
+ # Check for circular references
+ d_id = id(d)
+ if d_id in seen:
+ return "{...}" # Indicate circular reference
+ seen.add(d_id)
+
result = "{"
- for key, value in d.items():
- result += f"'{key}': {self.dict_to_string(value)}, "
+ try:
+ for key, value in d.items():
+ result += f"'{key}': {self.dict_to_string(value, seen)}, "
+ except TypeError as e:
+ return f"{{Error: {str(e)}}}"
+
result = result.rstrip(", ") + "}"
+ seen.remove(d_id)
return result📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def dict_to_string(self, d: dict) -> str: | |
| if not isinstance(d, dict): | |
| return str(d) | |
| result = "{" | |
| for key, value in d.items(): | |
| result += f"'{key}': {self.dict_to_string(value)}, " | |
| result = result.rstrip(", ") + "}" | |
| return result | |
| def dict_to_string(self, d: dict, seen=None) -> str: | |
| if seen is None: | |
| seen = set() | |
| if not isinstance(d, dict): | |
| return str(d) | |
| # Check for circular references | |
| d_id = id(d) | |
| if d_id in seen: | |
| return "{...}" # Indicate circular reference | |
| seen.add(d_id) | |
| result = "{" | |
| try: | |
| for key, value in d.items(): | |
| result += f"'{key}': {self.dict_to_string(value, seen)}, " | |
| except TypeError as e: | |
| return f"{{Error: {str(e)}}}" | |
| result = result.rstrip(", ") + "}" | |
| seen.remove(d_id) | |
| return result |
wren-ai-service/eval/pipelines.py
Outdated
| def ask_service(self): | ||
|
|
||
| return AskService( | ||
| { | ||
| "intent_classification": intent_classification.IntentClassification( | ||
| **self.pipe_components["intent_classification"], | ||
| ), | ||
| "data_assistance": data_assistance.DataAssistance( | ||
| **self.pipe_components["data_assistance"], | ||
| ), | ||
| "retrieval": retrieval.Retrieval( | ||
| **self.pipe_components["db_schema_retrieval"], | ||
| ), | ||
| "historical_question": historical_question.HistoricalQuestion( | ||
| **self.pipe_components["historical_question_retrieval"], | ||
| ), | ||
| "sql_generation": sql_generation.SQLGeneration( | ||
| **self.pipe_components["sql_generation"], | ||
| ), | ||
| "sql_correction": sql_correction.SQLCorrection( | ||
| **self.pipe_components["sql_correction"], | ||
| ), | ||
| } | ||
| ) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for missing components.
Similar to indexing_service, this function accesses self.pipe_components dictionary without checking if required components exist.
Add error handling to safely access required components:
def ask_service(self):
+ required_components = [
+ "intent_classification",
+ "data_assistance",
+ "db_schema_retrieval",
+ "historical_question_retrieval",
+ "sql_generation",
+ "sql_correction"
+ ]
+ missing_components = [comp for comp in required_components if comp not in self.pipe_components]
+ if missing_components:
+ raise ValueError(f"Missing required components: {missing_components}")
+
return AskService(📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def ask_service(self): | |
| return AskService( | |
| { | |
| "intent_classification": intent_classification.IntentClassification( | |
| **self.pipe_components["intent_classification"], | |
| ), | |
| "data_assistance": data_assistance.DataAssistance( | |
| **self.pipe_components["data_assistance"], | |
| ), | |
| "retrieval": retrieval.Retrieval( | |
| **self.pipe_components["db_schema_retrieval"], | |
| ), | |
| "historical_question": historical_question.HistoricalQuestion( | |
| **self.pipe_components["historical_question_retrieval"], | |
| ), | |
| "sql_generation": sql_generation.SQLGeneration( | |
| **self.pipe_components["sql_generation"], | |
| ), | |
| "sql_correction": sql_correction.SQLCorrection( | |
| **self.pipe_components["sql_correction"], | |
| ), | |
| } | |
| ) | |
| def ask_service(self): | |
| required_components = [ | |
| "intent_classification", | |
| "data_assistance", | |
| "db_schema_retrieval", | |
| "historical_question_retrieval", | |
| "sql_generation", | |
| "sql_correction" | |
| ] | |
| missing_components = [comp for comp in required_components if comp not in self.pipe_components] | |
| if missing_components: | |
| raise ValueError(f"Missing required components: {missing_components}") | |
| return AskService( | |
| { | |
| "intent_classification": intent_classification.IntentClassification( | |
| **self.pipe_components["intent_classification"], | |
| ), | |
| "data_assistance": data_assistance.DataAssistance( | |
| **self.pipe_components["data_assistance"], | |
| ), | |
| "retrieval": retrieval.Retrieval( | |
| **self.pipe_components["db_schema_retrieval"], | |
| ), | |
| "historical_question": historical_question.HistoricalQuestion( | |
| **self.pipe_components["historical_question_retrieval"], | |
| ), | |
| "sql_generation": sql_generation.SQLGeneration( | |
| **self.pipe_components["sql_generation"], | |
| ), | |
| "sql_correction": sql_correction.SQLCorrection( | |
| **self.pipe_components["sql_correction"], | |
| ), | |
| } | |
| ) |
wren-ai-service/eval/pipelines.py
Outdated
| def indexing_service(self): | ||
|
|
||
| return SemanticsPreparationService( | ||
| { | ||
| "db_schema": indexing.DBSchema( | ||
| **self.pipe_components["db_schema_indexing"], | ||
| ), | ||
| "historical_question": indexing.HistoricalQuestion( | ||
| **self.pipe_components["historical_question_indexing"], | ||
| ), | ||
| "table_description": indexing.TableDescription( | ||
| **self.pipe_components["table_description_indexing"], | ||
| ), | ||
| } | ||
| ) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for missing components.
The function accesses self.pipe_components dictionary without checking if required components exist. This could raise KeyError exceptions if any required component is missing.
Add error handling to safely access required components:
def indexing_service(self):
+ required_components = [
+ "db_schema_indexing",
+ "historical_question_indexing",
+ "table_description_indexing"
+ ]
+ missing_components = [comp for comp in required_components if comp not in self.pipe_components]
+ if missing_components:
+ raise ValueError(f"Missing required components: {missing_components}")
+
return SemanticsPreparationService(
{
"db_schema": indexing.DBSchema(📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def indexing_service(self): | |
| return SemanticsPreparationService( | |
| { | |
| "db_schema": indexing.DBSchema( | |
| **self.pipe_components["db_schema_indexing"], | |
| ), | |
| "historical_question": indexing.HistoricalQuestion( | |
| **self.pipe_components["historical_question_indexing"], | |
| ), | |
| "table_description": indexing.TableDescription( | |
| **self.pipe_components["table_description_indexing"], | |
| ), | |
| } | |
| ) | |
| def indexing_service(self): | |
| required_components = [ | |
| "db_schema_indexing", | |
| "historical_question_indexing", | |
| "table_description_indexing" | |
| ] | |
| missing_components = [comp for comp in required_components if comp not in self.pipe_components] | |
| if missing_components: | |
| raise ValueError(f"Missing required components: {missing_components}") | |
| return SemanticsPreparationService( | |
| { | |
| "db_schema": indexing.DBSchema( | |
| **self.pipe_components["db_schema_indexing"], | |
| ), | |
| "historical_question": indexing.HistoricalQuestion( | |
| **self.pipe_components["historical_question_indexing"], | |
| ), | |
| "table_description": indexing.TableDescription( | |
| **self.pipe_components["table_description_indexing"], | |
| ), | |
| } | |
| ) |
wren-ai-service/eval/pipelines.py
Outdated
|
|
||
| await self.indexing_service_var.prepare_semantics( | ||
| SemanticsPreparationRequest( | ||
| mdl=self.mdl_str_var, | ||
| mdl_hash=self.mdl_hash, | ||
| project_id=self.project_id | ||
| ), | ||
| service_metadata=self.service_metadata, | ||
| ) | ||
|
|
||
| # asking | ||
| ask_request = AskRequest( | ||
| query=prediction["input"], | ||
| contexts=documents, | ||
| samples=prediction["samples"], | ||
| exclude=[], | ||
| mdl_hash=self.mdl_hash, | ||
| project_id = self.project_id, | ||
|
|
||
| ) | ||
| ask_request.query_id = str(uuid.uuid4().int >> 65) | ||
| await self.ask_service_var.ask(ask_request, service_metadata=self.service_metadata) | ||
| # getting ask result | ||
| ask_result_response = self.ask_service_var.get_ask_result( | ||
| AskResultRequest( | ||
| query_id=ask_request.query_id, | ||
| ) | ||
| ) | ||
|
|
||
| prediction["actual_output"] = actual_output | ||
| prediction["retrieval_context"] = extract_units(documents) | ||
| while ( | ||
| ask_result_response.status != "finished" | ||
| and ask_result_response.status != "failed" | ||
| ): | ||
| # getting ask result | ||
| ask_result_response = self.ask_service_var.get_ask_result( | ||
| AskResultRequest( | ||
| query_id=ask_request.query_id, | ||
| ) | ||
| ) | ||
|
|
||
| # result = await self._retrieval.run(query=prediction["input"]) | ||
| # documents = result.get("construct_retrieval_results", []) | ||
| # actual_output = await self._generation.run( | ||
| # query=prediction["input"], | ||
| # contexts=documents, | ||
| # samples=prediction["samples"], | ||
| # exclude=[], | ||
| # ) | ||
|
|
||
| prediction["actual_output"] = ask_result_response.response[0].sql | ||
| #prediction["retrieval_context"] = extract_units(documents) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add timeout, backoff, and error handling to the polling loop.
The current implementation has several issues:
- No timeout mechanism for the polling loop
- No backoff strategy between retries
- No error handling for service calls
Add these safety mechanisms:
async def _process(self, prediction: dict, **_) -> dict:
+ MAX_RETRIES = 10
+ INITIAL_BACKOFF = 1 # seconds
+ MAX_BACKOFF = 32 # seconds
+
+ try:
await self.indexing_service_var.prepare_semantics(
SemanticsPreparationRequest(
mdl=self.mdl_str_var,
mdl_hash=self.mdl_hash,
project_id=self.project_id
),
service_metadata=self.service_metadata,
)
ask_request = AskRequest(
query=prediction["input"],
mdl_hash=self.mdl_hash,
project_id=self.project_id,
)
ask_request.query_id = str(uuid.uuid4().int >> 65)
await self.ask_service_var.ask(ask_request, service_metadata=self.service_metadata)
ask_result_response = self.ask_service_var.get_ask_result(
AskResultRequest(
query_id=ask_request.query_id,
)
)
+ retries = 0
+ backoff = INITIAL_BACKOFF
while (
ask_result_response.status != "finished"
and ask_result_response.status != "failed"
+ and retries < MAX_RETRIES
):
+ await asyncio.sleep(backoff)
+ backoff = min(backoff * 2, MAX_BACKOFF)
+ retries += 1
+
ask_result_response = self.ask_service_var.get_ask_result(
AskResultRequest(
query_id=ask_request.query_id,
)
)
+ if retries >= MAX_RETRIES:
+ raise TimeoutError("Ask service request timed out")
+
+ if ask_result_response.status == "failed":
+ raise RuntimeError(f"Ask service request failed: {ask_result_response.error}")
+
prediction["actual_output"] = ask_result_response.response[0].sql
+ except Exception as e:
+ logger.exception("Error in _process: %s", str(e))
+ prediction["actual_output"] = None
+ prediction["error"] = str(e)
return prediction📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| await self.indexing_service_var.prepare_semantics( | |
| SemanticsPreparationRequest( | |
| mdl=self.mdl_str_var, | |
| mdl_hash=self.mdl_hash, | |
| project_id=self.project_id | |
| ), | |
| service_metadata=self.service_metadata, | |
| ) | |
| # asking | |
| ask_request = AskRequest( | |
| query=prediction["input"], | |
| contexts=documents, | |
| samples=prediction["samples"], | |
| exclude=[], | |
| mdl_hash=self.mdl_hash, | |
| project_id = self.project_id, | |
| ) | |
| ask_request.query_id = str(uuid.uuid4().int >> 65) | |
| await self.ask_service_var.ask(ask_request, service_metadata=self.service_metadata) | |
| # getting ask result | |
| ask_result_response = self.ask_service_var.get_ask_result( | |
| AskResultRequest( | |
| query_id=ask_request.query_id, | |
| ) | |
| ) | |
| prediction["actual_output"] = actual_output | |
| prediction["retrieval_context"] = extract_units(documents) | |
| while ( | |
| ask_result_response.status != "finished" | |
| and ask_result_response.status != "failed" | |
| ): | |
| # getting ask result | |
| ask_result_response = self.ask_service_var.get_ask_result( | |
| AskResultRequest( | |
| query_id=ask_request.query_id, | |
| ) | |
| ) | |
| # result = await self._retrieval.run(query=prediction["input"]) | |
| # documents = result.get("construct_retrieval_results", []) | |
| # actual_output = await self._generation.run( | |
| # query=prediction["input"], | |
| # contexts=documents, | |
| # samples=prediction["samples"], | |
| # exclude=[], | |
| # ) | |
| prediction["actual_output"] = ask_result_response.response[0].sql | |
| #prediction["retrieval_context"] = extract_units(documents) | |
| async def _process(self, prediction: dict, **_) -> dict: | |
| MAX_RETRIES = 10 | |
| INITIAL_BACKOFF = 1 # seconds | |
| MAX_BACKOFF = 32 # seconds | |
| try: | |
| await self.indexing_service_var.prepare_semantics( | |
| SemanticsPreparationRequest( | |
| mdl=self.mdl_str_var, | |
| mdl_hash=self.mdl_hash, | |
| project_id=self.project_id | |
| ), | |
| service_metadata=self.service_metadata, | |
| ) | |
| ask_request = AskRequest( | |
| query=prediction["input"], | |
| mdl_hash=self.mdl_hash, | |
| project_id=self.project_id, | |
| ) | |
| ask_request.query_id = str(uuid.uuid4().int >> 65) | |
| await self.ask_service_var.ask(ask_request, service_metadata=self.service_metadata) | |
| ask_result_response = self.ask_service_var.get_ask_result( | |
| AskResultRequest( | |
| query_id=ask_request.query_id, | |
| ) | |
| ) | |
| retries = 0 | |
| backoff = INITIAL_BACKOFF | |
| while ( | |
| ask_result_response.status != "finished" | |
| and ask_result_response.status != "failed" | |
| and retries < MAX_RETRIES | |
| ): | |
| await asyncio.sleep(backoff) | |
| backoff = min(backoff * 2, MAX_BACKOFF) | |
| retries += 1 | |
| ask_result_response = self.ask_service_var.get_ask_result( | |
| AskResultRequest( | |
| query_id=ask_request.query_id, | |
| ) | |
| ) | |
| if retries >= MAX_RETRIES: | |
| raise TimeoutError("Ask service request timed out") | |
| if ask_result_response.status == "failed": | |
| raise RuntimeError(f"Ask service request failed: {ask_result_response.error}") | |
| prediction["actual_output"] = ask_result_response.response[0].sql | |
| except Exception as e: | |
| logger.exception("Error in _process: %s", str(e)) | |
| prediction["actual_output"] = None | |
| prediction["error"] = str(e) | |
| return prediction |
There was a problem hiding this comment.
Actionable comments posted: 1
🧹 Nitpick comments (2)
wren-ai-service/eval/pipelines.py (2)
375-380: Remove or document commented code.There are multiple lines of commented code without explanation. Either remove them if they're no longer needed or add a comment explaining why they're preserved.
- # document_store_provider.get_store(recreate_index=True) - # _indexing = indexing.Indexing( - # embedder_provider=embedder_provider, - # document_store_provider=document_store_provider, - # ) - # deploy_model(mdl, _indexing) + # TODO: Remove after confirming new component-based indexing is stable + # Legacy indexing code kept for reference: + # document_store_provider.get_store(recreate_index=True) + # _indexing = indexing.Indexing( + # embedder_provider=embedder_provider, + # document_store_provider=document_store_provider, + # ) + # deploy_model(mdl, _indexing)
485-488: Add type hints for service_metadata parameter.The
service_metadataparameter lacks type hints, which could lead to type-related issues.def init( name: Literal["retrieval", "generation", "ask"], meta: dict, mdl: dict, - service_metadata, + service_metadata: Dict[str, Any], pipe_components: Dict[str, Any], ) -> Eval:
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (2)
wren-ai-service/eval/pipelines.py(7 hunks)wren-ai-service/eval/prediction.py(4 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
wren-ai-service/eval/prediction.py
25-25: src.globals.create_service_container imported but unused
Remove unused import: src.globals.create_service_container
(F401)
145-145: Undefined name provider
(F821)
wren-ai-service/eval/pipelines.py
10-10: orjson imported but unused
Remove unused import: orjson
(F401)
15-15: src.config.settings imported but unused
Remove unused import: src.config.settings
(F401)
16-16: src.providers.generate_components imported but unused
Remove unused import: src.providers.generate_components
(F401)
24-24: src.web.v1.services.ask.AskResultResponse imported but unused
Remove unused import: src.web.v1.services.ask.AskResultResponse
(F401)
🔇 Additional comments (5)
wren-ai-service/eval/prediction.py (2)
53-56: LGTM! Configuration management improvement.Good refactor to use centralized settings with fallback values.
17-27:⚠️ Potential issueFix missing provider import causing runtime error.
The code attempts to use
provider.init_providerson line 145, but theprovidermodule is no longer imported.Add the following import:
+from src.providers import providerLikely invalid or redundant comment.
🧰 Tools
🪛 Ruff (0.8.2)
25-25:
src.globals.create_service_containerimported but unusedRemove unused import:
src.globals.create_service_container(F401)
wren-ai-service/eval/pipelines.py (3)
355-363: 🛠️ Refactor suggestionAdd protection against circular references and unhashable keys.
The dictionary to string conversion is vulnerable to:
- Infinite recursion from circular references
- TypeError from unhashable dictionary keys
Add these safety mechanisms:
- def dict_to_string(self, d: dict) -> str: + def dict_to_string(self, d: dict, seen=None) -> str: + if seen is None: + seen = set() + if not isinstance(d, dict): return str(d) + # Check for circular references + d_id = id(d) + if d_id in seen: + return "{...}" # Indicate circular reference + seen.add(d_id) result = "{" - for key, value in d.items(): - result += f"'{key}': {self.dict_to_string(value)}, " + try: + for key, value in d.items(): + result += f"'{key}': {self.dict_to_string(value, seen)}, " + except TypeError as e: + return f"{{Error: {str(e)}}}" result = result.rstrip(", ") + "}" + seen.remove(d_id) return resultLikely invalid or redundant comment.
364-388: 🛠️ Refactor suggestionAdd validation for required components.
The constructor should validate the presence of all required components before initialization to prevent runtime errors.
Add component validation:
def __init__( self, meta: dict, mdl: dict, service_metadata, pipe_components, ): super().__init__(meta, 3) + required_components = { + 'db_schema_indexing', + 'historical_question_indexing', + 'table_description_indexing', + 'intent_classification', + 'data_assistance', + 'db_schema_retrieval', + 'historical_question_retrieval', + 'sql_generation', + 'sql_correction' + } + + missing = required_components - set(pipe_components.keys()) + if missing: + raise ValueError(f"Missing required components: {missing}") + self.service_metadata = service_metadata self.pipe_components = pipe_components self.project_id = str(uuid.uuid4().int >> 65)Likely invalid or redundant comment.
398-445:⚠️ Potential issueAdd timeout and error handling to the polling loop.
The current polling implementation lacks several critical safety mechanisms:
- No timeout to prevent infinite loops
- No delay between retries
- No error handling for failed status
Add these safety mechanisms:
async def _process(self, prediction: dict, **_) -> dict: + MAX_RETRIES = 10 + RETRY_DELAY = 1 # seconds + + try: await self.indexing_service_var.prepare_semantics( SemanticsPreparationRequest( mdl=self.mdl_str_var, mdl_hash=self.mdl_hash, project_id=self.project_id ), service_metadata=self.service_metadata, ) ask_request = AskRequest( query=prediction["input"], mdl_hash=self.mdl_hash, project_id=self.project_id, ) ask_request.query_id = str(uuid.uuid4().int >> 65) await self.ask_service_var.ask(ask_request, service_metadata=self.service_metadata) ask_result_response = self.ask_service_var.get_ask_result( AskResultRequest( query_id=ask_request.query_id, ) ) + retries = 0 while ( ask_result_response.status != "finished" and ask_result_response.status != "failed" + and retries < MAX_RETRIES ): + await asyncio.sleep(RETRY_DELAY) + retries += 1 ask_result_response = self.ask_service_var.get_ask_result( AskResultRequest( query_id=ask_request.query_id, ) ) + if retries >= MAX_RETRIES: + raise TimeoutError(f"Ask service request timed out after {MAX_RETRIES} retries") + + if ask_result_response.status == "failed": + raise RuntimeError(f"Ask service request failed: {ask_result_response.error}") + + if not ask_result_response.response: + raise ValueError("Empty response from ask service") + prediction["actual_output"] = ask_result_response.response[0].sql + except Exception as e: + prediction["error"] = str(e) + prediction["actual_output"] = None return predictionLikely invalid or redundant comment.
wren-ai-service/eval/prediction.py
Outdated
| pipe_components = generate_components(settings.components) | ||
| meta = generate_meta( | ||
| path=path, | ||
| dataset=dataset, | ||
| pipe=pipe_name, | ||
| **providers, | ||
| **pipe_components["db_schema_retrieval"], | ||
| ) | ||
|
|
||
| service_metadata = create_service_metadata(pipe_components) | ||
| pipe = pipelines.init( | ||
| pipe_name, | ||
| meta, | ||
| mdl=dataset["mdl"], | ||
| providers=providers, | ||
| service_metadata=service_metadata, | ||
| pipe_components=pipe_components, |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for component generation.
The component generation and service metadata creation could fail silently. Consider adding error handling:
- pipe_components = generate_components(settings.components)
- meta = generate_meta(
- path=path,
- dataset=dataset,
- pipe=pipe_name,
- **pipe_components["db_schema_retrieval"],
- )
- service_metadata = create_service_metadata(pipe_components)
+ try:
+ pipe_components = generate_components(settings.components)
+ if not pipe_components.get("db_schema_retrieval"):
+ raise ValueError("Required component 'db_schema_retrieval' not found")
+
+ meta = generate_meta(
+ path=path,
+ dataset=dataset,
+ pipe=pipe_name,
+ **pipe_components["db_schema_retrieval"],
+ )
+ service_metadata = create_service_metadata(pipe_components)
+ except Exception as e:
+ raise RuntimeError(f"Failed to initialize components: {str(e)}") from e📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| pipe_components = generate_components(settings.components) | |
| meta = generate_meta( | |
| path=path, | |
| dataset=dataset, | |
| pipe=pipe_name, | |
| **providers, | |
| **pipe_components["db_schema_retrieval"], | |
| ) | |
| service_metadata = create_service_metadata(pipe_components) | |
| pipe = pipelines.init( | |
| pipe_name, | |
| meta, | |
| mdl=dataset["mdl"], | |
| providers=providers, | |
| service_metadata=service_metadata, | |
| pipe_components=pipe_components, | |
| try: | |
| pipe_components = generate_components(settings.components) | |
| if not pipe_components.get("db_schema_retrieval"): | |
| raise ValueError("Required component 'db_schema_retrieval' not found") | |
| meta = generate_meta( | |
| path=path, | |
| dataset=dataset, | |
| pipe=pipe_name, | |
| **pipe_components["db_schema_retrieval"], | |
| ) | |
| service_metadata = create_service_metadata(pipe_components) | |
| except Exception as e: | |
| raise RuntimeError(f"Failed to initialize components: {str(e)}") from e | |
| pipe = pipelines.init( | |
| pipe_name, | |
| meta, | |
| mdl=dataset["mdl"], | |
| service_metadata=service_metadata, | |
| pipe_components=pipe_components, |
1 participant
After the code is refactored, the evaluation will not work.
This PR is trying to make the evaluation work again.
But now it reports the error:
looks like engine doesn't load spider test data. I follow the instruction of https://github.com/Canner/WrenAI/tree/main/wren-ai-service/eval#eval-dataset-preparationif-using-spider-10-dataset put the data to
wren-ai-service/tools/dev/spider1.0ISummary by CodeRabbit
New Features
Refactor