Harrison/guardrails#4
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Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
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Greptile SummaryThis PR introduces a guardrails framework for
Confidence Score: 1/5Not safe to merge — multiple guardrail code paths crash at runtime before any useful work is done. The
Important Files Changed
Reviews (1): Last reviewed commit: "Update langchain/output_parsers/base.py" | Re-trigger Greptile |
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| class ValidationError(BaseModel): | ||
| error_message: str |
There was a problem hiding this comment.
ValidationError model defines its field as error_message, but every call site in this PR passes text=e — e.g., ValidationError(text=e) in both llm.py and output_parsers/base.py. Pydantic will raise a ValidationError (its own!) on any code path that triggers guardrail error handling, making the entire guardrails feature broken at runtime. The field must be renamed to text to match the call sites, and the exception must be coerced to str.
| class ValidationError(BaseModel): | |
| error_message: str | |
| class ValidationError(BaseModel): | |
| text: str |
| except Exception as e: | ||
| error = ValidationError(text=e) |
There was a problem hiding this comment.
e is an Exception object, but ValidationError.text (or error_message as currently defined) is typed as str. Passing the raw exception will fail Pydantic's type validation. It should be str(e).
| except Exception as e: | |
| error = ValidationError(text=e) | |
| except Exception as e: | |
| error = ValidationError(text=str(e)) |
| def check( | ||
| self, prompt_value: PromptValue, result: Any | ||
| ) -> Optional[ValidationError]: | ||
| try: | ||
| self.output_parser.parse(result) | ||
| return None | ||
| except Exception as e: | ||
| return ValidationError(text=e) | ||
|
|
||
| def fix( | ||
| self, prompt_value: PromptValue, result: Any, error: ValidationError | ||
| ) -> Any: | ||
| return self.fixer(prompt_value, result, error) |
There was a problem hiding this comment.
Fixer is an abstract class with a fix method, not a callable object. Calling self.fixer(prompt_value, result, error) will raise TypeError: 'Fixer' object is not callable at runtime. The correct call is self.fixer.fix(...). Additionally, the exception passed to ValidationError should be str(e) since the field is typed as str.
| def check( | |
| self, prompt_value: PromptValue, result: Any | |
| ) -> Optional[ValidationError]: | |
| try: | |
| self.output_parser.parse(result) | |
| return None | |
| except Exception as e: | |
| return ValidationError(text=e) | |
| def fix( | |
| self, prompt_value: PromptValue, result: Any, error: ValidationError | |
| ) -> Any: | |
| return self.fixer(prompt_value, result, error) | |
| def check( | |
| self, prompt_value: PromptValue, result: Any | |
| ) -> Optional[ValidationError]: | |
| try: | |
| self.output_parser.parse(result) | |
| return None | |
| except Exception as e: | |
| return ValidationError(text=str(e)) | |
| def fix( | |
| self, prompt_value: PromptValue, result: Any, error: ValidationError | |
| ) -> Any: | |
| return self.fixer.fix(prompt_value, result, error) |
| """Prompt object to use.""" | ||
| llm: BaseLanguageModel | ||
| output_key: str = "text" #: :meta private: | ||
| output_parser: Optional[OutputGuardrail] = None |
There was a problem hiding this comment.
output_parser is never read
output_parser: Optional[OutputGuardrail] = None is declared on LLMChain but is never consulted in _get_final_output, apply, or any other method. A user who sets this field expecting it to be applied will see it silently ignored. Either wire it into _get_final_output (e.g., prepend it to the guardrails loop) or remove the field.
| def _get_final_output(self, text: str, prompt_value: PromptValue) -> Any: | ||
| result: Any = text | ||
| for guardrail in self.guardrails: | ||
| if isinstance(guardrail, OutputGuardrail): | ||
| try: | ||
| result = guardrail.output_parser.parse(result) | ||
| error = None | ||
| except Exception as e: | ||
| error = ValidationError(text=e) | ||
| else: | ||
| error = guardrail.check(prompt_value, result) | ||
| if error is not None: | ||
| result = guardrail.fix(prompt_value, result, error) | ||
| return result |
There was a problem hiding this comment.
OutputGuardrail.check() bypassed for OutputGuardrail instances
When iterating self.guardrails, the code special-cases OutputGuardrail by calling guardrail.output_parser.parse(result) directly — duplicating the logic already encapsulated in OutputGuardrail.check(). Non-OutputGuardrail items go through guardrail.check(). This asymmetry means any future changes to OutputGuardrail.check won't be picked up here. The branch should call guardrail.check() uniformly for all guardrail types.
| class ValidationError(BaseModel): | ||
| error_message: str |
There was a problem hiding this comment.
pydantic.ValidationError
Defining a class named ValidationError in langchain.schema shadows Pydantic's own pydantic.ValidationError. Since from langchain.schema import ValidationError is used in llm.py and output_parsers/base.py, any except pydantic.ValidationError clauses in those files (or callers) may inadvertently catch the wrong class. Consider a more specific name such as GuardrailValidationError.
|
@codity review |
Policy Check Failed✗ 3/3 policy checks failed: • Need 2 more approval(s) (0/2) — comment LGTM or approve via review To merge this PR:
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PR SummaryWhat Changed
Key Changes by AreaSchema: Added Output Parsing: New LLM Chain: Added Files Changed
Review Focus Areas
ArchitectureDesign Decisions: The breaking change to Risks:
Merge StatusMERGEABLE — PR Score 69/100, above threshold (50). All gates passed. |
![codity-dm[bot]](https://avatars.githubusercontent.com/u/234452856?s=60&v=4){kind=small}
| result = guardrail.output_parser.parse(result) | ||
| error = None | ||
| except Exception as e: | ||
| error = ValidationError(text=e) |
There was a problem hiding this comment.
Any invalid model output that triggers an OutputGuardrail parse/check failure now crashes in create_outputs because both llm.py and output_parsers/base.py instantiate ValidationError with text=..., but schema.py changed ValidationError to require error_message; e.g. a parser rejecting the literal output not json raises a Pydantic validation error instead of invoking the fixer.
Also reported at: langchain/chains/llm.py L151
Construct ValidationError with the new field name everywhere, e.g. ValidationError(error_message=str(e)), and likewise update output_parsers/base.py.
Prompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert python developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/chains/llm.py
Lines: 147-147
Issue Type: functional-high
Severity: high
Issue Description:
Any invalid model output that triggers an OutputGuardrail parse/check failure now crashes in create_outputs because both llm.py and output_parsers/base.py instantiate ValidationError with `text=...`, but schema.py changed ValidationError to require `error_message`; e.g. a parser rejecting the literal output `not json` raises a Pydantic validation error instead of invoking the fixer.
_Also reported at: `langchain/chains/llm.py` L151_
Current Code:
error = ValidationError(text=e)
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow python best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
Security Scan Summary
No critical security issues detected Scan completed in 12.6sSecurity scan powered by Codity.ai |
License Compliance Scan
All licenses are low-risk and compliant Low Risk Licenses - Top licenses from 6 packagesBSD-3-Clause (2 packages) Powered by Codity.ai · Docs |
Code Quality Report — test-org-codity/langchain · PR #4Scanned: 2026-05-19 18:09 UTC | Score: 59/100 | Provider: github Executive Summary
Top Findings[CQ-LLM-001]
|
| File | Critical | High | Medium | Low | Total |
|---|---|---|---|---|---|
langchain/chains/llm.py |
0 | 0 | 3 | 7 | 10 |
langchain/output_parsers/base.py |
0 | 0 | 0 | 3 | 3 |
langchain/schema.py |
0 | 0 | 0 | 6 | 6 |
Recommendations
- Run automated tests after applying fixes to verify no regressions.
|
@codity review |
Policy Check Failed✗ 3/3 policy checks failed: • Need 2 more approval(s) (0/2) — comment LGTM or approve via review To merge this PR:
|
PR SummaryWhat Changed
Key Changes by AreaSchema: Added Output Parsing: Added LLMChain: Added guardrails support with Files Changed
Review Focus Areas
ArchitectureDesign Decisions: The tuple return from Risks:
Merge StatusNOT MERGEABLE — PR Score 53/100, below threshold (50)
|
| for guardrail in self.guardrails: | ||
| if isinstance(guardrail, OutputGuardrail): | ||
| try: | ||
| result = guardrail.output_parser.parse(result) |
There was a problem hiding this comment.
When any parser or guardrail raises, _get_final_output constructs ValidationError(text=e) but langchain/schema.py now defines ValidationError.error_message, so a malformed output such as non-JSON text under an OutputGuardrail turns a recoverable validation failure into a pydantic initialization exception.
Instantiate the new schema correctly in both call sites, e.g. ValidationError(error_message=str(e)), and update any guardrail implementations to populate/read error_message.
Prompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert python developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/chains/llm.py
Lines: 144-144
Issue Type: functional-high
Severity: high
Issue Description:
When any parser or guardrail raises, _get_final_output constructs ValidationError(text=e) but langchain/schema.py now defines ValidationError.error_message, so a malformed output such as non-JSON text under an OutputGuardrail turns a recoverable validation failure into a pydantic initialization exception.
Current Code:
try:
result = guardrail.output_parser.parse(result)
error = None
except Exception as e:
error = ValidationError(text=e)
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow python best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
| try: | ||
| result = guardrail.output_parser.parse(result) | ||
| error = None | ||
| except Exception as e: | ||
| error = ValidationError(text=e) |
There was a problem hiding this comment.
The broad exception handler hides parser and programmer errors; catch the parser's validation exception explicitly and let unexpected exceptions propagate.
Suggested fix
try:
result = guardrail.output_parser.parse(result)
error = None
except ValidationError as e:
error = ePrompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert python developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/chains/llm.py
Lines: 143-147
Issue Type: robustness-high
Severity: high
Issue Description:
The broad exception handler hides parser and programmer errors; catch the parser's validation exception explicitly and let unexpected exceptions propagate.
Current Code:
try:
result = guardrail.output_parser.parse(result)
error = None
except Exception as e:
error = ValidationError(text=e)
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow python best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
| try: | ||
| self.output_parser.parse(result) | ||
| return None | ||
| except Exception as e: | ||
| return ValidationError(text=e) |
There was a problem hiding this comment.
Catching Exception here hides all parser and runtime errors; catch the parser-specific exception types you expect and let unexpected failures propagate.
Suggested fix
try:
self.output_parser.parse(result)
return None
except (ValueError, TypeError) as e:
return ValidationError(text=str(e))Prompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert python developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/output_parsers/base.py
Lines: 40-44
Issue Type: robustness-medium
Severity: medium
Issue Description:
Catching Exception here hides all parser and runtime errors; catch the parser-specific exception types you expect and let unexpected failures propagate.
Current Code:
try:
self.output_parser.parse(result)
return None
except Exception as e:
return ValidationError(text=e)
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow python best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
| except Exception as e: | ||
| return ValidationError(text=e) |
There was a problem hiding this comment.
The exception object is passed directly into ValidationError.text, so convert it to a string before storing it.
Suggested fix
except (ValueError, TypeError) as e:
return ValidationError(text=str(e))Prompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert python developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/output_parsers/base.py
Lines: 43-44
Issue Type: functional-medium
Severity: medium
Issue Description:
The exception object is passed directly into ValidationError.text, so convert it to a string before storing it.
Current Code:
except Exception as e:
return ValidationError(text=e)
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow python best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
Security Scan Summary
No critical security issues detected Scan completed in 18.5sSecurity scan powered by Codity.ai |
License Compliance Scan
All licenses are low-risk and compliant Low Risk Licenses - Top licenses from 6 packagesBSD-3-Clause (2 packages) Powered by Codity.ai · Docs |
Code Quality Report — test-org-codity/langchain · PR #4Scanned: 2026-05-19 19:18 UTC | Score: 59/100 | Provider: github Executive Summary
Top Findings[CQ-LLM-001]
|
| File | Critical | High | Medium | Low | Total |
|---|---|---|---|---|---|
langchain/chains/llm.py |
0 | 0 | 3 | 7 | 10 |
langchain/output_parsers/base.py |
0 | 0 | 0 | 3 | 3 |
langchain/schema.py |
0 | 0 | 0 | 6 | 6 |
Recommendations
- Run automated tests after applying fixes to verify no regressions.
|
@codity review |
Policy Check Failed✗ 3/3 policy checks failed: • Need 2 more approval(s) (0/2) — comment LGTM or approve via review To merge this PR:
|
PR SummaryWhat Changed
Key Changes by AreaCore Schema ( Output Parsing ( LLM Chain ( Call Sites ( Files Changed
Review Focus Areas
ArchitectureDesign Decisions: The tuple return from Scalability & Extensibility: Out of scope. The framework provides extension points via Risks:
Merge StatusNOT MERGEABLE — PR Score 31/100, below threshold (50)
|
| for guardrail in self.guardrails: | ||
| if isinstance(guardrail, OutputGuardrail): | ||
| try: | ||
| result = guardrail.output_parser.parse(result) |
There was a problem hiding this comment.
If any guardrail parser/fixer raises (e.g. invalid output triggers OutputGuardrail on '{'), _get_final_output constructs ValidationError(text=e) but schema.ValidationError now requires error_message, so the guardrail path crashes instead of returning a fixed value.
Instantiate ValidationError with the declared field, e.g. ValidationError(error_message=str(e)), and make OutputGuardrail.check do the same.
Prompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert python developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/chains/llm.py
Lines: 144-144
Issue Type: functional-critical
Severity: critical
Issue Description:
If any guardrail parser/fixer raises (e.g. invalid output triggers OutputGuardrail on '{'), _get_final_output constructs ValidationError(text=e) but schema.ValidationError now requires error_message, so the guardrail path crashes instead of returning a fixed value.
Current Code:
try:
result = guardrail.output_parser.parse(result)
error = None
except Exception as e:
error = ValidationError(text=e)
else:
error = guardrail.check(prompt_value, result)
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow python best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
| # Get the text of the top generated string. | ||
| {self.output_key: generation[0].text} | ||
| for generation in response.generations | ||
| {self.output_key: self._get_final_output(generation[0].text, prompts[i])} |
There was a problem hiding this comment.
When an LLMChain is configured with an OutputGuardrail and the model returns invalid JSON like '{', create_outputs now stores the parsed/fixed object instead of the raw text, but cross-file callers such as QA generation still read res[0].text from generate() and therefore silently bypass guardrails/fixes on that trigger.
Either apply guardrails inside generate()/agenerate() so callers consuming LLMResult see the fixed output too, or update all generate() callers to use apply()/create_outputs() instead of reading generation.text directly.
Prompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert python developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/chains/llm.py
Lines: 160-160
Issue Type: functional-high
Severity: high
Issue Description:
When an LLMChain is configured with an OutputGuardrail and the model returns invalid JSON like '{', create_outputs now stores the parsed/fixed object instead of the raw text, but cross-file callers such as QA generation still read res[0].text from generate() and therefore silently bypass guardrails/fixes on that trigger.
Current Code:
{self.output_key: self._get_final_output(generation[0].text, prompts[i])}
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow python best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
|
|
||
| def _get_final_output(self, text: str, prompt_value: PromptValue) -> Any: | ||
| result: Any = text | ||
| for guardrail in self.guardrails: |
There was a problem hiding this comment.
For a chain constructed with output_parser=SomeParser() but no guardrails list, a trigger like model output 'not-json' is no longer parsed at all because create_outputs only consults self.guardrails and ignores the new output_parser field, breaking callers that expect parsed outputs after this refactor.
Also reported at: langchain/chains/llm.py L40–L41
Preserve previous/new contract by automatically running self.output_parser when set, or wrap it into guardrails during initialization so create_outputs always applies it.
Prompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert python developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/chains/llm.py
Lines: 141-141
Issue Type: functional-high
Severity: high
Issue Description:
For a chain constructed with output_parser=SomeParser() but no guardrails list, a trigger like model output 'not-json' is no longer parsed at all because create_outputs only consults self.guardrails and ignores the new output_parser field, breaking callers that expect parsed outputs after this refactor.
_Also reported at: `langchain/chains/llm.py` L40–L41_
Current Code:
for guardrail in self.guardrails:
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow python best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
Security Scan Summary
No critical security issues detected Scan completed in 19.4sSecurity scan powered by Codity.ai |
License Compliance Scan
All licenses are low-risk and compliant Low Risk Licenses - Top licenses from 6 packagesBSD-3-Clause (2 packages) Powered by Codity.ai · Docs |
Code Quality Report — test-org-codity/langchain · PR #4Scanned: 2026-05-20 17:56 UTC | Score: 51/100 | Provider: github Executive Summary
Top Findings[CQ-LLM-002]
|
| File | Critical | High | Medium | Low | Total |
|---|---|---|---|---|---|
langchain/chains/llm.py |
0 | 1 | 3 | 7 | 11 |
langchain/output_parsers/base.py |
0 | 0 | 0 | 3 | 3 |
langchain/schema.py |
0 | 0 | 0 | 6 | 6 |
Recommendations
- Resolve High severity issues, especially error handling gaps and performance bottlenecks.
- Run automated tests after applying fixes to verify no regressions.
|
@codity review |
1 similar comment
|
@codity review |
PR SummaryWhat Changed
Key Changes by AreaGuardrails Framework: New abstract classes in LLMChain Core: Added Chain Call Sites: Updated Files Changed
Review Focus Areas
ArchitectureDesign Decisions: The Risks: The return type change is intentional but creates migration burden for external Merge StatusNOT MERGEABLE — PR Score 42/100, below threshold (50)
|
Workflow DiagramsAutomatically generated sequence diagrams showing the workflows in this PR 1. AnalysisComplex complexity • Components: OutputGuardrail, Guardrail, Fixer sequenceDiagram
participant Client
participant LLMChain
participant Guardrail
participant OutputGuardrail
participant LLM
participant Fixer
Note over Client,Fixer: Guardrails Integration Workflow
Client->>LLMChain: apply(inputs)
LLMChain->>LLMChain: prep_prompts(inputs)
LLMChain->>LLM: generate_prompt(prompts, stop)
LLM-->>LLMChain: LLMResult
Note right of LLMChain: New signature returns Tuplebr/LLMResult and PromptValue list
LLMChain->>LLMChain: create_outputs(response, prompts)
loop For each generation with index i
LLMChain->>LLMChain: _get_final_output(text, prompts[i])
loop For each guardrail in guardrails
alt guardrail is OutputGuardrail
LLMChain->>OutputGuardrail: output_parser.parse(result)
alt parsing succeeds
OutputGuardrail-->>LLMChain: parsed result, no error
else parsing fails
OutputGuardrail-->>LLMChain: ValidationError
end
else guardrail is Guardrail
LLMChain->>Guardrail: check(prompt_value, result)
Guardrail-->>LLMChain: ValidationError or None
end
alt error is not None
LLMChain->>Fixer: fix(prompt_value, result, error)
Fixer-->>LLMChain: fixed result
end
end
end
LLMChain-->>Client: List of processed outputs
Note over Client,Fixer: Breaking change: generate now returnsbr/Tuple[LLMResult, List[PromptValue]]br/instead of just LLMResult
Note: Diagrams show detected patterns only. Complex workflows may require manual review. |
![codity-chait[bot]](https://avatars.githubusercontent.com/u/228176587?s=60&v=4){kind=small}
| self.output_parser.parse(result) | ||
| return None | ||
| except Exception as e: | ||
| return ValidationError(text=e) |
There was a problem hiding this comment.
ValidationError is constructed with text=e but the field is named error_message and expects a str, not an Exception. The code should pass the exception message as a string to the correct field name.
| return ValidationError(text=e) | |
| return ValidationError(error_message=str(e)) |
Prompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert bash developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/output_parsers/base.py
Lines: 44-44
Issue Type: functional-critical
Severity: critical
Issue Description:
ValidationError is constructed with `text=e` but the field is named `error_message` and expects a `str`, not an `Exception`. The code should pass the exception message as a string to the correct field name.
Current Code:
return ValidationError(text=e)
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow bash best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
| def fix( | ||
| self, prompt_value: PromptValue, result: Any, error: ValidationError | ||
| ) -> Any: | ||
| return self.fixer(prompt_value, result, error) |
There was a problem hiding this comment.
The OutputGuardrail.fix method incorrectly invokes self.fixer as a callable using self.fixer(...), but self.fixer is typed as Fixer, an abstract base class that only defines a fix(prompt_value, result, error) method and does not implement __call__. This causes a TypeError: 'Fixer' object is not callable at runtime. The correct invocation should call the fix method directly.
| return self.fixer(prompt_value, result, error) | |
| return self.fixer.fix(prompt_value, result, error) |
Prompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert bash developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/output_parsers/base.py
Lines: 49-49
Issue Type: functional-critical
Severity: critical
Issue Description:
The `OutputGuardrail.fix` method incorrectly invokes `self.fixer` as a callable using `self.fixer(...)`, but `self.fixer` is typed as `Fixer`, an abstract base class that only defines a `fix(prompt_value, result, error)` method and does not implement `__call__`. This causes a `TypeError: 'Fixer' object is not callable` at runtime. The correct invocation should call the `fix` method directly.
Current Code:
return self.fixer(prompt_value, result, error)
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow bash best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
| result = guardrail.output_parser.parse(result) | ||
| error = None | ||
| except Exception as e: | ||
| error = ValidationError(text=e) |
There was a problem hiding this comment.
The ValidationError is being constructed with an incorrect keyword argument text=e when it should use error_message=str(e). The field defined in schema.py is error_message: str, but the code passes text as the keyword argument. Additionally, e is an Exception instance, not a string, so it needs to be converted using str(e) to match the expected str type.
| error = ValidationError(text=e) | |
| error = ValidationError(error_message=str(e)) |
Prompt for AI assistance
Copy the prompt below and paste it into ChatGPT, Claude, or any LLM:
You are an expert bash developer with deep knowledge of security, performance, and best practices.
### Context
File: langchain/chains/llm.py
Lines: 147-147
Issue Type: functional-high
Severity: high
Issue Description:
The `ValidationError` is being constructed with an incorrect keyword argument `text=e` when it should use `error_message=str(e)`. The field defined in `schema.py` is `error_message: str`, but the code passes `text` as the keyword argument. Additionally, `e` is an `Exception` instance, not a string, so it needs to be converted using `str(e)` to match the expected `str` type.
Current Code:
error = ValidationError(text=e)
---
### Instructions
1. Fix the issue described above
2. Maintain the exact indentation and code style from the original
3. Follow bash best practices and language-specific idioms
4. Ensure the fix addresses the root cause, not just the symptoms
5. Add brief inline comments explaining the fix if needed
### Constraints
- Do not change functionality beyond fixing the identified issue
- Preserve existing variable names and function signatures unless they are part of the problem
- Ensure the fix is production-ready
---
Security Scan Summary
No critical security issues detected Scan completed in 12.7sSecurity scan powered by Codity.ai |
License Compliance Scan
All licenses are low-risk and compliant Low Risk Licenses - Top licenses from 6 packagesBSD-3-Clause (2 packages) Powered by Codity.ai · Docs |
Code Quality Report — test-org-codity/langchain · PR #4Scanned: 2026-06-06 18:32 UTC | Score: 54/100 | Provider: github Executive Summary
Top Findings[CQ-LLM-001]
|
| File | Critical | High | Medium | Low | Total |
|---|---|---|---|---|---|
langchain/chains/llm.py |
0 | 1 | 2 | 7 | 10 |
langchain/output_parsers/base.py |
0 | 0 | 0 | 3 | 3 |
langchain/schema.py |
0 | 0 | 0 | 6 | 6 |
Recommendations
- Resolve High severity issues, especially error handling gaps and performance bottlenecks.
- Run automated tests after applying fixes to verify no regressions.
2 participants
Fixes #
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