{"record":{"id":"23ae5afd8aae48b4","repo":"ScrapeGraphAI/Scrapegraph-ai","slug":"validation-code-generation-failed-str-e","errorCode":null,"errorMessage":"Validation code generation failed: {str(e)}","messagePattern":"Validation code generation failed: (.+?)","errorType":"error_code","errorClass":"CodeGenerationError","httpStatus":null,"severity":"error","filePath":"scrapegraphai/utils/code_error_correction.py","lineNumber":248,"sourceCode":"            input_variables=[\"analysis\", \"generated_code\", \"json_schema\"],\n        )\n        chain = prompt | llm_model | StrOutputParser()\n\n        # Execute chain with validated state\n        return chain.invoke(\n            {\n                \"analysis\": analysis,\n                \"generated_code\": validated_state.generated_code,\n                \"json_schema\": validated_state.json_schema,\n            }\n        )\n\n    except KeyError as e:\n        raise InvalidCorrectionStateError(\n            f\"Missing required key in state dictionary: {e}\"\n        )\n    except Exception as e:\n        raise CodeGenerationError(f\"Validation code generation failed: {str(e)}\")\n\n\ndef semantic_focused_code_generation(\n    state: Dict[str, Any], analysis: str, llm_model\n) -> str:\n    \"\"\"\n    Generates corrected code based on semantic error analysis.\n\n    Args:\n        state (dict): Contains the 'generated_code', 'execution_result', and 'reference_answer'.\n        analysis (str): The analysis of the semantic differences.\n        llm_model: The language model used for generating the corrected code.\n\n    Returns:\n        str: The corrected code.\n\n    Raises:\n        InvalidCorrectionStateError: If state is missing required keys or analysis is invalid.","sourceCodeStart":230,"sourceCodeEnd":266,"githubUrl":"https://github.com/ScrapeGraphAI/Scrapegraph-ai/blob/532dfffbf6ee823a6c9cf8cfedc24a93bf026780/scrapegraphai/utils/code_error_correction.py#L230-L266","documentation":"Generic failure of the validation-focused code-generation LLM chain; the underlying exception is appended to the message. Inputs passed validation, so the LLM call or prompt rendering is at fault.","triggerScenarios":"validation_focused_code_generation / validation_reasoning_loop with auth/network-failing llm_model, rate limits, or json_schema that cannot be serialized into the prompt.","commonSituations":"Bad provider credentials, quota exhausted, non-JSON-serializable schema objects (Pydantic models passed raw) blowing up template rendering.","solutions":["Read str(e) to identify the provider/prompt cause","Convert json_schema to a plain dict (e.g. schema.model_dump() or json.loads(schema.model_dump_json())) before passing state","Verify API key and retry transient errors with backoff"],"exampleFix":"# before\nstate[\"json_schema\"] = my_pydantic_model\n# after\nstate[\"json_schema\"] = my_pydantic_model.model_dump()","handlingStrategy":"retry","validationCode":"import json\njson.dumps(state[\"json_schema\"])  # schema must be JSON-serializable\nllm_model.invoke(\"ping\")","typeGuard":null,"tryCatchPattern":"from scrapegraphai.utils.code_error_correction import CodeGenerationError\ntry:\n    new_code = validation_focused_code_generation(state, analysis, llm_model)\nexcept CodeGenerationError as e:\n    logger.error(\"validation correction failed: %s\", e)\n    raise","preventionTips":["Convert Pydantic schemas with model_dump() before putting them in state","Verify API key and provider availability","Retry transient provider errors with backoff"],"tags":["llm","validation-correction","chain-invocation","serialization"],"backgroundTag":"llm-chain-invocation-failed","analyzedSha":"532dfffbf6ee823a6c9cf8cfedc24a93bf026780","analyzedAt":"2026-08-28T15:19:38.821Z","schemaVersion":2},"datasetVersion":"2026-08-28T16:17:29.566Z"}