fix: retry logic broken by merge conflict
This commit is contained in:
Timothy
+108
-83
@@ -807,97 +807,122 @@ Keep the same JSON structure but with shorter content values.
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f" ⚠ Response still truncated after {compaction_attempt} compaction attempts"
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)
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total_input_tokens += response.input_tokens
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total_output_tokens += response.output_tokens
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# Phase 2: Validation retry loop for Pydantic models
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max_validation_retries = ctx.node_spec.max_validation_retries if ctx.node_spec.output_model else 0
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validation_attempt = 0
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total_input_tokens = 0
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total_output_tokens = 0
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current_messages = messages.copy()
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# Log the response
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response_preview = (
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response.content[:200] if len(response.content) > 200 else response.content
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)
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if len(response.content) > 200:
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response_preview += "..."
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logger.info(f" ← Response: {response_preview}")
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while True:
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total_input_tokens += response.input_tokens
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total_output_tokens += response.output_tokens
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# If no output_model, break immediately (no validation needed)
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if ctx.node_spec.output_model is None:
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break
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# Log the response
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response_preview = (
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response.content[:200] if len(response.content) > 200 else response.content
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)
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if len(response.content) > 200:
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response_preview += "..."
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logger.info(f" ← Response: {response_preview}")
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# Try to parse and validate the response
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try:
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import json
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parsed = self._extract_json(response.content, ctx.node_spec.output_keys)
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# If no output_model, break immediately (no validation needed)
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if ctx.node_spec.output_model is None:
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break
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if isinstance(parsed, dict):
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from framework.graph.validator import OutputValidator
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validator = OutputValidator()
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validation_result, validated_model = validator.validate_with_pydantic(
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parsed, ctx.node_spec.output_model
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)
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# Try to parse and validate the response
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try:
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import json
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parsed = self._extract_json(response.content, ctx.node_spec.output_keys)
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if validation_result.success:
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# Validation passed, break out of retry loop
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model_name = ctx.node_spec.output_model.__name__
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logger.info(f" ✓ Pydantic validation passed for {model_name}")
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break
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else:
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# Validation failed
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validation_attempt += 1
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if isinstance(parsed, dict):
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from framework.graph.validator import OutputValidator
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validator = OutputValidator()
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validation_result, validated_model = validator.validate_with_pydantic(
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parsed, ctx.node_spec.output_model
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)
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if validation_attempt <= max_validation_retries:
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# Add validation feedback to messages and retry
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feedback = validator.format_validation_feedback(
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validation_result, ctx.node_spec.output_model
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)
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logger.warning(
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f" ⚠ Pydantic validation failed "
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f"(attempt {validation_attempt}/{max_validation_retries}): "
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f"{validation_result.error}"
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)
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logger.info(" 🔄 Retrying with validation feedback...")
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# Add the assistant's failed response and feedback
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current_messages.append({
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"role": "assistant",
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"content": response.content
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})
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current_messages.append({
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"role": "user",
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"content": feedback
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})
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continue # Retry the LLM call
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else:
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# Max retries exceeded
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latency_ms = int((time.time() - start) * 1000)
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err = validation_result.error
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logger.error(
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f" ✗ Pydantic validation failed after "
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f"{max_validation_retries} retries: {err}"
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)
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ctx.runtime.record_outcome(
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decision_id=decision_id,
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success=False,
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error=f"Validation failed: {validation_result.error}",
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tokens_used=total_input_tokens + total_output_tokens,
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latency_ms=latency_ms,
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)
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error_msg = (
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f"Pydantic validation failed after "
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f"{max_validation_retries} retries: {err}"
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)
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return NodeResult(
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success=False,
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error=error_msg,
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output=parsed,
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tokens_used=total_input_tokens + total_output_tokens,
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latency_ms=latency_ms,
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validation_errors=validation_result.errors,
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)
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else:
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# Not a dict, can't validate - break and let downstream handle
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if validation_result.success:
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# Validation passed, break out of retry loop
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model_name = ctx.node_spec.output_model.__name__
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logger.info(f" ✓ Pydantic validation passed for {model_name}")
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break
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except Exception:
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# JSON extraction failed - break and let downstream handle
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else:
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# Validation failed
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validation_attempt += 1
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if validation_attempt <= max_validation_retries:
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# Add validation feedback to messages and retry
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feedback = validator.format_validation_feedback(
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validation_result, ctx.node_spec.output_model
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)
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logger.warning(
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f" ⚠ Pydantic validation failed "
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f"(attempt {validation_attempt}/{max_validation_retries}): "
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f"{validation_result.error}"
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)
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logger.info(" 🔄 Retrying with validation feedback...")
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# Add the assistant's failed response and feedback
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current_messages.append({
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"role": "assistant",
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"content": response.content
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})
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current_messages.append({
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"role": "user",
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"content": feedback
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})
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# Re-call LLM with feedback
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if ctx.available_tools and self.tool_executor:
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response = ctx.llm.complete_with_tools(
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messages=current_messages,
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system=system,
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tools=ctx.available_tools,
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tool_executor=executor,
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max_tokens=ctx.max_tokens,
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)
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else:
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response = ctx.llm.complete(
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messages=current_messages,
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system=system,
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json_mode=use_json_mode,
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max_tokens=ctx.max_tokens,
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)
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continue # Retry validation
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else:
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# Max retries exceeded
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latency_ms = int((time.time() - start) * 1000)
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err = validation_result.error
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logger.error(
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f" ✗ Pydantic validation failed after "
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f"{max_validation_retries} retries: {err}"
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)
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ctx.runtime.record_outcome(
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decision_id=decision_id,
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success=False,
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error=f"Validation failed: {validation_result.error}",
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tokens_used=total_input_tokens + total_output_tokens,
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latency_ms=latency_ms,
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)
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error_msg = (
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f"Pydantic validation failed after "
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f"{max_validation_retries} retries: {err}"
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)
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return NodeResult(
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success=False,
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error=error_msg,
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output=parsed,
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tokens_used=total_input_tokens + total_output_tokens,
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latency_ms=latency_ms,
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validation_errors=validation_result.errors,
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)
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else:
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# Not a dict, can't validate - break and let downstream handle
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break
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except Exception:
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# JSON extraction failed - break and let downstream handle
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break
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latency_ms = int((time.time() - start) * 1000)
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