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https://github.com/Xe138/AI-Trader.git
synced 2026-04-01 17:17:24 -04:00
fix: remove incorrect tool_calls conversion logic
Systematic debugging revealed the root cause of Pydantic validation errors: - DeepSeek correctly returns tool_calls.arguments as JSON strings - My wrapper was incorrectly converting strings to dicts - This caused LangChain's parse_tool_call() to fail (json.loads(dict) error) - Failure created invalid_tool_calls with dict args (should be string) - Result: Pydantic validation error on invalid_tool_calls Solution: Remove all conversion logic. DeepSeek format is already correct. ToolCallArgsParsingWrapper now acts as a simple passthrough proxy. Trading session completes successfully with no errors. Fixes the systematic-debugging investigation that identified the issue was in our fix attempt, not in the original API response. Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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@@ -1,26 +1,24 @@
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"""
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Chat model wrapper to fix tool_calls args parsing issues.
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Chat model wrapper - Passthrough wrapper for ChatOpenAI models.
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Some AI providers (like DeepSeek) return tool_calls.args as JSON strings instead
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of dictionaries, causing Pydantic validation errors. This wrapper monkey-patches
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the model to fix args before AIMessage construction.
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Originally created to fix DeepSeek tool_calls arg parsing issues, but investigation
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revealed DeepSeek already returns the correct format (arguments as JSON strings).
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This wrapper is now a simple passthrough that proxies all calls to the underlying model.
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Kept for backward compatibility and potential future use.
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"""
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import json
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from typing import Any, List, Optional, Dict
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from functools import wraps
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from langchain_core.messages import AIMessage, BaseMessage
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from typing import Any
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class ToolCallArgsParsingWrapper:
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"""
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Wrapper around ChatOpenAI that fixes tool_calls args parsing.
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Passthrough wrapper around ChatOpenAI models.
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This fixes the Pydantic validation error:
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"Input should be a valid dictionary [type=dict_type, input_value='...', input_type=str]"
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After systematic debugging, determined that DeepSeek returns tool_calls.arguments
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as JSON strings (correct format), so no parsing/conversion is needed.
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Works by monkey-patching _create_chat_result to parse string args before
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AIMessage construction.
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This wrapper simply proxies all calls to the wrapped model.
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"""
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def __init__(self, model: Any, **kwargs):
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@@ -28,63 +26,10 @@ class ToolCallArgsParsingWrapper:
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Initialize wrapper around a chat model.
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Args:
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model: The chat model to wrap (should be ChatOpenAI instance)
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model: The chat model to wrap
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**kwargs: Additional parameters (ignored, for compatibility)
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"""
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self.wrapped_model = model
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self._patch_model()
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def _patch_model(self):
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"""Monkey-patch the model's _create_chat_result to fix tool_calls args"""
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if not hasattr(self.wrapped_model, '_create_chat_result'):
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# Model doesn't have this method (e.g., MockChatModel), skip patching
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return
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original_create_chat_result = self.wrapped_model._create_chat_result
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@wraps(original_create_chat_result)
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def patched_create_chat_result(response: Any, generation_info: Optional[Dict] = None):
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"""Patched version that fixes tool_calls args before AIMessage construction"""
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# Fix tool_calls in the response dict before passing to original method
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response_dict = response if isinstance(response, dict) else response.model_dump()
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# DIAGNOSTIC: Log response structure
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print(f"\n[DEBUG] Response keys: {response_dict.keys()}")
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if 'choices' in response_dict:
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print(f"[DEBUG] Number of choices: {len(response_dict['choices'])}")
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for i, choice in enumerate(response_dict['choices']):
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print(f"[DEBUG] Choice {i} keys: {choice.keys()}")
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if 'message' in choice:
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message = choice['message']
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print(f"[DEBUG] Message keys: {message.keys()}")
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# Check tool_calls structure
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if 'tool_calls' in message and message['tool_calls']:
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print(f"[DEBUG] Found {len(message['tool_calls'])} tool_calls")
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for j, tc in enumerate(message['tool_calls']):
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print(f"[DEBUG] tool_calls[{j}] keys: {tc.keys()}")
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if 'function' in tc:
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print(f"[DEBUG] tool_calls[{j}].function keys: {tc['function'].keys()}")
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if 'arguments' in tc['function']:
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args = tc['function']['arguments']
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print(f"[DEBUG] tool_calls[{j}].function.arguments type: {type(args)}")
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print(f"[DEBUG] tool_calls[{j}].function.arguments value: {repr(args)[:200]}")
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if 'invalid_tool_calls' in message:
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print(f"[DEBUG] Found invalid_tool_calls: {len(message['invalid_tool_calls'])} items")
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for j, inv in enumerate(message['invalid_tool_calls']):
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print(f"[DEBUG] invalid_tool_calls[{j}] keys: {inv.keys()}")
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if 'args' in inv:
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print(f"[DEBUG] invalid_tool_calls[{j}].args type: {type(inv['args'])}")
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print(f"[DEBUG] invalid_tool_calls[{j}].args value: {inv['args']}")
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# REMOVED: No conversion needed yet - gathering data first
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# Call original method with unmodified response
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return original_create_chat_result(response_dict, generation_info)
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# Replace the method
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self.wrapped_model._create_chat_result = patched_create_chat_result
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@property
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def _llm_type(self) -> str:
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@@ -94,21 +39,13 @@ class ToolCallArgsParsingWrapper:
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return "wrapped-chat-model"
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def __getattr__(self, name: str):
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"""Proxy all other attributes/methods to the wrapped model"""
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"""Proxy all attributes/methods to the wrapped model"""
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return getattr(self.wrapped_model, name)
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def bind_tools(self, tools: Any, **kwargs):
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"""
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Bind tools to the wrapped model.
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Since we patch the model in-place, we can just delegate to the wrapped model.
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"""
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"""Bind tools to the wrapped model"""
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return self.wrapped_model.bind_tools(tools, **kwargs)
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def bind(self, **kwargs):
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"""
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Bind settings to the wrapped model.
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Since we patch the model in-place, we can just delegate to the wrapped model.
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"""
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"""Bind settings to the wrapped model"""
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return self.wrapped_model.bind(**kwargs)
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