mirror of
https://github.com/Xe138/AI-Trader.git
synced 2026-04-02 01:27:24 -04:00
Bill
0641ce554a
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>
52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
"""
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Chat model wrapper - Passthrough wrapper for ChatOpenAI models.
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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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from typing import Any
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class ToolCallArgsParsingWrapper:
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"""
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Passthrough wrapper around ChatOpenAI models.
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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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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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"""
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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
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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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@property
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def _llm_type(self) -> str:
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"""Return identifier for this LLM type"""
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if hasattr(self.wrapped_model, '_llm_type'):
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return f"wrapped-{self.wrapped_model._llm_type}"
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return "wrapped-chat-model"
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def __getattr__(self, name: str):
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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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"""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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"""Bind settings to the wrapped model"""
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return self.wrapped_model.bind(**kwargs)
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