Tool calling support. Part I #102
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Looks good @rohitprasad15
Nothing blocking, just a few suggestions in the review.
| if isinstance(msg, dict): | ||
| return self._convert_dict_message(msg) | ||
| return self._convert_message_object(msg) |
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It looks like both _convert_dict_message and _convert_message_object logically do the same things. Would it be better to convert to one format and keep one of the convert message methods.
if isinstance(msg, dict):
msg = Message(msg)
return self._convert_message_object(msg)or the other way
if isinstance(msg, Message):
msg = dict(msg)
return self._convert_dict_message(msg)| tool_input = ( | ||
| tool_call["function"]["arguments"] | ||
| if isinstance(tool_call, dict) | ||
| else tool_call.function.arguments | ||
| ) | ||
| message_content.append( | ||
| { | ||
| "type": "tool_use", | ||
| "id": ( | ||
| tool_call["id"] if isinstance(tool_call, dict) else tool_call.id | ||
| ), | ||
| "name": ( | ||
| tool_call["function"]["name"] | ||
| if isinstance(tool_call, dict) | ||
| else tool_call.function.name | ||
| ), | ||
| "input": json.loads(tool_input), | ||
| } | ||
| ) |
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Similar thought, converting to either an object or dict at the beginning of this would create a consistency that would not require the if/else statements throughout the function call.
aisuite/utils/tool_manager.py
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Can you add unit tests for this?
|
@rohitprasad15 When can we expect tool calling in |
|
@ksolo - PTAL again. Added support for a bunch of other providers, and added test cases. |
Add tool calling support for below providers - OpenAI, Groq, Anthropic, AWS, & Mistral. OpenAI compatible SDKs need to changes for tool calling support. Adding utility ToolManager for users to easily supply tools, and parse model's request for tool usage.
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Add tool calling support for below providers -
OpenAI, Groq, Anthropic, AWS, & Mistral.
OpenAI compatible SDKs need to changes for
tool calling support.
Adding utility ToolManager for users to easily
supply tools, and parse model's request for
tool usage.