How to bind model-specific tools
Providers adopt different conventions for formatting tool schemas.For instance, OpenAI uses a format like this:
type: The type of the tool. At the time of writing, this is always"function".function: An object containing tool parameters.function.name: The name of the schema to output.function.description: A high level description of the schema to output.function.parameters: The nested details of the schema you want to extract, formatted as aJSON schema dict.
We can bind this model-specific format directly to the model as well if preferred. Here's an example:
from langchain_openaiimport ChatOpenAI
model= ChatOpenAI()
model_with_tools= model.bind(
tools=[
{
"type":"function",
"function":{
"name":"multiply",
"description":"Multiply two integers together.",
"parameters":{
"type":"object",
"properties":{
"a":{"type":"number","description":"First integer"},
"b":{"type":"number","description":"Second integer"},
},
"required":["a","b"],
},
},
}
]
)
model_with_tools.invoke("Whats 119 times 8?")
API Reference:ChatOpenAI
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mn4ELw1NbuE0DFYhIeK0GrPe', 'function': {'arguments': '{"a":119,"b":8}', 'name': 'multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 62, 'total_tokens': 79}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-353e8a9a-7125-4f94-8c68-4f3da4c21120-0', tool_calls=[{'name': 'multiply', 'args': {'a': 119, 'b': 8}, 'id': 'call_mn4ELw1NbuE0DFYhIeK0GrPe'}])
This is functionally equivalent to thebind_tools() method.