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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.
  • 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 a JSON schema dict.

We can bind this model-specific format directly to the model as well if preferred. Here's an example:

from langchain_openai import ChatOpenAI

model = ChatOpenAI()

model_with_tools = model.bind(
"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 the bind_tools() method.

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