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Functions

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FUNC add_tools_from_model_options 

add_tools_from_model_options(tools_dict: dict[str, Callable], model_options: dict[str, Any])
If model_options has tools, add those tools to the tools_dict.

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FUNC add_tools_from_context_actions 

add_tools_from_context_actions(tools_dict: dict[str, Callable], ctx_actions: list[Component | CBlock] | None)
If any of the actions in ctx_actions have tools in their template_representation, add those to the tools_dict.

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FUNC convert_tools_to_json 

convert_tools_to_json(tools: dict[str, Callable]) -> list[dict]
Convert tools to json dict representation. Notes:
  • Huggingface transformers library lets you pass in an array of functions but doesn’t like methods.
  • WatsonxAI uses from langchain_ibm.chat_models import convert_to_openai_tool in their demos, but it gives the same values.
  • OpenAI uses the same format / schema.

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FUNC json_extraction 

json_extraction(text: str) -> Generator[dict, None, None]
Yields the next valid json object in a given string.

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FUNC find_func 

find_func(d) -> tuple[str | None, Mapping | None]
Find the first function in a json-like dictionary. Most llms output tool requests in the form ...{"name": string, "arguments": {}}...

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FUNC parse_tools 

parse_tools(llm_response: str) -> list[tuple[str, Mapping]]
A simple parser that will scan a string for tools and attempt to extract them; only works for json based outputs.

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FUNC convert_function_to_tool 

convert_function_to_tool(func: Callable) -> Tool
Imported from Ollama.

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Classes

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CLASS SubscriptableBaseModel

Class imported from Ollama.
Methods:

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FUNC get 

get(self, key: str, default: Any = None) -> Any
Get.
msg = Message(role=‘user’) msg.get(‘role’) ‘user’ msg = Message(role=‘user’) msg.get(‘nonexistent’) msg = Message(role=‘user’) msg.get(‘nonexistent’, ‘default’) ‘default’ msg = Message(role=‘user’, tool_calls=[ Message.ToolCall(function=Message.ToolCall.Function(name=‘foo’, arguments=))]) msg.get(‘tool_calls’)[0][‘function’][‘name’] ‘foo’

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CLASS Tool

Class imported from Ollama.