This GIST conain Langchain related references.

Function_Call_Formula.ipynb

Function_Call_with_Ollama.py

from langchain_ollama.chat_models import ChatOllama
from langchain_core.tools import tool
from typing import Any, Dict, Optional, TypedDict
from langchain_core.runnables import RunnableConfig
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.tools import render_text_description

model=ChatOllama(model='gemma2:2b',temperature=0.3)

@tool
def multiply(x: float, y: float) -> float:
    """Multiply two numbers together."""
    return x * y

@tool
def add(x: int, y: int) -> int:
    "Add two numbers."
    return x + y

class ToolCallRequest(TypedDict):
    """A typed dict that shows the inputs into the invoke_tool function."""

    name: str
    arguments: Dict[str, Any]

def invoke_tool(tool_call_request: ToolCallRequest, config: Optional[RunnableConfig] = None):
    tool_name_to_tool = {tool.name: tool for tool in tools}
    name = tool_call_request["name"]
    requested_tool = tool_name_to_tool[name]
    return requested_tool.invoke(tool_call_request["arguments"], config=config)

tools = [multiply, add]

rendered_tools = render_text_description(tools)

system_prompt = f"""\
You are an assistant that has access to the following set of tools. 
Here are the names and descriptions for each tool:

{rendered_tools}

Given the user input, return the name and input of the tool to use. 
Return your response as a JSON blob with 'name' and 'arguments' keys.

The `arguments` should be a dictionary, with keys corresponding 
to the argument names and the values corresponding to the requested values.
"""

prompt = ChatPromptTemplate.from_messages(
    [("system", system_prompt), ("user", "{input}")]
)

chain = prompt | model | JsonOutputParser() | invoke_tool
response=chain.invoke({"input": "what's thirteen times 4"})
response

tool_call_example.ipynb