Custom Functions

This example code shows you how to leverage the OpenAI client and SeekrFlow's inference engine to create a custom unit conversion tool that can be configured dynamically.

Primary use cases for custom functions are:

Fetching up-to-date data to incorporate into the model's response (RAG): Stock market prices, exchange rates
Taking actions: Calling APIs, submitting forms, or taking agentic workflow actions (escalating a ticket).

Create the client and make an API request

import os  
import openai

# Set the API key
os.environ["OPENAI_API_KEY"] = "YOUR API KEY"

# Create the OpenAI client and retrieve the API key

client = openai.OpenAI(  
  base_url="<https://flow.seekr.com/v1/inference">,  
  api_key=os.environ.get("OPENAI_API_KEY"

)
  
# Send a request to the OpenAI API to leverage the specified Llama model as a unit conversion tool.
  
response = client.chat.completions.create(  
  model="meta-llama/Llama-3.1-8B-Instruct",  
  stream=False,  
  messages=[{  
    "role": "user",  
    "content": "Convert from 5 kilometers to miles"  
}],  
max_tokens=100,  
tools=\[{  
  "type": "function",  
  "function": {  
      "name": "convert_units",  
      "description": "Convert between different units of measurement",  
      "parameters": {  
          "type": "object",  
          "properties": {  
              "value": {"type": "number"},  
              "from_unit": {"type": "string"},  
              "to_unit": {"type": "string"} 

}, 

  "required": ["value", "from_unit", "to_unit"] 

}  
}  
}]  
)

Define and register a function from JSON

# Parse json and register

def register_from_json(json_obj):  
   code = f"def {json_obj['name']}({',    '.join(json_obj['args'])}):\\n{json_obj['docstring']}\\n{json_obj['code']}"  
   print(code)  
   namespace = {}  
   exec(code, namespace)  
   return namespace\[json_obj["name"]]

Run the unit conversion tool

This function executes the tool call, given an LLM response object.

# Execute our tool

def execute_tool_call(resp):  
    tool_call = resp.choices[0].message.tool_calls[0]
    
    func_name = tool_call.function.name  
		args = tool_call.function.arguments

		func = globals().get(func_name)
		if not func:
    	raise ValueError(f"Function {func_name} not found")

		if isinstance(args, str):
    	import json
    	args = json.loads(args)

		return func(**args)
  
execute_tool_call(response)

Sample output

This is the output expected in response to the request made earlier to convert 5 kilometers to miles.

3.106855