You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
263 lines
8.0 KiB
263 lines
8.0 KiB
from typing import Any, Dict, List, Optional, Union
|
|
|
|
import openai
|
|
import requests
|
|
from pydantic import BaseModel, validator
|
|
from tenacity import (
|
|
retry,
|
|
stop_after_attempt,
|
|
wait_random_exponential,
|
|
)
|
|
from termcolor import colored
|
|
|
|
|
|
class FunctionSpecification(BaseModel):
|
|
"""
|
|
Defines the specification for a function including its parameters and metadata.
|
|
|
|
Attributes:
|
|
-----------
|
|
name: str
|
|
The name of the function.
|
|
description: str
|
|
A brief description of what the function does.
|
|
parameters: Dict[str, Any]
|
|
The parameters required by the function, with their details.
|
|
required: Optional[List[str]]
|
|
List of required parameter names.
|
|
|
|
Methods:
|
|
--------
|
|
validate_params(params: Dict[str, Any]) -> None:
|
|
Validates the parameters against the function's specification.
|
|
|
|
|
|
|
|
Example:
|
|
|
|
# Example Usage
|
|
def get_current_weather(location: str, format: str) -> str:
|
|
``'
|
|
Example function to get current weather.
|
|
|
|
Args:
|
|
location (str): The city and state, e.g. San Francisco, CA.
|
|
format (str): The temperature unit, e.g. celsius or fahrenheit.
|
|
|
|
Returns:
|
|
str: Weather information.
|
|
'''
|
|
# Implementation goes here
|
|
return "Sunny, 23°C"
|
|
|
|
|
|
weather_function_spec = FunctionSpecification(
|
|
name="get_current_weather",
|
|
description="Get the current weather",
|
|
parameters={
|
|
"location": {"type": "string", "description": "The city and state"},
|
|
"format": {
|
|
"type": "string",
|
|
"enum": ["celsius", "fahrenheit"],
|
|
"description": "The temperature unit",
|
|
},
|
|
},
|
|
required=["location", "format"],
|
|
)
|
|
|
|
# Validating parameters for the function
|
|
params = {"location": "San Francisco, CA", "format": "celsius"}
|
|
weather_function_spec.validate_params(params)
|
|
|
|
# Calling the function
|
|
print(get_current_weather(**params))
|
|
"""
|
|
|
|
name: str
|
|
description: str
|
|
parameters: Dict[str, Any]
|
|
required: Optional[List[str]] = None
|
|
|
|
@validator("parameters")
|
|
def check_parameters(cls, params):
|
|
if not isinstance(params, dict):
|
|
raise ValueError("Parameters must be a dictionary.")
|
|
return params
|
|
|
|
def validate_params(self, params: Dict[str, Any]) -> None:
|
|
"""
|
|
Validates the parameters against the function's specification.
|
|
|
|
Args:
|
|
params (Dict[str, Any]): The parameters to validate.
|
|
|
|
Raises:
|
|
ValueError: If any required parameter is missing or if any parameter is invalid.
|
|
"""
|
|
for key, value in params.items():
|
|
if key in self.parameters:
|
|
self.parameters[key]
|
|
# Perform specific validation based on param_spec
|
|
# This can include type checking, range validation, etc.
|
|
else:
|
|
raise ValueError(f"Unexpected parameter: {key}")
|
|
|
|
for req_param in self.required or []:
|
|
if req_param not in params:
|
|
raise ValueError(
|
|
f"Missing required parameter: {req_param}"
|
|
)
|
|
|
|
|
|
class OpenAIFunctionCaller:
|
|
def __init__(
|
|
self,
|
|
openai_api_key: str,
|
|
model: str = "text-davinci-003",
|
|
max_tokens: int = 3000,
|
|
temperature: float = 0.5,
|
|
top_p: float = 1.0,
|
|
n: int = 1,
|
|
stream: bool = False,
|
|
stop: Optional[str] = None,
|
|
echo: bool = False,
|
|
frequency_penalty: float = 0.0,
|
|
presence_penalty: float = 0.0,
|
|
logprobs: Optional[int] = None,
|
|
best_of: int = 1,
|
|
logit_bias: Dict[str, float] = None,
|
|
user: str = None,
|
|
messages: List[Dict] = None,
|
|
timeout_sec: Union[float, None] = None,
|
|
):
|
|
self.openai_api_key = openai_api_key
|
|
self.model = model
|
|
self.max_tokens = max_tokens
|
|
self.temperature = temperature
|
|
self.top_p = top_p
|
|
self.n = n
|
|
self.stream = stream
|
|
self.stop = stop
|
|
self.echo = echo
|
|
self.frequency_penalty = frequency_penalty
|
|
self.presence_penalty = presence_penalty
|
|
self.logprobs = logprobs
|
|
self.best_of = best_of
|
|
self.logit_bias = logit_bias
|
|
self.user = user
|
|
self.messages = messages if messages is not None else []
|
|
self.timeout_sec = timeout_sec
|
|
|
|
def add_message(self, role: str, content: str):
|
|
self.messages.append({"role": role, "content": content})
|
|
|
|
@retry(
|
|
wait=wait_random_exponential(multiplier=1, max=40),
|
|
stop=stop_after_attempt(3),
|
|
)
|
|
def chat_completion_request(
|
|
self,
|
|
messages,
|
|
tools=None,
|
|
tool_choice=None,
|
|
):
|
|
headers = {
|
|
"Content-Type": "application/json",
|
|
"Authorization": "Bearer " + openai.api_key,
|
|
}
|
|
json_data = {"model": self.model, "messages": messages}
|
|
if tools is not None:
|
|
json_data.update({"tools": tools})
|
|
if tool_choice is not None:
|
|
json_data.update({"tool_choice": tool_choice})
|
|
try:
|
|
response = requests.post(
|
|
"https://api.openai.com/v1/chat/completions",
|
|
headers=headers,
|
|
json=json_data,
|
|
)
|
|
return response
|
|
except Exception as e:
|
|
print("Unable to generate ChatCompletion response")
|
|
print(f"Exception: {e}")
|
|
return e
|
|
|
|
def pretty_print_conversation(self, messages):
|
|
role_to_color = {
|
|
"system": "red",
|
|
"user": "green",
|
|
"assistant": "blue",
|
|
"tool": "magenta",
|
|
}
|
|
|
|
for message in messages:
|
|
if message["role"] == "system":
|
|
print(
|
|
colored(
|
|
f"system: {message['content']}\n",
|
|
role_to_color[message["role"]],
|
|
)
|
|
)
|
|
elif message["role"] == "user":
|
|
print(
|
|
colored(
|
|
f"user: {message['content']}\n",
|
|
role_to_color[message["role"]],
|
|
)
|
|
)
|
|
elif message["role"] == "assistant" and message.get(
|
|
"function_call"
|
|
):
|
|
print(
|
|
colored(
|
|
f"assistant: {message['function_call']}\n",
|
|
role_to_color[message["role"]],
|
|
)
|
|
)
|
|
elif message["role"] == "assistant" and not message.get(
|
|
"function_call"
|
|
):
|
|
print(
|
|
colored(
|
|
f"assistant: {message['content']}\n",
|
|
role_to_color[message["role"]],
|
|
)
|
|
)
|
|
elif message["role"] == "tool":
|
|
print(
|
|
colored(
|
|
(
|
|
f"function ({message['name']}):"
|
|
f" {message['content']}\n"
|
|
),
|
|
role_to_color[message["role"]],
|
|
)
|
|
)
|
|
|
|
def call(self, task: str, *args, **kwargs) -> Dict:
|
|
return openai.Completion.create(
|
|
engine=self.model,
|
|
prompt=task,
|
|
max_tokens=self.max_tokens,
|
|
temperature=self.temperature,
|
|
top_p=self.top_p,
|
|
n=self.n,
|
|
stream=self.stream,
|
|
stop=self.stop,
|
|
echo=self.echo,
|
|
frequency_penalty=self.frequency_penalty,
|
|
presence_penalty=self.presence_penalty,
|
|
logprobs=self.logprobs,
|
|
best_of=self.best_of,
|
|
logit_bias=self.logit_bias,
|
|
user=self.user,
|
|
messages=self.messages,
|
|
timeout_sec=self.timeout_sec,
|
|
*args,
|
|
**kwargs,
|
|
)
|
|
|
|
def run(self, task: str, *args, **kwargs) -> str:
|
|
response = self.call(task, *args, **kwargs)
|
|
return response["choices"][0]["text"].strip()
|