1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
|
from __future__ import annotations
from typing import List, Literal, Optional
from colorama import Fore
from autogpt.config import Config
from ..api_manager import ApiManager
from ..base import (
ChatModelResponse,
ChatSequence,
FunctionCallDict,
Message,
ResponseMessageDict,
)
from ..providers import openai as iopenai
from ..providers.openai import (
OPEN_AI_CHAT_MODELS,
OpenAIFunctionCall,
OpenAIFunctionSpec,
count_openai_functions_tokens,
)
from .token_counter import *
def call_ai_function(
function: str,
args: list,
description: str,
config: Config,
model: Optional[str] = None,
) -> str:
"""Call an AI function
This is a magic function that can do anything with no-code. See
https://github.com/Torantulino/AI-Functions for more info.
Args:
function (str): The function to call
args (list): The arguments to pass to the function
description (str): The description of the function
model (str, optional): The model to use. Defaults to None.
Returns:
str: The response from the function
"""
if model is None:
model = config.smart_llm
# For each arg, if any are None, convert to "None":
args = [str(arg) if arg is not None else "None" for arg in args]
# parse args to comma separated string
arg_str: str = ", ".join(args)
prompt = ChatSequence.for_model(
model,
[
Message(
"system",
f"You are now the following python function: ```# {description}"
f"\n{function}```\n\nOnly respond with your `return` value.",
),
Message("user", arg_str),
],
)
return create_chat_completion(prompt=prompt, temperature=0, config=config).content
def create_text_completion(
prompt: str,
config: Config,
model: Optional[str],
temperature: Optional[float],
max_output_tokens: Optional[int],
) -> str:
if model is None:
model = config.fast_llm
if temperature is None:
temperature = config.temperature
if config.use_azure:
kwargs = config.get_azure_kwargs(model)
else:
kwargs = {"model": model}
response = iopenai.create_text_completion(
prompt=prompt,
**kwargs,
temperature=temperature,
max_tokens=max_output_tokens,
api_key=config.openai_api_key,
)
logger.debug(f"Response: {response}")
return response.choices[0].text
# Overly simple abstraction until we create something better
def create_chat_completion(
prompt: ChatSequence,
config: Config,
functions: Optional[List[OpenAIFunctionSpec]] = None,
model: Optional[str] = None,
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
) -> ChatModelResponse:
"""Create a chat completion using the OpenAI API
Args:
messages (List[Message]): The messages to send to the chat completion
model (str, optional): The model to use. Defaults to None.
temperature (float, optional): The temperature to use. Defaults to 0.9.
max_tokens (int, optional): The max tokens to use. Defaults to None.
Returns:
str: The response from the chat completion
"""
if model is None:
model = prompt.model.name
if temperature is None:
temperature = config.temperature
if max_tokens is None:
prompt_tlength = prompt.token_length
max_tokens = OPEN_AI_CHAT_MODELS[model].max_tokens - prompt_tlength
logger.debug(f"Prompt length: {prompt_tlength} tokens")
if functions:
functions_tlength = count_openai_functions_tokens(functions, model)
max_tokens -= functions_tlength
logger.debug(f"Functions take up {functions_tlength} tokens in API call")
logger.debug(
f"{Fore.GREEN}Creating chat completion with model {model}, temperature {temperature}, max_tokens {max_tokens}{Fore.RESET}"
)
chat_completion_kwargs = {
"model": model,
"temperature": temperature,
"max_tokens": max_tokens,
}
for plugin in config.plugins:
if plugin.can_handle_chat_completion(
messages=prompt.raw(),
**chat_completion_kwargs,
):
message = plugin.handle_chat_completion(
messages=prompt.raw(),
**chat_completion_kwargs,
)
if message is not None:
return message
chat_completion_kwargs["api_key"] = config.openai_api_key
if config.use_azure:
chat_completion_kwargs.update(config.get_azure_kwargs(model))
if functions:
chat_completion_kwargs["functions"] = [
function.schema for function in functions
]
response = iopenai.create_chat_completion(
messages=prompt.raw(),
**chat_completion_kwargs,
)
logger.debug(f"Response: {response}")
if hasattr(response, "error"):
logger.error(response.error)
raise RuntimeError(response.error)
first_message: ResponseMessageDict = response.choices[0].message
content: str | None = first_message.get("content")
function_call: FunctionCallDict | None = first_message.get("function_call")
for plugin in config.plugins:
if not plugin.can_handle_on_response():
continue
# TODO: function call support in plugin.on_response()
content = plugin.on_response(content)
return ChatModelResponse(
model_info=OPEN_AI_CHAT_MODELS[model],
content=content,
function_call=OpenAIFunctionCall(
name=function_call["name"], arguments=function_call["arguments"]
)
if function_call
else None,
)
def check_model(
model_name: str,
model_type: Literal["smart_llm", "fast_llm"],
config: Config,
) -> str:
"""Check if model is available for use. If not, return gpt-3.5-turbo."""
openai_credentials = {
"api_key": config.openai_api_key,
}
if config.use_azure:
openai_credentials.update(config.get_azure_kwargs(model_name))
api_manager = ApiManager()
models = api_manager.get_models(**openai_credentials)
if any(model_name in m["id"] for m in models):
return model_name
logger.typewriter_log(
"WARNING: ",
Fore.YELLOW,
f"You do not have access to {model_name}. Setting {model_type} to "
f"gpt-3.5-turbo.",
)
return "gpt-3.5-turbo"
|
