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
|
from __future__ import annotations
from copy import deepcopy
from dataclasses import dataclass, field
from math import ceil, floor
from typing import TYPE_CHECKING, Literal, Optional, Type, TypedDict, TypeVar, overload
if TYPE_CHECKING:
from autogpt.llm.providers.openai import OpenAIFunctionCall
MessageRole = Literal["system", "user", "assistant", "function"]
MessageType = Literal["ai_response", "action_result"]
TText = list[int]
"""Token array representing tokenized text"""
class MessageDict(TypedDict):
role: MessageRole
content: str
class ResponseMessageDict(TypedDict):
role: Literal["assistant"]
content: Optional[str]
function_call: Optional[FunctionCallDict]
class FunctionCallDict(TypedDict):
name: str
arguments: str
@dataclass
class Message:
"""OpenAI Message object containing a role and the message content"""
role: MessageRole
content: str
type: MessageType | None = None
def raw(self) -> MessageDict:
return {"role": self.role, "content": self.content}
@dataclass
class ModelInfo:
"""Struct for model information.
Would be lovely to eventually get this directly from APIs, but needs to be scraped from
websites for now.
"""
name: str
max_tokens: int
prompt_token_cost: float
@dataclass
class CompletionModelInfo(ModelInfo):
"""Struct for generic completion model information."""
completion_token_cost: float
@dataclass
class ChatModelInfo(CompletionModelInfo):
"""Struct for chat model information."""
@dataclass
class TextModelInfo(CompletionModelInfo):
"""Struct for text completion model information."""
@dataclass
class EmbeddingModelInfo(ModelInfo):
"""Struct for embedding model information."""
embedding_dimensions: int
# Can be replaced by Self in Python 3.11
TChatSequence = TypeVar("TChatSequence", bound="ChatSequence")
@dataclass
class ChatSequence:
"""Utility container for a chat sequence"""
model: ChatModelInfo
messages: list[Message] = field(default_factory=list[Message])
@overload
def __getitem__(self, key: int) -> Message:
...
@overload
def __getitem__(self: TChatSequence, key: slice) -> TChatSequence:
...
def __getitem__(self: TChatSequence, key: int | slice) -> Message | TChatSequence:
if isinstance(key, slice):
copy = deepcopy(self)
copy.messages = self.messages[key]
return copy
return self.messages[key]
def __iter__(self):
return iter(self.messages)
def __len__(self):
return len(self.messages)
def add(
self,
message_role: MessageRole,
content: str,
type: MessageType | None = None,
) -> None:
self.append(Message(message_role, content, type))
def append(self, message: Message):
return self.messages.append(message)
def extend(self, messages: list[Message] | ChatSequence):
return self.messages.extend(messages)
def insert(self, index: int, *messages: Message):
for message in reversed(messages):
self.messages.insert(index, message)
@classmethod
def for_model(
cls: Type[TChatSequence],
model_name: str,
messages: list[Message] | ChatSequence = [],
**kwargs,
) -> TChatSequence:
from autogpt.llm.providers.openai import OPEN_AI_CHAT_MODELS
if not model_name in OPEN_AI_CHAT_MODELS:
raise ValueError(f"Unknown chat model '{model_name}'")
return cls(
model=OPEN_AI_CHAT_MODELS[model_name], messages=list(messages), **kwargs
)
@property
def token_length(self) -> int:
from autogpt.llm.utils import count_message_tokens
return count_message_tokens(self.messages, self.model.name)
def raw(self) -> list[MessageDict]:
return [m.raw() for m in self.messages]
def dump(self) -> str:
SEPARATOR_LENGTH = 42
def separator(text: str):
half_sep_len = (SEPARATOR_LENGTH - 2 - len(text)) / 2
return f"{floor(half_sep_len)*'-'} {text.upper()} {ceil(half_sep_len)*'-'}"
formatted_messages = "\n".join(
[f"{separator(m.role)}\n{m.content}" for m in self.messages]
)
return f"""
============== {__class__.__name__} ==============
Length: {self.token_length} tokens; {len(self.messages)} messages
{formatted_messages}
==========================================
"""
@dataclass
class LLMResponse:
"""Standard response struct for a response from an LLM model."""
model_info: ModelInfo
@dataclass
class EmbeddingModelResponse(LLMResponse):
"""Standard response struct for a response from an embedding model."""
embedding: list[float] = field(default_factory=list)
@dataclass
class ChatModelResponse(LLMResponse):
"""Standard response struct for a response from a chat LLM."""
content: Optional[str]
function_call: Optional[OpenAIFunctionCall]
|
