Spaces:
Runtime error
Runtime error
File size: 14,208 Bytes
ed4d993 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 |
"""Wrapper around Minimax chat models."""
import json
import logging
from contextlib import asynccontextmanager, contextmanager
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Type, Union
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import (
BaseChatModel,
agenerate_from_stream,
generate_from_stream,
)
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
logger = logging.getLogger(__name__)
@contextmanager
def connect_httpx_sse(client: Any, method: str, url: str, **kwargs: Any) -> Iterator:
from httpx_sse import EventSource
with client.stream(method, url, **kwargs) as response:
yield EventSource(response)
@asynccontextmanager
async def aconnect_httpx_sse(
client: Any, method: str, url: str, **kwargs: Any
) -> AsyncIterator:
from httpx_sse import EventSource
async with client.stream(method, url, **kwargs) as response:
yield EventSource(response)
def _convert_message_to_dict(message: BaseMessage) -> Dict[str, Any]:
"""Convert a LangChain messages to Dict."""
message_dict: Dict[str, Any]
if isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
else:
raise TypeError(f"Got unknown type '{message.__class__.__name__}'.")
return message_dict
def _convert_dict_to_message(dct: Dict[str, Any]) -> BaseMessage:
"""Convert a dict to LangChain message."""
role = dct.get("role")
content = dct.get("content", "")
if role == "assistant":
additional_kwargs = {}
tool_calls = dct.get("tool_calls", None)
if tool_calls is not None:
additional_kwargs["tool_calls"] = tool_calls
return AIMessage(content=content, additional_kwargs=additional_kwargs)
return ChatMessage(role=role, content=content) # type: ignore[arg-type]
def _convert_delta_to_message_chunk(
dct: Dict[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
role = dct.get("role")
content = dct.get("content", "")
additional_kwargs = {}
tool_calls = dct.get("tool_call", None)
if tool_calls is not None:
additional_kwargs["tool_calls"] = tool_calls
if role == "assistant" or default_class == AIMessageChunk:
return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
if role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role) # type: ignore[arg-type]
return default_class(content=content) # type: ignore[call-arg]
class MiniMaxChat(BaseChatModel):
"""MiniMax large language models.
To use, you should have the environment variable``MINIMAX_API_KEY`` set with
your API token, or pass it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain_community.chat_models import MiniMaxChat
llm = MiniMaxChat(model="abab5-chat")
"""
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "minimax"
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
return {
"model": self.model,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_p": self.top_p,
**self.model_kwargs,
}
_client: Any
model: str = "abab6.5-chat"
"""Model name to use."""
max_tokens: int = 256
"""Denotes the number of tokens to predict per generation."""
temperature: float = 0.7
"""A non-negative float that tunes the degree of randomness in generation."""
top_p: float = 0.95
"""Total probability mass of tokens to consider at each step."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
minimax_api_host: str = Field(
default="https://api.minimax.chat/v1/text/chatcompletion_v2", alias="base_url"
)
minimax_group_id: Optional[str] = Field(default=None, alias="group_id")
"""[DEPRECATED, keeping it for for backward compatibility] Group Id"""
minimax_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
"""Minimax API Key"""
streaming: bool = False
"""Whether to stream the results or not."""
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
@root_validator(allow_reuse=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["minimax_api_key"] = convert_to_secret_str(
get_from_dict_or_env(values, "minimax_api_key", "MINIMAX_API_KEY")
)
values["minimax_group_id"] = get_from_dict_or_env(
values, "minimax_group_id", "MINIMAX_GROUP_ID"
)
# Get custom api url from environment.
values["minimax_api_host"] = get_from_dict_or_env(
values,
"minimax_api_host",
"MINIMAX_API_HOST",
values["minimax_api_host"],
)
return values
def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
generations = []
if not isinstance(response, dict):
response = response.dict()
for res in response["choices"]:
message = _convert_dict_to_message(res["message"])
generation_info = dict(finish_reason=res.get("finish_reason"))
generations.append(
ChatGeneration(message=message, generation_info=generation_info)
)
token_usage = response.get("usage", {})
llm_output = {
"token_usage": token_usage,
"model_name": self.model,
}
return ChatResult(generations=generations, llm_output=llm_output)
def _create_payload_parameters( # type: ignore[no-untyped-def]
self, messages: List[BaseMessage], is_stream: bool = False, **kwargs
) -> Dict[str, Any]:
"""Create API request body parameters."""
message_dicts = [_convert_message_to_dict(m) for m in messages]
payload = self._default_params
payload["messages"] = message_dicts
payload.update(**kwargs)
if is_stream:
payload["stream"] = True
return payload
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
"""Generate next turn in the conversation.
Args:
messages: The history of the conversation as a list of messages. Code chat
does not support context.
stop: The list of stop words (optional).
run_manager: The CallbackManager for LLM run, it's not used at the moment.
stream: Whether to stream the results or not.
Returns:
The ChatResult that contains outputs generated by the model.
Raises:
ValueError: if the last message in the list is not from human.
"""
if not messages:
raise ValueError(
"You should provide at least one message to start the chat!"
)
is_stream = stream if stream is not None else self.streaming
if is_stream:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return generate_from_stream(stream_iter)
payload = self._create_payload_parameters(messages, **kwargs)
api_key = ""
if self.minimax_api_key is not None:
api_key = self.minimax_api_key.get_secret_value()
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
import httpx
with httpx.Client(headers=headers, timeout=60) as client:
response = client.post(self.minimax_api_host, json=payload)
response.raise_for_status()
return self._create_chat_result(response.json())
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
"""Stream the chat response in chunks."""
payload = self._create_payload_parameters(messages, is_stream=True, **kwargs)
api_key = ""
if self.minimax_api_key is not None:
api_key = self.minimax_api_key.get_secret_value()
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
import httpx
with httpx.Client(headers=headers, timeout=60) as client:
with connect_httpx_sse(
client, "POST", self.minimax_api_host, json=payload
) as event_source:
for sse in event_source.iter_sse():
chunk = json.loads(sse.data)
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
chunk = _convert_delta_to_message_chunk(
choice["delta"], AIMessageChunk
)
finish_reason = choice.get("finish_reason", None)
generation_info = (
{"finish_reason": finish_reason}
if finish_reason is not None
else None
)
chunk = ChatGenerationChunk(
message=chunk, generation_info=generation_info
)
yield chunk
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
if finish_reason is not None:
break
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
if not messages:
raise ValueError(
"You should provide at least one message to start the chat!"
)
is_stream = stream if stream is not None else self.streaming
if is_stream:
stream_iter = self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return await agenerate_from_stream(stream_iter)
payload = self._create_payload_parameters(messages, **kwargs)
api_key = ""
if self.minimax_api_key is not None:
api_key = self.minimax_api_key.get_secret_value()
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
import httpx
async with httpx.AsyncClient(headers=headers, timeout=60) as client:
response = await client.post(self.minimax_api_host, json=payload)
response.raise_for_status()
return self._create_chat_result(response.json())
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
payload = self._create_payload_parameters(messages, is_stream=True, **kwargs)
api_key = ""
if self.minimax_api_key is not None:
api_key = self.minimax_api_key.get_secret_value()
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
import httpx
async with httpx.AsyncClient(headers=headers, timeout=60) as client:
async with aconnect_httpx_sse(
client, "POST", self.minimax_api_host, json=payload
) as event_source:
async for sse in event_source.aiter_sse():
chunk = json.loads(sse.data)
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
chunk = _convert_delta_to_message_chunk(
choice["delta"], AIMessageChunk
)
finish_reason = choice.get("finish_reason", None)
generation_info = (
{"finish_reason": finish_reason}
if finish_reason is not None
else None
)
chunk = ChatGenerationChunk(
message=chunk, generation_info=generation_info
)
yield chunk
if run_manager:
await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
if finish_reason is not None:
break
|