Spaces:
Runtime error
Runtime error
File size: 3,137 Bytes
129cd69 |
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 |
"""Wrapper around Minimax chat models."""
import logging
from typing import Any, Dict, List, Optional, cast
from langchain_core.messages import (
AIMessage,
BaseMessage,
HumanMessage,
)
from langchain_core.outputs import ChatResult
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.llms.minimax import MinimaxCommon
from langchain.llms.utils import enforce_stop_tokens
logger = logging.getLogger(__name__)
def _parse_message(msg_type: str, text: str) -> Dict:
return {"sender_type": msg_type, "text": text}
def _parse_chat_history(history: List[BaseMessage]) -> List:
"""Parse a sequence of messages into history."""
chat_history = []
for message in history:
content = cast(str, message.content)
if isinstance(message, HumanMessage):
chat_history.append(_parse_message("USER", content))
if isinstance(message, AIMessage):
chat_history.append(_parse_message("BOT", content))
return chat_history
class MiniMaxChat(MinimaxCommon, BaseChatModel):
"""Wrapper around Minimax large language models.
To use, you should have the environment variable ``MINIMAX_GROUP_ID`` and
``MINIMAX_API_KEY`` set with your API token, or pass it as a named parameter to
the constructor.
Example:
.. code-block:: python
from langchain.chat_models import MiniMaxChat
llm = MiniMaxChat(model_name="abab5-chat")
"""
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = 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.
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!"
)
history = _parse_chat_history(messages)
payload = self._default_params
payload["messages"] = history
text = self._client.post(payload)
# This is required since the stop are not enforced by the model parameters
return text if stop is None else enforce_stop_tokens(text, stop)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
raise NotImplementedError(
"""Minimax AI doesn't support async requests at the moment."""
)
|