import logging
from typing import Any, AsyncIterator, Iterator, List, Optional
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,
ChatMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_community.llms.gigachat import _BaseGigaChat
logger = logging.getLogger(__name__)
def _convert_dict_to_message(message: Any) -> BaseMessage:
from gigachat.models import MessagesRole
if message.role == MessagesRole.SYSTEM:
return SystemMessage(content=message.content)
elif message.role == MessagesRole.USER:
return HumanMessage(content=message.content)
elif message.role == MessagesRole.ASSISTANT:
return AIMessage(content=message.content)
else:
raise TypeError(f"Got unknown role {message.role} {message}")
def _convert_message_to_dict(message: BaseMessage) -> Any:
from gigachat.models import Messages, MessagesRole
if isinstance(message, SystemMessage):
return Messages(role=MessagesRole.SYSTEM, content=message.content)
elif isinstance(message, HumanMessage):
return Messages(role=MessagesRole.USER, content=message.content)
elif isinstance(message, AIMessage):
return Messages(role=MessagesRole.ASSISTANT, content=message.content)
elif isinstance(message, ChatMessage):
return Messages(role=MessagesRole(message.role), content=message.content)
else:
raise TypeError(f"Got unknown type {message}")
[docs]class GigaChat(_BaseGigaChat, BaseChatModel):
"""`GigaChat` large language models API.
To use, you should pass login and password to access GigaChat API or use token.
Example:
.. code-block:: python
from langchain_community.chat_models import GigaChat
giga = GigaChat(credentials=..., verify_ssl_certs=False)
"""
def _build_payload(self, messages: List[BaseMessage]) -> Any:
from gigachat.models import Chat
payload = Chat(
messages=[_convert_message_to_dict(m) for m in messages],
profanity_check=self.profanity,
)
if self.temperature is not None:
payload.temperature = self.temperature
if self.max_tokens is not None:
payload.max_tokens = self.max_tokens
if self.verbose:
logger.info("Giga request: %s", payload.dict())
return payload
def _create_chat_result(self, response: Any) -> ChatResult:
generations = []
for res in response.choices:
message = _convert_dict_to_message(res.message)
finish_reason = res.finish_reason
gen = ChatGeneration(
message=message,
generation_info={"finish_reason": finish_reason},
)
generations.append(gen)
if finish_reason != "stop":
logger.warning(
"Giga generation stopped with reason: %s",
finish_reason,
)
if self.verbose:
logger.info("Giga response: %s", message.content)
llm_output = {"token_usage": response.usage, "model_name": response.model}
return ChatResult(generations=generations, llm_output=llm_output)
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if should_stream:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return generate_from_stream(stream_iter)
payload = self._build_payload(messages)
response = self._client.chat(payload)
return self._create_chat_result(response)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if should_stream:
stream_iter = self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return await agenerate_from_stream(stream_iter)
payload = self._build_payload(messages)
response = await self._client.achat(payload)
return self._create_chat_result(response)
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
payload = self._build_payload(messages)
for chunk in self._client.stream(payload):
if chunk.choices:
content = chunk.choices[0].delta.content
yield ChatGenerationChunk(message=AIMessageChunk(content=content))
if run_manager:
run_manager.on_llm_new_token(content)
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
payload = self._build_payload(messages)
async for chunk in self._client.astream(payload):
if chunk.choices:
content = chunk.choices[0].delta.content
yield ChatGenerationChunk(message=AIMessageChunk(content=content))
if run_manager:
await run_manager.on_llm_new_token(content)
[docs] def get_num_tokens(self, text: str) -> int:
"""Count approximate number of tokens"""
return round(len(text) / 4.6)