from __future__ import annotations import logging from functools import cached_property from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, Iterator, List, Optional from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) from langchain_core.language_models.llms import BaseLLM from langchain_core.load.serializable import Serializable from langchain_core.outputs import Generation, GenerationChunk, LLMResult from langchain_core.utils import pre_init from langchain_core.utils.pydantic import get_fields from pydantic import ConfigDict if TYPE_CHECKING: import gigachat import gigachat.models as gm logger = logging.getLogger(__name__) class _BaseGigaChat(Serializable): base_url: Optional[str] = None """ Base API URL """ auth_url: Optional[str] = None """ Auth URL """ credentials: Optional[str] = None """ Auth Token """ scope: Optional[str] = None """ Permission scope for access token """ access_token: Optional[str] = None """ Access token for GigaChat """ model: Optional[str] = None """Model name to use.""" user: Optional[str] = None """ Username for authenticate """ password: Optional[str] = None """ Password for authenticate """ timeout: Optional[float] = None """ Timeout for request """ verify_ssl_certs: Optional[bool] = None """ Check certificates for all requests """ ca_bundle_file: Optional[str] = None cert_file: Optional[str] = None key_file: Optional[str] = None key_file_password: Optional[str] = None # Support for connection to GigaChat through SSL certificates profanity: bool = True """ DEPRECATED: Check for profanity """ profanity_check: Optional[bool] = None """ Check for profanity """ streaming: bool = False """ Whether to stream the results or not. """ temperature: Optional[float] = None """ What sampling temperature to use. """ max_tokens: Optional[int] = None """ Maximum number of tokens to generate """ use_api_for_tokens: bool = False """ Use GigaChat API for tokens count """ verbose: bool = False """ Verbose logging """ top_p: Optional[float] = None """ top_p value to use for nucleus sampling. Must be between 0.0 and 1.0 """ repetition_penalty: Optional[float] = None """ The penalty applied to repeated tokens """ update_interval: Optional[float] = None """ Minimum interval in seconds that elapses between sending tokens """ @property def _llm_type(self) -> str: return "giga-chat-model" @property def lc_secrets(self) -> Dict[str, str]: return { "credentials": "GIGACHAT_CREDENTIALS", "access_token": "GIGACHAT_ACCESS_TOKEN", "password": "GIGACHAT_PASSWORD", "key_file_password": "GIGACHAT_KEY_FILE_PASSWORD", } @property def lc_serializable(self) -> bool: return True @cached_property def _client(self) -> gigachat.GigaChat: """Returns GigaChat API client""" import gigachat return gigachat.GigaChat( base_url=self.base_url, auth_url=self.auth_url, credentials=self.credentials, scope=self.scope, access_token=self.access_token, model=self.model, profanity_check=self.profanity_check, user=self.user, password=self.password, timeout=self.timeout, verify_ssl_certs=self.verify_ssl_certs, ca_bundle_file=self.ca_bundle_file, cert_file=self.cert_file, key_file=self.key_file, key_file_password=self.key_file_password, verbose=self.verbose, ) @pre_init def validate_environment(cls, values: Dict) -> Dict: """Validate authenticate data in environment and python package is installed.""" try: import gigachat # noqa: F401 except ImportError: raise ImportError( "Could not import gigachat python package. " "Please install it with `pip install gigachat`." ) fields = set(get_fields(cls).keys()) diff = set(values.keys()) - fields if diff: logger.warning(f"Extra fields {diff} in GigaChat class") if "profanity" in fields and values.get("profanity") is False: logger.warning( "'profanity' field is deprecated. Use 'profanity_check' instead." ) if values.get("profanity_check") is None: values["profanity_check"] = values.get("profanity") return values @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return { "temperature": self.temperature, "model": self.model, "profanity": self.profanity_check, "streaming": self.streaming, "max_tokens": self.max_tokens, "top_p": self.top_p, "repetition_penalty": self.repetition_penalty, } def tokens_count( self, input_: List[str], model: Optional[str] = None ) -> List[gm.TokensCount]: """Get tokens of string list""" return self._client.tokens_count(input_, model) async def atokens_count( self, input_: List[str], model: Optional[str] = None ) -> List[gm.TokensCount]: """Get tokens of strings list (async)""" return await self._client.atokens_count(input_, model) def get_models(self) -> gm.Models: """Get available models of Gigachat""" return self._client.get_models() async def aget_models(self) -> gm.Models: """Get available models of Gigachat (async)""" return await self._client.aget_models() def get_model(self, model: str) -> gm.Model: """Get info about model""" return self._client.get_model(model) async def aget_model(self, model: str) -> gm.Model: """Get info about model (async)""" return await self._client.aget_model(model) def get_num_tokens(self, text: str) -> int: """Count approximate number of tokens""" if self.use_api_for_tokens: return self.tokens_count([text])[0].tokens # type: ignore else: return round(len(text) / 4.6) class GigaChat(_BaseGigaChat, BaseLLM): """`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.llms import GigaChat giga = GigaChat(credentials=..., scope=..., verify_ssl_certs=False) """ payload_role: str = "user" def _build_payload(self, messages: List[str]) -> Dict[str, Any]: payload: Dict[str, Any] = { "messages": [{"role": self.payload_role, "content": m} for m in messages], } if self.model: payload["model"] = self.model if self.profanity_check is not None: payload["profanity_check"] = self.profanity_check if self.temperature is not None: payload["temperature"] = self.temperature if self.top_p is not None: payload["top_p"] = self.top_p if self.max_tokens is not None: payload["max_tokens"] = self.max_tokens if self.repetition_penalty is not None: payload["repetition_penalty"] = self.repetition_penalty if self.update_interval is not None: payload["update_interval"] = self.update_interval if self.verbose: logger.info("Giga request: %s", payload) return payload def _create_llm_result(self, response: Any) -> LLMResult: generations = [] for res in response.choices: finish_reason = res.finish_reason gen = Generation( text=res.message.content, 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", res.message.content) token_usage = response.usage llm_output = {"token_usage": token_usage, "model_name": response.model} return LLMResult(generations=generations, llm_output=llm_output) def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, stream: Optional[bool] = None, **kwargs: Any, ) -> LLMResult: should_stream = stream if stream is not None else self.streaming if should_stream: generation: Optional[GenerationChunk] = None stream_iter = self._stream( prompts[0], stop=stop, run_manager=run_manager, **kwargs ) for chunk in stream_iter: if generation is None: generation = chunk else: generation += chunk assert generation is not None return LLMResult(generations=[[generation]]) payload = self._build_payload(prompts) response = self._client.chat(payload) return self._create_llm_result(response) async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, stream: Optional[bool] = None, **kwargs: Any, ) -> LLMResult: should_stream = stream if stream is not None else self.streaming if should_stream: generation: Optional[GenerationChunk] = None stream_iter = self._astream( prompts[0], stop=stop, run_manager=run_manager, **kwargs ) async for chunk in stream_iter: if generation is None: generation = chunk else: generation += chunk assert generation is not None return LLMResult(generations=[[generation]]) payload = self._build_payload(prompts) response = await self._client.achat(payload) return self._create_llm_result(response) def _stream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[GenerationChunk]: payload = self._build_payload([prompt]) for chunk in self._client.stream(payload): if chunk.choices: content = chunk.choices[0].delta.content if run_manager: run_manager.on_llm_new_token(content) yield GenerationChunk(text=content) async def _astream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[GenerationChunk]: payload = self._build_payload([prompt]) async for chunk in self._client.astream(payload): if chunk.choices: content = chunk.choices[0].delta.content if run_manager: await run_manager.on_llm_new_token(content) yield GenerationChunk(text=content) model_config = ConfigDict( extra="allow", )