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260 lines
8.5 KiB
Python
260 lines
8.5 KiB
Python
9 months ago
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from __future__ import annotations
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import logging
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from functools import cached_property
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.llms import BaseLLM
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from langchain_core.load.serializable import Serializable
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from langchain_core.outputs import Generation, GenerationChunk, LLMResult
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from langchain_core.pydantic_v1 import root_validator
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logger = logging.getLogger(__name__)
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class _BaseGigaChat(Serializable):
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base_url: Optional[str] = None
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""" Base API URL """
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auth_url: Optional[str] = None
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""" Auth URL """
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credentials: Optional[str] = None
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""" Auth Token """
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scope: Optional[str] = None
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""" Permission scope for access token """
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access_token: Optional[str] = None
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""" Access token for GigaChat """
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model: Optional[str] = None
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"""Model name to use."""
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user: Optional[str] = None
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""" Username for authenticate """
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password: Optional[str] = None
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""" Password for authenticate """
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timeout: Optional[float] = None
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""" Timeout for request """
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verify_ssl_certs: Optional[bool] = None
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""" Check certificates for all requests """
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ca_bundle_file: Optional[str] = None
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cert_file: Optional[str] = None
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key_file: Optional[str] = None
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key_file_password: Optional[str] = None
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# Support for connection to GigaChat through SSL certificates
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profanity: bool = True
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""" Check for profanity """
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streaming: bool = False
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""" Whether to stream the results or not. """
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temperature: Optional[float] = None
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"""What sampling temperature to use."""
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max_tokens: Optional[int] = None
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""" Maximum number of tokens to generate """
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@property
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def _llm_type(self) -> str:
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return "giga-chat-model"
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {
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"credentials": "GIGACHAT_CREDENTIALS",
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"access_token": "GIGACHAT_ACCESS_TOKEN",
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"password": "GIGACHAT_PASSWORD",
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"key_file_password": "GIGACHAT_KEY_FILE_PASSWORD",
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}
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@property
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def lc_serializable(self) -> bool:
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return True
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@cached_property
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def _client(self) -> Any:
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"""Returns GigaChat API client"""
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import gigachat
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return gigachat.GigaChat(
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base_url=self.base_url,
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auth_url=self.auth_url,
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credentials=self.credentials,
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scope=self.scope,
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access_token=self.access_token,
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model=self.model,
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user=self.user,
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password=self.password,
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timeout=self.timeout,
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verify_ssl_certs=self.verify_ssl_certs,
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ca_bundle_file=self.ca_bundle_file,
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cert_file=self.cert_file,
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key_file=self.key_file,
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key_file_password=self.key_file_password,
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)
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate authenticate data in environment and python package is installed."""
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try:
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import gigachat # noqa: F401
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except ImportError:
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raise ImportError(
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"Could not import gigachat python package. "
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"Please install it with `pip install gigachat`."
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)
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return values
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {
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"temperature": self.temperature,
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"model": self.model,
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"profanity": self.profanity,
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"streaming": self.streaming,
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"max_tokens": self.max_tokens,
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}
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class GigaChat(_BaseGigaChat, BaseLLM):
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"""`GigaChat` large language models API.
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To use, you should pass login and password to access GigaChat API or use token.
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Example:
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.. code-block:: python
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from langchain_community.llms import GigaChat
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giga = GigaChat(credentials=..., verify_ssl_certs=False)
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"""
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def _build_payload(self, messages: List[str]) -> Dict[str, Any]:
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payload: Dict[str, Any] = {
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"messages": [{"role": "user", "content": m} for m in messages],
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"profanity_check": self.profanity,
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}
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if self.temperature is not None:
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payload["temperature"] = self.temperature
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if self.max_tokens is not None:
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payload["max_tokens"] = self.max_tokens
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if self.model:
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payload["model"] = self.model
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if self.verbose:
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logger.info("Giga request: %s", payload)
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return payload
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def _create_llm_result(self, response: Any) -> LLMResult:
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generations = []
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for res in response.choices:
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finish_reason = res.finish_reason
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gen = Generation(
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text=res.message.content,
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generation_info={"finish_reason": finish_reason},
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)
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generations.append([gen])
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if finish_reason != "stop":
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logger.warning(
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"Giga generation stopped with reason: %s",
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finish_reason,
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)
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if self.verbose:
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logger.info("Giga response: %s", res.message.content)
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token_usage = response.usage
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llm_output = {"token_usage": token_usage, "model_name": response.model}
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return LLMResult(generations=generations, llm_output=llm_output)
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def _generate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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stream: Optional[bool] = None,
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**kwargs: Any,
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) -> LLMResult:
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should_stream = stream if stream is not None else self.streaming
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if should_stream:
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generation: Optional[GenerationChunk] = None
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stream_iter = self._stream(
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prompts[0], stop=stop, run_manager=run_manager, **kwargs
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)
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for chunk in stream_iter:
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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return LLMResult(generations=[[generation]])
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payload = self._build_payload(prompts)
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response = self._client.chat(payload)
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return self._create_llm_result(response)
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async def _agenerate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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stream: Optional[bool] = None,
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**kwargs: Any,
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) -> LLMResult:
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should_stream = stream if stream is not None else self.streaming
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if should_stream:
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generation: Optional[GenerationChunk] = None
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stream_iter = self._astream(
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prompts[0], stop=stop, run_manager=run_manager, **kwargs
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)
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async for chunk in stream_iter:
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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return LLMResult(generations=[[generation]])
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payload = self._build_payload(prompts)
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response = await self._client.achat(payload)
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return self._create_llm_result(response)
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def _stream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[GenerationChunk]:
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payload = self._build_payload([prompt])
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for chunk in self._client.stream(payload):
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if chunk.choices:
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content = chunk.choices[0].delta.content
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yield GenerationChunk(text=content)
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if run_manager:
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run_manager.on_llm_new_token(content)
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async def _astream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[GenerationChunk]:
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payload = self._build_payload([prompt])
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async for chunk in self._client.astream(payload):
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if chunk.choices:
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content = chunk.choices[0].delta.content
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yield GenerationChunk(text=content)
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if run_manager:
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await run_manager.on_llm_new_token(content)
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def get_num_tokens(self, text: str) -> int:
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"""Count approximate number of tokens"""
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return round(len(text) / 4.6)
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