Source code for langchain_google_genai.llms

from __future__ import annotations

from enum import Enum, auto
from typing import Any, Callable, Dict, Iterator, List, Optional, Union

import google.api_core
import google.generativeai as genai  # type: ignore[import]
from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.llms import BaseLLM, create_base_retry_decorator
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
from langchain_core.utils import get_from_dict_or_env

from langchain_google_genai._enums import (
    HarmBlockThreshold,
    HarmCategory,
)


[docs]class GoogleModelFamily(str, Enum): GEMINI = auto() PALM = auto() @classmethod def _missing_(cls, value: Any) -> Optional["GoogleModelFamily"]: if "gemini" in value.lower(): return GoogleModelFamily.GEMINI elif "text-bison" in value.lower(): return GoogleModelFamily.PALM return None
def _create_retry_decorator( llm: BaseLLM, *, max_retries: int = 1, run_manager: Optional[ Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] ] = None, ) -> Callable[[Any], Any]: """Creates a retry decorator for Vertex / Palm LLMs.""" errors = [ google.api_core.exceptions.ResourceExhausted, google.api_core.exceptions.ServiceUnavailable, google.api_core.exceptions.Aborted, google.api_core.exceptions.DeadlineExceeded, google.api_core.exceptions.GoogleAPIError, ] decorator = create_base_retry_decorator( error_types=errors, max_retries=max_retries, run_manager=run_manager ) return decorator def _completion_with_retry( llm: GoogleGenerativeAI, prompt: LanguageModelInput, is_gemini: bool = False, stream: bool = False, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Any: """Use tenacity to retry the completion call.""" retry_decorator = _create_retry_decorator( llm, max_retries=llm.max_retries, run_manager=run_manager ) @retry_decorator def _completion_with_retry( prompt: LanguageModelInput, is_gemini: bool, stream: bool, **kwargs: Any ) -> Any: generation_config = kwargs.get("generation_config", {}) error_msg = ( "Your location is not supported by google-generativeai at the moment. " "Try to use VertexAI LLM from langchain_google_vertexai" ) try: if is_gemini: return llm.client.generate_content( contents=prompt, stream=stream, generation_config=generation_config, safety_settings=kwargs.pop("safety_settings", None), request_options={"timeout": llm.timeout} if llm.timeout else None, ) return llm.client.generate_text(prompt=prompt, **kwargs) except google.api_core.exceptions.FailedPrecondition as exc: if "location is not supported" in exc.message: raise ValueError(error_msg) return _completion_with_retry( prompt=prompt, is_gemini=is_gemini, stream=stream, **kwargs ) def _strip_erroneous_leading_spaces(text: str) -> str: """Strip erroneous leading spaces from text. The PaLM API will sometimes erroneously return a single leading space in all lines > 1. This function strips that space. """ has_leading_space = all(not line or line[0] == " " for line in text.split("\n")[1:]) if has_leading_space: return text.replace("\n ", "\n") else: return text class _BaseGoogleGenerativeAI(BaseModel): """Base class for Google Generative AI LLMs""" model: str = Field( ..., description="""The name of the model to use. Supported examples: - gemini-pro - models/text-bison-001""", ) """Model name to use.""" google_api_key: Optional[SecretStr] = None credentials: Any = None "The default custom credentials (google.auth.credentials.Credentials) to use " "when making API calls. If not provided, credentials will be ascertained from " "the GOOGLE_API_KEY envvar" temperature: float = 0.7 """Run inference with this temperature. Must by in the closed interval [0.0, 1.0].""" top_p: Optional[float] = None """Decode using nucleus sampling: consider the smallest set of tokens whose probability sum is at least top_p. Must be in the closed interval [0.0, 1.0].""" top_k: Optional[int] = None """Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.""" max_output_tokens: Optional[int] = None """Maximum number of tokens to include in a candidate. Must be greater than zero. If unset, will default to 64.""" n: int = 1 """Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.""" max_retries: int = 6 """The maximum number of retries to make when generating.""" timeout: Optional[float] = None """The maximum number of seconds to wait for a response.""" client_options: Optional[Dict] = Field( None, description=( "A dictionary of client options to pass to the Google API client, " "such as `api_endpoint`." ), ) transport: Optional[str] = Field( None, description="A string, one of: [`rest`, `grpc`, `grpc_asyncio`].", ) additional_headers: Optional[Dict[str, str]] = Field( None, description=( "A key-value dictionary representing additional headers for the model call" ), ) safety_settings: Optional[Dict[HarmCategory, HarmBlockThreshold]] = None """The default safety settings to use for all generations. For example: from google.generativeai.types.safety_types import HarmBlockThreshold, HarmCategory safety_settings = { HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH, HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, } """ # noqa: E501 @property def lc_secrets(self) -> Dict[str, str]: return {"google_api_key": "GOOGLE_API_KEY"} @property def _model_family(self) -> str: return GoogleModelFamily(self.model) @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return { "model": self.model, "temperature": self.temperature, "top_p": self.top_p, "top_k": self.top_k, "max_output_tokens": self.max_output_tokens, "candidate_count": self.n, }
[docs]class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM): """Google GenerativeAI models. Example: .. code-block:: python from langchain_google_genai import GoogleGenerativeAI llm = GoogleGenerativeAI(model="gemini-pro") """ client: Any #: :meta private: @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validates params and passes them to google-generativeai package.""" if values.get("credentials"): genai.configure( credentials=values.get("credentials"), transport=values.get("transport"), client_options=values.get("client_options"), ) else: google_api_key = get_from_dict_or_env( values, "google_api_key", "GOOGLE_API_KEY" ) if isinstance(google_api_key, SecretStr): google_api_key = google_api_key.get_secret_value() genai.configure( api_key=google_api_key, transport=values.get("transport"), client_options=values.get("client_options"), ) model_name = values["model"] safety_settings = values["safety_settings"] if safety_settings and ( not GoogleModelFamily(model_name) == GoogleModelFamily.GEMINI ): raise ValueError("Safety settings are only supported for Gemini models") if GoogleModelFamily(model_name) == GoogleModelFamily.GEMINI: values["client"] = genai.GenerativeModel( model_name=model_name, safety_settings=safety_settings ) else: values["client"] = genai if values["temperature"] is not None and not 0 <= values["temperature"] <= 1: raise ValueError("temperature must be in the range [0.0, 1.0]") if values["top_p"] is not None and not 0 <= values["top_p"] <= 1: raise ValueError("top_p must be in the range [0.0, 1.0]") if values["top_k"] is not None and values["top_k"] <= 0: raise ValueError("top_k must be positive") if values["max_output_tokens"] is not None and values["max_output_tokens"] <= 0: raise ValueError("max_output_tokens must be greater than zero") if values["timeout"] is not None and values["timeout"] <= 0: raise ValueError("timeout must be greater than zero") return values def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: generations: List[List[Generation]] = [] generation_config = { "stop_sequences": stop, "temperature": self.temperature, "top_p": self.top_p, "top_k": self.top_k, "max_output_tokens": self.max_output_tokens, "candidate_count": self.n, } for prompt in prompts: if self._model_family == GoogleModelFamily.GEMINI: res = _completion_with_retry( self, prompt=prompt, stream=False, is_gemini=True, run_manager=run_manager, generation_config=generation_config, safety_settings=kwargs.pop("safety_settings", None), ) candidates = [ "".join([p.text for p in c.content.parts]) for c in res.candidates ] generations.append([Generation(text=c) for c in candidates]) else: res = _completion_with_retry( self, model=self.model, prompt=prompt, stream=False, is_gemini=False, run_manager=run_manager, **generation_config, ) prompt_generations = [] for candidate in res.candidates: raw_text = candidate["output"] stripped_text = _strip_erroneous_leading_spaces(raw_text) prompt_generations.append(Generation(text=stripped_text)) generations.append(prompt_generations) return LLMResult(generations=generations) def _stream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[GenerationChunk]: generation_config = kwargs.get("generation_config", {}) if stop: generation_config["stop_sequences"] = stop for stream_resp in _completion_with_retry( self, prompt, stream=True, is_gemini=True, run_manager=run_manager, generation_config=generation_config, safety_settings=kwargs.pop("safety_settings", None), **kwargs, ): chunk = GenerationChunk(text=stream_resp.text) yield chunk if run_manager: run_manager.on_llm_new_token( stream_resp.text, chunk=chunk, verbose=self.verbose, ) @property def _llm_type(self) -> str: """Return type of llm.""" return "google_palm"
[docs] def get_num_tokens(self, text: str) -> int: """Get the number of tokens present in the text. Useful for checking if an input will fit in a model's context window. Args: text: The string input to tokenize. Returns: The integer number of tokens in the text. """ if self._model_family == GoogleModelFamily.GEMINI: result = self.client.count_tokens(text) token_count = result.total_tokens else: result = self.client.count_text_tokens(model=self.model, prompt=text) token_count = result["token_count"] return token_count