From fa3a45b8f20b0d218658c69aaaf008de8624d826 Mon Sep 17 00:00:00 2001 From: Leonid Kuligin Date: Sun, 23 Feb 2025 15:29:55 +0100 Subject: [PATCH] updating dependencies (#742) --- libs/genai/langchain_google_genai/_common.py | 95 +- .../langchain_google_genai/_function_utils.py | 33 +- .../langchain_google_genai/chat_models.py | 140 +-- libs/genai/langchain_google_genai/llms.py | 366 +------ libs/genai/poetry.lock | 997 ++++++++++-------- libs/genai/pyproject.toml | 2 +- .../integration_tests/test_chat_models.py | 2 +- .../tests/integration_tests/test_llms.py | 19 +- .../tests/integration_tests/test_standard.py | 38 +- .../tests/unit_tests/test_function_utils.py | 2 +- libs/genai/tests/unit_tests/test_llms.py | 9 +- 11 files changed, 737 insertions(+), 966 deletions(-) diff --git a/libs/genai/langchain_google_genai/_common.py b/libs/genai/langchain_google_genai/_common.py index 929c18c7..8d61b181 100644 --- a/libs/genai/langchain_google_genai/_common.py +++ b/libs/genai/langchain_google_genai/_common.py @@ -1,7 +1,9 @@ from importlib import metadata -from typing import Optional, Tuple, TypedDict +from typing import Any, Dict, Optional, Tuple, TypedDict from google.api_core.gapic_v1.client_info import ClientInfo +from langchain_core.utils import secret_from_env +from pydantic import BaseModel, Field, SecretStr from langchain_google_genai._enums import HarmBlockThreshold, HarmCategory @@ -12,6 +14,97 @@ class GoogleGenerativeAIError(Exception): """ +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] = Field( + alias="api_key", default_factory=secret_from_env("GOOGLE_API_KEY", default=None) + ) + """Google AI API key. + If not specified will be read from env var ``GOOGLE_API_KEY``.""" + 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] = Field(default=None, alias="max_tokens") + """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( + default=None, + description=( + "A dictionary of client options to pass to the Google API client, " + "such as `api_endpoint`." + ), + ) + transport: Optional[str] = Field( + default=None, + description="A string, one of: [`rest`, `grpc`, `grpc_asyncio`].", + ) + additional_headers: Optional[Dict[str, str]] = Field( + default=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 _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, + } + + def get_user_agent(module: Optional[str] = None) -> Tuple[str, str]: r"""Returns a custom user agent header. diff --git a/libs/genai/langchain_google_genai/_function_utils.py b/libs/genai/langchain_google_genai/_function_utils.py index 5afafef7..4ba4eea0 100644 --- a/libs/genai/langchain_google_genai/_function_utils.py +++ b/libs/genai/langchain_google_genai/_function_utils.py @@ -7,7 +7,6 @@ from typing import ( Any, Callable, - Collection, Dict, List, Literal, @@ -22,7 +21,6 @@ import google.ai.generativelanguage as glm import google.ai.generativelanguage_v1beta.types as gapic import proto # type: ignore[import] -from google.generativeai.types.content_types import ToolDict # type: ignore[import] from langchain_core.tools import BaseTool from langchain_core.tools import tool as callable_as_lc_tool from langchain_core.utils.function_calling import ( @@ -59,38 +57,21 @@ _ALLOWED_SCHEMA_FIELDS_SET = set(_ALLOWED_SCHEMA_FIELDS) -class _ToolDictLike(TypedDict): - function_declarations: _FunctionDeclarationLikeList - - -class _FunctionDeclarationDict(TypedDict): - name: str - description: str - parameters: Dict[str, Collection[str]] - - -class _ToolDict(TypedDict): - function_declarations: Sequence[_FunctionDeclarationDict] - - # Info: This is a FunctionDeclaration(=fc). _FunctionDeclarationLike = Union[ BaseTool, Type[BaseModel], gapic.FunctionDeclaration, Callable, Dict[str, Any] ] -# Info: This mean one tool. -_FunctionDeclarationLikeList = Sequence[_FunctionDeclarationLike] + +class _ToolDict(TypedDict): + function_declarations: Sequence[_FunctionDeclarationLike] # Info: This means one tool=Sequence of FunctionDeclaration # The dict should be gapic.Tool like. {"function_declarations": [ { "name": ...}. # OpenAI like dict is not be accepted. {{'type': 'function', 'function': {'name': ...} _ToolsType = Union[ - gapic.Tool, - ToolDict, - _ToolDictLike, - _FunctionDeclarationLikeList, - _FunctionDeclarationLike, + gapic.Tool, _ToolDict, _FunctionDeclarationLike, Sequence[_FunctionDeclarationLike] ] @@ -152,12 +133,12 @@ def convert_to_genai_function_declarations( gapic_tool = gapic.Tool() for tool in tools: if isinstance(tool, gapic.Tool): - gapic_tool.function_declarations.extend(tool.function_declarations) + gapic_tool.function_declarations.extend(tool.function_declarations) # type: ignore[union-attr] elif isinstance(tool, dict) and "function_declarations" not in tool: fd = _format_to_gapic_function_declaration(tool) gapic_tool.function_declarations.append(fd) elif isinstance(tool, dict): - function_declarations = cast(_ToolDictLike, tool)["function_declarations"] + function_declarations = cast(_ToolDict, tool)["function_declarations"] if not isinstance(function_declarations, collections.abc.Sequence): raise ValueError( "function_declarations should be a list" @@ -170,7 +151,7 @@ def convert_to_genai_function_declarations( ] gapic_tool.function_declarations.extend(fds) else: - fd = _format_to_gapic_function_declaration(tool) + fd = _format_to_gapic_function_declaration(tool) # type: ignore[arg-type] gapic_tool.function_declarations.append(fd) return gapic_tool diff --git a/libs/genai/langchain_google_genai/chat_models.py b/libs/genai/langchain_google_genai/chat_models.py index c50bd117..900dabfa 100644 --- a/libs/genai/langchain_google_genai/chat_models.py +++ b/libs/genai/langchain_google_genai/chat_models.py @@ -35,6 +35,7 @@ Content, FileData, FunctionCall, + FunctionDeclaration, FunctionResponse, GenerateContentRequest, GenerateContentResponse, @@ -44,12 +45,8 @@ ToolConfig, VideoMetadata, ) -from google.generativeai.caching import CachedContent # type: ignore[import] -from google.generativeai.types import Tool as GoogleTool # type: ignore[import] -from google.generativeai.types import caching_types, content_types -from google.generativeai.types.content_types import ( # type: ignore[import] - FunctionDeclarationType, - ToolDict, +from google.ai.generativelanguage_v1beta.types import ( + Tool as GoogleTool, ) from langchain_core.callbacks.manager import ( AsyncCallbackManagerForLLMRun, @@ -76,7 +73,7 @@ ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.runnables import Runnable, RunnablePassthrough -from langchain_core.utils import secret_from_env +from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_tool from pydantic import ( BaseModel, @@ -97,24 +94,32 @@ from langchain_google_genai._common import ( GoogleGenerativeAIError, SafetySettingDict, + _BaseGoogleGenerativeAI, get_client_info, ) from langchain_google_genai._function_utils import ( _tool_choice_to_tool_config, _ToolChoiceType, _ToolConfigDict, + _ToolDict, convert_to_genai_function_declarations, is_basemodel_subclass_safe, tool_to_dict, ) from langchain_google_genai._image_utils import ImageBytesLoader -from langchain_google_genai.llms import _BaseGoogleGenerativeAI from . import _genai_extension as genaix logger = logging.getLogger(__name__) +_FunctionDeclarationType = Union[ + FunctionDeclaration, + dict[str, Any], + Callable[..., Any], +] + + class ChatGoogleGenerativeAIError(GoogleGenerativeAIError): """ Custom exception class for errors associated with the `Google GenAI` API. @@ -786,11 +791,6 @@ class Joke(BaseModel): client: Any = Field(default=None, exclude=True) #: :meta private: async_client_running: Any = Field(default=None, exclude=True) #: :meta private: - google_api_key: Optional[SecretStr] = Field( - alias="api_key", default_factory=secret_from_env("GOOGLE_API_KEY", default=None) - ) - """Google AI API key. - If not specified will be read from env var ``GOOGLE_API_KEY``.""" default_metadata: Sequence[Tuple[str, str]] = Field( default_factory=list ) #: :meta private: @@ -938,8 +938,8 @@ def _generate( stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, *, - tools: Optional[Sequence[Union[ToolDict, GoogleTool]]] = None, - functions: Optional[Sequence[FunctionDeclarationType]] = None, + tools: Optional[Sequence[Union[_ToolDict, GoogleTool]]] = None, + functions: Optional[Sequence[_FunctionDeclarationType]] = None, safety_settings: Optional[SafetySettingDict] = None, tool_config: Optional[Union[Dict, _ToolConfigDict]] = None, generation_config: Optional[Dict[str, Any]] = None, @@ -972,8 +972,8 @@ async def _agenerate( stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, *, - tools: Optional[Sequence[Union[ToolDict, GoogleTool]]] = None, - functions: Optional[Sequence[FunctionDeclarationType]] = None, + tools: Optional[Sequence[Union[_ToolDict, GoogleTool]]] = None, + functions: Optional[Sequence[_FunctionDeclarationType]] = None, safety_settings: Optional[SafetySettingDict] = None, tool_config: Optional[Union[Dict, _ToolConfigDict]] = None, generation_config: Optional[Dict[str, Any]] = None, @@ -1021,8 +1021,8 @@ def _stream( stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, *, - tools: Optional[Sequence[Union[ToolDict, GoogleTool]]] = None, - functions: Optional[Sequence[FunctionDeclarationType]] = None, + tools: Optional[Sequence[Union[_ToolDict, GoogleTool]]] = None, + functions: Optional[Sequence[_FunctionDeclarationType]] = None, safety_settings: Optional[SafetySettingDict] = None, tool_config: Optional[Union[Dict, _ToolConfigDict]] = None, generation_config: Optional[Dict[str, Any]] = None, @@ -1083,8 +1083,8 @@ async def _astream( stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, *, - tools: Optional[Sequence[Union[ToolDict, GoogleTool]]] = None, - functions: Optional[Sequence[FunctionDeclarationType]] = None, + tools: Optional[Sequence[Union[_ToolDict, GoogleTool]]] = None, + functions: Optional[Sequence[_FunctionDeclarationType]] = None, safety_settings: Optional[SafetySettingDict] = None, tool_config: Optional[Union[Dict, _ToolConfigDict]] = None, generation_config: Optional[Dict[str, Any]] = None, @@ -1158,8 +1158,8 @@ def _prepare_request( messages: List[BaseMessage], *, stop: Optional[List[str]] = None, - tools: Optional[Sequence[Union[ToolDict, GoogleTool]]] = None, - functions: Optional[Sequence[FunctionDeclarationType]] = None, + tools: Optional[Sequence[Union[_ToolDict, GoogleTool]]] = None, + functions: Optional[Sequence[_FunctionDeclarationType]] = None, safety_settings: Optional[SafetySettingDict] = None, tool_config: Optional[Union[Dict, _ToolConfigDict]] = None, tool_choice: Optional[Union[_ToolChoiceType, bool]] = None, @@ -1251,8 +1251,8 @@ def with_structured_output( ) else: parser = JsonOutputToolsParser() - tool_choice = _get_tool_name(schema) if self._supports_tool_choice else None - llm = self.bind_tools([schema], tool_choice=tool_choice) + tool_choice = _get_tool_name(schema) if self._supports_tool_choice else None # type: ignore[arg-type] + llm = self.bind_tools([schema], tool_choice=tool_choice) # type: ignore[list-item] if include_raw: parser_with_fallback = RunnablePassthrough.assign( parsed=itemgetter("raw") | parser, parsing_error=lambda _: None @@ -1266,7 +1266,9 @@ def with_structured_output( def bind_tools( self, - tools: Sequence[Union[ToolDict, GoogleTool]], + tools: Sequence[ + dict[str, Any] | type | Callable[..., Any] | BaseTool | GoogleTool + ], tool_config: Optional[Union[Dict, _ToolConfigDict]] = None, *, tool_choice: Optional[Union[_ToolChoiceType, bool]] = None, @@ -1303,90 +1305,6 @@ def bind_tools( pass return self.bind(tools=formatted_tools, **kwargs) - def create_cached_content( - self, - contents: Union[List[BaseMessage], content_types.ContentsType], - *, - display_name: str | None = None, - tools: Union[ToolDict, GoogleTool, None] = None, - tool_choice: Optional[Union[_ToolChoiceType, bool]] = None, - ttl: Optional[caching_types.TTLTypes] = None, - expire_time: Optional[caching_types.ExpireTimeTypes] = None, - ) -> str: - """ - - Args: - display_name: The user-generated meaningful display name - of the cached content. `display_name` must be no - more than 128 unicode characters. - contents: Contents to cache. - tools: A list of `Tools` the model may use to generate response. - tool_choice: Which tool to require the model to call. - ttl: TTL for cached resource (in seconds). Defaults to 1 hour. - `ttl` and `expire_time` are exclusive arguments. - expire_time: Expiration time for cached resource. - `ttl` and `expire_time` are exclusive arguments. - """ - system: Optional[content_types.ContentType] = None - genai_contents: list = [] - if all(isinstance(c, BaseMessage) for c in contents): - system, genai_contents = _parse_chat_history( - contents, - convert_system_message_to_human=self.convert_system_message_to_human, - ) - elif any(isinstance(c, BaseMessage) for c in contents): - raise ValueError( - f"'contents' must either be a list of " - f"langchain_core.messages.BaseMessage or a list " - f"google.generativeai.types.content_types.ContentType, but not a mix " - f"of the two. Received {contents}" - ) - else: - for content in contents: - if hasattr(content, "role") and content.role == "system": - if system is not None: - warnings.warn( - "Received multiple pieces of content with role 'system'. " - "Should only be one set of system instructions. Ignoring " - "all but the first 'system' content." - ) - else: - system = content - elif isinstance(content, dict) and content.get("role") == "system": - if system is not None: - warnings.warn( - "Received multiple pieces of content with role 'system'. " - "Should only be one set of system instructions. Ignoring " - "all but the first 'system' content." - ) - else: - system = content - else: - genai_contents.append(content) - if tools: - genai_tools = [convert_to_genai_function_declarations(tools)] - else: - genai_tools = None - if tool_choice and genai_tools: - all_names = [f.name for t in genai_tools for f in t.function_declarations] - tool_config = _tool_choice_to_tool_config(tool_choice, all_names) - genai_tool_config = ToolConfig( - function_calling_config=tool_config["function_calling_config"] - ) - else: - genai_tool_config = None - cached_content = CachedContent.create( - model=self.model, - system_instruction=system, - contents=genai_contents, - display_name=display_name, - tools=genai_tools, - tool_config=genai_tool_config, - ttl=ttl, - expire_time=expire_time, - ) - return cached_content.name - @property def _supports_tool_choice(self) -> bool: return ( @@ -1397,7 +1315,7 @@ def _supports_tool_choice(self) -> bool: def _get_tool_name( - tool: Union[ToolDict, GoogleTool], + tool: Union[_ToolDict, GoogleTool], ) -> str: genai_tool = tool_to_dict(convert_to_genai_function_declarations([tool])) return [f["name"] for f in genai_tool["function_declarations"]][0] # type: ignore[index] diff --git a/libs/genai/langchain_google_genai/llms.py b/libs/genai/langchain_google_genai/llms.py index 0da089ce..b20e521e 100644 --- a/libs/genai/langchain_google_genai/llms.py +++ b/libs/genai/langchain_google_genai/llms.py @@ -1,208 +1,21 @@ from __future__ import annotations -from enum import Enum, auto -from typing import Any, Callable, Dict, Iterator, List, Optional, Union +from typing import Any, Iterator, List, Optional -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 LangSmithParams, LanguageModelInput -from langchain_core.language_models.llms import BaseLLM, create_base_retry_decorator +from langchain_core.language_models import LangSmithParams +from langchain_core.language_models.llms import BaseLLM +from langchain_core.messages import HumanMessage from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.utils import secret_from_env -from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator +from pydantic import ConfigDict, model_validator from typing_extensions import Self -from langchain_google_genai._enums import ( - HarmBlockThreshold, - HarmCategory, +from langchain_google_genai._common import ( + _BaseGoogleGenerativeAI, ) - - -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] = Field( - alias="api_key", default_factory=secret_from_env("GOOGLE_API_KEY", default=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] = Field(default=None, alias="max_tokens") - """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( - default=None, - description=( - "A dictionary of client options to pass to the Google API client, " - "such as `api_endpoint`." - ), - ) - transport: Optional[str] = Field( - default=None, - description="A string, one of: [`rest`, `grpc`, `grpc_asyncio`].", - ) - additional_headers: Optional[Dict[str, str]] = Field( - default=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, - } +from langchain_google_genai.chat_models import ChatGoogleGenerativeAI class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM): @@ -223,53 +36,21 @@ class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM): @model_validator(mode="after") def validate_environment(self) -> Self: """Validates params and passes them to google-generativeai package.""" - if self.credentials: - genai.configure( - credentials=self.credentials, - transport=self.transport, - client_options=self.client_options, - ) - else: - if isinstance(self.google_api_key, SecretStr): - google_api_key: Optional[str] = self.google_api_key.get_secret_value() - else: - google_api_key = self.google_api_key - genai.configure( - api_key=google_api_key, - transport=self.transport, - client_options=self.client_options, - ) - - model_name = self.model - - safety_settings = self.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: - self.client = genai.GenerativeModel( - model_name=model_name, safety_settings=safety_settings - ) - else: - self.client = genai - - if self.temperature is not None and not 0 <= self.temperature <= 1: - raise ValueError("temperature must be in the range [0.0, 1.0]") - - if self.top_p is not None and not 0 <= self.top_p <= 1: - raise ValueError("top_p must be in the range [0.0, 1.0]") - - if self.top_k is not None and self.top_k <= 0: - raise ValueError("top_k must be positive") - if self.max_output_tokens is not None and self.max_output_tokens <= 0: - raise ValueError("max_output_tokens must be greater than zero") - - if self.timeout is not None and self.timeout <= 0: - raise ValueError("timeout must be greater than zero") + self.client = ChatGoogleGenerativeAI( + api_key=self.google_api_key, + credentials=self.credentials, + temperature=self.temperature, + top_p=self.top_p, + top_k=self.top_k, + max_tokens=self.max_output_tokens, + timeout=self.timeout, + model=self.model, + client_options=self.client_options, + transport=self.transport, + additional_headers=self.additional_headers, + safety_settings=self.safety_settings, + ) return self @@ -290,58 +71,26 @@ def _generate( 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, - } + generations = [] 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), - ) - generation_info = None - if res.usage_metadata is not None: - generation_info = { - "usage_metadata": res.to_dict().get("usage_metadata") - } - - candidates = [ - "".join([p.text for p in c.content.parts]) for c in res.candidates + chat_result = self.client._generate( + [HumanMessage(content=prompt)], + stop=stop, + run_manager=run_manager, + **kwargs, + ) + generations.append( + [ + Generation( + text=g.message.content, + generation_info={ + **g.generation_info, + **{"usage_metadata": g.message.usage_metadata}, + }, + ) + for g in chat_result.generations ] - generations.append( - [ - Generation(text=c, generation_info=generation_info) - 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( @@ -351,31 +100,17 @@ def _stream( run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[GenerationChunk]: - 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, - } - generation_config = generation_config | kwargs.get("generation_config", {}) - - for stream_resp in _completion_with_retry( - self, - prompt, - stream=True, - is_gemini=True, + for stream_chunk in self.client._stream( + [HumanMessage(content=prompt)], + stop=stop, run_manager=run_manager, - generation_config=generation_config, - safety_settings=kwargs.pop("safety_settings", None), **kwargs, ): - chunk = GenerationChunk(text=stream_resp.text) + chunk = GenerationChunk(text=stream_chunk.message.content) yield chunk if run_manager: run_manager.on_llm_new_token( - stream_resp.text, + chunk.text, chunk=chunk, verbose=self.verbose, ) @@ -383,7 +118,7 @@ def _stream( @property def _llm_type(self) -> str: """Return type of llm.""" - return "google_palm" + return "google_gemini" def get_num_tokens(self, text: str) -> int: """Get the number of tokens present in the text. @@ -396,11 +131,4 @@ def get_num_tokens(self, text: str) -> int: 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 + return self.client.get_num_tokens(text) diff --git a/libs/genai/poetry.lock b/libs/genai/poetry.lock index eb9f85db..7b90448e 100644 --- a/libs/genai/poetry.lock +++ b/libs/genai/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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# noqa: E501 diff --git a/libs/genai/tests/integration_tests/test_llms.py b/libs/genai/tests/integration_tests/test_llms.py index 53b9ddb5..121f64f2 100644 --- a/libs/genai/tests/integration_tests/test_llms.py +++ b/libs/genai/tests/integration_tests/test_llms.py @@ -11,7 +11,7 @@ from langchain_google_genai import GoogleGenerativeAI, HarmBlockThreshold, HarmCategory -model_names = ["gemini-pro"] +model_names = ["gemini-1.5-flash-001"] @pytest.mark.parametrize( @@ -26,8 +26,8 @@ def test_google_generativeai_call(model_name: str) -> None: llm = GoogleGenerativeAI(max_tokens=10) # type: ignore[call-arg] output = llm("Say foo:") assert isinstance(output, str) - assert llm._llm_type == "google_palm" - assert llm.client.model_name == "models/gemini-pro" + assert llm._llm_type == "google_gemini" + assert llm.client.model == f"models/{model_name}" @pytest.mark.parametrize( @@ -35,12 +35,11 @@ def test_google_generativeai_call(model_name: str) -> None: model_names, ) def test_google_generativeai_generate(model_name: str) -> None: - n = 1 if model_name == "gemini-pro" else 2 - llm = GoogleGenerativeAI(temperature=0.3, n=n, model=model_name) + llm = GoogleGenerativeAI(temperature=0.3, model=model_name) output = llm.generate(["Say foo:"]) assert isinstance(output, LLMResult) assert len(output.generations) == 1 - assert len(output.generations[0]) == n + assert len(output.generations[0]) == 1 # check the usage data generation_info = output.generations[0][0].generation_info assert generation_info is not None @@ -54,20 +53,20 @@ async def test_google_generativeai_agenerate() -> None: def test_generativeai_stream() -> None: - llm = GoogleGenerativeAI(temperature=0, model="gemini-pro") + llm = GoogleGenerativeAI(temperature=0, model="gemini-1.5-flash-001") outputs = list(llm.stream("Please say foo:")) assert isinstance(outputs[0], str) def test_generativeai_get_num_tokens_gemini() -> None: - llm = GoogleGenerativeAI(temperature=0, model="gemini-pro") + llm = GoogleGenerativeAI(temperature=0, model="gemini-1.5-flash-001") output = llm.get_num_tokens("How are you?") assert output == 4 def test_safety_settings_gemini() -> None: # test with blocked prompt - llm = GoogleGenerativeAI(temperature=0, model="gemini-pro") + llm = GoogleGenerativeAI(temperature=0, model="gemini-1.5-flash-001") output = llm.generate(prompts=["how to make a bomb?"]) assert isinstance(output, LLMResult) assert len(output.generations[0]) > 0 @@ -92,7 +91,7 @@ def test_safety_settings_gemini() -> None: # test with safety filters on instantiation llm = GoogleGenerativeAI( - model="gemini-pro", + model="gemini-1.5-flash-001", safety_settings=safety_settings, temperature=0, ) diff --git a/libs/genai/tests/integration_tests/test_standard.py b/libs/genai/tests/integration_tests/test_standard.py index a4971ba3..dee8508c 100644 --- a/libs/genai/tests/integration_tests/test_standard.py +++ b/libs/genai/tests/integration_tests/test_standard.py @@ -1,10 +1,9 @@ """Standard LangChain interface tests""" -from typing import Dict, List, Literal, Type, cast +from typing import Dict, List, Literal, Type import pytest from langchain_core.language_models import BaseChatModel -from langchain_core.messages import AIMessage, HumanMessage, SystemMessage from langchain_core.rate_limiters import InMemoryRateLimiter from langchain_core.tools import BaseTool from langchain_tests.integration_tests import ChatModelIntegrationTests @@ -12,7 +11,6 @@ from langchain_google_genai import ChatGoogleGenerativeAI rate_limiter = InMemoryRateLimiter(requests_per_second=0.25) -rate_limiter_2_0 = InMemoryRateLimiter(requests_per_second=0.1) class TestGeminiAI2Standard(ChatModelIntegrationTests): @@ -23,8 +21,8 @@ def chat_model_class(self) -> Type[BaseChatModel]: @property def chat_model_params(self) -> dict: return { - "model": "models/gemini-2.0-flash-exp", - "rate_limiter": rate_limiter_2_0, + "model": "models/gemini-2.0-flash-001", + "rate_limiter": rate_limiter, } @pytest.mark.xfail( @@ -55,6 +53,10 @@ def test_structured_output_optional_param(self, model: BaseChatModel) -> None: def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None: super().test_bind_runnables_as_tools(model) + @pytest.mark.xfail(reason=("investigate")) + def test_tool_calling_with_no_arguments(self, model: BaseChatModel) -> None: + super().test_tool_calling_with_no_arguments(model) + class TestGeminiAIStandard(ChatModelIntegrationTests): @property @@ -107,31 +109,7 @@ def supported_usage_metadata_details( ] ], ]: - return {"invoke": ["cache_read_input"], "stream": ["cache_read_input"]} - - def invoke_with_cache_read_input(self, *, stream: bool = False) -> AIMessage: - llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro-001") - - with open(__file__, "r") as f: - code = f.read() * 40 - cached_content = llm.create_cached_content( - [ - SystemMessage("you are a good coder"), - HumanMessage(f"Here is a code file:\n\n```python\n{code}\n```"), - ], - ttl=120, - ) - cached_llm = llm.bind(cached_content=cached_content) - - input_ = "What does the above code do?" - - if stream: - full = None - for chunk in cached_llm.stream(input_): - full = full + chunk if full else chunk # type: ignore - return cast(AIMessage, full) - else: - return cast(AIMessage, cached_llm.invoke(input_)) + return {"invoke": [], "stream": []} # TODO: increase quota on gemini-1.5-pro-001 and test as well diff --git a/libs/genai/tests/unit_tests/test_function_utils.py b/libs/genai/tests/unit_tests/test_function_utils.py index 2faf03fa..eb6b21da 100644 --- a/libs/genai/tests/unit_tests/test_function_utils.py +++ b/libs/genai/tests/unit_tests/test_function_utils.py @@ -36,7 +36,7 @@ def possibly_none( return "value" # Convert to OpenAI, then to GenAI, then to dict - oai_tool = convert_to_openai_tool(possibly_none) + oai_tool: dict[str, Any] = convert_to_openai_tool(possibly_none) genai_tool = convert_to_genai_function_declarations([oai_tool]) genai_tool_dict = tool_to_dict(genai_tool) assert isinstance(genai_tool_dict, dict), "Expected a dict." diff --git a/libs/genai/tests/unit_tests/test_llms.py b/libs/genai/tests/unit_tests/test_llms.py index 29a68980..20f19d2d 100644 --- a/libs/genai/tests/unit_tests/test_llms.py +++ b/libs/genai/tests/unit_tests/test_llms.py @@ -1,11 +1,4 @@ -from langchain_google_genai.llms import GoogleGenerativeAI, GoogleModelFamily - - -def test_model_family() -> None: - model = GoogleModelFamily("gemini-pro") - assert model == GoogleModelFamily.GEMINI - model = GoogleModelFamily("gemini-ultra") - assert model == GoogleModelFamily.GEMINI +from langchain_google_genai.llms import GoogleGenerativeAI def test_tracing_params() -> None: