222 lines
8.7 KiB
Python
222 lines
8.7 KiB
Python
"""
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Copyright 2024, Zep Software, Inc.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import json
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import logging
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import typing
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from google import genai # type: ignore
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from google.genai import types # type: ignore
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from pydantic import BaseModel
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from ..prompts.models import Message
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from .client import LLMClient
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from .config import DEFAULT_MAX_TOKENS, LLMConfig, ModelSize
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from .errors import RateLimitError
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logger = logging.getLogger(__name__)
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DEFAULT_MODEL = 'gemini-2.0-flash'
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DEFAULT_THINKING_BUDGET = 0
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# Gemini models that support thinking capabilities
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GEMINI_THINKING_MODELS = [
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'gemini-2.5-pro',
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'gemini-2.5-flash',
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'gemini-2.5-flash-lite',
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]
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class GeminiClient(LLMClient):
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"""
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GeminiClient is a client class for interacting with Google's Gemini language models.
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This class extends the LLMClient and provides methods to initialize the client
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and generate responses from the Gemini language model.
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Attributes:
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model (str): The model name to use for generating responses.
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temperature (float): The temperature to use for generating responses.
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max_tokens (int): The maximum number of tokens to generate in a response.
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thinking_config (types.ThinkingConfig | None): Optional thinking configuration for models that support it.
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Methods:
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__init__(config: LLMConfig | None = None, cache: bool = False, thinking_config: types.ThinkingConfig | None = None):
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Initializes the GeminiClient with the provided configuration, cache setting, and optional thinking config.
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_generate_response(messages: list[Message]) -> dict[str, typing.Any]:
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Generates a response from the language model based on the provided messages.
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"""
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def __init__(
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self,
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config: LLMConfig | None = None,
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cache: bool = False,
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max_tokens: int = DEFAULT_MAX_TOKENS,
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thinking_config: types.ThinkingConfig | None = None,
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):
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"""
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Initialize the GeminiClient with the provided configuration, cache setting, and optional thinking config.
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Args:
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config (LLMConfig | None): The configuration for the LLM client, including API key, model, temperature, and max tokens.
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cache (bool): Whether to use caching for responses. Defaults to False.
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thinking_config (types.ThinkingConfig | None): Optional thinking configuration for models that support it.
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Only use with models that support thinking (gemini-2.5+). Defaults to None.
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"""
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if config is None:
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config = LLMConfig()
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super().__init__(config, cache)
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self.model = config.model
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# Configure the Gemini API
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self.client = genai.Client(
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api_key=config.api_key,
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)
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self.max_tokens = max_tokens
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self.thinking_config = thinking_config
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async def _generate_response(
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self,
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messages: list[Message],
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response_model: type[BaseModel] | None = None,
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max_tokens: int = DEFAULT_MAX_TOKENS,
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model_size: ModelSize = ModelSize.medium,
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) -> dict[str, typing.Any]:
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"""
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Generate a response from the Gemini language model.
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Args:
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messages (list[Message]): A list of messages to send to the language model.
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response_model (type[BaseModel] | None): An optional Pydantic model to parse the response into.
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max_tokens (int): The maximum number of tokens to generate in the response.
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Returns:
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dict[str, typing.Any]: The response from the language model.
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Raises:
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RateLimitError: If the API rate limit is exceeded.
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RefusalError: If the content is blocked by the model.
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Exception: If there is an error generating the response.
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"""
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try:
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gemini_messages: list[types.Content] = []
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# If a response model is provided, add schema for structured output
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system_prompt = ''
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if response_model is not None:
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# Get the schema from the Pydantic model
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pydantic_schema = response_model.model_json_schema()
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# Create instruction to output in the desired JSON format
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system_prompt += (
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f'Output ONLY valid JSON matching this schema: {json.dumps(pydantic_schema)}.\n'
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'Do not include any explanatory text before or after the JSON.\n\n'
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)
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# Add messages content
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# First check for a system message
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if messages and messages[0].role == 'system':
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system_prompt = f'{messages[0].content}\n\n {system_prompt}'
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messages = messages[1:]
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# Add the rest of the messages
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for m in messages:
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m.content = self._clean_input(m.content)
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gemini_messages.append(
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types.Content(role=m.role, parts=[types.Part.from_text(text=m.content)])
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)
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# Determine the model to be used
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model_to_use = self.model or DEFAULT_MODEL
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# Conditionally create thinking_config for models that support thinking
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thinking_config_arg = None
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if model_to_use in GEMINI_THINKING_MODELS:
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thinking_config_arg = types.ThinkingConfig(
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include_thoughts=False,
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thinking_budget=self.thinking_budget,
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)
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# Create generation config
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generation_config = types.GenerateContentConfig(
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temperature=self.temperature,
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max_output_tokens=max_tokens or self.max_tokens,
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response_mime_type='application/json' if response_model else None,
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response_schema=response_model if response_model else None,
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system_instruction=system_prompt,
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thinking_config=self.thinking_config,
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)
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# Generate content using the simple string approach
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response = await self.client.aio.models.generate_content(
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model=model_to_use,
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contents=gemini_messages,
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config=generation_config,
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)
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# If this was a structured output request, parse the response into the Pydantic model
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if response_model is not None:
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try:
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if not response.text:
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raise ValueError('No response text')
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validated_model = response_model.model_validate(json.loads(response.text))
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# Return as a dictionary for API consistency
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return validated_model.model_dump()
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except Exception as e:
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raise Exception(f'Failed to parse structured response: {e}') from e
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# Otherwise, return the response text as a dictionary
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return {'content': response.text}
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except Exception as e:
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# Check if it's a rate limit error
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if 'rate limit' in str(e).lower() or 'quota' in str(e).lower():
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raise RateLimitError from e
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logger.error(f'Error in generating LLM response: {e}')
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raise
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async def generate_response(
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self,
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messages: list[Message],
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response_model: type[BaseModel] | None = None,
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max_tokens: int | None = None,
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model_size: ModelSize = ModelSize.medium,
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) -> dict[str, typing.Any]:
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"""
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Generate a response from the Gemini language model.
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This method overrides the parent class method to provide a direct implementation.
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Args:
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messages (list[Message]): A list of messages to send to the language model.
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response_model (type[BaseModel] | None): An optional Pydantic model to parse the response into.
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max_tokens (int): The maximum number of tokens to generate in the response.
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Returns:
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dict[str, typing.Any]: The response from the language model.
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"""
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if max_tokens is None:
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max_tokens = self.max_tokens
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# Call the internal _generate_response method
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return await self._generate_response(
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messages=messages,
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response_model=response_model,
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max_tokens=max_tokens,
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model_size=model_size,
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)
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