""" Copyright 2024, Zep Software, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import json import logging import re import typing from typing import TYPE_CHECKING, ClassVar from pydantic import BaseModel from ..prompts.models import Message from .client import MULTILINGUAL_EXTRACTION_RESPONSES, LLMClient from .config import DEFAULT_MAX_TOKENS, LLMConfig, ModelSize from .errors import RateLimitError if TYPE_CHECKING: from google import genai from google.genai import types else: try: from google import genai from google.genai import types except ImportError: # If gemini client is not installed, raise an ImportError raise ImportError( 'google-genai is required for GeminiClient. ' 'Install it with: pip install graphiti-core[google-genai]' ) from None logger = logging.getLogger(__name__) DEFAULT_MODEL = 'gemini-2.5-flash' DEFAULT_SMALL_MODEL = 'gemini-2.5-flash-lite-preview-06-17' class GeminiClient(LLMClient): """ GeminiClient is a client class for interacting with Google's Gemini language models. This class extends the LLMClient and provides methods to initialize the client and generate responses from the Gemini language model. Attributes: model (str): The model name to use for generating responses. temperature (float): The temperature to use for generating responses. max_tokens (int): The maximum number of tokens to generate in a response. thinking_config (types.ThinkingConfig | None): Optional thinking configuration for models that support it. Methods: __init__(config: LLMConfig | None = None, cache: bool = False, thinking_config: types.ThinkingConfig | None = None): Initializes the GeminiClient with the provided configuration, cache setting, and optional thinking config. _generate_response(messages: list[Message]) -> dict[str, typing.Any]: Generates a response from the language model based on the provided messages. """ # Class-level constants MAX_RETRIES: ClassVar[int] = 2 def __init__( self, config: LLMConfig | None = None, cache: bool = False, max_tokens: int = DEFAULT_MAX_TOKENS, thinking_config: types.ThinkingConfig | None = None, client: 'genai.Client | None' = None, ): """ Initialize the GeminiClient with the provided configuration, cache setting, and optional thinking config. Args: config (LLMConfig | None): The configuration for the LLM client, including API key, model, temperature, and max tokens. cache (bool): Whether to use caching for responses. Defaults to False. thinking_config (types.ThinkingConfig | None): Optional thinking configuration for models that support it. Only use with models that support thinking (gemini-2.5+). Defaults to None. client (genai.Client | None): An optional async client instance to use. If not provided, a new genai.Client is created. """ if config is None: config = LLMConfig() super().__init__(config, cache) self.model = config.model if client is None: self.client = genai.Client(api_key=config.api_key) else: self.client = client self.max_tokens = max_tokens self.thinking_config = thinking_config def _check_safety_blocks(self, response) -> None: """Check if response was blocked for safety reasons and raise appropriate exceptions.""" # Check if the response was blocked for safety reasons if not (hasattr(response, 'candidates') and response.candidates): return candidate = response.candidates[0] if not (hasattr(candidate, 'finish_reason') and candidate.finish_reason == 'SAFETY'): return # Content was blocked for safety reasons - collect safety details safety_info = [] safety_ratings = getattr(candidate, 'safety_ratings', None) if safety_ratings: for rating in safety_ratings: if getattr(rating, 'blocked', False): category = getattr(rating, 'category', 'Unknown') probability = getattr(rating, 'probability', 'Unknown') safety_info.append(f'{category}: {probability}') safety_details = ( ', '.join(safety_info) if safety_info else 'Content blocked for safety reasons' ) raise Exception(f'Response blocked by Gemini safety filters: {safety_details}') def _check_prompt_blocks(self, response) -> None: """Check if prompt was blocked and raise appropriate exceptions.""" prompt_feedback = getattr(response, 'prompt_feedback', None) if not prompt_feedback: return block_reason = getattr(prompt_feedback, 'block_reason', None) if block_reason: raise Exception(f'Prompt blocked by Gemini: {block_reason}') def _get_model_for_size(self, model_size: ModelSize) -> str: """Get the appropriate model name based on the requested size.""" if model_size == ModelSize.small: return self.small_model or DEFAULT_SMALL_MODEL else: return self.model or DEFAULT_MODEL def salvage_json(self, raw_output: str) -> dict[str, typing.Any] | None: """ Attempt to salvage a JSON object if the raw output is truncated. This is accomplished by looking for the last closing bracket for an array or object. If found, it will try to load the JSON object from the raw output. If the JSON object is not valid, it will return None. Args: raw_output (str): The raw output from the LLM. Returns: dict[str, typing.Any]: The salvaged JSON object. None: If no salvage is possible. """ if not raw_output: return None # Try to salvage a JSON array array_match = re.search(r'\]\s*$', raw_output) if array_match: try: return json.loads(raw_output[:array_match.end()]) except Exception: pass # Try to salvage a JSON object obj_match = re.search(r'\}\s*$', raw_output) if obj_match: try: return json.loads(raw_output[:obj_match.end()]) except Exception: pass return None async def _generate_response( self, messages: list[Message], response_model: type[BaseModel] | None = None, max_tokens: int = DEFAULT_MAX_TOKENS, model_size: ModelSize = ModelSize.medium, ) -> dict[str, typing.Any]: """ Generate a response from the Gemini language model. Args: messages (list[Message]): A list of messages to send to the language model. response_model (type[BaseModel] | None): An optional Pydantic model to parse the response into. max_tokens (int): The maximum number of tokens to generate in the response. model_size (ModelSize): The size of the model to use (small or medium). Returns: dict[str, typing.Any]: The response from the language model. Raises: RateLimitError: If the API rate limit is exceeded. Exception: If there is an error generating the response or content is blocked. """ try: gemini_messages: typing.Any = [] # If a response model is provided, add schema for structured output system_prompt = '' if response_model is not None: # Get the schema from the Pydantic model pydantic_schema = response_model.model_json_schema() # Create instruction to output in the desired JSON format system_prompt += ( f'Output ONLY valid JSON matching this schema: {json.dumps(pydantic_schema)}.\n' 'Do not include any explanatory text before or after the JSON.\n\n' ) # Add messages content # First check for a system message if messages and messages[0].role == 'system': system_prompt = f'{messages[0].content}\n\n {system_prompt}' messages = messages[1:] # Add the rest of the messages for m in messages: m.content = self._clean_input(m.content) gemini_messages.append( types.Content(role=m.role, parts=[types.Part.from_text(text=m.content)]) ) # Get the appropriate model for the requested size model = self._get_model_for_size(model_size) # Create generation config generation_config = types.GenerateContentConfig( temperature=self.temperature, max_output_tokens=max_tokens or self.max_tokens, response_mime_type='application/json' if response_model else None, response_schema=response_model if response_model else None, system_instruction=system_prompt, thinking_config=self.thinking_config, ) # Generate content using the simple string approach response = await self.client.aio.models.generate_content( model=model, contents=gemini_messages, config=generation_config, ) # Always capture the raw output for debugging raw_output = getattr(response, 'text', None) # Check for safety and prompt blocks self._check_safety_blocks(response) self._check_prompt_blocks(response) # If this was a structured output request, parse the response into the Pydantic model if response_model is not None: try: if not raw_output: raise ValueError('No response text') validated_model = response_model.model_validate(json.loads(raw_output)) # Return as a dictionary for API consistency return validated_model.model_dump() except Exception as e: if raw_output: logger.error("🦀 LLM generation failed parsing as JSON, will try to salvage.") logger.error(self._get_failed_generation_log(gemini_messages, raw_output)) # Try to salvage salvaged = self.salvage_json(raw_output) if salvaged is not None: logger.warning("Salvaged partial JSON from truncated/malformed output.") return salvaged raise Exception(f'Failed to parse structured response: {e}') from e # Otherwise, return the response text as a dictionary return {'content': raw_output} except Exception as e: # Check if it's a rate limit error based on Gemini API error codes error_message = str(e).lower() if ( 'rate limit' in error_message or 'quota' in error_message or 'resource_exhausted' in error_message or '429' in str(e) ): raise RateLimitError from e logger.error(f'Error in generating LLM response: {e}') raise Exception from e async def generate_response( self, messages: list[Message], response_model: type[BaseModel] | None = None, max_tokens: int | None = None, model_size: ModelSize = ModelSize.medium, ) -> dict[str, typing.Any]: """ Generate a response from the Gemini language model with retry logic and error handling. This method overrides the parent class method to provide a direct implementation with advanced retry logic. Args: messages (list[Message]): A list of messages to send to the language model. response_model (type[BaseModel] | None): An optional Pydantic model to parse the response into. max_tokens (int | None): The maximum number of tokens to generate in the response. model_size (ModelSize): The size of the model to use (small or medium). Returns: dict[str, typing.Any]: The response from the language model. """ if max_tokens is None: max_tokens = self.max_tokens retry_count = 0 last_error = None last_output = None # Add multilingual extraction instructions messages[0].content += MULTILINGUAL_EXTRACTION_RESPONSES while retry_count < self.MAX_RETRIES: try: response = await self._generate_response( messages=messages, response_model=response_model, max_tokens=max_tokens, model_size=model_size, ) last_output = response.get('content') if isinstance(response, dict) and 'content' in response else None return response except RateLimitError as e: # Rate limit errors should not trigger retries (fail fast) raise e except Exception as e: last_error = e # Check if this is a safety block - these typically shouldn't be retried error_text = str(e) or (str(e.__cause__) if e.__cause__ else '') if 'safety' in error_text.lower() or 'blocked' in error_text.lower(): logger.warning(f'Content blocked by safety filters: {e}') raise Exception(f'Content blocked by safety filters: {e}') from e retry_count += 1 # Construct a detailed error message for the LLM error_context = ( f'The previous response attempt was invalid. ' f'Error type: {e.__class__.__name__}. ' f'Error details: {str(e)}. ' f'Please try again with a valid response, ensuring the output matches ' f'the expected format and constraints.' ) error_message = Message(role='user', content=error_context) messages.append(error_message) logger.warning( f'Retrying after application error (attempt {retry_count}/{self.MAX_RETRIES}): {e}' ) # If we exit the loop without returning, all retries are exhausted logger.error("🦀 LLM generation failed and retries are exhausted.") logger.error(self._get_failed_generation_log(messages, last_output)) logger.error(f'Max retries ({self.MAX_RETRIES}) exceeded. Last error: {last_error}') raise last_error or Exception("Max retries exceeded")