Mem0 1.0.0 (#3545)
This commit is contained in:
Parshva Daftari
@@ -10,6 +10,12 @@ from mem0.configs.llms.lmstudio import LMStudioConfig
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from mem0.configs.llms.ollama import OllamaConfig
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from mem0.configs.llms.openai import OpenAIConfig
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from mem0.configs.llms.vllm import VllmConfig
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from mem0.configs.rerankers.base import BaseRerankerConfig
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from mem0.configs.rerankers.cohere import CohereRerankerConfig
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from mem0.configs.rerankers.sentence_transformer import SentenceTransformerRerankerConfig
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from mem0.configs.rerankers.zero_entropy import ZeroEntropyRerankerConfig
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from mem0.configs.rerankers.llm import LLMRerankerConfig
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from mem0.configs.rerankers.huggingface import HuggingFaceRerankerConfig
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from mem0.embeddings.mock import MockEmbeddings
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@@ -219,3 +225,57 @@ class GraphStoreFactory:
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except (ImportError, AttributeError) as e:
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raise ImportError(f"Could not import MemoryGraph for provider '{provider_name}': {e}")
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return GraphClass(config)
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class RerankerFactory:
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"""
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Factory for creating reranker instances with appropriate configurations.
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Supports provider-specific configs following the same pattern as other factories.
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"""
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# Provider mappings with their config classes
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provider_to_class = {
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"cohere": ("mem0.reranker.cohere_reranker.CohereReranker", CohereRerankerConfig),
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"sentence_transformer": ("mem0.reranker.sentence_transformer_reranker.SentenceTransformerReranker", SentenceTransformerRerankerConfig),
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"zero_entropy": ("mem0.reranker.zero_entropy_reranker.ZeroEntropyReranker", ZeroEntropyRerankerConfig),
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"llm_reranker": ("mem0.reranker.llm_reranker.LLMReranker", LLMRerankerConfig),
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"huggingface": ("mem0.reranker.huggingface_reranker.HuggingFaceReranker", HuggingFaceRerankerConfig),
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}
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@classmethod
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def create(cls, provider_name: str, config: Optional[Union[BaseRerankerConfig, Dict]] = None, **kwargs):
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"""
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Create a reranker instance based on the provider and configuration.
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Args:
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provider_name: The reranker provider (e.g., 'cohere', 'sentence_transformer')
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config: Configuration object or dictionary
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**kwargs: Additional configuration parameters
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Returns:
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Reranker instance configured for the specified provider
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Raises:
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ImportError: If the provider class cannot be imported
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ValueError: If the provider is not supported
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"""
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if provider_name not in cls.provider_to_class:
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raise ValueError(f"Unsupported reranker provider: {provider_name}")
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class_path, config_class = cls.provider_to_class[provider_name]
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# Handle configuration
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if config is None:
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config = config_class(**kwargs)
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elif isinstance(config, dict):
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config = config_class(**config, **kwargs)
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elif not isinstance(config, BaseRerankerConfig):
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raise ValueError(f"Config must be a {config_class.__name__} instance or dict")
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# Import and create the reranker class
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try:
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reranker_class = load_class(class_path)
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except (ImportError, AttributeError) as e:
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raise ImportError(f"Could not import reranker for provider '{provider_name}': {e}")
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return reranker_class(config)
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@@ -0,0 +1,167 @@
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import os
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import json
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from typing import Optional, Dict, Any
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try:
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from google.oauth2 import service_account
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from google.auth import default
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import google.auth.credentials
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except ImportError:
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raise ImportError("google-auth is required for GCP authentication. Install with: pip install google-auth")
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class GCPAuthenticator:
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"""
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Centralized GCP authentication handler that supports multiple credential methods.
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Priority order:
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1. service_account_json (dict) - In-memory service account credentials
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2. credentials_path (str) - Path to service account JSON file
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3. Environment variables (GOOGLE_APPLICATION_CREDENTIALS)
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4. Default credentials (for environments like GCE, Cloud Run, etc.)
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"""
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@staticmethod
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def get_credentials(
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service_account_json: Optional[Dict[str, Any]] = None,
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credentials_path: Optional[str] = None,
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scopes: Optional[list] = None
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) -> tuple[google.auth.credentials.Credentials, Optional[str]]:
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"""
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Get Google credentials using the priority order defined above.
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Args:
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service_account_json: Service account credentials as a dictionary
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credentials_path: Path to service account JSON file
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scopes: List of OAuth scopes (optional)
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Returns:
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tuple: (credentials, project_id)
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Raises:
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ValueError: If no valid credentials are found
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"""
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credentials = None
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project_id = None
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# Method 1: Service account JSON (in-memory)
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if service_account_json:
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credentials = service_account.Credentials.from_service_account_info(
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service_account_json, scopes=scopes
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)
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project_id = service_account_json.get("project_id")
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# Method 2: Service account file path
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elif credentials_path and os.path.isfile(credentials_path):
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credentials = service_account.Credentials.from_service_account_file(
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credentials_path, scopes=scopes
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)
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# Extract project_id from the file
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with open(credentials_path, 'r') as f:
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cred_data = json.load(f)
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project_id = cred_data.get("project_id")
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# Method 3: Environment variable path
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elif os.getenv("GOOGLE_APPLICATION_CREDENTIALS"):
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env_path = os.getenv("GOOGLE_APPLICATION_CREDENTIALS")
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if os.path.isfile(env_path):
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credentials = service_account.Credentials.from_service_account_file(
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env_path, scopes=scopes
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)
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# Extract project_id from the file
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with open(env_path, 'r') as f:
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cred_data = json.load(f)
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project_id = cred_data.get("project_id")
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# Method 4: Default credentials (GCE, Cloud Run, etc.)
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if not credentials:
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try:
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credentials, project_id = default(scopes=scopes)
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except Exception as e:
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raise ValueError(
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f"No valid GCP credentials found. Please provide one of:\n"
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f"1. service_account_json parameter (dict)\n"
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f"2. credentials_path parameter (file path)\n"
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f"3. GOOGLE_APPLICATION_CREDENTIALS environment variable\n"
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f"4. Default credentials (if running on GCP)\n"
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f"Error: {e}"
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)
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return credentials, project_id
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@staticmethod
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def setup_vertex_ai(
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service_account_json: Optional[Dict[str, Any]] = None,
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credentials_path: Optional[str] = None,
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project_id: Optional[str] = None,
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location: str = "us-central1"
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) -> str:
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"""
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Initialize Vertex AI with proper authentication.
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Args:
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service_account_json: Service account credentials as dict
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credentials_path: Path to service account JSON file
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project_id: GCP project ID (optional, will be auto-detected)
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location: GCP location/region
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Returns:
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str: The project ID being used
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Raises:
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ValueError: If authentication fails
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"""
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try:
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import vertexai
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except ImportError:
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raise ImportError("google-cloud-aiplatform is required for Vertex AI. Install with: pip install google-cloud-aiplatform")
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credentials, detected_project_id = GCPAuthenticator.get_credentials(
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service_account_json=service_account_json,
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credentials_path=credentials_path,
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scopes=["https://www.googleapis.com/auth/cloud-platform"]
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)
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# Use provided project_id or fall back to detected one
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final_project_id = project_id or detected_project_id or os.getenv("GOOGLE_CLOUD_PROJECT")
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if not final_project_id:
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raise ValueError("Project ID could not be determined. Please provide project_id parameter or set GOOGLE_CLOUD_PROJECT environment variable.")
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vertexai.init(project=final_project_id, location=location, credentials=credentials)
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return final_project_id
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@staticmethod
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def get_genai_client(
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service_account_json: Optional[Dict[str, Any]] = None,
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credentials_path: Optional[str] = None,
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api_key: Optional[str] = None
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):
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"""
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Get a Google GenAI client with authentication.
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Args:
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service_account_json: Service account credentials as dict
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credentials_path: Path to service account JSON file
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api_key: API key (takes precedence over service account)
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Returns:
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Google GenAI client instance
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"""
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try:
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from google.genai import Client as GenAIClient
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except ImportError:
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raise ImportError("google-genai is required. Install with: pip install google-genai")
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# If API key is provided, use it directly
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if api_key:
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return GenAIClient(api_key=api_key)
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# Otherwise, try service account authentication
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credentials, _ = GCPAuthenticator.get_credentials(
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service_account_json=service_account_json,
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credentials_path=credentials_path,
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scopes=["https://www.googleapis.com/auth/generative-language"]
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)
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return GenAIClient(credentials=credentials)
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