Files
2025-03-20 12:57:32 +05:30

57 lines
2.6 KiB
Python

import os
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field, model_validator
class PineconeConfig(BaseModel):
"""Configuration for Pinecone vector database."""
collection_name: str = Field("mem0", description="Name of the index/collection")
embedding_model_dims: int = Field(1536, description="Dimensions of the embedding model")
client: Optional[Any] = Field(None, description="Existing Pinecone client instance")
api_key: Optional[str] = Field(None, description="API key for Pinecone")
environment: Optional[str] = Field(None, description="Pinecone environment")
serverless_config: Optional[Dict[str, Any]] = Field(None, description="Configuration for serverless deployment")
pod_config: Optional[Dict[str, Any]] = Field(None, description="Configuration for pod-based deployment")
hybrid_search: bool = Field(False, description="Whether to enable hybrid search")
metric: str = Field("cosine", description="Distance metric for vector similarity")
batch_size: int = Field(100, description="Batch size for operations")
extra_params: Optional[Dict[str, Any]] = Field(None, description="Additional parameters for Pinecone client")
@model_validator(mode="before")
@classmethod
def check_api_key_or_client(cls, values: Dict[str, Any]) -> Dict[str, Any]:
api_key, client = values.get("api_key"), values.get("client")
if not api_key and not client and "PINECONE_API_KEY" not in os.environ:
raise ValueError(
"Either 'api_key' or 'client' must be provided, or PINECONE_API_KEY environment variable must be set."
)
return values
@model_validator(mode="before")
@classmethod
def check_pod_or_serverless(cls, values: Dict[str, Any]) -> Dict[str, Any]:
pod_config, serverless_config = values.get("pod_config"), values.get("serverless_config")
if pod_config and serverless_config:
raise ValueError(
"Both 'pod_config' and 'serverless_config' cannot be specified. Choose one deployment option."
)
return values
@model_validator(mode="before")
@classmethod
def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
allowed_fields = set(cls.model_fields.keys())
input_fields = set(values.keys())
extra_fields = input_fields - allowed_fields
if extra_fields:
raise ValueError(
f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}"
)
return values
model_config = {
"arbitrary_types_allowed": True,
}