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,
    }