Add: Pinecone integration (#2395)

mem0/configs/vector_stores/pinecone.py

@@ -0,0 +1,56 @@
+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,
+    }