fix pinecone

docs/components/vectordbs/dbs/pinecone.mdx

@@ -2,6 +2,8 @@
 
 [Pinecone](https://www.pinecone.io/) is a fully managed vector database designed for machine learning applications, offering high performance vector search with low latency at scale. It's particularly well-suited for semantic search, recommendation systems, and other AI-powered applications.
 
+> **Note**: Before configuring Pinecone, you need to select an embedding model (e.g., OpenAI, Cohere, or custom models) and ensure the `embedding_model_dims` in your config matches your chosen model's dimensions. For example, OpenAI's text-embedding-ada-002 uses 1536 dimensions.
+
 ### Usage
 
 ```python
@@ -11,13 +13,17 @@ from mem0 import Memory
 os.environ["OPENAI_API_KEY"] = "sk-xx"
 os.environ["PINECONE_API_KEY"] = "your-api-key"
 
+# Example using serverless configuration
 config = {
     "vector_store": {
         "provider": "pinecone",
         "config": {
-            "collection_name": "memory_index",
-            "embedding_model_dims": 1536,
-            "environment": "us-west1-gcp",
+            "collection_name": "testing",
+            "embedding_model_dims": 1536,  # Matches OpenAI's text-embedding-3-small
+            "serverless_config": {
+                "cloud": "aws",  # Choose between 'aws' or 'gcp' or 'azure'
+                "region": "us-east-1"
+            },
             "metric": "cosine"
         }
     }
@@ -40,43 +46,43 @@ Here are the parameters available for configuring Pinecone:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collection_name` | Name of the index/collection | Required |
-| `embedding_model_dims` | Dimensions of the embedding model | Required |
+| `embedding_model_dims` | Dimensions of the embedding model (must match your chosen embedding model) | Required |
 | `client` | Existing Pinecone client instance | `None` |
 | `api_key` | API key for Pinecone | Environment variable: `PINECONE_API_KEY` |
 | `environment` | Pinecone environment | `None` |
-| `serverless_config` | Configuration for serverless deployment | `None` |
+| `serverless_config` | Configuration for serverless deployment (AWS or GCP or Azure) | `None` |
 | `pod_config` | Configuration for pod-based deployment | `None` |
 | `hybrid_search` | Whether to enable hybrid search | `False` |
 | `metric` | Distance metric for vector similarity | `"cosine"` |
 | `batch_size` | Batch size for operations | `100` |
 
-#### Serverless Config Example
+> **Important**: You must choose either `serverless_config` or `pod_config` for your deployment, but not both.
 
+#### Serverless Config Example
 ```python
 config = {
     "vector_store": {
         "provider": "pinecone",
         "config": {
             "collection_name": "memory_index",
-            "embedding_model_dims": 1536,
+            "embedding_model_dims": 1536,  # For OpenAI's text-embedding-3-small
             "serverless_config": {
-                "cloud": "aws",
-                "region": "us-west-2"
+                "cloud": "aws",  # or "gcp" or "azure"
+                "region": "us-east-1"  # Choose appropriate region
             }
         }
     }
 }
 ```
 
 #### Pod Config Example
-
 ```python
 config = {
     "vector_store": {
         "provider": "pinecone",
         "config": {
             "collection_name": "memory_index",
-            "embedding_model_dims": 1536,
+            "embedding_model_dims": 1536,  # For OpenAI's text-embedding-ada-002
             "pod_config": {
                 "environment": "gcp-starter",
                 "replicas": 1,

mem0/vector_stores/pinecone.py

@@ -199,12 +199,13 @@ def _create_filter(self, filters: Optional[Dict]) -> Dict:
 
         return pinecone_filter
 
-    def search(self, query: List[float], limit: int = 5, filters: Optional[Dict] = None) -> List[OutputData]:
+    def search(self, query: str, vectors: List[float], limit: int = 5, filters: Optional[Dict] = None) -> List[OutputData]:
         """
         Search for similar vectors.
 
         Args:
-            query (list): Query vector.
+            query (str): Query.
+            vectors (list): List of vectors to search.
             limit (int, optional): Number of results to return. Defaults to 5.
             filters (dict, optional): Filters to apply to the search. Defaults to None.
 
@@ -214,7 +215,7 @@ def search(self, query: List[float], limit: int = 5, filters: Optional[Dict] = N
         filter_dict = self._create_filter(filters) if filters else None
 
         query_params = {
-            "vector": query,
+            "vector": vectors,
             "top_k": limit,
             "include_metadata": True,
             "include_values": False,