# Pinecone

[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.

### Usage

```python
import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"
os.environ["PINECONE_API_KEY"] = "your-api-key"

config = {
    "vector_store": {
        "provider": "pinecone",
        "config": {
            "collection_name": "memory_index",
            "embedding_model_dims": 1536,
            "environment": "us-west1-gcp",
            "metric": "cosine"
        }
    }
}

m = Memory.from_config(config)
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
```

### Config

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 |
| `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` |
| `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

```python
config = {
    "vector_store": {
        "provider": "pinecone",
        "config": {
            "collection_name": "memory_index",
            "embedding_model_dims": 1536,
            "serverless_config": {
                "cloud": "aws",
                "region": "us-west-2"
            }
        }
    }
}
```

#### Pod Config Example

```python
config = {
    "vector_store": {
        "provider": "pinecone",
        "config": {
            "collection_name": "memory_index",
            "embedding_model_dims": 1536,
            "pod_config": {
                "environment": "gcp-starter",
                "replicas": 1,
                "pod_type": "starter"
            }
        }
    }
}
```