docs/swarms_memory/pinecone.md

# PineconeMemory Documentation

The `PineconeMemory` class provides a robust interface for integrating Pinecone-based Retrieval-Augmented Generation (RAG) systems. It allows for adding documents to a Pinecone index and querying the index for similar documents. The class supports custom embedding models, preprocessing functions, and other customizations to suit different use cases.



#### Parameters

| Parameter            | Type                                          | Default                           | Description                                                                                          |
|----------------------|-----------------------------------------------|-----------------------------------|------------------------------------------------------------------------------------------------------|
| `api_key`            | `str`                                         | -                                 | Pinecone API key.                                                                                    |
| `environment`        | `str`                                         | -                                 | Pinecone environment.                                                                                |
| `index_name`         | `str`                                         | -                                 | Name of the Pinecone index to use.                                                                   |
| `dimension`          | `int`                                         | `768`                             | Dimension of the document embeddings.                                                                |
| `embedding_model`    | `Optional[Any]`                               | `None`                            | Custom embedding model. Defaults to `SentenceTransformer('all-MiniLM-L6-v2')`.                       |
| `embedding_function` | `Optional[Callable[[str], List[float]]]`      | `None`                            | Custom embedding function. Defaults to `_default_embedding_function`.                                |
| `preprocess_function`| `Optional[Callable[[str], str]]`              | `None`                            | Custom preprocessing function. Defaults to `_default_preprocess_function`.                           |
| `postprocess_function`| `Optional[Callable[[List[Dict[str, Any]]], List[Dict[str, Any]]]]`| `None`              | Custom postprocessing function. Defaults to `_default_postprocess_function`.                         |
| `metric`             | `str`                                         | `'cosine'`                        | Distance metric for Pinecone index.                                                                  |
| `pod_type`           | `str`                                         | `'p1'`                            | Pinecone pod type.                                                                                   |
| `namespace`          | `str`                                         | `''`                              | Pinecone namespace.                                                                                  |
| `logger_config`      | `Optional[Dict[str, Any]]`                    | `None`                            | Configuration for the logger. Defaults to logging to `rag_wrapper.log` and console output.           |

### Methods

#### `_setup_logger`

```python
def _setup_logger(self, config: Optional[Dict[str, Any]] = None)
```

Sets up the logger with the given configuration.

#### `_default_embedding_function`

```python
def _default_embedding_function(self, text: str) -> List[float]
```

Generates embeddings using the default SentenceTransformer model.

#### `_default_preprocess_function`

```python
def _default_preprocess_function(self, text: str) -> str
```

Preprocesses the input text by stripping whitespace.

#### `_default_postprocess_function`

```python
def _default_postprocess_function(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]
```

Postprocesses the query results.

#### `add`

Adds a document to the Pinecone index.

| Parameter | Type                  | Default | Description                                   |
|-----------|-----------------------|---------|-----------------------------------------------|
| `doc`     | `str`                 | -       | The document to be added.                     |
| `metadata`| `Optional[Dict[str, Any]]` | `None`  | Additional metadata for the document.         |

#### `query`

Queries the Pinecone index for similar documents.

| Parameter | Type                    | Default | Description                                   |
|-----------|-------------------------|---------|-----------------------------------------------|
| `query`   | `str`                   | -       | The query string.                             |
| `top_k`   | `int`                   | `5`     | The number of top results to return.          |
| `filter`  | `Optional[Dict[str, Any]]` | `None`  | Metadata filter for the query.                |

## Usage


The `PineconeMemory` class is initialized with the necessary parameters to configure Pinecone and the embedding model. It supports a variety of custom configurations to suit different needs.

#### Example

```python
from swarms_memory import PineconeMemory

# Initialize PineconeMemory
memory = PineconeMemory(
    api_key="your-api-key",
    environment="us-west1-gcp",
    index_name="example-index",
    dimension=768
)
```

### Adding Documents

Documents can be added to the Pinecone index using the `add` method. The method accepts a document string and optional metadata.

#### Example

```python
doc = "This is a sample document to be added to the Pinecone index."
metadata = {"author": "John Doe", "date": "2024-07-08"}

memory.add(doc, metadata)
```

### Querying Documents

The `query` method allows for querying the Pinecone index for similar documents based on a query string. It returns the top `k` most similar documents.

#### Example

```python
query = "Sample query to find similar documents."
results = memory.query(query, top_k=5)

for result in results:
    print(result)
```

## Additional Information and Tips

### Custom Embedding and Preprocessing Functions

Custom embedding and preprocessing functions can be provided during initialization to tailor the document processing to specific requirements.

#### Example

```python
def custom_embedding_function(text: str) -> List[float]:
    # Custom embedding logic
    return [0.1, 0.2, 0.3]

def custom_preprocess_function(text: str) -> str:
    # Custom preprocessing logic
    return text.lower()

memory = PineconeMemory(
    api_key="your-api-key",
    environment="us-west1-gcp",
    index_name="example-index",
    embedding_function=custom_embedding_function,
    preprocess_function=custom_preprocess_function
)
```

### Logger Configuration

The logger can be configured to suit different logging needs. The default configuration logs to a file and the console.

#### Example

```python
logger_config = {
    "handlers": [
        {"sink": "custom_log.log", "rotation": "1 MB"},
        {"sink": lambda msg: print(msg, end="")},
    ]
}

memory = PineconeMemory(
    api_key="your-api-key",
    environment="us-west1-gcp",
    index_name="example-index",
    logger_config=logger_config
)
```

## References and Resources

- [Pinecone Documentation](https://docs.pinecone.io/)
- [SentenceTransformers Documentation](https://www.sbert.net/)
- [Loguru Documentation](https://loguru.readthedocs.io/en/stable/)

For further exploration and examples, refer to the official documentation and resources provided by Pinecone, SentenceTransformers, and Loguru.

This concludes the detailed documentation for the `PineconeMemory` class. The class offers a flexible and powerful interface for leveraging Pinecone's capabilities in retrieval-augmented generation systems. By supporting custom embeddings, preprocessing, and postprocessing functions, it can be tailored to a wide range of applications.