> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aurelio.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# semantic_router.index.pinecone

#### build\_records

```python theme={null}
def build_records(
    embeddings: List[List[float]],
    routes: List[str],
    utterances: List[str],
    function_schemas: Optional[Optional[List[Dict[str, Any]]]] = None,
    metadata_list: List[Dict[str, Any]] = [],
    sparse_embeddings: Optional[Optional[List[SparseEmbedding]]] = None
) -> List[Dict]
```

Build records for Pinecone upsert.

**Arguments**:

* `embeddings` (`List[List[float]]`): List of embeddings to upsert.
* `routes` (`List[str]`): List of routes to upsert.
* `utterances` (`List[str]`): List of utterances to upsert.
* `function_schemas` (`Optional[List[Dict[str, Any]]]`): List of function schemas to upsert.
* `metadata_list` (`List[Dict[str, Any]]`): List of metadata to upsert.
* `List[List[float]]`0 (`List[List[float]]`1): List of sparse embeddings to upsert.

**Returns**:

`List[List[float]]`2: List of records to upsert.

## PineconeRecord Objects

```python theme={null}
class PineconeRecord(BaseModel)
```

#### metadata

Additional metadata dictionary

#### \_\_init\_\_

```python theme={null}
def __init__(**data)
```

Initialize PineconeRecord.

**Arguments**:

* `**data` (`dict`): Keyword arguments to pass to the BaseModel constructor.

#### to\_dict

```python theme={null}
def to_dict()
```

Convert PineconeRecord to a dictionary.

**Returns**:

`dict`: Dictionary representation of the PineconeRecord.

## PineconeIndex Objects

```python theme={null}
class PineconeIndex(BaseIndex)
```

#### \_\_init\_\_

```python theme={null}
def __init__(api_key: Optional[str] = None,
             index_name: str = "index",
             dimensions: Optional[int] = None,
             metric: str = "dotproduct",
             cloud: str = "aws",
             region: str = "us-east-1",
             host: str = "",
             namespace: Optional[str] = "",
             base_url: Optional[str] = "https://api.pinecone.io",
             init_async_index: bool = False)
```

Initialize PineconeIndex.

**Arguments**:

* `api_key` (`Optional[str]`): Pinecone API key.
* `index_name` (`str`): Name of the index.
* `dimensions` (`Optional[int]`): Dimensions of the index.
* `metric` (`str`): Metric of the index.
* `cloud` (`str`): Cloud provider of the index.
* `Optional[str]`0 (`str`): Region of the index.
* `Optional[str]`2 (`str`): Host of the index.
* `Optional[str]`4 (`Optional[str]`): Namespace of the index.
* `Optional[str]`6 (`Optional[str]`): Base URL of the Pinecone API.
* `Optional[str]`8 (`Optional[str]`9): Whether to initialize the index asynchronously.

#### add

```python theme={null}
def add(embeddings: List[List[float]],
        routes: List[str],
        utterances: List[str],
        function_schemas: Optional[Optional[List[Dict[str, Any]]]] = None,
        metadata_list: List[Dict[str, Any]] = [],
        batch_size: int = 100,
        sparse_embeddings: Optional[Optional[List[SparseEmbedding]]] = None,
        **kwargs)
```

Add vectors to Pinecone in batches.

**Arguments**:

* `embeddings` (`List[List[float]]`): List of embeddings to upsert.
* `routes` (`List[str]`): List of routes to upsert.
* `utterances` (`List[str]`): List of utterances to upsert.
* `function_schemas` (`Optional[List[Dict[str, Any]]]`): List of function schemas to upsert.
* `metadata_list` (`List[Dict[str, Any]]`): List of metadata to upsert.
* `List[List[float]]`0 (`List[List[float]]`1): Number of vectors to upsert in a single batch.
* `List[List[float]]`2 (`List[List[float]]`3): List of sparse embeddings to upsert.

#### aadd

```python theme={null}
async def aadd(
        embeddings: List[List[float]],
        routes: List[str],
        utterances: List[str],
        function_schemas: Optional[Optional[List[Dict[str, Any]]]] = None,
        metadata_list: List[Dict[str, Any]] = [],
        batch_size: int = 100,
        sparse_embeddings: Optional[Optional[List[SparseEmbedding]]] = None,
        **kwargs)
```

Add vectors to Pinecone in batches.

**Arguments**:

* `embeddings` (`List[List[float]]`): List of embeddings to upsert.
* `routes` (`List[str]`): List of routes to upsert.
* `utterances` (`List[str]`): List of utterances to upsert.
* `function_schemas` (`Optional[List[Dict[str, Any]]]`): List of function schemas to upsert.
* `metadata_list` (`List[Dict[str, Any]]`): List of metadata to upsert.
* `List[List[float]]`0 (`List[List[float]]`1): Number of vectors to upsert in a single batch.
* `List[List[float]]`2 (`List[List[float]]`3): List of sparse embeddings to upsert.

#### delete

```python theme={null}
def delete(route_name: str) -> list[str]
```

Delete specified route from index if it exists. Returns the IDs of the vectors

deleted.

**Arguments**:

* `route_name` (`str`): Name of the route to delete.

**Returns**:

`list[str]`: List of IDs of the vectors deleted.

#### adelete

```python theme={null}
async def adelete(route_name: str) -> list[str]
```

Asynchronously delete specified route from index if it exists. Returns the IDs

of the vectors deleted.

**Arguments**:

* `route_name` (`str`): Name of the route to delete.

**Returns**:

`list[str]`: List of IDs of the vectors deleted.

#### delete\_all

```python theme={null}
def delete_all()
```

Delete all routes from index if it exists.

**Returns**:

`None`: None

#### describe

```python theme={null}
def describe() -> IndexConfig
```

Describe the index.

**Returns**:

`IndexConfig`: IndexConfig

#### is\_ready

```python theme={null}
def is_ready() -> bool
```

Checks if the index is ready to be used.

**Returns**:

`bool`: True if the index is ready, False otherwise.

#### query

```python theme={null}
def query(
    vector: np.ndarray,
    top_k: int = 5,
    route_filter: Optional[List[str]] = None,
    sparse_vector: dict[int, float] | SparseEmbedding | None = None
) -> Tuple[np.ndarray, List[str]]
```

Search the index for the query vector and return the top\_k results.

**Arguments**:

* `vector` (`np.ndarray`): The query vector to search for.
* `top_k` (`int, optional`): The number of top results to return, defaults to 5.
* `route_filter` (`Optional[List[str]], optional`): A list of route names to filter the search results, defaults to None.
* `sparse_vector` (`Optional[SparseEmbedding]`): An optional sparse vector to include in the query.
* `kwargs` (`Any`): Additional keyword arguments for the query, including sparse\_vector.

**Raises**:

* `np.ndarray`0: If the index is not populated.

**Returns**:

`np.ndarray`1: A tuple containing an array of scores and a list of route names.

#### aquery

```python theme={null}
async def aquery(
    vector: np.ndarray,
    top_k: int = 5,
    route_filter: Optional[List[str]] = None,
    sparse_vector: dict[int, float] | SparseEmbedding | None = None
) -> Tuple[np.ndarray, List[str]]
```

Asynchronously search the index for the query vector and return the top\_k results.

**Arguments**:

* `vector` (`np.ndarray`): The query vector to search for.
* `top_k` (`int, optional`): The number of top results to return, defaults to 5.
* `route_filter` (`Optional[List[str]], optional`): A list of route names to filter the search results, defaults to None.
* `kwargs` (`Any`): Additional keyword arguments for the query, including sparse\_vector.
* `sparse_vector` (`Optional[dict]`): An optional sparse vector to include in the query.

**Raises**:

* `np.ndarray`0: If the index is not populated.

**Returns**:

`np.ndarray`1: A tuple containing an array of scores and a list of route names.

#### aget\_routes

```python theme={null}
async def aget_routes() -> list[tuple]
```

Asynchronously get a list of route and utterance objects currently

stored in the index.

**Returns**:

`List[Tuple]`: A list of (route\_name, utterance) objects.

#### delete\_index

```python theme={null}
def delete_index()
```

Delete the index.

**Returns**:

`None`: None

#### adelete\_index

```python theme={null}
async def adelete_index()
```

Asynchronously delete the index.

#### ais\_ready

```python theme={null}
async def ais_ready(client_only: bool = False) -> bool
```

Checks if class attributes exist to be used for async operations.

**Arguments**:

* `client_only` (`bool, optional`): Whether to check only the client attributes. If False
  attributes will be checked for both client and index operations. If True
  only attributes for client operations will be checked. Defaults to False.

**Returns**:

`bool`: True if the class attributes exist, False otherwise.

#### \_\_len\_\_

```python theme={null}
def __len__()
```

Returns the total number of vectors in the index. If the index is not initialized

returns 0.

**Returns**:

`int`: The total number of vectors.

#### alen

```python theme={null}
async def alen()
```

Async version of **len**. Returns the total number of vectors in the index.

If the index is not initialized, initializes it first or returns 0.

**Returns**:

`int`: The total number of vectors.