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build_records

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.

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PineconeRecord Objects

class PineconeRecord(BaseModel)

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metadata

Additional metadata dictionary

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__init__

def __init__(**data)
Initialize PineconeRecord. Arguments:
  • **data (dict): Keyword arguments to pass to the BaseModel constructor.

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to_dict

def to_dict()
Convert PineconeRecord to a dictionary. Returns: dict: Dictionary representation of the PineconeRecord.

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PineconeIndex Objects

class PineconeIndex(BaseIndex)

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__init__

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.

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add

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.

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aadd

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.

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delete

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.

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adelete

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.

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delete_all

def delete_all()
Delete all routes from index if it exists. Returns: None: None

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describe

def describe() -> IndexConfig
Describe the index. Returns: IndexConfig: IndexConfig

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is_ready

def is_ready() -> bool
Checks if the index is ready to be used. Returns: bool: True if the index is ready, False otherwise.

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query

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.ndarray0: If the index is not populated.
Returns: np.ndarray1: A tuple containing an array of scores and a list of route names.

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aquery

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.ndarray0: If the index is not populated.
Returns: np.ndarray1: A tuple containing an array of scores and a list of route names.

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aget_routes

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.

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delete_index

def delete_index()
Delete the index. Returns: None: None

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adelete_index

async def adelete_index()
Asynchronously delete the index.

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ais_ready

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.

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__len__

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.

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alen

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.