autogen.agentchat.contrib.vectordb.qdrant.QdrantVectorDB

QdrantVectorDB

QdrantVectorDB(
    *,
    client=None,
    embedding_function: EmbeddingFunction = None,
    content_payload_key: str = '_content',
    metadata_payload_key: str = '_metadata',
    collection_options: dict = {},
    **kwargs: Any
)

A vector database implementation that uses Qdrant as the backend.
Initialize the vector database.

Parameters:

Name	Description
client=None
embedding_function	Type: EmbeddingFunction Default: None
content_payload_key	Type: str Default: ‘_content’
metadata_payload_key	Type: str Default: ‘_metadata’
collection_options	Type: dict Default: {}
**kwargs	Type: Any

Class Attributes

active_collection

embedding_function

type

Instance Methods

create_collection

create_collection(
    self,
    collection_name: str,
    overwrite: bool = False,
    get_or_create: bool = True
) -> None

Create a collection in the vector database.
Case 1. if the collection does not exist, create the collection.
Case 2. the collection exists, if overwrite is True, it will overwrite the collection.
Case 3. the collection exists and overwrite is False, if get_or_create is True, it will get the collection, otherwise it raise a ValueError.

Parameters:

Name	Description
collection_name	str	The name of the collection. Type: str
overwrite	bool	Whether to overwrite the collection if it exists. Default is False. Type: bool Default: False
get_or_create	bool	Whether to get the collection if it exists. Default is True. Type: bool Default: True

Returns:

Type	Description
None	Any | The collection object.

delete_collection

delete_collection(self, collection_name: str) -> None

Delete the collection from the vector database.

Parameters:

Name	Description
collection_name	str	The name of the collection. Type: str

Returns:

Type	Description
None	Any

delete_docs

delete_docs(
    self,
    ids: list[str | int],
    collection_name: str = None,
    **kwargs
) -> None

Delete documents from the collection of the vector database.

Parameters:

Name	Description
ids	List[ItemID]	A list of document ids. Each id is a typed ItemID. Type: list[str | int]
collection_name	str	The name of the collection. Default is None. Type: str Default: None
**kwargs

Returns:

Type	Description
None	None

get_collection

get_collection(self, collection_name: str = None) -> 

Get the collection from the vector database.

Parameters:

Name	Description
collection_name	str	The name of the collection. Type: str Default: None

get_docs_by_ids

get_docs_by_ids(
    self,
    ids: list[str | int] = None,
    collection_name: str = None,
    include=True,
    **kwargs
) -> list[Document]

Retrieve documents from the collection of the vector database based on the ids.

Parameters:

Name	Description
ids	List[ItemID]	A list of document ids. If None, will return all the documents. Default is None. Type: list[str | int] Default: None
collection_name	str	The name of the collection. Default is None. Type: str Default: None
include=True
**kwargs

Returns:

Type	Description
list[Document]	List[Document] | The results.

insert_docs

insert_docs(
    self,
    docs: list[Document],
    collection_name: str = None,
    upsert: bool = False
) -> None

Insert documents into the collection of the vector database.

Parameters:

Name	Description
docs	List[Document]	A list of documents. Each document is a TypedDict Document. Type: list[Document]
collection_name	str	The name of the collection. Default is None. Type: str Default: None
upsert	bool	Whether to update the document if it exists. Default is False. Type: bool Default: False

Returns:

Type	Description
None	None

retrieve_docs

retrieve_docs(
    self,
    queries: list[str],
    collection_name: str = None,
    n_results: int = 10,
    distance_threshold: float = 0,
    **kwargs: Any
) -> list[list[tuple[Document, float]]]

Retrieve documents from the collection of the vector database based on the queries.

Parameters:

Name	Description
queries	List[str]	A list of queries. Each query is a string. Type: list[str]
collection_name	str	The name of the collection. Default is None. Type: str Default: None
n_results	int	The number of relevant documents to return. Default is 10. Type: int Default: 10
distance_threshold	float	The threshold for the distance score, only distance smaller than it will be returned. Don’t filter with it if 0. Default is 0. Type: float Default: 0
**kwargs	Type: Any

Returns:

Type	Description
list[list[tuple[Document, float]]]	QueryResults | The query results. Each query result is a list of list of tuples containing the document and the distance.

update_docs

update_docs(
    self,
    docs: list[Document],
    collection_name: str = None
) -> None

Update documents in the collection of the vector database.

Parameters:

Name	Description
docs	List[Document]	A list of documents. Type: list[Document]
collection_name	str	The name of the collection. Default is None. Type: str Default: None

Returns:

Type	Description
None	None