autogen.agentchat.contrib.vectordb.pgvectordb.PGVectorDB

PGVectorDB

PGVectorDB(
    *,
    conn: ForwardRef('Connection') | None = None,
    connection_string: str | None = None,
    host: str | None = None,
    port: int | str | None = None,
    dbname: str | None = None,
    username: str | None = None,
    password: str | None = None,
    connect_timeout: int | None = 10,
    embedding_function: Callable = None,
    metadata: dict[str, Any] | None = None
)

A vector database that uses PGVector as the backend.
Initialize the vector database.
Note: connection_string or host + port + dbname must be specified

Parameters:

Name	Description
conn	Type: ForwardRef(‘psycopg.Connection’) | None Default: None
connection_string	Type: str | None Default: None
host	Type: str | None Default: None
port	Type: int | str | None Default: None
dbname	Type: str | None Default: None
username	Type: str | None Default: None
password	Type: str | None Default: None
connect_timeout	Type: int | None Default: 10
embedding_function	Type: Callable Default: None
metadata	Type: dict[str, typing.Any] | None Default: None

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
) -> Collection

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
Collection	Collection | 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	None

delete_docs

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

Returns:

Type	Description
None	None

establish_connection

establish_connection(
    self,
    conn: ForwardRef('Connection') | None = None,
    connection_string: str | None = None,
    host: str | None = None,
    port: int | str | None = None,
    dbname: str | None = None,
    username: str | None = None,
    password: str | None = None,
    connect_timeout: int | None = 10
) -> Connection

Establishes a connection to a PostgreSQL database using psycopg.

Parameters:

Name	Description
conn	An existing psycopg connection object. If provided, this connection will be used. Type: ForwardRef(‘psycopg.Connection’) | None Default: None
connection_string	A string containing the connection information. If provided, a new connection will be established using this string. Type: str | None Default: None
host	The hostname of the PostgreSQL server. Used if connection_string is not provided. Type: str | None Default: None
port	The port number to connect to at the server host. Used if connection_string is not provided. Type: int | str | None Default: None
dbname	The database name. Used if connection_string is not provided. Type: str | None Default: None
username	The username to connect as. Used if connection_string is not provided. Type: str | None Default: None
password	The user’s password. Used if connection_string is not provided. Type: str | None Default: None
connect_timeout	Maximum wait for connection, in seconds. The default is 10 seconds. Type: int | None Default: 10

Returns:

Type	Description
psycopg.Connection	A psycopg.Connection object representing the established connection.

get_collection

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

Get the collection from the vector database.

Parameters:

Name	Description
collection_name	str	The name of the collection. Default is None. If None, return the current active collection. Type: str Default: None

Returns:

Type	Description
Collection	Collection | The collection object.

get_docs_by_ids

get_docs_by_ids(
    self,
    ids: list[str | int] = None,
    collection_name: str = None,
    include=None,
    **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=None
**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 = -1
) -> 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 -1. Type: float Default: -1

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