clip_client.client module#

class clip_client.client.Client(server, credential={}, **kwargs)[source]#

Bases: object

Create a Clip client object that connects to the Clip server. Server scheme is in the format of scheme://netloc:port, where

scheme: one of grpc, websocket, http, grpcs, websockets, https

netloc: the server ip address or hostname

port: the public port of the server

Parameters

server (str) – the server URI
credential (dict) – the credential for authentication {'Authentication': '<token>'}

profile(content='')[source]#

Profiling a single query’s roundtrip including network and computation latency. Results is summarized in a table. :type content: Optional[str] :param content: the content to be sent for profiling. By default it sends an empty Document

that helps you understand the network latency.

Return type: Dict[str, float]
Returns: the latency report in a dict.

encode(content: Iterable[str], *, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100) → np.ndarray[source]#
encode(content: Union[DocumentArray, Iterable[Document]], *, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100) → DocumentArray

async aencode(content: Iterator[str], *, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100) → np.ndarray[source]#
async aencode(content: Union[DocumentArray, Iterable[Document]], *, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100) → DocumentArray

rank(docs, **kwargs)[source]#

Rank image-text matches according to the server CLIP model. Given a Document with nested matches, where the root is image/text and the matches is in another modality, i.e. text/image; this method ranks the matches according to the CLIP model. Each match now has a new score inside clip_score and matches are sorted descendingly according to this score. More details can be found in: https://github.com/openai/CLIP#usage

Parameters: docs (Union[ForwardRef, Iterable[ForwardRef]]) – the input Documents
Return type: DocumentArray
Returns: the ranked Documents in a DocumentArray.

async arank(docs, **kwargs)[source]#

Return type: DocumentArray

index(content: Iterable[str], *, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[Dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100)[source]#
index(content: Union[DocumentArray, Iterable[Document]], *, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100) → DocumentArray

async aindex(content: Iterator[str], *, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[Dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100)[source]#
async aindex(content: Union[DocumentArray, Iterable[Document]], *, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100)

search(content: Iterable[str], *, limit: int = 10, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[Dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100) → DocumentArray[source]#

search(content: Union[DocumentArray, Iterable[Document]], *, limit: int = 10, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100) → DocumentArray

Return type: DocumentArray

async asearch(content: Iterator[str], *, limit: int = 10, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[Dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100)[source]#
async asearch(content: Union[DocumentArray, Iterable[Document]], *, limit: int = 10, batch_size: Optional[int] = None, show_progress: bool = False, parameters: Optional[dict] = None, on_done: Optional[CallbackFnType] = None, on_error: Optional[CallbackFnType] = None, on_always: Optional[CallbackFnType] = None, prefetch: int = 100)