a bŠÝgõã@s8dZddlmZmZmZddlmZGdd„deƒZdS)z+Wrapper around Xinference embedding models.é)ÚAnyÚListÚOptional)Ú Embeddingscs„eZdZUdZeed<eeed<eeed<deeeedœ‡fdd„ Ze ee e e d œd d„Zee e dœd d„Z‡Z S)ÚXinferenceEmbeddingsaqXinference embedding models. To use, you should have the xinference library installed: .. code-block:: bash pip install xinference If you're simply using the services provided by Xinference, you can utilize the xinference_client package: .. code-block:: bash pip install xinference_client Check out: https://github.com/xorbitsai/inference To run, you need to start a Xinference supervisor on one server and Xinference workers on the other servers. Example: To start a local instance of Xinference, run .. code-block:: bash $ xinference You can also deploy Xinference in a distributed cluster. Here are the steps: Starting the supervisor: .. code-block:: bash $ xinference-supervisor If you're simply using the services provided by Xinference, you can utilize the xinference_client package: .. code-block:: bash pip install xinference_client Starting the worker: .. code-block:: bash $ xinference-worker Then, launch a model using command line interface (CLI). Example: .. code-block:: bash $ xinference launch -n orca -s 3 -q q4_0 It will return a model UID. Then you can use Xinference Embedding with LangChain. Example: .. code-block:: python from langchain_community.embeddings import XinferenceEmbeddings xinference = XinferenceEmbeddings( server_url="http://0.0.0.0:9997", model_uid = {model_uid} # replace model_uid with the model UID return from launching the model ) ÚclientÚ server_urlÚ model_uidN)rr c s¨zddlm}WnRtybzddlm}Wn.ty\}ztdƒ|‚WYd}~n d}~00Yn0tƒ ¡|dur~tdƒ‚|durŽtdƒ‚||_||_||ƒ|_ dS)Nr)Ú RESTfulClientzƒCould not import RESTfulClient from xinference. Please install it with `pip install xinference` or `pip install xinference_client`.zPlease provide server URLzPlease provide the model UID) Zxinference.clientr ÚImportErrorZxinference_clientÚsuperÚ__init__Ú ValueErrorrr r)Úselfrr r Úe©Ú __class__©úw/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/embeddings/xinference.pyr Rs&ÿý zXinferenceEmbeddings.__init__)ÚtextsÚreturncs.|j |j¡‰‡fdd„|Dƒ}dd„|DƒS)zµEmbed a list of documents using Xinference. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. cs"g|]}ˆ |¡ddd‘qS)ÚdatarÚ embedding)Úcreate_embedding)Ú.0Útext©ÚmodelrrÚ xsz8XinferenceEmbeddings.embed_documents..cSsg|]}ttt|ƒƒ‘qSr)ÚlistÚmapÚfloat)rrrrrr{ó)rÚ get_modelr )rrZ embeddingsrrrÚembed_documentsns ÿz$XinferenceEmbeddings.embed_documents)rrcCs6|j |j¡}| |¡}|ddd}ttt|ƒƒS)zžEmbed a query of documents using Xinference. Args: text: The text to embed. Returns: Embeddings for the text. rrr)rr#r rrr r!)rrrZ embedding_resrrrrÚembed_query}s z XinferenceEmbeddings.embed_query)NN)Ú__name__Ú __module__Ú__qualname__Ú__doc__rÚ__annotations__rÚstrr rr!r$r%Ú __classcell__rrrrrs CÿÿrN)r)ÚtypingrrrZlangchain_core.embeddingsrrrrrrÚs