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#: ../../source/models/model_abilities/embed.rst:5
msgid "Embeddings"
msgstr "intégré"

#: ../../source/models/model_abilities/embed.rst:8
msgid "Learn how to create text embeddings in Xinference."
msgstr "Apprenez à créer des embeddings de texte dans Xinference."

#: ../../source/models/model_abilities/embed.rst:12
msgid "Introduction"
msgstr "Introduction"

#: ../../source/models/model_abilities/embed.rst:14
msgid ""
"Text embeddings are used to quantify how related different pieces of text"
" are. They can be used in various applications including search, "
"clustering, recommendations, anomaly detection, diversity measurement, "
"and classification."
msgstr ""
"Les plongements de texte sont utilisés pour quantifier la similarité "
"entre différents textes. Ils peuvent être appliqués à diverses "
"applications, notamment la recherche, le clustering, la recommandation, "
"la détection d'anomalies, les mesures de diversité et la classification."

#: ../../source/models/model_abilities/embed.rst:17
msgid ""
"An embedding is a vector of floating point numbers. The proximity between"
" two vectors serves as an indicator of their similarity. Less distance "
"implies a higher correlation, while a larger distance indicates reduced "
"correlation."
msgstr ""
"Un embedding est un vecteur de nombres flottants. La proximité entre deux"
" vecteurs peut servir d'indicateur de leur similarité. Une distance plus "
"faible indique une corrélation plus élevée, tandis qu'une distance plus "
"grande signifie une corrélation réduite."

#: ../../source/models/model_abilities/embed.rst:20
msgid ""
"Embedding models in Xinference can be invoked through the Embeddings API "
"to create embeddings. The Embeddings API mimics OpenAI's `create "
"embeddings API <https://platform.openai.com/docs/api-"
"reference/embeddings/create>`_."
msgstr ""
"Les modèles d'embedding dans Xinference peuvent être appelés via "
"l'Embeddings API pour créer des embeddings. L'Embeddings API imite l'`API"
" de création d'embeddings d'OpenAI <https://platform.openai.com/docs/api-"
"reference/embeddings/create>`_."

#: ../../source/models/model_abilities/embed.rst:27
msgid "API ENDPOINT"
msgstr "Point de terminaison API"

#: ../../source/models/model_abilities/embed.rst:28
msgid "OpenAI-compatible ENDPOINT"
msgstr "Endpoint compatible avec OpenAI"

#: ../../source/models/model_abilities/embed.rst:30
msgid "Embeddings API"
msgstr "API Embeddings"

#: ../../source/models/model_abilities/embed.rst:31
msgid "/v1/embeddings"
msgstr "/v1/embeddings"

#: ../../source/models/model_abilities/embed.rst:35
msgid "Supported models"
msgstr "Liste des modèles pris en charge"

#: ../../source/models/model_abilities/embed.rst:37
msgid ""
"You can examine all the :ref:`builtin embedding models in Xinference "
"<models_embedding_index>`."
msgstr ""
"Vous pouvez consulter tous les modèles d'intégration intégrés de "
":ref:`Xinference <models_embedding_index>`."

#: ../../source/models/model_abilities/embed.rst:40
msgid "Embedding engines"
msgstr "Moteurs d'embedding"

#: ../../source/models/model_abilities/embed.rst:42
msgid ""
"When launching an embedding model, you can pick the serving engine with "
"the ``model_engine`` parameter (``--model-engine`` on the command line):"
msgstr ""
"Lors du lancement d'un modèle d'embedding, vous pouvez choisir le moteur "
"de service avec le paramètre ``model_engine`` (``--model-engine`` en "
"ligne de commande) :"

#: ../../source/models/model_abilities/embed.rst:45
msgid ""
"``sentence_transformers``: the default engine, available for all "
"embedding models."
msgstr ""
"``sentence_transformers`` : le moteur par défaut, disponible pour tous "
"les modèles d'embedding."

#: ../../source/models/model_abilities/embed.rst:47
msgid ""
"``vllm``: high-throughput serving for supported model families — "
"currently models whose names start with ``bge``, ``gte``, ``text2vec``, "
"``m3e``, ``Qwen3``, or ``bce`` (e.g. ``bce-embedding-base_v1``)."
msgstr ""
"``vllm`` : service à haut débit pour les familles de modèles prises en "
"charge — actuellement les modèles dont le nom commence par ``bge``, "
"``gte``, ``text2vec``, ``m3e``, ``Qwen3`` ou ``bce`` (par ex. ``bce-"
"embedding-base_v1``)."

#: ../../source/models/model_abilities/embed.rst:50
msgid ""
"``flag``: FlagEmbedding-based engine; also supports hybrid (sparse+dense)"
" output, see the FAQ below."
msgstr ""
"``flag`` : moteur basé sur FlagEmbedding ; il prend également en charge "
"la sortie hybride (sparse+dense), voir la FAQ ci-dessous."

#: ../../source/models/model_abilities/embed.rst:52
msgid "``llama.cpp``: serve GGUF-format embedding models."
msgstr "``llama.cpp`` : servir des modèles d'embedding au format GGUF."

#: ../../source/models/model_abilities/embed.rst:55
msgid "Truncating input"
msgstr "Troncature de l'entrée"

#: ../../source/models/model_abilities/embed.rst:57
msgid ""
"The Embeddings API accepts an optional ``truncate_prompt_tokens`` "
"parameter to cap the token length of each input before encoding:"
msgstr ""
"L'API Embeddings accepte un paramètre optionnel "
"``truncate_prompt_tokens`` pour plafonner la longueur en tokens de chaque"
" entrée avant l'encodage :"

#: ../../source/models/model_abilities/embed.rst:60
msgid "unset / ``null``: no truncation."
msgstr "non défini / ``null`` : aucune troncature."

#: ../../source/models/model_abilities/embed.rst:61
msgid "a positive integer ``N``: truncate each input to at most ``N`` tokens."
msgstr "un entier positif ``N`` : tronquer chaque entrée à au plus ``N`` tokens."

#: ../../source/models/model_abilities/embed.rst:62
msgid "``-1``: truncate to the model's own maximum input length."
msgstr "``-1`` : tronquer à la longueur d'entrée maximale du modèle."

#: ../../source/models/model_abilities/embed.rst:77
msgid "Quickstart"
msgstr "Démarrage rapide"

#: ../../source/models/model_abilities/embed.rst:79
msgid ""
"We can try Embeddings API out either via cURL, OpenAI Client, or "
"Xinference's python client:"
msgstr ""
"Nous pouvons essayer l'API Embeddings via cURL, le client OpenAI ou le "
"client Python de Xinference."

#: ../../source/models/model_abilities/embed.rst:140
msgid "FAQ"
msgstr "FAQ"

#: ../../source/models/model_abilities/embed.rst:143
msgid "Does the LLM in Xinference support Embeddings API?"
msgstr "LLM prend-il en charge l'API Embeddings dans Xinference ?"

#: ../../source/models/model_abilities/embed.rst:145
msgid ""
"No. Xinference doesn't provide embed API for LLMs due to considerations "
"of performance."
msgstr ""
"Non. Xinference ne fournit pas d'API embed pour les LLM pour des raisons "
"de performance."

#: ../../source/models/model_abilities/embed.rst:149
msgid "Does Embeddings API provides integration method for LangChain?"
msgstr "L'API Embeddings fournit-elle une méthode d'intégration avec LangChain ?"

#: ../../source/models/model_abilities/embed.rst:151
msgid ""
"Yes, you can refer to the related sections in LangChain's respective "
"official Xinference documentation. Here is the link: `Text Embedding "
"Models: Xinference "
"<https://python.langchain.com/docs/integrations/text_embedding/xinference>`_"
msgstr ""
"Oui, vous pouvez consulter la documentation officielle de Xinference pour"
" la partie concernant LangChain. Voici le lien : `Text Embedding Models: "
"Xinference "
"<https://python.langchain.com/docs/integrations/text_embedding/xinference>`_"

#: ../../source/models/model_abilities/embed.rst:156
msgid "Does Embeddings API support hrbrid model?"
msgstr "L'API Embeddings prend-elle en charge les modèles hybrides ?"

#: ../../source/models/model_abilities/embed.rst:158
msgid ""
"Yes, you can use ``flag`` as the engine to deploy the model and call "
"Embeddings API by setting the extra parameter ``return_parse=True`` which"
" will return sparse vectors."
msgstr ""
"Oui, vous pouvez utiliser ``flag`` comme moteur pour déployer le modèle "
"et appeler l'API Embeddings en définissant le paramètre supplémentaire "
"``return_parse=True``, ce qui renverra des vecteurs creux (sparse)."

#~ msgid ""
#~ "You can find more examples of "
#~ "``embed`` ability in the tutorial "
#~ "notebook:"
#~ msgstr ""
#~ "Vous pouvez trouver plus d'exemples sur"
#~ " la capacité ``embed`` dans le cahier"
#~ " de tutoriel."

#~ msgid "LangChain Streamlit Doc Chat"
#~ msgstr "Documentation de dialogue LangChain Streamlit"

#~ msgid "Learn from an example demonstrating how to use embed API via LangChain"
#~ msgstr ""
#~ "Apprenez à utiliser l'API d'embedding "
#~ "via LangChain à partir d'un exemple."

