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

#: ../../source/models/model_abilities/embed.rst:8
msgid "Learn how to create text embeddings in Xinference."
msgstr "Aprende cómo crear incrustaciones de texto en Xinference."

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

#: ../../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 ""
"Los embeddings de texto se utilizan para cuantificar la relevancia entre "
"diferentes textos. Se pueden aplicar en diversas aplicaciones, incluyendo"
" búsqueda, agrupamiento, recomendaciones, detección de anomalías, "
"medición de diversidad y clasificación."

#: ../../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 es un vector de números de punto flotante. La proximidad "
"entre dos vectores puede servir como indicador de su similitud. Cuanto "
"menor es la distancia, mayor es la correlación, mientras que una "
"distancia mayor indica una correlación reducida."

#: ../../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 ""
"El modelo de incrustación en Xinference puede ser invocado a través de la"
" API de Embeddings para crear incrustaciones. La API de Embeddings imita "
"la `API de creación de incrustaciones de OpenAI "
"<https://platform.openai.com/docs/api-reference/embeddings/create>`_."

#: ../../source/models/model_abilities/embed.rst:27
msgid "API ENDPOINT"
msgstr "API endpoint"

#: ../../source/models/model_abilities/embed.rst:28
msgid "OpenAI-compatible ENDPOINT"
msgstr "Punto final compatible con OpenAI"

#: ../../source/models/model_abilities/embed.rst:30
msgid "Embeddings API"
msgstr "API de 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 "Lista de modelos compatibles"

#: ../../source/models/model_abilities/embed.rst:37
msgid ""
"You can examine all the :ref:`builtin embedding models in Xinference "
"<models_embedding_index>`."
msgstr ""
"Puedes ver todos los modelos de incrustación integrados en "
":ref:`Xinference <models_embedding_index>`."

#: ../../source/models/model_abilities/embed.rst:40
msgid "Embedding engines"
msgstr "Motores de embeddings"

#: ../../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 ""
"Al lanzar un modelo de embedding, puedes elegir el motor de servicio con "
"el parámetro ``model_engine`` (``--model-engine`` en la línea de "
"comandos):"

#: ../../source/models/model_abilities/embed.rst:45
msgid ""
"``sentence_transformers``: the default engine, available for all "
"embedding models."
msgstr ""
"``sentence_transformers``: el motor predeterminado, disponible para todos"
" los modelos de 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``: servicio de alto rendimiento para las familias de modelos "
"compatibles; actualmente, los modelos cuyos nombres comienzan por "
"``bge``, ``gte``, ``text2vec``, ``m3e``, ``Qwen3`` o ``bce`` (por "
"ejemplo, ``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``: motor basado en FlagEmbedding; también admite salida híbrida "
"(dispersa + densa), consulta las preguntas frecuentes más abajo."

#: ../../source/models/model_abilities/embed.rst:52
msgid "``llama.cpp``: serve GGUF-format embedding models."
msgstr "``llama.cpp``: sirve modelos de embedding en formato GGUF."

#: ../../source/models/model_abilities/embed.rst:55
msgid "Truncating input"
msgstr "Truncar la entrada"

#: ../../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 ""
"La API de Embeddings acepta un parámetro opcional "
"``truncate_prompt_tokens`` para limitar la longitud en tokens de cada "
"entrada antes de la codificación:"

#: ../../source/models/model_abilities/embed.rst:60
msgid "unset / ``null``: no truncation."
msgstr "sin establecer / ``null``: sin truncamiento."

#: ../../source/models/model_abilities/embed.rst:61
msgid "a positive integer ``N``: truncate each input to at most ``N`` tokens."
msgstr "un entero positivo ``N``: trunca cada entrada a un máximo de ``N`` tokens."

#: ../../source/models/model_abilities/embed.rst:62
msgid "``-1``: truncate to the model's own maximum input length."
msgstr "``-1``: trunca a la longitud máxima de entrada propia del modelo."

#: ../../source/models/model_abilities/embed.rst:77
msgid "Quickstart"
msgstr "Inicio rápido"

#: ../../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 ""
"Podemos probar la API de Embeddings mediante cURL, el cliente de OpenAI o"
" el cliente Python de Xinference."

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

#: ../../source/models/model_abilities/embed.rst:143
msgid "Does the LLM in Xinference support Embeddings API?"
msgstr "¿LLM admite la API de Embeddings en Xinference?"

#: ../../source/models/model_abilities/embed.rst:145
msgid ""
"No. Xinference doesn't provide embed API for LLMs due to considerations "
"of performance."
msgstr ""
"No. Xinference no proporciona una API de embeddings para los LLM por "
"consideraciones de rendimiento."

#: ../../source/models/model_abilities/embed.rst:149
msgid "Does Embeddings API provides integration method for LangChain?"
msgstr "¿La API de Embeddings proporciona un método de integración con 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 ""
"Sí, puedes consultar la documentación oficial de Xinference en la parte "
"relacionada con LangChain. Aquí está el enlace: `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 "¿La API de Embeddings admite modelos híbridos?"

#: ../../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 ""
"Sí, puedes usar ``flag`` como motor para desplegar el modelo y llamar a "
"la API de Embeddings estableciendo el parámetro adicional "
"``return_parse=True``, que devolverá vectores dispersos."

#~ msgid ""
#~ "You can find more examples of "
#~ "``embed`` ability in the tutorial "
#~ "notebook:"
#~ msgstr ""
#~ "Puedes encontrar más ejemplos sobre la"
#~ " capacidad de ``embed`` en el "
#~ "cuaderno de tutoriales."

#~ msgid "LangChain Streamlit Doc Chat"
#~ msgstr "LangChain Streamlit documentación de diálogo"

#~ msgid "Learn from an example demonstrating how to use embed API via LangChain"
#~ msgstr ""
#~ "Aprende cómo usar la API de "
#~ "embeddings a través de LangChain con "
#~ "un ejemplo."

