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Embeddings HuggingFace Inference Node#

Verwenden Sie den Embeddings HuggingFace Inference Node, um Embeddings für einen bestimmten Text zu generieren.

Auf dieser Seite finden Sie die Node-Parameter für den Embeddings HuggingFace Inference Node sowie Links zu weiteren Ressourcen.

Credentials

Sie finden Authentifizierungsinformationen für diesen Node hier.

Parameter resolution in sub-nodes

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five name values, the expression {{ $json.name }} resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five name values, the expression {{ $json.name }} always resolves to the first name.

Node-Parameter#

  • Model: Wählen Sie das Modell aus, das zum Generieren des Embeddings verwendet werden soll.

Weitere Informationen zu verfügbaren Modellen finden Sie in der Hugging Face-Modell-Dokumentation.

Node-Optionen#

  • Custom Inference Endpoint: Geben Sie die URL Ihres bereitgestellten Modells ein, das von HuggingFace gehostet wird. Wenn Sie dies festlegen, ignoriert Localmind Automate den Model Name.

Weitere Informationen finden Sie im HuggingFace-Leitfaden zur Inferenz.

Verwandte Ressourcen#

Weitere Informationen zum Dienst finden Sie in der Langchain-Dokumentation zu HuggingFace Inference Embeddings.

View Localmind Automate's Advanced AI documentation.

AI glossary#

  • completion: Completions are the responses generated by a model like GPT.
  • hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
  • vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
  • vector store: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.