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Nappai Documentation

AI/ML Embeddings

AI/ML Embeddings turns text into a numeric fingerprint of its meaning using the models of AI/ML API. With that fingerprint, NappAI can find the documents that say something similar to what you are looking for, even when they use different words, for example in a knowledge base.

You connect the component to a vector database, which uses it both to store your documents and to search them. Use it if your company already has an AI/ML API account, which gives access to models from several providers with a single account.

When to use it

  • Give an agent the knowledge in your manuals, contracts or FAQs by storing them in a vector database.
  • Build an internal search that finds documents by their meaning, not only by exact words.
  • You already have an AI/ML API account and prefer not to open one with each provider.
  • You need a multilingual model for documents in several languages.

Configuration

FieldWhat it does
Credential (required)The credential of the service AI/ML API. Pick one with Choose an option or create one with Add new credential. See Credentials.
Model Name (required)The model that calculates the fingerprints. Pick one from the list: text-embedding-3-large, text-embedding-3-small, text-embedding-ada-002, text-multilingual-embedding-002, textembedding-gecko-multilingual@001, textembedding-gecko@001 or textembedding-gecko@003.

Output

Example

An office supplies distributor, Suministros Ebro, wants its assistant to answer using its product catalogue and its terms of sale.

  1. In a loading flow, split the documents into chunks with Recursive Character Text Splitter and pass them to Chroma DB with Operation set to Add.
  2. Add AI/ML Embeddings and connect its Embeddings output to the Embedding input of Chroma DB.
  3. In the assistant flow, use the same Chroma DB collection with the same AI/ML Embeddings component to find the chunks that answer each question.