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Nappai Documentation

LM Studio Embeddings

LM Studio Embeddings turns text into a numeric fingerprint of its meaning using a model loaded in LM Studio, the application for running AI models on your own computer or server. 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 you want the text to stay on a server your company controls.

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.
  • Your company already has a server running LM Studio and does not want to depend on an outside service.

Configuration

FieldWhat it does
LM Studio Base URL (required)The address of your LM Studio server. It must be reachable from NappAI.
Model (required)The LM Studio model that calculates the fingerprints. Press the refresh button to load the list of models on your server and pick an embeddings model.

Advanced options

FieldWhat it does
Model TemperatureA setting inherited from models that write text. Default: 0.1. You do not need to change it.

Output

Example

An employment advisory firm, Asesores Ribera, wants its assistant to answer using its contract templates, without them leaving its server.

  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 LM Studio 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 LM Studio Embeddings component to find the chunks that answer each question.