Google Generative AI Embeddings

Google Generative AI Embeddings turns text into a numeric fingerprint of its meaning using Google Gemini models. 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 works with Google Gemini.

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 use Google Gemini as your AI model and want to use the same account.

Configuration

Field	What it does
Credential (required)	The credential of the service Google Gemini. Pick one with Choose an option or create one with Add new credential. See Credentials.
Model Name	The name of the Gemini model that calculates the fingerprints. Default: `models/gemini-embedding-001`.

Output

Embeddings: the model that calculates the fingerprints, ready to use. Connect it to the Embedding input of a vector database: Astra DB, Cassandra, Chroma DB, Couchbase, Elasticsearch, FAISS, Milvus, MongoDB Vector Store, PGVector, Pinecone, Qdrant, Redis, Supabase Vector Store, Upstash, Vectara, Weaviate Cloud (v4). Other components with an input of this type accept it too, such as Semantic Text Splitter or Text Embedder.

Example

A language school, Academia Lingua, wants its assistant to answer using its internal rules and its course sheets.

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.
Add Google Generative AI Embeddings and connect its Embeddings output to the Embedding input of Chroma DB.
In the assistant flow, use the same Chroma DB collection with the same Google Generative AI Embeddings component to find the chunks that answer each question.