Weaviate Similarity Search Gemini Embeddings Byov

Similarity Search Gemini Embeddings Byov

vector-searchvector-databaseretrieval-augmented-generationgooglellm-frameworksweaviate-featuresfunction-callingweaviate-recipesmodel-providersPythongenerative-ai

alph-notebooks/weaviate-recipes / similarity_search_gemini_embeddings_byov.ipynb

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Vector search with Gemini Embeddings

This tutorial shows you, how to use the generally available (GA) Gemini Embedding model gemini-embedding-001 in Weaviate.

The Gemini Embedding model is initialised from Google's Gemini LLM. It builds its representations on top of the knowledge already present in Gemini's parameters.

Model overview:

Free and paid ($0.15/1M tokens) tiers available
Multilingual capabilities in over 100 languages
Leverages Matryoshka Representation Learning for flexible output dimensions (768, 1536, 3,072)
Maximum input sequence length of 2,048 tokens

Note, if you have been using the experimental gemini-embedding-exp-03-07, you won't need to re-embed your contents according to the release blog, but it will no longer be supported by the Gemini API on August 14, 2025.

For more information, you can check out the following resources:

You can access the gemini-embedding-001 model directly in Weaviate by using the Google module. Please note: you will need to use Weaviate 1.32.1 or later.

Dependencies

[1]

Connect to Weaviate

You can connect to Weaviate in different ways.

Weaviate Cloud (WCD): Managed services for development and production environments.
Embedded Weaviate: Experimental in-memory option without API keys for quick experimentation.
Local Weaviate instance e.g. deployed with Docker

You can get 14 days of free access to Weaviate Cloud's Sandbox by creating an account in Weaviate Cloud (WCD), where you can also obtain your API key. No name, no credit card required.

Alternatively, you can enable option 2 for quick experimentation.

[3]

INFO:weaviate-client:Binary /root/.cache/weaviate-embedded did not exist. Downloading binary from https://github.com/weaviate/weaviate/releases/download/v1.30.5/weaviate-v1.30.5-Linux-amd64.tar.gz
INFO:weaviate-client:Started /root/.cache/weaviate-embedded: process ID 752

True

Create a collection

Collection stores your data and vector embeddings.

[4]

Successfully created collection: MyCollection.

Import the data

[5]

Number of embeddings: 10
Length of each embedding: 3072

[6]

Data import complete

Quick check to see if all objects are in.

[7]

Query Weaviate: Vector Search (vector embeddings)

Search Weaviate with a vector embedding with near_vector.

See the docs for more.

[8]

The capital of Germany is Berlin.
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Die Hauptstadt Deutschlands ist Berlin.
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Der schnelle braune Fuchs springt über den faulen Hund.
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