Chapter 11

Gemini API Qdrant Examples

This folder contains example notebooks demonstrating how to combine the Gemini API with the Qdrant vector database to enable semantic search and recommendation features using embeddings.

Notebooks

Similarity Search using Qdrant Load website data, build a semantic search system using embeddings from the Gemini API, store the embeddings in a Qdrant vector DB, and perform similarity search using Gemini-powered queries.
Movie Recommendation using Qdrant Process and embed a large movie dataset with the Gemini API, index movie vectors in Qdrant, and build a semantic movie recommender that returns similar movies based on user input using vector similarity search.
Hybrid Search & Reranking with Qdrant: Under the Hood of Legal AI Embed and index a legal dataset with the Gemini API and Qdrant, combining dense (based on Matryoshka Representations of Gemini embeddings) and sparse (based on the Qdrant's custom keywords-based retriever miniCOIL) vectors for hybrid search to ensure high accuracy, citation-grounded legal domain question answering.

These examples show how to:

Embed unstructured text data using Gemini's embedding model.
Store and search high-dimensional vectors in Qdrant.
Use Gemini queries to semantically match user input to relevant content.

You can use these templates as a foundation for building search, recommendation, or AI assistant systems using Gemini and Qdrant.

Chapter 11

Gemini API Qdrant Examples

This folder contains example notebooks demonstrating how to combine the Gemini API with the Qdrant vector database to enable semantic search and recommendation features using embeddings.

Notebooks

Similarity Search using Qdrant Load website data, build a semantic search system using embeddings from the Gemini API, store the embeddings in a Qdrant vector DB, and perform similarity search using Gemini-powered queries.
Movie Recommendation using Qdrant Process and embed a large movie dataset with the Gemini API, index movie vectors in Qdrant, and build a semantic movie recommender that returns similar movies based on user input using vector similarity search.
Hybrid Search & Reranking with Qdrant: Under the Hood of Legal AI Embed and index a legal dataset with the Gemini API and Qdrant, combining dense (based on Matryoshka Representations of Gemini embeddings) and sparse (based on the Qdrant's custom keywords-based retriever miniCOIL) vectors for hybrid search to ensure high accuracy, citation-grounded legal domain question answering.

These examples show how to:

Embed unstructured text data using Gemini's embedding model.
Store and search high-dimensional vectors in Qdrant.
Use Gemini queries to semantically match user input to relevant content.

You can use these templates as a foundation for building search, recommendation, or AI assistant systems using Gemini and Qdrant.

Gemini API Qdrant Examples