From 1f45dd7ed9bff6d7cee4488e46d1128b6e757331 Mon Sep 17 00:00:00 2001 From: Guillaume Vernade Date: Mon, 16 Jun 2025 11:49:06 +0200 Subject: [PATCH] Fixing links --- examples/qdrant/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/qdrant/README.md b/examples/qdrant/README.md index 9ee091c24..6c2a53254 100644 --- a/examples/qdrant/README.md +++ b/examples/qdrant/README.md @@ -8,10 +8,10 @@ This folder contains example notebooks demonstrating how to combine the **Gemini ### Notebooks -* **[Similarity Search using Qdrant](../examples/qdrant/Qdrant_similarity_search.ipynb)** +* **[Similarity Search using Qdrant](./Qdrant_similarity_search.ipynb)** 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](../examples/qdrant/Movie_Recommendation.ipynb)** +* **[Movie Recommendation using Qdrant](./Movie_Recommendation.ipynb)** 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. --- @@ -22,4 +22,4 @@ These examples show how to: * 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. \ No newline at end of file +You can use these templates as a foundation for building search, recommendation, or AI assistant systems using Gemini and Qdrant.