#vector-db

Professional diagram following Retrieval Augmented Generation architecture. Components: 1. Document Loader -> Splitting -> Embeddings. 2. Vector DB Storage. 3. Query Rewrite -> Retrieval -> Re-ranking. 4. Contextual Prompt -> LLM Generation. Use blue/violet gradients and high-quality technical icons.

Visualize how similarity search works. Features: 1. Input text field. 2. Results list with 'Similarity Score' (0-1.0). 3. 3D t-SNE scatter plot showing vector clusters. 4. Filter by namespace/metadata. 5. Performance info (Query time in ms). Compatible with Pinecone, Chroma, and Weaviate APIs.