VectorDB and RAG Resources

Curated videos, demos, notebooks, and reading for vector search and RAG with ApertureDB.

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Interactive Demo

Try semantic search in the ApertureDB Web UI — no setup required:

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Videos

Customer Story

How ApertureDB powers real-world multimodal AI applications.

Embeddings and Semantic Search

Generate CLIP embeddings for video frames and run semantic video search.

Extract text from PDF documents, generate embeddings, and store them as descriptors in ApertureDB.

End-to-end face detection and similarity search pipeline using ApertureDB Workflows.

RAG Applications

Crawl a website, embed the content, and serve a RAG chatbot using ApertureDB Workflows.

AI Agents and Memory

Expose ApertureDB as a tool for AI agents via the Model Context Protocol.

For more videos, visit the ApertureData YouTube channel.

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Notebooks and Tutorials

• Vector Search Quickstart — store, search, and rerank embeddings
• Image Similarity Search — face search with FaceNet on the CelebA dataset
• RAG on Wikipedia — Cohere embeddings + LangChain RAG chain
• Website Chatbot — local LLM + LangChain + ApertureDB
• LangChain Integration — use ApertureDB as a LangChain vector store
• LlamaIndex Integration — ApertureDBVectorStore with VectorStoreIndex
• Embeddings Extraction Workflow — images, PDFs, and video frames

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Blog Posts

• Vector Databases and Beyond for Multimodal AI: Part 1
• Vector Databases and Beyond for Multimodal AI: Part 2
• Are Vector Databases Enough for Visual Data Use Cases?
• Building Real World RAG-based Applications with ApertureDB
• Can a RAG Chatbot Really Improve Content?
• Smarter Agents Start with Smarter Data

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Community and User Contributions

• Agentic RAG with ApertureDB and HuggingFace SmolAgents
• Secure Your Agentic RAG Chatbots with Realm Labs and ApertureDB
• RAG Overview — Example built with ApertureDB
• RAG Workshop: Optimization
• RAG Workshop: Agentic RAG
• Semantic Search from Podcasts — 3-part series
• A Quick Comparison of Vector Databases for RAG Systems (AIMon)
• How to Improve RAG Accuracy with AIMon, ApertureDB, and LlamaIndex
• Managing Video Data with ApertureDB and Twelve Labs

Agent Memory — by Ayesha Imran

• Engineering the Memory Layer For An AI Agent To Navigate Large-scale Event Data — Part 1
• Engineering An AI Agent To Navigate Large-scale Event Data — Part 2

GraphRAG — by Ayesha Imran

• Automating Knowledge Graph Creation with Gemini and ApertureDB — Part 1
• Automating Knowledge Graph Creation with Gemini and ApertureDB — Part 2
• Automating Knowledge Graph Creation with Gemini and ApertureDB — Part 3
• Automating Knowledge Graph Creation with Gemini and ApertureDB — Part 4 (GraphRAG Evaluation)

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API and SDK Reference

• AddDescriptorSet — create a vector index (engines, metrics, dimensions)
• AddDescriptor — add a vector with metadata
• FindDescriptor — KNN search parameters, constraints, distances
• UpdateDescriptor — update metadata on stored vectors
• DeleteDescriptor — remove vectors
• Descriptors Python SDK — find_similar, find_similar_mmr
• DescriptorDataCSV — bulk ingestion from CSV + npz files

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Performance

• Vector Search Benchmarks — throughput and latency vs. Pinecone, Weaviate, Qdrant, Milvus
• Performance Whitepaper (access required)