Best of Vector Search — December 2024

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·1y

   Building a RAG app using Llama-3.3

   Meta released Llama-3.3, and this post provides a hands-on demo for building a RAG app using it. The app allows users to interact with a document via chat. It uses LlamaIndex for orchestration, Qdrant for a self-hosted vector database, and Ollama for serving Llama-3.3 locally. The implementation steps include loading and parsing a knowledge base, creating embeddings, indexing and storing them, defining a custom prompt template, and setting up a query engine.

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2. 2
   Article

   Towards Dev·1y

   Mastering Chunking for Effective RAG: Beyond Basics with Qdrant and Reranking

   Chunking is essential in Retrieval-Augmented Generation (RAG) workflows, breaking large documents into manageable pieces to optimize data ingestion. Different chunking strategies, such as semantic chunking and topic node parsing, enhance the effectiveness of RAG pipelines when combined with Qdrant’s hybrid vector search and reranking methods. An evaluation framework assesses the quality of RAG pipelines through metrics like faithfulness, answer relevancy, and answer correctness, providing insights into which combinations perform best.

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3. 3
   Article

   Towards AI·1y

   Building Multimodal RAG Application #5: Multimodal Retrieval from Vector Stores

   Multimodal RAG combines textual and visual data to improve the retrieval process, enhancing the accuracy and detail of large language models. This guide covers setting up multimodal retrieval using the LanceDB vector database, highlighting installation, configuration, and ingestion of text and image data using LangChain. It concludes with a practical walkthrough for performing efficient multimodal searches.

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