#54 Things are never boring with RAG! Vector Store, Vector Search, Knowledge Base, and more!

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

This week, we dive into our beloved RAG, but all new things. This week’s resources focus a lot on how to make RAG work for you and what you need for it. You might also enjoy the practical tutorials on building an AI research agent using Pydantic AI and the step-by-step guide on fine-tuning the PaliGemma2 model for object detection. As always, our community has built yet another useful resource for you to test and shared some interesting collaboration possibilities. Enjoy the read!

What’s AI Weekly

Whether you’re building recommendation systems like Netflix, Spotify, or any AI-driven application, vector databases provide the performance, scalability, and flexibility needed to handle large, complex datasets. This week in What’s AI, we dive into what precisely a vector database is, how it stores and searches data, the difference between indexing and a database, and the newest trends in vector databases. These are all really useful concepts for an AI engineer today playing with LLMs. Read the entire article here or watch the video on YouTube.

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

Learn AI Together Community section!

Featured Community post from the Discord

Max.berry_33008 has created a library of 24,000 prompts across 270 topics & featuring 90 prompt techniques. It includes domains such as human understanding, abstract reasoning, natural sciences, social sciences, humanities, applications of engineering and technology, and more. Download it here and support a fellow community member. If you have any questions or feedback, write it in the thread!

AI poll of the week!

Since most of you use 4o, we would love to know what tasks you use it for and where you think it lags behind. Tell us in the thread!

Collaboration Opportunities

The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!

1. Swangaaw4 is looking for a partner to build a portfolio and work on projects. If you are also building your portfolio, reach out in the thread!

2. _collaborator012 is looking for someone passionate about learning AI and can help train their models in English. If this sounds exciting, connect in the thread!

Meme of the week!

Meme shared by ghost_in_the_machine

TAI Curated section

Article of the week

Pydantic AI + Web Scraper + Llama 3.3 Python = Powerful AI Research Agent By Gao Dalie (高達烈)

This article details building a powerful AI research agent using Pydantic AI, a web scraper (Tavily), and Llama 3.3. It highlights Pydantic AI’s role in simplifying AI agent development, emphasizing its type-safe operations, structured response validation, and dependency injection system. The agent leverages Llama 3.3’s capabilities for information retrieval and summarization. A Streamlit application showcases the agent’s functionality: users input a query, and the agent scrapes data, processes it using Llama 3.3, and presents a structured summary including the title, main article, and bullet points. It also compares Pydantic AI with LangChain and LlamaIndex, noting Pydantic AI’s focus on production reliability and type safety.

Our must-read articles

1. Build a Company Brain With AI and RAG By Igor Novikov

This article discusses using AI and Retrieval-Augmented Generation (RAG) to build a company knowledge base. It addresses the limitations of traditional search methods, highlighting LLMs’ ability to understand complex queries and generate meaningful answers. However, LLMs alone lack access to company-specific data, necessitating a retriever to fetch relevant information from various sources (databases, documents, etc.). It details the challenges of handling large documents and datasets and the importance of re-ranking retrieved information to ensure relevance. Vector databases are presented as efficient storage solutions for semantic search, with options like QDrant and Pinecone discussed. It emphasizes the role of LLamaindex in building RAG systems, managing data ingestion, indexing, and querying. Further, it also explores challenges like access control, hallucinations (and mitigation strategies), knowledge graphs for enhanced data organization, and handling numerical computations within the RAG framework, suggesting integrating tools like Elasticsearch or dedicated scripting languages for complex calculations.

2. Mastering Object Detection: Fine-Tuning PaliGemma2 with a Step-by-Step Guide By Isuru Lakshan Ekanayaka

This article provides a detailed guide to fine-tuning the PaliGemma2 model for object detection. It introduces PaliGemma2, highlighting its multimodal architecture combining SigLIP-So400m vision encoding with Gemma 2 language models. It then covers prerequisites, including GPU access and setting up the Google Colab environment and API keys. Data preparation using Roboflow, model loading and configuration PaliGemma2 (including optional LoRA/QLoRA), and data loader creation are explained. It also explains the fine-tuning process, followed by instructions for running inference and evaluating the model using metrics like Mean Average Precision (mAP) and a confusion matrix. Finally, it offers best practices for fine-tuning, emphasizing data quality, parameter optimization, and leveraging transfer learning techniques. The article includes code snippets and visual examples throughout.

3. The GenAI DLP Black Book: Everything You Need to Know About Data Leakage from LLM By Mohit Sewak, Ph.D.

This article examines data leakage in LLMs. It explores several types of leakage: training data regurgitation, where LLMs reveal information from their training sets; prompt hijacking, where attackers use cleverly crafted prompts to elicit sensitive data; and parameter sniffing, involving attacks on the model’s internal parameters. The article details how these leaks occur, citing examples of real-world incidents, and explores the roles of developers, users, and attackers in these events. Finally, it outlines countermeasures such as differential privacy, federated learning, data sanitization, adversarial training, robust prompt management, and memory optimization, emphasizing a layered approach to security and building user trust in AI.

4. Will Long Context Language Models Replace RAG? By Claudio Giorgio Giancaterino

This article explores the potential of long-context language models to replace Retrieval-Augmented Generation (RAG) architectures. Using Gemini 1.5 Flash, the study compared three approaches: native language models, Naive RAG, and Advanced RAG across five complex questions. The research evaluated performance using ROUGE and BERTScore metrics, investigating whether long context models can effectively process and retrieve information without traditional RAG techniques. It demonstrated that long-context language models show promise but do not comprehensively outperform RAG systems. Advanced RAG strategies slightly improved information retrieval accuracy, particularly post-retrieval re-ranking techniques. It highlights the potential of long context models to simplify information processing by reducing RAG architecture complexity. However, the author concluded it is premature to completely replace RAG systems, suggesting continued technological evolution and potential hybrid approaches that leverage the strengths of both methodologies.

5. HNSW — Small World, Yes! But how in the world is it Navigable? By Allohvk

This article explains the Hierarchical Navigable Small World (HNSW) algorithm, a popular vector search method. It explores the “small-world phenomenon,” illustrating how seemingly distant individuals are connected through short chains of acquaintances, referencing Milgram’s experiment and Facebook’s network analysis. It also uses graph theory to model this phenomenon, discussing various graph models — from random graphs to Watts-Strogatz models — that capture the characteristic short path lengths and high clustering coefficients of small-world networks. The core of the article details HNSW, explaining its construction and search processes and emphasizing its hierarchical structure, which allows for efficient approximate nearest neighbor search in high-dimensional vector spaces.

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