Best of Vector Search — September 2024

1. 1
   Article

   GoPenAI·2y

   Build an Advanced RAG App: Query Routing

   This post explores how to build an advanced RAG application using a technique called Query Routing. Query Routing enables the application to make decisions based on a user's query, selecting the most appropriate action from predefined choices such as retrieving context from multiple data sources, using different indexes, or performing a web search. Various types of Query Routers are discussed, including LLM Selector Router, LLM Function Calling Router, Semantic Router, and more. Example implementations demonstrate how to create Query Routers and enhance the decision-making capabilities of RAG applications.

   71

   3
2. 2
   Article

   Community Picks·2y

   Building an Advanced RAG System With Self-Querying Retrieval

   Learn how to build an advanced Retrieval Augmented Generation (RAG) system that leverages self-querying retrieval to improve search relevance. This tutorial covers extracting metadata filters from natural language queries, combining metadata filtering with vector search, and generating structured outputs using LLMs. The guide focuses on developing an investment assistant to answer financial questions using MongoDB as the vector store and LangGraph for orchestration.

   37
3. 3
   Article

   InfoWorld·2y

   Using PostgreSQL as a vector database in RAG

   Learn how to build a local retrieval-augmented generation (RAG) application using PostgreSQL with the pgvector extension, Ollama, and the Llama 3 large language model. This guide describes how Postgres can store both vector and tabular data, making it a versatile option for medium-sized RAG applications. It covers setting up a vector database, ingesting text from multiple sources, conducting similarity searches, and querying a large language model to generate answers. Practical coding examples and step-by-step instructions are provided to help developers get started quickly.

   26
4. 4
   Article

   Redis·2y

   Get better RAG responses with Ragas

   Many teams struggle to measure the performance of their Retrieval Augmented Generation (RAG) applications due to issues like hallucinations and incorrect data. A metrics-driven approach, using frameworks like RAG Assessment (Ragas), can improve evaluation by quantitatively measuring faithfulness, answer relevancy, context precision, and context recall. This ensures engineers can optimize their systems without relying on anecdotal evidence. Using tools like OpenAI, LangChain, and Redis, developers can establish and test baseline metrics efficiently.

   24
5. 5
   Article

   Baeldung·2y

   Building a RAG App Using MongoDB and Spring AI

   Learn how to build a Retrieval-Augmented Generation (RAG) Wiki application using MongoDB and Spring AI. The tutorial details setting up MongoDB Atlas Vector Search for storing documents, adding necessary dependencies, and configuring the application to save and retrieve documents based on context. The application leverages a vector store for similarity search and utilizes LLM for generating responses, making it suitable for developing chatbots, automated wikis, and search engines.

   22
6. 6
   Article

   InfoWorld·2y

   Why vector databases aren’t just databases

   Vector databases are specialized databases designed for handling unstructured data and supporting modern AI workloads like generative AI, machine learning, and natural language processing. Unlike traditional databases focused on structured data and transactional workloads, vector databases excel in real-time similarity searches and relevance ranking. They are highly effective in advanced search, recommendation systems, and retrieval-augmented generation (RAG) for large language models. They also integrate traditional filtering capabilities to enhance search results and are optimized for scalability and speed in AI-driven applications.

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7. 7
   Article

   Towards Data Science·2y

   How to Implement Graph RAG Using Knowledge Graphs and Vector Databases

   Learn to implement Graph RAG using knowledge graphs and vector databases to enhance semantic search, similarity search, and retrieval-augmented generation. The tutorial covers different retrieval methods including vector-based retrieval, prompt-to-query retrieval, and hybrid approaches, using real-world examples and code snippets.

   15
8. 8
   Article

   BigData Boutique blog·2y

   Implementing Semantic Image Search with Amazon OpenSearch Service

   Semantic search, powered by large multi-modal models like CLIP and Amazon OpenSearch Service, enhances image retrieval accuracy by addressing the limitations of traditional tag-based approaches. By embedding images and search queries into vectors within the same semantic space, this method allows for a more intuitive and accurate search experience. This process involves image embedding, vector indexing, query embedding, and nearest neighbor search, resulting in more relevant search results.

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