freeCodeCamp·2yLearn Generative AI in 30 Hours
Learn Generative AI through a comprehensive course on the freeCodeCamp.org YouTube channel. The course covers various aspects of generative AI and its applications, including frameworks and models. It includes hands-on projects and is taught by leading experts in the field.
Community Picks·2yThe State of Data Engineering 2024
The 2024 State of Data Engineering report discusses the influence of GenAI on software infrastructure, the expansion of product offerings due to the economic downturn, and the impact of open table formats and their catalogs in the data lake industry. It also highlights the importance of data version control and observability in AI/ML systems.
GoPenAI·2yBuild 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.
GoPenAI·2yAnthropic’s New RAG Approach
LLMs excel at general tasks but struggle with specialized domains. Fine-tuning enhances their performance in targeted areas, but it's complex and costly. Retrieval-Augmented Generation (RAG) offers a solution by connecting LLMs directly to knowledge bases, enabling domain-specific data retrieval without extensive retraining. Techniques like Contextual Retrieval and BM25 integration improve accuracy by situating chunks within their full context. This approach balances semantic understanding with traditional keyword search, addressing challenges like incomplete responses.
Tech With Tim·2yBuild a Full RAG Based AI App Including Deployment In Under 20 Minutes
Learn how to build a full RAG-based AI app using Vex, a platform that allows you to quickly build and deploy applications. The app demonstrated in the post is a travel assistant that provides personalized recommendations based on travel plans and preferences.
Weaviate·2yVector Search Explained
Vector search leverages numerical representations called vector embeddings to retrieve similar items based on semantic meaning rather than exact text matches. It is used in modern AI applications, such as image retrieval, recommendation systems, and search engines. Implementing vector search involves converting data and query into vector embeddings and calculating their similarity using distance metrics like cosine similarity. Vector databases and Approximate Nearest Neighbor algorithms significantly speed up search times, making them suitable for large-scale datasets. Use cases include search systems, recommendation systems, and Retrieval Augmented Generation (RAG).
Daily Dose of Data Science | Avi Chawla | Substack·1yBuilding 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.
neo4j·2yGraph vs. Vector RAG — Benchmarking, Optimization Levers, and a Financial Analysis Example
Exploring the use of graph and vector search in retrieval-augmented generation (RAG) systems, focusing on their application in financial analysis. Discusses the differences between graph and vector search, optimization levers for graph search, and the combination of both methods in RAG. Highlights the benefits of graph databases for modeling complex relationships and dependencies, as well as the limitations and complementarity of vector search. Demonstrates the application of graph and vector search in a financial report RAG example.
Tech With Tim·2yAdvanced Multi-Agent AI App Walkthrough (Python, Langflow, Streamlit & More!)
This post provides a walkthrough on building an advanced multi-agent AI application using Python, Langflow, Streamlit, and other tools. The application can handle multiple tasks with different language models and integrates a full front end for interaction. Key technologies include Langflow for low-code AI flows, Streamlit for front-end development, and Astrab for a vector database to implement retrieval augmented generation features. The tutorial offers a comprehensive guide on setting up the application, integrating AI features, customizing flow, and connecting with different tools and APIs.
Community Picks·2yBuilding 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.
MongoDB·2yMongoDB AI Course in Partnership with Andrew Ng and DeepLearning.AI
MongoDB has partnered with Andrew Ng and DeepLearning.AI to launch a course focused on bridging database technology with modern AI applications. Titled 'Prompt Compression and Query Optimization,' this course covers Retrieval Augmented Generation (RAG) and uses MongoDB Atlas Vector Search to teach developers how to build efficient AI applications. The course also introduces techniques like prompt compression to reduce operational costs and showcases MongoDB's flexible document model ideal for AI data handling. It's tailored for developers interested in vector search, RAG applications, and AI optimization strategies.
Community Picks·2yA Broke B**ch’s Guide to Tech Start-up: Choosing Vector Database — Part 1 Self-Hosted
Vector databases are crucial for GenAI applications, offering augmented knowledge bases for language models with support for fuzzy searching using text or media embeddings. The post evaluates various self-hosted vector databases like MongoDB, ChromaDB, Weaviate, Milvus, Neo4j, KDB.AI, PostgreSQL, and SQLite. Recommendations include using Docker for ease of setup and highlighting the benefits and limitations of each option. The guide emphasizes starting with self-hosted instances to control costs while prototyping and suggests evaluating multiple databases to find the optimal setup for your application.
Medium·2yHow to Build RAG Applications with Pinecone Serverless, OpenAI, Langchain and Python
Learn how to build RAG applications using Pinecone Serverless, OpenAI, Langchain, and Python. Discover why Pinecone is a preferred vector database, and follow a step-by-step guide on building RAG apps. Upsert data to the vector database and query it to retrieve relevant information.
AI in Plain English·2yYour guide to Vector Databases
This post provides a guide to understanding Vector Databases, including their definition, functionality, and applications. It also highlights the features of a good Vector Database and offers best practices for choosing the right one. The post includes a list of over 80 Vector Databases for AI projects.
InfoWorld·2yUsing 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.
RUBYLAND·2yBuilding a Personal RAG Application for PDF-Based Question Answering
Learn about building a Retrieval-Augmented Generation (RAG) application for PDF-based question answering using LLMs, embedding models, and vector databases. This guide utilizes Meta's Llama3, Qdrant VectorDB, and Llama Index for embedding with Python, providing a way to interact with PDF content through natural, conversational queries.
GoPenAI·2yIntroduction to Retrieval-Augmented Generation (RAG): A Beginner’s Guide
Introduction to Retrieval-Augmented Generation (RAG): A Beginner's Guide. RAG combines retrieval and generative AI techniques to ensure accurate and meaningful responses. The RAG process involves document ingestion, retrieval, and response generation. RAG systems provide precise and top-notch text responses, elevating the performance of AI applications.
Redis·2yGet 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.
Baeldung·2yBuilding 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.
Towards AI·2yWhat are Vector Databases?
Vector databases are designed specifically for storing vector embeddings and enhance operations such as semantic search, recommendation engines, and advanced AI/ML processes. They leverage similarity metrics for data retrieval and offer benefits like scalability, flexibility, ultra-low latency, high performance, and optimized storage and memory.
Towards Data Science·2yScaling RAG from POC to Production
Retrieval Augmented Generation (RAG) is becoming a key architecture for large-scale applications of AI, balancing the capabilities of large language models with the accuracy of indexed data. Scaling from a proof of concept (POC) to production presents multiple challenges, including performance, data management, and risk mitigation. Addressing these challenges involves architectural components such as scalable vector databases, caching mechanisms, advanced search techniques, and a Responsible AI layer. Strategic planning and integration into existing workflows are crucial for successful scaling.
Community Picks·2yLLM app dev using AWS Bedrock and Langchain
The post explains how to develop applications using Large Language Models (LLMs) with Amazon Bedrock and Langchain to perform tasks like Question Answering over large document corpora. It introduces the concept of retrieval-augmented generation (RAG), which uses document processing and vector embedding to fetch relevant document chunks for question answering. The process includes setting up LLM and embedding models, loading and splitting documents into chunks, creating a vector database using SingleStoreDB, and performing similarity searches to generate context-aware responses.
Community Picks·2ySemantic search using OpenAI, pg_embedding and Neon
Learn how to build a semantic search app using OpenAI, Neon, and pg_embedding. The app transforms user queries into vector embeddings to perform vector similarity searches, retrieving the most relevant results based on meaning instead of keyword matches. The methodology includes generating embeddings, storing them in a Postgres database using pg_embedding, and retrieving similar items through vector similarity search. Step-by-step instructions and code are included for building the app, from gathering data to deploying the frontend and API.
Towards AI·2yPrompt Like a Pro Using DSPy: A Guide to Build a Better Local RAG Model using DSPy, Qdrant, and Ollama
Manual prompting in AI is becoming obsolete, with DSPy offering a revolutionary approach to optimize language model (LM) prompts algorithmically. DSPy uses signatures, modules, metrics, and optimizers to attain consistent and reproducible results across different LMs. This guide details the step-by-step process of integrating DSPy with Qdrant for vector databases and Ollama for local LLM deployments. Highlights include dataset loading, creating a vector database, and implementing a Chain of Thought Reasoning with a RAG model.