Retrieval. Augmented. Generation!
8 lessons
Vectorize RAG-as-a-Service handles the messy, hard parts of AI development, so you can focus on building your applications.
This lesson demonstrates building a Retrieval Augmented Generation (RAG) pipeline using the Vectorize platform to quickly process and query large PDF datasets, such as Denver's zoning regulations. The tutorial covers key steps including data ingestion from Dropbox, configuring extraction strategies, selecting a vector database (like Pinecone), and deploying a functional chatbot for efficient question answering.
Master efficient document searching with vectorize.io's RAG pipeline builder; optimize your search by testing various embedding models and chunking strategies to achieve optimal performance using metrics like NDCG and Recall.
This lesson shows how to build a Slack bot that uses Vectorize's RAG pipeline and OpenAI to answer questions. The process involves setting up a Slack app, configuring an n8n workflow to connect to Vectorize and OpenAI, and optimizing prompt engineering for accurate responses.
Master Retrieval Augmented Generation (RAG) by leveraging vector databases for efficient similarity searches within unstructured data. Optimize your RAG system through strategic chunking, meticulous metadata design, and continuous data updates to ensure accuracy and real-time relevance.
This lesson demonstrates building and deploying a Retrieval-Augmented Generation (RAG) chatbot using Vectorize, integrating LLMs like OpenAI and vector databases for efficient information retrieval. The process covers pipeline creation, data ingestion, chatbot integration, and deployment on platforms like Vercel, emphasizing ease of use and scalability.
This lesson teaches you to build a Retrieval Augmented Generation (RAG) pipeline using Vectorize and Qdrant. It covers web crawling, vector database integration, and the configuration of a conversational AI system for efficient information retrieval from online sources.
This lesson explores the architecture of an AI-powered technical support agent, detailing its interaction with LLMs and various tools via function calls to automate responses and escalate complex issues to human agents. The system leverages OpenAI's API, incorporating features like persistent storage, error handling, and a chain-of-thought prompting approach for improved accuracy and efficiency.
This lesson teaches you to build a Retrieval Augmented Generation (RAG) pipeline using Vectorize, integrating it with Swarm for multi-agent system development. It covers pipeline creation, deployment, and integration with Swarm, showcasing how to use Vectorize's features and pre-built Swarm agent code for efficient data retrieval.
