RAG, MCP, AI Agents: Production-Ready Retrieval Systems

Define retrieval-augmented generation and vector databases, and explain chunking, embeddings, and memory management for scalable, private data queries.

Rag uses a two-phase offline ingestion and online retrieval from a vector database, while cag preloads all knowledge into the model’s context window for fast, single-pass responses.

Explore the three vector database categories—cloud managed, self-hosted open source, and hybrid—along with examples like Pinecone, Amazon Kendra, and Supabase to optimize speed and accuracy.

Explore key considerations for selecting a vector database for production-ready retrieval systems, covering scalability, performance, cost models, ease of integration, feature sets, multi-tenancy, backups, and community support.

Learn to implement production-ready retrieval augmented generation pipelines in n8n by integrating Pinecone vector store, OpenAI embeddings, and Google Drive data for effective document retrieval.

Compare Weaviate and Pinecone, highlighting open source self-hosting, flexible deployment, and semantic search versus a fully managed cloud service, with insights on scalability, security, cost, and data residency.

Learn to self-host Weaviate with Docker Compose, configuring services, ports, and authentication, and enable text to vec OpenAI and generative OpenAI modules for semantic search.

Explore the agno framework, a full-stack Python platform for building multi-agent systems with memory, knowledge, and reasoning, with easy integration of Pinecone and OpenAI.

Navigate a FastAPI and Agno basic setup with a hands-on tour of creating knowledge bases, saving data to a vector database, and performing hybrid near-text searches.

Set up a simple agno project on a native vba library with a framework, enabling health checks, authentication, and a chat and search flow for knowledge base loaded from urls.

Build a website knowledge-base agent with the Agno framework, transforming site content into a vector-backed knowledge base, using multi-query retrieval and code-ready examples for AI agents.

This lecture demonstrates building a team of AI agents for RAG-based coding tasks, using a website knowledge base and vector database to generate runnable Python code for image comparison.

Query expansion adds related terms and synonyms to user queries, boosting recall and relevance in retrieval systems. It follows a multi-step pipeline to improve hit accuracy and reduce follow-up clarifications.

Learn how synonym expansion creates a domain-specific layer that maps key legal terms to precise synonyms, boosting recall while preserving legal precision through controlled terminology and targeted retrieval.

Boost RAG speed by implementing prompt caching to reuse identical queries, dramatically lowering latency and OpenAI costs while storing prompts with hashing and an LRU cache.

Tune vector store indexes with NSW to balance accuracy and latency by adjusting m, f construction, and query-time f across millions of embeddings.

Contextual reranking reorders retrieved results using session history and metadata to boost relevance, especially in onboarding flows and multi-turn chats, improving response accuracy.

Blend embedding similarity, bm25, and feedback signals to rerank results, then synthesize a final answer using a hybrid vector database workflow for production-ready retrieval systems.

Enable MCP, an open standard acting as a universal AI remote, letting models discover and call tools such as web search, CRM, and databases via a JSON handshake.

Build a quick MVP workflow on anytime to reveal how MVPs run behind the scenes and empower you to create MVP flows with an AI agent, Pinecone, and MCP client.

Learn to use MCP to call external servers or build a custom MCP layer, integrate MCP tools with AI agents, and interact with Pine Cone via file-system navigation.