The Complete RAG & Vectorless RAG Course: Build AI Apps

Explore the traditional rag pipeline—documents, embeddings, vectors, vector databases, and semantic search—then examine vectorless rag with page index for faster AI retrieval.

Compare traditional rag and vectorless rag, weighing cost, speed, infrastructure, and complexity. Highlight use cases, practical architecture, and the project-based implementation that the course offers.

Explore LangChain, a framework that bridges large language models with external data, tools, and memory to build real-world AI apps, from personal assistants to business automation.

Explore the essential lang chain packages, from community tools and text splitter to hugging face models and chroma memory, and learn installation with requirements.txt, python.env, and ipykernel.

Discover how lang chain functions as a cohesive pipeline—from document loading and text splitting to embeddings, vector databases, retrievers, and LLMs—forming a robust RAG system.

Generate a HuggingFace access token by creating an account, navigating to access tokens, and copying the API key to the environment variable file for use in AI apps.

Learn to generate and securely store a Grok API key to enable fast LLM inference and run models with Grok in your apps.

Generate and copy your Tavili API key to enable real-time web search in AI apps by signing up at tavili.com and creating the key in the dashboard.

Generate the pinecone api key, log in or sign up, and navigate to api key settings to create and copy your key for use with LangChain and Pinecone embeddings.

Configure the Pinecone environment for LangChain apps by creating an index, choosing a region like US East 1, and setting the API key and environment.

Learn to securely manage API keys by creating a .env file and loading secrets into your Python project with the python.env package, avoiding hardcoding and secret exposure.

Build your first ai application with LangChain by using chat grok to connect to a lemma 3.18 billion parameter instant model, invoke prompts, and run text-based questions end-to-end.

Explore retrieval augmented generation, a two-step process that retrieves relevant data from vector databases via similarity search and generates grounded answers with an llm.

Learn why large language models alone fall short due to hallucination and knowledge cutoff, and how retrieval augmented generation uses external data to build reliable AI apps.

Discover how LangChain enables scalable real-world ai systems by implementing a rag workflow with a retriever, vector database, embeddings, and an llm chain for context-driven answers.

Implement hugging face embeddings in langchain for real-world rag applications by creating query and document embeddings with all-mini-lm-l6v2 and 384-dimensional vectors for semantic search.

Explore offline local AI apps using OLAMA embeddings in LANCHAIN, create embeddings with LAMA 3.2, and prepare vectors from small docs and queries for RAC pipeline and future vector databases.

Learn how vector databases store embeddings and enable semantic search and similarity retrieval in a LangChain rag pipeline.

Explore vector indexing fundamentals with flat, IVF, and HNSW, comparing accuracy, speed, and use cases to optimize similarity search in LangChain and production-grade AI apps.

Discover how vector databases, embeddings, and retrieval augmented generation power accurate, grounded ai by loading documents, chunking, creating embeddings, storing in vector databases, and using similarity search with an llm.

Explore faiss, a high-performance vector index, and build a local vector search workflow with LangChain, in-memory docstore, and olama embeddings, including flat inner-product search and index persistence.

Connect embeddings to Pinecone's scalable cloud vector infrastructure, create an index, upload documents, and run similarity searches with metadata filters using LangChain integration.

Load a pdf into a langchain pipeline using the pypdf loader, setting environment variables from a .env file, and prepare for embedding and a retriever pipeline.

Convert text chunks into 384-dim vectors using HuggingFace embeddings with the AllMiniLM L6v2 model, store them in a files vector index, and enable semantic search via an in-memory doc store.

Understand how the retriever uses the FIAS vector store to perform a top-k search and return the top 10 relevant chunks for the generator.

Explore why vector databases become expensive at scale, from embedding generation costs to retrieval latency, and learn how vectorless rack approaches address infrastructure and scalability challenges.

Uncover the hidden problems with chunking and embeddings in retrieval systems. See how vectorless RAG uses structure, hierarchy, and intelligent indexing to improve retrieval quality.

Understand how traditional rag pipelines fail due to irrelevant chunks and semantic mismatches. Learn how retrieval quality shapes model accuracy, mitigating hallucinations with re-ranking, context optimization, and structured retrieval.

Learn when to use traditional rag versus vectorless rag for large unstructured data, semantic search, and enterprise-scale retrieval. Understand infrastructure, chunking quality, and precision in choosing the right architecture.

Explore page index, a structure-aware retrieval approach that navigates hierarchical sections and summaries like a human. Shift from vector similarity to reasoning-based navigation over documents.

Install and set up project dependencies using a requirements.txt file and pip install -r requirements.txt, covering libraries like streamlit, Lanchain, Lanchain OpenAI, PyPDF, Python env, and page index.

Discover how retrieval works without vector databases by using page index's hierarchical tree structure, node summaries, and structured reasoning to navigate and extract relevant content.

Explore the two-part architecture of vectorless AI systems, from indexing with table-of-contents aware and section-aware splitting to retrieval driven by reasoning over a hierarchical knowledge tree with node summaries.

Build a complete vectorless retrieval system from scratch that creates a structured knowledge index and enables reasoning-based, grounded answers without embeddings or vector databases.

Load and process PDFs by preserving pages, extracting sections, and forming structured knowledge for a vectorless rag pipeline, then save and load it as JSON.

Create a vectorless knowledge index from pdf sections by extracting sections and metadata with an LLM, storing a structured document_index.json as the system's brain for vector-free retrieval.

Explore a reasoning-based retrieval in a vectorless rag system using a knowledge index and metadata, with an llm selecting top nodes without embeddings.

Learn to generate grounded artificial intelligence answers by transforming retrieved nodes into a context, then prompt a language model to deliver evidence-based responses with reduced hallucination.

Connects all components of the vectorless rack into an intelligent pipeline by running runVectorlessRack, orchestrating build index, retrieval, reasoning, and grounded answer generation into a complete AI product.

Build a professional vectorless rag app with a streamlit interface that uploads pdfs, accepts questions, and shows retrieval reasoning and grounded answers.