RAG & Knowledge Base Toolkit
Everything you need to build retrieval-augmented apps: frameworks, vector databases, and orchestration.
LlamaIndex Framework Tutorial
A hands-on tutorial for LlamaIndex, the framework designed for building RAG applications that connect LLMs to custom data sources. It covers data ingestion pipelines, indexing strategies, query engines, and agent-based retrieval, with practical examples for PDF, web, and database connectors. Aimed at developers who want to build domain-specific AI assistants grounded in their own documents.
Haystack RAG Framework
Haystack is an open-source framework by deepset for building production-ready LLM applications, specializing in retrieval-augmented generation (RAG) and question-answering pipelines. It offers modular components for document processing, embedding, and retrieval that connect easily with models from OpenAI, Hugging Face, and more.
Qdrant Vector Database
Qdrant is a high-performance open-source vector database and similarity search engine designed for production use. Written in Rust, it powers RAG pipelines, recommendation systems, and semantic search with rich payload filtering, multi-tenancy support, and both self-hosted and managed cloud deployment options.
Dify LLM App Development Platform
Dify is an open-source LLM application development platform that combines visual workflow orchestration, a built-in RAG engine, and Agent capabilities. It lets developers and non-technical users quickly build, test, and deploy AI-powered apps with support for multiple model providers and seamless API integration.
LangChain Framework Tutorial
An in-depth tutorial on the LangChain framework, walking through its architecture and core modules—Chains, Agents, and Memory—with hands-on examples. It also covers RAG integration, custom tool development, and debugging strategies, making it a solid starting point for developers ready to move beyond simple prompts and build production-grade LLM applications.
Pinecone
Pinecone is a fully managed vector database built for production RAG and semantic search, offering serverless scaling, hybrid search, and metadata filtering. It removes the operational burden of running vector infrastructure yourself, letting teams focus on application logic — with usage-based pricing and a free starter tier.
Chroma
Chroma is a developer-friendly open-source embedding database that gets RAG prototypes running in minutes with a simple Python or JavaScript API. It runs in-process for local development and scales through its hosted cloud offering, making it the easiest entry point into vector search for most developers.