Build RAG from Scratch with ChromaDB and Python, Bypassing LangChain

This article demonstrates how to build a Retrieval-Augmented Generation (RAG) pipeline from scratch using Python, ChromaDB, and OpenAI, bypassing frameworks like LangChain. It details the process of chunking documents, generating embeddings, storing them in ChromaDB, and querying for relevant context to feed into a language model. The author highlights the benefits of this approach, including increased transparency, fewer dependencies, greater control, and easier debugging compared to using abstraction layers. AI

IMPACT Enables developers to build RAG systems with greater control and transparency by avoiding abstraction layers.

RANK_REASON Tutorial on using specific tools (ChromaDB, Python, OpenAI) for a common AI task (RAG) without a popular framework (LangChain).

Read on dev.to — MCP tag →

• ChromaDB
• LangChain
• OpenAI
• Python
• retrieval-augmented generation
• SQLite

• product
• infra

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