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Build a simple RAG pipeline from scratch with Python and Claude Haiku

By PulseAugur Editorial · [1 sources] ·

This tutorial demonstrates how to build a Retrieval-Augmented Generation (RAG) pipeline from scratch using Python, without relying on heavy frameworks or cloud services. The process involves four core steps: chunking source documents into manageable passages, embedding these chunks into vectors, storing them in a local vector database (chromadb), and then retrieving relevant chunks to augment an LLM's response. The guide uses the `sentence-transformers` library for embeddings and Anthropic's Claude Haiku 4.5 model for generation, highlighting the cost-effectiveness and simplicity of this approach for understanding RAG fundamentals. AI

IMPACT Provides a foundational understanding of RAG pipelines, enabling developers to build custom solutions without heavy dependencies.

RANK_REASON The item describes a tutorial for building a RAG pipeline using specific libraries, which falls under tooling.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Build a simple RAG pipeline from scratch with Python and Claude Haiku

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The item describes a tutorial for building a RAG pipeline using specific libraries, which falls under tooling.
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  1. dev.to — LLM tag TIER_1 English(EN) · aiunplugged ·

    Build a RAG pipeline from scratch — the actually simple version

    <p>Originally published on <a href="https://aiunplugged.in/blog/build-rag-pipeline-from-scratch/" rel="noopener noreferrer">aiunplugged.in</a> — cross-posting for the Dev.to community.</p> <p>Every RAG tutorial online starts with LangChain, LlamaIndex, or a hosted vector database…

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