A Natural Language Search Engine Over S3 Documents

📰 Medium · RAG

Build a natural language search engine over S3 documents using LLM integration and RAG with FastAPI, OpenSearch, Ollama, and LocalStack

advanced Published 28 Jun 2026

Action Steps

Build a local pipeline using FastAPI, OpenSearch, Ollama, and LocalStack to integrate LLMs and RAG
Configure OpenSearch to index S3 documents and enable search functionality
Use Ollama to integrate LLMs into the search engine and enable natural language querying
Implement RAG to improve search results and provide more accurate answers
Test and debug the pipeline to ensure seamless integration and functionality

Who Needs to Know This

This project is beneficial for backend developers and ML engineers who want to integrate LLMs and RAG into their applications, allowing them to build more efficient and accurate search engines.

Key Insight

💡 Integrating LLMs and RAG into a search engine can significantly improve search results and provide more accurate answers

Key Takeaways

Build a natural language search engine over S3 documents using LLM integration and RAG with FastAPI, OpenSearch, Ollama, and LocalStack

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Title: A Natural Language Search Engine Over S3 Documents

URL Source: https://medium.com/@towfiq106/a-natural-language-search-engine-over-s3-documents-d6321704290d?source=rss------rag-5

Published Time: 2026-06-28T13:55:33Z

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# A Natural Language Search Engine Over S3 Documents

[![Image 2: Towfiqul Islam](https://miro.medium.com/v2/resize:fill:32:32/0*3XVzmL4wdP4Eqh07.)](https://medium.com/@towfiq106?source=post_page---byline--d6321704290d---------------------------------------)

[Towfiqul Islam](https://medium.com/@towfiq106?source=post_page---byline--d6321704290d---------------------------------------)

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## I Built a Natural Language Search Engine Over S3 Documents as a Learning Project — Here’s Everything I Learned

_A curious developer’s journey into LLM integration and RAG: wiring FastAPI, OpenSearch, Ollama, and LocalStack into a fully local pipeline — and all the painful bugs in between._

## The Problem — and Why I Built This

I’m not an ML engineer. I’m a backend developer who got curious about LLMs and wanted to understand RAG from the inside — not by reading papers, but by building something real with it.

**_The trend is hard to ignore_**: every team is asking how to make their inte

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A Natural Language Search Engine Over S3 Documents

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