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GaiKT
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RAG Chatbot Microservice

Extract text from PDF and Markdown documents, index them, and serve AI chatbot responses via an API.

Updated May 25, 2026
Search Tags:#FastAPI#document indexing#PDF text extraction#AI-driven search#AI chatbot
One-click install
npx skills add https://github.com/GaiKT/Simple-Rag-service --skill rag-chatbot-microservice
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: RAG Chatbot Microservice
Source: https://github.com/GaiKT/Simple-Rag-service/tree/main
Command: npx skills add https://github.com/GaiKT/Simple-Rag-service --skill rag-chatbot-microservice

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires fastapi, uvicorn, chromadb, sentence-transformers, groq, pypdf, pdfplumber, python-dotenv, httpx, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill turns your PDFs and text documents into an AI-powered chatbot that can answer questions about the content.

Core Features & Use Cases

  • PDF to Text: Extract text from PDFs and Markdown files.
  • AI Chatbot: Generate answers to questions using the extracted text.
  • Use Case: With this Skill, you can create a chatbot that can answer questions about your company policies, product specifications, or any other document you have on hand.

Quick Start

Run the 'ingest.py' script to index your documents, then start the chat server with 'uvicorn main:app --reload' and ask questions through the terminal using 'python chat.py'.

Frequently Asked Questions about RAG Chatbot Microservice

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a FastAPI chatbot that answers questions from PDF documents?▼

To build a FastAPI chatbot for PDF documents, you extract text, index the content in ChromaDB, and expose an endpoint for AI-generated responses. This Skill provides the scripts to automate document indexing and query processing.

How does AI-driven search work with extracted PDF text?▼

AI-driven search with extracted PDF text works by indexing document chunks in ChromaDB and using Sentence Transformers to match user queries. The Groq API then generates answers based on the retrieved text context.

Do I need a Groq API key to run an AI chatbot with FastAPI?▼

Yes, you need a Groq API key to run this AI chatbot, as the Groq dependency drives the AI-generated responses. You must configure it in your environment variables using python-dotenv before starting the server.

What is the best way to extract text from PDFs for document indexing?▼

The best way to extract text from PDFs for document indexing here is using the included pypdf and pdfplumber libraries. The ingest.py script processes these files, extracts the text, and prepares it for ChromaDB indexing.

Can I use ChromaDB and Sentence Transformers for an AI chatbot microservice?▼

Yes, you can use ChromaDB and Sentence Transformers for an AI chatbot microservice. ChromaDB handles the vector storage for document indexing, while Sentence Transformers embed the text for AI-driven search.

What are the limitations of using a FastAPI microservice for PDF chatbots?▼

Limitations of this FastAPI PDF chatbot include reliance on the quality of PDF text extraction and the processing speed of the Sentence Transformers model. Complex PDF layouts may also result in fragmented text, affecting the AI-generated answers.

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