rag-pipeline-builder

Build RAG pipelines ingesting markdown into Qdrant with FastAPI streaming endpoints.

1|Updated Dec 2, 2025

Search Tags:#fastapi #rag #openai #qdrant #retrieval-augmented generation #document-ingestion

Ready to run

npx skills add https://github.com/MrOwaisAbdullah/ai-humanoid-robotics --skill rag-pipeline-builder

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-pipeline-builder
Source: https://github.com/MrOwaisAbdullah/ai-humanoid-robotics/tree/main/.claude/skills/rag-pipeline-builder
Command: npx skills add https://github.com/MrOwaisAbdullah/ai-humanoid-robotics --skill rag-pipeline-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tiktoken, qdrant-client, openai, and includes scripts (resource) components.

What problem does it solve?

This Skill provides a ready-to-use RAG pipeline builder to scaffold production-grade retrieval-augmented generation systems with a lightweight, LangChain-free stack, including document ingestion, vector storage, and FastAPI endpoints.

Core Features & Use Cases

Lightweight, LangChain-free RAG pipeline: chunking, embeddings, Qdrant storage, and streaming endpoints.
FastAPI-based API with OpenAI models for embeddings and generation, suitable for production deployment.
Ingestion and testing tooling to curate and verify document pipelines.

Quick Start

Install dependencies from templates/requirements.txt, configure environment variables, ingest your markdown docs into Qdrant, then run uvicorn on the provided FastAPI template.

Frequently Asked Questions about rag-pipeline-builder

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

FAQPage Schema

How do I build a RAG pipeline with FastAPI and Qdrant without using LangChain?▼

You can build a LangChain-free RAG pipeline by ingesting markdown documents, chunking content, generating OpenAI embeddings, storing vectors in Qdrant, and exposing FastAPI endpoints for streaming QA.

How do I ingest markdown documentation into a Qdrant vector database for retrieval?▼

To ingest markdown documentation into Qdrant, the pipeline chunks the content, generates embeddings using OpenAI, and stores the vectors directly in the Qdrant database for fast retrieval.

Can I use OpenAI models for both embeddings and generation in a production RAG API?▼

Yes, this RAG pipeline uses OpenAI models for both generating embeddings during ingestion and powering context-aware generation in the FastAPI streaming QA endpoints for production deployment.

What is the best way to chunk technical documentation for retrieval-augmented generation?▼

The best way to chunk technical documentation for retrieval-augmented generation is applying precise chunking logic directly during the markdown ingestion phase before generating OpenAI embeddings.

Do I need tiktoken to build a RAG pipeline with FastAPI and Qdrant?▼

Yes, you need the tiktoken dependency alongside qdrant-client and openai to handle tokenization and direct control over ingestion, retrieval, and generation logic in the FastAPI RAG pipeline.

Why use a LangChain-free architecture for retrieval-augmented generation?▼

A LangChain-free architecture provides direct control over ingestion, retrieval, and generation logic, offering a lightweight stack for production-grade retrieval-augmented generation without framework overhead.