mrowaisabdullah/ai-humanoid-robotics

Overview

This skill provides a complete, production-ready RAG (Retrieval-Augmented Generation) pipeline implementation that avoids LangChain and other heavy frameworks. It includes markdown-aware document chunking, OpenAI embedding generation, Qdrant vector storage, and FastAPI streaming endpoints for real-time responses. The design emphasizes minimal dependencies, control over ingestion and retrieval logic, and easy deployment.

How this skill works

Documents are preprocessed with a RecursiveTextSplitter that protects code blocks, then split by paragraphs, lines, and sentences to create semantically coherent chunks sized by token counts via tiktoken. Chunks are embedded using OpenAI's AsyncOpenAI client in batched calls and stored in Qdrant using the native qdrant-client. At query time, the system performs semantic similarity search in Qdrant, assembles context windows, and streams generated answers via FastAPI endpoints using a lightweight response generator.

When to use it

• Building high-performance RAG systems where you need full control over ingestion and retrieval logic.
• Implementing semantic search for technical documentation, code bases, or markdown-heavy content.
• Deploying a small-footprint service with faster startup and smaller Docker images.
• Creating streaming API endpoints for chat-like user experiences with low latency.
• Preferring explicit, debuggable pipeline steps rather than abstracted frameworks.

Best practices

• Protect code blocks and other special markup during chunking to preserve meaning and avoid breaking examples.
• Tune chunk_size and overlap based on your model context window; use tiktoken to count tokens, not characters.
• Batch embedding calls to OpenAI to reduce latency and cost; handle rate limits and retries.
• Use metadata on upserted points (source file, position, headings) to trace answers back to originals.
• Limit retrieved context by relevance score and token budget before sending to the generator to avoid context drift.

Example use cases

• Enterprise docs search that returns exact code snippets and contextual answers from internal Markdown manuals.
• Customer support assistant that retrieves relevant KB entries and streams concise answers to users.
• Developer tooling that searches across code samples, READMEs, and design docs to support pair-programming flows.
• Small-scale, private deployments where reducing third-party dependency surface is a priority.
• Prototyping a custom RAG stack quickly and then incrementally optimizing ingestion and retrieval logic.