building-rag-systems - Build Production RAG Systems
Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval using Qdrant and OpenAI.
Tags
Updated: 2026-09-24Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Split text by semantic boundaries
- Detect document changes via hash
- Generate batched embeddings using OpenAI
- Create Qdrant payload indexes
- Build multi-condition vector filters
- Expand context via chunk chain
- Retrieve ordered document chunks
Inputs
- Markdown documents
- Qdrant connection parameters
- OpenAI API key
- Search query filter parameters
Outputs
- Indexed vector collection in Qdrant
- Filtered document chunk search results
- Expanded context chunk sequences
Requirements
- Python 3 environment with qdrant-client, openai, pydantic, and python-frontmatter
- OpenAI API access
- Running Qdrant vector database server