AI SummaryA lightweight production-ready RAG system prompt that integrates contextual chunking and vector retrieval via Chroma, designed for developers building AI-enhanced code editors and IDEs. Developers working with Claude, ChatGPT, or Cursor who need retrieval-augmented generation without heavy infrastructure will benefit from this ready-to-deploy foundation.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to add the "RAG-prod — System Prompt" prompt rules to my project. Repository: https://github.com/MirxaWaqarBaig/RAG-prod Please read the repo to find the rules/prompt file, then: 1. Download it to the correct location (.cursorrules, .windsurfrules, .github/prompts/, or project root — based on the file type) 2. If there's an existing rules file, merge the new rules in rather than overwriting 3. Confirm what was added
Description
System Prompt for RAG-prod
System Prompt RAG (Prompt-Only)
A trimmed-down version of the production Graph RAG server focused on contextual chunking, Chroma vector retrieval, and a unified system-prompt generation path. Neo4j knowledge-graph features and SQL/business-data fusion are deliberately removed to keep the stack lightweight.
Contents
• core.py, core_hybrid.py, cache.py: Retrieval helpers, hybrid fusion, optional PostgreSQL cache manager. • optimized_chunking.py, local_qwen_llm.py: Contextual chunk pipeline and optional Qwen enhancer. • build_chroma_only.py: CLI to rebuild contextual chunks and refresh the Chroma index. • system_rag_service.py, system_rag_server.py: System-prompt RAG service and ZMQ entrypoint. • rag_system_prompt.md: System prompt used for both concise and detailed responses. • input/, input_chunked/, chromadb/, cache/: Document sources, generated chunks, vector store, and auxiliary cache data.
Getting Started
• Install dependencies: pip install -r requirements.txt • Populate input/ with .txt documents. • Run python build_chroma_only.py to generate contextual chunks and build the Chroma index. • Launch the server: python system_rag_server.py serve Environment variables can be stored in .env.example (copy to .env), e.g. LLM credentials. PostgreSQL cache is optional—if the connection fails the server simply operates without caching.
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Works With
Any AI assistant that accepts custom rules or system prompts