CodeR-RAG

Retrieval-Augmented Generation for Code Intelligence

CodeR-RAG is an end-to-end retrieval-augmented system built on top of CodeR, a state-of-the-art code embedding model trained using a massive synthetic dataset (CodeR-Pile). This framework brings high-accuracy code retrieval, multi-language code understanding, and task-aware augmentation directly to LLM-based developer tools.

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Features

State-of-the-Art Code Retrieval

Powered by CodeR embeddings, optimized for:

• Text2Code (e.g., natural language → code examples)
• Code2Text (summaries, explanations, API usage)
• Code2Code (similar code, translation, refinement)
• Hybrid retrieval (bug fixing, optimization, style, security)

Full RAG Pipeline

The system includes:

• Document ingestion + code parsing
• Embedding generation (instruction-aware InfoNCE)
• Vector storage (FAISS / LanceDB / Milvus)
• Query routing + retrieval
• Augmentation formatting
• LLM generation modules

Multi-language Support

CodeR-RAG supports retrieval across 20 programming languages, including: Python, Java, Go, C++, TypeScript, Rust, C#, PHP…

Synthetic Data Extensions

Includes tools and prompts to extend CodeR-Pile using the DRU principle:

• Diversity (multiple task types)
• Reliability (LLM and GPT-filtered)
• Usability (instruction-aware queries)

Modular and Production-Ready

Easily swap:

• Embedding models (CodeR or custom)
• LLMs for generation
• Vector DB backends
• Chunking & parsing strategies