Rag for code documentation

Skill Goodnight77/rag-skills/skills/data-type-handling/rag-for-code-documentation

Handle code-aware retrieval by preserving symbols, file structure, and API context.From its SKILL.md

Install

npx -y skills add Goodnight77/rag-skills --skill rag-for-code-documentation

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SKILL.md

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Overview

RAG for code documentation requires specialized handling due to code's structured nature, syntax-specific patterns, and the importance of preserving function signatures, imports, and contextual relationships. This skill covers embedding code snippets, handling API references, and retrieving code-aware context.

Problem Statement

Generic RAG approaches struggle with code-related queries:

Standard chunking breaks function/class structures mid-signature
Language models don't understand code syntax and semantics as well as natural language
Code requires preserving structural relationships (imports, dependencies, inheritance)
API queries need exact matches alongside semantic understanding
Code examples need context to be useful (imports, dependencies)

Key Concepts

Code-Aware Chunking: Preserving function/class boundaries and syntactic units
Code-Specific Embeddings: Models trained on code (CodeBERT, StarCoder) vs. general embeddings
Syntax Preservation: Maintaining formatting, indentation, and structure
Context Tracking: Following import chains and dependency relationships
Hybrid Search for Code: Combining semantic search with AST-based matching

Implementation Guide

Step 1: Code-Aware Document Parsing

Parse code files into structured chunks that preserve syntactic units.

Why: AST-based parsing preserves code structure that would be lost with generic text chunking.

Step 2: Code-Specific Embedding

Use embeddings designed for code or augment text embeddings with code-aware features.

Why: Code-specific embeddings capture semantic meaning in code (function relationships, patterns) that general embeddings miss.

Step 2.5: Store Code Chunks in Vector DB

Store code chunks with rich metadata for effective retrieval.

Why: Rich metadata enables filtering by language, type, file path, and other code-specific attributes.

Step 3: Implement Code-Aware Search

Search with code-specific considerations.

Why: Code-aware search combines semantic understanding with precise filtering for programming-specific use cases.

Step 4: Handle API Documentation

Specialized handling for API reference queries.

Why: API queries often require exact matching (function/class names) rather than pure semantic search.

Practical references include the CodeBERT paper, GraphCodeBERT paper, StarCoder paper, and GitHub code search.

When to Use This Skill

Building code search or documentation assistants
When users query code syntax, APIs, or programming concepts
For IDE integrations and code completion systems
When building technical documentation with code examples
For troubleshooting programming queries

When NOT to Use This Skill

For natural language-only content (use general RAG)
When code structure isn't important for retrieval
For very simple codebases where grep suffices
When embedding code-specific models is not feasible

Related Skills

Semantic Chunking - For document chunks
Choosing Vector DB by Datatype - Database selection
Hierarchical Chunking - For nested code structures

Metrics & Success Criteria

Code Retrieval Accuracy: Exact matches for function/class names > 90%
Semantic Understanding: NDCG@10 for code-related queries > 0.7
Context Preservation: Relevant imports included > 80% of time
Syntax Preservation: Code formatting maintained 100%
Query Latency: < 200ms for typical code queries