Custom Code RAG

Retrieval-Augmented Generation (RAG) is a technique that enhances AI models by retrieving relevant information before generating responses. ByteBuddy's Custom Code RAG feature allows you to optimize how the AI understands and interacts with your codebase.

Understanding Code RAG

What is RAG?

RAG combines two components:

Retrieval: Finding relevant information from your codebase
Generation: Using that information to generate better responses

How Code RAG Works in ByteBuddy

ByteBuddy's Code RAG process:

Context Analysis: Analyze the current code context
Relevant Code Retrieval: Find related code segments
Documentation Integration: Include relevant documentation
Enhanced Response Generation: Generate informed responses

Basic RAG Configuration

Enabling Code RAG

Enable RAG in your configuration:

yaml

# .bytebuddy/config.yaml
rag:
  enabled: true
  codeRag:
    enabled: true
    maxContextTokens: 8192
    similarityThreshold: 0.7

Simple Code RAG Setup

Basic configuration for getting started:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    sources:
      - type: "localFiles"
        paths:
          - "src/**/*.js"
          - "src/**/*.ts"
        exclude:
          - "node_modules/**"
          - "dist/**"
          - "*.test.*"

Advanced RAG Configuration

Multiple Data Sources

Configure multiple sources for RAG:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    sources:
      # Local source code
      - type: "localFiles"
        name: "source-code"
        paths:
          - "src/**/*.js"
          - "src/**/*.ts"
        exclude:
          - "node_modules/**"
          - "dist/**"
        weight: 0.8 # Higher priority

      # Documentation files
      - type: "localFiles"
        name: "documentation"
        paths:
          - "docs/**/*.md"
          - "README.md"
        weight: 0.6

      # Test files
      - type: "localFiles"
        name: "tests"
        paths:
          - "tests/**/*.js"
          - "tests/**/*.ts"
        weight: 0.4

      # External API documentation
      - type: "http"
        name: "api-docs"
        url: "https://api.company.com/docs"
        format: "json"
        weight: 0.5

Custom Retrieval Strategies

Define how code is retrieved and ranked:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    retrievalStrategy:
      type: "hybrid"
      components:
        - type: "semantic"
          weight: 0.7
          model: "embeddings"

        - type: "lexical"
          weight: 0.3
          algorithm: "bm25"

    ranking:
      algorithm: "rrf" # Reciprocal Rank Fusion
      weights:
        semantic: 0.7
        lexical: 0.3

Semantic Code Indexing

Embedding Models

Configure embedding models for semantic search:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    embeddings:
      model: "openai/text-embedding-ada-002"
      apiKey: "${OPENAI_API_KEY}"
      batchSize: 100
      cacheEnabled: true

    # Alternative: local embeddings
    # embeddings:
    #   model: "sentence-transformers/all-MiniLM-L6-v2"
    #   type: "local"

Indexing Configuration

Control how code is indexed:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    indexing:
      chunkSize: 1000
      overlap: 200
      maxFileSize: "1MB"
      updateFrequency: "1h"

    preprocessing:
      removeComments: false
      extractFunctions: true
      extractClasses: true
      extractVariables: false

Custom RAG Pipelines

Defining Pipelines

Create custom RAG pipelines for specific use cases:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    pipelines:
      - name: "code-completion"
        description: "Pipeline for code completion tasks"
        stages:
          - name: "context-extraction"
            action: "extractContext"
            parameters:
              includeSurroundingLines: 50

          - name: "relevant-code-retrieval"
            action: "retrieveRelevantCode"
            parameters:
              maxResults: 10
              similarityThreshold: 0.8

          - name: "documentation-retrieval"
            action: "retrieveDocumentation"
            parameters:
              maxResults: 5

          - name: "context-assembly"
            action: "assembleContext"
            parameters:
              maxTokens: 4096

Pipeline Routing

Route different queries to different pipelines:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    pipelineRouting:
      - condition:
          queryType: "code-completion"
        pipeline: "code-completion"

      - condition:
          queryType: "bug-fixing"
        pipeline: "bug-fixing-pipeline"

      - condition:
          queryType: "refactoring"
        pipeline: "refactoring-pipeline"

      - condition:
          default: true
        pipeline: "general-purpose"

Performance Optimization

Caching Strategies

Optimize RAG performance with caching:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    caching:
      enabled: true
      ttl: "24h"
      maxSize: "1GB"

      layers:
        - type: "memory"
          maxSize: "100MB"

        - type: "disk"
          path: "~/.bytebuddy/cache/rag"
          maxSize: "1GB"

        - type: "remote"
          url: "redis://localhost:6379"
          enabled: false

Resource Management

Control resource usage:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    resources:
      maxConcurrency: 4
      timeout: 30
      memoryLimit: "512MB"

    rateLimiting:
      enabled: true
      requestsPerMinute: 60
      burstLimit: 10

Integration with Other Features

RAG and Continuous AI

Combine RAG with Continuous AI:

yaml

continuousAI:
  enabled: true
  workflows:
    - name: "smart-code-review"
      actions:
        - action: "reviewCode"
          useRag: true
          ragPipeline: "code-review-pipeline"

rag:
  enabled: true
  codeRag:
    enabled: true
    pipelines:
      - name: "code-review-pipeline"
        stages:
          - name: "similar-code-finding"
            action: "findSimilarCode"

          - name: "best-practices-retrieval"
            action: "retrieveBestPractices"

          - name: "anti-patterns-check"
            action: "checkAntiPatterns"

RAG and Rules

Enhance rules with RAG context:

yaml

rules:
  - name: "security-review"
    prompt: |
      Review this code for security issues, considering:
      {{rag_context.security_patterns}}

      Common vulnerabilities to check:
      {{rag_context.common_vulnerabilities}}

    useRag: true
    ragContext:
      - "security_patterns"
      - "common_vulnerabilities"
      - "framework_security_guidelines"

Custom Processors

Pre-processing

Add custom pre-processing steps:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    preprocessors:
      - name: "code-normalizer"
        type: "custom"
        script: "./scripts/normalize-code.js"

      - name: "comment-remover"
        type: "builtin"
        action: "removeComments"

      - name: "framework-identifier"
        type: "custom"
        script: "./scripts/identify-framework.js"

Post-processing

Add custom post-processing steps:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    postprocessors:
      - name: "relevance-filter"
        type: "custom"
        script: "./scripts/filter-by-relevance.js"
        parameters:
          minScore: 0.7

      - name: "duplicate-remover"
        type: "builtin"
        action: "removeDuplicates"

      - name: "format-enforcer"
        type: "custom"
        script: "./scripts/enforce-format.js"

Example Configurations

Web Development RAG Setup

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    sources:
      - type: "localFiles"
        name: "frontend-code"
        paths:
          - "src/components/**/*.jsx"
          - "src/pages/**/*.jsx"
          - "src/hooks/**/*.js"
        exclude:
          - "node_modules/**"
          - "build/**"
        weight: 0.9

      - type: "localFiles"
        name: "backend-code"
        paths:
          - "src/controllers/**/*.js"
          - "src/models/**/*.js"
          - "src/routes/**/*.js"
        weight: 0.8

      - type: "localFiles"
        name: "documentation"
        paths:
          - "docs/**/*.md"
          - "README.md"
          - "SPECIFICATION.md"
        weight: 0.7

    embeddings:
      model: "openai/text-embedding-ada-002"
      apiKey: "${OPENAI_API_KEY}"

    indexing:
      chunkSize: 800
      overlap: 100

    pipelines:
      - name: "react-development"
        stages:
          - name: "component-finder"
            action: "findSimilarComponents"

          - name: "hook-recommender"
            action: "recommendHooks"

          - name: "best-practices"
            action: "retrieveReactBestPractices"

Data Science RAG Setup

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    sources:
      - type: "localFiles"
        name: "notebooks"
        paths:
          - "notebooks/**/*.ipynb"
          - "scripts/**/*.py"
        weight: 0.9

      - type: "localFiles"
        name: "data-pipelines"
        paths:
          - "src/data/**/*.py"
          - "src/models/**/*.py"
        weight: 0.8

      - type: "http"
        name: "ml-documentation"
        url: "https://scikit-learn.org/stable/modules/"
        format: "html"
        weight: 0.6

    embeddings:
      model: "sentence-transformers/all-MiniLM-L6-v2"
      type: "local"

    preprocessing:
      extractFunctions: true
      extractClasses: true
      extractDocstrings: true

    pipelines:
      - name: "ml-model-development"
        stages:
          - name: "algorithm-finder"
            action: "findSimilarAlgorithms"

          - name: "parameter-suggestions"
            action: "suggestParameters"

          - name: "evaluation-methods"
            action: "retrieveEvaluationMethods"

Monitoring and Analytics

RAG Metrics

Track RAG performance:

yaml

rag:
  enabled: true
  codeRag:
    enabled: true
    analytics:
      enabled: true
      metrics:
        - "retrievalAccuracy"
        - "generationQuality"
        - "responseTime"
        - "cacheHitRate"

      logging:
        level: "info"
        outputFile: "~/.bytebuddy/logs/rag.log"

      dashboards:
        enabled: true
        port: 8080

Performance Profiling

Profile RAG performance:

bash

# Profile RAG operations
bytebuddy profile --rag

# View detailed metrics
bytebuddy metrics --rag

# Generate performance report
bytebuddy report --rag --output rag-performance.md

Troubleshooting

Common Issues

Poor Retrieval Quality

Check embedding model quality
Adjust similarity thresholds
Review chunking strategy
Verify data sources are properly indexed

Slow Performance

Enable caching
Reduce max results
Optimize chunk sizes
Check resource limits

Missing Context

Expand data sources
Adjust retrieval strategies
Fine-tune preprocessing
Review exclusion patterns

Debugging Commands

bash

# Test RAG retrieval
bytebuddy rag test --query "how to handle user authentication"

# View indexed documents
bytebuddy rag index list

# Rebuild index
bytebuddy rag index rebuild

# Check embedding quality
bytebuddy rag embeddings test

Best Practices

Indexing Strategy

Chunk Appropriately: Balance chunk size for context vs. specificity
Update Regularly: Keep indices fresh with regular updates
Exclude Noise: Remove irrelevant files from indexing
Prioritize Sources: Weight important sources higher

Performance Optimization

Use Caching: Enable multi-layer caching
Limit Results: Retrieve only necessary information
Preprocess Data: Clean and normalize data before indexing
Monitor Resources: Keep track of memory and CPU usage

Quality Assurance

Test Retrieval: Regularly test retrieval accuracy
Review Results: Manually review generated responses
Gather Feedback: Collect user feedback on RAG quality
Iterate Improvements: Continuously refine RAG configuration

Next Steps

After configuring Custom Code RAG, explore these related guides:

Continuous AI - Automate development workflows
Plan Mode Guide - Complex task planning and execution
Understanding Configs - Deep dive into all configuration options

Custom Code RAG ​

Understanding Code RAG ​

What is RAG? ​

How Code RAG Works in ByteBuddy ​

Basic RAG Configuration ​

Enabling Code RAG ​

Simple Code RAG Setup ​

Advanced RAG Configuration ​

Multiple Data Sources ​

Custom Retrieval Strategies ​

Semantic Code Indexing ​

Embedding Models ​

Indexing Configuration ​

Custom RAG Pipelines ​

Defining Pipelines ​

Pipeline Routing ​

Performance Optimization ​

Caching Strategies ​

Resource Management ​

Integration with Other Features ​

RAG and Continuous AI ​

RAG and Rules ​

Custom Processors ​

Pre-processing ​

Post-processing ​

Example Configurations ​

Web Development RAG Setup ​

Data Science RAG Setup ​

Monitoring and Analytics ​

RAG Metrics ​

Performance Profiling ​

Troubleshooting ​

Common Issues ​

Poor Retrieval Quality ​

Slow Performance ​

Missing Context ​

Debugging Commands ​

Best Practices ​

Indexing Strategy ​

Performance Optimization ​

Quality Assurance ​

Next Steps ​

Custom Code RAG

Understanding Code RAG

What is RAG?

How Code RAG Works in ByteBuddy

Basic RAG Configuration

Enabling Code RAG

Simple Code RAG Setup

Advanced RAG Configuration

Multiple Data Sources

Custom Retrieval Strategies

Semantic Code Indexing

Embedding Models

Indexing Configuration

Custom RAG Pipelines

Defining Pipelines

Pipeline Routing

Performance Optimization

Caching Strategies

Resource Management

Integration with Other Features

RAG and Continuous AI

RAG and Rules

Custom Processors

Pre-processing

Post-processing

Example Configurations

Web Development RAG Setup

Data Science RAG Setup

Monitoring and Analytics

RAG Metrics

Performance Profiling

Troubleshooting

Common Issues

Poor Retrieval Quality

Slow Performance

Missing Context

Debugging Commands

Best Practices

Indexing Strategy

Performance Optimization

Quality Assurance

Next Steps