Building a Codebase RAG System

Building a Codebase RAG System: From Theory to Practice

Have you ever wondered how modern IDEs like Cursor or GitHub Copilot understand your codebase so well? The answer lies in a powerful technique called Retrieval Augmented Generation (RAG), combined with sophisticated code parsing and semantic search. In this article, I'll walk you through building a codebase RAG system from scratch, sharing insights from my experience developing an intelligent code assistant that helps developers understand and navigate their codebases through natural language queries.

The Problem: Understanding Large Codebases

Modern software development often involves working with massive codebases. Whether you're:

  • Joining a new project and trying to understand its architecture
  • Debugging a complex issue across multiple files
  • Looking for specific functionality without knowing exact method names
  • Understanding how different components interact

Traditional methods like grep or IDE search fall short when dealing with semantic queries like "How does the authentication flow work?" or "Where is the payment processing logic implemented?"

Why Not Just Use GPT-4?

While GPT-4 is impressive, it has significant limitations when dealing with codebases:

  1. No Codebase Context: GPT-4 doesn't know about your specific codebase, its architecture, or implementation details.
  2. Hallucination Risk: Without proper grounding, it might generate plausible but incorrect answers.
  3. Limited Knowledge: It can't access your private code, custom libraries, or project-specific patterns.

The Solution: Codebase RAG

RAG solves these problems by:

  1. Indexing your codebase intelligently
  2. Retrieving relevant context for queries
  3. Using that context to generate accurate, grounded responses

Let's break down how to build this system.

Part 1: Codebase Indexing

The Challenge of Code Chunking

Unlike regular text, code has a specific structure and syntax. Simple chunking strategies like fixed-size windows or paragraph-based splitting don't work well. We need to preserve code's semantic meaning.

Consider this Python code:

class UserManager:
    def __init__(self, db_connection):
        self.db = db_connection

    def authenticate_user(self, username, password):
        user = self.db.find_user(username)
        if user and user.verify_password(password):
            return user
        return None

If we chunk this naively, we might split it like this:

# Chunk 1
class UserManager:
    def __init__(self, db_connection):
        self.db = db_connection

# Chunk 2
    def authenticate_user(self, username, password):
        user = self.db.find_user(username)
        if user and user.verify_password(password):
            return user
        return None

This breaks the semantic meaning. The authenticate_user method loses its connection to the UserManager class.

Enter Tree-sitter

Tree-sitter, a parser generator tool, solves this problem by understanding code structure. It creates an Abstract Syntax Tree (AST) that preserves the hierarchical relationships in your code.

Here's how we use tree-sitter in our implementation:

from tree_sitter_languages import get_parser

def extract_code_units(code, language):
    parser = get_parser(language)
    tree = parser.parse(bytes(code, "utf8"))

    # Define queries for different code constructs
    class_query = """
        (class_definition
            name: (identifier) @class.name
            body: (block) @class.body
        ) @class.def
    """

    method_query = """
        (function_definition
            name: (identifier) @method.name
            parameters: (parameters) @method.params
            body: (block) @method.body
        ) @method.def
    """

    # Extract meaningful code units
    units = []
    for node in tree.root_node.children:
        if node.type == "class_definition":
            units.append({
                "type": "class",
                "name": node.child_by_field_name("name").text.decode(),
                "code": node.text.decode()
            })
        elif node.type == "function_definition":
            units.append({
                "type": "method",
                "name": node.child_by_field_name("name").text.decode(),
                "code": node.text.decode()
            })

    return units

Building Code References

Understanding how code elements are used across the codebase is crucial. We track references like this:

def find_references(codebase, symbol_name):
    references = []
    for file_path, code in codebase.items():
        tree = parser.parse(bytes(code, "utf8"))

        # Find all occurrences of the symbol
        for node in tree.root_node.children:
            if node.type == "identifier" and node.text.decode() == symbol_name:
                # Get the surrounding context
                parent = node.parent
                references.append({
                    "file": file_path,
                    "line": node.start_point[0],
                    "context": parent.text.decode()
                })

    return references

Part 2: Advanced Retrieval Techniques

Embeddings and Vector Search

Once we have our code chunks, we need to make them searchable. We use embeddings to convert code into vectors that capture semantic meaning.

from sentence_transformers import SentenceTransformer

def create_embeddings(code_units):
    model = SentenceTransformer('all-MiniLM-L6-v2')

    embeddings = []
    for unit in code_units:
        # Combine code with any documentation
        text = f"{unit['code']}\n{unit.get('doc', '')}"
        embedding = model.encode(text)
        embeddings.append({
            "unit": unit,
            "embedding": embedding
        })

    return embeddings

Improving Search Quality

Simple vector search isn't always enough. We implement several techniques to improve results:

  1. HyDE (Hypothetical Document Embeddings)
def generate_hyde_query(original_query, llm):
    prompt = f"""
    Given the query: "{original_query}"
    Generate a code snippet or technical description that would answer this query.
    Focus on implementation details, method names, and class structures.
    """
    return llm.generate(prompt)
  1. Hybrid Search
def hybrid_search(query, codebase, vector_db, bm25_index):
    # Get semantic search results
    semantic_results = vector_db.search(query, k=10)

    # Get keyword search results
    keyword_results = bm25_index.search(query, k=10)

    # Combine and rerank results
    combined_results = merge_results(semantic_results, keyword_results)
    return rerank_results(query, combined_results)
  1. Reranking with Cross-Encoders
from sentence_transformers import CrossEncoder

def rerank_results(query, results):
    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')

    # Prepare pairs for scoring
    pairs = [[query, result['text']] for result in results]

    # Get relevance scores
    scores = cross_encoder.predict(pairs)

    # Sort results by score
    scored_results = list(zip(results, scores))
    scored_results.sort(key=lambda x: x[1], reverse=True)

    return [result for result, score in scored_results]

Putting It All Together

The complete system flow looks like this:

  1. Indexing Phase:

    • Parse codebase using tree-sitter
    • Extract meaningful code units
    • Generate embeddings
    • Store in vector database

  2. Query Phase:

    • Generate HyDE query
    • Perform hybrid search
    • Rerank results
    • Generate response using LLM

Here's a simplified version of the main query handler:

async def handle_code_query(query: str, codebase: Codebase):
    # Generate HyDE query
    hyde_query = generate_hyde_query(query)

    # Perform hybrid search
    results = await hybrid_search(hyde_query, codebase)

    # Prepare context for LLM
    context = prepare_context(results)

    # Generate response
    response = await generate_response(query, context)

    return response

Lessons Learned

  1. Code Structure Matters: Preserving code structure during chunking is crucial for meaningful search results.
  2. Multiple Search Strategies: Combining semantic and keyword search provides better results than either alone.
  3. Context is Key: The quality of LLM responses heavily depends on the quality of retrieved context.
  4. Performance Trade-offs: There's always a balance between search quality and response time.

Future Improvements

  1. Incremental Indexing: Update indexes efficiently as code changes
  2. Better Embeddings: Fine-tune embeddings specifically for code
  3. Caching: Implement smart caching for frequent queries
  4. Evaluation Metrics: Develop better ways to measure search quality

Resources and References

Conclusion

Building a codebase RAG system is challenging but rewarding. The combination of tree-sitter for code parsing, embeddings for semantic search, and advanced retrieval techniques creates a powerful tool for understanding and navigating codebases.

The field is evolving rapidly, with new techniques and models emerging regularly. I encourage you to experiment with these concepts and contribute to the growing body of knowledge in this area.

Remember: The goal isn't just to build a tool that answers questions about code - it's to make codebases more accessible and understandable for developers, ultimately leading to better software development practices.

This article is based on my experience building an intelligent code assistant and incorporates insights from various sources including academic papers, technical blogs, and discussions with fellow developers. The examples and code snippets are simplified for clarity but demonstrate the core concepts.