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ChunkHound

Under the Hood

Architecture Overview

Section titled “Architecture Overview”

ChunkHound uses a local-first architecture with embedded databases and universal code parsing. The system is built around the cAST (Chunking via Abstract Syntax Trees) algorithm for intelligent code segmentation:

Database Layer

DuckDB (primary) - OLAP columnar database with HNSW vector indexing LanceDB (experimental) - Purpose-built vector database with Apache Arrow format

Parsing Engine

Tree-sitter - Universal AST parser supporting 20+ languages Language-agnostic - Same semantic concepts across all languages

Flexible Providers

Pluggable backends - OpenAI, VoyageAI, Ollama Cloud & Local - Run with APIs or fully offline with local models

Advanced Algorithms

cAST - Semantic code chunking preserving AST structure Two-Hop Search - Context-aware search with reranking

ChunkHound’s local-first architecture provides key advantages: Privacy - Your code never leaves your machine. Speed - No network latency or API rate limits. Reliability - Works offline and in air-gapped environments. Cost - No per-token charges for indexing large codebases.

The cAST Algorithm

Section titled “The cAST Algorithm”

When AI assistants search your codebase, they need code split into “chunks” - searchable pieces small enough to understand but large enough to be meaningful. The challenge: how do you split code without breaking its logic?

Research Foundation: ChunkHound implements the cAST (Chunking via Abstract Syntax Trees) algorithm developed by researchers at Carnegie Mellon University and Augment Code. This approach demonstrates significant improvements in code retrieval and generation tasks.

Three Approaches Compared

Section titled “Three Approaches Compared”

1. Naive Fixed-Size Chunking

Split every 1000 characters regardless of code structure:

def authenticate_user(username, password):
    if not username or not password:
        return False


    hashed = hash_password(password)
    user = database.get_u
# CHUNK BOUNDARY CUTS HERE ❌
ser(username)
    return user and user.password_hash == hashed

Problem: Functions get cut in half, breaking meaning.

2. Naive AST Chunking

Split only at function/class boundaries:

# Chunk 1: Tiny function (50 characters)
def get_name(self):
    return self.name


# Chunk 2: Massive function (5000 characters)
def process_entire_request(self, request):
    # ... 200 lines of complex logic ...

Problem: Creates chunks that are too big or too small.

3. Smart cAST Algorithm (ChunkHound’s Solution)

Respects code boundaries AND enforces size limits:

# Right-sized chunks that preserve meaning
def authenticate_user(username, password):    # ✅ Complete function
    if not username or not password:          #    fits in one chunk
        return False
    hashed = hash_password(password)
    user = database.get_user(username)
    return user and user.password_hash == hashed


def hash_password(password):                  # ✅ Small adjacent functions
def validate_email(email):                   #    merged together
def sanitize_input(data):
    # All fit together in one chunk

How cAST Works

Section titled “How cAST Works”

The algorithm is surprisingly simple:

  1. Parse code into a syntax tree (AST) using Tree-sitter
  2. Walk the tree top-down (classes → functions → statements)
  3. For each piece:
    • If it fits in size limit (1200 chars) → make it a chunk
    • If too big → split at smart boundaries (;, }, line breaks)
    • If too small → merge with neighboring pieces
  4. Result: Every chunk is meaningful code that fits in context window

Performance: The research paper shows cAST provides 4.3 point gain in Recall@5 on RepoEval retrieval and 2.67 point gain in Pass@1 on SWE-bench generation tasks.

Why This Matters for AI

Section titled “Why This Matters for AI”
  • Better Search: Find complete functions, not fragments
  • Better Context: AI sees full logic flow, not half-statements
  • Better Results: AI gives accurate suggestions based on complete code understanding
  • Research-Backed: Peer-reviewed algorithm with proven performance gains

Traditional chunking gives AI puzzle pieces. cAST gives it complete pictures.

Learn More: Read the full cAST research paper for implementation details and benchmarks.

Semantic Search Architecture

Section titled “Semantic Search Architecture”

ChunkHound provides two search modes depending on your embedding provider’s capabilities. The system uses vector embeddings from providers like OpenAI, VoyageAI, or local models via Ollama.

Section titled “Regular Semantic Search”

The standard approach used by most embedding providers:

Query
"database timeout"
↓
Embedding
[0.2, -0.1, 0.8, ...]
↓
Search
Find nearest neighbors in vector space
↓
Results
SQL connection timeout
DB retry logic
Connection pool config

How it works:

  1. Convert query to embedding vector
  2. Search the vector index for nearest neighbors
  3. Return top-k most similar code chunks
Section titled “Two-Hop Semantic Search”

Advanced search for providers with reranking (VoyageAI, custom servers):

Query
"user authentication"
↓
Stage 1: Get immediate candidates
Embedding
[0.2, -0.1, 0.8, ...]
↓
Search
Find nearest neighbors in vector space
↓
Initial Results
validateUser()
checkAuth()
loginHandler()
↓
Stage 2: Semantic expansion
Find Neighbors
For each top result, find semantically similar chunks
↓
Expanded Set
validateUser()
checkAuth()
loginHandler()
hashPassword()
generateToken()
createSession()
getUserProfile()
↓
Stage 3: Rerank against original query
Rerank
Sort by relevance to original query
↓
Final Results
validateUser()
hashPassword()
checkAuth()
createSession()

Why it’s better:

  • Semantic bridging: Discovers related concepts through intermediate connections
  • Example: Search “authentication” → finds validateLogin() → discovers related hashPassword() through semantic similarity
  • Context expansion: Finds supporting functions you might not think to search for
  • Research foundation: Based on advanced retrieval techniques in RAG systems

Learn More

Section titled “Learn More”

Research & Documentation

Section titled “Research & Documentation”

Database Technologies

Section titled “Database Technologies”

Embedding Providers

Section titled “Embedding Providers”