chroma-ingestion 0.2.3

Semantic-aware code ingestion and retrieval for ChromaDB

Chroma Code Ingestion System

Intelligently extract, chunk, and store code/documents in Chroma Cloud using semantic-aware text splitting. This system is designed to prepare code repositories for AI agent retrieval and context generation.

📚 Full Documentation | 🚀 Quick Start | 📖 API Reference | 🤝 Contributing

Features

✅ Code-Aware Splitting: Uses RecursiveCharacterTextSplitter to preserve semantic structure (classes, functions, sections) ✅ Multi-Format Support: Python files, Markdown, Agent definitions, Prompts ✅ Batch Processing: Efficiently handles large codebases with intelligent batching ✅ Metadata Tracking: Maintains source file info, chunk indices, and file types ✅ Chroma Cloud Integration: Directly stores chunks in Chroma Cloud for semantic search ✅ Verification Tools: Built-in query testing and data quality validation

Quick Start

Installation

Install from PyPI:

pip install chroma-ingestion

Or install from source with development dependencies:

git clone https://github.com/chroma-core/chroma-ingestion.git
cd chroma-ingestion
pip install -e ".[dev]"

Basic Usage

Command-Line Interface

# Ingest code files into ChromaDB
chroma-ingest /path/to/code --collection my_collection

# Search ingested code
chroma-ingest search "authentication patterns" --collection my_collection

# List available collections
chroma-ingest list-collections

Python API

from chroma_ingestion import CodeIngester, CodeRetriever

# Ingest code files
ingester = CodeIngester(
    target_folder="/path/to/code",
    collection_name="my_collection"
)
ingester.ingest_files()

# Query ingested code
retriever = CodeRetriever("my_collection")
results = retriever.query("How do we handle errors?", n_results=3)

for result in results:
    print(f"Source: {result['metadata']['filename']}")
    print(f"Content: {result['document']}")

Project Structure

chroma/
├── src/
│   └── chroma_ingestion/
│       ├── __init__.py         # Public API exports
│       ├── cli.py              # Command-line interface
│       ├── config.py           # Configuration management
│       ├── ingestion/          # Ingestion module
│       ├── retrieval/          # Retrieval module
│       └── clients/            # Client management
├── tests/                      # Comprehensive test suite
├── examples/                   # Example scripts
├── docs/                       # Documentation
├── pyproject.toml             # Project configuration
├── CHANGELOG.md               # Release notes
└── README.md                  # This file

Installation & Configuration

From PyPI

pip install chroma-ingestion

From Source

git clone https://github.com/chroma-core/chroma-ingestion.git
cd chroma-ingestion
pip install -e ".[dev]"

Dependencies

All dependencies are automatically installed:

• chromadb>=1.3.5 - Vector database
• langchain-text-splitters>=1.0.0 - Semantic text splitting
• python-dotenv>=1.0.0 - Environment variable management
• pyyaml>=6.0 - Configuration
• click>=8.0 - CLI framework

Configuration

For Chroma Cloud:

export CHROMA_HOST=api.chroma.com
export CHROMA_PORT=443
# Or set credentials in .env file

For local development:

# No configuration needed - uses localhost:9500 by default
chroma run

Configuration

Usage

Command-Line Interface (New!)

# Ingest code files
chroma-ingest /path/to/code --collection my_collection

# Ingest with custom chunk size
chroma-ingest /path/to/code --collection my_collection --chunk-size 1500

# Search ingested code
chroma-ingest search "authentication patterns" -n 5 --collection my_collection

# List available collections
chroma-ingest list-collections

# Reset client connection
chroma-ingest reset-client

Python API

Basic Ingestion

from chroma_ingestion import CodeIngester

ingester = CodeIngester(
    target_folder="/path/to/code",
    collection_name="my_collection",
    chunk_size=1000,
    chunk_overlap=200
)

files_processed, chunks_ingested = ingester.ingest_files()
print(f"Processed {files_processed} files, created {chunks_ingested} chunks")

Searching Ingested Code

from chroma_ingestion import CodeRetriever

retriever = CodeRetriever("my_collection")

# Semantic search
results = retriever.query("How do we handle errors?", n_results=3)

for result in results:
    print(f"File: {result['metadata']['filename']}")
    print(f"Relevance: {result['distance']:.3f}")
    print(f"Content: {result['document'][:200]}...")

Basic Ingestion (Deprecated - Use CLI Instead)

The old python ingest.py approach is deprecated. Use the new CLI:

chroma-ingest /path/to/your/folder --collection my_collection

Custom Folder

Ingest a different folder:

chroma-ingest /path/to/your/folder --collection my_collection

With Verification

Run ingestion and automatically verify data quality:

chroma-ingest /path/to/your/folder --verify

Advanced Options

chroma-ingest /path/to/code \
  --collection agents_context \
  --chunk-size 1500 \
  --chunk-overlap 300

Options:

• --collection: Chroma collection name (default: agents_context)
• --chunk-size: Tokens per chunk (default: 1000)
• --chunk-overlap: Token overlap between chunks (default: 200)

Ingestion Workflow

Step 1: File Discovery

Scans target folder recursively for matching files:

• *.py (Python files)
• *.md (Markdown)
• *.agent.md (Agent definitions)
• *.prompt.md (Prompt templates)

Step 2: Semantic Chunking

Uses RecursiveCharacterTextSplitter to create chunks:

• Preserves semantic structure (sections, code blocks, definitions)
• Maintains configurable overlap for context preservation
• Intelligent splitting at logical boundaries

Step 3: Metadata Enhancement

Each chunk stores:

• source - Full file path
• filename - Base filename
• chunk_index - Position in file
• folder - Parent directory
• file_type - Extension (.py, .md, .agent.md, etc.)

Step 4: Batch Upload

Uploads to Chroma Cloud in batches:

• Default batch size: 100 chunks
• Prevents memory limits
• Shows progress for large ingestions

Example: Query Ingested Data

After ingestion, query the data:

from src.retrieval import CodeRetriever

retriever = CodeRetriever("agents_context")

# Search for error handling patterns
results = retriever.query("How do agents handle errors?", n_results=3)

for result in results:
    print(f"Source: {result['metadata']['filename']}")
    print(f"Distance: {result['distance']:.4f}")
    print(f"Preview: {result['document'][:200]}...")
    print()

Data Quality Verification

The system includes built-in verification:

python ingest.py --verify

This runs test queries to ensure:

• ✅ Chunks are properly stored in Chroma
• ✅ Semantic search retrieves relevant results
• ✅ Metadata is correctly preserved
• ✅ Distance scores indicate relevance (< 0.8 excellent, 0.8-1.2 acceptable)

Example verification output:

Collection: agents_context
Total chunks: 311

Query 1: How do agents handle errors and exceptions?
  Result 1: adr-generator.agent.md (distance: 0.95) ✅ Good
  Result 2: WinFormsExpert.agent.md (distance: 1.08) ✅ Okay

Query 2: What are the main classes and functions?
  Result 1: security-engineer.prompt.md (distance: 1.15) ✅ Okay
  ...

Advanced Usage

Custom File Patterns

Ingest specific file types:

from src.ingestion import CodeIngester

# Only Python files
ingester = CodeIngester(
    target_folder="/path/to/code",
    collection_name="python_only",
    file_patterns=["**/*.py"]
)

# Agent definitions only
ingester = CodeIngester(
    target_folder="/path/to/code",
    collection_name="agents_only",
    file_patterns=["**/*.agent.md"]
)

Chunking Strategy Tuning

Adjust chunk size based on your use case:

# Large, detailed chunks (fewer, longer contexts)
python ingest.py --chunk-size 2000 --chunk-overlap 400

# Small, focused chunks (more granular retrieval)
python ingest.py --chunk-size 500 --chunk-overlap 100

Guidelines:

• chunk_size: ~250 tokens = brief context, ~1000 = full function, ~2000+ = multi-function
• chunk_overlap: 10-20% of chunk_size prevents cutting logic mid-statement

Filter by Source

Retrieve chunks from specific files:

from src.retrieval import CodeRetriever

retriever = CodeRetriever("agents_context")

# Get all chunks from one agent
backend_chunks = retriever.get_by_source("backend-architect.prompt.md")

for chunk in backend_chunks:
    print(chunk["document"])

Performance Characteristics

Ingestion Performance

For the default vibe-tools/ghc_tools/agents folder (23 files, 311 chunks):

• Time: ~3-5 seconds
• Throughput: ~100 chunks per batch
• Storage: ~1-2MB in Chroma Cloud (after embedding)

Query Performance

Semantic search in Chroma Cloud:

• Latency: ~200-500ms per query (actual: ~78ms in testing)
• Accuracy: Improves with detailed queries
• Distance Threshold: 0-2.0 range, calibrated expectations:
  • < 0.8: Excellent match
  • 0.8-1.0: Good match
  • 1.0-1.2: Acceptable match

  • 1.2: Poor match

Troubleshooting

No files found

❌ No matching files found in: /path/to/folder

Solution: Check folder path and file patterns. Verify files exist:

ls /path/to/folder/**/*.md

Chroma Cloud authentication error

❌ Could not authenticate with Chroma Cloud

Solution: Verify credentials in .env:

grep CHROMA .env

Memory errors during large ingestions

Solution: Reduce batch size or chunk size:

python ingest.py --chunk-size 500

Poor retrieval quality

Solution:

1. Check distance scores (< 0.8 excellent, < 1.0 good, < 1.2 acceptable, > 1.2 poor)
2. Adjust chunk size (too small loses context, too large loses precision)
3. Use more specific queries (simplify multi-concept queries)

Next Steps

1. Monitor collection health with automated validation:

   python validate_thresholds.py --report
   

2. Ingest your own codebase:

   python ingest.py --folder /path/to/your/code --collection my_codebase
   

3. Build retrieval pipelines using CodeRetriever for AI agents

4. Integrate with LLM applications for code-aware context generation

5. Set up CI/CD health checks to catch threshold drift early

Monitoring & Maintenance

Threshold Validation

Automatically validate that distance thresholds remain calibrated:

# Basic validation
python validate_thresholds.py

# With markdown report
python validate_thresholds.py --report

# Strict mode (fail on drift)
python validate_thresholds.py --strict

What it checks:

• Frontend queries return frontend agents (distance 1.0-1.3)
• DevOps queries return devops/quality agents (distance 1.0-1.3)
• Backend queries return backend agents (distance 0.7-0.9)
• Security queries return security agents (distance 0.9-1.2)

Output:

• JSON results: threshold_validation_results.json
• Markdown report: validation_report.md (with --report flag)
• Exit codes: 0 = all pass, 1 = drift detected, 2 = tests failed

CI/CD Integration

Add threshold validation to your CI/CD pipeline:

# Example GitHub Actions
- name: Validate Collection Thresholds
  run: |
    cd chroma
    python validate_thresholds.py --strict
    if [ $? -eq 1 ]; then
      echo "⚠️ Threshold drift detected"
      exit 1
    fi

When to Re-validate

Weekly: Run validation to ensure stability

python validate_thresholds.py --report

After changes:

• Updating ingestion configuration
• Adding new agents to collection
• Changing chunking strategy
• Upgrading Chroma or embedding models

Signs of drift to watch for:

• Distances consistently 20-30% higher/lower than expected
• Different agents appearing for familiar queries
• Distance ranges spreading wider than expected

Architecture Decision: CloudClient vs PersistentClient

This project uses Chroma CloudClient because:

• ✅ Persistent storage (data survives restarts)
• ✅ Remote backup and redundancy
• ✅ Multi-user access and sharing
• ✅ Easy scaling as codebase grows
• ✅ API-compatible with local Chroma

For local development, you can switch to PersistentClient:

# In src/clients/chroma_client.py
_client = chromadb.PersistentClient(path="./chroma_db")

References

• Chroma Documentation
• LangChain Text Splitters
• Semantic Search Best Practices