git-rag-embedder 0.1.0

Minimalist library to convert Git repositories and local directories into embeddings

GitRAG Embedder: Git Repository to Embeddings Pipeline

🚀 Overview

GitRAG Embedder is a focused library that converts Git repositories into embedding vectors. Pure processing pipeline - from repository cloning to embedding generation.

✨ Features

🔍 Repository Processing

• Git Integration: Clone and process any Git repository
• Multi-Format Support: Process .py, .md, .rst, .txt files
• Smart Chunking: Configurable text splitting with overlap
• Selective Processing: Filter by file type and directory
• Batch Processing: Efficient handling of large codebases

📊 Multiple Embedding Backends

• OpenAI Embeddings: Support for text-embedding-ada-002, text-embedding-3-small/large
• Sentence Transformers: Local models like all-MiniLM-L6-v2
• HuggingFace Transformers: Custom transformer models
• Batch Processing: Efficient batch embedding generation
• Automatic Retries: Built-in error handling and retries

📦 Installation

pip install git-rag-embedder

Optional dependencies (install as needed):

# For OpenAI embeddings
pip install openai

# For Sentence Transformers
pip install sentence-transformers

# For HuggingFace embeddings  
pip install transformers torch

🚀 Quick Start

Basic Usage

from git_rag_embedder import GitRAGEmbedder

# Initialize with default settings (Sentence Transformers)
embedder = GitRAGEmbedder()

# Process a repository into embeddings
embeddings = embedder.process_repository(
    "https://github.com/username/repository"
)

print(f"Generated {len(embeddings)} embedding vectors")

Using Different Backends

# OpenAI embeddings
embedder = GitRAGEmbedder(
    embedding_backend="openai",
    model="text-embedding-3-small",
    api_key="your-openai-key"
)

# Sentence Transformers
embedder = GitRAGEmbedder(
    embedding_backend="sentence_transformers", 
    model_name="all-MiniLM-L6-v2"
)

# HuggingFace Transformers
embedder = GitRAGEmbedder(
    embedding_backend="huggingface",
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)

Advanced Configuration

embeddings = embedder.process_repository(
    repo_url="https://github.com/username/repository",
    chunk_size=1000,
    chunk_overlap=150,
    extensions={'.py', '.md', '.txt'},
    exclude_dirs={'tests', 'docs', 'node_modules'},
    batch_size=32,
    max_files=1000
)

⚙️ Configuration

Processing Parameters

# All configuration options
embeddings = embedder.process_repository(
    repo_url="https://github.com/user/repo",  # Git URL or local path
    chunk_size=1000,           # Characters per chunk
    chunk_overlap=150,         # Overlap between chunks  
    extensions={'.py', '.md'}, # File types to process
    exclude_dirs={'tests'},    # Directories to skip
    batch_size=32,             # Processing batch size
    max_files=1000            # Maximum files to process
)

Available Embedding Models

OpenAI Models:

• text-embedding-ada-002 (1536 dim)
• text-embedding-3-small (1536 dim)
• text-embedding-3-large (3072 dim)

Sentence Transformers Models:

• all-MiniLM-L6-v2 (384 dim)
• all-mpnet-base-v2 (768 dim)
• multi-qa-mpnet-base-dot-v1 (768 dim)

HuggingFace Models:

• Any sentence transformer compatible model

📊 Output Format

Embedding Structure

Each embedding contains:

{
    'content': 'def calculate_sum(a, b):\n    return a + b',
    'file_path': 'src/math_utils.py', 
    'file_extension': '.py',
    'embedding': [0.123, -0.456, 0.789, ...],  # Vector array
    'embedding_dimension': 384,
    'embedding_model': 'sentence_transformers',
    'embedding_norm': 1.234,  # L2 norm of the vector
    'metadata': {
        'chunk_size': 245,
        'token_count': 45
    }
}

🔧 API Reference

GitRAGEmbedder Class

class GitRAGEmbedder:
    def __init__(
        self,
        embedding_backend: str = "sentence_transformers",
        **backend_kwargs
    )
    
    def process_repository(
        self,
        repo_url: str,
        chunk_size: int = 1000,
        chunk_overlap: int = 150,
        extensions: Set[str] = None,
        exclude_dirs: Set[str] = None,
        batch_size: int = 32,
        max_files: int = 1000
    ) -> List[Dict[str, Any]]

EmbeddingGenerator Class

class EmbeddingGenerator:
    def __init__(self, backend: str = "sentence_transformers", **backend_kwargs)
    
    def generate_embeddings(
        self,
        chunks: List[Dict[str, Any]],
        batch_size: int = 32,
        max_retries: int = 3
    ) -> List[Dict[str, Any]]
    
    def embed_single_text(self, text: str) -> List[float]
    def get_embedding_dimension(self) -> int

Available Backends

• OpenAIEmbeddingBackend - For OpenAI embedding API
• SentenceTransformersBackend - For local sentence transformer models
• HuggingFaceEmbeddingBackend - For HuggingFace transformer models

🏗️ Advanced Usage

Custom Processing Pipeline

from git_rag_embedder import GitRAGEmbedder, EmbeddingGenerator

# Step-by-step processing
embedder = GitRAGEmbedder()

# Extract and chunk documents
documents = embedder.extract_documents("https://github.com/user/repo")

# Use different embedding backend for generation
embedding_gen = EmbeddingGenerator(
    backend="openai",
    model="text-embedding-3-small",
    api_key="your-key"
)

embeddings = embedding_gen.generate_embeddings(documents)

Multiple Repositories

repositories = [
    "https://github.com/org/repo1",
    "https://github.com/org/repo2", 
    "/path/to/local/repo"
]

all_embeddings = []
for repo in repositories:
    embeddings = embedder.process_repository(repo)
    all_embeddings.extend(embeddings)

Save/Load Embeddings

# Save for later use
embedder.save_embeddings(embeddings, "my_embeddings.json")

# Load saved embeddings
loaded_embeddings = embedder.load_embeddings("my_embeddings.json")

🔍 Backend Details

OpenAI Backend

backend = OpenAIEmbeddingBackend(
    api_key="your-key",  # Optional, uses OPENAI_API_KEY env var
    model="text-embedding-3-small"
)

Sentence Transformers Backend

backend = SentenceTransformersBackend(
    model_name="all-MiniLM-L6-v2"  # Any sentence-transformers model
)

HuggingFace Backend

backend = HuggingFaceEmbeddingBackend(
    model_name="sentence-transformers/all-MiniLM-L6-v2"  # Any HF model
)

📝 License

MIT License - see LICENSE file for details.

📞 Support

• Issues: GitHub Issues

---

Focus: Pure Git repository to embedding conversion pipeline. No search, no quality metrics, just embeddings.

---

Star this repository if you find it helpful! ⭐