Building a RAG System with Ollama and LanceDB: A Comprehensive Tutorial

This tutorial walks through building a Retrieval-Augmented Generation (RAG) system for BBC News data using Ollama for embeddings and language modeling, and LanceDB for vector storage.

System Architecture

The system consists of several key components:

1. LLM Interface: An async interface for large language models
2. Embedder: Handles document and query embedding
3. Vector Store: Manages storage and retrieval of embedded documents
4. Component Factory: Creates instances of the above components
5. Main RAG System: Orchestrates the entire pipeline

Prerequisites

• Python 3.7+
• Ollama running locally (default: http://localhost:11434)
• Required packages:
• httpx
• pandas
• lancedb
• pydantic

Component Breakdown

1. LLM Implementation (ollama.py)

The AsyncOllamaLLM class provides an async interface to Ollama’s API:

class AsyncOllamaLLM(LLM):
    def __init__(self, model_name: str = "llama3.1", base_url: str = "http://localhost:11434"):
        self.model_name = model_name
        self.base_url = base_url
        self.client = httpx.AsyncClient()

Key features:

• Async HTTP client for API communication
• Support for both streaming and non-streaming generation
• Context manager for proper resource cleanup

2. Embedder Implementation (ollama_embedder.py)

The AsyncOllamaEmbedder handles document embedding:

class AsyncOllamaEmbedder(Embedder):
    def __init__(self, model_name: str = "mxbai-embed-large", base_url: str = "http://localhost:11434"):
        self.model_name = model_name
        self.base_url = base_url
        self.client = httpx.AsyncClient()

Features:

• Integration with LanceDB’s embedding registry
• Async document and query embedding
• Dimension information for vector storage

3. Vector Store Implementation (lancedb_store.py)

The AsyncLanceDBStore manages embedded documents:

class AsyncLanceDBStore(VectorStore):
    def __init__(self, embedder, db_path: str = "./lancedb", table_name: str = "documents"):
        self.db = lancedb.connect(db_path)
        self.table_name = table_name

Key capabilities:

• Async document storage with metadata
• Vector similarity search
• Thread-safe operations with async locks

Setting Up the RAG System

1. First, create a configuration dictionary:

config = {
    "llm": {
        "type": "ollama",
        "model_name": "llama3.2"
    },
    "embedder": {
        "type": "ollama",
        "model_name": "nomic-embed-text"
    },
    "vector_store": {
        "type": "lancedb",
        "db_path": "./data/lancedb",
        "table_name": "documents"
    }
}

2. Initialize the RAG system:

rag = BBCNewsRAG(config)
await rag.initialize()

3. Ingest your data:

df = pd.read_csv('data/data.txt')
await rag.ingest_data(df)

4. Query the system:

response = await rag.query(
    "Who is Aarin Chiekrie?",
    system_prompt="You are a helpful assistant that provides accurate information based on the news articles."
)

Best Practices

1. Resource Management

• Always use async context managers or explicit cleanup
• Close connections properly using await rag.close()

1. Error Handling

• Implement proper error handling for API calls
• Check for missing or malformed data

1. Configuration

• Keep configuration separate from code
• Use environment variables for sensitive information

1. Performance

• Use connection pooling for HTTP clients
• Implement caching where appropriate
• Use async operations for I/O-bound tasks

Common Pitfalls

1. Not properly closing async resources
2. Forgetting to handle API rate limits
3. Missing error handling for embedding operations
4. Not considering thread safety in vector store operations

Advanced Usage

Custom Embedding Models

You can extend the system to use different embedding models:

class CustomEmbedder(Embedder):
    async def embed_documents(self, documents: List[str]) -> List[List[float]]:
        # Your custom embedding logic here
        pass

Custom Vector Stores

Implement different vector stores by extending the base class:

class CustomVectorStore(VectorStore):
    async def store_embeddings(self, documents, embeddings, metadata=None):
        # Your custom storage logic here
        pass

Conclusion

This RAG system provides a robust foundation for building document-based question-answering systems. Its modular design allows for easy extension and customization, while the async implementation ensures efficient resource usage.

For production deployment, consider:

• Implementing proper logging
• Adding monitoring and metrics
• Setting up proper error handling and retries
• Implementing caching mechanisms
• Adding authentication and authorization

The system can be extended to handle different document types, embedding models, and vector stores by implementing the appropriate interfaces.

Source code

https://gist.github.com/dewmal/e8f0296bd9743d3fa9dd5841a65d3cdd