Custom RAG System with Ollama

Retrieval-Augmented Generation (RAG) systems are gaining significant traction for their ability to retrieve relevant information and generate human-like responses. This article provides an in-depth walkthrough of building a custom RAG system integrated with Ollama, a cutting-edge LLM service. We'll also delve into key considerations like quantization, architecture choices, and performance optimization.

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Why RAG Over Fine-Tuning?

Fine-tuning involves adapting a model to specific tasks by modifying its weights. While this can yield high performance, it comes with challenges:

• Resource Intensive: Requires substantial compute power.

• Data Dependency: Needs large labeled datasets.

• Limited Flexibility: Fine-tuned models struggle to generalize outside their domain.

RAG, on the other hand, decouples knowledge retrieval from generation:

• Efficiency: Leverages external knowledge stores.

• Scalability: Easily update or expand knowledge without retraining.

• Domain Agnosticism: Adapts to various domains with minimal setup.

Ollama

Ollama is an advanced LLM service designed to provide developers with powerful and flexible language models. Built with simplicity and efficiency in mind, it supports:

• Customizable Models: Choose or fine-tune models for specific use cases.

• Local and API Access: Seamless integration for on-premise or cloud deployments.

• High Performance: Optimized for speed and accuracy, making it ideal for real-time applications like RAG systems. Ollama empowers developers to build intelligent systems that deliver accurate and context-aware responses.

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System Architecture

Our RAG system consists of three core layers:

1. Document Processing & Chunking

• File Types Supported: PDFs, DOCX, Markdown, Text files.

• Chunking Strategy: Divides text into manageable pieces (e.g., 512 tokens) with overlaps to maintain context continuity.

• Tools:

  • PyPDF2 for PDFs.

  • docx for Word files.

  • markdown for MD files.

2. Embedding Generation

• Embedding Models: Supports local models like SentenceTransformer and API-based models like Hugging Face.

• Quantization Benefits:

  • Reduces memory footprint.

  • Improves inference speed with minimal accuracy loss.

• Key Metrics:

  • Embedding time.

  • Quality of embeddings (cosine similarity).

3. Vector Search & Response Generation

• Vector Search:

  • Uses similarity metrics to retrieve the most relevant document chunks.

  • Frameworks: FAISS or custom databases for optimized searches.

• Response Generation:

  • Integrates Ollama’s LLMs for contextual answers.

  • Supports configurable prompts and fallback mechanisms.

Quantization vs Model Size vs Performance

Quantization has emerged as a game-changer in model optimization. Here are some practical insights:

1. Quantization vs Model Size

   • Smaller models benefit significantly from quantization.

   • Large models (e.g., 13B+ parameters) show diminishing returns with aggressive quantization.

2. Quantization vs Performance

   • Int8 quantization offers up to 4x speedups with negligible accuracy loss.

   • Beyond Int8 (e.g., Int4), performance can degrade on complex queries.

3. Practical Takeaway

   • Choose quantization based on your hardware (e.g., GPU vs CPU).

   • Test accuracy trade-offs with your dataset.

Key Features of Our Implementation

1. Async Operations

• Leverages asyncio for concurrent document processing and querying.

• Boosts throughput in multi-user scenarios.

2. Configurable Settings

• Chunk Size: Default 512 tokens.

• Overlap: Default 50 tokens.

• Embedding Model: Configurable via the UI.

3. Error Handling & Metrics

• Comprehensive logging ensures traceability.

• Metrics capture performance at every stage:

•   {
      "embedding_time": 0.05,
      "search_time": 0.10,
      "llm_time": 0.20,
      "total_time": 0.35
    }
  

Practical Implementation

Step 1: Document Upload

A simple UI allows users to upload documents, which are then processed into text chunks.

Step 2: Querying

• Users input a query.

• The system retrieves relevant chunks and generates a response.

  

  Figure : Querying with Multiple Local Models on Ollama

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    Figure : Response for DeepSeek r1(10B Parameters)

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  Figure : Response for Ilama(3B Parameters)

Best Practices

1. Text Preprocessing: Ensure UTF-8 encoding and clean unnecessary symbols.

2. Efficient Indexing: Optimize vector search for speed.

3. Context Management: Avoid exceeding token limits by prioritizing the most relevant chunks.

4. Fallback Mechanisms: Handle cases where context is insufficient.

Conclusion

Integrated with Ollama's advanced capabilities, we bridge the gap between raw data and actionable insights. This approach empowers developers to build scalable, efficient, and context-aware systems that adapt seamlessly across diverse domains.

Key Trends to Watch:

• Quantization will continue to dominate, pushing the boundaries of model efficiency without compromising accuracy.

• Hybrid architectures combining RAG with fine-tuned components could emerge for domain-specific applications.

• The rise of modular, API-driven solutions like Ollama emphasizes the shift towards composable AI systems.