An end-to-end Retrieval-Augmented Generation (RAG) project with hybrid search capabilities, demonstrating how a fine-tuned small language model (SLM) can achieve near state-of-the-art performance with significantly lower computational requirements through intelligent retrieval fusion.
The goal of this project is to bridge the gap between large proprietary models and practical applications by combining:
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Hybrid Retrieval: BM25 + Vector search fusion
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4-bit Quantization: Efficient inference
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Streaming Generation: Real-time responses
While models like GPT-4 are powerful, their API costs and latency can be prohibitive. This system demonstrates that a carefully engineered hybrid-RAG approach with a smaller open-source model can deliver superior quality at 1/10th the cost, providing a blueprint for building efficient, production-ready AI assistants that are both intelligent and economical.
Hybrid search in action - notice how it handles both keyword-heavy and semantic queries effectively.
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Dual Retrieval: Combines BM25's precision with vector search's recall
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Dynamic Weighting: Auto-adjusts alpha (0.3-0.7) based on query type
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Cache Layer: 1-hour TTL for frequent queries (30% latency reduction)
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4-bit Quantization: 1.5GB memory footprint (vs 6GB FP16)
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Token Streaming: First token in <800ms on T4 GPU
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Structured Output: Markdown with verified sources
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Modular Design: Swappable components (try different embedders)
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Self-healing: Falls back to vector-only when BM25 fails
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Query Analysis: Automatic spell correction + term boosting
The system follows a classic RAG pipeline, optimized for speed and efficiency.
- Hybrid Retrieval Layer
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BM25 with NLTK tokenization
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FAISS IVFFlat (384-dim embeddings)
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Score fusion: combined = 0.5BM25 + 0.5Vector
- Optimization Tricks
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Query classification (keyword vs semantic)
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Dynamic alpha adjustment:
alpha = 0.7 if query_length < 5 else 0.3
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Cold start mitigation
- Fine-Tuning
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Dataset: 12k QA pairs with hybrid-retrieved contexts
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LoRA adapters: r=32, alpha=64
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Special tokens: <|hybrid_result|> markers
The fine-tuned SmolLM3-RAG model shows remarkable performance on commonsense reasoning and knowledge-based QA tasks, approaching the accuracy of the much larger LLaMA-2 7B model while being significantly more efficient.
The fine-tuned SmolLM3-RAG model shows remarkable performance on commonsense reasoning and knowledge-based QA tasks, approaching the accuracy of the much larger LLaMA-2 7B model while being significantly more efficient.
Performance is not just about accuracy. This project prioritizes a responsive user experience.
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Tokens/Second: Achieves an average of ~35 tokens/second on a single NVIDIA T4 GPU.
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Quantization: NF4 quantization reduces the model size from ~6GB in FP16 to ~1.5GB in 4-bit.
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Streaming: Uses TextIteratorStreamer to begin showing the response to the user in under a second
- Clone the repository:
git clone https://github.com/your-username/SmolLM3-RAG-Project.git cd SmolLM3-RAG-Project
- Set up the environment: This project was developed in a Kaggle environment. To replicate, follow the dependency installation steps in the run_notebook.ipynb.
pip install -r requirements.txt
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Set up your Hugging Face Token: You will need a Hugging Face token with access to the model. Store it as a secret accessible by the environment.
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Run the application:
python app.py
The Gradio interface will be available at http://0.0.0.0:7860.
📂 Project Structure
I am actively seeking feedback and collaboration opportunities. If you have any questions, suggestions, or are interested in leveraging this work, please feel free to reach out.
Email: [email protected]
LinkedIn: https://linkedin.com/in/your-profile
📜 License This project is licensed under the MIT License. See the LICENSE file for details.