Скачать или смотреть Low-Cost, Accurate RAG System Using SLMs for any type of document (DataSheets, Invoices ,CVs..)

Many organizations want tMany organizations want to leverage Retrieval-Augmented Generation (RAG), but they face recurring obstacles:
❗ High operational cost
❗ Privacy and data-security concerns
❗ Heavy dependence on large cloud-based LLMs to handle Text & Image data

✅ Solution: A Fully Local RAG System Powered by SLMs
Using Phi-3 Mini and Gemma 4B, I designed a RAG pipeline that is:
✓ Low cost
✓ Accurate and reliable
✓ 100% private (local-only)
✓ Capable of handling both text and images
Yes SLMs can handle vision tasks in a RAG setup.

🧩 Pipeline Overview
1️⃣ Document Intake
Solar panel datasheets (1:30)

2️⃣ Pre-Processing & Image Handling
PDF → Markdown conversion via Tool Maker (1:52)
Image extraction (Maker) + image summarization using Moondream:latest (2:16–2:41)
➝ Enables multimodal context inside RAG (text + images)

3️⃣ Embedding & Chunking
BAAI/bge-small-en v1.5 for lightweight, high-quality embeddings (3:46)

4️⃣ Retrieval Pipeline
Hybrid search (semantic + BM25 keyword)
Flashrank for final reranking (4:29)

5️⃣ MVP Application
Built with Streamlit for rapid testing and demos (6:03)

📊 Evaluation Layer
To ensure reliability and transparency:
DeepEval for RAG metrics
User feedback integration
SLM confidence scoring to estimate response reliability (11:17)

💡 Key Takeaways (11:54)
SLM-based RAG can deliver LLM-level accuracy at a fraction of the cost
Full local deployment guarantees privacy—ideal for sensitive industries
SLMs combined with the right pipeline can successfully handle multimodal (text + image) use cases
The solution is efficient, production-friendly, and budget-friendlyo leverage Retrieval-Augmented Generation (RAG), but they face recurring obstacles:
❗ High operational cost
❗ Privacy and data-security concerns
❗ Heavy dependence on large cloud-based LLMs to handle Text & Image data

✅ Solution: A Fully Local RAG System Powered by SLMs
Using Phi-3 Mini and Gemma 4B, I designed a RAG pipeline that is:
✓ Low cost
✓ Accurate and reliable
✓ 100% private (local-only)
✓ Capable of handling both text and images
Yes SLMs can handle vision tasks in a RAG setup.

🧩 Pipeline Overview
1️⃣ Document Intake
Solar panel datasheets (1:30)

2️⃣ Pre-Processing & Image Handling
PDF → Markdown conversion via Tool Maker (1:52)
Image extraction (Maker) + image summarization using Moondream:latest (2:16–2:41)
➝ Enables multimodal context inside RAG (text + images)

3️⃣ Embedding & Chunking
BAAI/bge-small-en v1.5 for lightweight, high-quality embeddings (3:46)

4️⃣ Retrieval Pipeline
Hybrid search (semantic + BM25 keyword)
Flashrank for final reranking (4:29)

5️⃣ MVP Application
Built with Streamlit for rapid testing and demos (6:03)

📊 Evaluation Layer
To ensure reliability and transparency:
DeepEval for RAG metrics
User feedback integration
SLM confidence scoring to estimate response reliability (11:17)

💡 Key Takeaways (11:54)
SLM-based RAG can deliver LLM-level accuracy at a fraction of the cost
Full local deployment guarantees privacy—ideal for sensitive industries
SLMs combined with the right pipeline can successfully handle multimodal (text + image) use cases
The solution is efficient, production-friendly, and budget-friendly