Multilingual Knowledge RAG Bot – Cross-Lingual Retrieval-Augmented Generation

This model is designed for cross-lingual question answering using Retrieval-Augmented Generation (RAG).
It can take documents in multiple languages — Urdu, Hindi, Spanish, English — and answer in the same or different language.

Key Features

• LLM Used: Meta-Llama-3-8B-Instruct
• Embedding Model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
• RAG Pipeline: FAISS-based vector search + context injection
• Training/Processing: Implemented entirely in Google Colab using open-source tools only
• Zero paid APIs — 100% free and deployable

Techniques Used

• Vector Database: FAISS for similarity search
• Cross-Lingual Embeddings: multilingual sentence transformers
• Prompt Engineering: Context-aware question answering
• Open-Source Deployment Ready: Hugging Face Spaces compatible

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License

Apache-2.0