hamzi275/multilingual

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

License

Apache-2.0