An end-to-end Retrieval-Augmented Generation (RAG) system for grounded financial document intelligence using semantic search, embeddings, vector retrieval, and LLMs.
- Build keyword search using TF-IDF
- Implement semantic search with embeddings
- Create a vector retrieval pipeline
- Develop a complete RAG system
- Evaluate retrieval and answer quality
- Deploy as an API using FastAPI and Docker
- Problem Framing
- Text Preprocessing
- TF-IDF Search
- Embedding-Based Search
- Vector Store Integration
- Retriever Pipeline
- RAG Pipeline
- Evaluation
- API Deployment
- Python
- Sentence Transformers
- FAISS
- LangChain
- FastAPI
- Docker
🚧 Under Development