RAG BOT
JAN 2026
Semantic retrieval and question-answering systems using vector embeddings and similarity search to answer contextual queries grounded in document corpora.
OVERVIEW
RAG Bot explores multiple specialized retrieval-augmented generation workflows, each designed to retrieve contextually relevant information from document collections and generate accurate responses using modern NLP techniques.
THE IDEA
Investigate and implement Retrieval-Augmented Generation as a practical architecture for domain-specific question-answering systems that ground responses in verified document content rather than relying on model memory alone.
WORKFLOW
KEY FEATURES
- Document ingestion and chunking pipeline
- Semantic embedding generation
- Vector similarity search with Qdrant
- Context retrieval and ranking pipeline
- Response generation grounded in retrieved context
- Specialized QA configurations for custom corpora
ENGINEERING APPROACH
Documents are chunked and embedded into a Qdrant vector store. At query time, semantic similarity search retrieves the most relevant chunks, which are passed as grounded context for response generation via LangChain orchestration.