RAG Pipeline Demo
Type a question below or choose a preset query to visualize how our Retrieval-Augmented Generation (RAG) system reads internal files and builds responses.
Query Embedding Generation
The user's text query is processed via our custom encoder to generate a dense, high-dimensional vector representation capturing semantic intent.
Vector DB Retrieval & Similarity Match
The embedded query vector is run against our secure Qdrant database to retrieve the top matching document chunks based on cosine similarity.
Prompt Augmentation & Context Engineering
The retrieved text chunks are formatted as references, combined with the user query, and fed into our fine-tuned LLM system prompt.
Response Synthesis & Citation Output
The model synthesizes the answer, strictly using the augmented context to avoid hallucination, complete with clickable citation markers.