1. Data Loading & Query Input:
a. A user submits a natural language query via an interface or API, and the system receives the query as input.
b. The input is passed to a vectorizer, which converts the natural language query into a vector representation using vectorization techniques (e.g., BERT or Sentence Transformers).
2. Document Retrieval:
a. The vectorized query is sent to the retriever, which searches the knowledge base for the most relevant document snippets.
b. Retrieval can leverage sparse retrieval techniques (e.g., BM25) or dense retrieval techniques (e.g., DPR) to improve matching efficiency and precision.
3. Generator Processing & Natural Language Generation:
a. The retrieved document snippets are fed into the generator (e.g., GPT, BART, or T5), which produces a natural language answer based on the query and document content.
b. The generator combines external retrieval results with the linguistic knowledge of pre-trained models to ensure answers are precise and natural.
4. Result Output:
a. The system-generated answer is returned to the user via the API or interface, ensuring coherence and factual accuracy.
5. Feedback & Optimization:
a. Users can provide feedback on the generated answers, and the system optimizes retrieval and generation processes based on this feedback.
b. Through fine-tuning model parameters or adjusting retrieval weights, the system iteratively improves performance to achieve higher accuracy and efficiency for future queries.