| import gradio as gr | |
| import faiss | |
| import numpy as np | |
| from sentence_transformers import SentenceTransformer | |
| import openai | |
| import pickle | |
| from utils import rag_based_generation, search_with_metadata_and_reranking | |
| embedder = SentenceTransformer("sentence-transformers/all-mpnet-base-v2") | |
| index = faiss.read_index('faiss_index.index') | |
| chunked_documents = np.load('chunked_documents.npy', allow_pickle=True) | |
| def rag_interface(query, author=None, category=None): | |
| try: | |
| filters = {} | |
| if author: | |
| filters['author'] = author | |
| if category: | |
| filters['categories'] = category | |
| generated_answer = rag_based_generation(query, index, chunked_documents, embedder, filters=filters, top_k=5) | |
| return generated_answer | |
| except Exception as e: | |
| import traceback | |
| return f"An error occurred: {str(e)}\n{traceback.format_exc()}" | |
| rag_demo = gr.Interface( | |
| fn=rag_interface, | |
| inputs=[gr.Textbox(label="Query"), gr.Textbox(label="Author (optional)"), gr.Textbox(label="Category (optional)")], | |
| outputs="text", | |
| title="Search for Articles!" | |
| ) | |
| rag_demo.launch() | |