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| import os,re | |
| import gradio as gr | |
| import nest_asyncio | |
| from langchain import PromptTemplate | |
| from llama_index.core import PromptTemplate, StorageContext, load_index_from_storage | |
| from llama_index.llms.openai import OpenAI | |
| import networkx as nx | |
| from pyvis.network import Network | |
| from IPython.display import HTML, Markdown, display | |
| # Define constants for index paths | |
| os.environ["OPENAI_API_KEY"] = os.getenv('oai') | |
| KG_INDEX_PATH = "./telcom_full_property_kg_processed_dynamicpath2_withpackagedata_category/" | |
| KG_PLOT_PATH = KG_INDEX_PATH + "/full_kg.html" | |
| RAG_INDEX_PATH = "./telcom_RAG_full_withpackagedata_category/" | |
| # Load Graph-RAG index | |
| graph_rag_index = load_index_from_storage( | |
| StorageContext.from_defaults(persist_dir=KG_INDEX_PATH) | |
| ) | |
| # Load RAG index | |
| rag_index = load_index_from_storage( | |
| StorageContext.from_defaults(persist_dir=RAG_INDEX_PATH) | |
| ) | |
| def query_tqa(query, search_level): | |
| """ | |
| Query both Graph-RAG and RAG models and return their responses and references. | |
| Args: | |
| query (str): The query to be processed. | |
| search_level (str): The level of search to be performed. | |
| Returns: | |
| tuple: Responses and references from both Graph-RAG and RAG models. | |
| """ | |
| grag_response, grag_reference, grag_reference_text = query_graph_rag_qa(query, search_level) | |
| rag_response, rag_reference, rag_reference_text = query_rag_qa(query, search_level) | |
| return grag_response, grag_reference, grag_reference_text, rag_response, rag_reference, rag_reference_text | |
| def eval_llm(query, rag_response, grag_response): | |
| """ | |
| Evaluate responses from both Graph-RAG and RAG models using an LLM. | |
| Args: | |
| query (str): The query that was used to generate the responses. | |
| rag_response (str): The response from the RAG model. | |
| grag_response (str): The response from the Graph-RAG model. | |
| Returns: | |
| tuple: Evaluation results for both responses. | |
| """ | |
| grag_eval = evaluate_llm(query, grag_response) | |
| rag_eval = evaluate_llm(query, rag_response) | |
| return grag_eval, rag_eval | |
| with gr.Blocks() as demo: | |
| gr.Markdown("<h1>Telcom Graph-RAG v0.1</h1>") | |
| with gr.Tab("Virtual Assistant"): | |
| with gr.Row(): | |
| query_input = gr.Textbox(label="Input Your Query..") | |
| search_level = gr.Slider(minimum=1, maximum=50, value=3, step=5, label="Search level") | |
| ask_button = gr.Button("Ask TelcomVA!!") | |
| with gr.Row(): | |
| with gr.Accordion("Graph-RAG!", open=True): | |
| grag_output = gr.Textbox(label="Response") | |
| grag_reference = gr.Textbox(label="Triplets") | |
| grag_reference_text = gr.Textbox(label="Extracted Reference raw") | |
| with gr.Accordion("RAG", open=True): | |
| rag_output = gr.Textbox(label="Response") | |
| rag_reference = gr.Textbox(label="Extracted Reference") | |
| rag_reference_text = gr.Textbox(label="Extracted Reference raw") | |
| with gr.Row(): | |
| grag_performance = gr.Textbox(label="Graph-RAG Performance") | |
| rag_performance = gr.Textbox(label="RAG Performance") | |
| eval_button = gr.Button("Evaluate LLMs!!") | |
| with gr.Accordion("Explore KG!", open=False): | |
| kg_output = gr.HTML() | |
| plot_button = gr.Button("Plot Full KG!!") | |
| ask_button.click(query_tqa, | |
| inputs=[query_input,search_level], | |
| outputs=[ | |
| grag_output, | |
| grag_reference, | |
| grag_reference_text, | |
| rag_output, | |
| rag_reference, | |
| rag_reference_text | |
| ] | |
| ) | |
| eval_button.click(eval_llm, | |
| inputs=[query_input,rag_output,grag_output], | |
| outputs=[ | |
| grag_performance, | |
| rag_performance | |
| ] | |
| ) | |
| plot_button.click(plot_full_kg, outputs=kg_output) | |
| examples = gr.Examples( | |
| examples=[ | |
| ["what are the upselling ideas for roaming package you can recommend for customer Rina Wati."], | |
| ], | |
| inputs=[query_input] | |
| ) | |
| demo.launch(auth=(os.getenv('id'), os.getenv('pass')), share=True) | |