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import os |
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import openai |
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from llama_index import StorageContext, load_index_from_storage |
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import logging |
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import sys |
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logging.basicConfig(stream=sys.stdout, level=logging.INFO) |
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logging.getLogger().handlers = [] |
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logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) |
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openai.api_key = os.environ['OPENAI_API_KEY'] |
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from llama_index import load_indices_from_storage |
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storage_context = StorageContext.from_defaults(persist_dir='./storage') |
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indx = load_indices_from_storage(storage_context) |
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from llama_index.query_engine import CitationQueryEngine |
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query_engine = CitationQueryEngine.from_args(index=indx[1], similarity_top_k=2) |
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list_query_engine = indx[0].as_query_engine( |
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response_mode='tree_summarize', |
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use_async=True, |
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) |
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vector_query_engine = indx[1].as_query_engine(similarity_top_k=2) |
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import gradio as gr |
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def chatbot(input_text, is_qen): |
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engine = query_engine if is_qen else vector_query_engine |
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response = engine.query(input_text) |
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return response |
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iface = gr.Interface(fn=chatbot, |
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inputs=[gr.inputs.Textbox(lines=7, default="What are examples for short, intermediate and long acting glucocorticoids?", label="Enter your text here"), gr.Checkbox(label="Use Citation Engine instead of Query Engine.")], |
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outputs="text", |
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title="Project Rang and Dale") |
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iface.launch() |
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