convert file address

examples/app.py

@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+import gradio as gr
+from langchain.agents import (
+    load_tools,
+    initialize_agent,
+    AgentType
+)
+from pilot.model.vicuna_llm import VicunaRequestLLM, VicunaEmbeddingLLM
+from llama_index import LLMPredictor, LangchainEmbedding, ServiceContext
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from llama_index import Document, GPTSimpleVectorIndex
+
+def agent_demo():
+    llm = VicunaRequestLLM()
+
+    tools = load_tools(['python_repl'], llm=llm)
+    agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
+    agent.run(
+        "Write a SQL script that Query 'select count(1)!'"
+    )
+
+def knowledged_qa_demo(text_list):
+    llm_predictor = LLMPredictor(llm=VicunaRequestLLM())
+    hfemb = VicunaEmbeddingLLM()
+    embed_model = LangchainEmbedding(hfemb)
+    documents = [Document(t) for t in text_list]
+
+    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)
+    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context) 
+    return index
+
+
+def get_answer(q):
+    base_knowledge = """ """ 
+    text_list = [base_knowledge]
+    index = knowledged_qa_demo(text_list)
+    response = index.query(q)
+    return response.response
+
+def get_similar(q):
+    from pilot.vector_store.extract_tovec import knownledge_tovec, knownledge_tovec_st
+    docsearch = knownledge_tovec_st("./datasets/plan.md")
+    docs = docsearch.similarity_search_with_score(q, k=1)
+
+    for doc in docs:
+        dc, s = doc 
+        print(s)
+        yield dc.page_content 
+
+if __name__ == "__main__":
+    # agent_demo()
+
+    with gr.Blocks() as demo:
+        gr.Markdown("数据库智能助手")
+        with gr.Tab("知识问答"):
+            text_input = gr.TextArea()
+            text_output = gr.TextArea()
+            text_button = gr.Button()
+        
+        text_button.click(get_similar, inputs=text_input, outputs=text_output)
+
+    demo.queue(concurrency_count=3).launch(server_name="0.0.0.0")
+