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examples/app.py

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+#!/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")
+   

examples/embdserver.py

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+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+import requests
+import json
+import time
+import uuid
+from urllib.parse import urljoin
+import gradio as gr
+from pilot.configs.model_config import *
+from pilot.conversation import conv_qa_prompt_template, conv_templates
+from langchain.prompts import PromptTemplate
+
+vicuna_stream_path = "generate_stream"
+
+def generate(query):
+
+    template_name = "conv_one_shot"
+    state = conv_templates[template_name].copy()
+
+    pt = PromptTemplate(
+        template=conv_qa_prompt_template,
+        input_variables=["context", "question"]
+    )
+
+    result = pt.format(context="This page covers how to use the Chroma ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Chroma wrappers.",
+              question=query)
+
+    print(result)
+
+    state.append_message(state.roles[0], result)
+    state.append_message(state.roles[1], None)
+
+    prompt = state.get_prompt()
+    params = {
+        "model": "vicuna-13b",
+        "prompt": prompt,
+        "temperature": 0.7,
+        "max_new_tokens": 1024,
+        "stop": "###"
+    }
+
+    response = requests.post(
+        url=urljoin(VICUNA_MODEL_SERVER, vicuna_stream_path), data=json.dumps(params)
+    )
+
+    skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("</s>") * 3
+    for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
+        if chunk:
+            data = json.loads(chunk.decode())
+            if data["error_code"] == 0:
+                output = data["text"][skip_echo_len:].strip()
+                state.messages[-1][-1] = output + "▌"
+                yield(output) 
+ 
+if __name__ == "__main__":
+    print(LLM_MODEL)
+    with gr.Blocks() as demo:
+        gr.Markdown("数据库SQL生成助手")
+        with gr.Tab("SQL生成"):
+            text_input = gr.TextArea()
+            text_output = gr.TextArea()
+            text_button = gr.Button("提交")
+        
+
+        text_button.click(generate, inputs=text_input, outputs=text_output)
+
+    demo.queue(concurrency_count=3).launch(server_name="0.0.0.0") 
+
+