2023-06-30 11:42:09 +08:00
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from vector_store import get_client
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from split_markdown import split_markdown
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from embedding import get_embedding
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import gradio as gr
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import os
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2024-02-22 14:45:20 +08:00
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import json
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2023-06-30 11:42:09 +08:00
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from llm.wenxin import Wenxin, ModelName
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from dotenv import load_dotenv
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load_dotenv()
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chroma_client = get_client()
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collection = chroma_client.get_collection(name="amis")
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wenxin = Wenxin()
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text_blocks_by_id = {}
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2024-02-22 14:45:20 +08:00
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with open(os.path.join(os.path.dirname(__file__), 'text.json'), 'rb') as f:
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text_blocks_by_id = json.load(f)
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2023-06-30 11:42:09 +08:00
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def get_prompt(context, query):
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return f"""
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请只根据下面的资料回答问题，如果无法根据这些资料回答，回答“找不到相关答案”：
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资料：
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{context}
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问题是：{query}
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回答："""
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def get_context(search_result, include_code=True, max_length=1024):
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context = ""
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doc_ids = []
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for doc_id in search_result['ids'][0]:
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doc_id = doc_id.split("_")[0]
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if doc_id not in doc_ids:
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doc_ids.append(doc_id)
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for doc_id in doc_ids:
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markdown_block = text_blocks_by_id[doc_id]
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block_text = markdown_block.gen_text(512, include_code)
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if (len(context) + len(block_text)) < max_length:
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context += block_text + "\n\n"
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return context
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query = gr.Textbox(label="问题")
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include_code = gr.Checkbox(value=True, label="提示词中是否要包含 amis schema",
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info="包含的好处是大模型会返回 json，但也会导致内容太长，只能提供少量段落给大模型，导致错过重要资料")
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n_result = gr.Number(
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value=10, precision=0, label="向量搜索查询返回个数")
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bot_result = gr.Textbox(label="文心的回答")
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bot_turbo_result = gr.Textbox(label="文心 Turbo 的回答")
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booomz_result = gr.Textbox(label="开源 BLOOMZ 的回答")
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prompt = gr.Textbox(label="提示词")
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vector_search_result = gr.Dataframe(
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label="向量相关搜索结果，这个结果只是为了辅助调试，确认是因为没找到相关内容还是大模型没能理解",
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headers=["相关段落", "所属文档"],
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datatype=["str", "str"],
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col_count=(2, "dynamic"),
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wrap=True
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)
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def amis_search(query, n_result=10, include_code=True):
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if query.strip() == "":
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return "必须有输入", "", "", []
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search_result = collection.query(
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query_embeddings=get_embedding(query).tolist(),
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n_results=n_result
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)
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context = get_context(search_result, include_code)
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if (context == ""):
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return "检索不到相关内容", "", "", []
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prompt = get_prompt(context, query)
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bot_result = wenxin.generate(prompt, ModelName.ERNIE_BOT)
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# bloomz_result = wenxin.generate(prompt, ModelName.BLOOMZ_7B)
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markdown_blocks = []
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index = 0
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for doc in search_result['documents'][0]:
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markdown_block = []
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markdown_block.append(doc)
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if index < len(search_result['metadatas'][0]):
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source = search_result['metadatas'][0][index]['source'].replace(
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'docs/zh-CN/', '')
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markdown_block.append(
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source)
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else:
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print("index out of range", doc)
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markdown_blocks.append(markdown_block)
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index += 1
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return bot_result, prompt, markdown_blocks
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demo = gr.Interface(amis_search, title="amis 文档问答机器人", inputs=[
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query, n_result, include_code], outputs=[bot_result, prompt, vector_search_result])
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if __name__ == '__main__':
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demo.queue(concurrency_count=10).launch(share=False, server_name="0.0.0.0")
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