更新python后端
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BBIT-Kai
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d029ad67",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_milvus import BM25BuiltInFunction, Milvus\n",
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"from typing import List\n",
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"URI = \"http://10.10.10.9:19530\"\n",
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"tongyiKey = \"sk-9464b2498c184982a9fe9d2c2e725ab5\"\n",
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"from langchain_community.embeddings import DashScopeEmbeddings\n",
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"embeddings = DashScopeEmbeddings(\n",
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" model=\"text-embedding-v3\",\n",
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" dashscope_api_key= tongyiKey, \n",
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")\n",
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"memVectorstore = Milvus(\n",
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" embedding_function=embeddings,\n",
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" connection_args={\"uri\": URI, \"token\": \"root:Milvus\", \"db_name\": \"bbit_ai_lab\"},\n",
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" collection_name=\"memory\",\n",
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" index_params={\"index_type\": \"FLAT\", \"metric_type\": \"L2\"},\n",
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" consistency_level=\"Strong\",\n",
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" auto_id=True,\n",
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"\n",
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" primary_field = \"id\",\n",
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" text_field=\"text\",\n",
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" vector_field=\"vector\",\n",
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" partition_key_field = \"ai_id\",\n",
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" enable_dynamic_field = True,\n",
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" drop_old=False, # set to True if seeking to drop the collection with that name if it exists\n",
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")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a480053b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_memory_by_key_words(key_words: str, ai_ids: List[str]) -> str:\n",
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" print(\"ai_id是：\" , ai_ids)\n",
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" \"\"\"\n",
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" 根据关键词和 ai_ids 列表，在知识库中检索相关内容，并返回整理后的文本字符串\n",
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" \"\"\"\n",
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" # 构建过滤表达式：只查 kn_ids 范围内的\n",
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" if ai_ids:\n",
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" ids_expr = \" or \".join([f'ai_id == \"{kid}\"' for kid in ai_ids])\n",
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" expr = f\"({ids_expr})\"\n",
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" else:\n",
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" expr = \"\" # 不限制 kn_id todo 实际上应该不反悔任何内容\n",
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" \n",
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" result = knVectorstore.similarity_search(\n",
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" query=key_words,\n",
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" k=5, # 可调节返回条数\n",
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" expr=expr\n",
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" )\n",
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" \n",
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" # 整理成字符串\n",
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" doc_texts = []\n",
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" for idx, doc in enumerate(result, start=1):\n",
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" text = doc.page_content.strip()\n",
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" if text:\n",
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" # 可以加个编号，便于LLM区分\n",
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" doc_texts.append(f\"[记忆{idx}]: {text}\")\n",
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" \n",
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" # 拼成一个大字符串，用换行隔开\n",
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" combined_text = \"\\n\\n\".join(doc_texts)\n",
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" return combined_text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "36759de5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ai_id是： ['3730f279-8b56-46ec-bde9-8a9e6c27f021']\n"
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]
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},
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{
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"ename": "NameError",
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"evalue": "name 'knVectorstore' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_memory_by_key_words\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m共育室 部署 地方\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m3730f279-8b56-46ec-bde9-8a9e6c27f021\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
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"Cell \u001b[0;32mIn[2], line 13\u001b[0m, in \u001b[0;36mget_memory_by_key_words\u001b[0;34m(key_words, ai_ids)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 11\u001b[0m expr \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# 不限制 kn_id todo 实际上应该不反悔任何内容\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mknVectorstore\u001b[49m\u001b[38;5;241m.\u001b[39msimilarity_search(\n\u001b[1;32m 14\u001b[0m query\u001b[38;5;241m=\u001b[39mkey_words,\n\u001b[1;32m 15\u001b[0m k\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, \u001b[38;5;66;03m# 可调节返回条数\u001b[39;00m\n\u001b[1;32m 16\u001b[0m expr\u001b[38;5;241m=\u001b[39mexpr\n\u001b[1;32m 17\u001b[0m )\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# 整理成字符串\u001b[39;00m\n\u001b[1;32m 20\u001b[0m doc_texts \u001b[38;5;241m=\u001b[39m []\n",
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"\u001b[0;31mNameError\u001b[0m: name 'knVectorstore' is not defined"
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]
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}
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],
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"source": [
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"get_memory_by_key_words(\"共育室 部署 地方\",[\"3730f279-8b56-46ec-bde9-8a9e6c27f021\"])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "lang",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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