更新python后端

bbit_ai/test/milvus/main.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dfb008fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from openai import OpenAI\n",
+    "from glob import glob\n",
+    "from pymilvus import MilvusClient\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "eaa97ad1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = OpenAI(\n",
+    "    api_key= \"sk-9464b2498c184982a9fe9d2c2e725ab5\",  # 如果您没有配置环境变量，请在此处用您的API Key进行替换\n",
+    "    base_url=\"https://dashscope.aliyuncs.com/compatible-mode/v1\"  # 百炼服务的base_url\n",
+    ")\n",
+    "def emb_text(text):\n",
+    "    return client.embeddings.create(\n",
+    "        model=\"text-embedding-v4\",\n",
+    "        input=text,\n",
+    "        dimensions=1024, # 指定向量维度（仅 text-embedding-v3及 text-embedding-v4支持该参数）\n",
+    "        encoding_format=\"float\"\n",
+    "    ).data[0].embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9df315ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1024\n",
+      "[-0.017507297918200493, 0.02571254037320614, 0.02589302882552147, -0.02639283984899521, -0.013571279123425484, -0.0032158030662685633, -0.006428135093301535, 0.02458796463906765, -0.059366535395383835, 0.13083963096141815]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 测试\n",
+    "test_embedding = emb_text(\"This is a test\")\n",
+    "embedding_dim = len(test_embedding)\n",
+    "print(embedding_dim)\n",
+    "print(test_embedding[:10])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95d0a121",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Milvus数据库配置\n",
+    "milvus_client = MilvusClient(uri=\"http://10.10.10.9:19530\")\n",
+    "collection_name = \"my_rag_collection\"\n",
+    "embedding_dim = 1024\n",
+    "\n",
+    "if milvus_client.has_collection(collection_name):\n",
+    "    milvus_client.drop_collection(collection_name)\n",
+    "milvus_client.create_collection(\n",
+    "    collection_name=collection_name,\n",
+    "    dimension=embedding_dim,\n",
+    "    metric_type=\"IP\",  # Inner product distance\n",
+    "    consistency_level=\"Bounded\",  # Supported values are (`\"Strong\"`, `\"Session\"`, `\"Bounded\"`, `\"Eventually\"`). See https://milvus.io/docs/consistency.md#Consistency-Level for more details.\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e09edfec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Creating embeddings: 100%|██████████| 72/72 [00:11<00:00,  6.46it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'insert_count': 72, 'ids': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71], 'cost': 0}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 从文件中插入数据\n",
+    "text_lines = []\n",
+    "for file_path in glob(\"milvus_docs/en/faq/*.md\", recursive=True):\n",
+    "    with open(file_path, \"r\") as file:\n",
+    "        file_text = file.read()\n",
+    "\n",
+    "    text_lines += file_text.split(\"# \")\n",
+    "\n",
+    "data = []\n",
+    "\n",
+    "for i, line in enumerate(tqdm(text_lines, desc=\"Creating embeddings\")):\n",
+    "    data.append({\"id\": i, \"vector\": emb_text(line), \"text\": line})\n",
+    "\n",
+    "milvus_client.insert(collection_name=collection_name, data=data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f3007553",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Milvus 是一个开源的向量数据库，主要用于高效地存储、管理和检索大规模的向量数据。它广泛应用于机器学习、推荐系统、图像识别等需要处理高维数据的场景。\n"
+     ]
+    }
+   ],
+   "source": [
+    "question = \"milvus是什么，用中文回答\"\n",
+    "search_res = milvus_client.search(\n",
+    "    collection_name=collection_name,\n",
+    "    data=[\n",
+    "        emb_text(question)\n",
+    "    ],  # Use the `emb_text` function to convert the question to an embedding vector\n",
+    "    limit=3,  # Return top 3 results\n",
+    "    search_params={\"metric_type\": \"IP\", \"params\": {}},  # Inner product distance\n",
+    "    output_fields=[\"text\"],  # Return the text field\n",
+    ")\n",
+    "import json\n",
+    "# 获取答案\n",
+    "retrieved_lines_with_distances = [\n",
+    "    (res[\"entity\"][\"text\"], res[\"distance\"]) for res in search_res[0]\n",
+    "]\n",
+    "context = \"\\n\".join(\n",
+    "    [line_with_distance[0] for line_with_distance in retrieved_lines_with_distances]\n",
+    ")\n",
+    "SYSTEM_PROMPT = \"\"\"\n",
+    "Human: You are an AI assistant. You are able to find answers to the questions from the contextual passage snippets provided.\n",
+    "\"\"\"\n",
+    "USER_PROMPT = f\"\"\"\n",
+    "Use the following pieces of information enclosed in <context> tags to provide an answer to the question enclosed in <question> tags.\n",
+    "<context>\n",
+    "{context}\n",
+    "</context>\n",
+    "<question>\n",
+    "{question}\n",
+    "</question>\n",
+    "\"\"\"\n",
+    "response = client.chat.completions.create(\n",
+    "    model='qwen-turbo',\n",
+    "    messages=[\n",
+    "        {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+    "        {\"role\": \"user\", \"content\": USER_PROMPT},\n",
+    "    ],\n",
+    ")\n",
+    "print(response.choices[0].message.content)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "077922d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-09-15 15:12:53,649 [ERROR][handler]: RPC error: [drop_database], <MilvusException: (code=65535, message=can not drop default database)>, <Time:{'RPC start': '2025-09-15 15:12:53.638539', 'RPC error': '2025-09-15 15:12:53.649605'}> (decorators.py:140)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Database 'default' already exists.\n",
+      "Collection 'my_rag_collection' has been dropped.\n",
+      "Collection 'bbit_ai_lab_knowledge' has been dropped.\n",
+      "An error occurred: <MilvusException: (code=65535, message=can not drop default database)>\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pymilvus import Collection, MilvusException, connections, db, utility\n",
+    "\n",
+    "conn = connections.connect(host=\"10.10.10.9\", port=19530)\n",
+    "\n",
+    "# Check if the database exists\n",
+    "db_name = \"default\"\n",
+    "\n",
+    "try:\n",
+    "    existing_databases = db.list_database()\n",
+    "    if db_name in existing_databases:\n",
+    "        print(f\"Database '{db_name}' already exists.\")\n",
+    "\n",
+    "        # Use the database context\n",
+    "        db.using_database(db_name)\n",
+    "\n",
+    "        # Drop all collections in the database\n",
+    "        collections = utility.list_collections()\n",
+    "        for collection_name in collections:\n",
+    "            collection = Collection(name=collection_name)\n",
+    "            collection.drop()\n",
+    "            print(f\"Collection '{collection_name}' has been dropped.\")\n",
+    "\n",
+    "        db.drop_database(db_name)\n",
+    "        print(f\"Database '{db_name}' has been deleted.\")\n",
+    "    else:\n",
+    "        print(f\"Database '{db_name}' does not exist.\")\n",
+    "        database = db.create_database(db_name)\n",
+    "        print(f\"Database '{db_name}' created successfully.\")\n",
+    "except MilvusException as e:\n",
+    "    print(f\"An error occurred: {e}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lang",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}