更新python后端
This commit is contained in:
BBIT-Kai
@@ -0,0 +1,126 @@
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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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@@ -0,0 +1,114 @@
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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": 21,
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"id": "d029ad67",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[460823023525530114, 460823023525530115]"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain_milvus import BM25BuiltInFunction, Milvus\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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"vectorstore = 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=\"knowledge\",\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 = \"kn_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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"\n",
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"from langchain.schema import Document\n",
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"\n",
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"docs = [\n",
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" Document(\n",
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" page_content=\"这是第一条文本\",\n",
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" metadata={\n",
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" \"kn_id\": \"8ecd1179-4194-4b80-bc39-5addc678df4b\",\n",
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" \"is_active\": True,\n",
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" }\n",
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" ),\n",
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" Document(\n",
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" page_content=\"这是第二条文本\",\n",
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" metadata={\n",
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" \"kn_id\": \"8ecd1179-4194-4b80-bc39-5addc678df4b\",\n",
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" \"is_active\": True,\n",
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" }\n",
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" )\n",
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"]\n",
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"\n",
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"vectorstore.add_documents(docs)\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": null,
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"id": "a480053b",
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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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"*这是第一条文本 [{'kn_id': '8ecd1179-4194-4b80-bc39-5addc678df4b', 'id': 460823023525530108, 'is_active': True}]\n",
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"*这是第一条文本 [{'kn_id': '8ecd1179-4194-4b80-bc39-5addc678df4b', 'id': 460823023525530110, 'is_active': True}]\n"
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]
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}
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],
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"source": [
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"results = vectorstore.similarity_search(\n",
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" \"\",\n",
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" k=2,\n",
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" expr='kn_id == \"8ecd1179-4194-4b80-bc39-5addc678df4b\"',\n",
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")\n",
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"for res in results:\n",
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" print(f\"*{res.page_content} [{res.metadata}]\")"
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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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@@ -0,0 +1,260 @@
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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": 11,
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"id": "dfb008fd",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from openai import OpenAI\n",
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"from glob import glob\n",
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"from pymilvus import MilvusClient\n",
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"from tqdm import tqdm"
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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": 7,
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"id": "eaa97ad1",
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"metadata": {},
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"outputs": [],
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"source": [
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"client = OpenAI(\n",
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" 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",
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" base_url=\"https://dashscope.aliyuncs.com/compatible-mode/v1\" # 百炼服务的base_url\n",
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")\n",
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"def emb_text(text):\n",
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" return client.embeddings.create(\n",
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" model=\"text-embedding-v4\",\n",
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" input=text,\n",
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" dimensions=1024, # 指定向量维度（仅 text-embedding-v3及 text-embedding-v4支持该参数）\n",
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" encoding_format=\"float\"\n",
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" ).data[0].embedding"
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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": null,
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"id": "9df315ea",
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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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"1024\n",
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"[-0.017507297918200493, 0.02571254037320614, 0.02589302882552147, -0.02639283984899521, -0.013571279123425484, -0.0032158030662685633, -0.006428135093301535, 0.02458796463906765, -0.059366535395383835, 0.13083963096141815]\n"
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]
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}
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],
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"source": [
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"# 测试\n",
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"test_embedding = emb_text(\"This is a test\")\n",
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"embedding_dim = len(test_embedding)\n",
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"print(embedding_dim)\n",
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"print(test_embedding[:10])\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": null,
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"id": "95d0a121",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Milvus数据库配置\n",
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"milvus_client = MilvusClient(uri=\"http://10.10.10.9:19530\")\n",
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"collection_name = \"my_rag_collection\"\n",
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"embedding_dim = 1024\n",
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"\n",
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"if milvus_client.has_collection(collection_name):\n",
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" milvus_client.drop_collection(collection_name)\n",
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"milvus_client.create_collection(\n",
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" collection_name=collection_name,\n",
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" dimension=embedding_dim,\n",
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" metric_type=\"IP\", # Inner product distance\n",
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" consistency_level=\"Bounded\", # Supported values are (`\"Strong\"`, `\"Session\"`, `\"Bounded\"`, `\"Eventually\"`). See https://milvus.io/docs/consistency.md#Consistency-Level for more details.\n",
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")"
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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": null,
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"id": "e09edfec",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Creating embeddings: 100%|██████████| 72/72 [00:11<00:00, 6.46it/s]\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'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}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 从文件中插入数据\n",
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"text_lines = []\n",
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"for file_path in glob(\"milvus_docs/en/faq/*.md\", recursive=True):\n",
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" with open(file_path, \"r\") as file:\n",
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" file_text = file.read()\n",
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"\n",
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" text_lines += file_text.split(\"# \")\n",
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"\n",
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"data = []\n",
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"\n",
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"for i, line in enumerate(tqdm(text_lines, desc=\"Creating embeddings\")):\n",
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" data.append({\"id\": i, \"vector\": emb_text(line), \"text\": line})\n",
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"\n",
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"milvus_client.insert(collection_name=collection_name, data=data)"
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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": 25,
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"id": "f3007553",
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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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"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"
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||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
Reference in New Issue
Block a user