11_open_source_embedding.ipynb

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   "source": [
    "import openai, os\n",
    "import faiss\n",
    "from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTFaissIndex, ServiceContext\n",
    "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
    "from langchain.text_splitter import SpacyTextSplitter, CharacterTextSplitter\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "\n",
    "openai.api_key = \"\""
   ]
  },
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      "WARNING:root:Created a chunk of size 130, which is longer than the specified 100\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: sentence-transformers/paraphrase-multilingual-mpnet-base-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device: cpu\n"
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     "output_type": "stream",
     "text": [
      "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
      "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 3198 tokens\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from llama_index.node_parser import SimpleNodeParser\n",
    "\n",
    "text_splitter = CharacterTextSplitter(separator=\"\\n\\n\", chunk_size=100, chunk_overlap=20)\n",
    "parser = SimpleNodeParser(text_splitter=text_splitter)\n",
    "documents = SimpleDirectoryReader('./data/faq/').load_data()\n",
    "nodes = parser.get_nodes_from_documents(documents)\n",
    "\n",
    "embed_model = LangchainEmbedding(HuggingFaceEmbeddings(\n",
    "    model_name=\"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\"\n",
    "))\n",
    "service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
    "\n",
    "dimension = 768\n",
    "faiss_index = faiss.IndexFlatIP(dimension)\n",
    "index = GPTFaissIndex(nodes=nodes,faiss_index=faiss_index, service_context=service_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36;1m\u001b[1;3m> Got node text: Q: 支持哪些省份配送？\n",
      "A: 我们支持全国大部分省份的配送，包括北京、上海、天津、重庆、河北、山西、辽宁、吉林、黑龙江、江苏、浙江、安徽、福建、江西、山东、河南、湖北、湖南、广东、海南、四川、贵州、云南、陕西、甘肃、青海、台湾、内蒙古、广西、西藏、宁夏和新疆...\n",
      "\u001b[0m"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 341 tokens\n",
      "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 24 tokens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "是的，我们支持海南省的配送。\n"
     ]
    }
   ],
   "source": [
    "from llama_index import QueryMode\n",
    "\n",
    "openai.api_key = os.environ.get(\"OPENAI_API_KEY\")\n",
    "\n",
    "response = index.query(\n",
    "    \"请问你们海南能发货吗？\", \n",
    "    mode=QueryMode.EMBEDDING,\n",
    "    verbose=True, \n",
    ")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "version_major": 2,
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36;1m\u001b[1;3m> Got node text: Q: 提供哪些快递公司的服务？\n",
      "A: 我们与顺丰速运、圆通速递、申通快递、韵达快递、中通快递、百世快递等多家知名快递公司合作。...\n",
      "\u001b[0m"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 281 tokens\n",
      "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 27 tokens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "我们与顺丰速运、圆通速递、申通快递、韵达快递、中通快递、百世快递等多家知名快递公司合作，用它们的服务送货。\n"
     ]
    }
   ],
   "source": [
    "response = index.query(\n",
    "    \"你们用哪些快递公司送货？\", \n",
    "    mode=QueryMode.EMBEDDING,\n",
    "    verbose=True, \n",
    ")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36;1m\u001b[1;3m> Got node text: Q: 退货政策是什么？\n",
      "A: 自收到商品之日起7天内，如产品未使用、包装完好，您可以申请退货。某些特殊商品可能不支持退货，请在购买前查看商品详情页面的退货政策。...\n",
      "\u001b[0m"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 393 tokens\n",
      "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 27 tokens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "我们的退货政策是自收到商品之日起7天内，如产品未使用、包装完好，您可以申请退货。某些特殊商品可能不支持退货，请在购买前查看商品详情页面的退货政策。\n"
     ]
    }
   ],
   "source": [
    "response = index.query(\n",
    "    \"你们的退货政策是怎么样的？\", \n",
    "    mode=QueryMode.EMBEDDING,\n",
    "    verbose=True, \n",
    ")\n",
    "print(response)"
   ]
  }
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