langchain/docs/docs/integrations/vectorstores/dashvector.ipynb

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   "source": [
    "# DashVector\n",
    "\n",
    "> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.\n",
    "\n",
    "This notebook shows how to use functionality related to the `DashVector` vector database.\n",
    "\n",
    "To use DashVector, you must have an API key.\n",
    "Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html)."
   ]
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   "source": [
    "## Install"
   ]
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   "source": [
    "%pip install --upgrade --quiet  dashvector dashscope"
   ]
  },
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   "source": [
    "We want to use `DashScopeEmbeddings` so we also have to get the Dashscope API Key."
   ]
  },
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   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "os.environ[\"DASHVECTOR_API_KEY\"] = getpass.getpass(\"DashVector API Key:\")\n",
    "os.environ[\"DASHSCOPE_API_KEY\"] = getpass.getpass(\"DashScope API Key:\")"
   ]
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   "source": [
    "## Example"
   ]
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   "source": [
    "from langchain_community.embeddings.dashscope import DashScopeEmbeddings\n",
    "from langchain_community.vectorstores import DashVector\n",
    "from langchain_text_splitters import CharacterTextSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "pycharm": {
     "is_executing": true,
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   "source": [
    "from langchain_community.document_loaders import TextLoader\n",
    "\n",
    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
    "documents = loader.load()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "docs = text_splitter.split_documents(documents)\n",
    "\n",
    "embeddings = DashScopeEmbeddings()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "name": "#%% md\n"
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   },
   "source": [
    "We can create DashVector from documents."
   ]
  },
  {
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   "outputs": [],
   "source": [
    "dashvector = DashVector.from_documents(docs, embeddings)\n",
    "\n",
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "docs = dashvector.similarity_search(query)\n",
    "print(docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can add texts with meta datas and ids, and search with meta filter."
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Document(page_content='baz', metadata={'key': 2})]\n"
     ]
    }
   ],
   "source": [
    "texts = [\"foo\", \"bar\", \"baz\"]\n",
    "metadatas = [{\"key\": i} for i in range(len(texts))]\n",
    "ids = [\"0\", \"1\", \"2\"]\n",
    "\n",
    "dashvector.add_texts(texts, metadatas=metadatas, ids=ids)\n",
    "\n",
    "docs = dashvector.similarity_search(\"foo\", filter=\"key = 2\")\n",
    "print(docs)"
   ]
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