langchain/docs/docs/integrations/vectorstores/tair.ipynb

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   "source": [
    "# Tair\n",
    "\n",
    ">[Tair](https://www.alibabacloud.com/help/en/tair/latest/what-is-tair) is a cloud native in-memory database service developed by `Alibaba Cloud`. \n",
    "It provides rich data models and enterprise-grade capabilities to support your real-time online scenarios while maintaining full compatibility with open-source `Redis`. `Tair` also introduces persistent memory-optimized instances that are based on the new non-volatile memory (NVM) storage medium.\n",
    "\n",
    "This notebook shows how to use functionality related to the `Tair` vector database.\n",
    "\n",
    "To run, you should have a `Tair` instance up and running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings.fake import FakeEmbeddings\n",
    "from langchain_community.vectorstores import Tair\n",
    "from langchain_text_splitters import CharacterTextSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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 = FakeEmbeddings(size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Connect to Tair using the `TAIR_URL` environment variable \n",
    "```\n",
    "export TAIR_URL=\"redis://{username}:{password}@{tair_address}:{tair_port}\"\n",
    "```\n",
    "\n",
    "or the keyword argument `tair_url`.\n",
    "\n",
    "Then store documents and embeddings into Tair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tair_url = \"redis://localhost:6379\"\n",
    "\n",
    "# drop first if index already exists\n",
    "Tair.drop_index(tair_url=tair_url)\n",
    "\n",
    "vector_store = Tair.from_documents(docs, embeddings, tair_url=tair_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query similar documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "docs = vector_store.similarity_search(query)\n",
    "docs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tair Hybrid Search Index build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop first if index already exists\n",
    "Tair.drop_index(tair_url=tair_url)\n",
    "\n",
    "vector_store = Tair.from_documents(\n",
    "    docs, embeddings, tair_url=tair_url, index_params={\"lexical_algorithm\": \"bm25\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tair Hybrid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "# hybrid_ratio: 0.5 hybrid search, 0.9999 vector search, 0.0001 text search\n",
    "kwargs = {\"TEXT\": query, \"hybrid_ratio\": 0.5}\n",
    "docs = vector_store.similarity_search(query, **kwargs)\n",
    "docs[0]"
   ]
  }
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