langchain/docs/extras/integrations/text_embedding/awadb.ipynb

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   "source": [
    "# AwaDB\n",
    "\n",
    ">[AwaDB](https://github.com/awa-ai/awadb) is an AI Native database for the search and storage of embedding vectors used by LLM Applications.\n",
    "\n",
    "This notebook explains how to use `AwaEmbeddings` in LangChain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ab948fc",
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   "source": [
    "# pip install awadb"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## import the library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5709b030",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import AwaEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1756b1ba",
   "metadata": {},
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   "source": [
    "Embedding = AwaEmbeddings()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2a098d",
   "metadata": {},
   "source": [
    "# Set embedding model\n",
    "Users can use `Embedding.set_model()` to specify the embedding model. \\\n",
    "The input of this function is a string which represents the model's name. \\\n",
    "The list of currently supported models can be obtained [here](https://github.com/awa-ai/awadb) \\ \\ \n",
    "\n",
    "The **default model** is `all-mpnet-base-v2`, it can be used without setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "584b9af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"our embedding test\"\n",
    "\n",
    "Embedding.set_model(\"all-mpnet-base-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be18b873",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_query = Embedding.embed_query(\"The test information\")\n",
    "res_document = Embedding.embed_documents([\"test1\", \"another test\"])"
   ]
  }
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