Add TablestoreVectorStore.

docs/docs/integrations/providers/alibaba_cloud.mdx

@@ -89,3 +89,11 @@ See [installation instructions and a usage example](/docs/integrations/vectorsto
 ```python
 from langchain_community.vectorstores import Hologres
 ```
+
+### Tablestore
+
+See [installation instructions and a usage example](/docs/integrations/vectorstores/tablestore).
+
+```python
+from langchain_community.vectorstores import TablestoreVectorStore
+```

docs/docs/integrations/vectorstores/tablestore.ipynb

@@ -0,0 +1,385 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# TablestoreVectorStore\n",
+    "\n",
+    "> [Tablestore](https://www.aliyun.com/product/ots) is a fully managed NoSQL cloud database service that enables storage of a massive amount of structured\n",
+    "and semi-structured data.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `Tablestore` vector database.\n",
+    "\n",
+    "To use Tablestore, you must create an instance.\n",
+    "Here are the [creating instance instructions](https://help.aliyun.com/zh/tablestore/getting-started/manage-the-wide-column-model-in-the-tablestore-console)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": "## Setup"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain-community tablestore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## Initialization"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-20T11:10:04.469458Z",
+     "start_time": "2024-08-20T11:09:49.541150Z"
+    },
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"end_point\"] = getpass.getpass(\"Tablestore end_point:\")\n",
+    "os.environ[\"instance_name\"] = getpass.getpass(\"Tablestore instance_name:\")\n",
+    "os.environ[\"access_key_id\"] = getpass.getpass(\"Tablestore access_key_id:\")\n",
+    "os.environ[\"access_key_secret\"] = getpass.getpass(\"Tablestore access_key_secret:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "Create vector store. "
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-20T11:10:07.911086Z",
+     "start_time": "2024-08-20T11:10:07.351293Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import tablestore\n",
+    "from langchain_community.embeddings import FakeEmbeddings\n",
+    "from langchain_community.vectorstores import TablestoreVectorStore\n",
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "test_embedding_dimension_size = 4\n",
+    "embeddings = FakeEmbeddings(size=test_embedding_dimension_size)\n",
+    "\n",
+    "store = TablestoreVectorStore(\n",
+    "    embedding=embeddings,\n",
+    "    endpoint=os.getenv(\"end_point\"),\n",
+    "    instance_name=os.getenv(\"instance_name\"),\n",
+    "    access_key_id=os.getenv(\"access_key_id\"),\n",
+    "    access_key_secret=os.getenv(\"access_key_secret\"),\n",
+    "    vector_dimension=test_embedding_dimension_size,\n",
+    "    # metadata mapping is used to filter non-vector fields.\n",
+    "    metadata_mappings=[\n",
+    "        tablestore.FieldSchema(\n",
+    "            \"type\", tablestore.FieldType.KEYWORD, index=True, enable_sort_and_agg=True\n",
+    "        ),\n",
+    "        tablestore.FieldSchema(\n",
+    "            \"time\", tablestore.FieldType.LONG, index=True, enable_sort_and_agg=True\n",
+    "        ),\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## Manage vector store"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "Create table and index."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-20T11:10:10.875422Z",
+     "start_time": "2024-08-20T11:10:10.566400Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "store.create_table_if_not_exist()\n",
+    "store.create_search_index_if_not_exist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "Add documents."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-20T11:10:14.974253Z",
+     "start_time": "2024-08-20T11:10:14.894190Z"
+    },
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['1', '2', '3', '4', '5']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "store.add_documents(\n",
+    "    [\n",
+    "        Document(\n",
+    "            id=\"1\", page_content=\"1 hello world\", metadata={\"type\": \"pc\", \"time\": 2000}\n",
+    "        ),\n",
+    "        Document(\n",
+    "            id=\"2\", page_content=\"abc world\", metadata={\"type\": \"pc\", \"time\": 2009}\n",
+    "        ),\n",
+    "        Document(\n",
+    "            id=\"3\", page_content=\"3 text world\", metadata={\"type\": \"sky\", \"time\": 2010}\n",
+    "        ),\n",
+    "        Document(\n",
+    "            id=\"4\", page_content=\"hi world\", metadata={\"type\": \"sky\", \"time\": 2030}\n",
+    "        ),\n",
+    "        Document(\n",
+    "            id=\"5\", page_content=\"hi world\", metadata={\"type\": \"sky\", \"time\": 2030}\n",
+    "        ),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": "Delete document."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-20T11:10:17.408739Z",
+     "start_time": "2024-08-20T11:10:17.269593Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "store.delete([\"3\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": "Get documents."
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## Query vector store"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-20T11:10:19.379617Z",
+     "start_time": "2024-08-20T11:10:19.339970Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='1', metadata={'embedding': '[1.3296732307905934, 0.0037521341868022385, 0.9821875819319514, 2.5644103644492393]', 'time': 2000, 'type': 'pc'}, page_content='1 hello world'),\n",
+       " None,\n",
+       " Document(id='5', metadata={'embedding': '[1.4558082172139821, -1.6441137122167426, -0.13113098640337423, -1.889685473174525]', 'time': 2030, 'type': 'sky'}, page_content='hi world')]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "store.get_by_ids([\"1\", \"3\", \"5\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "Similarity search."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-20T11:10:21.306717Z",
+     "start_time": "2024-08-20T11:10:21.284244Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='1', metadata={'embedding': [1.3296732307905934, 0.0037521341868022385, 0.9821875819319514, 2.5644103644492393], 'time': 2000, 'type': 'pc'}, page_content='1 hello world'),\n",
+       " Document(id='4', metadata={'embedding': [-0.3310144199800685, 0.29250046478723635, -0.0646862290377582, -0.23664360156781225], 'time': 2030, 'type': 'sky'}, page_content='hi world')]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "store.similarity_search(query=\"hello world\", k=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "Similarity search with filters. "
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-20T11:10:23.231425Z",
+     "start_time": "2024-08-20T11:10:23.213046Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='5', metadata={'embedding': [1.4558082172139821, -1.6441137122167426, -0.13113098640337423, -1.889685473174525], 'time': 2030, 'type': 'sky'}, page_content='hi world'),\n",
+       " Document(id='4', metadata={'embedding': [-0.3310144199800685, 0.29250046478723635, -0.0646862290377582, -0.23664360156781225], 'time': 2030, 'type': 'sky'}, page_content='hi world')]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "store.similarity_search(\n",
+    "    query=\"hello world\",\n",
+    "    k=10,\n",
+    "    tablestore_filter_query=tablestore.BoolQuery(\n",
+    "        must_queries=[tablestore.TermQuery(field_name=\"type\", column_value=\"sky\")],\n",
+    "        should_queries=[tablestore.RangeQuery(field_name=\"time\", range_from=2020)],\n",
+    "        must_not_queries=[tablestore.TermQuery(field_name=\"type\", column_value=\"pc\")],\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage for retrieval-augmented generation\n",
+    "\n",
+    "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
+    "\n",
+    "- [Tutorials](/docs/tutorials/)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `TablestoreVectorStore` features and configurations head to the API reference:\n",
+    " https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.tablestore.TablestoreVectorStore.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}