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community[minor]: Add TablestoreVectorStore (#25767)
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Thank you for contributing to LangChain!

- [x] **PR title**:  community: add TablestoreVectorStore



- [x] **PR message**: 
    - **Description:** add TablestoreVectorStore
    - **Dependencies:** none


- [x] **Add tests and docs**: If you're adding a new integration, please
include
  1. a test for the integration: yes
  2. an example notebook showing its use: yes

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Bagatur <[email protected]>
Co-authored-by: Bagatur <[email protected]>
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@xjtushilei @baskaryan
3 people authored Dec 13, 2024
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8 changes: 8 additions & 0 deletions docs/docs/integrations/providers/alibaba_cloud.mdx
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Expand Up @@ -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
```
385 changes: 385 additions & 0 deletions docs/docs/integrations/vectorstores/tablestore.ipynb
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{
"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"
},
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"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"pygments_lexer": "ipython3",
"version": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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