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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 <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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8
docs/docs/integrations/providers/alibaba_cloud.mdx
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@ -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
docs/docs/integrations/vectorstores/tablestore.ipynb Normal file
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@ -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
}

5
libs/community/langchain_community/vectorstores/__init__.py
View File

@ -245,6 +245,9 @@ if TYPE_CHECKING:
from langchain_community.vectorstores.surrealdb import (
SurrealDBStore,
)
from langchain_community.vectorstores.tablestore import (
TablestoreVectorStore,
)
from langchain_community.vectorstores.tair import (
Tair,
)
@ -391,6 +394,7 @@ __all__ = [
"StarRocks",
"SupabaseVectorStore",
"SurrealDBStore",
"TablestoreVectorStore",
"Tair",
"TencentVectorDB",
"TiDBVectorStore",
@ -495,6 +499,7 @@ _module_lookup = {
"StarRocks": "langchain_community.vectorstores.starrocks",
"SupabaseVectorStore": "langchain_community.vectorstores.supabase",
"SurrealDBStore": "langchain_community.vectorstores.surrealdb",
"TablestoreVectorStore": "langchain_community.vectorstores.tablestore",
"Tair": "langchain_community.vectorstores.tair",
"TencentVectorDB": "langchain_community.vectorstores.tencentvectordb",
"TiDBVectorStore": "langchain_community.vectorstores.tidb_vector",

564
libs/community/langchain_community/vectorstores/tablestore.py Normal file
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@ -0,0 +1,564 @@
import json
import logging
import uuid
from typing import (
Any,
Iterable,
List,
Optional,
Sequence,
Tuple,
)
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
logger = logging.getLogger(__name__)
class TablestoreVectorStore(VectorStore):
"""`Tablestore` vector store.
To use, you should have the ``tablestore`` python package installed.
Example:
.. code-block:: python
import os
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import TablestoreVectorStore
import tablestore
embeddings = OpenAIEmbeddings()
store = TablestoreVectorStore(
embeddings,
endpoint=os.getenv("end_point"),
instance_name=os.getenv("instance_name"),
access_key_id=os.getenv("access_key_id"),
access_key_secret=os.getenv("access_key_secret"),
vector_dimension=512,
# metadata mapping is used to filter non-vector fields.
metadata_mappings=[
tablestore.FieldSchema(
"type",
tablestore.FieldType.KEYWORD,
index=True,
enable_sort_and_agg=True
),
tablestore.FieldSchema(
"time",
tablestore.FieldType.LONG,
index=True,
enable_sort_and_agg=True
),
]
)
"""
def __init__(
self,
embedding: Embeddings,
*,
endpoint: Optional[str] = None,
instance_name: Optional[str] = None,
access_key_id: Optional[str] = None,
access_key_secret: Optional[str] = None,
table_name: Optional[str] = "langchain_vector_store_ots_v1",
index_name: Optional[str] = "langchain_vector_store_ots_index_v1",
text_field: Optional[str] = "content",
vector_field: Optional[str] = "embedding",
vector_dimension: int = 512,
vector_metric_type: Optional[str] = "cosine",
metadata_mappings: Optional[List[Any]] = None,
):
try:
import tablestore
except ImportError:
raise ImportError(
"Could not import tablestore python package. "
"Please install it with `pip install tablestore`."
)
self.__embedding = embedding
self.__tablestore_client = tablestore.OTSClient(
endpoint,
access_key_id,
access_key_secret,
instance_name,
retry_policy=tablestore.WriteRetryPolicy(),
)
self.__table_name = table_name
self.__index_name = index_name
self.__vector_dimension = vector_dimension
self.__vector_field = vector_field
self.__text_field = text_field
if vector_metric_type == "cosine":
self.__vector_metric_type = tablestore.VectorMetricType.VM_COSINE
elif vector_metric_type == "euclidean":
self.__vector_metric_type = tablestore.VectorMetricType.VM_EUCLIDEAN
elif vector_metric_type == "dot_product":
self.__vector_metric_type = tablestore.VectorMetricType.VM_DOT_PRODUCT
else:
raise ValueError(
f"Unsupported vector_metric_type operator: {vector_metric_type}"
)
self.__metadata_mappings = [
tablestore.FieldSchema(
self.__text_field,
tablestore.FieldType.TEXT,
index=True,
enable_sort_and_agg=False,
store=False,
analyzer=tablestore.AnalyzerType.MAXWORD,
),
tablestore.FieldSchema(
self.__vector_field,
tablestore.FieldType.VECTOR,
vector_options=tablestore.VectorOptions(
data_type=tablestore.VectorDataType.VD_FLOAT_32,
dimension=self.__vector_dimension,
metric_type=self.__vector_metric_type,
),
),
]
if metadata_mappings:
for mapping in metadata_mappings:
if not isinstance(mapping, tablestore.FieldSchema):
raise ValueError(
f"meta_data mapping should be an "
f"instance of tablestore.FieldSchema, "
f"bug got {type(mapping)}"
)
if (
mapping.field_name == text_field
or mapping.field_name == vector_field
):
continue
self.__metadata_mappings.append(mapping)
def create_table_if_not_exist(self) -> None:
"""Create table if not exist."""
try:
import tablestore
except ImportError:
raise ImportError(
"Could not import tablestore python package. "
"Please install it with `pip install tablestore`."
)
table_list = self.__tablestore_client.list_table()
if self.__table_name in table_list:
logger.info("Tablestore system table[%s] already exists", self.__table_name)
return None
logger.info(
"Tablestore system table[%s] does not exist, try to create the table.",
self.__table_name,
)
schema_of_primary_key = [("id", "STRING")]
table_meta = tablestore.TableMeta(self.__table_name, schema_of_primary_key)
table_options = tablestore.TableOptions()
reserved_throughput = tablestore.ReservedThroughput(
tablestore.CapacityUnit(0, 0)
)
try:
self.__tablestore_client.create_table(
table_meta, table_options, reserved_throughput
)
logger.info("Tablestore create table[%s] successfully.", self.__table_name)
except tablestore.OTSClientError as e:
logger.exception(
"Tablestore create system table[%s] failed with client error, "
"http_status:%d, error_message:%s",
self.__table_name,
e.get_http_status(),
e.get_error_message(),
)
except tablestore.OTSServiceError as e:
logger.exception(
"Tablestore create system table[%s] failed with client error, "
"http_status:%d, error_code:%s, error_message:%s, request_id:%s",
self.__table_name,
e.get_http_status(),
e.get_error_code(),
e.get_error_message(),
e.get_request_id(),
)
def create_search_index_if_not_exist(self) -> None:
"""Create search index if not exist."""
try:
import tablestore
except ImportError:
raise ImportError(
"Could not import tablestore python package. "
"Please install it with `pip install tablestore`."
)
search_index_list = self.__tablestore_client.list_search_index(
table_name=self.__table_name
)
if self.__index_name in [t[1] for t in search_index_list]:
logger.info("Tablestore system index[%s] already exists", self.__index_name)
return None
index_meta = tablestore.SearchIndexMeta(self.__metadata_mappings)
self.__tablestore_client.create_search_index(
self.__table_name, self.__index_name, index_meta
)
logger.info(
"Tablestore create system index[%s] successfully.", self.__index_name
)
def delete_table_if_exists(self) -> None:
"""Delete table if exists."""
search_index_list = self.__tablestore_client.list_search_index(
table_name=self.__table_name
)
for resp_tuple in search_index_list:
self.__tablestore_client.delete_search_index(resp_tuple[0], resp_tuple[1])
self.__tablestore_client.delete_table(self.__table_name)
def delete_search_index(self, table_name: str, index_name: str) -> None:
"""Delete search index."""
self.__tablestore_client.delete_search_index(table_name, index_name)
def __write_row(
self, row_id: str, content: str, embedding_vector: List[float], meta_data: dict
) -> None:
try:
import tablestore
except ImportError:
raise ImportError(
"Could not import tablestore python package. "
"Please install it with `pip install tablestore`."
)
primary_key = [("id", row_id)]
attribute_columns = [
(self.__text_field, content),
(self.__vector_field, json.dumps(embedding_vector)),
]
for k, v in meta_data.items():
item = (k, v)
attribute_columns.append(item)
row = tablestore.Row(primary_key, attribute_columns)
try:
self.__tablestore_client.put_row(self.__table_name, row)
logger.debug(
"Tablestore put row successfully. id:%s, content:%s, meta_data:%s",
row_id,
content,
meta_data,
)
except tablestore.OTSClientError as e:
logger.exception(
"Tablestore put row failed with client error:%s, "
"id:%s, content:%s, meta_data:%s",
e,
row_id,
content,
meta_data,
)
except tablestore.OTSServiceError as e:
logger.exception(
"Tablestore put row failed with client error:%s, id:%s, content:%s, "
"meta_data:%s, http_status:%d, "
"error_code:%s, error_message:%s, request_id:%s",
e,
row_id,
content,
meta_data,
e.get_http_status(),
e.get_error_code(),
e.get_error_message(),
e.get_request_id(),
)
def __delete_row(self, row_id: str) -> None:
try:
import tablestore
except ImportError:
raise ImportError(
"Could not import tablestore python package. "
"Please install it with `pip install tablestore`."
)
primary_key = [("id", row_id)]
try:
self.__tablestore_client.delete_row(self.__table_name, primary_key, None)
logger.info("Tablestore delete row successfully. id:%s", row_id)
except tablestore.OTSClientError as e:
logger.exception(
"Tablestore delete row failed with client error:%s, id:%s", e, row_id
)
except tablestore.OTSServiceError as e:
logger.exception(
"Tablestore delete row failed with client error:%s, "
"id:%s, http_status:%d, error_code:%s, error_message:%s, request_id:%s",
e,
row_id,
e.get_http_status(),
e.get_error_code(),
e.get_error_message(),
e.get_request_id(),
)
def __get_row(self, row_id: str) -> Document:
try:
import tablestore
except ImportError:
raise ImportError(
"Could not import tablestore python package. "
"Please install it with `pip install tablestore`."
)
primary_key = [("id", row_id)]
try:
_, row, _ = self.__tablestore_client.get_row(
self.__table_name, primary_key, None, None, 1
)
logger.debug("Tablestore get row successfully. id:%s", row_id)
if row is None:
raise ValueError("Can't not find row_id:%s in tablestore." % row_id)
document_id = row.primary_key[0][1]
meta_data = {}
text = ""
for col in row.attribute_columns:
key = col[0]
val = col[1]
if key == self.__text_field:
text = val
continue
meta_data[key] = val
return Document(
id=document_id,
page_content=text,
metadata=meta_data,
)
except tablestore.OTSClientError as e:
logger.exception(
"Tablestore get row failed with client error:%s, id:%s", e, row_id
)
raise e
except tablestore.OTSServiceError as e:
logger.exception(
"Tablestore get row failed with client error:%s, "
"id:%s, http_status:%d, error_code:%s, error_message:%s, request_id:%s",
e,
row_id,
e.get_http_status(),
e.get_error_code(),
e.get_error_message(),
e.get_request_id(),
)
raise e
def _tablestore_search(
self,
query_embedding: List[float],
k: int = 5,
tablestore_filter_query: Optional[Any] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
try:
import tablestore
except ImportError:
raise ImportError(
"Could not import tablestore python package. "
"Please install it with `pip install tablestore`."
)
if tablestore_filter_query:
if not isinstance(tablestore_filter_query, tablestore.Query):
raise ValueError(
f"table_store_filter_query should be "
f"an instance of tablestore.Query, "
f"bug got {type(tablestore_filter_query)}"
)
if "knn_top_k" in kwargs:
knn_top_k = kwargs["knn_top_k"]
else:
knn_top_k = k
ots_query = tablestore.KnnVectorQuery(
field_name=self.__vector_field,
top_k=knn_top_k,
float32_query_vector=query_embedding,
filter=tablestore_filter_query,
)
sort = tablestore.Sort(
sorters=[tablestore.ScoreSort(sort_order=tablestore.SortOrder.DESC)]
)
search_query = tablestore.SearchQuery(
ots_query, limit=k, get_total_count=False, sort=sort
)
try:
search_response = self.__tablestore_client.search(
table_name=self.__table_name,
index_name=self.__index_name,
search_query=search_query,
columns_to_get=tablestore.ColumnsToGet(
return_type=tablestore.ColumnReturnType.ALL
),
)
logger.info(
"Tablestore search successfully. request_id:%s",
search_response.request_id,
)
tuple_list = []
for hit in search_response.search_hits:
row = hit.row
score = hit.score
document_id = row[0][0][1]
meta_data = {}
text = ""
for col in row[1]:
key = col[0]
val = col[1]
if key == self.__text_field:
text = val
continue
if key == self.__vector_field:
val = json.loads(val)
meta_data[key] = val
doc = Document(
id=document_id,
page_content=text,
metadata=meta_data,
)
tuple_list.append((doc, score))
return tuple_list
except tablestore.OTSClientError as e:
logger.exception("Tablestore search failed with client error:%s", e)
raise e
except tablestore.OTSServiceError as e:
logger.exception(
"Tablestore search failed with client error:%s, "
"http_status:%d, error_code:%s, error_message:%s, request_id:%s",
e,
e.get_http_status(),
e.get_error_code(),
e.get_error_message(),
e.get_request_id(),
)
raise e
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
ids = ids or [str(uuid.uuid4().hex) for _ in texts]
text_list = list(texts)
embeddings = self.__embedding.embed_documents(text_list)
for i in range(len(ids)):
row_id = ids[i]
text = text_list[i]
embedding_vector = embeddings[i]
metadata = dict()
if metadatas and metadatas[i]:
metadata = metadatas[i]
self.__write_row(
row_id=row_id,
content=text,
embedding_vector=embedding_vector,
meta_data=metadata,
)
return ids
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
if ids:
for row_id in ids:
self.__delete_row(row_id)
return True
def get_by_ids(self, ids: Sequence[str], /) -> List[Document]:
return [self.__get_row(row_id) for row_id in ids]
def similarity_search(
self,
query: str,
k: int = 4,
tablestore_filter_query: Optional[Any] = None,
**kwargs: Any,
) -> List[Document]:
return [
doc
for (doc, score) in self.similarity_search_with_score(
query, k=k, tablestore_filter_query=tablestore_filter_query, **kwargs
)
]
def similarity_search_with_score(
self,
query: str,
k: int = 4,
tablestore_filter_query: Optional[Any] = None,
*args: Any,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
query_embedding = self.__embedding.embed_query(query)
return self._tablestore_search(
query_embedding,
k=k,
tablestore_filter_query=tablestore_filter_query,
**kwargs,
)
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
tablestore_filter_query: Optional[Any] = None,
**kwargs: Any,
) -> List[Document]:
return [
doc
for (doc, score) in self._tablestore_search(
embedding,
k=k,
tablestore_filter_query=tablestore_filter_query,
**kwargs,
)
]
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
endpoint: Optional[str] = None,
instance_name: Optional[str] = None,
access_key_id: Optional[str] = None,
access_key_secret: Optional[str] = None,
table_name: Optional[str] = "langchain_vector_store_ots_v1",
index_name: Optional[str] = "langchain_vector_store_ots_index_v1",
text_field: Optional[str] = "content",
vector_field: Optional[str] = "embedding",
vector_dimension: int = 512,
vector_metric_type: Optional[str] = "cosine",
metadata_mappings: Optional[List[Any]] = None,
**kwargs: Any,
) -> "TablestoreVectorStore":
store = cls(
embedding=embedding,
endpoint=endpoint,
instance_name=instance_name,
access_key_id=access_key_id,
access_key_secret=access_key_secret,
table_name=table_name,
index_name=index_name,
text_field=text_field,
vector_field=vector_field,
vector_dimension=vector_dimension,
vector_metric_type=vector_metric_type,
metadata_mappings=metadata_mappings,
)
store.create_table_if_not_exist()
store.create_search_index_if_not_exist()
store.add_texts(texts, metadatas)
return store

93
libs/community/tests/integration_tests/vectorstores/test_tablestore.py Normal file
View File

@ -0,0 +1,93 @@
"""Test tablestore functionality."""
import os
import pytest
from langchain_core.documents import Document
from langchain_community.embeddings import FakeEmbeddings
from langchain_community.vectorstores.tablestore import TablestoreVectorStore
def test_tablestore() -> None:
"""Test end to end construction and search."""
test_embedding_dimension_size = 4
embeddings = FakeEmbeddings(size=test_embedding_dimension_size)
end_point = os.getenv("end_point")
instance_name = os.getenv("instance_name")
access_key_id = os.getenv("access_key_id")
access_key_secret = os.getenv("access_key_secret")
if (
end_point is None
or instance_name is None
or access_key_id is None
or access_key_secret is None
):
pytest.skip(
"end_point is None or instance_name is None or "
"access_key_id is None or access_key_secret is None"
)
"""
1. create vector store
"""
store = TablestoreVectorStore(
embedding=embeddings,
endpoint=end_point,
instance_name=instance_name,
access_key_id=access_key_id,
access_key_secret=access_key_secret,
vector_dimension=test_embedding_dimension_size,
)
"""
2. create table and index. (only needs to be run once)
"""
store.create_table_if_not_exist()
store.create_search_index_if_not_exist()
"""
3. add document
"""
store.add_documents(
[
Document(
id="1",
page_content="1 hello world",
metadata={"type": "pc", "time": 2000},
),
Document(
id="2", page_content="abc world", metadata={"type": "pc", "time": 2009}
),
Document(
id="3",
page_content="3 text world",
metadata={"type": "sky", "time": 2010},
),
Document(
id="4", page_content="hi world", metadata={"type": "sky", "time": 2030}
),
Document(
id="5", page_content="hi world", metadata={"type": "sky", "time": 2030}
),
]
)
"""
4. delete document
"""
assert store.delete(["3"])
"""
5. get document
"""
get_docs = store.get_by_ids(["1", "4"])
assert len(get_docs) == 2
assert get_docs[0].id == "1"
assert get_docs[1].id == "4"
"""
6. similarity_search
"""
search_result = store.similarity_search_with_score(query="hello world", k=2)
assert len(search_result) == 2

1
libs/community/tests/unit_tests/vectorstores/test_imports.py
View File

@ -84,6 +84,7 @@ EXPECTED_ALL = [
"StarRocks",
"SupabaseVectorStore",
"SurrealDBStore",
"TablestoreVectorStore",
"Tair",
"TencentVectorDB",
"TiDBVectorStore",

1
libs/community/tests/unit_tests/vectorstores/test_indexing_docs.py
View File

@ -88,6 +88,7 @@ def test_compatible_vectorstore_documentation() -> None:
"SingleStoreDB",
"SupabaseVectorStore",
"SurrealDBStore",
"TablestoreVectorStore",
"TileDB",
"TimescaleVector",
"TencentVectorDB",