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community[minor]: Add TablestoreVectorStore (#25767)
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>
This commit is contained in:
ScriptShi
GitHub
@@ -245,6 +245,9 @@ if TYPE_CHECKING:
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from langchain_community.vectorstores.surrealdb import (
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SurrealDBStore,
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)
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from langchain_community.vectorstores.tablestore import (
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TablestoreVectorStore,
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)
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from langchain_community.vectorstores.tair import (
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Tair,
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)
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@@ -391,6 +394,7 @@ __all__ = [
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"StarRocks",
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"SupabaseVectorStore",
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"SurrealDBStore",
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"TablestoreVectorStore",
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"Tair",
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"TencentVectorDB",
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"TiDBVectorStore",
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@@ -495,6 +499,7 @@ _module_lookup = {
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"StarRocks": "langchain_community.vectorstores.starrocks",
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"SupabaseVectorStore": "langchain_community.vectorstores.supabase",
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"SurrealDBStore": "langchain_community.vectorstores.surrealdb",
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"TablestoreVectorStore": "langchain_community.vectorstores.tablestore",
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"Tair": "langchain_community.vectorstores.tair",
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"TencentVectorDB": "langchain_community.vectorstores.tencentvectordb",
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"TiDBVectorStore": "langchain_community.vectorstores.tidb_vector",
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564
libs/community/langchain_community/vectorstores/tablestore.py
Normal file
564
libs/community/langchain_community/vectorstores/tablestore.py
Normal file
@@ -0,0 +1,564 @@
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import json
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import logging
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import uuid
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from typing import (
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Any,
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Iterable,
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List,
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Optional,
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Sequence,
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Tuple,
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)
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.vectorstores import VectorStore
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logger = logging.getLogger(__name__)
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class TablestoreVectorStore(VectorStore):
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"""`Tablestore` vector store.
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To use, you should have the ``tablestore`` python package installed.
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Example:
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.. code-block:: python
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import os
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from langchain_openai import OpenAIEmbeddings
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from langchain_community.vectorstores import TablestoreVectorStore
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import tablestore
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embeddings = OpenAIEmbeddings()
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store = TablestoreVectorStore(
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embeddings,
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endpoint=os.getenv("end_point"),
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instance_name=os.getenv("instance_name"),
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access_key_id=os.getenv("access_key_id"),
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access_key_secret=os.getenv("access_key_secret"),
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vector_dimension=512,
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# metadata mapping is used to filter non-vector fields.
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metadata_mappings=[
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tablestore.FieldSchema(
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"type",
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tablestore.FieldType.KEYWORD,
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index=True,
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enable_sort_and_agg=True
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),
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tablestore.FieldSchema(
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"time",
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tablestore.FieldType.LONG,
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index=True,
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enable_sort_and_agg=True
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),
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]
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)
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"""
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def __init__(
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self,
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embedding: Embeddings,
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*,
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endpoint: Optional[str] = None,
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instance_name: Optional[str] = None,
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access_key_id: Optional[str] = None,
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access_key_secret: Optional[str] = None,
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table_name: Optional[str] = "langchain_vector_store_ots_v1",
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index_name: Optional[str] = "langchain_vector_store_ots_index_v1",
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text_field: Optional[str] = "content",
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vector_field: Optional[str] = "embedding",
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vector_dimension: int = 512,
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vector_metric_type: Optional[str] = "cosine",
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metadata_mappings: Optional[List[Any]] = None,
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):
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try:
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import tablestore
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except ImportError:
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raise ImportError(
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"Could not import tablestore python package. "
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"Please install it with `pip install tablestore`."
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)
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self.__embedding = embedding
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self.__tablestore_client = tablestore.OTSClient(
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endpoint,
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access_key_id,
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access_key_secret,
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instance_name,
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retry_policy=tablestore.WriteRetryPolicy(),
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)
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self.__table_name = table_name
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self.__index_name = index_name
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self.__vector_dimension = vector_dimension
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self.__vector_field = vector_field
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self.__text_field = text_field
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if vector_metric_type == "cosine":
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self.__vector_metric_type = tablestore.VectorMetricType.VM_COSINE
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elif vector_metric_type == "euclidean":
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self.__vector_metric_type = tablestore.VectorMetricType.VM_EUCLIDEAN
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elif vector_metric_type == "dot_product":
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self.__vector_metric_type = tablestore.VectorMetricType.VM_DOT_PRODUCT
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else:
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raise ValueError(
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f"Unsupported vector_metric_type operator: {vector_metric_type}"
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)
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self.__metadata_mappings = [
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tablestore.FieldSchema(
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self.__text_field,
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tablestore.FieldType.TEXT,
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index=True,
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enable_sort_and_agg=False,
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store=False,
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analyzer=tablestore.AnalyzerType.MAXWORD,
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),
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tablestore.FieldSchema(
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self.__vector_field,
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tablestore.FieldType.VECTOR,
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vector_options=tablestore.VectorOptions(
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data_type=tablestore.VectorDataType.VD_FLOAT_32,
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dimension=self.__vector_dimension,
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metric_type=self.__vector_metric_type,
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),
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),
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]
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if metadata_mappings:
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for mapping in metadata_mappings:
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if not isinstance(mapping, tablestore.FieldSchema):
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raise ValueError(
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f"meta_data mapping should be an "
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f"instance of tablestore.FieldSchema, "
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f"bug got {type(mapping)}"
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)
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if (
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mapping.field_name == text_field
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or mapping.field_name == vector_field
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):
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continue
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self.__metadata_mappings.append(mapping)
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def create_table_if_not_exist(self) -> None:
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"""Create table if not exist."""
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try:
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import tablestore
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except ImportError:
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raise ImportError(
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"Could not import tablestore python package. "
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"Please install it with `pip install tablestore`."
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)
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table_list = self.__tablestore_client.list_table()
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if self.__table_name in table_list:
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logger.info("Tablestore system table[%s] already exists", self.__table_name)
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return None
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logger.info(
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"Tablestore system table[%s] does not exist, try to create the table.",
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self.__table_name,
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)
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schema_of_primary_key = [("id", "STRING")]
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table_meta = tablestore.TableMeta(self.__table_name, schema_of_primary_key)
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table_options = tablestore.TableOptions()
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reserved_throughput = tablestore.ReservedThroughput(
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tablestore.CapacityUnit(0, 0)
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)
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try:
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self.__tablestore_client.create_table(
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table_meta, table_options, reserved_throughput
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)
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logger.info("Tablestore create table[%s] successfully.", self.__table_name)
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except tablestore.OTSClientError as e:
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logger.exception(
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"Tablestore create system table[%s] failed with client error, "
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"http_status:%d, error_message:%s",
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self.__table_name,
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e.get_http_status(),
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e.get_error_message(),
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)
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except tablestore.OTSServiceError as e:
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logger.exception(
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"Tablestore create system table[%s] failed with client error, "
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"http_status:%d, error_code:%s, error_message:%s, request_id:%s",
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self.__table_name,
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e.get_http_status(),
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e.get_error_code(),
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e.get_error_message(),
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e.get_request_id(),
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)
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def create_search_index_if_not_exist(self) -> None:
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"""Create search index if not exist."""
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try:
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import tablestore
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except ImportError:
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raise ImportError(
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"Could not import tablestore python package. "
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"Please install it with `pip install tablestore`."
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)
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search_index_list = self.__tablestore_client.list_search_index(
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table_name=self.__table_name
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)
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if self.__index_name in [t[1] for t in search_index_list]:
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logger.info("Tablestore system index[%s] already exists", self.__index_name)
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return None
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index_meta = tablestore.SearchIndexMeta(self.__metadata_mappings)
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self.__tablestore_client.create_search_index(
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self.__table_name, self.__index_name, index_meta
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)
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logger.info(
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"Tablestore create system index[%s] successfully.", self.__index_name
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)
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def delete_table_if_exists(self) -> None:
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"""Delete table if exists."""
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search_index_list = self.__tablestore_client.list_search_index(
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table_name=self.__table_name
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)
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for resp_tuple in search_index_list:
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self.__tablestore_client.delete_search_index(resp_tuple[0], resp_tuple[1])
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self.__tablestore_client.delete_table(self.__table_name)
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def delete_search_index(self, table_name: str, index_name: str) -> None:
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"""Delete search index."""
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self.__tablestore_client.delete_search_index(table_name, index_name)
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def __write_row(
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self, row_id: str, content: str, embedding_vector: List[float], meta_data: dict
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) -> None:
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try:
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import tablestore
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except ImportError:
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raise ImportError(
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"Could not import tablestore python package. "
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"Please install it with `pip install tablestore`."
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)
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primary_key = [("id", row_id)]
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attribute_columns = [
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(self.__text_field, content),
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(self.__vector_field, json.dumps(embedding_vector)),
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]
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for k, v in meta_data.items():
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item = (k, v)
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attribute_columns.append(item)
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row = tablestore.Row(primary_key, attribute_columns)
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try:
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self.__tablestore_client.put_row(self.__table_name, row)
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logger.debug(
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"Tablestore put row successfully. id:%s, content:%s, meta_data:%s",
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row_id,
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content,
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meta_data,
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)
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except tablestore.OTSClientError as e:
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logger.exception(
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"Tablestore put row failed with client error:%s, "
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"id:%s, content:%s, meta_data:%s",
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e,
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row_id,
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content,
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meta_data,
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)
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except tablestore.OTSServiceError as e:
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logger.exception(
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"Tablestore put row failed with client error:%s, id:%s, content:%s, "
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"meta_data:%s, http_status:%d, "
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"error_code:%s, error_message:%s, request_id:%s",
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e,
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row_id,
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content,
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meta_data,
|
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e.get_http_status(),
|
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e.get_error_code(),
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e.get_error_message(),
|
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e.get_request_id(),
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)
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def __delete_row(self, row_id: str) -> None:
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try:
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import tablestore
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except ImportError:
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raise ImportError(
|
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"Could not import tablestore python package. "
|
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"Please install it with `pip install tablestore`."
|
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)
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primary_key = [("id", row_id)]
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try:
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self.__tablestore_client.delete_row(self.__table_name, primary_key, None)
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logger.info("Tablestore delete row successfully. id:%s", row_id)
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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,
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row_id,
|
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e.get_http_status(),
|
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e.get_error_code(),
|
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e.get_error_message(),
|
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e.get_request_id(),
|
||||
)
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|
||||
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
|
||||
@@ -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
|
||||
@@ -84,6 +84,7 @@ EXPECTED_ALL = [
|
||||
"StarRocks",
|
||||
"SupabaseVectorStore",
|
||||
"SurrealDBStore",
|
||||
"TablestoreVectorStore",
|
||||
"Tair",
|
||||
"TencentVectorDB",
|
||||
"TiDBVectorStore",
|
||||
|
||||
@@ -88,6 +88,7 @@ def test_compatible_vectorstore_documentation() -> None:
|
||||
"SingleStoreDB",
|
||||
"SupabaseVectorStore",
|
||||
"SurrealDBStore",
|
||||
"TablestoreVectorStore",
|
||||
"TileDB",
|
||||
"TimescaleVector",
|
||||
"TencentVectorDB",
|
||||
|
||||
Reference in New Issue
Block a user