mirror of https://github.com/hwchase17/langchain
Integrate Clickhouse as Vector Store (#5650)
<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> #### Description This PR is mainly to integrate open source version of ClickHouse as Vector Store as it is easy for both local development and adoption of LangChain for enterprises who already have large scale clickhouse deployment. ClickHouse is a open source real-time OLAP database with full SQL support and a wide range of functions to assist users in writing analytical queries. Some of these functions and data structures perform distance operations between vectors, [enabling ClickHouse to be used as a vector database](https://clickhouse.com/blog/vector-search-clickhouse-p1). Recently added ClickHouse capabilities like [Approximate Nearest Neighbour (ANN) indices](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) support faster approximate matching of vectors and provide a promising development aimed to further enhance the vector matching capabilities of ClickHouse. In LangChain, some ClickHouse based commercial variant vector stores like [Chroma](https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/chroma.py) and [MyScale](https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/myscale.py), etc are already integrated, but for some enterprises with large scale Clickhouse clusters deployment, it will be more straightforward to upgrade existing clickhouse infra instead of moving to another similar vector store solution, so we believe it's a valid requirement to integrate open source version of ClickHouse as vector store. As `clickhouse-connect` is already included by other integrations, this PR won't include any new dependencies. #### Before submitting <!-- If you're adding a new integration, please include: 1. Added a test for the integration: https://github.com/haoch/langchain/blob/clickhouse/tests/integration_tests/vectorstores/test_clickhouse.py 2. Added an example notebook and document showing its use: * Notebook: https://github.com/haoch/langchain/blob/clickhouse/docs/modules/indexes/vectorstores/examples/clickhouse.ipynb * Doc: https://github.com/haoch/langchain/blob/clickhouse/docs/integrations/clickhouse.md See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> 1. Added a test for the integration: https://github.com/haoch/langchain/blob/clickhouse/tests/integration_tests/vectorstores/test_clickhouse.py 2. Added an example notebook and document showing its use: * Notebook: https://github.com/haoch/langchain/blob/clickhouse/docs/modules/indexes/vectorstores/examples/clickhouse.ipynb * Doc: https://github.com/haoch/langchain/blob/clickhouse/docs/integrations/clickhouse.md #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> @hwchase17 @dev2049 Could you please help review? --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>pull/5753/head
Hao Chen
1 year ago
committed by
GitHub
parent
2f2d27fd82
commit
a4c9053d40
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# ClickHouse
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This page covers how to use ClickHouse Vector Search within LangChain.
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[ClickHouse](https://clickhouse.com) is a open source real-time OLAP database with full SQL support and a wide range of functions to assist users in writing analytical queries. Some of these functions and data structures perform distance operations between vectors, enabling ClickHouse to be used as a vector database.
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Due to the fully parallelized query pipeline, ClickHouse can process vector search operations very quickly, especially when performing exact matching through a linear scan over all rows, delivering processing speed comparable to dedicated vector databases.
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High compression levels, tunable through custom compression codecs, enable very large datasets to be stored and queried. ClickHouse is not memory-bound, allowing multi-TB datasets containing embeddings to be queried.
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The capabilities for computing the distance between two vectors are just another SQL function and can be effectively combined with more traditional SQL filtering and aggregation capabilities. This allows vectors to be stored and queried alongside metadata, and even rich text, enabling a broad array of use cases and applications.
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Finally, experimental ClickHouse capabilities like [Approximate Nearest Neighbour (ANN) indices](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) support faster approximate matching of vectors and provide a promising development aimed to further enhance the vector matching capabilities of ClickHouse.
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## Installation
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- Install clickhouse server by [binary](https://clickhouse.com/docs/en/install) or [docker image](https://hub.docker.com/r/clickhouse/clickhouse-server/)
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- Install the Python SDK with `pip install clickhouse-connect`
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### Configure clickhouse vector index
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Customize `ClickhouseSettings` object with parameters
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```python
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from langchain.vectorstores import ClickHouse, ClickhouseSettings
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config = ClickhouseSettings(host="<clickhouse-server-host>", port=8123, ...)
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index = Clickhouse(embedding_function, config)
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index.add_documents(...)
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```
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## Wrappers
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supported functions:
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- `add_texts`
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- `add_documents`
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- `from_texts`
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- `from_documents`
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- `similarity_search`
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- `asimilarity_search`
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- `similarity_search_by_vector`
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- `asimilarity_search_by_vector`
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- `similarity_search_with_relevance_scores`
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### VectorStore
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There exists a wrapper around open source Clickhouse database, allowing you to use it as a vectorstore,
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whether for semantic search or similar example retrieval.
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To import this vectorstore:
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```python
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from langchain.vectorstores import Clickhouse
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```
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For a more detailed walkthrough of the MyScale wrapper, see [this notebook](../modules/indexes/vectorstores/examples/clickhouse.ipynb)
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"""Wrapper around open source ClickHouse VectorSearch capability."""
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from __future__ import annotations
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import json
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import logging
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from hashlib import sha1
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from threading import Thread
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from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
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from pydantic import BaseSettings
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.vectorstores.base import VectorStore
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logger = logging.getLogger()
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def has_mul_sub_str(s: str, *args: Any) -> bool:
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for a in args:
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if a not in s:
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return False
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return True
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class ClickhouseSettings(BaseSettings):
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"""ClickHouse Client Configuration
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Attribute:
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clickhouse_host (str) : An URL to connect to MyScale backend.
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Defaults to 'localhost'.
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clickhouse_port (int) : URL port to connect with HTTP. Defaults to 8443.
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username (str) : Username to login. Defaults to None.
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password (str) : Password to login. Defaults to None.
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index_type (str): index type string.
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index_param (list): index build parameter.
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index_query_params(dict): index query parameters.
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database (str) : Database name to find the table. Defaults to 'default'.
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table (str) : Table name to operate on.
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Defaults to 'vector_table'.
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metric (str) : Metric to compute distance,
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supported are ('angular', 'euclidean', 'manhattan', 'hamming',
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'dot'). Defaults to 'angular'.
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https://github.com/spotify/annoy/blob/main/src/annoymodule.cc#L149-L169
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column_map (Dict) : Column type map to project column name onto langchain
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semantics. Must have keys: `text`, `id`, `vector`,
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must be same size to number of columns. For example:
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.. code-block:: python
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{
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'id': 'text_id',
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'uuid': 'global_unique_id'
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'embedding': 'text_embedding',
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'document': 'text_plain',
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'metadata': 'metadata_dictionary_in_json',
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}
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Defaults to identity map.
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"""
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host: str = "localhost"
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port: int = 8123
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username: Optional[str] = None
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password: Optional[str] = None
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index_type: str = "annoy"
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# Annoy supports L2Distance and cosineDistance.
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index_param: Optional[Union[List, Dict]] = [100, "'L2Distance'"]
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index_query_params: Dict[str, str] = {}
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column_map: Dict[str, str] = {
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"id": "id",
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"uuid": "uuid",
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"document": "document",
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"embedding": "embedding",
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"metadata": "metadata",
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}
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database: str = "default"
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table: str = "langchain"
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metric: str = "angular"
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def __getitem__(self, item: str) -> Any:
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return getattr(self, item)
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class Config:
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env_file = ".env"
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env_prefix = "clickhouse_"
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env_file_encoding = "utf-8"
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class Clickhouse(VectorStore):
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"""Wrapper around ClickHouse vector database
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You need a `clickhouse-connect` python package, and a valid account
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to connect to ClickHouse.
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ClickHouse can not only search with simple vector indexes,
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it also supports complex query with multiple conditions,
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constraints and even sub-queries.
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For more information, please visit
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[ClickHouse official site](https://clickhouse.com/clickhouse)
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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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config: Optional[ClickhouseSettings] = None,
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**kwargs: Any,
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) -> None:
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"""ClickHouse Wrapper to LangChain
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embedding_function (Embeddings):
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config (ClickHouseSettings): Configuration to ClickHouse Client
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Other keyword arguments will pass into
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[clickhouse-connect](https://docs.clickhouse.com/)
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"""
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try:
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from clickhouse_connect import get_client
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except ImportError:
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raise ValueError(
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"Could not import clickhouse connect python package. "
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"Please install it with `pip install clickhouse-connect`."
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)
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try:
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from tqdm import tqdm
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self.pgbar = tqdm
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except ImportError:
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# Just in case if tqdm is not installed
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self.pgbar = lambda x, **kwargs: x
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super().__init__()
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if config is not None:
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self.config = config
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else:
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self.config = ClickhouseSettings()
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assert self.config
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assert self.config.host and self.config.port
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assert (
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self.config.column_map
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and self.config.database
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and self.config.table
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and self.config.metric
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)
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for k in ["id", "embedding", "document", "metadata", "uuid"]:
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assert k in self.config.column_map
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assert self.config.metric in [
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"angular",
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"euclidean",
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"manhattan",
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"hamming",
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"dot",
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]
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# initialize the schema
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dim = len(embedding.embed_query("test"))
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index_params = (
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(
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",".join([f"'{k}={v}'" for k, v in self.config.index_param.items()])
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if self.config.index_param
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else ""
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)
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if isinstance(self.config.index_param, Dict)
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else ",".join([str(p) for p in self.config.index_param])
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if isinstance(self.config.index_param, List)
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else self.config.index_param
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)
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self.schema = f"""\
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CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
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{self.config.column_map['id']} Nullable(String),
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{self.config.column_map['document']} Nullable(String),
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{self.config.column_map['embedding']} Array(Float32),
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{self.config.column_map['metadata']} JSON,
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{self.config.column_map['uuid']} UUID DEFAULT generateUUIDv4(),
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CONSTRAINT cons_vec_len CHECK length({self.config.column_map['embedding']}) = {dim},
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INDEX vec_idx {self.config.column_map['embedding']} TYPE \
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{self.config.index_type}({index_params}) GRANULARITY 1000
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) ENGINE = MergeTree ORDER BY uuid SETTINGS index_granularity = 8192\
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"""
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self.dim = dim
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self.BS = "\\"
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self.must_escape = ("\\", "'")
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self.embedding_function = embedding
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self.dist_order = "ASC" # Only support ConsingDistance and L2Distance
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# Create a connection to clickhouse
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self.client = get_client(
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host=self.config.host,
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port=self.config.port,
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username=self.config.username,
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password=self.config.password,
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**kwargs,
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)
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# Enable JSON type
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self.client.command("SET allow_experimental_object_type=1")
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# Enable Annoy index
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self.client.command("SET allow_experimental_annoy_index=1")
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self.client.command(self.schema)
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def escape_str(self, value: str) -> str:
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return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
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def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
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ks = ",".join(column_names)
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_data = []
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for n in transac:
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n = ",".join([f"'{self.escape_str(str(_n))}'" for _n in n])
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_data.append(f"({n})")
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i_str = f"""
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INSERT INTO TABLE
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{self.config.database}.{self.config.table}({ks})
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VALUES
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{','.join(_data)}
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"""
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return i_str
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def _insert(self, transac: Iterable, column_names: Iterable[str]) -> None:
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_insert_query = self._build_insert_sql(transac, column_names)
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self.client.command(_insert_query)
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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batch_size: int = 32,
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ids: Optional[Iterable[str]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Insert more texts through the embeddings and add to the VectorStore.
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Args:
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texts: Iterable of strings to add to the VectorStore.
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ids: Optional list of ids to associate with the texts.
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batch_size: Batch size of insertion
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metadata: Optional column data to be inserted
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Returns:
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List of ids from adding the texts into the VectorStore.
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"""
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# Embed and create the documents
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ids = ids or [sha1(t.encode("utf-8")).hexdigest() for t in texts]
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colmap_ = self.config.column_map
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transac = []
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column_names = {
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colmap_["id"]: ids,
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colmap_["document"]: texts,
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colmap_["embedding"]: self.embedding_function.embed_documents(list(texts)),
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}
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metadatas = metadatas or [{} for _ in texts]
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column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
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assert len(set(colmap_) - set(column_names)) >= 0
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keys, values = zip(*column_names.items())
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try:
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t = None
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for v in self.pgbar(
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zip(*values), desc="Inserting data...", total=len(metadatas)
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):
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assert (
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len(v[keys.index(self.config.column_map["embedding"])]) == self.dim
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)
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transac.append(v)
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if len(transac) == batch_size:
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if t:
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t.join()
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t = Thread(target=self._insert, args=[transac, keys])
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t.start()
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transac = []
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if len(transac) > 0:
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if t:
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t.join()
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self._insert(transac, keys)
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return [i for i in ids]
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except Exception as e:
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logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
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return []
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[Dict[Any, Any]]] = None,
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config: Optional[ClickhouseSettings] = None,
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text_ids: Optional[Iterable[str]] = None,
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batch_size: int = 32,
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**kwargs: Any,
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) -> Clickhouse:
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"""Create ClickHouse wrapper with existing texts
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Args:
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embedding_function (Embeddings): Function to extract text embedding
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texts (Iterable[str]): List or tuple of strings to be added
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config (ClickHouseSettings, Optional): ClickHouse configuration
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text_ids (Optional[Iterable], optional): IDs for the texts.
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Defaults to None.
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batch_size (int, optional): Batchsize when transmitting data to ClickHouse.
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Defaults to 32.
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metadata (List[dict], optional): metadata to texts. Defaults to None.
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Other keyword arguments will pass into
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[clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driver-api)
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Returns:
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ClickHouse Index
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"""
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ctx = cls(embedding, config, **kwargs)
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ctx.add_texts(texts, ids=text_ids, batch_size=batch_size, metadatas=metadatas)
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return ctx
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def __repr__(self) -> str:
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"""Text representation for ClickHouse Vector Store, prints backends, username
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and schemas. Easy to use with `str(ClickHouse())`
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Returns:
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repr: string to show connection info and data schema
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"""
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_repr = f"\033[92m\033[1m{self.config.database}.{self.config.table} @ "
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_repr += f"{self.config.host}:{self.config.port}\033[0m\n\n"
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_repr += f"\033[1musername: {self.config.username}\033[0m\n\nTable Schema:\n"
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_repr += "-" * 51 + "\n"
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for r in self.client.query(
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f"DESC {self.config.database}.{self.config.table}"
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).named_results():
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_repr += (
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f"|\033[94m{r['name']:24s}\033[0m|\033[96m{r['type']:24s}\033[0m|\n"
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)
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_repr += "-" * 51 + "\n"
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return _repr
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def _build_query_sql(
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self, q_emb: List[float], topk: int, where_str: Optional[str] = None
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) -> str:
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q_emb_str = ",".join(map(str, q_emb))
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if where_str:
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where_str = f"PREWHERE {where_str}"
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else:
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where_str = ""
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settings_strs = []
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if self.config.index_query_params:
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for k in self.config.index_query_params:
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settings_strs.append(f"SETTING {k}={self.config.index_query_params[k]}")
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q_str = f"""
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SELECT {self.config.column_map['document']},
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{self.config.column_map['metadata']}, dist
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FROM {self.config.database}.{self.config.table}
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{where_str}
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ORDER BY L2Distance({self.config.column_map['embedding']}, [{q_emb_str}])
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AS dist {self.dist_order}
|
||||
LIMIT {topk} {' '.join(settings_strs)}
|
||||
"""
|
||||
return q_str
|
||||
|
||||
def similarity_search(
|
||||
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
|
||||
) -> List[Document]:
|
||||
"""Perform a similarity search with ClickHouse
|
||||
|
||||
Args:
|
||||
query (str): query string
|
||||
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
|
||||
where_str (Optional[str], optional): where condition string.
|
||||
Defaults to None.
|
||||
|
||||
NOTE: Please do not let end-user to fill this and always be aware
|
||||
of SQL injection. When dealing with metadatas, remember to
|
||||
use `{self.metadata_column}.attribute` instead of `attribute`
|
||||
alone. The default name for it is `metadata`.
|
||||
|
||||
Returns:
|
||||
List[Document]: List of Documents
|
||||
"""
|
||||
return self.similarity_search_by_vector(
|
||||
self.embedding_function.embed_query(query), k, where_str, **kwargs
|
||||
)
|
||||
|
||||
def similarity_search_by_vector(
|
||||
self,
|
||||
embedding: List[float],
|
||||
k: int = 4,
|
||||
where_str: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Perform a similarity search with ClickHouse by vectors
|
||||
|
||||
Args:
|
||||
query (str): query string
|
||||
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
|
||||
where_str (Optional[str], optional): where condition string.
|
||||
Defaults to None.
|
||||
|
||||
NOTE: Please do not let end-user to fill this and always be aware
|
||||
of SQL injection. When dealing with metadatas, remember to
|
||||
use `{self.metadata_column}.attribute` instead of `attribute`
|
||||
alone. The default name for it is `metadata`.
|
||||
|
||||
Returns:
|
||||
List[Document]: List of (Document, similarity)
|
||||
"""
|
||||
q_str = self._build_query_sql(embedding, k, where_str)
|
||||
try:
|
||||
return [
|
||||
Document(
|
||||
page_content=r[self.config.column_map["document"]],
|
||||
metadata=r[self.config.column_map["metadata"]],
|
||||
)
|
||||
for r in self.client.query(q_str).named_results()
|
||||
]
|
||||
except Exception as e:
|
||||
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
|
||||
return []
|
||||
|
||||
def similarity_search_with_relevance_scores(
|
||||
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
|
||||
) -> List[Tuple[Document, float]]:
|
||||
"""Perform a similarity search with ClickHouse
|
||||
|
||||
Args:
|
||||
query (str): query string
|
||||
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
|
||||
where_str (Optional[str], optional): where condition string.
|
||||
Defaults to None.
|
||||
|
||||
NOTE: Please do not let end-user to fill this and always be aware
|
||||
of SQL injection. When dealing with metadatas, remember to
|
||||
use `{self.metadata_column}.attribute` instead of `attribute`
|
||||
alone. The default name for it is `metadata`.
|
||||
|
||||
Returns:
|
||||
List[Document]: List of documents
|
||||
"""
|
||||
q_str = self._build_query_sql(
|
||||
self.embedding_function.embed_query(query), k, where_str
|
||||
)
|
||||
try:
|
||||
return [
|
||||
(
|
||||
Document(
|
||||
page_content=r[self.config.column_map["document"]],
|
||||
metadata=r[self.config.column_map["metadata"]],
|
||||
),
|
||||
r["dist"],
|
||||
)
|
||||
for r in self.client.query(q_str).named_results()
|
||||
]
|
||||
except Exception as e:
|
||||
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
|
||||
return []
|
||||
|
||||
def drop(self) -> None:
|
||||
"""
|
||||
Helper function: Drop data
|
||||
"""
|
||||
self.client.command(
|
||||
f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}"
|
||||
)
|
||||
|
||||
@property
|
||||
def metadata_column(self) -> str:
|
||||
return self.config.column_map["metadata"]
|
||||
@ -0,0 +1,108 @@
|
||||
"""Test ClickHouse functionality."""
|
||||
import pytest
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.vectorstores import Clickhouse, ClickhouseSettings
|
||||
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
|
||||
|
||||
|
||||
def test_clickhouse() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
config = ClickhouseSettings()
|
||||
config.table = "test_clickhouse"
|
||||
docsearch = Clickhouse.from_texts(texts, FakeEmbeddings(), config=config)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
|
||||
docsearch.drop()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_clickhouse_async() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
config = ClickhouseSettings()
|
||||
config.table = "test_clickhouse_async"
|
||||
docsearch = Clickhouse.from_texts(
|
||||
texts=texts, embedding=FakeEmbeddings(), config=config
|
||||
)
|
||||
output = await docsearch.asimilarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
|
||||
docsearch.drop()
|
||||
|
||||
|
||||
def test_clickhouse_with_metadatas() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
config = ClickhouseSettings()
|
||||
config.table = "test_clickhouse_with_metadatas"
|
||||
docsearch = Clickhouse.from_texts(
|
||||
texts=texts,
|
||||
embedding=FakeEmbeddings(),
|
||||
config=config,
|
||||
metadatas=metadatas,
|
||||
)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo", metadata={"page": "0"})]
|
||||
docsearch.drop()
|
||||
|
||||
|
||||
def test_clickhouse_with_metadatas_with_relevance_scores() -> None:
|
||||
"""Test end to end construction and scored search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
config = ClickhouseSettings()
|
||||
config.table = "test_clickhouse_with_metadatas_with_relevance_scores"
|
||||
docsearch = Clickhouse.from_texts(
|
||||
texts=texts, embedding=FakeEmbeddings(), metadatas=metadatas, config=config
|
||||
)
|
||||
output = docsearch.similarity_search_with_relevance_scores("foo", k=1)
|
||||
assert output[0][0] == Document(page_content="foo", metadata={"page": "0"})
|
||||
docsearch.drop()
|
||||
|
||||
|
||||
def test_clickhouse_search_filter() -> None:
|
||||
"""Test end to end construction and search with metadata filtering."""
|
||||
texts = ["far", "bar", "baz"]
|
||||
metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
|
||||
config = ClickhouseSettings()
|
||||
config.table = "test_clickhouse_search_filter"
|
||||
docsearch = Clickhouse.from_texts(
|
||||
texts=texts, embedding=FakeEmbeddings(), metadatas=metadatas, config=config
|
||||
)
|
||||
output = docsearch.similarity_search(
|
||||
"far", k=1, where_str=f"{docsearch.metadata_column}.first_letter='f'"
|
||||
)
|
||||
assert output == [Document(page_content="far", metadata={"first_letter": "f"})]
|
||||
output = docsearch.similarity_search(
|
||||
"bar", k=1, where_str=f"{docsearch.metadata_column}.first_letter='b'"
|
||||
)
|
||||
assert output == [Document(page_content="bar", metadata={"first_letter": "b"})]
|
||||
docsearch.drop()
|
||||
|
||||
|
||||
def test_clickhouse_with_persistence() -> None:
|
||||
"""Test end to end construction and search, with persistence."""
|
||||
config = ClickhouseSettings()
|
||||
config.table = "test_clickhouse_with_persistence"
|
||||
texts = [
|
||||
"foo",
|
||||
"bar",
|
||||
"baz",
|
||||
]
|
||||
docsearch = Clickhouse.from_texts(
|
||||
texts=texts, embedding=FakeEmbeddings(), config=config
|
||||
)
|
||||
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
|
||||
|
||||
# Get a new VectorStore with same config
|
||||
# it will reuse the table spontaneously
|
||||
# unless you drop it
|
||||
docsearch = Clickhouse(embedding=FakeEmbeddings(), config=config)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
|
||||
# Clean up
|
||||
docsearch.drop()
|
||||
Loading…
Reference in New Issue