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from langchain_community.vectorstores import Clickhouse, ClickhouseSettings |
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from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store |
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from langflow.helpers.data import docs_to_data |
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from langflow.inputs import BoolInput, FloatInput |
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from langflow.io import ( |
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DataInput, |
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DictInput, |
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DropdownInput, |
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HandleInput, |
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IntInput, |
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MultilineInput, |
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SecretStrInput, |
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StrInput, |
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) |
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from langflow.schema import Data |
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class ClickhouseVectorStoreComponent(LCVectorStoreComponent): |
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display_name = "Clickhouse" |
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description = "Clickhouse Vector Store with search capabilities" |
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documentation = "https://python.langchain.com/v0.2/docs/integrations/vectorstores/clickhouse/" |
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name = "Clickhouse" |
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icon = "Clickhouse" |
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inputs = [ |
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StrInput(name="host", display_name="hostname", required=True, value="localhost"), |
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IntInput(name="port", display_name="port", required=True, value=8123), |
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StrInput(name="database", display_name="database", required=True), |
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StrInput(name="table", display_name="Table name", required=True), |
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StrInput(name="username", display_name="The ClickHouse user name.", required=True), |
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SecretStrInput(name="password", display_name="The password for username.", required=True), |
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DropdownInput( |
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name="index_type", |
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display_name="index_type", |
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options=["annoy", "vector_similarity"], |
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info="Type of the index.", |
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value="annoy", |
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advanced=True, |
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), |
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DropdownInput( |
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name="metric", |
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display_name="metric", |
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options=["angular", "euclidean", "manhattan", "hamming", "dot"], |
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info="Metric to compute distance.", |
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value="angular", |
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advanced=True, |
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), |
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BoolInput( |
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name="secure", |
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display_name="Use https/TLS. This overrides inferred values from the interface or port arguments.", |
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value=False, |
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advanced=True, |
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), |
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StrInput(name="index_param", display_name="Param of the index", value="'L2Distance',100", advanced=True), |
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DictInput(name="index_query_params", display_name="index query params", advanced=True), |
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MultilineInput(name="search_query", display_name="Search Query"), |
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DataInput(name="ingest_data", display_name="Ingest Data", is_list=True), |
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HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]), |
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IntInput( |
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name="number_of_results", |
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display_name="Number of Results", |
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info="Number of results to return.", |
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value=4, |
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advanced=True, |
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), |
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FloatInput(name="score_threshold", display_name="Score threshold", advanced=True), |
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] |
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@check_cached_vector_store |
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def build_vector_store(self) -> Clickhouse: |
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try: |
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import clickhouse_connect |
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except ImportError as e: |
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msg = ( |
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"Failed to import Clickhouse dependencies. " |
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"Install it using `pip install langflow[clickhouse-connect] --pre`" |
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) |
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raise ImportError(msg) from e |
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try: |
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client = clickhouse_connect.get_client(host=self.host, username=self.username, password=self.password) |
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client.command("SELECT 1") |
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except Exception as e: |
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msg = f"Failed to connect to Clickhouse: {e}" |
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raise ValueError(msg) from e |
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documents = [] |
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for _input in self.ingest_data or []: |
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if isinstance(_input, Data): |
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documents.append(_input.to_lc_document()) |
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else: |
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documents.append(_input) |
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kwargs = {} |
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if self.index_param: |
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kwargs["index_param"] = self.index_param.split(",") |
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if self.index_query_params: |
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kwargs["index_query_params"] = self.index_query_params |
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settings = ClickhouseSettings( |
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table=self.table, |
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database=self.database, |
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host=self.host, |
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index_type=self.index_type, |
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metric=self.metric, |
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password=self.password, |
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port=self.port, |
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secure=self.secure, |
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username=self.username, |
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**kwargs, |
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) |
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if documents: |
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clickhouse_vs = Clickhouse.from_documents(documents=documents, embedding=self.embedding, config=settings) |
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else: |
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clickhouse_vs = Clickhouse(embedding=self.embedding, config=settings) |
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return clickhouse_vs |
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def search_documents(self) -> list[Data]: |
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vector_store = self.build_vector_store() |
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if self.search_query and isinstance(self.search_query, str) and self.search_query.strip(): |
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kwargs = {} |
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if self.score_threshold: |
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kwargs["score_threshold"] = self.score_threshold |
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docs = vector_store.similarity_search(query=self.search_query, k=self.number_of_results, **kwargs) |
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data = docs_to_data(docs) |
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self.status = data |
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return data |
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return [] |
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