feat: add component clickhouse for vector database

pyproject.toml

@@ -108,6 +108,7 @@ nltk = "^3.9.1"
 bson = "^0.5.10"
 lark = "^1.2.2"
 jq = "^1.8.0"
+clickhouse-connect = {version = "0.7.19", optional = true, extras = ["clickhouse-connect"]}
 
 
 [tool.poetry.group.dev.dependencies]

src/backend/base/langflow/components/vectorstores/Clickhouse.py

@@ -0,0 +1,134 @@
+from typing import List
+
+from langchain_community.vectorstores import Clickhouse, ClickhouseSettings
+
+from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
+from langflow.helpers.data import docs_to_data
+from langflow.inputs import BoolInput, FloatInput
+from langflow.io import (
+    HandleInput,
+    IntInput,
+    StrInput,
+    SecretStrInput,
+    DataInput,
+    DropdownInput,
+    MultilineInput,
+    DictInput,
+)
+from langflow.schema import Data
+
+
+class ClickhouseVectorStoreComponent(LCVectorStoreComponent):
+    display_name = "Clickhouse"
+    description = "Clickhouse Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/v0.2/docs/integrations/vectorstores/clickhouse/"
+    name = "Clickhouse"
+    icon = "Clickhouse"
+
+    inputs = [
+        StrInput(name="host", display_name="hostname", required=True, value="localhost"),
+        IntInput(name="port", display_name="port", required=True, value=8123),
+        StrInput(name="database", display_name="database", required=True),
+        StrInput(name="table", display_name="Table name", required=True),
+        StrInput(name="username", display_name="The ClickHouse user name.", required=True),
+        SecretStrInput(name="password", display_name="The password for username.", required=True),
+        DropdownInput(
+            name="index_type",
+            display_name="index_type",
+            options=["annoy", "vector_similarity"],
+            info="Type of the index.",
+            value="annoy",
+            advanced=True,
+        ),
+        DropdownInput(
+            name="metric",
+            display_name="metric",
+            options=["angular", "euclidean", "manhattan", "hamming", "dot"],
+            info="Metric to compute distance.",
+            value="angular",
+            advanced=True,
+        ),
+        BoolInput(
+            name="secure",
+            display_name="Use https/TLS. This overrides inferred values from the interface or port arguments.",
+            value=False,
+            advanced=True,
+        ),
+        StrInput(name="index_param", display_name="Param of the index", value="'L2Distance',100", advanced=True),
+        DictInput(name="index_query_params", display_name="index query params", advanced=True),
+        MultilineInput(name="search_query", display_name="Search Query"),
+        DataInput(name="ingest_data", display_name="Ingest Data", is_list=True),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+        FloatInput(name="score_threshold", display_name="Score threshold", advanced=True),
+    ]
+
+    @check_cached_vector_store
+    def build_vector_store(self) -> Clickhouse:
+        try:
+            import clickhouse_connect  # type: ignore
+        except ImportError as e:
+            raise ImportError(
+                "Failed to import Clickhouse dependencies. Install it using `pip install langflow[clickhouse-connect] --pre`"
+            ) from e
+
+        try:
+            client = clickhouse_connect.get_client(host=self.host, username=self.username, password=self.password)
+            client.command("SELECT 1")
+        except Exception as e:
+            raise ValueError(f"Failed to connect to Clickhouse: {e}")
+
+        documents = []
+        for _input in self.ingest_data or []:
+            if isinstance(_input, Data):
+                documents.append(_input.to_lc_document())
+            else:
+                documents.append(_input)
+
+        kwargs = {}
+        if self.index_param:
+            kwargs["index_param"] = self.index_param.split(",")
+        if self.index_query_params:
+            kwargs["index_query_params"] = self.index_query_params
+
+        settings = ClickhouseSettings(
+            table=self.table,
+            database=self.database,
+            host=self.host,
+            index_type=self.index_type,
+            metric=self.metric,
+            password=self.password,
+            port=self.port,
+            secure=self.secure,
+            username=self.username,
+            **kwargs,
+        )
+        if documents:
+            clickhouse_vs = Clickhouse.from_documents(documents=documents, embedding=self.embedding, config=settings)
+
+        else:
+            clickhouse_vs = Clickhouse(embedding=self.embedding, config=settings)
+
+        return clickhouse_vs
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self.build_vector_store()
+
+        if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():
+            kwargs = {}
+            if self.score_threshold:
+                kwargs["score_threshold"] = self.score_threshold
+
+            docs = vector_store.similarity_search(query=self.search_query, k=self.number_of_results, **kwargs)
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []