feat: Add milvus vector store (#2634)

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

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

@@ -0,0 +1,123 @@
+from typing import List
+
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.helpers.data import docs_to_data
+from langflow.io import (
+    DataInput,
+    StrInput,
+    IntInput,
+    FloatInput,
+    BoolInput,
+    DictInput,
+    MultilineInput,
+    DropdownInput,
+    SecretStrInput,
+    HandleInput,
+)
+from langflow.schema import Data
+
+
+class MilvusVectorStoreComponent(LCVectorStoreComponent):
+    """Milvus vector store with search capabilities"""
+
+    display_name: str = "Milvus"
+    description: str = "Milvus vector store with search capabilities"
+    documentation = "https://python.langchain.com/docs/integrations/vectorstores/milvus"
+    name = "Milvus"
+    icon = "Milvus"
+
+    inputs = [
+        StrInput(name="collection_name", display_name="Collection Name", value="langflow"),
+        StrInput(name="collection_description", display_name="Collection Description", value=""),
+        StrInput(
+            name="uri",
+            display_name="Connection URI",
+            value="http://localhost:19530",
+        ),
+        SecretStrInput(
+            name="password",
+            display_name="Connection Password",
+            value="",
+            info="Ignore this field if no password is required to make connection.",
+        ),
+        DictInput(name="connection_args", display_name="Other Connection Arguments", advanced=True),
+        StrInput(name="primary_field", display_name="Primary Field Name", value="pk"),
+        StrInput(name="text_field", display_name="Text Field Name", value="text"),
+        StrInput(name="vector_field", display_name="Vector Field Name", value="vector"),
+        DropdownInput(
+            name="consistency_level",
+            display_name="Consistencey Level",
+            options=["Bounded", "Session", "Strong", "Eventual"],
+            value="Session",
+            advanced=True,
+        ),
+        DictInput(name="index_params", display_name="Index Parameters", advanced=True),
+        DictInput(name="search_params", display_name="Search Parameters", advanced=True),
+        BoolInput(name="drop_old", display_name="Drop Old Collection", value=False, advanced=True),
+        FloatInput(name="timeout", display_name="Timeout", 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,
+        ),
+    ]
+
+    def build_vector_store(self):
+        try:
+            from langchain_milvus.vectorstores import Milvus as LangchainMilvus
+        except ImportError:
+            raise ImportError(
+                "Could not import Milvus integration package. " "Please install it with `pip install langchain-milvus`."
+            )
+        self.connection_args.update(uri=self.uri, token=self.password)
+        milvus_store = LangchainMilvus(
+            embedding_function=self.embedding,
+            collection_name=self.collection_name,
+            collection_description=self.collection_description,
+            connection_args=self.connection_args,
+            consistency_level=self.consistency_level,
+            index_params=self.index_params,
+            search_params=self.search_params,
+            drop_old=self.drop_old,
+            auto_id=True,
+            primary_field=self.primary_field,
+            text_field=self.text_field,
+            vector_field=self.vector_field,
+            timeout=self.timeout,
+        )
+
+        documents = []
+        for _input in self.ingest_data or []:
+            if isinstance(_input, Data):
+                documents.append(_input.to_lc_document())
+            else:
+                documents.append(_input)
+
+        if documents:
+            milvus_store.add_documents(documents)
+
+        return milvus_store
+
+    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():
+            docs = vector_store.similarity_search(
+                query=self.search_query,
+                k=self.number_of_results,
+            )
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []