Add support for Couchbase vector store (#1901)

* add couchbase vector store support

* add docs + minor changes

* Fix lint issues

* remove stray lines

* Add required validation and minor changes

* Address Comments

---------

Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>

src/backend/base/langflow/components/vectorsearch/CouchbaseSearch.py

@@ -0,0 +1,73 @@
+from typing import List, Optional
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Couchbase import CouchbaseComponent
+from langflow.field_typing import Embeddings, NestedDict, Text
+from langflow.schema import Record
+
+
+class CouchbaseSearchComponent(LCVectorStoreComponent):
+    display_name = "Couchbase Search"
+    description = "Search a Couchbase Vector Store for similar documents."
+    documentation = "https://python.langchain.com/docs/integrations/vectorstores/couchbase"
+    icon = "Couchbase"
+    field_order = [
+        "couchbase_connection_string",
+        "couchbase_username",
+        "couchbase_password",
+        "bucket_name",
+        "scope_name",
+        "collection_name",
+        "index_name",
+    ]
+
+    def build_config(self):
+        return {
+            "input_value": {"display_name": "Input"},
+            "embedding": {"display_name": "Embedding"},
+            "couchbase_connection_string": {"display_name": "Couchbase Cluster connection string","required": True},
+            "couchbase_username": {"display_name": "Couchbase username","required": True},
+            "couchbase_password": {
+                "display_name": "Couchbase password",
+                "password": True,
+                "required": True
+            },
+            "bucket_name": {"display_name": "Bucket Name","required": True},
+            "scope_name": {"display_name": "Scope Name","required": True},
+            "collection_name": {"display_name": "Collection Name","required": True},
+            "index_name": {"display_name": "Index Name","required": True},
+            "number_of_results": {
+                "display_name": "Number of Results",
+                "info": "Number of results to return.",
+                "advanced": True,
+            },
+        }
+
+    def build(  # type: ignore[override]
+        self,
+        input_value: Text,
+        embedding: Embeddings,
+        number_of_results: int = 4,
+        bucket_name: str = "",
+        scope_name: str = "",
+        collection_name: str = "",
+        index_name: str = "",
+        couchbase_connection_string: str = "",
+        couchbase_username: str = "",
+        couchbase_password: str = "",
+    ) -> List[Record]:
+        vector_store = CouchbaseComponent().build(
+            couchbase_connection_string=couchbase_connection_string,
+            couchbase_username=couchbase_username,
+            couchbase_password=couchbase_password,
+            bucket_name=bucket_name,
+            scope_name=scope_name,
+            collection_name=collection_name,
+            embedding=embedding,
+            index_name=index_name,
+        )
+        if not vector_store:
+            raise ValueError("Failed to create Couchbase Vector Store")
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type="similarity", k=number_of_results
+        )

src/backend/base/langflow/components/vectorsearch/__init__.py

@@ -9,10 +9,12 @@ from .SupabaseVectorStoreSearch import SupabaseSearchComponent
 from .VectaraSearch import VectaraSearchComponent
 from .WeaviateSearch import WeaviateSearchVectorStore
 from .pgvectorSearch import PGVectorSearchComponent
+from .Couchbase import CouchbaseSearchComponent # type: ignore
 
 __all__ = [
     "AstraDBSearchComponent",
     "ChromaSearchComponent",
+    "CouchbaseSearchComponent",
     "FAISSSearchComponent",
     "MongoDBAtlasSearchComponent",
     "PineconeSearchComponent",

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

@@ -0,0 +1,95 @@
+from typing import List, Optional, Union
+
+from langchain.schema import BaseRetriever
+
+from langchain_community.vectorstores import CouchbaseVectorStore 
+
+from langflow.custom import CustomComponent
+from langflow.field_typing import Embeddings, VectorStore
+from langflow.schema import Record
+
+from datetime import timedelta
+
+from couchbase.auth import PasswordAuthenticator # type: ignore
+from couchbase.cluster import Cluster # type: ignore
+from couchbase.options import ClusterOptions # type: ignore
+
+
+class CouchbaseComponent(CustomComponent):
+    display_name = "Couchbase"
+    description = "Construct a `Couchbase Vector Search` vector store from raw documents."
+    documentation = "https://python.langchain.com/docs/integrations/vectorstores/couchbase"
+    icon = "Couchbase"
+    field_order = [
+        "couchbase_connection_string",
+        "couchbase_username",
+        "couchbase_password",
+        "bucket_name",
+        "scope_name",
+        "collection_name",
+        "index_name",
+    ]
+
+    def build_config(self):
+        return {
+            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
+            "embedding": {"display_name": "Embedding"},
+            "couchbase_connection_string": {"display_name": "Couchbase Cluster connection string","required": True},
+            "couchbase_username": {"display_name": "Couchbase username","required": True},
+            "couchbase_password": {
+                "display_name": "Couchbase password",
+                "password": True,
+                "required": True
+            },
+            "bucket_name": {"display_name": "Bucket Name","required": True},
+            "scope_name": {"display_name": "Scope Name","required": True},
+            "collection_name": {"display_name": "Collection Name","required": True},
+            "index_name": {"display_name": "Index Name","required": True},
+        }
+
+    def build(
+        self,
+        embedding: Embeddings,
+        inputs: Optional[List[Record]] = None,
+        bucket_name: str = "",
+        scope_name: str = "",
+        collection_name: str = "",
+        index_name: str = "",
+        couchbase_connection_string: str = "",
+        couchbase_username: str = "",
+        couchbase_password: str = "",
+    ) -> Union[VectorStore, BaseRetriever]:
+        try:
+            auth = PasswordAuthenticator(couchbase_username, couchbase_password)
+            options = ClusterOptions(auth)
+            cluster = Cluster(couchbase_connection_string, options)
+
+            cluster.wait_until_ready(timedelta(seconds=5))
+        except Exception as e:
+            raise ValueError(f"Failed to connect to Couchbase: {e}")
+        documents = []
+        for _input in inputs or []:
+            if isinstance(_input, Record):
+                documents.append(_input.to_lc_document())
+            else:
+                documents.append(_input)
+        if documents:
+            vector_store = CouchbaseVectorStore.from_documents(
+                documents=documents,
+                cluster=cluster,
+                bucket_name=bucket_name,
+                scope_name=scope_name,
+                collection_name=collection_name,
+                embedding=embedding,
+                index_name=index_name,
+            )
+        else:
+            vector_store = CouchbaseVectorStore(
+                cluster=cluster,
+                bucket_name=bucket_name,
+                scope_name=scope_name,
+                collection_name=collection_name,
+                embedding=embedding,
+                index_name=index_name,
+            )
+        return vector_store

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

@@ -9,10 +9,12 @@ from .SupabaseVectorStore import SupabaseComponent
 from .Vectara import VectaraComponent
 from .Weaviate import WeaviateVectorStoreComponent
 from .pgvector import PGVectorComponent
+from .Couchbase import CouchbaseComponent
 
 __all__ = [
     "AstraDBVectorStoreComponent",
     "ChromaComponent",
+    "CouchbaseComponent",
     "FAISSComponent",
     "MongoDBAtlasComponent",
     "PineconeComponent",