From a97b5eee751f71427be628755744c914e3e4852d Mon Sep 17 00:00:00 2001
From: joaoguilhermeS <j.guilherme.s.oliveira2@gmail.com>
Date: Sat, 22 Jun 2024 16:46:28 -0300
Subject: [PATCH] chore: Updating Courchbase Vector Store parameters format

---
 .../components/vectorstores/Couchbase.py      | 66 ++++++++-----------
 1 file changed, 27 insertions(+), 39 deletions(-)

diff --git a/src/backend/base/langflow/components/vectorstores/Couchbase.py b/src/backend/base/langflow/components/vectorstores/Couchbase.py
index 56a01d0db..1355f7802 100644
--- a/src/backend/base/langflow/components/vectorstores/Couchbase.py
+++ b/src/backend/base/langflow/components/vectorstores/Couchbase.py
@@ -12,29 +12,26 @@ from langflow.schema import Data
 class CouchbaseVectorStoreComponent(LCVectorStoreComponent):
     display_name = "Couchbase"
     description = "Couchbase Vector Store with search capabilities"
-    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/couchbase"
+    documentation = "https://python.langchain.com/v0.1/docs/integrations/document_loaders/couchbase/"
     icon = "Couchbase"
 
     inputs = [
-        StrInput(name="couchbase_connection_string", display_name="Couchbase Cluster connection string", required=True),
+        SecretStrInput(
+            name="couchbase_connection_string", display_name="Couchbase Cluster connection string", required=True
+        ),
         StrInput(name="couchbase_username", display_name="Couchbase username", required=True),
         SecretStrInput(name="couchbase_password", display_name="Couchbase password", required=True),
         StrInput(name="bucket_name", display_name="Bucket Name", required=True),
         StrInput(name="scope_name", display_name="Scope Name", required=True),
         StrInput(name="collection_name", display_name="Collection Name", required=True),
         StrInput(name="index_name", display_name="Index Name", required=True),
-        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        MultilineInput(name="search_query", display_name="Search Query"),
         DataInput(
-            name="vector_store_inputs",
-            display_name="Vector Store Inputs",
+            name="ingest_data",
+            display_name="Ingest Data",
             is_list=True,
         ),
-        BoolInput(
-            name="add_to_vector_store",
-            display_name="Add to Vector Store",
-            info="If true, the Vector Store Inputs will be added to the Vector Store.",
-        ),
-        MultilineInput(name="search_input", display_name="Search Input"),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -66,33 +63,24 @@ class CouchbaseVectorStoreComponent(LCVectorStoreComponent):
         except Exception as e:
             raise ValueError(f"Failed to connect to Couchbase: {e}")
 
-        if self.add_to_vector_store:
-            documents = []
-            for _input in self.vector_store_inputs or []:
-                if isinstance(_input, Data):
-                    documents.append(_input.to_lc_document())
-                else:
-                    documents.append(_input)
-
-            if documents:
-                couchbase_vs = CouchbaseVectorStore.from_documents(
-                    documents=documents,
-                    cluster=cluster,
-                    bucket_name=self.bucket_name,
-                    scope_name=self.scope_name,
-                    collection_name=self.collection_name,
-                    embedding=self.embedding,
-                    index_name=self.index_name,
-                )
+        documents = []
+        for _input in self.ingest_data or []:
+            if isinstance(_input, Data):
+                documents.append(_input.to_lc_document())
             else:
-                couchbase_vs = CouchbaseVectorStore(
-                    cluster=cluster,
-                    bucket_name=self.bucket_name,
-                    scope_name=self.scope_name,
-                    collection_name=self.collection_name,
-                    embedding=self.embedding,
-                    index_name=self.index_name,
-                )
+                documents.append(_input)
+
+        if documents:
+            couchbase_vs = CouchbaseVectorStore.from_documents(
+                documents=documents,
+                cluster=cluster,
+                bucket_name=self.bucket_name,
+                scope_name=self.scope_name,
+                collection_name=self.collection_name,
+                embedding=self.embedding,
+                index_name=self.index_name,
+            )
+
         else:
             couchbase_vs = CouchbaseVectorStore(
                 cluster=cluster,
@@ -108,9 +96,9 @@ class CouchbaseVectorStoreComponent(LCVectorStoreComponent):
     def search_documents(self) -> List[Data]:
         vector_store = self._build_couchbase()
 
-        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+        if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():
             docs = vector_store.similarity_search(
-                query=self.search_input,
+                query=self.search_query,
                 k=self.number_of_results,
             )