<feat>: update vectorstores to new format:

- CassandraVectorStoreComponent;
  - CouchbaseVectorStoreComponent;
  - FaissVectorStoreComponent;
  - MongoVectorStoreComponent;
  - PGVectorStoreComponent;
  - PineconeVectorStoreComponent;
  - QdrantVectorStoreComponent;
  - SupabaseVectorStoreComponent;
  - UpstashVectorStoreComponent;
  - VectaraVectorStoreComponent;
  - WeaviateVectorStoreComponent;

Fixes:
  - set token of CassandraVectorStoreComponent as secret;
  - set couchbase_password of CouchbaseVectorStoreComponent
as secret;
  - set mongodb_atlas_cluster_uri of MongoVectorStoreCompon
ent as secret;
  - set pg_server_url of PGVectorStoreComponent as secret;
  - set pinecone_api_key of PineconeVectorStoreComponent as
secret;
  - set api_key of QdrantVectorStoreComponent as secret;
  - set supabase_service_key of SupabaseVectorStoreComponen
t as secret;
  - set index_token of UpstashVectorStoreComponent as secre
t;
  - set vectara_api_key of VectaraVectorStoreComponent as s
ecret;
  - set api_key of WeaviateVectorStoreComponent as secret;

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

@@ -1,7 +1,16 @@
 from loguru import logger
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent
-from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput
+from langflow.io import (
+    BoolInput,
+    DropdownInput,
+    HandleInput,
+    IntInput,
+    MultilineInput,
+    SecretStrInput,
+    StrInput,
+    DataInput,
+)
 from langflow.schema import Data
 
 
@@ -29,10 +38,9 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
             info="API endpoint URL for the Astra DB service.",
             value="ASTRA_DB_API_ENDPOINT",
         ),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
         HandleInput(

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

@@ -1,15 +1,23 @@
 from typing import List
 
 from langchain_community.vectorstores import Cassandra
-from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import Component
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
-from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.io import (
+    BoolInput,
+    DropdownInput,
+    HandleInput,
+    IntInput,
+    SecretStrInput,
+    TextInput,
+    DataInput,
+    MultilineInput,
+)
 from langflow.schema import Data
 
 
-class CassandraVectorStoreComponent(Component):
+class CassandraVectorStoreComponent(LCVectorStoreComponent):
     display_name = "Cassandra"
     description = "Cassandra Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/cassandra"
@@ -22,14 +30,14 @@ class CassandraVectorStoreComponent(Component):
             info="Authentication token for accessing Cassandra on Astra DB.",
             required=True,
         ),
-        StrInput(name="database_id", display_name="Database ID", info="The Astra database ID.", required=True),
+        TextInput(name="database_id", display_name="Database ID", info="The Astra database ID.", required=True),
-        StrInput(
+        TextInput(
             name="table_name",
             display_name="Table Name",
             info="The name of the table where vectors will be stored.",
             required=True,
         ),
-        StrInput(
+        TextInput(
             name="keyspace",
             display_name="Keyspace",
             info="Optional key space within Astra DB. The keyspace should already be created.",
@@ -48,7 +56,7 @@ class CassandraVectorStoreComponent(Component):
             value=16,
             advanced=True,
         ),
-        StrInput(
+        TextInput(
             name="body_index_options",
             display_name="Body Index Options",
             info="Optional options used to create the body index.",
@@ -63,10 +71,9 @@ class CassandraVectorStoreComponent(Component):
             advanced=True,
         ),
         HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
         BoolInput(
@@ -74,7 +81,7 @@ class CassandraVectorStoreComponent(Component):
             display_name="Add to Vector Store",
             info="If true, the Vector Store Inputs will be added to the Vector Store.",
         ),
-        StrInput(name="search_input", display_name="Search Input"),
+        MultilineInput(name="search_input", display_name="Search Input"),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -84,17 +91,6 @@ class CassandraVectorStoreComponent(Component):
         ),
     ]
 
-    outputs = [
-        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Cassandra),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-            output_type=BaseRetriever,
-        ),
-        Output(display_name="Search Results", name="search_results", method="search_documents"),
-    ]
-
     def build_vector_store(self) -> Cassandra:
         return self._build_cassandra()
 

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

@@ -7,7 +7,7 @@ from loguru import logger
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.base.vectorstores.utils import chroma_collection_to_data
-from langflow.io import BoolInput, DataInput, DropdownInput, HandleInput, IntInput, StrInput, TextInput
+from langflow.io import BoolInput, DataInput, DropdownInput, HandleInput, IntInput, StrInput, MultilineInput
 from langflow.schema import Data
 
 if TYPE_CHECKING:
@@ -34,13 +34,14 @@ class ChromaVectorStoreComponent(LCVectorStoreComponent):
             name="persist_directory",
             display_name="Persist Directory",
         ),
-        TextInput(
+        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"]),
         StrInput(
@@ -96,7 +97,7 @@ class ChromaVectorStoreComponent(LCVectorStoreComponent):
         ),
     ]
 
-    def build_vector_store(self) -> "Chroma":
+    def build_vector_store(self) -> Chroma:
         """
         Builds the Chroma object.
         """

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

@@ -2,15 +2,14 @@ from datetime import timedelta
 from typing import List
 
 from langchain_community.vectorstores import CouchbaseVectorStore
-from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import Component
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
-from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.io import BoolInput, HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
 from langflow.schema import Data
 
 
-class CouchbaseVectorStoreComponent(Component):
+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"
@@ -25,10 +24,9 @@ class CouchbaseVectorStoreComponent(Component):
         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"]),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
         BoolInput(
@@ -36,7 +34,7 @@ class CouchbaseVectorStoreComponent(Component):
             display_name="Add to Vector Store",
             info="If true, the Vector Store Inputs will be added to the Vector Store.",
         ),
-        StrInput(name="search_input", display_name="Search Input"),
+        MultilineInput(name="search_input", display_name="Search Input"),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -46,22 +44,6 @@ class CouchbaseVectorStoreComponent(Component):
         ),
     ]
 
-    outputs = [
-        Output(
-            display_name="Vector Store",
-            name="vector_store",
-            method="build_vector_store",
-            output_type=CouchbaseVectorStore,
-        ),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-            output_type=BaseRetriever,
-        ),
-        Output(display_name="Search Results", name="search_results", method="search_documents"),
-    ]
-
     def build_vector_store(self) -> CouchbaseVectorStore:
         return self._build_couchbase()
 

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

@@ -6,7 +6,7 @@ from loguru import logger
 from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.field_typing import Text
 from langflow.helpers.data import docs_to_data
-from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.io import BoolInput, HandleInput, IntInput, StrInput, DataInput, MultilineInput
 from langflow.schema import Data
 
 
@@ -32,10 +32,9 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
             value="langflow_index",
         ),
         HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
-        StrInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
         BoolInput(
@@ -48,9 +47,9 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
             display_name="Allow Dangerous Deserialization",
             info="Set to True to allow loading pickle files from untrusted sources. Only enable this if you trust the source of the data.",
             advanced=True,
-            value=False,
+            value=True,
         ),
-        StrInput(
+        MultilineInput(
             name="search_input",
             display_name="Search Input",
         ),
@@ -63,24 +62,6 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
         ),
     ]
 
-    outputs = [
-        Output(
-            display_name="Vector Store",
-            name="vector_store",
-            method="build_vector_store",
-        ),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-        ),
-        Output(
-            display_name="Search Results",
-            name="search_results",
-            method="search_documents",
-        ),
-    ]
-
     def build_vector_store(self) -> FAISS:
         """
         Builds the FAISS object.
@@ -100,19 +81,12 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
             faiss = FAISS.from_documents(documents=documents, embedding=self.embedding)
             faiss.save_local(Text(path), self.index_name)
         else:
-            try:
+            faiss = FAISS.load_local(
-                faiss = FAISS.load_local(
+                folder_path=Text(path),
-                    folder_path=Text(path),
+                embeddings=self.embedding,
-                    embeddings=self.embedding,
+                index_name=self.index_name,
-                    index_name=self.index_name,
+                allow_dangerous_deserialization=self.allow_dangerous_deserialization,
-                    allow_dangerous_deserialization=self.allow_dangerous_deserialization,
+            )
-                )
-            except Exception as e:
-                raise ValueError(
-                    "Failed to load the FAISS index. Make sure the index was created with trusted data. "
-                    "If you trust the data source, you can set `allow_dangerous_deserialization` to `True` "
-                    "in the component's advanced settings to enable deserialization."
-                ) from e
 
         return faiss
 
@@ -124,19 +98,12 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
             raise ValueError("Folder path is required to load the FAISS index.")
         path = self.resolve_path(self.folder_path)
 
-        try:
+        vector_store = FAISS.load_local(
-            vector_store = FAISS.load_local(
+            folder_path=Text(path),
-                folder_path=Text(path),
+            embeddings=self.embedding,
-                embeddings=self.embedding,
+            index_name=self.index_name,
-                index_name=self.index_name,
+            allow_dangerous_deserialization=self.allow_dangerous_deserialization,
-                allow_dangerous_deserialization=self.allow_dangerous_deserialization,
+        )
-            )
-        except Exception as e:
-            raise ValueError(
-                "Failed to load the FAISS index. Make sure the index was created with trusted data. "
-                "If you trust the data source, you can set `allow_dangerous_deserialization` to `True` "
-                "in the component's advanced settings to enable deserialization."
-            ) from e
 
         if not vector_store:
             raise ValueError("Failed to load the FAISS index.")

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

@@ -1,30 +1,28 @@
 from typing import List
 
 from langchain_community.vectorstores import MongoDBAtlasVectorSearch
-from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import Component
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
-from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.io import BoolInput, HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
 from langflow.schema import Data
 
 
-class MongoVectorStoreComponent(Component):
+class MongoVectorStoreComponent(LCVectorStoreComponent):
     display_name = "MongoDB Atlas"
     description = "MongoDB Atlas Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/mongodb_atlas"
     icon = "MongoDB"
 
     inputs = [
-        StrInput(name="mongodb_atlas_cluster_uri", display_name="MongoDB Atlas Cluster URI", required=True),
+        SecretStrInput(name="mongodb_atlas_cluster_uri", display_name="MongoDB Atlas Cluster URI", required=True),
         StrInput(name="db_name", display_name="Database 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"]),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
         BoolInput(
@@ -32,7 +30,7 @@ class MongoVectorStoreComponent(Component):
             display_name="Add to Vector Store",
             info="If true, the Vector Store Inputs will be added to the Vector Store.",
         ),
-        StrInput(name="search_input", display_name="Search Input"),
+        MultilineInput(name="search_input", display_name="Search Input"),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -42,22 +40,6 @@ class MongoVectorStoreComponent(Component):
         ),
     ]
 
-    outputs = [
-        Output(
-            display_name="Vector Store",
-            name="vector_store",
-            method="build_vector_store",
-            output_type=MongoDBAtlasVectorSearch,
-        ),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-            output_type=BaseRetriever,
-        ),
-        Output(display_name="Search Results", name="search_results", method="search_documents"),
-    ]
-
     def build_vector_store(self) -> MongoDBAtlasVectorSearch:
         return self._build_mongodb_atlas()
 
@@ -83,12 +65,7 @@ class MongoVectorStoreComponent(Component):
 
             if documents:
                 vector_store = MongoDBAtlasVectorSearch.from_documents(
-                    documents=documents,
+                    documents=documents, embedding=self.embedding, collection=collection, index_name=self.index_name
-                    embedding=self.embedding,
-                    collection=collection,
-                    db_name=self.db_name,
-                    index_name=self.index_name,
-                    mongodb_atlas_cluster_uri=self.mongodb_atlas_cluster_uri,
                 )
             else:
                 vector_store = MongoDBAtlasVectorSearch(
@@ -106,14 +83,31 @@ class MongoVectorStoreComponent(Component):
         return vector_store
 
     def search_documents(self) -> List[Data]:
+        from typing import Union
+        from bson import ObjectId
+        from langchain.schema import Document
+        import json
+
         vector_store = self._build_mongodb_atlas()
 
-        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+        class JSONEncoder(json.JSONEncoder):
+            def default(self, o):
+                if isinstance(o, Union[ObjectId, Document]):
+                    return str(o)
+                return super(JSONEncoder, self).default(o)
+
+        if self.search_input and isinstance(self.search_input, str):
             docs = vector_store.similarity_search(
                 query=self.search_input,
                 k=self.number_of_results,
             )
-
+            for index in range(len(docs)):
+                docs[index].metadata = {
+                    key: str(docs[index].metadata[key])
+                    if isinstance(docs[index].metadata[key], ObjectId)
+                    else docs[index].metadata[key]
+                    for key in docs[index].metadata
+                }
             data = docs_to_data(docs)
             self.status = data
             return data

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

@@ -1,15 +1,23 @@
 from typing import List
 
-from langchain_core.retrievers import BaseRetriever
 from langchain_pinecone import Pinecone
 
-from langflow.custom import Component
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
-from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.io import (
+    BoolInput,
+    DropdownInput,
+    HandleInput,
+    IntInput,
+    StrInput,
+    SecretStrInput,
+    DataInput,
+    MultilineInput,
+)
 from langflow.schema import Data
 
 
-class PineconeVectorStoreComponent(Component):
+class PineconeVectorStoreComponent(LCVectorStoreComponent):
     display_name = "Pinecone"
     description = "Pinecone Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone"
@@ -34,18 +42,18 @@ class PineconeVectorStoreComponent(Component):
             value="text",
             advanced=True,
         ),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "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.",
+            value=True,
         ),
-        StrInput(name="search_input", display_name="Search Input"),
+        MultilineInput(name="search_input", display_name="Search Input"),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -55,17 +63,6 @@ class PineconeVectorStoreComponent(Component):
         ),
     ]
 
-    outputs = [
-        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Pinecone),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-            output_type=BaseRetriever,
-        ),
-        Output(display_name="Search Results", name="search_results", method="search_documents"),
-    ]
-
     def build_vector_store(self) -> Pinecone:
         return self._build_pinecone()
 

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

@@ -1,15 +1,23 @@
 from typing import List
 
 from langchain_community.vectorstores import Qdrant
-from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import Component
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
-from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.io import (
+    BoolInput,
+    DropdownInput,
+    HandleInput,
+    IntInput,
+    StrInput,
+    SecretStrInput,
+    DataInput,
+    MultilineInput,
+)
 from langflow.schema import Data
 
 
-class QdrantVectorStoreComponent(Component):
+class QdrantVectorStoreComponent(LCVectorStoreComponent):
     display_name = "Qdrant"
     description = "Qdrant Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant"
@@ -35,10 +43,9 @@ class QdrantVectorStoreComponent(Component):
         StrInput(name="content_payload_key", display_name="Content Payload Key", value="page_content", advanced=True),
         StrInput(name="metadata_payload_key", display_name="Metadata Payload Key", value="metadata", advanced=True),
         HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
         BoolInput(
@@ -46,7 +53,7 @@ class QdrantVectorStoreComponent(Component):
             display_name="Add to Vector Store",
             info="If true, the Vector Store Inputs will be added to the Vector Store.",
         ),
-        StrInput(name="search_input", display_name="Search Input"),
+        MultilineInput(name="search_input", display_name="Search Input"),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -56,17 +63,6 @@ class QdrantVectorStoreComponent(Component):
         ),
     ]
 
-    outputs = [
-        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Qdrant),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-            output_type=BaseRetriever,
-        ),
-        Output(display_name="Search Results", name="search_results", method="search_documents"),
-    ]
-
     def build_vector_store(self) -> Qdrant:
         return self._build_qdrant()
 
@@ -91,7 +87,6 @@ class QdrantVectorStoreComponent(Component):
 
         # Remove None values from server_kwargs
         server_kwargs = {k: v for k, v in server_kwargs.items() if v is not None}
-
         if self.add_to_vector_store:
             documents = []
             for _input in self.vector_store_inputs or []:
@@ -101,9 +96,7 @@ class QdrantVectorStoreComponent(Component):
                     documents.append(_input)
 
             if documents:
-                qdrant = Qdrant.from_documents(
+                qdrant = Qdrant.from_documents(documents, embedding=self.embedding, **qdrant_kwargs)
-                    documents, embedding=self.embedding, client_kwargs=server_kwargs, **qdrant_kwargs
-                )
             else:
                 from qdrant_client import QdrantClient
 

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

@@ -1,14 +1,15 @@
-from typing import Optional, cast
+from typing import List
 
 from langchain_community.vectorstores.redis import Redis
-from langchain_core.embeddings import Embeddings
 
-from langflow.custom import CustomComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
-from langflow.field_typing import VectorStore
+from langflow.helpers.data import docs_to_data
+from langflow.io import HandleInput, IntInput, StrInput, SecretStrInput, MultilineInput, DataInput
 from langflow.schema import Data
+from langchain.text_splitter import CharacterTextSplitter
 
 
-class RedisVectorStoreComponent(CustomComponent):
+class RedisVectorStoreComponent(LCVectorStoreComponent):
     """
     A custom component for implementing a Vector Store using Redis.
     """
@@ -17,67 +18,77 @@ class RedisVectorStoreComponent(CustomComponent):
     description: str = "Implementation of Vector Store using Redis"
     documentation = "https://python.langchain.com/docs/integrations/vectorstores/redis"
 
-    def build_config(self):
+    inputs = [
-        """
+        SecretStrInput(name="redis_server_url", display_name="Redis Server Connection String", required=True),
-        Builds the configuration for the component.
+        StrInput(
+            name="redis_index_name",
+            display_name="Redis Index",
+        ),
+        StrInput(name="code", display_name="Code", advanced=True),
+        StrInput(
+            name="schema",
+            display_name="Schema",
+            field_type=[".yaml"],
+        ),
+        DataInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            is_list=True,
+        ),
+        MultilineInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+    ]
 
-        Returns:
+    def build_vector_store(self) -> Redis:
-        - dict: A dictionary containing the configuration options for the component.
-        """
-        return {
-            "index_name": {"display_name": "Index Name", "value": "your_index"},
-            "code": {"show": False, "display_name": "Code"},
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Data"]},
-            "embedding": {"display_name": "Embedding"},
-            "schema": {"display_name": "Schema", "file_types": [".yaml"]},
-            "redis_server_url": {
-                "display_name": "Redis Server Connection String",
-                "advanced": False,
-            },
-            "redis_index_name": {"display_name": "Redis Index", "advanced": False},
-        }
-
-    def build(
-        self,
-        embedding: Embeddings,
-        redis_server_url: str,
-        redis_index_name: str,
-        schema: Optional[str] = None,
-        inputs: Optional[Data] = None,
-    ) -> VectorStore:
-        """
-        Builds the Vector Store or BaseRetriever object.
-
-        Args:
-        - embedding (Embeddings): The embeddings to use for the Vector Store.
-        - documents (Optional[Document]): The documents to use for the Vector Store.
-        - redis_index_name (str): The name of the Redis index.
-        - redis_server_url (str): The URL for the Redis server.
-
-        Returns:
-        - VectorStore: The Vector Store object.
-        """
         documents = []
-        for _input in inputs or []:
+
+        for _input in self.vector_store_inputs or []:
             if isinstance(_input, Data):
                 documents.append(_input.to_lc_document())
             else:
                 documents.append(_input)
+        with open("docuemnts.txt", "w") as f:
+            f.write(str(documents))
+
         if not documents:
-            if schema is None:
+            if self.schema is None:
                 raise ValueError("If no documents are provided, a schema must be provided.")
             redis_vs = Redis.from_existing_index(
-                embedding=embedding,
+                embedding=self.embedding,
-                index_name=redis_index_name,
+                index_name=self.redis_index_name,
-                schema=schema,
+                schema=self.schema,
                 key_prefix=None,
-                redis_url=redis_server_url,
+                redis_url=self.redis_server_url,
             )
         else:
+            text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+            docs = text_splitter.split_documents(documents)
             redis_vs = Redis.from_documents(
-                documents=documents,  # type: ignore
+                documents=docs,
-                embedding=embedding,
+                embedding=self.embedding,
-                redis_url=redis_server_url,
+                redis_url=self.redis_server_url,
-                index_name=redis_index_name,
+                index_name=self.redis_index_name,
             )
-        return cast(VectorStore, redis_vs)
+        return redis_vs
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_redis()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
+            )
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []

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

@@ -1,16 +1,15 @@
 from typing import List
 
 from langchain_community.vectorstores import SupabaseVectorStore
-from langchain_core.retrievers import BaseRetriever
 from supabase.client import Client, create_client
 
-from langflow.custom import Component
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
-from langflow.io import HandleInput, IntInput, Output, StrInput
+from langflow.io import HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
 from langflow.schema import Data
 
 
-class SupabaseVectorStoreComponent(Component):
+class SupabaseVectorStoreComponent(LCVectorStoreComponent):
     display_name = "Supabase"
     description = "Supabase Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase"
@@ -18,17 +17,16 @@ class SupabaseVectorStoreComponent(Component):
 
     inputs = [
         StrInput(name="supabase_url", display_name="Supabase URL", required=True),
-        StrInput(name="supabase_service_key", display_name="Supabase Service Key", required=True),
+        SecretStrInput(name="supabase_service_key", display_name="Supabase Service Key", required=True),
         StrInput(name="table_name", display_name="Table Name", advanced=True),
         StrInput(name="query_name", display_name="Query Name"),
         HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
-        StrInput(name="search_input", display_name="Search Input"),
+        MultilineInput(name="search_input", display_name="Search Input"),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -38,22 +36,6 @@ class SupabaseVectorStoreComponent(Component):
         ),
     ]
 
-    outputs = [
-        Output(
-            display_name="Vector Store",
-            name="vector_store",
-            method="build_vector_store",
-            output_type=SupabaseVectorStore,
-        ),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-            output_type=BaseRetriever,
-        ),
-        Output(display_name="Search Results", name="search_results", method="search_documents"),
-    ]
-
     def build_vector_store(self) -> SupabaseVectorStore:
         return self._build_supabase()
 

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

@@ -1,15 +1,14 @@
 from typing import List
 
 from langchain_community.vectorstores import UpstashVectorStore
-from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import Component
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
-from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.io import BoolInput, HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
 from langflow.schema import Data
 
 
-class UpstashVectorStoreComponent(Component):
+class UpstashVectorStoreComponent(LCVectorStoreComponent):
     display_name = "Upstash"
     description = "Upstash Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/upstash"
@@ -17,7 +16,7 @@ class UpstashVectorStoreComponent(Component):
 
     inputs = [
         StrInput(name="index_url", display_name="Index URL", info="The URL of the Upstash index.", required=True),
-        StrInput(
+        SecretStrInput(
             name="index_token", display_name="Index Token", info="The token for the Upstash index.", required=True
         ),
         StrInput(
@@ -33,10 +32,9 @@ class UpstashVectorStoreComponent(Component):
             input_types=["Embeddings"],
             info="To use Upstash's embeddings, don't provide an embedding.",
         ),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
         BoolInput(
@@ -44,7 +42,7 @@ class UpstashVectorStoreComponent(Component):
             display_name="Add to Vector Store",
             info="If true, the Vector Store Inputs will be added to the Vector Store.",
         ),
-        StrInput(name="search_input", display_name="Search Input"),
+        MultilineInput(name="search_input", display_name="Search Input"),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -54,22 +52,6 @@ class UpstashVectorStoreComponent(Component):
         ),
     ]
 
-    outputs = [
-        Output(
-            display_name="Vector Store",
-            name="vector_store",
-            method="build_vector_store",
-            output_type=UpstashVectorStore,
-        ),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-            output_type=BaseRetriever,
-        ),
-        Output(display_name="Search Results", name="search_results", method="search_documents"),
-    ]
-
     def build_vector_store(self) -> UpstashVectorStore:
         return self._build_upstash()
 

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

@@ -2,15 +2,14 @@ from typing import List
 
 from langchain_community.embeddings import FakeEmbeddings
 from langchain_community.vectorstores import Vectara
-from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import Component
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
-from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.io import BoolInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
 from langflow.schema import Data
 
 
-class VectaraVectorStoreComponent(Component):
+class VectaraVectorStoreComponent(LCVectorStoreComponent):
     display_name = "Vectara"
     description = "Vectara Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/vectara"
@@ -20,10 +19,9 @@ class VectaraVectorStoreComponent(Component):
         StrInput(name="vectara_customer_id", display_name="Vectara Customer ID", required=True),
         StrInput(name="vectara_corpus_id", display_name="Vectara Corpus ID", required=True),
         SecretStrInput(name="vectara_api_key", display_name="Vectara API Key", required=True),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
         BoolInput(
@@ -31,7 +29,7 @@ class VectaraVectorStoreComponent(Component):
             display_name="Add to Vector Store",
             info="If true, the Vector Store Inputs will be added to the Vector Store.",
         ),
-        StrInput(name="search_input", display_name="Search Input"),
+        MultilineInput(name="search_input", display_name="Search Input"),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -41,17 +39,6 @@ class VectaraVectorStoreComponent(Component):
         ),
     ]
 
-    outputs = [
-        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Vectara),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-            output_type=BaseRetriever,
-        ),
-        Output(display_name="Search Results", name="search_results", method="search_documents"),
-    ]
-
     def build_vector_store(self) -> Vectara:
         return self._build_vectara()
 

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

@@ -2,15 +2,14 @@ from typing import List
 
 import weaviate  # type: ignore
 from langchain_community.vectorstores import Weaviate
-from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import Component
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
-from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.io import BoolInput, HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
 from langflow.schema import Data
 
 
-class WeaviateVectorStoreComponent(Component):
+class WeaviateVectorStoreComponent(LCVectorStoreComponent):
     display_name = "Weaviate"
     description = "Weaviate Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/weaviate"
@@ -22,10 +21,9 @@ class WeaviateVectorStoreComponent(Component):
         StrInput(name="index_name", display_name="Index Name", required=True),
         StrInput(name="text_key", display_name="Text Key", value="text", advanced=True),
         HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
         BoolInput(
@@ -33,7 +31,7 @@ class WeaviateVectorStoreComponent(Component):
             display_name="Add to Vector Store",
             info="If true, the Vector Store Inputs will be added to the Vector Store.",
         ),
-        StrInput(name="search_input", display_name="Search Input"),
+        MultilineInput(name="search_input", display_name="Search Input"),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -44,17 +42,6 @@ class WeaviateVectorStoreComponent(Component):
         BoolInput(name="search_by_text", display_name="Search By Text", advanced=True),
     ]
 
-    outputs = [
-        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Weaviate),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-            output_type=BaseRetriever,
-        ),
-        Output(display_name="Search Results", name="search_results", method="search_documents"),
-    ]
-
     def build_vector_store(self) -> Weaviate:
         return self._build_weaviate()
 

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

@@ -1,28 +1,26 @@
 from typing import List
 
-from langchain.schema import BaseRetriever
 from langchain_community.vectorstores import PGVector
 
-from langflow.custom import Component
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
-from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.io import BoolInput, HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
 from langflow.schema import Data
 
 
-class PGVectorStoreComponent(Component):
+class PGVectorStoreComponent(LCVectorStoreComponent):
     display_name = "PGVector"
     description = "PGVector Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pgvector"
     icon = "PGVector"
 
     inputs = [
-        StrInput(name="pg_server_url", display_name="PostgreSQL Server Connection String", required=True),
+        SecretStrInput(name="pg_server_url", display_name="PostgreSQL Server Connection String", required=True),
         StrInput(name="collection_name", display_name="Table", required=True),
         HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
-        HandleInput(
+        DataInput(
             name="vector_store_inputs",
             display_name="Vector Store Inputs",
-            input_types=["Document", "Data"],
             is_list=True,
         ),
         BoolInput(
@@ -30,7 +28,7 @@ class PGVectorStoreComponent(Component):
             display_name="Add to Vector Store",
             info="If true, the Vector Store Inputs will be added to the Vector Store.",
         ),
-        StrInput(name="search_input", display_name="Search Input"),
+        MultilineInput(name="search_input", display_name="Search Input"),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -40,17 +38,6 @@ class PGVectorStoreComponent(Component):
         ),
     ]
 
-    outputs = [
-        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=PGVector),
-        Output(
-            display_name="Base Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-            output_type=BaseRetriever,
-        ),
-        Output(display_name="Search Results", name="search_results", method="search_documents"),
-    ]
-
     def build_vector_store(self) -> PGVector:
         return self._build_pgvector()