diff --git a/poetry.lock b/poetry.lock
index 9ed84f1c307..294eb6640c4 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -5111,6 +5111,23 @@ langchain = ["langchain (>=0.2.0,<0.3.0)"]
 litellm = ["litellm (>=1.40.15,<2.0.0)"]
 openai = ["openai (>=1.42.0,<2.0.0)"]
 
+[[package]]
+name = "lark"
+version = "1.2.2"
+description = "a modern parsing library"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "lark-1.2.2-py3-none-any.whl", hash = "sha256:c2276486b02f0f1b90be155f2c8ba4a8e194d42775786db622faccd652d8e80c"},
+    {file = "lark-1.2.2.tar.gz", hash = "sha256:ca807d0162cd16cef15a8feecb862d7319e7a09bdb13aef927968e45040fed80"},
+]
+
+[package.extras]
+atomic-cache = ["atomicwrites"]
+interegular = ["interegular (>=0.3.1,<0.4.0)"]
+nearley = ["js2py"]
+regex = ["regex"]
+
 [[package]]
 name = "litellm"
 version = "1.44.8"
@@ -11826,4 +11843,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "d6fd6b327ba3ded7e8eefd2505c0cc6f15d4a5a9f1fd34020dc25324e9f13be1"
+content-hash = "0be9d1ea13484a0ccf92511c188edaab862ab3b883813efaca2f9bfbbfccd2a8"
diff --git a/pyproject.toml b/pyproject.toml
index 8bbc52561c5..38af95d4cd2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -106,6 +106,7 @@ composio-langchain = "^0.5.8"
 spider-client = "^0.0.27"
 nltk = "^3.9.1"
 bson = "^0.5.10"
+lark = "^1.2.2"
 
 
 [tool.poetry.group.dev.dependencies]
diff --git a/src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py b/src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py
index 149c7325c85..b75829cb41b 100644
--- a/src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py
+++ b/src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py
@@ -1,70 +1,76 @@
-# from langflow.field_typing import Data
+from typing import List
+
 from langchain.chains.query_constructor.base import AttributeInfo
 from langchain.retrievers.self_query.base import SelfQueryRetriever
-from langchain_core.vectorstores import VectorStore
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import LanguageModel, Text
+from langflow.custom import Component
+from langflow.inputs import HandleInput, MessageTextInput
+from langflow.io import Output
 from langflow.schema import Data
 from langflow.schema.message import Message
 
 
-class SelfQueryRetrieverComponent(CustomComponent):
-    display_name: str = "Self Query Retriever"
-    description: str = "Retriever that uses a vector store and an LLM to generate the vector store queries."
+class SelfQueryRetrieverComponent(Component):
+    display_name = "Self Query Retriever"
+    description = "Retriever that uses a vector store and an LLM to generate the vector store queries."
     name = "SelfQueryRetriever"
     icon = "LangChain"
 
-    def build_config(self):
-        return {
-            "query": {
-                "display_name": "Query",
-                "input_types": ["Message", "Text"],
-                "info": "Query to be passed as input.",
-            },
-            "vectorstore": {
-                "display_name": "Vector Store",
-                "info": "Vector Store to be passed as input.",
-            },
-            "attribute_infos": {
-                "display_name": "Metadata Field Info",
-                "info": "Metadata Field Info to be passed as input.",
-            },
-            "document_content_description": {
-                "display_name": "Document Content Description",
-                "info": "Document Content Description to be passed as input.",
-            },
-            "llm": {
-                "display_name": "LLM",
-                "info": "LLM to be passed as input.",
-            },
-        }
+    inputs = [
+        HandleInput(
+            name="query",
+            display_name="Query",
+            info="Query to be passed as input.",
+            input_types=["Message", "Text"],
+        ),
+        HandleInput(
+            name="vectorstore",
+            display_name="Vector Store",
+            info="Vector Store to be passed as input.",
+            input_types=["VectorStore"],
+        ),
+        HandleInput(
+            name="attribute_infos",
+            display_name="Metadata Field Info",
+            info="Metadata Field Info to be passed as input.",
+            input_types=["Data"],
+            is_list=True,
+        ),
+        MessageTextInput(
+            name="document_content_description",
+            display_name="Document Content Description",
+            info="Document Content Description to be passed as input.",
+        ),
+        HandleInput(
+            name="llm",
+            display_name="LLM",
+            info="LLM to be passed as input.",
+            input_types=["LanguageModel"],
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Retrieved Documents", name="documents", method="retrieve_documents"),
+    ]
 
-    def build(
-        self,
-        query: Message,
-        vectorstore: VectorStore,
-        attribute_infos: list[Data],
-        document_content_description: Text,
-        llm: LanguageModel,
-    ) -> Data:
-        metadata_field_infos = [AttributeInfo(**value.data) for value in attribute_infos]
+    def retrieve_documents(self) -> List[Data]:
+        metadata_field_infos = [AttributeInfo(**value.data) for value in self.attribute_infos]
         self_query_retriever = SelfQueryRetriever.from_llm(
-            llm=llm,
-            vectorstore=vectorstore,
-            document_contents=document_content_description,
+            llm=self.llm,
+            vectorstore=self.vectorstore,
+            document_contents=self.document_content_description,
             metadata_field_info=metadata_field_infos,
             enable_limit=True,
         )
 
-        if isinstance(query, Message):
-            input_text = query.text
-        elif isinstance(query, str):
-            input_text = query
+        if isinstance(self.query, Message):
+            input_text = self.query.text
+        elif isinstance(self.query, str):
+            input_text = self.query
+        else:
+            raise ValueError(f"Query type {type(self.query)} not supported.")
 
-        if not isinstance(query, str):
-            raise ValueError(f"Query type {type(query)} not supported.")
         documents = self_query_retriever.invoke(input=input_text, config={"callbacks": self.get_langchain_callbacks()})
         data = [Data.from_document(document) for document in documents]
         self.status = data
-        return data  # type: ignore
+        return data