diff --git a/src/backend/langflow/components/retrievers/VectaraSelfQueryRetriever.py b/src/backend/langflow/components/retrievers/VectaraSelfQueryRetriever.py
deleted file mode 100644
index dec8efdc2..000000000
--- a/src/backend/langflow/components/retrievers/VectaraSelfQueryRetriever.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from typing import Optional, List
-from langflow import CustomComponent
-import json
-from langchain.schema import BaseRetriever
-from langchain.schema.vectorstore import VectorStore
-from langchain.base_language import BaseLanguageModel
-from langchain.retrievers.self_query.base import SelfQueryRetriever
-from langchain.chains.query_constructor.base import AttributeInfo
-
-
-class VectaraComponent(CustomComponent):
-    display_name: str = "Vectara Self Query Retriever for Vectara Vector Store"
-    description: str = "Implementation of Vectara Self Query Retriever"
-    documentation = (
-        "https://python.langchain.com/docs/integrations/vectorstores/vectara"
-    )
-    beta = True
-    field_config = {
-        "code": {"show": False},
-        "vectorstore": {
-            "display_name": "Vectara Vector Store", 
-            "info": "Input Vectara Vectore Store"
-            },
-        "llm": {
-            "display_name": "LLM", 
-            "info": "For self query retriever"
-            },
-        "document_content_description":{
-            "display_name": "Document Content Description", 
-            "info": "For self query retriever",
-            },
-        "metadata_field_info": {
-            "display_name": "Metadata Field Info", 
-            "info": "Check dictionary format in documentation for self query retriever",
-            "info": "Each metadata field is a string in the form of json containing additional search metadata.\nExample input: {\"name\":\"speech\",\"description\":\"what name of the speech\",\"type\":\"string or list[string]\"}.\nThe keys should remain constant",
-            },
-    }
-
-    def build(
-        self,
-        vectorstore: VectorStore = None,
-        document_content_description: str = None,
-        llm: BaseLanguageModel = None,
-        metadata_field_info: List[str] = None,
-    ) -> BaseRetriever:
-        
-        metadata_field_obj = []
-
-        for meta in metadata_field_info:
-            meta_obj = json.loads(meta)
-            if 'name' not in meta_obj or 'description' not in meta_obj or 'type' not in meta_obj :
-                raise Exception('Incorrect metadata field info format.')
-            attribute_info = AttributeInfo(
-                name = meta_obj['name'],
-                description = meta_obj['description'],
-                type = meta_obj['type'],
-            )
-            metadata_field_obj.append(attribute_info)
-
-        return SelfQueryRetriever.from_llm(
-            llm,
-            vectorstore, 
-            document_content_description, 
-            metadata_field_obj, 
-            verbose=True
-        )
-    
- 
\ No newline at end of file