refactor: Update SelfQueryRetrieverComponent build method to handle different input types

src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py

@@ -4,7 +4,7 @@ from langchain.retrievers.self_query.base import SelfQueryRetriever
 from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import BaseLanguageModel, Text
 from langflow.schema import Record
 from langflow.schema.message import Message
 
@@ -14,25 +14,54 @@ class SelfQueryRetrieverComponent(CustomComponent):
     description: str = "Retriever that uses a vector store and an LLM to generate the vector store queries."
     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.",
+            },
+        }
+
     def build(
         self,
         query: Message,
         vectorstore: VectorStore,
-        metadata_field_info: list[AttributeInfo],
+        attribute_infos: list[Record],
-        document_content_description: str,
+        document_content_description: Text,
         llm: BaseLanguageModel,
     ) -> Record:
-        metadata_field_info = [i[0] for i in metadata_field_info]
+        metadata_field_infos = [AttributeInfo(**record.data) for record in attribute_infos]
-
         self_query_retriever = SelfQueryRetriever.from_llm(
-            llm,
+            llm=llm,
-            vectorstore,
+            vectorstore=vectorstore,
-            document_content_description,
+            document_contents=document_content_description,
-            metadata_field_info,
+            metadata_field_info=metadata_field_infos,
             enable_limit=True,
         )
 
-        input_text = query.text
+        if isinstance(query, Message):
+            input_text = query.text
+        elif isinstance(query, str):
+            input_text = query
+        else:
+            raise ValueError(f"Query type {type(query)} not supported.")
         documents = self_query_retriever.invoke(input=input_text)
         records = [Record.from_document(document) for document in documents]
         self.status = records