Add experimental components for listing flows, getting notified, and executing runnables

src/backend/langflow/components/experimental/SQLExecutor.py

@@ -11,7 +11,10 @@ class SQLExecutorComponent(CustomComponent):
 
     def build_config(self):
         return {
-            "database": {"display_name": "Database"},
+            "database_url": {
+                "display_name": "Database URL",
+                "info": "The URL of the database.",
+            },
             "include_columns": {
                 "display_name": "Include Columns",
                 "info": "Include columns in the result.",
@@ -26,15 +29,24 @@ class SQLExecutorComponent(CustomComponent):
             },
         }
 
+    def clean_up_uri(self, uri: str) -> str:
+        if uri.startswith("postgresql://"):
+            uri = uri.replace("postgresql://", "postgres://")
+        return uri.strip()
+
     def build(
         self,
         query: str,
-        database: SQLDatabase,
+        database_url: str,
         include_columns: bool = False,
         passthrough: bool = False,
         add_error: bool = False,
     ) -> Text:
         error = None
+        try:
+            database = SQLDatabase.from_uri(database_url)
+        except Exception as e:
+            raise ValueError(f"An error occurred while connecting to the database: {e}")
         try:
             tool = QuerySQLDataBaseTool(db=database)
             result = tool.run(query, include_columns=include_columns)

src/backend/langflow/components/helpers/SharedState.py

@@ -1,41 +0,0 @@
-from typing import Optional
-
-from langflow import CustomComponent
-from langflow.schema import Record
-
-
-class SharedState(CustomComponent):
-    display_name = "Shared State"
-    description = "A component to share state between components."
-
-    def build_config(self):
-        return {
-            "name": {"display_name": "Name", "info": "The name of the state."},
-            "record": {"display_name": "Record", "info": "The record to store."},
-            "append": {
-                "display_name": "Append",
-                "info": "If True, the record will be appended to the state.",
-            },
-        }
-
-    def build(
-        self, name: str, record: Optional[Record] = None, append: bool = False
-    ) -> Record:
-        if record:
-            if append:
-                self.append_state(name, record)
-            else:
-                self.update_state(name, record)
-
-        state = self.get_state(name)
-        if state and not isinstance(state, Record):
-            if isinstance(state, str):
-                state = Record(text=state)
-            elif isinstance(state, dict):
-                state = Record(data=state)
-            else:
-                state = Record(text=str(state))
-        elif not state:
-            state = Record(text="")
-        self.status = state
-        return state

src/backend/langflow/components/helpers/ShouldRunNext.py

@@ -1,49 +0,0 @@
-# Implement ShouldRunNext component
-from typing import Text
-from langchain_core.prompts import PromptTemplate
-
-from langflow import CustomComponent
-from langflow.field_typing import BaseLanguageModel, Prompt
-
-
-class ShouldRunNext(CustomComponent):
-    display_name = "Should Run Next"
-    description = "Decides whether to run the next component."
-
-    def build_config(self):
-        return {
-            "prompt": {
-                "display_name": "Prompt",
-                "info": "The prompt to use for the decision. It should generate a boolean response (True or False).",
-            },
-            "llm": {
-                "display_name": "LLM",
-                "info": "The language model to use for the decision.",
-            },
-        }
-
-    def build(self, template: Prompt, llm: BaseLanguageModel, **kwargs) -> dict:
-        # This is a simple component that always returns True
-        prompt_template = PromptTemplate.from_template(Text(template))
-
-        attributes_to_check = ["text", "page_content"]
-        for key, value in kwargs.items():
-            for attribute in attributes_to_check:
-                if hasattr(value, attribute):
-                    kwargs[key] = getattr(value, attribute)
-
-        chain = prompt_template | llm
-        result = chain.invoke(kwargs)
-        if hasattr(result, "content") and isinstance(result.content, str):
-            result = result.content
-        elif isinstance(result, str):
-            result = result
-        else:
-            result = result.get("response")
-
-        if result.lower() not in ["true", "false"]:
-            raise ValueError("The prompt should generate a boolean response (True or False).")
-        # The string should be the words true or false
-        # if not raise an error
-        bool_result = result.lower() == "true"
-        return {"condition": bool_result, "result": kwargs}