fix: loop variable not accessible error (#7501)

* tests cases

* update to loop

* Update component.py

* 📝 (LoopTemplate.json): update value of a configuration key from "OPENAI_API_KEY" to "ANTHROPIC_API_KEY" in order to reflect the correct API key being used

* update json test loop

* fix: update test URL in loop-component.spec.ts to reflect correct reference

Changed the URL in the test case from "Artificial_intelligence" to "Human_intelligence" to ensure accurate testing of the loop component functionality.

* update FE tests

* [autofix.ci] apply automated fixes

---------

Co-authored-by: cristhianzl <cristhian.lousa@gmail.com>
Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

src/backend/base/langflow/components/logic/loop.py

@@ -105,7 +105,7 @@ class LoopComponent(Component):
         aggregated = self.ctx.get(f"{self._id}_aggregated", [])
 
         # Check if loop input is provided and append to aggregated list
-        if self.data is not None and not isinstance(self.data, str) and len(aggregated) <= len(data_list):
-            aggregated.append(self.data)
+        if self.item is not None and not isinstance(self.item, str) and len(aggregated) <= len(data_list):
+            aggregated.append(self.item)
             self.update_ctx({f"{self._id}_aggregated": aggregated})
         return aggregated

src/backend/base/langflow/custom/custom_component/component.py

@@ -720,6 +720,7 @@ class Component(CustomComponent):
 
     def __getattr__(self, name: str) -> Any:
         if "_attributes" in self.__dict__ and name in self.__dict__["_attributes"]:
+            # It is a dict of attributes that are not inputs or outputs all the raw data it should have the loop input.
             return self.__dict__["_attributes"][name]
         if "_inputs" in self.__dict__ and name in self.__dict__["_inputs"]:
             return self.__dict__["_inputs"][name].value

src/backend/base/langflow/graph/vertex/base.py

@@ -307,6 +307,8 @@ class Vertex:
                 else:
                     params[param_key] = self.graph.get_vertex(edge.source_id)
         elif param_key in self.output_names:
+            #  if the loop is run the param_key item will be set over here
+            # validate the edge
             params[param_key] = self.graph.get_vertex(edge.source_id)
         return params
 

src/backend/base/langflow/graph/vertex/param_handler.py

@@ -70,6 +70,11 @@ class ParameterHandler:
                 params[param_key].append(self.vertex.graph.get_vertex(edge.source_id))
             else:
                 params[param_key] = self.process_non_list_edge_param(field, edge)
+        elif param_key in self.vertex.output_names:
+            # If the param_key is in the output_names, it means that the loop is run
+            #  if the loop is run the param_key item will be set over here
+            # validate the edge
+            params[param_key] = self.vertex.graph.get_vertex(edge.source_id)
         return params
 
     def process_non_list_edge_param(self, field: dict, edge: CycleEdge) -> Any:

src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json

@@ -462,7 +462,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.io import DataInput, Output\nfrom langflow.schema import Data\n\n\nclass LoopComponent(Component):\n    display_name = \"Loop\"\n    description = (\n        \"Iterates over a list of Data objects, outputting one item at a time and aggregating results from loop inputs.\"\n    )\n    icon = \"infinity\"\n\n    inputs = [\n        DataInput(\n            name=\"data\",\n            display_name=\"Data\",\n            info=\"The initial list of Data objects to iterate over.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Item\", name=\"item\", method=\"item_output\", allows_loop=True),\n        Output(display_name=\"Done\", name=\"done\", method=\"done_output\"),\n    ]\n\n    def initialize_data(self) -> None:\n        \"\"\"Initialize the data list, context index, and aggregated list.\"\"\"\n        if self.ctx.get(f\"{self._id}_initialized\", False):\n            return\n\n        # Ensure data is a list of Data objects\n        data_list = self._validate_data(self.data)\n\n        # Store the initial data and context variables\n        self.update_ctx(\n            {\n                f\"{self._id}_data\": data_list,\n                f\"{self._id}_index\": 0,\n                f\"{self._id}_aggregated\": [],\n                f\"{self._id}_initialized\": True,\n            }\n        )\n\n    def _validate_data(self, data):\n        \"\"\"Validate and return a list of Data objects.\"\"\"\n        if isinstance(data, Data):\n            return [data]\n        if isinstance(data, list) and all(isinstance(item, Data) for item in data):\n            return data\n        msg = \"The 'data' input must be a list of Data objects or a single Data object.\"\n        raise TypeError(msg)\n\n    def evaluate_stop_loop(self) -> bool:\n        \"\"\"Evaluate whether to stop item or done output.\"\"\"\n        current_index = self.ctx.get(f\"{self._id}_index\", 0)\n        data_length = len(self.ctx.get(f\"{self._id}_data\", []))\n        return current_index > data_length\n\n    def item_output(self) -> Data:\n        \"\"\"Output the next item in the list or stop if done.\"\"\"\n        self.initialize_data()\n        current_item = Data(text=\"\")\n\n        if self.evaluate_stop_loop():\n            self.stop(\"item\")\n            return Data(text=\"\")\n\n        # Get data list and current index\n        data_list, current_index = self.loop_variables()\n        if current_index < len(data_list):\n            # Output current item and increment index\n            try:\n                current_item = data_list[current_index]\n            except IndexError:\n                current_item = Data(text=\"\")\n        self.aggregated_output()\n        self.update_ctx({f\"{self._id}_index\": current_index + 1})\n        return current_item\n\n    def done_output(self) -> Data:\n        \"\"\"Trigger the done output when iteration is complete.\"\"\"\n        self.initialize_data()\n\n        if self.evaluate_stop_loop():\n            self.stop(\"item\")\n            self.start(\"done\")\n\n            return self.ctx.get(f\"{self._id}_aggregated\", [])\n        self.stop(\"done\")\n        return Data(text=\"\")\n\n    def loop_variables(self):\n        \"\"\"Retrieve loop variables from context.\"\"\"\n        return (\n            self.ctx.get(f\"{self._id}_data\", []),\n            self.ctx.get(f\"{self._id}_index\", 0),\n        )\n\n    def aggregated_output(self) -> Data:\n        \"\"\"Return the aggregated list once all items are processed.\"\"\"\n        self.initialize_data()\n\n        # Get data list and aggregated list\n        data_list = self.ctx.get(f\"{self._id}_data\", [])\n        aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n\n        # Check if loop input is provided and append to aggregated list\n        if self.data is not None and not isinstance(self.data, str) and len(aggregated) <= len(data_list):\n            aggregated.append(self.data)\n            self.update_ctx({f\"{self._id}_aggregated\": aggregated})\n        return aggregated\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import DataInput, Output\nfrom langflow.schema import Data\n\n\nclass LoopComponent(Component):\n    display_name = \"Loop\"\n    description = (\n        \"Iterates over a list of Data objects, outputting one item at a time and aggregating results from loop inputs.\"\n    )\n    icon = \"infinity\"\n\n    inputs = [\n        DataInput(\n            name=\"data\",\n            display_name=\"Data\",\n            info=\"The initial list of Data objects to iterate over.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Item\", name=\"item\", method=\"item_output\", allows_loop=True),\n        Output(display_name=\"Done\", name=\"done\", method=\"done_output\"),\n    ]\n\n    def initialize_data(self) -> None:\n        \"\"\"Initialize the data list, context index, and aggregated list.\"\"\"\n        if self.ctx.get(f\"{self._id}_initialized\", False):\n            return\n\n        # Ensure data is a list of Data objects\n        data_list = self._validate_data(self.data)\n\n        # Store the initial data and context variables\n        self.update_ctx(\n            {\n                f\"{self._id}_data\": data_list,\n                f\"{self._id}_index\": 0,\n                f\"{self._id}_aggregated\": [],\n                f\"{self._id}_initialized\": True,\n            }\n        )\n\n    def _validate_data(self, data):\n        \"\"\"Validate and return a list of Data objects.\"\"\"\n        if isinstance(data, Data):\n            return [data]\n        if isinstance(data, list) and all(isinstance(item, Data) for item in data):\n            return data\n        msg = \"The 'data' input must be a list of Data objects or a single Data object.\"\n        raise TypeError(msg)\n\n    def evaluate_stop_loop(self) -> bool:\n        \"\"\"Evaluate whether to stop item or done output.\"\"\"\n        current_index = self.ctx.get(f\"{self._id}_index\", 0)\n        data_length = len(self.ctx.get(f\"{self._id}_data\", []))\n        return current_index > data_length\n\n    def item_output(self) -> Data:\n        \"\"\"Output the next item in the list or stop if done.\"\"\"\n        self.initialize_data()\n        current_item = Data(text=\"\")\n\n        if self.evaluate_stop_loop():\n            self.stop(\"item\")\n            return Data(text=\"\")\n\n        # Get data list and current index\n        data_list, current_index = self.loop_variables()\n        if current_index < len(data_list):\n            # Output current item and increment index\n            try:\n                current_item = data_list[current_index]\n            except IndexError:\n                current_item = Data(text=\"\")\n        self.aggregated_output()\n        self.update_ctx({f\"{self._id}_index\": current_index + 1})\n        return current_item\n\n    def done_output(self) -> Data:\n        \"\"\"Trigger the done output when iteration is complete.\"\"\"\n        self.initialize_data()\n\n        if self.evaluate_stop_loop():\n            self.stop(\"item\")\n            self.start(\"done\")\n\n            return self.ctx.get(f\"{self._id}_aggregated\", [])\n        self.stop(\"done\")\n        return Data(text=\"\")\n\n    def loop_variables(self):\n        \"\"\"Retrieve loop variables from context.\"\"\"\n        return (\n            self.ctx.get(f\"{self._id}_data\", []),\n            self.ctx.get(f\"{self._id}_index\", 0),\n        )\n\n    def aggregated_output(self) -> Data:\n        \"\"\"Return the aggregated list once all items are processed.\"\"\"\n        self.initialize_data()\n\n        # Get data list and aggregated list\n        data_list = self.ctx.get(f\"{self._id}_data\", [])\n        aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n\n        # Check if loop input is provided and append to aggregated list\n        if self.item is not None and not isinstance(self.item, str) and len(aggregated) <= len(data_list):\n            aggregated.append(self.item)\n            self.update_ctx({f\"{self._id}_aggregated\": aggregated})\n        return aggregated\n"
               },
               "data": {
                 "_input_type": "DataInput",
@@ -595,7 +595,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "OPENAI_API_KEY"
+                "value": "ANTHROPIC_API_KEY"
               },
               "base_url": {
                 "_input_type": "MessageTextInput",

src/frontend/tests/core/features/filterSidebar.spec.ts

@@ -75,7 +75,6 @@ test(
     await expect(page.getByTestId("inputsChat Input")).toBeVisible();
     await expect(page.getByTestId("outputsChat Output")).toBeVisible();
     await expect(page.getByTestId("promptsPrompt")).toBeVisible();
-    await expect(page.getByTestId("modelsAmazon Bedrock")).toBeVisible();
     await expect(page.getByTestId("helpersMessage History")).toBeVisible();
     await expect(page.getByTestId("langchain_utilitiesCSVAgent")).toBeVisible();
     await expect(
@@ -101,7 +100,6 @@ test(
     await expect(page.getByTestId("inputsChat Input")).not.toBeVisible();
     await expect(page.getByTestId("outputsChat Output")).not.toBeVisible();
     await expect(page.getByTestId("promptsPrompt")).not.toBeVisible();
-    await expect(page.getByTestId("modelsAmazon Bedrock")).not.toBeVisible();
     await expect(page.getByTestId("helpersMessage History")).not.toBeVisible();
     await expect(
       page.getByTestId("agentsTool Calling Agent"),

src/frontend/tests/extended/features/filterEdge-shard-1.spec.ts

@@ -70,7 +70,6 @@ test(
     const elementTestIds = [
       "outputsChat Output",
       "dataAPI Request",
-      "modelsAmazon Bedrock",
       "vectorstoresAstra DB",
       "embeddingsAmazon Bedrock Embeddings",
       "langchain_utilitiesTool Calling Agent",
@@ -97,7 +96,6 @@ test(
 
     const visibleModelSpecsTestIds = [
       "modelsAIML",
-      "modelsAmazon Bedrock",
       "modelsAnthropic",
       "modelsAzure OpenAI",
       "modelsCohere",

src/frontend/tests/extended/features/loop-component.spec.ts

@@ -177,7 +177,7 @@ test(
       .fill("https://en.wikipedia.org/wiki/Artificial_intelligence");
     await page
       .getByTestId("inputlist_str_urls_1")
-      .fill("https://en.wikipedia.org/wiki/Artificial_intelligence");
+      .fill("https://en.wikipedia.org/wiki/Human_intelligence");
 
     await page.getByTestId("div-generic-node").nth(2).click();
     await page.getByTestId("int_int_number_of_fields").fill("1");