⚡️ Speed up function replace_api_key_with_env_var_name by 23% in PR #12129 (fix-api-key-components)

src/backend/base/langflow/api/utils/__init__.py

@@ -36,6 +36,7 @@
     parse_value,
     raise_error_if_astra_cloud_env,
     remove_api_keys,
+    replace_api_key_with_env_var_name,
     validate_is_component,
     verify_public_flow_and_get_user,
 )
@@ -75,6 +76,7 @@
     "parse_value",
     "raise_error_if_astra_cloud_env",
     "remove_api_keys",
+    "replace_api_key_with_env_var_name",
     "validate_is_component",
     "verify_public_flow_and_get_user",
 ]

src/backend/base/langflow/api/utils/core.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import re
 import uuid
 from ast import literal_eval
 from datetime import timedelta
@@ -28,6 +29,8 @@
     from langflow.services.chat.service import ChatService
     from langflow.services.store.schema import StoreComponentCreate
 
+_VARIABLE_NAME_PATTERN = re.compile(r"[A-Za-z][A-Za-z0-9_]*")
+
 
 API_WORDS = ["api", "key", "token"]
 
@@ -68,14 +71,61 @@
     return "api" in word and ("key" in word or ("token" in word and "tokens" not in word))
 
 
+def _get_provider_from_template(template: dict) -> str | None:
+    """Return provider name from template's model field, if any."""
+    model_field = template.get("model")
+    if not isinstance(model_field, dict):
+        return None
+    raw = model_field.get("value")
+    if isinstance(raw, list) and len(raw) > 0 and isinstance(raw[0], dict):
+        return raw[0].get("provider")
+    return None
+
+
+def _looks_like_variable_name(value: Any) -> bool:
+    """Return True if value looks like a variable name."""
+    if not isinstance(value, str):
+        return False
+    stripped = value.strip()
+    if not stripped:
+        return False
+    return _VARIABLE_NAME_PATTERN.fullmatch(stripped) is not None
+
+
+def replace_api_key_with_env_var_name(flow: dict) -> dict:
+    """Normalize api_key to a variable name when possible, never export raw keys."""
+    nodes = flow.get("data", {}).get("nodes", [])
+    for node in nodes:
+        node_data = node.get("data")
+        if not isinstance(node_data, dict):
+            continue
+        node_inner = node_data.get("node")
+        if not isinstance(node_inner, dict):
+            continue
+        template = node_inner.get("template")
+        if not isinstance(template, dict):
+            continue
+        for value in template.values():
+            if isinstance(value, dict) and value.get("name") == "api_key" and value.get("password"):
+                current = value.get("value")
+                if _looks_like_variable_name(current):
+                    break  # keep user's custom variable name
+                # raw secret or other string: clear it
+                value["value"] = None
+                break
+    return flow
+
+
 def remove_api_keys(flow: dict):
-    """Remove api keys from flow data."""
+    """Clear secret values from flow data."""
+    flow = replace_api_key_with_env_var_name(flow)
     for node in flow.get("data", {}).get("nodes", []):
         node_data = node.get("data").get("node")
         template = node_data.get("template")
         for value in template.values():
             if isinstance(value, dict) and "name" in value and has_api_terms(value["name"]) and value.get("password"):
-                value["value"] = None
+                if value.get("name") != "api_key":
+                    value["value"] = None
 
     return flow
 

src/backend/base/langflow/api/v1/flows.py

@@ -21,7 +21,14 @@
 from sqlmodel import and_, col, select
 from sqlmodel.ext.asyncio.session import AsyncSession
 
-from langflow.api.utils import CurrentActiveUser, DbSession, cascade_delete_flow, remove_api_keys, validate_is_component
+from langflow.api.utils import (
+    CurrentActiveUser,
+    DbSession,
+    cascade_delete_flow,
+    remove_api_keys,
+    replace_api_key_with_env_var_name,
+    validate_is_component,
+)
 from langflow.api.v1.schemas import FlowListCreate
 from langflow.helpers.user import get_user_by_flow_id_or_endpoint_name
 from langflow.initial_setup.constants import STARTER_FOLDER_NAME
@@ -480,6 +487,8 @@ async def update_flow(
         if flow.endpoint_name is None or flow.endpoint_name == "":
             update_data["endpoint_name"] = None
 
+        # Never persist raw api_key; use env var name so import resolves from env
+        update_data = replace_api_key_with_env_var_name(update_data)
         if settings_service.settings.remove_api_keys:
             update_data = remove_api_keys(update_data)
 
@@ -672,6 +681,8 @@ async def _update_existing_flow(
     if "folder_id" not in update_data or update_data.get("folder_id") is None:
         update_data.pop("folder_id", None)
 
+    # Never persist raw api_key; use env var name so import resolves from env
+    update_data = replace_api_key_with_env_var_name(update_data)
     if settings_service.settings.remove_api_keys:
         update_data = remove_api_keys(update_data)
 

src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json

@@ -1033,7 +1033,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.unified_models import (\n    apply_provider_variable_config_to_build_config,\n    get_language_model_options,\n    get_llm,\n    update_model_options_in_build_config,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, StrInput\nfrom lfx.io import IntInput, MessageInput, ModelInput, MultilineInput, SecretStrInput, SliderInput\n\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass LanguageModelComponent(LCModelComponent):\n    display_name = \"Language Model\"\n    description = \"Runs a language model given a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-models\"\n    icon = \"brain-circuit\"\n    category = \"models\"\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Model Provider API key\",\n            required=False,\n            show=True,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"ollama_base_url\",\n            display_name=\"Ollama API URL\",\n            info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n            value=DEFAULT_OLLAMA_URL,\n            show=False,\n            real_time_refresh=True,\n        ),\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Input\",\n            info=\"The input text to send to the model\",\n        ),\n        MultilineInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"A system message that helps set the behavior of the assistant\",\n            advanced=False,\n        ),\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=\"Whether to stream the response\",\n            value=False,\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            info=\"Controls randomness in responses\",\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=self.api_key,\n            temperature=self.temperature,\n            stream=self.stream,\n            max_tokens=getattr(self, \"max_tokens\", None),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            ollama_base_url=getattr(self, \"ollama_base_url\", None),\n        )\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        \"\"\"Dynamically update build config with user-filtered model options.\"\"\"\n        # Update model options\n        build_config = update_model_options_in_build_config(\n            component=self,\n            build_config=build_config,\n            cache_key_prefix=\"language_model_options\",\n            get_options_func=get_language_model_options,\n            field_name=field_name,\n            field_value=field_value,\n        )\n\n        # Hide all provider-specific fields by default\n        for field in [\"api_key\", \"base_url_ibm_watsonx\", \"project_id\", \"ollama_base_url\"]:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n                build_config[field][\"required\"] = False\n\n        # Show/configure provider-specific fields based on selected model\n        # Get current model value - from field_value if model is being changed, otherwise from build_config\n        current_model_value = field_value if field_name == \"model\" else build_config.get(\"model\", {}).get(\"value\")\n        if isinstance(current_model_value, list) and len(current_model_value) > 0:\n            selected_model = current_model_value[0]\n            provider = selected_model.get(\"provider\", \"\")\n\n            if provider:\n                # Apply provider variable configuration (required_for_component, advanced, env var fallback)\n                build_config = apply_provider_variable_config_to_build_config(build_config, provider)\n\n        return build_config\n"
+                "value": "from lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.unified_models import (\n    apply_provider_variable_config_to_build_config,\n    get_language_model_options,\n    get_llm,\n    get_model_providers,\n    update_model_options_in_build_config,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, StrInput\nfrom lfx.io import IntInput, MessageInput, ModelInput, MultilineInput, SecretStrInput, SliderInput\n\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass LanguageModelComponent(LCModelComponent):\n    display_name = \"Language Model\"\n    description = \"Runs a language model given a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-models\"\n    icon = \"brain-circuit\"\n    category = \"models\"\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Model Provider API key\",\n            required=False,\n            show=True,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"ollama_base_url\",\n            display_name=\"Ollama API URL\",\n            info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n            value=DEFAULT_OLLAMA_URL,\n            show=False,\n            real_time_refresh=True,\n        ),\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Input\",\n            info=\"The input text to send to the model\",\n        ),\n        MultilineInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"A system message that helps set the behavior of the assistant\",\n            advanced=False,\n        ),\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=\"Whether to stream the response\",\n            value=False,\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            info=\"Controls randomness in responses\",\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=self.api_key,\n            temperature=self.temperature,\n            stream=self.stream,\n            max_tokens=getattr(self, \"max_tokens\", None),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            ollama_base_url=getattr(self, \"ollama_base_url\", None),\n        )\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        \"\"\"Dynamically update build config with user-filtered model options.\"\"\"\n        # Update model options\n        build_config = update_model_options_in_build_config(\n            component=self,\n            build_config=build_config,\n            cache_key_prefix=\"language_model_options\",\n            get_options_func=get_language_model_options,\n            field_name=field_name,\n            field_value=field_value,\n        )\n\n        # Hide all provider-specific fields by default\n        for field in [\"api_key\", \"base_url_ibm_watsonx\", \"project_id\", \"ollama_base_url\"]:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n                build_config[field][\"required\"] = False\n\n        # Show/configure provider-specific fields based on selected model\n        current_model_value = field_value if field_name == \"model\" else build_config.get(\"model\", {}).get(\"value\")\n        provider = \"\"\n        if isinstance(current_model_value, list) and current_model_value:\n            selected_model = current_model_value[0]\n            provider = selected_model.get(\"provider\", \"\") or \"\"\n\n        if provider:\n            build_config = apply_provider_variable_config_to_build_config(build_config, provider)\n        else:\n            # No provider selected yet (e.g. new node): pre-fill api_key from first provider that has env configured\n            for p in get_model_providers():\n                build_config = apply_provider_variable_config_to_build_config(build_config, p)\n                if build_config.get(\"api_key\", {}).get(\"value\"):\n                    break\n\n        return build_config\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",