Add PythonREPLToolComponent to tools/__init__.py and create PythonREPLTool.py (#1639)

* re-add --fix

* Add PythonREPLToolComponent to tools/__init__.py and create PythonREPLTool.py

* Refactor PythonREPLToolComponent to use build_status_from_tool in PythonREPLTool.py

* Refactor model_specs imports in ChatLiteLLMSpecs.py

* Refactor imports in various files

* Refactor model_specs imports and class names in AnthropicLLMSpecs.py and AnthropicSpecs.py

docs/docs/components/custom.mdx

@@ -37,7 +37,7 @@ The CustomComponent class serves as the foundation for creating custom component
   | _`langflow.field_typing.Prompt`_                         |
   | _`langchain.chains.base.Chain`_                           |
   | _`langchain.PromptTemplate`_                              |
-  | _`langchain.llms.base.BaseLLM`_                           |
+  | _`from langchain.schema.language_model import BaseLanguageModel`_                           |
   | _`langchain.Tool`_                                        |
   | _`langchain.document_loaders.base.BaseLoader`_            |
   | _`langchain.schema.Document`_                             |

docs/docs/guidelines/custom-component.mdx

@@ -131,7 +131,7 @@ class MyComponent(CustomComponent):
 
 ---
 
-The [Return Type Annotation](https://docs.python.org/3/library/typing.html) of the _`build`_ method defines the component type (e.g., Chain, BaseLLM, or basic Python types). Check out all supported types in the [component reference](../components/custom).
+The [Return Type Annotation](https://docs.python.org/3/library/typing.html) of the _`build`_ method defines the component type (e.g., Chain, BaseLanguageModel, or basic Python types). Check out all supported types in the [component reference](../components/custom).
 
 ```python
 from langflow.custom import CustomComponent

src/backend/base/langflow/__main__.py

@@ -21,8 +21,6 @@ from langflow.main import setup_app
 from langflow.services.database.utils import session_getter
 from langflow.services.deps import get_db_service, get_settings_service
 from langflow.services.utils import initialize_services, initialize_settings_service
-from langflow.utils.logger import configure, logger
-                                     initialize_settings_service)
 from langflow.utils.logger import configure, logger
 
 console = Console()
@@ -102,12 +100,8 @@ def update_settings(
 
 @app.command()
 def run(
-    host: str = typer.Option(
+    host: str = typer.Option("127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"),
-        "127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"
+    workers: int = typer.Option(1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"),
-    ),
-    workers: int = typer.Option(
-        1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"
-    ),
     timeout: int = typer.Option(300, help="Worker timeout in seconds."),
     port: int = typer.Option(7860, help="Port to listen on.", envvar="LANGFLOW_PORT"),
     components_path: Optional[Path] = typer.Option(
@@ -115,19 +109,11 @@ def run(
         help="Path to the directory containing custom components.",
         envvar="LANGFLOW_COMPONENTS_PATH",
     ),
-    config: str = typer.Option(
+    config: str = typer.Option(Path(__file__).parent / "config.yaml", help="Path to the configuration file."),
-        Path(__file__).parent / "config.yaml", help="Path to the configuration file."
-    ),
     # .env file param
-    env_file: Path = typer.Option(
+    env_file: Path = typer.Option(None, help="Path to the .env file containing environment variables."),
-        None, help="Path to the .env file containing environment variables."
+    log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"),
-    ),
+    log_file: Path = typer.Option("logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"),
-    log_level: str = typer.Option(
-        "critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"
-    ),
-    log_file: Path = typer.Option(
-        "logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"
-    ),
     cache: Optional[str] = typer.Option(
         envvar="LANGFLOW_LANGCHAIN_CACHE",
         help="Type of cache to use. (InMemoryCache, SQLiteCache)",
@@ -221,9 +207,7 @@ def wait_for_server_ready(host, port):
 
 
 def run_on_mac_or_linux(host, port, log_level, options, app):
-    webapp_process = Process(
+    webapp_process = Process(target=run_langflow, args=(host, port, log_level, options, app))
-        target=run_langflow, args=(host, port, log_level, options, app)
-    )
     webapp_process.start()
     wait_for_server_ready(host, port)
 
@@ -319,9 +303,7 @@ def build_new_version_notice(current_version: str, package_name: str):
                 f"A new pre-release version of {package_name} is available: {latest_version}",
             )
     else:
-        latest_version = httpx.get(f"https://pypi.org/pypi/{package_name}/json").json()[
+        latest_version = httpx.get(f"https://pypi.org/pypi/{package_name}/json").json()["info"]["version"]
-            "info"
-        ]["version"]
         if not version_is_prerelease(latest_version):
             return (
                 False,
@@ -345,9 +327,7 @@ def fetch_latest_version(package_name: str, include_prerelease: bool) -> str:
 
 def build_version_notice(current_version: str, package_name: str) -> str:
     latest_version = fetch_latest_version(package_name, is_prerelease(current_version))
-    if latest_version and pkg_version.parse(current_version) < pkg_version.parse(
+    if latest_version and pkg_version.parse(current_version) < pkg_version.parse(latest_version):
-        latest_version
-    ):
         release_type = "pre-release" if is_prerelease(latest_version) else "version"
         return f"A new {release_type} of {package_name} is available: {latest_version}"
     return ""
@@ -396,9 +376,7 @@ def print_banner(host: str, port: int):
             from importlib import metadata
 
             langflow_base_version = metadata.version("langflow-base")
-            is_pre_release |= is_prerelease(
+            is_pre_release |= is_prerelease(langflow_base_version)  # Update pre-release status
-                langflow_base_version
-            )  # Update pre-release status
             notice = build_version_notice(langflow_base_version, "langflow-base")
             notice = stylize_text(notice, "langflow-base", is_pre_release)
             if notice:
@@ -417,9 +395,7 @@ def print_banner(host: str, port: int):
         notices.append(f"Run '{pip_command}' to update.")
 
     styled_notices = [f"[bold]{notice}[/bold]" for notice in notices if notice]
-    styled_package_name = stylize_text(
+    styled_package_name = stylize_text(package_name, package_name, any("pre-release" in notice for notice in notices))
-        package_name, package_name, any("pre-release" in notice for notice in notices)
-    )
 
     title = f"[bold]Welcome to :chains: {styled_package_name}[/bold]\n"
     info_text = "Collaborate, and contribute at our [bold][link=https://github.com/langflow-ai/langflow]GitHub Repo[/link][/bold] :rocket:"
@@ -462,12 +438,8 @@ def run_langflow(host, port, log_level, options, app):
 @app.command()
 def superuser(
     username: str = typer.Option(..., prompt=True, help="Username for the superuser."),
-    password: str = typer.Option(
+    password: str = typer.Option(..., prompt=True, hide_input=True, help="Password for the superuser."),
-        ..., prompt=True, hide_input=True, help="Password for the superuser."
+    log_level: str = typer.Option("error", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"),
-    ),
-    log_level: str = typer.Option(
-        "error", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"
-    ),
 ):
     """
     Create a superuser.
@@ -494,11 +466,23 @@ def superuser(
 
 
 @app.command()
-def migration(test: bool = typer.Option(True, help="Run migrations in test mode.")):
+def migration(
+    test: bool = typer.Option(True, help="Run migrations in test mode."),
+    fix: bool = typer.Option(
+        False,
+        help="Fix migrations. This is a destructive operation, and should only be used if you know what you are doing.",
+    ),
+):
     """
     Run or test migrations.
     """
-    initialize_services()
+    if fix:
+        if not typer.confirm(
+            "This will delete all data necessary to fix migrations. Are you sure you want to continue?"
+        ):
+            raise typer.Abort()
+
+    initialize_services(fix_migration=fix)
     db_service = get_db_service()
     if not test:
         db_service.run_migrations()

src/backend/base/langflow/base/tools/base.py

@@ -0,0 +1,23 @@
+from langflow.field_typing import Tool
+
+
+def build_status_from_tool(tool: Tool):
+    """
+    Builds a status string representation of a tool.
+
+    Args:
+        tool (Tool): The tool object to build the status for.
+
+    Returns:
+        str: The status string representation of the tool, including its name, description, and arguments (if any).
+    """
+    description_repr = repr(tool.description).strip("'")
+    args_str = "\n".join(
+        [
+            f"- {arg_name}: {arg_data['description']}"
+            for arg_name, arg_data in tool.args.items()
+            if "description" in arg_data
+        ]
+    )
+    status = f"Name: {tool.name}\nDescription: {description_repr}"
+    return status + (f"\nArguments:\n{args_str}" if args_str else "")

src/backend/base/langflow/components/agents/XMLAgent.py

@@ -4,7 +4,7 @@ from langchain.agents import create_xml_agent
 from langchain_core.prompts import PromptTemplate
 
 from langflow.base.agents.agent import LCAgentComponent
-from langflow.field_typing import BaseLLM, BaseMemory, Text, Tool
+from langflow.field_typing import BaseLanguageModel, BaseMemory, Text, Tool
 
 
 class XMLAgentComponent(LCAgentComponent):
@@ -66,7 +66,7 @@ class XMLAgentComponent(LCAgentComponent):
     async def build(
         self,
         input_value: str,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         tools: List[Tool],
         prompt: str,
         memory: Optional[BaseMemory] = None,

src/backend/base/langflow/components/model_specs/AmazonBedrockSpecs.py

@@ -1,6 +1,5 @@
 from typing import Optional
-
+from langflow.field_typing import BaseLanguageModel
-from langchain.llms.base import BaseLLM
 from langchain_community.llms.bedrock import Bedrock
 
 from langflow.interface.custom.custom_component import CustomComponent
@@ -46,7 +45,7 @@ class AmazonBedrockComponent(CustomComponent):
         endpoint_url: Optional[str] = None,
         streaming: bool = False,
         cache: Optional[bool] = None,
-    ) -> BaseLLM:
+    ) -> BaseLanguageModel:
         try:
             output = Bedrock(
                 credentials_profile_name=credentials_profile_name,

src/backend/base/langflow/components/model_specs/AnthropicLLMSpecs.py

@@ -1,14 +1,14 @@
 from typing import Optional
 
 from langchain.llms.base import BaseLanguageModel
-from langchain_community.chat_models.anthropic import ChatAnthropic
+from langchain_anthropic import ChatAnthropic
 from pydantic.v1 import SecretStr
 
 from langflow.interface.custom.custom_component import CustomComponent
 
 
-class AnthropicLLM(CustomComponent):
+class ChatAntropicSpecsComponent(CustomComponent):
-    display_name: str = "AnthropicLLM"
+    display_name: str = "Anthropic"
     description: str = "Anthropic Chat&Completion large language models."
     icon = "Anthropic"
 

src/backend/base/langflow/components/model_specs/AnthropicSpecs.py

@@ -1,49 +0,0 @@
-from typing import Optional
-
-from langchain_community.llms.anthropic import Anthropic
-from pydantic.v1 import SecretStr
-
-from langflow.field_typing import BaseLanguageModel, NestedDict
-from langflow.interface.custom.custom_component import CustomComponent
-
-
-class AnthropicComponent(CustomComponent):
-    display_name = "Anthropic"
-    description = "Anthropic large language models."
-    icon = "Anthropic"
-
-    def build_config(self):
-        return {
-            "anthropic_api_key": {
-                "display_name": "Anthropic API Key",
-                "type": str,
-                "password": True,
-            },
-            "anthropic_api_url": {
-                "display_name": "Anthropic API URL",
-                "type": str,
-            },
-            "model_kwargs": {
-                "display_name": "Model Kwargs",
-                "field_type": "NestedDict",
-                "advanced": True,
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "field_type": "float",
-            },
-        }
-
-    def build(
-        self,
-        anthropic_api_key: str,
-        anthropic_api_url: str,
-        model_kwargs: NestedDict = {},
-        temperature: Optional[float] = None,
-    ) -> BaseLanguageModel:
-        return Anthropic(
-            anthropic_api_key=SecretStr(anthropic_api_key),
-            anthropic_api_url=anthropic_api_url,
-            model_kwargs=model_kwargs,
-            temperature=temperature,
-        )

src/backend/base/langflow/components/model_specs/BaiduQianfanChatEndpointsSpecs.py

@@ -1,9 +1,9 @@
 from typing import Optional
 
-from langchain.llms.base import BaseLLM
 from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
 from pydantic.v1 import SecretStr
 
+from langflow.field_typing import BaseLanguageModel
 from langflow.interface.custom.custom_component import CustomComponent
 
 
@@ -79,7 +79,7 @@ class QianfanChatEndpointComponent(CustomComponent):
         temperature: Optional[float] = None,
         penalty_score: Optional[float] = None,
         endpoint: Optional[str] = None,
-    ) -> BaseLLM:
+    ) -> BaseLanguageModel:
         try:
             output = QianfanChatEndpoint(  # type: ignore
                 model=model,

src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py

@@ -1,9 +1,9 @@
 from typing import Optional
 
 from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint
-from langchain.llms.base import BaseLLM
 
 from langflow.interface.custom.custom_component import CustomComponent
+from langflow.field_typing import BaseLanguageModel
 
 
 class QianfanLLMEndpointComponent(CustomComponent):
@@ -78,7 +78,7 @@ class QianfanLLMEndpointComponent(CustomComponent):
         temperature: Optional[float] = None,
         penalty_score: Optional[float] = None,
         endpoint: Optional[str] = None,
-    ) -> BaseLLM:
+    ) -> BaseLanguageModel:
         try:
             output = QianfanLLMEndpoint(  # type: ignore
                 model=model,

src/backend/base/langflow/components/model_specs/ChatAnthropicSpecs.py

@@ -1,67 +1,89 @@
-from typing import Callable, Optional, Union
+from typing import Optional
 
-from langchain_community.chat_models.anthropic import ChatAnthropic
+from langchain_anthropic import ChatAnthropic
 from pydantic.v1.types import SecretStr
 
+
 from langflow.custom import CustomComponent
 from langflow.field_typing import BaseLanguageModel
 
 
-class ChatAnthropicComponent(CustomComponent):
+class AnthropicLLM(CustomComponent):
-    display_name = "ChatAnthropic"
+    display_name: str = "Anthropic"
-    description = "`Anthropic` chat large language models."
+    description: str = "Generate text using Anthropic Chat&Completion LLMs."
-    documentation = "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic"
     icon = "Anthropic"
 
+    field_order = [
+        "model",
+        "anthropic_api_key",
+        "max_tokens",
+        "temperature",
+        "anthropic_api_url",
+    ]
+
     def build_config(self):
         return {
+            "model": {
+                "display_name": "Model Name",
+                "options": [
+                    "claude-3-opus-20240229",
+                    "claude-3-sonnet-20240229",
+                    "claude-3-haiku-20240307",
+                    "claude-2.1",
+                    "claude-2.0",
+                    "claude-instant-1.2",
+                    "claude-instant-1",
+                ],
+                "info": "Name of the model to use.",
+                "required": True,
+                "value": "claude-3-opus-20240229",
+            },
             "anthropic_api_key": {
                 "display_name": "Anthropic API Key",
-                "field_type": "str",
+                "required": True,
                 "password": True,
-            },
+                "info": "Your Anthropic API key.",
-            "model_kwargs": {
-                "display_name": "Model Kwargs",
-                "field_type": "dict",
-                "advanced": True,
-            },
-            "model_name": {
-                "display_name": "Model Name",
-                "field_type": "str",
-                "advanced": False,
-                "required": False,
-                "options": ["claude-3-opus-20240229", "claude-3-sonnet-20240229", "claude-3-haiku-20240307"],
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "field_type": "float",
             },
             "max_tokens": {
                 "display_name": "Max Tokens",
                 "field_type": "int",
-                "advanced": False,
+                "advanced": True,
-                "required": False,
+                "value": 256,
             },
-            "top_k": {"display_name": "Top K", "field_type": "int", "advanced": True},
+            "temperature": {
-            "top_p": {"display_name": "Top P", "field_type": "float", "advanced": True},
+                "display_name": "Temperature",
+                "field_type": "float",
+                "value": 0.1,
+            },
+            "anthropic_api_url": {
+                "display_name": "Anthropic API URL",
+                "advanced": True,
+                "info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
+            },
+            "code": {"show": False},
         }
 
     def build(
         self,
-        anthropic_api_key: str,
+        model: str,
-        model_kwargs: dict = {},
+        anthropic_api_key: Optional[str] = None,
-        model_name: str = "claude-3-opus-20240229",
+        max_tokens: Optional[int] = None,
         temperature: Optional[float] = None,
-        max_tokens: Optional[int] = 1024,
+        anthropic_api_url: Optional[str] = None,
-        top_k: Optional[int] = None,
+    ) -> BaseLanguageModel:
-        top_p: Optional[float] = None,
+        # Set default API endpoint if not provided
-    ) -> Union[BaseLanguageModel, Callable]:
+        if not anthropic_api_url:
-        return ChatAnthropic(
+            anthropic_api_url = "https://api.anthropic.com"
-            anthropic_api_key=SecretStr(anthropic_api_key),
+
-            model_kwargs=model_kwargs,
+        try:
-            model_name=model_name,
+            output = ChatAnthropic(
-            temperature=temperature,
+                model_name=model,
-            max_tokens=max_tokens,  # type: ignore
+                anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),
-            top_k=top_k,
+                max_tokens_to_sample=max_tokens,  # type: ignore
-            top_p=top_p,
+                temperature=temperature,
-        )
+                anthropic_api_url=anthropic_api_url,
+            )
+        except Exception as e:
+            raise ValueError("Could not connect to Anthropic API.") from e
+
+        return output

src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Dict, Optional
 
 from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException
 from langflow.field_typing import BaseLanguageModel

src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py

@@ -1,9 +1,9 @@
-from typing import Optional, Union
+from typing import Optional
 
-from langchain.llms import BaseLLM
+from langflow.field_typing import BaseLanguageModel
 from langchain_community.chat_models.openai import ChatOpenAI
 
-from langflow.field_typing import BaseLanguageModel, NestedDict
+from langflow.field_typing import NestedDict
 from langflow.interface.custom.custom_component import CustomComponent
 
 
@@ -68,7 +68,7 @@ class ChatOpenAIComponent(CustomComponent):
         openai_api_base: Optional[str] = None,
         openai_api_key: Optional[str] = None,
         temperature: float = 0.7,
-    ) -> Union[BaseLanguageModel, BaseLLM]:
+    ) -> BaseLanguageModel:
         if not openai_api_base:
             openai_api_base = "https://api.openai.com/v1"
         return ChatOpenAI(

src/backend/base/langflow/components/model_specs/ChatVertexAISpecs.py

@@ -1,6 +1,5 @@
-from typing import List, Optional, Union
+from typing import List, Optional
 
-from langchain.llms import BaseLLM
 from langchain_community.chat_models.vertexai import ChatVertexAI
 from langchain_core.messages.base import BaseMessage
 
@@ -74,7 +73,7 @@ class ChatVertexAIComponent(CustomComponent):
         top_k: int = 40,
         top_p: float = 0.95,
         verbose: bool = False,
-    ) -> Union[BaseLanguageModel, BaseLLM]:
+    ) -> BaseLanguageModel:
         return ChatVertexAI(
             credentials=credentials,
             examples=examples,

src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from langchain.llms.base import BaseLLM
+from langflow.field_typing import BaseLanguageModel
 from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint
 
 from langflow.interface.custom.custom_component import CustomComponent
@@ -32,7 +32,7 @@ class HuggingFaceEndpointsComponent(CustomComponent):
         task: str = "text2text-generation",
         huggingfacehub_api_token: Optional[str] = None,
         model_kwargs: Optional[dict] = None,
-    ) -> BaseLLM:
+    ) -> BaseLanguageModel:
         try:
             output = HuggingFaceEndpoint(  # type: ignore
                 endpoint_url=endpoint_url,

src/backend/base/langflow/components/model_specs/OllamaLLMSpecs.py

@@ -1,6 +1,6 @@
 from typing import List, Optional
 
-from langchain.llms.base import BaseLLM
+from langflow.field_typing import BaseLanguageModel
 from langchain_community.llms.ollama import Ollama
 
 from langflow.interface.custom.custom_component import CustomComponent
@@ -118,7 +118,7 @@ class OllamaLLM(CustomComponent):
         tfs_z: Optional[float] = None,
         top_k: Optional[int] = None,
         top_p: Optional[int] = None,
-    ) -> BaseLLM:
+    ) -> BaseLanguageModel:
         if not base_url:
             base_url = "http://localhost:11434"
 

src/backend/base/langflow/components/model_specs/VertexAISpecs.py

@@ -1,6 +1,6 @@
-from typing import Callable, Dict, Optional, Union
+from typing import Dict, Optional
 
-from langchain.llms import BaseLLM
+from langflow.field_typing import BaseLanguageModel
 from langchain_community.llms.vertexai import VertexAI
 
 from langflow.interface.custom.custom_component import CustomComponent
@@ -129,7 +129,7 @@ class VertexAIComponent(CustomComponent):
         top_p: float = 0.95,
         tuned_model_name: Optional[str] = None,
         verbose: bool = False,
-    ) -> Union[BaseLLM, Callable]:
+    ) -> BaseLanguageModel:
         return VertexAI(
             credentials=credentials,
             location=location,

src/backend/base/langflow/components/model_specs/__init__.py

@@ -1,6 +1,6 @@
 from .AmazonBedrockSpecs import AmazonBedrockComponent
-from .AnthropicLLMSpecs import AnthropicLLM
+from .AnthropicLLMSpecs import ChatAntropicSpecsComponent
-from .AnthropicSpecs import AnthropicComponent
+
 from .AzureChatOpenAISpecs import AzureChatOpenAISpecsComponent
 from .BaiduQianfanChatEndpointsSpecs import QianfanChatEndpointComponent
 from .BaiduQianfanLLMEndpointsSpecs import QianfanLLMEndpointComponent
@@ -17,8 +17,7 @@ from .VertexAISpecs import VertexAIComponent
 
 __all__ = [
     "AmazonBedrockComponent",
-    "AnthropicLLM",
+    "ChatAntropicSpecsComponent",
-    "AnthropicComponent",
     "AzureChatOpenAISpecsComponent",
     "QianfanChatEndpointComponent",
     "QianfanLLMEndpointComponent",

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

@@ -1,8 +1,8 @@
-from typing import Callable, Optional, Union
+from typing import Optional
 
 from langchain.retrievers import MultiQueryRetriever
 
-from langflow.field_typing import BaseLLM, BaseRetriever, PromptTemplate
+from langflow.field_typing import BaseRetriever, PromptTemplate, BaseLanguageModel
 from langflow.interface.custom.custom_component import CustomComponent
 
 
@@ -39,11 +39,11 @@ class MultiQueryRetrieverComponent(CustomComponent):
 
     def build(
         self,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         retriever: BaseRetriever,
         prompt: Optional[PromptTemplate] = None,
         parser_key: str = "lines",
-    ) -> Union[Callable, MultiQueryRetriever]:
+    ) -> MultiQueryRetriever:
         if not prompt:
             return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, parser_key=parser_key)
         else:

src/backend/base/langflow/components/toolkits/VectorStoreInfo.py

@@ -1,5 +1,3 @@
-from typing import Callable, Union
-
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
 from langchain_community.vectorstores import VectorStore
 
@@ -22,5 +20,5 @@ class VectorStoreInfoComponent(CustomComponent):
         vectorstore: VectorStore,
         description: str,
         name: str,
-    ) -> Union[VectorStoreInfo, Callable]:
+    ) -> VectorStoreInfo:
         return VectorStoreInfo(vectorstore=vectorstore, description=description, name=name)

src/backend/base/langflow/components/tools/PythonREPLTool.py

@@ -0,0 +1,68 @@
+import importlib
+
+from langchain.agents import Tool
+from langchain_experimental.utilities import PythonREPL
+
+from langflow.base.tools.base import build_status_from_tool
+from langflow.custom import CustomComponent
+
+
+class PythonREPLToolComponent(CustomComponent):
+    display_name = "Python REPL Tool"
+    description = "A tool for running Python code in a REPL environment."
+
+    def build_config(self):
+        return {
+            "name": {"display_name": "Name", "info": "The name of the tool."},
+            "description": {"display_name": "Description", "info": "A description of the tool."},
+            "global_imports": {
+                "display_name": "Global Imports",
+                "info": "A list of modules to import globally, e.g. ['math', 'numpy'].",
+            },
+        }
+
+    def get_globals(self, globals: list[str]) -> dict:
+        """
+        Retrieves the global variables from the specified modules.
+
+        Args:
+            globals (list[str]): A list of module names.
+
+        Returns:
+            dict: A dictionary containing the global variables from the specified modules.
+        """
+        global_dict = {}
+        for module in globals:
+            try:
+                module = importlib.import_module(module)
+                global_dict[module.__name__] = module
+            except ImportError:
+                print(f"Could not import module {module}")
+        return global_dict
+
+    def build(
+        self,
+        name: str = "python_repl",
+        description: str = "A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",
+        global_imports: list[str] = ["math"],
+    ) -> Tool:
+        """
+        Builds a Python REPL tool.
+
+        Args:
+            name (str, optional): The name of the tool. Defaults to "python_repl".
+            description (str, optional): The description of the tool. Defaults to "A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`. ".
+            global_imports (list[str], optional): A list of global imports to be available in the Python REPL. Defaults to ["math"].
+
+        Returns:
+            Tool: The built Python REPL tool.
+        """
+        _globals = self.get_globals(global_imports)
+        python_repl = PythonREPL(_globals=_globals)
+        tool = Tool(
+            name=name,
+            description=description,
+            func=python_repl.run,
+        )
+        self.status = build_status_from_tool(tool)
+        return tool

src/backend/base/langflow/components/tools/__init__.py

@@ -1,5 +1,7 @@
+from .PythonREPLTool import PythonREPLToolComponent
 from .RetrieverTool import RetrieverToolComponent
-from .SearchAPITool import SearchApiToolComponent
 from .SearchApi import SearchApi
+from .SearchAPITool import SearchApiToolComponent
 
-__all__ = ["RetrieverToolComponent", "SearchApiToolComponent", "SearchApi"]
+
+__all__ = ["RetrieverToolComponent", "SearchApiToolComponent", "SearchApi", "PythonREPLToolComponent"]

src/backend/base/langflow/server.py

@@ -35,11 +35,7 @@ class LangflowApplication(BaseApplication):
         super().__init__()
 
     def load_config(self):
-        config = {
+        config = {key: value for key, value in self.options.items() if key in self.cfg.settings and value is not None}
-            key: value
-            for key, value in self.options.items()
-            if key in self.cfg.settings and value is not None
-        }
         for key, value in config.items():
             self.cfg.set(key.lower(), value)
 

src/backend/base/langflow/services/database/service.py

@@ -1,6 +1,5 @@
 from datetime import datetime
 import time
-from datetime import datetime
 from pathlib import Path
 from typing import TYPE_CHECKING
 
@@ -37,10 +36,7 @@ class DatabaseService(Service):
     def _create_engine(self) -> "Engine":
         """Create the engine for the database."""
         settings_service = get_settings_service()
-        if (
+        if settings_service.settings.DATABASE_URL and settings_service.settings.DATABASE_URL.startswith("sqlite"):
-            settings_service.settings.DATABASE_URL
-            and settings_service.settings.DATABASE_URL.startswith("sqlite")
-        ):
             connect_args = {"check_same_thread": False}
         else:
             connect_args = {}
@@ -52,9 +48,7 @@ class DatabaseService(Service):
 
     def __exit__(self, exc_type, exc_value, traceback):
         if exc_type is not None:  # If an exception has been raised
-            logger.error(
+            logger.error(f"Session rollback because of exception: {exc_type.__name__} {exc_value}")
-                f"Session rollback because of exception: {exc_type.__name__} {exc_value}"
-            )
             self._session.rollback()
         else:
             self._session.commit()
@@ -71,9 +65,7 @@ class DatabaseService(Service):
         settings_service = get_settings_service()
         if settings_service.auth_settings.AUTO_LOGIN:
             with Session(self.engine) as session:
-                flows = session.exec(
+                flows = session.exec(select(models.Flow).where(models.Flow.user_id is None)).all()
-                    select(models.Flow).where(models.Flow.user_id is None)
-                ).all()
                 if flows:
                     logger.debug("Migrating flows to default superuser")
                     username = settings_service.auth_settings.SUPERUSER
@@ -103,9 +95,7 @@ class DatabaseService(Service):
             expected_columns = list(model.model_fields.keys())
 
             try:
-                available_columns = [
+                available_columns = [col["name"] for col in inspector.get_columns(table)]
-                    col["name"] for col in inspector.get_columns(table)
-                ]
             except sa.exc.NoSuchTableError:
                 logger.debug(f"Missing table: {table}")
                 return False
@@ -169,9 +159,7 @@ class DatabaseService(Service):
             buffer.write(f"{datetime.now().isoformat()}: Checking migrations\n")
             command.check(alembic_cfg)
         except Exception as exc:
-            if isinstance(
+            if isinstance(exc, (util.exc.CommandError, util.exc.AutogenerateDiffsDetected)):
-                exc, (util.exc.CommandError, util.exc.AutogenerateDiffsDetected)
-            ):
                 command.upgrade(alembic_cfg, "head")
                 time.sleep(3)
 
@@ -208,10 +196,7 @@ class DatabaseService(Service):
         # We will check that all models are in the database
         # and that the database is up to date with all columns
         sql_models = [models.Flow, models.User, models.ApiKey]
-        return [
+        return [TableResults(sql_model.__tablename__, self.check_table(sql_model)) for sql_model in sql_models]
-            TableResults(sql_model.__tablename__, self.check_table(sql_model))
-            for sql_model in sql_models
-        ]
 
     def check_table(self, model):
         results = []
@@ -220,9 +205,7 @@ class DatabaseService(Service):
         expected_columns = list(model.__fields__.keys())
         available_columns = []
         try:
-            available_columns = [
+            available_columns = [col["name"] for col in inspector.get_columns(table_name)]
-                col["name"] for col in inspector.get_columns(table_name)
-            ]
             results.append(Result(name=table_name, type="table", success=True))
         except sa.exc.NoSuchTableError:
             logger.error(f"Missing table: {table_name}")
@@ -253,9 +236,7 @@ class DatabaseService(Service):
             try:
                 table.create(self.engine, checkfirst=True)
             except OperationalError as oe:
-                logger.warning(
+                logger.warning(f"Table {table} already exists, skipping. Exception: {oe}")
-                    f"Table {table} already exists, skipping. Exception: {oe}"
-                )
             except Exception as exc:
                 logger.error(f"Error creating table {table}: {exc}")
                 raise RuntimeError(f"Error creating table {table}") from exc
@@ -267,9 +248,7 @@ class DatabaseService(Service):
             if table not in table_names:
                 logger.error("Something went wrong creating the database and tables.")
                 logger.error("Please check your database settings.")
-                raise RuntimeError(
+                raise RuntimeError("Something went wrong creating the database and tables.")
-                    "Something went wrong creating the database and tables."
-                )
 
         logger.debug("Database and tables created successfully")
 

src/backend/base/langflow/template/frontend_node/llms.py

@@ -63,8 +63,8 @@ class LLMFrontendNode(FrontendNode):
             field.info = OPENAI_API_BASE_INFO
 
     def add_extra_base_classes(self) -> None:
-        if "BaseLLM" not in self.base_classes:
+        if "BaseLanguageModel" not in self.base_classes:
-            self.base_classes.append("BaseLLM")
+            self.base_classes.append("BaseLanguageModel")
 
     @staticmethod
     def format_azure_field(field: TemplateField):

src/backend/base/langflow/utils/logger.py

@@ -26,10 +26,7 @@ def patching(record):
 
 
 def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None):
-    if (
+    if os.getenv("LANGFLOW_LOG_LEVEL", "").upper() in VALID_LOG_LEVELS and log_level is None:
-        os.getenv("LANGFLOW_LOG_LEVEL", "").upper() in VALID_LOG_LEVELS
-        and log_level is None
-    ):
         log_level = os.getenv("LANGFLOW_LOG_LEVEL")
     if log_level is None:
         log_level = "ERROR"
@@ -77,11 +74,7 @@ def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None):
 
 
 def setup_uvicorn_logger():
-    loggers = (
+    loggers = (logging.getLogger(name) for name in logging.root.manager.loggerDict if name.startswith("uvicorn."))
-        logging.getLogger(name)
-        for name in logging.root.manager.loggerDict
-        if name.startswith("uvicorn.")
-    )
     for uvicorn_logger in loggers:
         uvicorn_logger.handlers = []
     logging.getLogger("uvicorn").handlers = [InterceptHandler()]
@@ -111,6 +104,4 @@ class InterceptHandler(logging.Handler):
             frame = frame.f_back
             depth += 1
 
-        logger.opt(depth=depth, exception=record.exc_info).log(
+        logger.opt(depth=depth, exception=record.exc_info).log(level, record.getMessage())
-            level, record.getMessage()
-        )

src/frontend/tests/custom_component_full.ts

@@ -1,6 +1,6 @@
 export const custom = `from langflow.custom import CustomComponent
 
-from langchain.llms.base import BaseLLM
+from langflow.field_typing import BaseLanguageModel
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
 from langchain_core.documents import Document
@@ -15,6 +15,6 @@ class YourComponent(CustomComponent):
     def build_config(self):
         return { "file": { "file_type": ["json"], } }
 
-    def build(self, url: str,file:str,integer:int,nested:NestedDict,flt:float,boolean:bool,lisst:list[str],dictionary:dict, llm: BaseLLM, prompt: PromptTemplate) -> Document:
+    def build(self, url: str,file:str,integer:int,nested:NestedDict,flt:float,boolean:bool,lisst:list[str],dictionary:dict, llm: BaseLanguageModel, prompt: PromptTemplate) -> Document:
 
         return "test"`;

src/frontend/tests/end-to-end/dropdownComponent.spec.ts

@@ -177,7 +177,7 @@ test("dropDownComponent", async ({ page }) => {
     .click();
   await page.locator("textarea").press("Control+a");
   const emptyOptionsCode = `from typing import Optional
-from langchain.llms.base import BaseLLM
+from langflow.field_typing import BaseLanguageModel
 from langchain_community.llms.bedrock import Bedrock
 
 from langflow.interface.custom.custom_component import CustomComponent
@@ -212,7 +212,7 @@ class AmazonBedrockComponent(CustomComponent):
         endpoint_url: Optional[str] = None,
         streaming: bool = False,
         cache: Optional[bool] = None,
-    ) -> BaseLLM:
+    ) -> BaseLanguageModel:
         try:
             output = Bedrock(
                 credentials_profile_name=credentials_profile_name,

tests/conftest.py

@@ -22,9 +22,6 @@ from langflow.services.database.models.flow.model import Flow, FlowCreate
 from langflow.services.database.models.user.model import User, UserCreate
 from langflow.services.database.utils import session_getter
 from langflow.services.deps import get_db_service
-from sqlmodel import Session, SQLModel, create_engine, select
-from sqlmodel.pool import StaticPool
-from typer.testing import CliRunner
 
 if TYPE_CHECKING:
     from langflow.services.database.service import DatabaseService

tests/test_custom_component.py

@@ -6,21 +6,15 @@ import pytest
 from langchain_core.documents import Document
 
 from langflow.interface.custom.base import CustomComponent
-from langflow.interface.custom.code_parser.code_parser import (
+from langflow.interface.custom.code_parser.code_parser import CodeParser, CodeSyntaxError
-    CodeParser,
+from langflow.interface.custom.custom_component.component import Component, ComponentCodeNullError
-    CodeSyntaxError,
-)
-from langflow.interface.custom.custom_component.component import (
-    Component,
-    ComponentCodeNullError,
-)
 from langflow.services.database.models.flow import Flow, FlowCreate
 
 code_default = """
 from langflow.field_typing import Prompt
 from langflow.interface.custom.custom_component import CustomComponent
 
-from langchain.llms.base import BaseLLM
+from langflow.field_typing import BaseLanguageModel
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
 from langchain_core.documents import Document
@@ -32,7 +26,7 @@ class YourComponent(CustomComponent):
     description: str = "Your description"
     field_config = { "url": { "multiline": True, "required": True } }
 
-    def build(self, url: str, llm: BaseLLM, template: Prompt) -> Document:
+    def build(self, url: str, llm: BaseLanguageModel, template: Prompt) -> Document:
         response = requests.get(url)
         prompt = PromptTemplate.from_template(template)
         chain = LLMChain(llm=llm, prompt=prompt)