🔥 refactor(loading.py): remove unused imports and functions

The imports and functions that were not being used were removed to improve the code's readability and maintainability.

src/backend/langflow/interface/initialize/loading.py

@@ -1,22 +1,11 @@
 import json
-from typing import Any, Callable, Dict, Optional
+from typing import Any, Callable, Dict, Sequence
 
 from langchain.agents import ZeroShotAgent
 from langchain.agents import agent as agent_module
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.base import BaseToolkit
-from langchain.agents.load_tools import (
+from langchain.agents.tools import BaseTool
-    _BASE_TOOLS,
-    _EXTRA_LLM_TOOLS,
-    _EXTRA_OPTIONAL_TOOLS,
-    _LLM_TOOLS,
-)
-from langchain.agents.loading import load_agent_from_config
-from langchain.agents.tools import Tool
-from langchain.base_language import BaseLanguageModel
-from langchain.callbacks.base import BaseCallbackManager
-from langchain.chains.loading import load_chain_from_config
-from langchain.llms.loading import load_llm_from_config
 from langflow.interface.initialize.vector_store import (
     initialize_chroma,
     initialize_faiss,
@@ -31,9 +20,8 @@ from langflow.interface.custom_lists import CUSTOM_NODES
 from langflow.interface.importing.utils import get_function, import_by_type
 from langflow.interface.toolkits.base import toolkits_creator
 from langflow.interface.chains.base import chain_creator
-from langflow.interface.types import get_type_list
 from langflow.interface.utils import load_file_into_dict
-from langflow.utils import util, validate
+from langflow.utils import validate
 
 
 def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
@@ -237,49 +225,15 @@ def replace_zero_shot_prompt_with_prompt_template(nodes):
     return nodes
 
 
-def load_langchain_type_from_config(config: Dict[str, Any]):
-    """Load langchain type from config"""
-    # Get type list
-    type_list = get_type_list()
-    if config["_type"] in type_list["agents"]:
-        config = util.update_verbose(config, new_value=False)
-        return load_agent_executor_from_config(config, verbose=True)
-    elif config["_type"] in type_list["chains"]:
-        config = util.update_verbose(config, new_value=False)
-        return load_chain_from_config(config, verbose=True)
-    elif config["_type"] in type_list["llms"]:
-        config = util.update_verbose(config, new_value=True)
-        return load_llm_from_config(config)
-    else:
-        raise ValueError("Type should be either agent, chain or llm")
-
-
-def load_agent_executor_from_config(
-    config: dict,
-    llm: Optional[BaseLanguageModel] = None,
-    tools: Optional[list[Tool]] = None,
-    callback_manager: Optional[BaseCallbackManager] = None,
-    **kwargs: Any,
-):
-    tools = load_tools_from_config(config["allowed_tools"])
-    config["allowed_tools"] = [tool.name for tool in tools] if tools else []
-    agent_obj = load_agent_from_config(config, llm, tools, **kwargs)
-
-    return AgentExecutor.from_agent_and_tools(
-        agent=agent_obj,
-        tools=tools,
-        callback_manager=callback_manager,
-        **kwargs,
-    )
-
-
 def load_agent_executor(agent_class: type[agent_module.Agent], params, **kwargs):
     """Load agent executor from agent class, tools and chain"""
-    allowed_tools = params.get("allowed_tools", [])
+    allowed_tools: Sequence[BaseTool] = params.get("allowed_tools", [])
     llm_chain = params["llm_chain"]
     # if allowed_tools is not a list or set, make it a list
-    if not isinstance(allowed_tools, (list, set)):
+    if not isinstance(allowed_tools, (list, set)) and isinstance(
-        allowed_tools = [allowed_tools]
+        allowed_tools, BaseTool
+    ):
+        allowed_tools: Sequence[BaseTool] = [allowed_tools]
     tool_names = [tool.name for tool in allowed_tools]
     # Agent class requires an output_parser but Agent classes
     # have a default output_parser.
@@ -297,46 +251,6 @@ def load_toolkits_executor(node_type: str, toolkit: BaseToolkit, params: dict):
         return create_function(llm=llm, toolkit=toolkit)
 
 
-def load_tools_from_config(tool_list: list[dict]) -> list:
-    """Load tools based on a config list.
-
-    Args:
-        config: config list.
-
-    Returns:
-        List of tools.
-    """
-    tools = []
-    for tool in tool_list:
-        tool_type = tool.pop("_type")
-        llm_config = tool.pop("llm", None)
-        llm = load_llm_from_config(llm_config) if llm_config else None
-        kwargs = tool
-        if tool_type in _BASE_TOOLS:
-            tools.append(_BASE_TOOLS[tool_type]())
-        elif tool_type in _LLM_TOOLS:
-            if llm is None:
-                raise ValueError(f"Tool {tool_type} requires an LLM to be provided")
-            tools.append(_LLM_TOOLS[tool_type](llm))
-        elif tool_type in _EXTRA_LLM_TOOLS:
-            if llm is None:
-                raise ValueError(f"Tool {tool_type} requires an LLM to be provided")
-            _get_llm_tool_func, extra_keys = _EXTRA_LLM_TOOLS[tool_type]
-            if missing_keys := set(extra_keys).difference(kwargs):
-                raise ValueError(
-                    f"Tool {tool_type} requires some parameters that were not "
-                    f"provided: {missing_keys}"
-                )
-            tools.append(_get_llm_tool_func(llm=llm, **kwargs))
-        elif tool_type in _EXTRA_OPTIONAL_TOOLS:
-            _get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type]
-            kwargs = {k: value for k, value in kwargs.items() if value}
-            tools.append(_get_tool_func(**kwargs))
-        else:
-            raise ValueError(f"Got unknown tool {tool_type}")
-    return tools
-
-
 def build_prompt_template(prompt, tools):
     """Build PromptTemplate from ZeroShotPrompt"""
     prefix = prompt["node"]["template"]["prefix"]["value"]