feat: implemented file loading and toolkit

src/backend/langflow/graph/base.py

@@ -3,19 +3,22 @@
 #   - Defer prompts building to the last moment or when they have all the tools
 #   - Build each inner agent first, then build the outer agent
 
-from copy import deepcopy
 import types
+from copy import deepcopy
 from typing import Any, Dict, List
-from langflow.interface.listing import ALL_TYPES_DICT
+
+from langflow.graph.utils import load_dict
 from langflow.interface import loading
+from langflow.interface.listing import ALL_TYPES_DICT
 from langflow.interface.tools.base import tool_creator
 
 
 class Node:
-    def __init__(self, data: Dict):
+    def __init__(self, data: Dict, base_type: str | None = None) -> None:
         self.id: str = data["id"]
         self._data = data
         self.edges: List[Edge] = []
+        self.base_type: str | None = base_type
         self._parse_data()
         self._built_object = None
         self._built = False
@@ -44,6 +47,11 @@ class Node:
         self.node_type = (
             self.data["type"] if "Tool" not in self.output else template_dict["_type"]
         )
+        if self.base_type is None:
+            for base_type, value in ALL_TYPES_DICT.items():
+                if self.node_type in value:
+                    self.base_type = base_type
+                    break
 
     def _build_params(self):
         # Some params are required, some are optional
@@ -71,7 +79,18 @@ class Node:
                 continue
             # If the type is not transformable to a python base class
             # then we need to get the edge that connects to this node
-            if value["type"] not in ["str", "bool", "code"]:
+            if value["type"] == "file":
+                # Load the type in value.get('suffixes') using
+                # what is inside value.get('content')
+                # value.get('value') is the file name
+                type_to_load = value.get("suffixes")
+                file_name = value.get("value")
+                content = value.get("content")
+                # Now
+                loaded_dict = load_dict(file_name, content, type_to_load)
+                params[key] = loaded_dict
+
+            elif value["type"] not in ["str", "bool", "code", "int", "float"]:
                 # Get the edge that connects to this node
                 edge = next(
                     (
@@ -138,17 +157,15 @@ class Node:
         # Get the class from LANGCHAIN_TYPES_DICT
         # and instantiate it with the params
         # and return the instance
-        for base_type, value in ALL_TYPES_DICT.items():
-            if base_type == "tools":
-                value = tool_creator.type_to_loader_dict
 
-            if self.node_type in value:
-                self._built_object = loading.instantiate_class(
-                    node_type=self.node_type,
-                    base_type=base_type,
-                    params=self.params,
-                )
-                break
+        try:
+            self._built_object = loading.instantiate_class(
+                node_type=self.node_type,
+                base_type=self.base_type,
+                params=self.params,
+            )
+        except Exception as exc:
+            raise ValueError(f"Error building node {self.node_type}") from exc
 
         if self._built_object is None:
             raise ValueError(f"Node type {self.node_type} not found")

src/backend/langflow/interface/toolkits/base.py

@@ -1,12 +1,28 @@
-from langflow.interface.base import LangChainTypeCreator
-from langflow.utils.util import build_template_from_class
-from typing import Dict, List
+from typing import Callable, Dict, List
+
 from langchain.agents import agent_toolkits
-from langflow.interface.importing.utils import import_class
+
+from langflow.interface.base import LangChainTypeCreator
+from langflow.interface.importing.utils import import_class, import_module
+from langflow.utils.util import build_template_from_class
 
 
 class ToolkitCreator(LangChainTypeCreator):
     type_name: str = "toolkits"
+    all_types: List[str] = agent_toolkits.__all__
+    create_functions: Dict = {
+        "JsonToolkit": [],
+        "SQLDatabaseToolkit": [],
+        "OpenAPIToolkit": ["create_openapi_agent"],
+        "VectorStoreToolkit": [
+            "create_vectorstore_agent",
+            "create_vectorstore_router_agent",
+            "VectorStoreInfo",
+        ],
+        "ZapierToolkit": [],
+        "PandasToolkit": ["create_pandas_dataframe_agent"],
+        "CSVToolkit": ["create_csv_agent"],
+    }
 
     @property
     def type_to_loader_dict(self) -> Dict:
@@ -19,6 +35,7 @@ class ToolkitCreator(LangChainTypeCreator):
                 for toolkit_name in agent_toolkits.__all__
                 if not toolkit_name.islower()
             }
+
         return self.type_dict
 
     def get_signature(self, name: str) -> Dict | None:
@@ -30,5 +47,17 @@ class ToolkitCreator(LangChainTypeCreator):
     def to_list(self) -> List[str]:
         return list(self.type_to_loader_dict.keys())
 
+    def get_create_function(self, name: str) -> Callable | None:
+        if loader_name := self.create_functions.get(name, None):
+            # import loader
+            return import_module(
+                f"from langchain.agents.agent_toolkits import {loader_name[0]}"
+            )
+        return None
+
+    def has_create_function(self, name: str) -> bool:
+        # check if the function list is not empty
+        return bool(self.create_functions.get(name, None))
+
 
 toolkits_creator = ToolkitCreator()