Merge remote-tracking branch 'origin/dev' into fix_toolkits

src/backend/langflow/config.yaml

@@ -52,7 +52,6 @@ llms:
   - OpenAI
   # - AzureOpenAI
   - ChatOpenAI
-  - HuggingFaceHub
   - LlamaCpp
   - CTransformers
   - Cohere
@@ -66,9 +65,9 @@ prompts:
   - ZeroShotPrompt
 textsplitters:
   - CharacterTextSplitter
-  - RecursiveCharacterTextSplitter
-  - LatexTextSplitter
-  - PythonCodeTextSplitter
+  # - RecursiveCharacterTextSplitter
+  # - LatexTextSplitter
+  # - PythonCodeTextSplitter
 toolkits:
   - OpenAPIToolkit
   - JsonToolkit

src/backend/langflow/graph/base.py

@@ -7,7 +7,6 @@ import contextlib
 import inspect
 import types
 import warnings
-from copy import deepcopy
 from typing import Any, Dict, List, Optional
 
 from langflow.cache import base as cache_utils

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

@@ -1,6 +1,6 @@
 from typing import Dict, List, Optional
 
-from langchain.agents import loading
+from langchain.agents import types
 
 from langflow.custom.customs import get_custom_nodes
 from langflow.interface.agents.custom import CUSTOM_AGENTS
@@ -16,7 +16,7 @@ class AgentCreator(LangChainTypeCreator):
     @property
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:
-            self.type_dict = loading.AGENT_TO_CLASS
+            self.type_dict = types.AGENT_TO_CLASS
             # Add JsonAgent to the list of agents
             for name, agent in CUSTOM_AGENTS.items():
                 # TODO: validate AgentType

src/backend/langflow/interface/loading.py

@@ -31,7 +31,7 @@ from langflow.utils import util, validate
 def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
     """Instantiate class from module type and key, and params"""
     params = convert_params_to_sets(params)
-
+    params = convert_kwargs(params)
     if node_type in CUSTOM_AGENTS:
         custom_agent = CUSTOM_AGENTS.get(node_type)
         if custom_agent:
@@ -50,6 +50,16 @@ def convert_params_to_sets(params):
     return params
 
 
+def convert_kwargs(params):
+    # if *kwargs are passed as a string, convert to dict
+    # first find any key that has kwargs in it
+    kwargs_keys = [key for key in params.keys() if "kwargs" in key]
+    for key in kwargs_keys:
+        if isinstance(params[key], str):
+            params[key] = json.loads(params[key])
+    return params
+
+
 def instantiate_based_on_type(class_object, base_type, node_type, params):
     if base_type == "agents":
         return instantiate_agent(class_object, params)

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

@@ -39,7 +39,7 @@ class TextSplitterCreator(LangChainTypeCreator):
                 "type": "int",
                 "required": True,
                 "show": True,
-                "value": 4000,
+                "value": 1000,
                 "name": "chunk_size",
                 "display_name": "Chunk Size",
             }

src/backend/langflow/template/base.py

@@ -1 +0,0 @@
-

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

@@ -3,7 +3,7 @@ from typing import List, Optional
 
 from pydantic import BaseModel
 
-from langflow.template.constants import FORCE_SHOW_FIELDS
+from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS
 from langflow.template.field.base import TemplateField
 from langflow.template.template.base import Template
 from langflow.utils import constants

src/backend/langflow/template/frontend_node/prompts.py

@@ -2,7 +2,11 @@ from typing import Optional
 
 from langchain.agents.mrkl import prompt
 
-from langflow.template.constants import DEFAULT_PROMPT, HUMAN_PROMPT, SYSTEM_PROMPT
+from langflow.template.frontend_node.constants import (
+    DEFAULT_PROMPT,
+    HUMAN_PROMPT,
+    SYSTEM_PROMPT,
+)
 from langflow.template.field.base import TemplateField
 from langflow.template.frontend_node.base import FrontendNode
 from langflow.template.template.base import Template

src/backend/langflow/template/frontend_node/utilities.py

@@ -1,3 +1,4 @@
+import ast
 import json
 from typing import Optional
 
@@ -12,7 +13,7 @@ class UtilitiesFrontendNode(FrontendNode):
         # field.field_type could be "Literal['news', 'search', 'places', 'images']
         # we need to convert it to a list
         if "Literal" in field.field_type:
-            field.options = eval(field.field_type.replace("Literal", ""))
+            field.options = ast.literal_eval(field.field_type.replace("Literal", ""))
             field.is_list = True
             field.field_type = "str"
 

src/backend/langflow/template/nodes.py

@@ -1 +0,0 @@
-

src/backend/langflow/utils/util.py

@@ -6,7 +6,7 @@ from typing import Dict, Optional
 
 from docstring_parser import parse  # type: ignore
 
-from langflow.template.constants import FORCE_SHOW_FIELDS
+from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS
 from langflow.utils import constants