Merge branch 'dev' into vecstores

src/backend/langflow/config.yaml

@@ -29,6 +29,7 @@ llms:
   # - AzureOpenAI
   - ChatOpenAI
   - HuggingFaceHub
+  - LlamaCpp
 
 tools:
   - Search

src/backend/langflow/graph/base.py

@@ -4,6 +4,7 @@
 #   - Build each inner agent first, then build the outer agent
 
 import types
+import warnings
 from copy import deepcopy
 from typing import Any, Dict, List, Optional
 
@@ -119,7 +120,13 @@ class Node:
                     params[key] = edges[0].source
 
             elif value["required"] or value.get("value"):
-                params[key] = value["value"]
+                # If value does not have value this still passes
+                # but then gives a keyError
+                # so we need to check if value has value
+                new_value = value.get("value")
+                if new_value is None:
+                    warnings.warn(f"Value for {key} in {self.node_type} is None. ")
+                params[key] = new_value
 
         # Add _type to params
         self.params = params

src/backend/langflow/graph/constants.py

@@ -1 +1 @@
-DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any"]
+DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any", "prompt"]

src/backend/langflow/graph/nodes.py

@@ -54,7 +54,10 @@ class PromptNode(Node):
         tools: Optional[Union[List[Node], List[ToolNode]]] = None,
     ) -> Any:
         if not self._built or force:
-            if "input_variables" not in self.params:
+            if (
+                "input_variables" not in self.params
+                or self.params["input_variables"] is None
+            ):
                 self.params["input_variables"] = []
             # Check if it is a ZeroShotPrompt and needs a tool
             if "ShotPrompt" in self.node_type:
@@ -74,7 +77,6 @@ class PromptNode(Node):
             for param in prompt_params:
                 prompt_text = self.params[param]
                 variables = extract_input_variables_from_prompt(prompt_text)
-
                 self.params["input_variables"].extend(variables)
             self.params["input_variables"] = list(set(self.params["input_variables"]))
 

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

@@ -1,9 +1,10 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Type
 
 from langflow.custom.customs import get_custom_nodes
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import chain_type_to_cls_dict
 from langflow.settings import settings
+from langflow.template.nodes import ChainFrontendNode
 from langflow.utils.util import build_template_from_class
 
 # Assuming necessary imports for Field, Template, and FrontendNode classes
@@ -12,6 +13,10 @@ from langflow.utils.util import build_template_from_class
 class ChainCreator(LangChainTypeCreator):
     type_name: str = "chains"
 
+    @property
+    def frontend_node_class(self) -> Type[ChainFrontendNode]:
+        return ChainFrontendNode
+
     @property
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:

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

@@ -1,14 +1,19 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Type
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import llm_type_to_cls_dict
 from langflow.settings import settings
+from langflow.template.nodes import LLMFrontendNode
 from langflow.utils.util import build_template_from_class
 
 
 class LLMCreator(LangChainTypeCreator):
     type_name: str = "llms"
 
+    @property
+    def frontend_node_class(self) -> Type[LLMFrontendNode]:
+        return LLMFrontendNode
+
     @property
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:

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

@@ -73,9 +73,14 @@ class ToolCreator(LangChainTypeCreator):
         base_classes = ["Tool"]
         all_tools = {}
         for tool in self.type_to_loader_dict.keys():
-            if tool_params := get_tool_params(get_tool_by_name(tool)):
+            tool_fcn = get_tool_by_name(tool)
+            if tool_params := get_tool_params(tool_fcn):
                 tool_name = tool_params.get("name") or str(tool)
-                all_tools[tool_name] = {"type": tool, "params": tool_params}
+                all_tools[tool_name] = {
+                    "type": tool,
+                    "params": tool_params,
+                    "fcn": tool_fcn,
+                }
 
         # Raise error if name is not in tools
         if name not in all_tools.keys():
@@ -83,15 +88,21 @@ class ToolCreator(LangChainTypeCreator):
 
         tool_type: str = all_tools[name]["type"]  # type: ignore
 
-        if tool_type in _BASE_TOOLS:
+        if all_tools[tool_type]["fcn"] in _BASE_TOOLS.values():
             params = []
-        elif tool_type in _LLM_TOOLS:
+        elif all_tools[tool_type]["fcn"] in _LLM_TOOLS.values():
             params = ["llm"]
-        elif tool_type in _EXTRA_LLM_TOOLS:
-            _, extra_keys = _EXTRA_LLM_TOOLS[tool_type]
+        elif all_tools[tool_type]["fcn"] in [
+            val[0] for val in _EXTRA_LLM_TOOLS.values()
+        ]:
+            n_dict = {val[0]: val[1] for val in _EXTRA_LLM_TOOLS.values()}
+            extra_keys = n_dict[all_tools[tool_type]["fcn"]]
             params = ["llm"] + extra_keys
-        elif tool_type in _EXTRA_OPTIONAL_TOOLS:
-            _, extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type]
+        elif all_tools[tool_type]["fcn"] in [
+            val[0] for val in _EXTRA_OPTIONAL_TOOLS.values()
+        ]:
+            n_dict = {val[0]: val[1] for val in _EXTRA_OPTIONAL_TOOLS.values()}  # type: ignore
+            extra_keys = n_dict[all_tools[tool_type]["fcn"]]
             params = extra_keys
         elif tool_type == "Tool":
             params = ["name", "description", "func"]
@@ -104,7 +115,6 @@ class ToolCreator(LangChainTypeCreator):
         elif tool_type in FILE_TOOLS:
             params = all_tools[name]["params"]  # type: ignore
             base_classes += [name]
-
         else:
             params = []
 

src/backend/langflow/template/nodes.py

@@ -345,3 +345,41 @@ class MemoryFrontendNode(FrontendNode):
             field.field_type = "int"
             field.value = 10
             field.display_name = "Memory Size"
+
+
+class ChainFrontendNode(FrontendNode):
+    @staticmethod
+    def format_field(field: TemplateField, name: Optional[str] = None) -> None:
+        FrontendNode.format_field(field, name)
+
+        if "key" in field.name:
+            field.password = False
+            field.show = False
+
+
+class LLMFrontendNode(FrontendNode):
+    @staticmethod
+    def format_field(field: TemplateField, name: Optional[str] = None) -> None:
+        display_names_dict = {
+            "huggingfacehub_api_token": "HuggingFace Hub API Token",
+        }
+        FrontendNode.format_field(field, name)
+        SHOW_FIELDS = ["repo_id", "task", "model_kwargs"]
+        if field.name in SHOW_FIELDS:
+            field.show = True
+
+        if "api" in field.name and ("key" in field.name or "token" in field.name):
+            field.password = True
+            field.show = True
+            field.required = True
+
+        if field.name == "task":
+            field.required = True
+            field.show = True
+            field.is_list = True
+            field.options = ["text-generation", "text2text-generation"]
+
+        if display_name := display_names_dict.get(field.name):
+            field.display_name = display_name
+        if field.name == "model_kwargs":
+            field.field_type = "code"