🐛 fix(loading.py): include "config" in the list of keys to check for *kwargs

✨ feat(constants.py): add default config for CTransformers
🚀 feat(llms.py): add method to format ctransformers field in LLMFrontendNode
The fix in loading.py ensures that the *kwargs are converted to a dictionary when the key contains "config". The addition of the default config for CTransformers in constants.py provides a default configuration for the CTransformers model. The new method in llms.py formats the ctransformers field in the LLMFrontendNode.

src/backend/langflow/interface/loading.py

@@ -53,8 +53,8 @@ def convert_params_to_sets(params):
 
 def convert_kwargs(params):
     # if *kwargs are passed as a string, convert to dict
-    # first find any key that has kwargs in it
+    # first find any key that has kwargs or config in it
-    kwargs_keys = [key for key in params.keys() if "kwargs" in key]
+    kwargs_keys = [key for key in params.keys() if "kwargs" in key or "config" in key]
     for key in kwargs_keys:
         if isinstance(params[key], str):
             params[key] = json.loads(params[key])
@@ -82,10 +82,16 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
         return instantiate_utility(node_type, class_object, params)
     elif base_type == "chains":
         return instantiate_chains(node_type, class_object, params)
+    elif base_type == "llms":
+        return instantiate_llm(node_type, class_object, params)
     else:
         return class_object(**params)
 
 
+def instantiate_llm(node_type, class_object, params):
+    return class_object(**params)
+
+
 def instantiate_chains(node_type, class_object, params):
     if "retriever" in params and hasattr(params["retriever"], "as_retriever"):
         params["retriever"] = params["retriever"].as_retriever()

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

@@ -32,3 +32,20 @@ You are a good listener and you can talk about anything.
 HUMAN_PROMPT = "{input}"
 
 QA_CHAIN_TYPES = ["stuff", "map_reduce", "map_rerank", "refine"]
+
+CTRANSFORMERS_DEFAULT_CONFIG = {
+    "top_k": 40,
+    "top_p": 0.95,
+    "temperature": 0.8,
+    "repetition_penalty": 1.1,
+    "last_n_tokens": 64,
+    "seed": -1,
+    "max_new_tokens": 256,
+    "stop": None,
+    "stream": False,
+    "reset": True,
+    "batch_size": 8,
+    "threads": -1,
+    "context_length": -1,
+    "gpu_layers": 0,
+}

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

@@ -1,7 +1,9 @@
+import json
 from typing import Optional
 
 from langflow.template.field.base import TemplateField
 from langflow.template.frontend_node.base import FrontendNode
+from langflow.template.frontend_node.constants import CTRANSFORMERS_DEFAULT_CONFIG
 
 
 class LLMFrontendNode(FrontendNode):
@@ -31,6 +33,13 @@ class LLMFrontendNode(FrontendNode):
         field.show = True
         field.advanced = not field.required
 
+    @staticmethod
+    def format_ctransformers_field(field: TemplateField):
+        if field.name == "config":
+            field.show = True
+            field.advanced = True
+            field.value = json.dumps(CTRANSFORMERS_DEFAULT_CONFIG, indent=2)
+
     @staticmethod
     def format_field(field: TemplateField, name: Optional[str] = None) -> None:
         display_names_dict = {
@@ -38,6 +47,7 @@ class LLMFrontendNode(FrontendNode):
         }
         FrontendNode.format_field(field, name)
         LLMFrontendNode.format_openai_field(field)
+        LLMFrontendNode.format_ctransformers_field(field)
         if name and "azure" in name.lower():
             LLMFrontendNode.format_azure_field(field)
         if name and "llama" in name.lower():