Merge branch 'form_io' of github.com:logspace-ai/langflow into form_io

src/backend/langflow/api/utils.py

@@ -37,11 +37,11 @@ def build_input_keys_response(langchain_object):
         langchain_object.memory, "memory_variables"
     ):
         # Remove memory variables from input keys
-        input_keys_response["input_keys"] = [
-            key
-            for key in input_keys_response["input_keys"]
+        input_keys_response["input_keys"] = {
+            key: value
+            for key, value in input_keys_response["input_keys"].items()
             if key not in langchain_object.memory.memory_variables
-        ]
+        }
         # Add memory variables to memory_keys
         input_keys_response["memory_keys"] = langchain_object.memory.memory_variables
 

src/backend/langflow/api/v1/chat.py

@@ -128,6 +128,8 @@ async def stream_build(flow_id: str):
                 yield str(StreamData(event="message", data=input_keys_response))
 
             chat_manager.set_cache(flow_id, langchain_object)
+            # We need to reset the chat history
+            chat_manager.chat_history.empty_history(flow_id)
         except Exception as exc:
             logger.error("Error while building the flow: %s", exc)
             yield str(StreamData(event="error", data={"error": str(exc)}))

src/backend/langflow/api/v1/validate.py

@@ -61,6 +61,11 @@ def post_validate_prompt(prompt: ValidatePromptRequest):
                     logger.exception(exc)
                     raise HTTPException(status_code=500, detail=str(exc)) from exc
 
+        # Now we will set the field "input_variables" to the new list of variables
+        # if it exists
+        if "input_variables" in prompt.frontend_node.template:
+            prompt.frontend_node.template["input_variables"]["value"] = input_variables
+
         return PromptValidationResponse(
             input_variables=input_variables,
             frontend_node=prompt.frontend_node,

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

@@ -110,7 +110,21 @@ def instantiate_prompt(node_type, class_object, params):
     if node_type == "ChatPromptTemplate":
         return class_object.from_messages(**params)
 
-    return class_object(**params)
+    prompt = class_object(**params)
+
+    # Now we go through input_variables
+    # Check if they are in params, if so
+    # get their values and set them
+    format_kwargs = {}
+    for input_variable in prompt.input_variables:
+        if input_variable in params:
+            input_value = params[input_variable]
+            format_kwargs[input_variable] = input_value
+
+    if format_kwargs:
+        prompt = prompt.partial(**format_kwargs)
+
+    return prompt
 
 
 def instantiate_tool(node_type, class_object, params):