Remove Prompt

src/backend/base/langflow/base/models/model.py

@@ -7,7 +7,6 @@ from langchain_core.language_models.llms import LLM
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 
 from langflow.custom import Component
-from langflow.field_typing.prompt import Prompt
 from langflow.schema.message import Message
 
 
@@ -86,7 +85,7 @@ class LCModelComponent(Component):
         return status_message
 
     def get_chat_result(
-        self, runnable: BaseChatModel, stream: bool, input_value: str | Prompt, system_message: Optional[str] = None
+        self, runnable: BaseChatModel, stream: bool, input_value: str | Message, system_message: Optional[str] = None
     ):
         messages: list[Union[HumanMessage, SystemMessage]] = []
         if not input_value and not system_message:

src/backend/base/langflow/field_typing/__init__.py

@@ -25,7 +25,6 @@ from .constants import (
     Tool,
     VectorStore,
 )
-from .prompt import Prompt
 from .range_spec import RangeSpec
 
 
@@ -45,7 +44,6 @@ def __getattr__(name: str) -> Any:
     # This is to avoid circular imports
     if name == "Input":
         return _import_input_class()
-    elif name == "RangeSpec":
         return RangeSpec
     elif name == "Output":
         return _import_output_class()
@@ -76,7 +74,6 @@ __all__ = [
     "Input",
     "NestedDict",
     "Object",
-    "Prompt",
     "PromptTemplate",
     "RangeSpec",
     "Text",

src/backend/base/langflow/field_typing/constants.py

@@ -15,9 +15,6 @@ from langchain_core.tools import Tool
 from langchain_core.vectorstores import VectorStore
 from langchain_text_splitters import TextSplitter
 
-from langflow.field_typing.prompt import Prompt
-
-# Type alias for more complex dicts
 NestedDict = Dict[str, Union[str, Dict]]
 
 
@@ -60,5 +57,4 @@ CUSTOM_COMPONENT_SUPPORTED_TYPES = {
     "Text": Text,
     "Object": Object,
     "Callable": Callable,
-    "Prompt": Prompt,
 }

src/backend/base/langflow/field_typing/prompt.py

@@ -1,43 +0,0 @@
-from langchain_core.load import load
-from langchain_core.messages import HumanMessage
-from langchain_core.prompts import BaseChatPromptTemplate, ChatPromptTemplate, PromptTemplate
-
-from langflow.base.prompts.utils import dict_values_to_string
-from langflow.schema.message import Message
-from langflow.schema.data import Data
-
-
-class Prompt(Data):
-    def load_lc_prompt(self):
-        if "prompt" not in self:
-            raise ValueError("Prompt is required.")
-        return load(self.prompt)
-
-    @classmethod
-    def from_lc_prompt(
-        cls,
-        prompt: BaseChatPromptTemplate,
-    ):
-        prompt_json = prompt.to_json()
-        return cls(prompt=prompt_json)
-
-    def format_text(self):
-        prompt_template = PromptTemplate.from_template(self.template)
-        variables_with_str_values = dict_values_to_string(self.variables)
-        formatted_prompt = prompt_template.format(**variables_with_str_values)
-        self.text = formatted_prompt
-        return formatted_prompt
-
-    @classmethod
-    async def from_template_and_variables(cls, template: str, variables: dict):
-        instance = cls(template=template, variables=variables)
-        contents = [{"type": "text", "text": instance.format_text()}]
-        # Get all Message instances from the kwargs
-        for value in variables.values():
-            if isinstance(value, Message):
-                content_dicts = await value.get_file_content_dicts()
-                contents.extend(content_dicts)
-        prompt_template = ChatPromptTemplate.from_messages([HumanMessage(content=contents)])
-        instance.messages = prompt_template.messages
-        instance.prompt = prompt_template.to_json()
-        return instance