🔀 chore(field_typing): add support for additional types in field_typing/__init__.py

🔀 chore(field_typing): add support for Data type in field_typing/base.py

🔀 chore(field_typing): add constants.py file to define supported types in field_typing

🔀 chore(interface/custom): remove duplicate imports and use field_typing types

🔀 chore(utils): fix syntax error in get_base_classes function

src/backend/langflow/field_typing/__init__.py

@@ -1,3 +1,8 @@
-from .base import NestedDict
+from .base import NestedDict, Data
+from .constants import *
 
-__all__ = ["NestedDict"]
+__all__ = (
+    ["NestedDict", "Data"]
+    + list(LANGCHAIN_BASE_TYPES.keys())
+    + list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys())
+)

src/backend/langflow/field_typing/base.py

@@ -1,4 +1,5 @@
-from typing import Union, Dict
+from typing import Union, Dict, Any
 
 # Type alias for more complex dicts
 NestedDict = Dict[str, Union[str, Dict]]
+Data = Any

src/backend/langflow/field_typing/constants.py

@@ -0,0 +1,43 @@
+from langchain.agents.agent import AgentExecutor
+from langchain.chains.base import Chain
+from langchain.document_loaders.base import BaseLoader
+from langchain.llms.base import BaseLLM
+from langchain.memory.chat_memory import BaseChatMemory
+from langchain.prompts import PromptTemplate
+from langchain.schema import BaseOutputParser, BaseRetriever, Document
+from langchain.schema.embeddings import Embeddings
+from langchain.schema.memory import BaseMemory
+from langchain.text_splitter import TextSplitter
+from langchain.tools import Tool
+from langchain.vectorstores.base import VectorStore
+from langflow.field_typing import Data, NestedDict
+
+
+LANGCHAIN_BASE_TYPES = {
+    "Chain": Chain,
+    "AgentExecutor": AgentExecutor,
+    "Tool": Tool,
+    "BaseLLM": BaseLLM,
+    "PromptTemplate": PromptTemplate,
+    "BaseLoader": BaseLoader,
+    "Document": Document,
+    "TextSplitter": TextSplitter,
+    "VectorStore": VectorStore,
+    "Embeddings": Embeddings,
+    "BaseRetriever": BaseRetriever,
+    "BaseOutputParser": BaseOutputParser,
+    "BaseMemory": BaseMemory,
+    "BaseChatMemory": BaseChatMemory,
+}
+# Langchain base types plus Python base types
+CUSTOM_COMPONENT_SUPPORTED_TYPES = {
+    **LANGCHAIN_BASE_TYPES,
+    "str": str,
+    "int": int,
+    "float": float,
+    "bool": bool,
+    "list": list,
+    "dict": dict,
+    "NestedDict": NestedDict,
+    "Data": Data,
+}

src/backend/langflow/interface/custom/constants.py

@@ -1,65 +1,15 @@
-from langchain.prompts import PromptTemplate
-from langchain.chains.base import Chain
-from langchain.document_loaders.base import BaseLoader
-from langchain.schema.embeddings import Embeddings
-from langchain.llms.base import BaseLLM
-from langchain.schema import BaseRetriever, Document
-from langchain.text_splitter import TextSplitter
-from langchain.tools import Tool
-from langchain.vectorstores.base import VectorStore
-from langchain.schema import BaseOutputParser
-from langchain.schema.memory import BaseMemory
-from langchain.memory.chat_memory import BaseChatMemory
-from langchain.agents.agent import AgentExecutor
-
-LANGCHAIN_BASE_TYPES = {
-    "Chain": Chain,
-    "AgentExecutor": AgentExecutor,
-    "Tool": Tool,
-    "BaseLLM": BaseLLM,
-    "PromptTemplate": PromptTemplate,
-    "BaseLoader": BaseLoader,
-    "Document": Document,
-    "TextSplitter": TextSplitter,
-    "VectorStore": VectorStore,
-    "Embeddings": Embeddings,
-    "BaseRetriever": BaseRetriever,
-    "BaseOutputParser": BaseOutputParser,
-    "BaseMemory": BaseMemory,
-    "BaseChatMemory": BaseChatMemory,
-}
-
-# Langchain base types plus Python base types
-CUSTOM_COMPONENT_SUPPORTED_TYPES = {
-    **LANGCHAIN_BASE_TYPES,
-    "str": str,
-    "int": int,
-    "float": float,
-    "bool": bool,
-    "list": list,
-    "dict": dict,
-}
-
-
 DEFAULT_CUSTOM_COMPONENT_CODE = """from langflow import CustomComponent
 
-from langchain.llms.base import BaseLLM
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain.schema import Document
+from langflow.field_typing import *
 
-import requests
-
-class YourComponent(CustomComponent):
+class Component(CustomComponent):
     display_name: str = "Custom Component"
     description: str = "Create any custom component you want!"
 
     def build_config(self):
-        return { "url": { "multiline": True, "required": True } }
+        return { "param": { "display_name": "Parameter" } }
+
+    def build(self, param: Data) -> Data:
+        return params
 
-    def build(self, url: str, llm: BaseLLM, prompt: PromptTemplate) -> Document:
-        response = requests.get(url)
-        chain = LLMChain(llm=llm, prompt=prompt)
-        result = chain.run(response.text[:300])
-        return Document(page_content=str(result))
 """

src/backend/langflow/utils/util.py

@@ -191,7 +191,9 @@ def get_base_classes(cls):
     """Get the base classes of a class.
     These are used to determine the output of the nodes.
     """
-    if bases := cls.__bases__:
+
+    if hasattr(cls, "__bases__") and cls.__bases__:
+        bases = cls.__bases__
         result = []
         for base in bases:
             if any(type in base.__module__ for type in ["pydantic", "abc"]):