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

src/backend/langflow/template/field/base.py

@@ -6,24 +6,58 @@ from pydantic import BaseModel
 
 class TemplateFieldCreator(BaseModel, ABC):
     field_type: str = "str"
+    """The type of field this is. Default is a string."""
+
     required: bool = False
+    """Specifies if the field is required. Defaults to False."""
+
     placeholder: str = ""
+    """A placeholder string for the field. Default is an empty string."""
+
     is_list: bool = False
+    """Defines if the field is a list. Default is False."""
+
     show: bool = True
+    """Should the field be shown. Defaults to True."""
+
     multiline: bool = False
+    """Defines if the field will allow the user to open a text editor. Default is False."""
+
     value: Any = None
+    """The value of the field. Default is None."""
+
     suffixes: list[str] = []
-    fileTypes: list[str] = []
+    """List of suffixes for a file field. Default is an empty list."""
+
     file_types: list[str] = []
+    """List of file types associated with the field. Default is an empty list. (duplicate)"""
+
     file_path: Union[str, None] = None
+    """The file path of the field if it is a file. Defaults to None."""
+
     password: bool = False
+    """Specifies if the field is a password. Defaults to False."""
+
     options: list[str] = []
+    """List of options for the field. Only used when is_list=True. Default is an empty list."""
+
     name: str = ""
+    """Name of the field. Default is an empty string."""
+
     display_name: Optional[str] = None
+    """Display name of the field. Defaults to None."""
+
     advanced: bool = False
+    """Specifies if the field will an advanced parameter (hidden). Defaults to False."""
+
     input_types: list[str] = []
+    """List of input types for the handle when the field has more than one type. Default is an empty list."""
+
     dynamic: bool = False
+    """Specifies if the field is dynamic. Defaults to False."""
+
     info: Optional[str] = ""
+    """Additional information about the field to be shown in the tooltip. Defaults to an empty string."""
 
     def to_dict(self):
         result = self.dict()

tests/conftest.py

@@ -135,6 +135,24 @@ from langchain.chains.base import Chain
 from langchain.prompts import StringPromptTemplate
 from langflow.interface.custom.base import CustomComponent
 
+class MyCustomChain(Chain):
+    """
+    An example of a custom chain.
+    """
+
+from typing import Any, Dict, List, Optional
+
+from pydantic import Extra
+
+from langchain.schema import BaseLanguageModel, Document
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForChainRun,
+    CallbackManagerForChainRun,
+)
+from langchain.chains.base import Chain
+from langchain.prompts import StringPromptTemplate
+from langflow.interface.custom.base import CustomComponent
+
 class MyCustomChain(Chain):
     """
     An example of a custom chain.
@@ -232,7 +250,60 @@ class CustomChain(CustomComponent):
         "llm": {"field_type": "BaseLanguageModel"},
     }
 
-    def build(self, prompt: StringPromptTemplate, llm: BaseLanguageModel, input: str) -> Document:
+    def build(self, prompt, llm, input: str) -> Document:
         chain = MyCustomChain(prompt=prompt, llm=llm)
-        return chain(input)
-'''
+        return chain(input)'''
+
+
+@pytest.fixture
+def data_processing():
+    return """
+import pandas as pd
+from langchain.schema import Document
+from langflow.interface.custom.base import CustomComponent
+
+class CSVLoaderComponent(CustomComponent):
+    display_name: str = "CSV Loader"
+    field_config = {
+        "filename": {"field_type": "str", "required": True},
+        "column_name": {"field_type": "str", "required": True},
+    }
+
+    def build(self, filename: str, column_name: str) -> Document:
+        # Load the CSV file
+        df = pd.read_csv(filename)
+
+        # Verify the column exists
+        if column_name not in df.columns:
+            raise ValueError(f"Column '{column_name}' not found in the CSV file")
+
+        # Convert each row of the specified column to a document object
+        documents = []
+        for content in df[column_name]:
+            metadata = {"filename": filename}
+            documents.append(Document(page_content=str(content), metadata=metadata))
+
+        return documents
+"""
+
+
+@pytest.fixture
+def filter_docs():
+    return """
+from langchain.schema import Document
+from langflow.interface.custom.base import CustomComponent
+from typing import List
+
+class DocumentFilterByLengthComponent(CustomComponent):
+    display_name: str = "Document Filter By Length"
+    field_config = {
+        "documents": {"field_type": "Document", "required": True},
+        "max_length": {"field_type": "int", "required": True},
+    }
+
+    def build(self, documents: List[Document], max_length: int) -> List[Document]:
+        # Filter the documents by length
+        filtered_documents = [doc for doc in documents if len(doc.page_content) <= max_length]
+
+        return filtered_documents
+"""