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

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
+"""