📦 chore(LanguageRecursiveTextSplitter.py): add LanguageRecursiveTextSplitter component to split text into chunks based on language

📝 docs(LanguageRecursiveTextSplitter.py): add documentation link for LanguageRecursiveTextSplitter component
🔧 refactor(LanguageRecursiveTextSplitter.py): refactor build_config method to use options from Language enum for separator_type
🔧 refactor(LanguageRecursiveTextSplitter.py): refactor build method to split text into chunks based on specified length and overlap
🔧 refactor(LanguageRecursiveTextSplitter.py): refactor build method to handle chunk_size and chunk_overlap as integers
🔧 refactor(LanguageRecursiveTextSplitter.py): refactor build method to use RecursiveCharacterTextSplitter from langchain.text_splitter
🔧 refactor(LanguageRecursiveTextSplitter.py): refactor build method to split documents using RecursiveCharacterTextSplitter and return the chunks

src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py

@@ -0,0 +1,85 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.text_splitter import Language
+from langchain.schema import Document
+from langflow.utils.util import build_loader_repr_from_documents
+
+
+class LanguageRecursiveTextSplitterComponent(CustomComponent):
+    display_name: str = "Language Recursive Text Splitter"
+    description: str = "Split text into chunks of a specified length based on language."
+    documentation: str = "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter"
+
+    def build_config(self):
+        options = [x.value for x in Language]
+        return {
+            "documents": {
+                "display_name": "Documents",
+                "info": "The documents to split.",
+            },
+            "separator_type": {
+                "display_name": "Separator Type",
+                "info": "The type of separator to use.",
+                "field_type": "str",
+                "options": options,
+                "value": "Python",
+            },
+            "separators": {
+                "display_name": "Separators",
+                "info": "The characters to split on.",
+                "is_list": True,
+            },
+            "chunk_size": {
+                "display_name": "Chunk Size",
+                "info": "The maximum length of each chunk.",
+                "field_type": "int",
+                "value": 1000,
+            },
+            "chunk_overlap": {
+                "display_name": "Chunk Overlap",
+                "info": "The amount of overlap between chunks.",
+                "field_type": "int",
+                "value": 200,
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        documents: Document,
+        chunk_size: Optional[int] = 1000,
+        chunk_overlap: Optional[int] = 200,
+        separator_type: Optional[str] = "Python",
+    ) -> Document:
+        """
+        Split text into chunks of a specified length.
+
+        Args:
+            separators (list[str]): The characters to split on.
+            chunk_size (int): The maximum length of each chunk.
+            chunk_overlap (int): The amount of overlap between chunks.
+            length_function (function): The function to use to calculate the length of the text.
+
+        Returns:
+            list[str]: The chunks of text.
+        """
+        from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+        # Make sure chunk_size and chunk_overlap are ints
+        try:
+            chunk_size = int(chunk_size)
+            chunk_overlap = int(chunk_overlap)
+        except Exception as e:
+            raise ValueError(
+                "chunk_size and chunk_overlap must be integers."
+                " Received chunk_size={chunk_size} and chunk_overlap={chunk_overlap}."
+            ) from e
+        splitter = RecursiveCharacterTextSplitter.from_language(
+            language=Language(separator_type),
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+        )
+
+        docs = splitter.split_documents(documents)
+        self.repr_value = build_loader_repr_from_documents(docs)
+        return docs