🔀 chore(config): add DocArrayHnswSearch and DocArrayInMemorySearch to vectorstores

🐛 fix(base.py): correctly handle nested lists in Node.build() method
✨ feat(vector_store): add VectorStoreFrontendNode to handle vector store templates
🐛 fix(util.py): add build_template_from_method to correctly build templates from class methods
The configuration file now includes two new vector stores, DocArrayHnswSearch and DocArrayInMemorySearch. The Node.build() method now correctly handles nested lists. A new VectorStoreFrontendNode has been added to handle vector store templates. The build_template_from_method function has been added to correctly build templates from class methods.

Issue #335

src/backend/langflow/config.yaml

@@ -83,6 +83,8 @@ embeddings:
 
 vectorstores:
   - Chroma
+  - DocArrayHnswSearch
+  - DocArrayInMemorySearch
 
 documentloaders:
   - AirbyteJSONLoader

src/backend/langflow/graph/base.py

@@ -180,7 +180,13 @@ class Node:
             elif isinstance(value, list) and all(
                 isinstance(node, Node) for node in value
             ):
-                self.params[key] = [node.build() for node in value]  # type: ignore
+                self.params[key] = []
+                for node in value:
+                    built = node.build()
+                    if isinstance(built, list):
+                        self.params[key].extend(built)
+                    else:
+                        self.params[key].append(built)
 
         # Get the class from LANGCHAIN_TYPES_DICT
         # and instantiate it with the params

src/backend/langflow/interface/vector_store/base.py

@@ -1,15 +1,20 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Type
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import vectorstores_type_to_cls_dict
 from langflow.settings import settings
+from langflow.template.nodes import VectorStoreFrontendNode
 from langflow.utils.logger import logger
-from langflow.utils.util import build_template_from_class
+from langflow.utils.util import build_template_from_class, build_template_from_method
 
 
 class VectorstoreCreator(LangChainTypeCreator):
     type_name: str = "vectorstores"
 
+    @property
+    def frontend_node_class(self) -> Type[VectorStoreFrontendNode]:
+        return VectorStoreFrontendNode
+
     @property
     def type_to_loader_dict(self) -> Dict:
         return vectorstores_type_to_cls_dict
@@ -17,25 +22,29 @@ class VectorstoreCreator(LangChainTypeCreator):
     def get_signature(self, name: str) -> Optional[Dict]:
         """Get the signature of an embedding."""
         try:
-            signature = build_template_from_class(name, vectorstores_type_to_cls_dict)
+            signature = build_template_from_method(
+                name,
+                type_to_cls_dict=vectorstores_type_to_cls_dict,
+                method_name="from_texts",
+            )
 
             # TODO: Use FrontendendNode class to build the signature
-            signature["template"] = {
-                "documents": {
-                    "type": "TextSplitter",
-                    "required": True,
-                    "show": True,
-                    "name": "documents",
-                    "display_name": "Text Splitter",
-                },
-                "embedding": {
-                    "type": "Embeddings",
-                    "required": True,
-                    "show": True,
-                    "name": "embedding",
-                    "display_name": "Embedding",
-                },
-            }
+            # signature["template"] = {
+            #     "documents": {
+            #         "type": "TextSplitter",
+            #         "required": True,
+            #         "show": True,
+            #         "name": "documents",
+            #         "display_name": "Text Splitter",
+            #     },
+            #     "embedding": {
+            #         "type": "Embeddings",
+            #         "required": True,
+            #         "show": True,
+            #         "name": "embedding",
+            #         "display_name": "Embedding",
+            #     },
+            # }
             return signature
 
         except ValueError as exc:

src/backend/langflow/template/nodes.py

@@ -628,3 +628,32 @@ class EmbeddingFrontendNode(FrontendNode):
         FrontendNode.format_field(field, name)
         if field.name == "headers":
             field.show = False
+
+
+class VectorStoreFrontendNode(FrontendNode):
+    @staticmethod
+    def format_field(field: TemplateField, name: Optional[str] = None) -> None:
+        FrontendNode.format_field(field, name)
+        if field.name == "texts":
+            field.name = "documents"
+            field.field_type = "TextSplitter"
+            field.display_name = "Text Splitter"
+            field.required = True
+            field.show = True
+            field.advanced = False
+
+        if "embedding" in field.name:
+            # for backwards compatibility
+            field.name = "embedding"
+            field.required = True
+            field.show = True
+            field.advanced = False
+            field.display_name = "Embedding"
+            field.field_type = "Embeddings"
+
+        elif field.name == "n_dim":
+            field.show = True
+            field.advanced = True
+        elif field.name == "work_dir":
+            field.show = True
+            field.advanced = False

src/backend/langflow/utils/util.py

@@ -160,6 +160,70 @@ def build_template_from_class(
             }
 
 
+def build_template_from_method(
+    class_name: str,
+    method_name: str,
+    type_to_cls_dict: Dict,
+    add_function: bool = False,
+):
+    classes = [item.__name__ for item in type_to_cls_dict.values()]
+
+    # Raise error if class_name is not in classes
+    if class_name not in classes:
+        raise ValueError(f"{class_name} not found.")
+
+    for _type, v in type_to_cls_dict.items():
+        if v.__name__ == class_name:
+            _class = v
+
+            # Check if the method exists in this class
+            if not hasattr(_class, method_name):
+                raise ValueError(
+                    f"Method {method_name} not found in class {class_name}"
+                )
+
+            # Get the method
+            method = getattr(_class, method_name)
+
+            # Get the docstring
+            docs = parse(method.__doc__)
+
+            # Get the signature of the method
+            sig = inspect.signature(method)
+
+            # Get the parameters of the method
+            params = sig.parameters
+
+            # Initialize the variables dictionary with method parameters
+            variables = {
+                "_type": _type,
+                **{
+                    name: {
+                        "default": param.default
+                        if param.default != param.empty
+                        else None,
+                        "type": param.annotation
+                        if param.annotation != param.empty
+                        else None,
+                        "required": param.default == param.empty,
+                    }
+                    for name, param in params.items()
+                },
+            }
+
+            base_classes = get_base_classes(_class)
+
+            # Adding function to base classes to allow the output to be a function
+            if add_function:
+                base_classes.append("function")
+
+            return {
+                "template": format_dict(variables, class_name),
+                "description": docs.short_description or "",
+                "base_classes": base_classes,
+            }
+
+
 def get_base_classes(cls):
     """Get the base classes of a class.
     These are used to determine the output of the nodes.