feat: add NestedDictInput filter and non-vector search for AstraVecto…

…rStoreComponent (#4564)

* NestedDictInput filter and non-vector search for AstraVectorStoreComponent

* [autofix.ci] apply automated fixes

* addressing Ruff linting

* [autofix.ci] apply automated fixes

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Eric Hare <[email protected]>

src/backend/base/langflow/components/vectorstores/astradb.py

@@ -2,10 +2,11 @@
 
 import orjson
 from astrapy.admin import parse_api_endpoint
+from langchain_astradb import AstraDBVectorStore
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
 from langflow.helpers import docs_to_data
-from langflow.inputs import DictInput, FloatInput, MessageTextInput
+from langflow.inputs import DictInput, FloatInput, MessageTextInput, NestedDictInput
 from langflow.io import (
     BoolInput,
     DataInput,
@@ -26,6 +27,8 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
     name = "AstraDB"
     icon: str = "AstraDB"
 
+    _cached_vector_store: AstraDBVectorStore | None = None
+
     VECTORIZE_PROVIDERS_MAPPING = {
         "Azure OpenAI": ["azureOpenAI", ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"]],
         "Hugging Face - Dedicated": ["huggingfaceDedicated", ["endpoint-defined-model"]],
@@ -201,11 +204,17 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
             value=0,
             advanced=True,
         ),
-        DictInput(
-            name="search_filter",
+        NestedDictInput(
+            name="advanced_search_filter",
             display_name="Search Metadata Filter",
             info="Optional dictionary of filters to apply to the search query.",
             advanced=True,
+        ),
+        DictInput(
+            name="search_filter",
+            display_name="[DEPRECATED] Search Metadata Filter",
+            info="Deprecated: use advanced_search_filter. Optional dictionary of filters to apply to the search query.",
+            advanced=True,
             is_list=True,
         ),
     ]
@@ -482,43 +491,69 @@ def _map_search_type(self) -> str:
         return "similarity"
 
     def _build_search_args(self):
-        args = {
-            "k": self.number_of_results,
-            "score_threshold": self.search_score_threshold,
-        }
+        query = self.search_input if isinstance(self.search_input, str) and self.search_input.strip() else None
+        search_filter = (
+            {k: v for k, v in self.search_filter.items() if k and v and k.strip()} if self.search_filter else None
+        )
+
+        if query:
+            args = {
+                "query": query,
+                "search_type": self._map_search_type(),
+                "k": self.number_of_results,
+                "score_threshold": self.search_score_threshold,
+            }
+        elif self.advanced_search_filter or search_filter:
+            args = {
+                "n": self.number_of_results,
+            }
+        else:
+            return {}
+
+        filter_arg = self.advanced_search_filter or {}
+
+        if search_filter:
+            self.log(self.log(f"`search_filter` is deprecated. Use `advanced_search_filter`. Cleaned: {search_filter}"))
+            filter_arg.update(search_filter)
+
+        if filter_arg:
+            args["filter"] = filter_arg
 
-        if self.search_filter:
-            clean_filter = {k: v for k, v in self.search_filter.items() if k and v}
-            if len(clean_filter) > 0:
-                args["filter"] = clean_filter
         return args
 
     def search_documents(self, vector_store=None) -> list[Data]:
-        if not vector_store:
-            vector_store = self.build_vector_store()
+        vector_store = vector_store or self.build_vector_store()
 
         self.log(f"Search input: {self.search_input}")
         self.log(f"Search type: {self.search_type}")
         self.log(f"Number of results: {self.number_of_results}")
 
-        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
-            try:
-                search_type = self._map_search_type()
-                search_args = self._build_search_args()
+        try:
+            search_args = self._build_search_args()
+        except Exception as e:
+            msg = f"Error in AstraDBVectorStore._build_search_args: {e}"
+            raise ValueError(msg) from e
 
-                docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)
-            except Exception as e:
-                msg = f"Error performing search in AstraDBVectorStore: {e}"
-                raise ValueError(msg) from e
+        if not search_args:
+            self.log("No search input or filters provided. Skipping search.")
+            return []
+
+        docs = []
+        search_method = "search" if "query" in search_args else "metadata_search"
+
+        try:
+            self.log(f"Calling vector_store.{search_method} with args: {search_args}")
+            docs = getattr(vector_store, search_method)(**search_args)
+        except Exception as e:
+            msg = f"Error performing {search_method} in AstraDBVectorStore: {e}"
+            raise ValueError(msg) from e
 
-            self.log(f"Retrieved documents: {len(docs)}")
+        self.log(f"Retrieved documents: {len(docs)}")
 
-            data = docs_to_data(docs)
-            self.log(f"Converted documents to data: {len(data)}")
-            self.status = data
-            return data
-        self.log("No search input provided. Skipping search.")
-        return []
+        data = docs_to_data(docs)
+        self.log(f"Converted documents to data: {len(data)}")
+        self.status = data
+        return data
 
     def get_retriever_kwargs(self):
         search_args = self._build_search_args()