feat(cassandra/astradb): hybrid search support (langflow-ai#2396)

* cassandra/astradb: hybrid search support

* fix

* fix

(cherry picked from commit 30c369f)

src/backend/base/langflow/base/vectorstores/model.py

@@ -79,7 +79,7 @@ def build_base_retriever(self) -> Retriever:  # type: ignore[type-var]
         """
         vector_store = self.build_vector_store()
         if hasattr(vector_store, "as_retriever"):
-            retriever = vector_store.as_retriever()
+            retriever = vector_store.as_retriever(**self.get_retriever_kwargs())
             if self.status is None:
                 self.status = "Retriever built successfully."
             return retriever
@@ -106,3 +106,9 @@ def search_documents(self) -> List[Data]:
         )
         self.status = search_results
         return search_results
+
+    def get_retriever_kwargs(self):
+        """
+        Get the retriever kwargs. Implementations can override this method to provide custom retriever kwargs.
+        """
+        return {}

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

@@ -1,6 +1,9 @@
 from loguru import logger
 
+from langchain_core.vectorstores import VectorStore
 from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.helpers import docs_to_data
+from langflow.inputs import FloatInput, DictInput
 from langflow.io import (
     BoolInput,
     DataInput,
@@ -20,6 +23,8 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
     documentation: str = "https://python.langchain.com/docs/integrations/vectorstores/astradb"
     icon: str = "AstraDB"
 
+    _cached_vectorstore: VectorStore = None
+
     inputs = [
         StrInput(
             name="collection_name",
@@ -124,23 +129,40 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
             info="Optional dictionary defining the indexing policy for the collection.",
             advanced=True,
         ),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            advanced=True,
+            value=4,
+        ),
         DropdownInput(
             name="search_type",
             display_name="Search Type",
-            options=["Similarity", "MMR"],
+            info="Search type to use",
+            options=["Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)"],
             value="Similarity",
             advanced=True,
         ),
-        IntInput(
-            name="number_of_results",
-            display_name="Number of Results",
-            info="Number of results to return.",
+        FloatInput(
+            name="search_score_threshold",
+            display_name="Search Score Threshold",
+            info="Minimum similarity score threshold for search results. (when using 'Similarity with score threshold')",
+            value=0,
             advanced=True,
-            value=4,
+        ),
+        DictInput(
+            name="search_filter",
+            display_name="Search Metadata Filter",
+            info="Optional dictionary of filters to apply to the search query.",
+            advanced=True,
+            is_list=True,
         ),
     ]
 
     def _build_vector_store_no_ingest(self):
+        if self._cached_vectorstore:
+            return self._cached_vectorstore
         try:
             from langchain_astradb import AstraDBVectorStore
             from langchain_astradb.utils.astradb import SetupMode
@@ -199,13 +221,13 @@ def _build_vector_store_no_ingest(self):
         except Exception as e:
             raise ValueError(f"Error initializing AstraDBVectorStore: {str(e)}") from e
 
+        self._cached_vectorstore = vector_store
+
         return vector_store
 
     def build_vector_store(self):
         vector_store = self._build_vector_store_no_ingest()
-        if hasattr(self, "ingest_data") and self.ingest_data:
-            logger.debug("Ingesting data into the Vector Store.")
-            self._add_documents_to_vector_store(vector_store)
+        self._add_documents_to_vector_store(vector_store)
         return vector_store
 
     def _add_documents_to_vector_store(self, vector_store):
@@ -216,7 +238,7 @@ def _add_documents_to_vector_store(self, vector_store):
             else:
                 raise ValueError("Vector Store Inputs must be Data objects.")
 
-        if documents and self.embedding is not None:
+        if documents:
             logger.debug(f"Adding {len(documents)} documents to the Vector Store.")
             try:
                 vector_store.add_documents(documents)
@@ -225,36 +247,56 @@ def _add_documents_to_vector_store(self, vector_store):
         else:
             logger.debug("No documents to add to the Vector Store.")
 
+    def _map_search_type(self):
+        if self.search_type == "Similarity with score threshold":
+            return "similarity_score_threshold"
+        elif self.search_type == "MMR (Max Marginal Relevance)":
+            return "mmr"
+        else:
+            return "similarity"
+
     def search_documents(self) -> list[Data]:
         vector_store = self._build_vector_store_no_ingest()
+        self._add_documents_to_vector_store(vector_store)
 
         logger.debug(f"Search input: {self.search_input}")
         logger.debug(f"Search type: {self.search_type}")
         logger.debug(f"Number of results: {self.number_of_results}")
 
         if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
             try:
-                if self.search_type == "Similarity":
-                    docs = vector_store.similarity_search(
-                        query=self.search_input,
-                        k=self.number_of_results,
-                    )
-                elif self.search_type == "MMR":
-                    docs = vector_store.max_marginal_relevance_search(
-                        query=self.search_input,
-                        k=self.number_of_results,
-                    )
-                else:
-                    raise ValueError(f"Invalid search type: {self.search_type}")
+                search_type = self._map_search_type()
+                search_args = self._build_search_args()
+
+                docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)
             except Exception as e:
                 raise ValueError(f"Error performing search in AstraDBVectorStore: {str(e)}") from e
 
             logger.debug(f"Retrieved documents: {len(docs)}")
 
-            data = [Data.from_document(doc) for doc in docs]
+            data = docs_to_data(docs)
             logger.debug(f"Converted documents to data: {len(data)}")
             self.status = data
             return data
         else:
             logger.debug("No search input provided. Skipping search.")
             return []
+
+    def _build_search_args(self):
+        args = {
+            "k": self.number_of_results,
+            "score_threshold": self.search_score_threshold,
+        }
+
+        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 get_retriever_kwargs(self):
+        search_args = self._build_search_args()
+        return {
+            "search_type": self._map_search_type(),
+            "search_kwargs": search_args,
+        }