feat: add support for DataStax HCD vector store

pyproject.toml

@@ -72,7 +72,7 @@ assemblyai = "^0.26.0"
 litellm = "^1.44.0"
 chromadb = "^0.4"
 langchain-anthropic = "^0.1.23"
-langchain-astradb = "^0.3.3"
+langchain-astradb = "^0.3.5"
 langchain-openai = "0.1.22"
 zep-python = { version = "^2.0.0rc5", allow-prereleases = true }
 langchain-google-vertexai = "1.0.10"

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

@@ -0,0 +1,320 @@
+from loguru import logger
+
+from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
+from langflow.helpers import docs_to_data
+from langflow.inputs import DictInput, FloatInput
+from langflow.io import (
+    BoolInput,
+    DataInput,
+    DropdownInput,
+    HandleInput,
+    IntInput,
+    MultilineInput,
+    SecretStrInput,
+    StrInput,
+)
+from langflow.schema import Data
+
+
+class HCDVectorStoreComponent(LCVectorStoreComponent):
+    display_name: str = "Hyper-Converged Database"
+    description: str = "Implementation of Vector Store using Hyper-Converged Database (HCD) with search capabilities"
+    documentation: str = "https://python.langchain.com/docs/integrations/vectorstores/astradb"
+    name = "HCD"
+    icon: str = "HCD"
+
+    inputs = [
+        StrInput(
+            name="collection_name",
+            display_name="Collection Name",
+            info="The name of the collection within HCD where the vectors will be stored.",
+            required=True,
+        ),
+        StrInput(
+            name="username",
+            display_name="HCD Username",
+            info="Authentication username for accessing HCD.",
+            value="hcd-superuser",
+            required=True,
+        ),
+        SecretStrInput(
+            name="password",
+            display_name="HCD Password",
+            info="Authentication password for accessing HCD.",
+            value="HCD_PASSWORD",
+            required=True,
+        ),
+        SecretStrInput(
+            name="api_endpoint",
+            display_name="HCD API Endpoint",
+            info="API endpoint URL for the HCD service.",
+            value="HCD_API_ENDPOINT",
+            required=True,
+        ),
+        MultilineInput(
+            name="search_input",
+            display_name="Search Input",
+        ),
+        DataInput(
+            name="ingest_data",
+            display_name="Ingest Data",
+            is_list=True,
+        ),
+        StrInput(
+            name="namespace",
+            display_name="Namespace",
+            info="Optional namespace within HCD to use for the collection.",
+            value="default_namespace",
+            advanced=True,
+        ),
+        MultilineInput(
+            name="ca_certificate",
+            display_name="CA Certificate",
+            info="Optional CA certificate for TLS connections to HCD.",
+            advanced=True,
+        ),
+        DropdownInput(
+            name="metric",
+            display_name="Metric",
+            info="Optional distance metric for vector comparisons in the vector store.",
+            options=["cosine", "dot_product", "euclidean"],
+            advanced=True,
+        ),
+        IntInput(
+            name="batch_size",
+            display_name="Batch Size",
+            info="Optional number of data to process in a single batch.",
+            advanced=True,
+        ),
+        IntInput(
+            name="bulk_insert_batch_concurrency",
+            display_name="Bulk Insert Batch Concurrency",
+            info="Optional concurrency level for bulk insert operations.",
+            advanced=True,
+        ),
+        IntInput(
+            name="bulk_insert_overwrite_concurrency",
+            display_name="Bulk Insert Overwrite Concurrency",
+            info="Optional concurrency level for bulk insert operations that overwrite existing data.",
+            advanced=True,
+        ),
+        IntInput(
+            name="bulk_delete_concurrency",
+            display_name="Bulk Delete Concurrency",
+            info="Optional concurrency level for bulk delete operations.",
+            advanced=True,
+        ),
+        DropdownInput(
+            name="setup_mode",
+            display_name="Setup Mode",
+            info="Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
+            options=["Sync", "Async", "Off"],
+            advanced=True,
+            value="Sync",
+        ),
+        BoolInput(
+            name="pre_delete_collection",
+            display_name="Pre Delete Collection",
+            info="Boolean flag to determine whether to delete the collection before creating a new one.",
+            advanced=True,
+        ),
+        StrInput(
+            name="metadata_indexing_include",
+            display_name="Metadata Indexing Include",
+            info="Optional list of metadata fields to include in the indexing.",
+            advanced=True,
+        ),
+        HandleInput(
+            name="embedding",
+            display_name="Embedding or Astra Vectorize",
+            input_types=["Embeddings", "dict"],
+            info="Allows either an embedding model or an Astra Vectorize configuration.",  # TODO: This should be optional, but need to refactor langchain-astradb first.
+        ),
+        StrInput(
+            name="metadata_indexing_exclude",
+            display_name="Metadata Indexing Exclude",
+            info="Optional list of metadata fields to exclude from the indexing.",
+            advanced=True,
+        ),
+        StrInput(
+            name="collection_indexing_policy",
+            display_name="Collection Indexing Policy",
+            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",
+            info="Search type to use",
+            options=["Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)"],
+            value="Similarity",
+            advanced=True,
+        ),
+        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,
+        ),
+        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,
+        ),
+    ]
+
+    @check_cached_vector_store
+    def build_vector_store(self):
+        try:
+            from langchain_astradb import AstraDBVectorStore
+            from langchain_astradb.utils.astradb import SetupMode
+        except ImportError:
+            raise ImportError(
+                "Could not import langchain Astra DB integration package. "
+                "Please install it with `pip install langchain-astradb`."
+            )
+
+        try:
+            from astrapy.constants import Environment
+            from astrapy.authentication import UsernamePasswordTokenProvider
+        except ImportError:
+            raise ImportError(
+                "Could not import astrapy integration package. " "Please install it with `pip install astrapy`."
+            )
+
+        try:
+            if not self.setup_mode:
+                self.setup_mode = self._inputs["setup_mode"].options[0]
+
+            setup_mode_value = SetupMode[self.setup_mode.upper()]
+        except KeyError:
+            raise ValueError(f"Invalid setup mode: {self.setup_mode}")
+
+        if not isinstance(self.embedding, dict):
+            embedding_dict = {"embedding": self.embedding}
+        else:
+            from astrapy.info import CollectionVectorServiceOptions
+
+            dict_options = self.embedding.get("collection_vector_service_options", {})
+            dict_options["authentication"] = {
+                k: v for k, v in dict_options.get("authentication", {}).items() if k and v
+            }
+            dict_options["parameters"] = {k: v for k, v in dict_options.get("parameters", {}).items() if k and v}
+            embedding_dict = {
+                "collection_vector_service_options": CollectionVectorServiceOptions.from_dict(dict_options)
+            }
+            collection_embedding_api_key = self.embedding.get("collection_embedding_api_key")
+            if collection_embedding_api_key:
+                embedding_dict["collection_embedding_api_key"] = collection_embedding_api_key
+
+        token_provider = UsernamePasswordTokenProvider(self.username, self.password)
+        vector_store_kwargs = {
+            **embedding_dict,
+            "collection_name": self.collection_name,
+            "token": token_provider,
+            "api_endpoint": self.api_endpoint,
+            "namespace": self.namespace,
+            "metric": self.metric or None,
+            "batch_size": self.batch_size or None,
+            "bulk_insert_batch_concurrency": self.bulk_insert_batch_concurrency or None,
+            "bulk_insert_overwrite_concurrency": self.bulk_insert_overwrite_concurrency or None,
+            "bulk_delete_concurrency": self.bulk_delete_concurrency or None,
+            "setup_mode": setup_mode_value,
+            "pre_delete_collection": self.pre_delete_collection or False,
+            "environment": Environment.HCD,
+        }
+
+        if self.metadata_indexing_include:
+            vector_store_kwargs["metadata_indexing_include"] = self.metadata_indexing_include
+        elif self.metadata_indexing_exclude:
+            vector_store_kwargs["metadata_indexing_exclude"] = self.metadata_indexing_exclude
+        elif self.collection_indexing_policy:
+            vector_store_kwargs["collection_indexing_policy"] = self.collection_indexing_policy
+
+        try:
+            vector_store = AstraDBVectorStore(**vector_store_kwargs)
+        except Exception as e:
+            raise ValueError(f"Error initializing AstraDBVectorStore: {str(e)}") from e
+
+        self._add_documents_to_vector_store(vector_store)
+        return vector_store
+
+    def _add_documents_to_vector_store(self, vector_store):
+        documents = []
+        for _input in self.ingest_data or []:
+            if isinstance(_input, Data):
+                documents.append(_input.to_lc_document())
+            else:
+                raise ValueError("Vector Store Inputs must be Data objects.")
+
+        if documents:
+            logger.debug(f"Adding {len(documents)} documents to the Vector Store.")
+            try:
+                vector_store.add_documents(documents)
+            except Exception as e:
+                raise ValueError(f"Error adding documents to AstraDBVectorStore: {str(e)}") from e
+        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 _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 search_documents(self) -> list[Data]:
+        vector_store = self.build_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:
+                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 = 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 get_retriever_kwargs(self):
+        search_args = self._build_search_args()
+        return {
+            "search_type": self._map_search_type(),
+            "search_kwargs": search_args,
+        }