feat: Add AstraGraphVectorStoreComponent

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

@@ -0,0 +1,305 @@
+import os
+
+import orjson
+from astrapy.admin import parse_api_endpoint
+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 AstraGraphVectorStoreComponent(LCVectorStoreComponent):
+    display_name: str = "Astra DB Graph"
+    description: str = "Implementation of Graph Vector Store using Astra DB"
+    documentation: str = "https://python.langchain.com/api_reference/astradb/graph_vectorstores/langchain_astradb.graph_vectorstores.AstraDBGraphVectorStore.html"
+    name = "AstraDBGraph"
+    icon: str = "AstraDB"
+
+    inputs = [
+        SecretStrInput(
+            name="token",
+            display_name="Astra DB Application Token",
+            info="Authentication token for accessing Astra DB.",
+            value="ASTRA_DB_APPLICATION_TOKEN",
+            required=True,
+            advanced=os.getenv("ASTRA_ENHANCED", "false").lower() == "true",
+        ),
+        SecretStrInput(
+            name="api_endpoint",
+            display_name="Database" if os.getenv("ASTRA_ENHANCED", "false").lower() == "true" else "API Endpoint",
+            info="API endpoint URL for the Astra DB service.",
+            value="ASTRA_DB_API_ENDPOINT",
+            required=True,
+        ),
+        StrInput(
+            name="collection_name",
+            display_name="Collection Name",
+            info="The name of the collection within Astra DB where the vectors will be stored.",
+            required=True,
+        ),
+        StrInput(
+            name="link_to_metadata_key",
+            display_name="Outgoing links metadata key",
+            info="Metadata key used for outgoing links.",
+            advanced=True,
+        ),
+        StrInput(
+            name="link_from_metadata_key",
+            display_name="Incoming links metadata key",
+            info="Metadata key used for incoming links.",
+            advanced=True,
+        ),
+        StrInput(
+            name="namespace",
+            display_name="Namespace",
+            info="Optional namespace within Astra DB to use for the collection.",
+            advanced=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 Astra DB to use for the collection.",
+            advanced=True,
+        ),
+        HandleInput(
+            name="embedding",
+            display_name="Embedding Model",
+            input_types=["Embeddings"],
+            info="Embedding model.",
+            required=True,
+        ),
+        DropdownInput(
+            name="metric",
+            display_name="Metric",
+            info="Optional distance metric for vector comparisons in the vector store.",
+            options=["cosine", "dot_product", "euclidean"],
+            value="cosine",
+            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', or 'Off'.",
+            options=["Sync", "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,
+            value=False,
+        ),
+        StrInput(
+            name="metadata_indexing_include",
+            display_name="Metadata Indexing Include",
+            info="Optional list of metadata fields to include in the indexing.",
+            advanced=True,
+            is_list=True,
+        ),
+        StrInput(
+            name="metadata_indexing_exclude",
+            display_name="Metadata Indexing Exclude",
+            info="Optional list of metadata fields to exclude from the indexing.",
+            advanced=True,
+            is_list=True,
+        ),
+        StrInput(
+            name="collection_indexing_policy",
+            display_name="Collection Indexing Policy",
+            info='Optional JSON string for the "indexing" field of the collection. '
+            "See https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html#the-indexing-option",
+            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 AstraDBGraphVectorStore
+            from langchain_astradb.utils.astradb import SetupMode
+        except ImportError as e:
+            msg = (
+                "Could not import langchain Astra DB integration package. "
+                "Please install it with `pip install langchain-astradb`."
+            )
+            raise ImportError(msg) from e
+
+        try:
+            vector_store = AstraDBGraphVectorStore(
+                embedding=self.embedding,
+                collection_name=self.collection_name,
+                link_to_metadata_key=self.link_to_metadata_key or "links_to",
+                link_from_metadata_key=self.link_from_metadata_key or "links_from",
+                token=self.token,
+                api_endpoint=self.api_endpoint,
+                namespace=self.namespace or None,
+                environment=parse_api_endpoint(self.api_endpoint).environment,
+                metric=self.metric,
+                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=SetupMode[self.setup_mode.upper()],
+                pre_delete_collection=self.pre_delete_collection,
+                metadata_indexing_include=[s for s in self.metadata_indexing_include if s],
+                metadata_indexing_exclude=[s for s in self.metadata_indexing_exclude if s],
+                collection_indexing_policy=orjson.dumps(self.collection_indexing_policy)
+                if self.collection_indexing_policy
+                else None,
+            )
+        except Exception as e:
+            msg = f"Error initializing AstraDBGraphVectorStore: {e}"
+            raise ValueError(msg) from e
+
+        self._add_documents_to_vector_store(vector_store)
+
+        return vector_store
+
+    def _add_documents_to_vector_store(self, vector_store) -> None:
+        documents = []
+        for _input in self.ingest_data or []:
+            if isinstance(_input, Data):
+                documents.append(_input.to_lc_document())
+            else:
+                msg = "Vector Store Inputs must be Data objects."
+                raise TypeError(msg)
+
+        if documents:
+            logger.debug(f"Adding {len(documents)} documents to the Vector Store.")
+            try:
+                vector_store.add_documents(documents)
+            except Exception as e:
+                msg = f"Error adding documents to AstraDBGraphVectorStore: {e}"
+                raise ValueError(msg) from e
+        else:
+            logger.debug("No documents to add to the Vector Store.")
+
+    def _map_search_type(self) -> str:
+        if self.search_type == "Similarity with score threshold":
+            return "similarity_score_threshold"
+        if self.search_type == "MMR (Max Marginal Relevance)":
+            return "mmr"
+        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, vector_store=None) -> list[Data]:
+        if not vector_store:
+            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:
+                msg = f"Error performing search in AstraDBGraphVectorStore: {e}"
+                raise ValueError(msg) 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
+        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,
+        }