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import os |
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import orjson |
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from astrapy.admin import parse_api_endpoint |
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from loguru import logger |
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from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store |
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from langflow.helpers import docs_to_data |
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from langflow.inputs import DictInput, FloatInput |
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from langflow.io import ( |
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BoolInput, |
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DataInput, |
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DropdownInput, |
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HandleInput, |
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IntInput, |
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MultilineInput, |
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SecretStrInput, |
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StrInput, |
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) |
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from langflow.schema import Data |
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class AstraDBGraphVectorStoreComponent(LCVectorStoreComponent): |
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display_name: str = "Astra DB Graph" |
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description: str = "Implementation of Graph Vector Store using Astra DB" |
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documentation: str = "https://python.langchain.com/api_reference/astradb/graph_vectorstores/langchain_astradb.graph_vectorstores.AstraDBGraphVectorStore.html" |
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name = "AstraDBGraph" |
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icon: str = "AstraDB" |
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inputs = [ |
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SecretStrInput( |
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name="token", |
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display_name="Astra DB Application Token", |
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info="Authentication token for accessing Astra DB.", |
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value="ASTRA_DB_APPLICATION_TOKEN", |
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required=True, |
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advanced=os.getenv("ASTRA_ENHANCED", "false").lower() == "true", |
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), |
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SecretStrInput( |
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name="api_endpoint", |
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display_name="Database" if os.getenv("ASTRA_ENHANCED", "false").lower() == "true" else "API Endpoint", |
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info="API endpoint URL for the Astra DB service.", |
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value="ASTRA_DB_API_ENDPOINT", |
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required=True, |
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), |
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StrInput( |
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name="collection_name", |
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display_name="Collection Name", |
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info="The name of the collection within Astra DB where the vectors will be stored.", |
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required=True, |
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), |
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StrInput( |
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name="metadata_incoming_links_key", |
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display_name="Metadata incoming links key", |
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info="Metadata key used for incoming links.", |
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advanced=True, |
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), |
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MultilineInput( |
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name="search_input", |
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display_name="Search Input", |
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), |
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DataInput( |
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name="ingest_data", |
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display_name="Ingest Data", |
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is_list=True, |
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), |
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StrInput( |
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name="keyspace", |
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display_name="Keyspace", |
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info="Optional keyspace within Astra DB to use for the collection.", |
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advanced=True, |
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), |
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HandleInput( |
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name="embedding", |
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display_name="Embedding Model", |
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input_types=["Embeddings"], |
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info="Embedding model.", |
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required=True, |
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), |
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DropdownInput( |
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name="metric", |
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display_name="Metric", |
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info="Optional distance metric for vector comparisons in the vector store.", |
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options=["cosine", "dot_product", "euclidean"], |
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value="cosine", |
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advanced=True, |
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), |
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IntInput( |
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name="batch_size", |
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display_name="Batch Size", |
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info="Optional number of data to process in a single batch.", |
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advanced=True, |
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), |
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IntInput( |
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name="bulk_insert_batch_concurrency", |
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display_name="Bulk Insert Batch Concurrency", |
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info="Optional concurrency level for bulk insert operations.", |
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advanced=True, |
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), |
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IntInput( |
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name="bulk_insert_overwrite_concurrency", |
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display_name="Bulk Insert Overwrite Concurrency", |
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info="Optional concurrency level for bulk insert operations that overwrite existing data.", |
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advanced=True, |
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), |
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IntInput( |
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name="bulk_delete_concurrency", |
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display_name="Bulk Delete Concurrency", |
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info="Optional concurrency level for bulk delete operations.", |
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advanced=True, |
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), |
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DropdownInput( |
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name="setup_mode", |
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display_name="Setup Mode", |
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info="Configuration mode for setting up the vector store, with options like 'Sync', or 'Off'.", |
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options=["Sync", "Off"], |
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advanced=True, |
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value="Sync", |
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), |
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BoolInput( |
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name="pre_delete_collection", |
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display_name="Pre Delete Collection", |
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info="Boolean flag to determine whether to delete the collection before creating a new one.", |
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advanced=True, |
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value=False, |
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), |
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StrInput( |
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name="metadata_indexing_include", |
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display_name="Metadata Indexing Include", |
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info="Optional list of metadata fields to include in the indexing.", |
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advanced=True, |
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is_list=True, |
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), |
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StrInput( |
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name="metadata_indexing_exclude", |
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display_name="Metadata Indexing Exclude", |
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info="Optional list of metadata fields to exclude from the indexing.", |
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advanced=True, |
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is_list=True, |
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), |
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StrInput( |
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name="collection_indexing_policy", |
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display_name="Collection Indexing Policy", |
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info='Optional JSON string for the "indexing" field of the collection. ' |
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"See https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html#the-indexing-option", |
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advanced=True, |
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), |
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IntInput( |
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name="number_of_results", |
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display_name="Number of Results", |
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info="Number of results to return.", |
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advanced=True, |
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value=4, |
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), |
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DropdownInput( |
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name="search_type", |
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display_name="Search Type", |
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info="Search type to use", |
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options=["Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)"], |
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value="Similarity", |
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advanced=True, |
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), |
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FloatInput( |
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name="search_score_threshold", |
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display_name="Search Score Threshold", |
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info="Minimum similarity score threshold for search results. " |
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"(when using 'Similarity with score threshold')", |
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value=0, |
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advanced=True, |
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), |
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DictInput( |
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name="search_filter", |
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display_name="Search Metadata Filter", |
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info="Optional dictionary of filters to apply to the search query.", |
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advanced=True, |
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is_list=True, |
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), |
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] |
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@check_cached_vector_store |
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def build_vector_store(self): |
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try: |
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from langchain_astradb import AstraDBGraphVectorStore |
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from langchain_astradb.utils.astradb import SetupMode |
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except ImportError as e: |
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msg = ( |
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"Could not import langchain Astra DB integration package. " |
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"Please install it with `pip install langchain-astradb`." |
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) |
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raise ImportError(msg) from e |
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try: |
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if not self.setup_mode: |
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self.setup_mode = self._inputs["setup_mode"].options[0] |
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setup_mode_value = SetupMode[self.setup_mode.upper()] |
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except KeyError as e: |
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msg = f"Invalid setup mode: {self.setup_mode}" |
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raise ValueError(msg) from e |
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try: |
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vector_store = AstraDBGraphVectorStore( |
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embedding=self.embedding, |
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collection_name=self.collection_name, |
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metadata_incoming_links_key=self.metadata_incoming_links_key or "incoming_links", |
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token=self.token, |
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api_endpoint=self.api_endpoint, |
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namespace=self.keyspace or None, |
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environment=parse_api_endpoint(self.api_endpoint).environment if self.api_endpoint else None, |
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metric=self.metric or None, |
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batch_size=self.batch_size or None, |
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bulk_insert_batch_concurrency=self.bulk_insert_batch_concurrency or None, |
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bulk_insert_overwrite_concurrency=self.bulk_insert_overwrite_concurrency or None, |
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bulk_delete_concurrency=self.bulk_delete_concurrency or None, |
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setup_mode=setup_mode_value, |
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pre_delete_collection=self.pre_delete_collection, |
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metadata_indexing_include=[s for s in self.metadata_indexing_include if s] or None, |
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metadata_indexing_exclude=[s for s in self.metadata_indexing_exclude if s] or None, |
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collection_indexing_policy=orjson.dumps(self.collection_indexing_policy) |
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if self.collection_indexing_policy |
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else None, |
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) |
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except Exception as e: |
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msg = f"Error initializing AstraDBGraphVectorStore: {e}" |
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raise ValueError(msg) from e |
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self._add_documents_to_vector_store(vector_store) |
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return vector_store |
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def _add_documents_to_vector_store(self, vector_store) -> None: |
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documents = [] |
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for _input in self.ingest_data or []: |
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if isinstance(_input, Data): |
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documents.append(_input.to_lc_document()) |
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else: |
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msg = "Vector Store Inputs must be Data objects." |
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raise TypeError(msg) |
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if documents: |
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logger.debug(f"Adding {len(documents)} documents to the Vector Store.") |
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try: |
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vector_store.add_documents(documents) |
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except Exception as e: |
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msg = f"Error adding documents to AstraDBGraphVectorStore: {e}" |
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raise ValueError(msg) from e |
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else: |
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logger.debug("No documents to add to the Vector Store.") |
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def _map_search_type(self) -> str: |
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if self.search_type == "Similarity with score threshold": |
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return "similarity_score_threshold" |
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if self.search_type == "MMR (Max Marginal Relevance)": |
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return "mmr" |
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return "similarity" |
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def _build_search_args(self): |
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args = { |
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"k": self.number_of_results, |
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"score_threshold": self.search_score_threshold, |
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} |
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if self.search_filter: |
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clean_filter = {k: v for k, v in self.search_filter.items() if k and v} |
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if len(clean_filter) > 0: |
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args["filter"] = clean_filter |
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return args |
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def search_documents(self, vector_store=None) -> list[Data]: |
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if not vector_store: |
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vector_store = self.build_vector_store() |
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logger.debug(f"Search input: {self.search_input}") |
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logger.debug(f"Search type: {self.search_type}") |
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logger.debug(f"Number of results: {self.number_of_results}") |
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if self.search_input and isinstance(self.search_input, str) and self.search_input.strip(): |
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try: |
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search_type = self._map_search_type() |
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search_args = self._build_search_args() |
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docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args) |
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except Exception as e: |
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msg = f"Error performing search in AstraDBGraphVectorStore: {e}" |
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raise ValueError(msg) from e |
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logger.debug(f"Retrieved documents: {len(docs)}") |
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data = docs_to_data(docs) |
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logger.debug(f"Converted documents to data: {len(data)}") |
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self.status = data |
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return data |
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logger.debug("No search input provided. Skipping search.") |
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return [] |
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def get_retriever_kwargs(self): |
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search_args = self._build_search_args() |
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return { |
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"search_type": self._map_search_type(), |
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"search_kwargs": search_args, |
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} |
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