from __future__ import annotations import uuid import warnings from concurrent.futures import ThreadPoolExecutor from typing import ( TYPE_CHECKING, Any, Awaitable, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, TypeVar, Union, ) import numpy as np from langchain_core._api.deprecation import deprecated from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.runnables.utils import gather_with_concurrency from langchain_core.utils.iter import batch_iterate from langchain_core.vectorstores import VectorStore from langchain_community.utilities.astradb import ( SetupMode, _AstraDBCollectionEnvironment, ) from langchain_community.vectorstores.utils import maximal_marginal_relevance if TYPE_CHECKING: from astrapy.db import AstraDB as LibAstraDB from astrapy.db import AsyncAstraDB ADBVST = TypeVar("ADBVST", bound="AstraDB") T = TypeVar("T") U = TypeVar("U") DocDict = Dict[str, Any] # dicts expressing entries to insert # Batch/concurrency default values (if parameters not provided): # Size of batches for bulk insertions: # (20 is the max batch size for the HTTP API at the time of writing) DEFAULT_BATCH_SIZE = 20 # Number of threads to insert batches concurrently: DEFAULT_BULK_INSERT_BATCH_CONCURRENCY = 16 # Number of threads in a batch to insert pre-existing entries: DEFAULT_BULK_INSERT_OVERWRITE_CONCURRENCY = 10 # Number of threads (for deleting multiple rows concurrently): DEFAULT_BULK_DELETE_CONCURRENCY = 20 def _unique_list(lst: List[T], key: Callable[[T], U]) -> List[T]: visited_keys: Set[U] = set() new_lst = [] for item in lst: item_key = key(item) if item_key not in visited_keys: visited_keys.add(item_key) new_lst.append(item) return new_lst @deprecated( since="0.0.21", removal="0.3.0", alternative_import="langchain_astradb.AstraDBVectorStore", ) class AstraDB(VectorStore): @staticmethod def _filter_to_metadata(filter_dict: Optional[Dict[str, Any]]) -> Dict[str, Any]: if filter_dict is None: return {} else: metadata_filter = {} for k, v in filter_dict.items(): if k and k[0] == "$": if isinstance(v, list): metadata_filter[k] = [AstraDB._filter_to_metadata(f) for f in v] else: metadata_filter[k] = AstraDB._filter_to_metadata(v) # type: ignore[assignment] else: metadata_filter[f"metadata.{k}"] = v return metadata_filter def __init__( self, *, embedding: Embeddings, collection_name: str, token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[LibAstraDB] = None, async_astra_db_client: Optional[AsyncAstraDB] = None, namespace: Optional[str] = None, metric: Optional[str] = None, batch_size: Optional[int] = None, bulk_insert_batch_concurrency: Optional[int] = None, bulk_insert_overwrite_concurrency: Optional[int] = None, bulk_delete_concurrency: Optional[int] = None, setup_mode: SetupMode = SetupMode.SYNC, pre_delete_collection: bool = False, ) -> None: """Wrapper around DataStax Astra DB for vector-store workloads. For quickstart and details, visit https://docs.datastax.com/en/astra/astra-db-vector/ Example: .. code-block:: python from langchain_community.vectorstores import AstraDB from langchain_openai.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() vectorstore = AstraDB( embedding=embeddings, collection_name="my_store", token="AstraCS:...", api_endpoint="https://-.apps.astra.datastax.com" ) vectorstore.add_texts(["Giraffes", "All good here"]) results = vectorstore.similarity_search("Everything's ok", k=1) Args: embedding: embedding function to use. collection_name: name of the Astra DB collection to create/use. token: API token for Astra DB usage. api_endpoint: full URL to the API endpoint, such as `https://-us-east1.apps.astra.datastax.com`. astra_db_client: *alternative to token+api_endpoint*, you can pass an already-created 'astrapy.db.AstraDB' instance. async_astra_db_client: *alternative to token+api_endpoint*, you can pass an already-created 'astrapy.db.AsyncAstraDB' instance. namespace: namespace (aka keyspace) where the collection is created. Defaults to the database's "default namespace". metric: similarity function to use out of those available in Astra DB. If left out, it will use Astra DB API's defaults (i.e. "cosine" - but, for performance reasons, "dot_product" is suggested if embeddings are normalized to one). batch_size: Size of batches for bulk insertions. bulk_insert_batch_concurrency: Number of threads or coroutines to insert batches concurrently. bulk_insert_overwrite_concurrency: Number of threads or coroutines in a batch to insert pre-existing entries. bulk_delete_concurrency: Number of threads (for deleting multiple rows concurrently). pre_delete_collection: whether to delete the collection before creating it. If False and the collection already exists, the collection will be used as is. Note: For concurrency in synchronous :meth:`~add_texts`:, as a rule of thumb, on a typical client machine it is suggested to keep the quantity bulk_insert_batch_concurrency * bulk_insert_overwrite_concurrency much below 1000 to avoid exhausting the client multithreading/networking resources. The hardcoded defaults are somewhat conservative to meet most machines' specs, but a sensible choice to test may be: - bulk_insert_batch_concurrency = 80 - bulk_insert_overwrite_concurrency = 10 A bit of experimentation is required to nail the best results here, depending on both the machine/network specs and the expected workload (specifically, how often a write is an update of an existing id). Remember you can pass concurrency settings to individual calls to :meth:`~add_texts` and :meth:`~add_documents` as well. """ self.embedding = embedding self.collection_name = collection_name self.token = token self.api_endpoint = api_endpoint self.namespace = namespace # Concurrency settings self.batch_size: int = batch_size or DEFAULT_BATCH_SIZE self.bulk_insert_batch_concurrency: int = ( bulk_insert_batch_concurrency or DEFAULT_BULK_INSERT_BATCH_CONCURRENCY ) self.bulk_insert_overwrite_concurrency: int = ( bulk_insert_overwrite_concurrency or DEFAULT_BULK_INSERT_OVERWRITE_CONCURRENCY ) self.bulk_delete_concurrency: int = ( bulk_delete_concurrency or DEFAULT_BULK_DELETE_CONCURRENCY ) # "vector-related" settings self.metric = metric embedding_dimension: Union[int, Awaitable[int], None] = None if setup_mode == SetupMode.ASYNC: embedding_dimension = self._aget_embedding_dimension() elif setup_mode == SetupMode.SYNC: embedding_dimension = self._get_embedding_dimension() self.astra_env = _AstraDBCollectionEnvironment( collection_name=collection_name, token=token, api_endpoint=api_endpoint, astra_db_client=astra_db_client, async_astra_db_client=async_astra_db_client, namespace=namespace, setup_mode=setup_mode, pre_delete_collection=pre_delete_collection, embedding_dimension=embedding_dimension, metric=metric, ) self.astra_db = self.astra_env.astra_db self.async_astra_db = self.astra_env.async_astra_db self.collection = self.astra_env.collection self.async_collection = self.astra_env.async_collection def _get_embedding_dimension(self) -> int: return len(self.embedding.embed_query(text="This is a sample sentence.")) async def _aget_embedding_dimension(self) -> int: return len(await self.embedding.aembed_query(text="This is a sample sentence.")) @property def embeddings(self) -> Embeddings: return self.embedding @staticmethod def _dont_flip_the_cos_score(similarity0to1: float) -> float: """Keep similarity from client unchanged ad it's in [0:1] already.""" return similarity0to1 def _select_relevance_score_fn(self) -> Callable[[float], float]: """ The underlying API calls already returns a "score proper", i.e. one in [0, 1] where higher means more *similar*, so here the final score transformation is not reversing the interval: """ return self._dont_flip_the_cos_score def clear(self) -> None: """Empty the collection of all its stored entries.""" self.astra_env.ensure_db_setup() self.collection.delete_many({}) async def aclear(self) -> None: """Empty the collection of all its stored entries.""" await self.astra_env.aensure_db_setup() await self.async_collection.delete_many({}) # type: ignore[union-attr] def delete_by_document_id(self, document_id: str) -> bool: """ Remove a single document from the store, given its document ID. Args: document_id: The document ID Returns True if a document has indeed been deleted, False if ID not found. """ self.astra_env.ensure_db_setup() deletion_response = self.collection.delete_one(document_id) # type: ignore[union-attr] return ((deletion_response or {}).get("status") or {}).get( "deletedCount", 0 ) == 1 async def adelete_by_document_id(self, document_id: str) -> bool: """ Remove a single document from the store, given its document ID. Args: document_id: The document ID Returns True if a document has indeed been deleted, False if ID not found. """ await self.astra_env.aensure_db_setup() deletion_response = await self.async_collection.delete_one(document_id) return ((deletion_response or {}).get("status") or {}).get( "deletedCount", 0 ) == 1 def delete( self, ids: Optional[List[str]] = None, concurrency: Optional[int] = None, **kwargs: Any, ) -> Optional[bool]: """Delete by vector ids. Args: ids: List of ids to delete. concurrency: max number of threads issuing single-doc delete requests. Defaults to instance-level setting. Returns: True if deletion is successful, False otherwise. """ if kwargs: warnings.warn( "Method 'delete' of AstraDB vector store invoked with " f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), " "which will be ignored." ) if ids is None: raise ValueError("No ids provided to delete.") _max_workers = concurrency or self.bulk_delete_concurrency with ThreadPoolExecutor(max_workers=_max_workers) as tpe: _ = list( tpe.map( self.delete_by_document_id, ids, ) ) return True async def adelete( self, ids: Optional[List[str]] = None, concurrency: Optional[int] = None, **kwargs: Any, ) -> Optional[bool]: """Delete by vector ids. Args: ids: List of ids to delete. concurrency: max concurrency of single-doc delete requests. Defaults to instance-level setting. **kwargs: Other keyword arguments that subclasses might use. Returns: True if deletion is successful, False otherwise. """ if kwargs: warnings.warn( "Method 'adelete' of AstraDB vector store invoked with " f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), " "which will be ignored." ) if ids is None: raise ValueError("No ids provided to delete.") return all( await gather_with_concurrency( concurrency, *[self.adelete_by_document_id(doc_id) for doc_id in ids] ) ) def delete_collection(self) -> None: """ Completely delete the collection from the database (as opposed to :meth:`~clear`, which empties it only). Stored data is lost and unrecoverable, resources are freed. Use with caution. """ self.astra_env.ensure_db_setup() self.astra_db.delete_collection( collection_name=self.collection_name, ) async def adelete_collection(self) -> None: """ Completely delete the collection from the database (as opposed to :meth:`~aclear`, which empties it only). Stored data is lost and unrecoverable, resources are freed. Use with caution. """ await self.astra_env.aensure_db_setup() await self.async_astra_db.delete_collection( collection_name=self.collection_name, ) @staticmethod def _get_documents_to_insert( texts: Iterable[str], embedding_vectors: List[List[float]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, ) -> List[DocDict]: if ids is None: ids = [uuid.uuid4().hex for _ in texts] if metadatas is None: metadatas = [{} for _ in texts] # documents_to_insert = [ { "content": b_txt, "_id": b_id, "$vector": b_emb, "metadata": b_md, } for b_txt, b_emb, b_id, b_md in zip( texts, embedding_vectors, ids, metadatas, ) ] # make unique by id, keeping the last uniqued_documents_to_insert = _unique_list( documents_to_insert[::-1], lambda document: document["_id"], )[::-1] return uniqued_documents_to_insert @staticmethod def _get_missing_from_batch( document_batch: List[DocDict], insert_result: Dict[str, Any] ) -> Tuple[List[str], List[DocDict]]: if "status" not in insert_result: raise ValueError( f"API Exception while running bulk insertion: {str(insert_result)}" ) batch_inserted = insert_result["status"]["insertedIds"] # estimation of the preexisting documents that failed missed_inserted_ids = {document["_id"] for document in document_batch} - set( batch_inserted ) errors = insert_result.get("errors", []) # careful for other sources of error other than "doc already exists" num_errors = len(errors) unexpected_errors = any( error.get("errorCode") != "DOCUMENT_ALREADY_EXISTS" for error in errors ) if num_errors != len(missed_inserted_ids) or unexpected_errors: raise ValueError( f"API Exception while running bulk insertion: {str(errors)}" ) # deal with the missing insertions as upserts missing_from_batch = [ document for document in document_batch if document["_id"] in missed_inserted_ids ] return batch_inserted, missing_from_batch def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, *, batch_size: Optional[int] = None, batch_concurrency: Optional[int] = None, overwrite_concurrency: Optional[int] = None, **kwargs: Any, ) -> List[str]: """Run texts through the embeddings and add them to the vectorstore. If passing explicit ids, those entries whose id is in the store already will be replaced. Args: texts: Texts to add to the vectorstore. metadatas: Optional list of metadatas. ids: Optional list of ids. batch_size: Number of documents in each API call. Check the underlying Astra DB HTTP API specs for the max value (20 at the time of writing this). If not provided, defaults to the instance-level setting. batch_concurrency: number of threads to process insertion batches concurrently. Defaults to instance-level setting if not provided. overwrite_concurrency: number of threads to process pre-existing documents in each batch (which require individual API calls). Defaults to instance-level setting if not provided. Note: There are constraints on the allowed field names in the metadata dictionaries, coming from the underlying Astra DB API. For instance, the `$` (dollar sign) cannot be used in the dict keys. See this document for details: https://docs.datastax.com/en/astra/astra-db-vector/api-reference/data-api.html Returns: The list of ids of the added texts. """ if kwargs: warnings.warn( "Method 'add_texts' of AstraDB vector store invoked with " f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), " "which will be ignored." ) self.astra_env.ensure_db_setup() embedding_vectors = self.embedding.embed_documents(list(texts)) documents_to_insert = self._get_documents_to_insert( texts, embedding_vectors, metadatas, ids ) def _handle_batch(document_batch: List[DocDict]) -> List[str]: im_result = self.collection.insert_many( documents=document_batch, options={"ordered": False}, partial_failures_allowed=True, ) batch_inserted, missing_from_batch = self._get_missing_from_batch( document_batch, im_result ) def _handle_missing_document(missing_document: DocDict) -> str: replacement_result = self.collection.find_one_and_replace( filter={"_id": missing_document["_id"]}, replacement=missing_document, ) return replacement_result["data"]["document"]["_id"] _u_max_workers = ( overwrite_concurrency or self.bulk_insert_overwrite_concurrency ) with ThreadPoolExecutor(max_workers=_u_max_workers) as tpe2: batch_replaced = list( tpe2.map( _handle_missing_document, missing_from_batch, ) ) return batch_inserted + batch_replaced _b_max_workers = batch_concurrency or self.bulk_insert_batch_concurrency with ThreadPoolExecutor(max_workers=_b_max_workers) as tpe: all_ids_nested = tpe.map( _handle_batch, batch_iterate( batch_size or self.batch_size, documents_to_insert, ), ) return [iid for id_list in all_ids_nested for iid in id_list] async def aadd_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, *, batch_size: Optional[int] = None, batch_concurrency: Optional[int] = None, overwrite_concurrency: Optional[int] = None, **kwargs: Any, ) -> List[str]: """Run texts through the embeddings and add them to the vectorstore. If passing explicit ids, those entries whose id is in the store already will be replaced. Args: texts: Texts to add to the vectorstore. metadatas: Optional list of metadatas. ids: Optional list of ids. batch_size: Number of documents in each API call. Check the underlying Astra DB HTTP API specs for the max value (20 at the time of writing this). If not provided, defaults to the instance-level setting. batch_concurrency: number of threads to process insertion batches concurrently. Defaults to instance-level setting if not provided. overwrite_concurrency: number of threads to process pre-existing documents in each batch (which require individual API calls). Defaults to instance-level setting if not provided. Note: There are constraints on the allowed field names in the metadata dictionaries, coming from the underlying Astra DB API. For instance, the `$` (dollar sign) cannot be used in the dict keys. See this document for details: https://docs.datastax.com/en/astra/astra-db-vector/api-reference/data-api.html Returns: The list of ids of the added texts. """ if kwargs: warnings.warn( "Method 'aadd_texts' of AstraDB vector store invoked with " f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), " "which will be ignored." ) await self.astra_env.aensure_db_setup() embedding_vectors = await self.embedding.aembed_documents(list(texts)) documents_to_insert = self._get_documents_to_insert( texts, embedding_vectors, metadatas, ids ) async def _handle_batch(document_batch: List[DocDict]) -> List[str]: im_result = await self.async_collection.insert_many( documents=document_batch, options={"ordered": False}, partial_failures_allowed=True, ) batch_inserted, missing_from_batch = self._get_missing_from_batch( document_batch, im_result ) async def _handle_missing_document(missing_document: DocDict) -> str: replacement_result = await self.async_collection.find_one_and_replace( filter={"_id": missing_document["_id"]}, replacement=missing_document, ) return replacement_result["data"]["document"]["_id"] _u_max_workers = ( overwrite_concurrency or self.bulk_insert_overwrite_concurrency ) batch_replaced = await gather_with_concurrency( _u_max_workers, *[_handle_missing_document(doc) for doc in missing_from_batch], ) return batch_inserted + batch_replaced _b_max_workers = batch_concurrency or self.bulk_insert_batch_concurrency all_ids_nested = await gather_with_concurrency( _b_max_workers, *[ _handle_batch(batch) for batch in batch_iterate( batch_size or self.batch_size, documents_to_insert, ) ], ) return [iid for id_list in all_ids_nested for iid in id_list] def similarity_search_with_score_id_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, ) -> List[Tuple[Document, float, str]]: """Return docs most similar to embedding vector with score and id. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of (Document, score, id), the most similar to the query vector. """ self.astra_env.ensure_db_setup() metadata_parameter = self._filter_to_metadata(filter) # hits = list( self.collection.paginated_find( filter=metadata_parameter, sort={"$vector": embedding}, options={"limit": k, "includeSimilarity": True}, projection={ "_id": 1, "content": 1, "metadata": 1, }, ) ) # return [ ( Document( page_content=hit["content"], metadata=hit["metadata"], ), hit["$similarity"], hit["_id"], ) for hit in hits ] async def asimilarity_search_with_score_id_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, ) -> List[Tuple[Document, float, str]]: """Return docs most similar to embedding vector with score and id. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of (Document, score, id), the most similar to the query vector. """ await self.astra_env.aensure_db_setup() metadata_parameter = self._filter_to_metadata(filter) # return [ ( Document( page_content=hit["content"], metadata=hit["metadata"], ), hit["$similarity"], hit["_id"], ) async for hit in self.async_collection.paginated_find( filter=metadata_parameter, sort={"$vector": embedding}, options={"limit": k, "includeSimilarity": True}, projection={ "_id": 1, "content": 1, "metadata": 1, }, ) ] def similarity_search_with_score_id( self, query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, ) -> List[Tuple[Document, float, str]]: """Return docs most similar to the query with score and id. Args: query: Query to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of (Document, score, id), the most similar to the query. """ embedding_vector = self.embedding.embed_query(query) return self.similarity_search_with_score_id_by_vector( embedding=embedding_vector, k=k, filter=filter, ) async def asimilarity_search_with_score_id( self, query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, ) -> List[Tuple[Document, float, str]]: """Return docs most similar to the query with score and id. Args: query: Query to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of (Document, score, id), the most similar to the query. """ embedding_vector = await self.embedding.aembed_query(query) return await self.asimilarity_search_with_score_id_by_vector( embedding=embedding_vector, k=k, filter=filter, ) def similarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, ) -> List[Tuple[Document, float]]: """Return docs most similar to embedding vector with score. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of (Document, score), the most similar to the query vector. """ return [ (doc, score) for (doc, score, doc_id) in self.similarity_search_with_score_id_by_vector( embedding=embedding, k=k, filter=filter, ) ] async def asimilarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, ) -> List[Tuple[Document, float]]: """Return docs most similar to embedding vector with score. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of (Document, score), the most similar to the query vector. """ return [ (doc, score) for ( doc, score, doc_id, ) in await self.asimilarity_search_with_score_id_by_vector( embedding=embedding, k=k, filter=filter, ) ] def similarity_search( self, query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Document]: """Return docs most similar to query. Args: query: Query to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of Documents most similar to the query. """ embedding_vector = self.embedding.embed_query(query) return self.similarity_search_by_vector( embedding_vector, k, filter=filter, ) async def asimilarity_search( self, query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Document]: """Return docs most similar to query. Args: query: Query to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of Documents most similar to the query. """ embedding_vector = await self.embedding.aembed_query(query) return await self.asimilarity_search_by_vector( embedding_vector, k, filter=filter, ) def similarity_search_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Document]: """Return docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of Documents most similar to the query vector. """ return [ doc for doc, _ in self.similarity_search_with_score_by_vector( embedding, k, filter=filter, ) ] async def asimilarity_search_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Document]: """Return docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of Documents most similar to the query vector. """ return [ doc for doc, _ in await self.asimilarity_search_with_score_by_vector( embedding, k, filter=filter, ) ] def similarity_search_with_score( self, query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, ) -> List[Tuple[Document, float]]: """Return docs most similar to query with score. Args: query: Query to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of (Document, score), the most similar to the query vector. """ embedding_vector = self.embedding.embed_query(query) return self.similarity_search_with_score_by_vector( embedding_vector, k, filter=filter, ) async def asimilarity_search_with_score( self, query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, ) -> List[Tuple[Document, float]]: """Return docs most similar to query with score. Args: query: Query to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of (Document, score), the most similar to the query vector. """ embedding_vector = await self.embedding.aembed_query(query) return await self.asimilarity_search_with_score_by_vector( embedding_vector, k, filter=filter, ) @staticmethod def _get_mmr_hits( embedding: List[float], k: int, lambda_mult: float, prefetch_hits: List[DocDict] ) -> List[Document]: mmr_chosen_indices = maximal_marginal_relevance( np.array(embedding, dtype=np.float32), [prefetch_hit["$vector"] for prefetch_hit in prefetch_hits], k=k, lambda_mult=lambda_mult, ) mmr_hits = [ prefetch_hit for prefetch_index, prefetch_hit in enumerate(prefetch_hits) if prefetch_index in mmr_chosen_indices ] return [ Document( page_content=hit["content"], metadata=hit["metadata"], ) for hit in mmr_hits ] def max_marginal_relevance_search_by_vector( self, embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. filter: Filter on the metadata to apply. Returns: The list of Documents selected by maximal marginal relevance. """ self.astra_env.ensure_db_setup() metadata_parameter = self._filter_to_metadata(filter) prefetch_hits = list( self.collection.paginated_find( filter=metadata_parameter, sort={"$vector": embedding}, options={"limit": fetch_k, "includeSimilarity": True}, projection={ "_id": 1, "content": 1, "metadata": 1, "$vector": 1, }, ) ) return self._get_mmr_hits(embedding, k, lambda_mult, prefetch_hits) async def amax_marginal_relevance_search_by_vector( self, embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. filter: Filter on the metadata to apply. Returns: The list of Documents selected by maximal marginal relevance. """ await self.astra_env.aensure_db_setup() metadata_parameter = self._filter_to_metadata(filter) prefetch_hits = [ hit async for hit in self.async_collection.paginated_find( filter=metadata_parameter, sort={"$vector": embedding}, options={"limit": fetch_k, "includeSimilarity": True}, projection={ "_id": 1, "content": 1, "metadata": 1, "$vector": 1, }, ) ] return self._get_mmr_hits(embedding, k, lambda_mult, prefetch_hits) def max_marginal_relevance_search( self, query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Query to look up documents similar to. k: Number of Documents to return. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. filter: Filter on the metadata to apply. Returns: The list of Documents selected by maximal marginal relevance. """ embedding_vector = self.embedding.embed_query(query) return self.max_marginal_relevance_search_by_vector( embedding_vector, k, fetch_k, lambda_mult=lambda_mult, filter=filter, ) async def amax_marginal_relevance_search( self, query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Query to look up documents similar to. k: Number of Documents to return. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. filter: Filter on the metadata to apply. Returns: The list of Documents selected by maximal marginal relevance. """ embedding_vector = await self.embedding.aembed_query(query) return await self.amax_marginal_relevance_search_by_vector( embedding_vector, k, fetch_k, lambda_mult=lambda_mult, filter=filter, ) @classmethod def _from_kwargs( cls: Type[ADBVST], embedding: Embeddings, **kwargs: Any, ) -> ADBVST: known_kwargs = { "collection_name", "token", "api_endpoint", "astra_db_client", "async_astra_db_client", "namespace", "metric", "batch_size", "bulk_insert_batch_concurrency", "bulk_insert_overwrite_concurrency", "bulk_delete_concurrency", "batch_concurrency", "overwrite_concurrency", } if kwargs: unknown_kwargs = set(kwargs.keys()) - known_kwargs if unknown_kwargs: warnings.warn( "Method 'from_texts' of AstraDB vector store invoked with " f"unsupported arguments ({', '.join(sorted(unknown_kwargs))}), " "which will be ignored." ) collection_name: str = kwargs["collection_name"] token = kwargs.get("token") api_endpoint = kwargs.get("api_endpoint") astra_db_client = kwargs.get("astra_db_client") async_astra_db_client = kwargs.get("async_astra_db_client") namespace = kwargs.get("namespace") metric = kwargs.get("metric") return cls( embedding=embedding, collection_name=collection_name, token=token, api_endpoint=api_endpoint, astra_db_client=astra_db_client, async_astra_db_client=async_astra_db_client, namespace=namespace, metric=metric, batch_size=kwargs.get("batch_size"), bulk_insert_batch_concurrency=kwargs.get("bulk_insert_batch_concurrency"), bulk_insert_overwrite_concurrency=kwargs.get( "bulk_insert_overwrite_concurrency" ), bulk_delete_concurrency=kwargs.get("bulk_delete_concurrency"), ) @classmethod def from_texts( cls: Type[ADBVST], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any, ) -> ADBVST: """Create an Astra DB vectorstore from raw texts. Args: texts: the texts to insert. embedding: the embedding function to use in the store. metadatas: metadata dicts for the texts. ids: ids to associate to the texts. **kwargs: you can pass any argument that you would to :meth:`~add_texts` and/or to the 'AstraDB' constructor (see these methods for details). These arguments will be routed to the respective methods as they are. Returns: an `AstraDb` vectorstore. """ astra_db_store = AstraDB._from_kwargs(embedding, **kwargs) astra_db_store.add_texts( texts=texts, metadatas=metadatas, ids=ids, batch_size=kwargs.get("batch_size"), batch_concurrency=kwargs.get("batch_concurrency"), overwrite_concurrency=kwargs.get("overwrite_concurrency"), ) return astra_db_store # type: ignore[return-value] @classmethod async def afrom_texts( cls: Type[ADBVST], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any, ) -> ADBVST: """Create an Astra DB vectorstore from raw texts. Args: texts: the texts to insert. embedding: the embedding function to use in the store. metadatas: metadata dicts for the texts. ids: ids to associate to the texts. **kwargs: you can pass any argument that you would to :meth:`~add_texts` and/or to the 'AstraDB' constructor (see these methods for details). These arguments will be routed to the respective methods as they are. Returns: an `AstraDb` vectorstore. """ astra_db_store = AstraDB._from_kwargs(embedding, **kwargs) await astra_db_store.aadd_texts( texts=texts, metadatas=metadatas, ids=ids, batch_size=kwargs.get("batch_size"), batch_concurrency=kwargs.get("batch_concurrency"), overwrite_concurrency=kwargs.get("overwrite_concurrency"), ) return astra_db_store # type: ignore[return-value] @classmethod def from_documents( cls: Type[ADBVST], documents: List[Document], embedding: Embeddings, **kwargs: Any, ) -> ADBVST: """Create an Astra DB vectorstore from a document list. Utility method that defers to 'from_texts' (see that one). Args: see 'from_texts', except here you have to supply 'documents' in place of 'texts' and 'metadatas'. Returns: an `AstraDB` vectorstore. """ return super().from_documents(documents, embedding, **kwargs)