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777 lines
28 KiB
Python
777 lines
28 KiB
Python
from __future__ import annotations
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import uuid
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import warnings
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from concurrent.futures import ThreadPoolExecutor
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from typing import (
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Any,
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Callable,
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Dict,
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Iterable,
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List,
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Optional,
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Set,
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Tuple,
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Type,
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TypeVar,
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)
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import numpy as np
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.utils.iter import batch_iterate
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from langchain_core.vectorstores import VectorStore
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from langchain_community.vectorstores.utils import maximal_marginal_relevance
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ADBVST = TypeVar("ADBVST", bound="AstraDB")
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T = TypeVar("T")
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U = TypeVar("U")
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DocDict = Dict[str, Any] # dicts expressing entries to insert
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# Batch/concurrency default values (if parameters not provided):
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# Size of batches for bulk insertions:
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# (20 is the max batch size for the HTTP API at the time of writing)
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DEFAULT_BATCH_SIZE = 20
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# Number of threads to insert batches concurrently:
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DEFAULT_BULK_INSERT_BATCH_CONCURRENCY = 16
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# Number of threads in a batch to insert pre-existing entries:
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DEFAULT_BULK_INSERT_OVERWRITE_CONCURRENCY = 10
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# Number of threads (for deleting multiple rows concurrently):
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DEFAULT_BULK_DELETE_CONCURRENCY = 20
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def _unique_list(lst: List[T], key: Callable[[T], U]) -> List[T]:
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visited_keys: Set[U] = set()
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new_lst = []
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for item in lst:
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item_key = key(item)
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if item_key not in visited_keys:
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visited_keys.add(item_key)
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new_lst.append(item)
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return new_lst
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class AstraDB(VectorStore):
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"""Wrapper around DataStax Astra DB for vector-store workloads.
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To use it, you need a recent installation of the `astrapy` library
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and an Astra DB cloud database.
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For quickstart and details, visit:
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docs.datastax.com/en/astra/home/astra.html
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Example:
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.. code-block:: python
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from langchain_community.vectorstores import AstraDB
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from langchain_community.embeddings.openai import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings()
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vectorstore = AstraDB(
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embedding=embeddings,
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collection_name="my_store",
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token="AstraCS:...",
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api_endpoint="https://<DB-ID>-us-east1.apps.astra.datastax.com"
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)
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vectorstore.add_texts(["Giraffes", "All good here"])
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results = vectorstore.similarity_search("Everything's ok", k=1)
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Constructor Args (only keyword-arguments accepted):
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embedding (Embeddings): embedding function to use.
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collection_name (str): name of the Astra DB collection to create/use.
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token (Optional[str]): API token for Astra DB usage.
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api_endpoint (Optional[str]): full URL to the API endpoint,
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such as "https://<DB-ID>-us-east1.apps.astra.datastax.com".
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astra_db_client (Optional[Any]): *alternative to token+api_endpoint*,
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you can pass an already-created 'astrapy.db.AstraDB' instance.
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namespace (Optional[str]): namespace (aka keyspace) where the
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collection is created. Defaults to the database's "default namespace".
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metric (Optional[str]): similarity function to use out of those
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available in Astra DB. If left out, it will use Astra DB API's
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defaults (i.e. "cosine" - but, for performance reasons,
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"dot_product" is suggested if embeddings are normalized to one).
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Advanced arguments (coming with sensible defaults):
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batch_size (Optional[int]): Size of batches for bulk insertions.
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bulk_insert_batch_concurrency (Optional[int]): Number of threads
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to insert batches concurrently.
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bulk_insert_overwrite_concurrency (Optional[int]): Number of
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threads in a batch to insert pre-existing entries.
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bulk_delete_concurrency (Optional[int]): Number of threads
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(for deleting multiple rows concurrently).
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pre_delete_collection (Optional[bool]): whether to delete the collection
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before creating it. If False and the collection already exists,
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the collection will be used as is.
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A note on concurrency: as a rule of thumb, on a typical client machine
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it is suggested to keep the quantity
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bulk_insert_batch_concurrency * bulk_insert_overwrite_concurrency
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much below 1000 to avoid exhausting the client multithreading/networking
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resources. The hardcoded defaults are somewhat conservative to meet
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most machines' specs, but a sensible choice to test may be:
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bulk_insert_batch_concurrency = 80
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bulk_insert_overwrite_concurrency = 10
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A bit of experimentation is required to nail the best results here,
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depending on both the machine/network specs and the expected workload
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(specifically, how often a write is an update of an existing id).
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Remember you can pass concurrency settings to individual calls to
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add_texts and add_documents as well.
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"""
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@staticmethod
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def _filter_to_metadata(filter_dict: Optional[Dict[str, str]]) -> Dict[str, Any]:
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if filter_dict is None:
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return {}
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else:
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return {f"metadata.{mdk}": mdv for mdk, mdv in filter_dict.items()}
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def __init__(
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self,
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*,
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embedding: Embeddings,
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collection_name: str,
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token: Optional[str] = None,
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api_endpoint: Optional[str] = None,
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astra_db_client: Optional[Any] = None, # 'astrapy.db.AstraDB' if passed
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namespace: Optional[str] = None,
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metric: Optional[str] = None,
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batch_size: Optional[int] = None,
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bulk_insert_batch_concurrency: Optional[int] = None,
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bulk_insert_overwrite_concurrency: Optional[int] = None,
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bulk_delete_concurrency: Optional[int] = None,
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pre_delete_collection: bool = False,
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) -> None:
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"""
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Create an AstraDB vector store object. See class docstring for help.
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"""
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try:
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from astrapy.db import (
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AstraDB as LibAstraDB,
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)
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from astrapy.db import (
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AstraDBCollection as LibAstraDBCollection,
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)
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except (ImportError, ModuleNotFoundError):
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raise ImportError(
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"Could not import a recent astrapy python package. "
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"Please install it with `pip install --upgrade astrapy`."
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)
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# Conflicting-arg checks:
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if astra_db_client is not None:
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if token is not None or api_endpoint is not None:
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raise ValueError(
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"You cannot pass 'astra_db_client' to AstraDB if passing "
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"'token' and 'api_endpoint'."
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)
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self.embedding = embedding
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self.collection_name = collection_name
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self.token = token
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self.api_endpoint = api_endpoint
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self.namespace = namespace
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# Concurrency settings
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self.batch_size: int = batch_size or DEFAULT_BATCH_SIZE
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self.bulk_insert_batch_concurrency: int = (
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bulk_insert_batch_concurrency or DEFAULT_BULK_INSERT_BATCH_CONCURRENCY
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)
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self.bulk_insert_overwrite_concurrency: int = (
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bulk_insert_overwrite_concurrency
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or DEFAULT_BULK_INSERT_OVERWRITE_CONCURRENCY
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)
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self.bulk_delete_concurrency: int = (
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bulk_delete_concurrency or DEFAULT_BULK_DELETE_CONCURRENCY
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)
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# "vector-related" settings
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self._embedding_dimension: Optional[int] = None
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self.metric = metric
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if astra_db_client is not None:
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self.astra_db = astra_db_client
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else:
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self.astra_db = LibAstraDB(
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token=self.token,
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api_endpoint=self.api_endpoint,
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namespace=self.namespace,
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)
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if not pre_delete_collection:
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self._provision_collection()
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else:
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self.clear()
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self.collection = LibAstraDBCollection(
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collection_name=self.collection_name,
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astra_db=self.astra_db,
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)
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def _get_embedding_dimension(self) -> int:
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if self._embedding_dimension is None:
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self._embedding_dimension = len(
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self.embedding.embed_query("This is a sample sentence.")
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)
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return self._embedding_dimension
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def _drop_collection(self) -> None:
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"""
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Drop the collection from storage.
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This is meant as an internal-usage method, no members
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are set other than actual deletion on the backend.
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"""
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_ = self.astra_db.delete_collection(
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collection_name=self.collection_name,
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)
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return None
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def _provision_collection(self) -> None:
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"""
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Run the API invocation to create the collection on the backend.
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Internal-usage method, no object members are set,
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other than working on the underlying actual storage.
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"""
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_ = self.astra_db.create_collection(
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dimension=self._get_embedding_dimension(),
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collection_name=self.collection_name,
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metric=self.metric,
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)
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return None
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@property
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def embeddings(self) -> Embeddings:
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return self.embedding
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@staticmethod
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def _dont_flip_the_cos_score(similarity0to1: float) -> float:
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"""Keep similarity from client unchanged ad it's in [0:1] already."""
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return similarity0to1
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def _select_relevance_score_fn(self) -> Callable[[float], float]:
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"""
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The underlying API calls already returns a "score proper",
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i.e. one in [0, 1] where higher means more *similar*,
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so here the final score transformation is not reversing the interval:
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"""
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return self._dont_flip_the_cos_score
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def clear(self) -> None:
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"""Empty the collection of all its stored entries."""
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self._drop_collection()
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self._provision_collection()
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return None
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def delete_by_document_id(self, document_id: str) -> bool:
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"""
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Remove a single document from the store, given its document_id (str).
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Return True if a document has indeed been deleted, False if ID not found.
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"""
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deletion_response = self.collection.delete(document_id)
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return ((deletion_response or {}).get("status") or {}).get(
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"deletedCount", 0
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) == 1
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def delete(
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self,
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ids: Optional[List[str]] = None,
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concurrency: Optional[int] = None,
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**kwargs: Any,
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) -> Optional[bool]:
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"""Delete by vector ids.
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Args:
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ids (Optional[List[str]]): List of ids to delete.
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concurrency (Optional[int]): max number of threads issuing
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single-doc delete requests. Defaults to instance-level setting.
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Returns:
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Optional[bool]: True if deletion is successful,
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False otherwise, None if not implemented.
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"""
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if kwargs:
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warnings.warn(
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"Method 'delete' of AstraDB vector store invoked with "
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f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), "
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"which will be ignored."
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)
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if ids is None:
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raise ValueError("No ids provided to delete.")
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_max_workers = concurrency or self.bulk_delete_concurrency
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with ThreadPoolExecutor(max_workers=_max_workers) as tpe:
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_ = list(
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tpe.map(
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self.delete_by_document_id,
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ids,
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)
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)
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return True
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def delete_collection(self) -> None:
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"""
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Completely delete the collection from the database (as opposed
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to 'clear()', which empties it only).
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Stored data is lost and unrecoverable, resources are freed.
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Use with caution.
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"""
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self._drop_collection()
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return None
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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ids: Optional[List[str]] = None,
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*,
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batch_size: Optional[int] = None,
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batch_concurrency: Optional[int] = None,
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overwrite_concurrency: Optional[int] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Run texts through the embeddings and add them to the vectorstore.
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If passing explicit ids, those entries whose id is in the store already
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will be replaced.
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Args:
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texts (Iterable[str]): Texts to add to the vectorstore.
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metadatas (Optional[List[dict]], optional): Optional list of metadatas.
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ids (Optional[List[str]], optional): Optional list of ids.
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batch_size (Optional[int]): Number of documents in each API call.
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Check the underlying Astra DB HTTP API specs for the max value
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(20 at the time of writing this). If not provided, defaults
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to the instance-level setting.
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batch_concurrency (Optional[int]): number of threads to process
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insertion batches concurrently. Defaults to instance-level
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setting if not provided.
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overwrite_concurrency (Optional[int]): number of threads to process
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pre-existing documents in each batch (which require individual
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API calls). Defaults to instance-level setting if not provided.
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A note on metadata: there are constraints on the allowed field names
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in this dictionary, coming from the underlying Astra DB API.
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For instance, the `$` (dollar sign) cannot be used in the dict keys.
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See this document for details:
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docs.datastax.com/en/astra-serverless/docs/develop/dev-with-json.html
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Returns:
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List[str]: List of ids of the added texts.
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"""
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if kwargs:
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warnings.warn(
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"Method 'add_texts' of AstraDB vector store invoked with "
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f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), "
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"which will be ignored."
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)
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_texts = list(texts)
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if ids is None:
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ids = [uuid.uuid4().hex for _ in _texts]
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if metadatas is None:
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metadatas = [{} for _ in _texts]
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#
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embedding_vectors = self.embedding.embed_documents(_texts)
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documents_to_insert = [
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{
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"content": b_txt,
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"_id": b_id,
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"$vector": b_emb,
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"metadata": b_md,
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}
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for b_txt, b_emb, b_id, b_md in zip(
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_texts,
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embedding_vectors,
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ids,
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metadatas,
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)
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]
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# make unique by id, keeping the last
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uniqued_documents_to_insert = _unique_list(
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documents_to_insert[::-1],
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lambda document: document["_id"],
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)[::-1]
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all_ids = []
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def _handle_batch(document_batch: List[DocDict]) -> List[str]:
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im_result = self.collection.insert_many(
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documents=document_batch,
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options={"ordered": False},
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partial_failures_allowed=True,
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)
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if "status" not in im_result:
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raise ValueError(
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f"API Exception while running bulk insertion: {str(im_result)}"
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)
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batch_inserted = im_result["status"]["insertedIds"]
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# estimation of the preexisting documents that failed
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missed_inserted_ids = {
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document["_id"] for document in document_batch
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} - set(batch_inserted)
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errors = im_result.get("errors", [])
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# careful for other sources of error other than "doc already exists"
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num_errors = len(errors)
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unexpected_errors = any(
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error.get("errorCode") != "DOCUMENT_ALREADY_EXISTS" for error in errors
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)
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if num_errors != len(missed_inserted_ids) or unexpected_errors:
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raise ValueError(
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f"API Exception while running bulk insertion: {str(errors)}"
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)
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# deal with the missing insertions as upserts
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missing_from_batch = [
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document
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for document in document_batch
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if document["_id"] in missed_inserted_ids
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]
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def _handle_missing_document(missing_document: DocDict) -> str:
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replacement_result = self.collection.find_one_and_replace(
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filter={"_id": missing_document["_id"]},
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replacement=missing_document,
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)
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return replacement_result["data"]["document"]["_id"]
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_u_max_workers = (
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overwrite_concurrency or self.bulk_insert_overwrite_concurrency
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)
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with ThreadPoolExecutor(max_workers=_u_max_workers) as tpe2:
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batch_replaced = list(
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tpe2.map(
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_handle_missing_document,
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missing_from_batch,
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)
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)
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upsert_ids = batch_inserted + batch_replaced
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return upsert_ids
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_b_max_workers = batch_concurrency or self.bulk_insert_batch_concurrency
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with ThreadPoolExecutor(max_workers=_b_max_workers) as tpe:
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all_ids_nested = tpe.map(
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_handle_batch,
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batch_iterate(
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batch_size or self.batch_size,
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uniqued_documents_to_insert,
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),
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)
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all_ids = [iid for id_list in all_ids_nested for iid in id_list]
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return all_ids
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|
|
def similarity_search_with_score_id_by_vector(
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self,
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embedding: List[float],
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|
k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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|
) -> List[Tuple[Document, float, str]]:
|
|
"""Return docs most similar to embedding vector.
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|
Args:
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embedding (str): Embedding to look up documents similar to.
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k (int): Number of Documents to return. Defaults to 4.
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Returns:
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List of (Document, score, id), the most similar to the query vector.
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"""
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|
metadata_parameter = self._filter_to_metadata(filter)
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#
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hits = list(
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self.collection.paginated_find(
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filter=metadata_parameter,
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sort={"$vector": embedding},
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options={"limit": k, "includeSimilarity": True},
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projection={
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"_id": 1,
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"content": 1,
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"metadata": 1,
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},
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)
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)
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#
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return [
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(
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Document(
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page_content=hit["content"],
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metadata=hit["metadata"],
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),
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hit["$similarity"],
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hit["_id"],
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)
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for hit in hits
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]
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|
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def similarity_search_with_score_id(
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self,
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query: str,
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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) -> List[Tuple[Document, float, str]]:
|
|
embedding_vector = self.embedding.embed_query(query)
|
|
return self.similarity_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, str]] = None,
|
|
) -> List[Tuple[Document, float]]:
|
|
"""Return docs most similar to embedding vector.
|
|
|
|
Args:
|
|
embedding (str): Embedding to look up documents similar to.
|
|
k (int): Number of Documents to return. Defaults to 4.
|
|
Returns:
|
|
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,
|
|
)
|
|
]
|
|
|
|
def similarity_search(
|
|
self,
|
|
query: str,
|
|
k: int = 4,
|
|
filter: Optional[Dict[str, str]] = None,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
embedding_vector = self.embedding.embed_query(query)
|
|
return self.similarity_search_by_vector(
|
|
embedding_vector,
|
|
k,
|
|
filter=filter,
|
|
)
|
|
|
|
def similarity_search_by_vector(
|
|
self,
|
|
embedding: List[float],
|
|
k: int = 4,
|
|
filter: Optional[Dict[str, str]] = None,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
return [
|
|
doc
|
|
for doc, _ in self.similarity_search_with_score_by_vector(
|
|
embedding,
|
|
k,
|
|
filter=filter,
|
|
)
|
|
]
|
|
|
|
def similarity_search_with_score(
|
|
self,
|
|
query: str,
|
|
k: int = 4,
|
|
filter: Optional[Dict[str, str]] = None,
|
|
) -> List[Tuple[Document, float]]:
|
|
embedding_vector = self.embedding.embed_query(query)
|
|
return self.similarity_search_with_score_by_vector(
|
|
embedding_vector,
|
|
k,
|
|
filter=filter,
|
|
)
|
|
|
|
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, str]] = 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.
|
|
Returns:
|
|
List of Documents selected by maximal marginal relevance.
|
|
"""
|
|
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,
|
|
},
|
|
)
|
|
)
|
|
|
|
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(
|
|
self,
|
|
query: str,
|
|
k: int = 4,
|
|
fetch_k: int = 20,
|
|
lambda_mult: float = 0.5,
|
|
filter: Optional[Dict[str, str]] = 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 (str): Text to look up documents similar to.
|
|
k (int = 4): Number of Documents to return.
|
|
fetch_k (int = 20): Number of Documents to fetch to pass to MMR algorithm.
|
|
lambda_mult (float = 0.5): 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.
|
|
Optional.
|
|
Returns:
|
|
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,
|
|
)
|
|
|
|
@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 (List[str]): the texts to insert.
|
|
embedding (Embeddings): the embedding function to use in the store.
|
|
metadatas (Optional[List[dict]]): metadata dicts for the texts.
|
|
ids (Optional[List[str]]): ids to associate to the texts.
|
|
*Additional arguments*: you can pass any argument that you would
|
|
to 'add_texts' and/or to the 'AstraDB' class constructor
|
|
(see these methods for details). These arguments will be
|
|
routed to the respective methods as they are.
|
|
|
|
Returns:
|
|
an `AstraDb` vectorstore.
|
|
"""
|
|
|
|
known_kwargs = {
|
|
"collection_name",
|
|
"token",
|
|
"api_endpoint",
|
|
"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")
|
|
namespace = kwargs.get("namespace")
|
|
metric = kwargs.get("metric")
|
|
|
|
astra_db_store = cls(
|
|
embedding=embedding,
|
|
collection_name=collection_name,
|
|
token=token,
|
|
api_endpoint=api_endpoint,
|
|
astra_db_client=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"),
|
|
)
|
|
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
|
|
|
|
@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)
|