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This PR adds support for the Azure Cosmos DB MongoDB vCore Vector Store https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/ https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-search Summary: - **Description:** added vector store integration for Azure Cosmos DB MongoDB vCore Vector Store, - **Issue:** the issue # it fixes #11627, - **Dependencies:** pymongo dependency, - **Tag maintainer:** @hwchase17, - **Twitter handle:** @izzyacademy --------- Co-authored-by: Israel Ekpo <israel.ekpo@gmail.com> Co-authored-by: Israel Ekpo <44282278+izzyacademy@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Israel Ekpo
GitHub
@@ -37,6 +37,12 @@ def _import_alibaba_cloud_open_search_settings() -> Any:
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return AlibabaCloudOpenSearchSettings
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def _import_azure_cosmos_db() -> Any:
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from langchain.vectorstores.azure_cosmos_db import AzureCosmosDBVectorSearch
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return AzureCosmosDBVectorSearch
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def _import_elastic_knn_search() -> Any:
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from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
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@@ -398,6 +404,8 @@ def __getattr__(name: str) -> Any:
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return _import_alibaba_cloud_open_search()
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elif name == "AlibabaCloudOpenSearchSettings":
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return _import_alibaba_cloud_open_search_settings()
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elif name == "AzureCosmosDBVectorSearch":
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return _import_azure_cosmos_db()
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elif name == "ElasticKnnSearch":
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return _import_elastic_knn_search()
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elif name == "ElasticVectorSearch":
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@@ -588,4 +596,5 @@ __all__ = [
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"Zilliz",
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"Zilliz",
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"TencentVectorDB",
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"AzureCosmosDBVectorSearch",
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]
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421
libs/langchain/langchain/vectorstores/azure_cosmos_db.py
Normal file
421
libs/langchain/langchain/vectorstores/azure_cosmos_db.py
Normal file
@@ -0,0 +1,421 @@
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from __future__ import annotations
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import logging
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from enum import Enum
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from typing import (
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TYPE_CHECKING,
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Any,
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Dict,
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Generator,
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Iterable,
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List,
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Optional,
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Tuple,
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TypeVar,
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Union,
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)
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import numpy as np
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from langchain.docstore.document import Document
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from langchain.vectorstores.base import VectorStore
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from langchain.vectorstores.utils import maximal_marginal_relevance
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if TYPE_CHECKING:
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from pymongo.collection import Collection
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from langchain.schema.embeddings import Embeddings
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# Before Python 3.11 native StrEnum is not available
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class CosmosDBSimilarityType(str, Enum):
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COS = "COS" # CosineSimilarity
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IP = "IP" # inner - product
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L2 = "L2" # Euclidean distance
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CosmosDBDocumentType = TypeVar("CosmosDBDocumentType", bound=Dict[str, Any])
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logger = logging.getLogger(__name__)
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DEFAULT_INSERT_BATCH_SIZE = 128
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class AzureCosmosDBVectorSearch(VectorStore):
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"""`Azure Cosmos DB for MongoDB vCore` vector store.
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To use, you should have both:
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- the ``pymongo`` python package installed
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- a connection string associated with a MongoDB VCore Cluster
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Example:
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. code-block:: python
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from langchain.vectorstores import AzureCosmosDBVectorSearch
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from langchain.embeddings.openai import OpenAIEmbeddings
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from pymongo import MongoClient
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mongo_client = MongoClient("<YOUR-CONNECTION-STRING>")
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collection = mongo_client["<db_name>"]["<collection_name>"]
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embeddings = OpenAIEmbeddings()
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vectorstore = AzureCosmosDBVectorSearch(collection, embeddings)
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"""
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def __init__(
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self,
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collection: Collection[CosmosDBDocumentType],
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embedding: Embeddings,
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*,
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index_name: str = "vectorSearchIndex",
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text_key: str = "textContent",
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embedding_key: str = "vectorContent",
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):
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"""Constructor for AzureCosmosDBVectorSearch
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Args:
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collection: MongoDB collection to add the texts to.
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embedding: Text embedding model to use.
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index_name: Name of the Atlas Search index.
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text_key: MongoDB field that will contain the text
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for each document.
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embedding_key: MongoDB field that will contain the embedding
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for each document.
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"""
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self._collection = collection
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self._embedding = embedding
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self._index_name = index_name
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self._text_key = text_key
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self._embedding_key = embedding_key
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@property
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def embeddings(self) -> Embeddings:
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return self._embedding
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def get_index_name(self) -> str:
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"""Returns the index name
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Returns:
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Returns the index name
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"""
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return self._index_name
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@classmethod
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def from_connection_string(
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cls,
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connection_string: str,
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namespace: str,
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embedding: Embeddings,
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**kwargs: Any,
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) -> AzureCosmosDBVectorSearch:
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"""Creates an Instance of AzureCosmosDBVectorSearch from a Connection String
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Args:
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connection_string: The MongoDB vCore instance connection string
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namespace: The namespace (database.collection)
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embedding: The embedding utility
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**kwargs: Dynamic keyword arguments
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Returns:
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an instance of the vector store
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"""
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try:
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from pymongo import MongoClient
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except ImportError:
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raise ImportError(
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"Could not import pymongo, please install it with "
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"`pip install pymongo`."
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)
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client: MongoClient = MongoClient(connection_string)
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db_name, collection_name = namespace.split(".")
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collection = client[db_name][collection_name]
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return cls(collection, embedding, **kwargs)
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def index_exists(self) -> bool:
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"""Verifies if the specified index name during instance
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construction exists on the collection
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Returns:
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Returns True on success and False if no such index exists
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on the collection
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"""
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cursor = self._collection.list_indexes()
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index_name = self._index_name
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for res in cursor:
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current_index_name = res.pop("name")
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if current_index_name == index_name:
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return True
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return False
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def delete_index(self) -> None:
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"""Deletes the index specified during instance construction if it exists"""
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if self.index_exists():
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self._collection.drop_index(self._index_name)
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# Raises OperationFailure on an error (e.g. trying to drop
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# an index that does not exist)
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def create_index(
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self,
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num_lists: int = 100,
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dimensions: int = 1536,
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similarity: CosmosDBSimilarityType = CosmosDBSimilarityType.COS,
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) -> dict[str, Any]:
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"""Creates an index using the index name specified at
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instance construction
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Setting the numLists parameter correctly is important for achieving
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good accuracy and performance.
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Since the vector store uses IVF as the indexing strategy,
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you should create the index only after you
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have loaded a large enough sample documents to ensure that the
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centroids for the respective buckets are
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faily distributed.
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We recommend that numLists is set to documentCount/1000 for up
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to 1 million documents
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and to sqrt(documentCount) for more than 1 million documents.
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As the number of items in your database grows, you should
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tune numLists to be larger
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in order to achieve good latency performance for vector search.
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If you're experimenting with a new scenario or creating a
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small demo, you can start with numLists
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set to 1 to perform a brute-force search across all vectors.
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This should provide you with the most
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accurate results from the vector search, however be aware that
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the search speed and latency will be slow.
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After your initial setup, you should go ahead and tune
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the numLists parameter using the above guidance.
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Args:
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num_lists: This integer is the number of clusters that the
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inverted file (IVF) index uses to group the vector data.
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We recommend that numLists is set to documentCount/1000
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for up to 1 million documents and to sqrt(documentCount)
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for more than 1 million documents.
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Using a numLists value of 1 is akin to performing
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brute-force search, which has limited performance
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dimensions: Number of dimensions for vector similarity.
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The maximum number of supported dimensions is 2000
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similarity: Similarity metric to use with the IVF index.
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Possible options are:
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- CosmosDBSimilarityType.COS (cosine distance),
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- CosmosDBSimilarityType.L2 (Euclidean distance), and
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- CosmosDBSimilarityType.IP (inner product).
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Returns:
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An object describing the created index
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"""
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# prepare the command
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create_index_commands = {
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"createIndexes": self._collection.name,
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"indexes": [
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{
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"name": self._index_name,
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"key": {"vectorContent": "cosmosSearch"},
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"cosmosSearchOptions": {
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"kind": "vector-ivf",
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"numLists": num_lists,
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"similarity": similarity,
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"dimensions": dimensions,
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},
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}
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],
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}
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# retrieve the database object
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current_database = self._collection.database
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# invoke the command from the database object
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create_index_responses: dict[str, Any] = current_database.command(
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create_index_commands
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)
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return create_index_responses
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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[str, Any]]] = None,
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**kwargs: Any,
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) -> List:
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batch_size = kwargs.get("batch_size", DEFAULT_INSERT_BATCH_SIZE)
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_metadatas: Union[List, Generator] = metadatas or ({} for _ in texts)
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texts_batch = []
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metadatas_batch = []
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result_ids = []
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for i, (text, metadata) in enumerate(zip(texts, _metadatas)):
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texts_batch.append(text)
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metadatas_batch.append(metadata)
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if (i + 1) % batch_size == 0:
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result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
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texts_batch = []
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metadatas_batch = []
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if texts_batch:
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result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
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return result_ids
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def _insert_texts(self, texts: List[str], metadatas: List[Dict[str, Any]]) -> List:
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"""Used to Load Documents into the collection
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Args:
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texts: The list of documents strings to load
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metadatas: The list of metadata objects associated with each document
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Returns:
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"""
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# If the text is empty, then exit early
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if not texts:
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return []
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# Embed and create the documents
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embeddings = self._embedding.embed_documents(texts)
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to_insert = [
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{self._text_key: t, self._embedding_key: embedding, **m}
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for t, m, embedding in zip(texts, metadatas, embeddings)
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]
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# insert the documents in Cosmos DB
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insert_result = self._collection.insert_many(to_insert) # type: ignore
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return insert_result.inserted_ids
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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collection: Optional[Collection[CosmosDBDocumentType]] = None,
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**kwargs: Any,
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) -> AzureCosmosDBVectorSearch:
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if collection is None:
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raise ValueError("Must provide 'collection' named parameter.")
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vectorstore = cls(collection, embedding, **kwargs)
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vectorstore.add_texts(texts, metadatas=metadatas)
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return vectorstore
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def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
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if ids is None:
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raise ValueError("No document ids provided to delete.")
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for document_id in ids:
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self.delete_document_by_id(document_id)
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return True
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def delete_document_by_id(self, document_id: Optional[str] = None) -> None:
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"""Removes a Specific Document by Id
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Args:
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document_id: The document identifier
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"""
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try:
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from bson.objectid import ObjectId
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except ImportError as e:
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raise ImportError(
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"Unable to import bson, please install with `pip install bson`."
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) from e
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if document_id is None:
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raise ValueError("No document id provided to delete.")
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self._collection.delete_one({"_id": ObjectId(document_id)})
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def _similarity_search_with_score(
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self, embeddings: List[float], k: int = 4
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) -> List[Tuple[Document, float]]:
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"""Returns a list of documents with their scores
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Args:
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embeddings: The query vector
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k: the number of documents to return
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Returns:
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A list of documents closest to the query vector
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"""
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pipeline: List[dict[str, Any]] = [
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{
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"$search": {
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"cosmosSearch": {
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"vector": embeddings,
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"path": self._embedding_key,
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"k": k,
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},
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"returnStoredSource": True,
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}
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},
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{
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"$project": {
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"similarityScore": {"$meta": "searchScore"},
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"document": "$$ROOT",
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}
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},
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]
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cursor = self._collection.aggregate(pipeline)
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docs = []
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for res in cursor:
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score = res.pop("similarityScore")
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document_object_field = res.pop("document")
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text = document_object_field.pop(self._text_key)
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docs.append(
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(Document(page_content=text, metadata=document_object_field), score)
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)
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return docs
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def similarity_search_with_score(
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self, query: str, k: int = 4
|
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) -> List[Tuple[Document, float]]:
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embeddings = self._embedding.embed_query(query)
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docs = self._similarity_search_with_score(embeddings=embeddings, k=k)
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return docs
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|
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def similarity_search(
|
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self, query: str, k: int = 4, **kwargs: Any
|
||||
) -> List[Document]:
|
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docs_and_scores = self.similarity_search_with_score(query, k=k)
|
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return [doc for doc, _ in docs_and_scores]
|
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|
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def max_marginal_relevance_search_by_vector(
|
||||
self,
|
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embedding: List[float],
|
||||
k: int = 4,
|
||||
fetch_k: int = 20,
|
||||
lambda_mult: float = 0.5,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
# Retrieves the docs with similarity scores
|
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# sorted by similarity scores in DESC order
|
||||
docs = self._similarity_search_with_score(embedding, k=fetch_k)
|
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|
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# Re-ranks the docs using MMR
|
||||
mmr_doc_indexes = maximal_marginal_relevance(
|
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np.array(embedding),
|
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[doc.metadata[self._embedding_key] for doc, _ in docs],
|
||||
k=k,
|
||||
lambda_mult=lambda_mult,
|
||||
)
|
||||
mmr_docs = [docs[i][0] for i in mmr_doc_indexes]
|
||||
return mmr_docs
|
||||
|
||||
def max_marginal_relevance_search(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
fetch_k: int = 20,
|
||||
lambda_mult: float = 0.5,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
# compute the embeddings vector from the query string
|
||||
embeddings = self._embedding.embed_query(query)
|
||||
|
||||
docs = self.max_marginal_relevance_search_by_vector(
|
||||
embeddings, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult
|
||||
)
|
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return docs
|
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@@ -0,0 +1,435 @@
|
||||
"""Test AzureCosmosDBVectorSearch functionality."""
|
||||
import logging
|
||||
import os
|
||||
from time import sleep
|
||||
from typing import Any, Generator, Optional, Union
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.embeddings import OpenAIEmbeddings
|
||||
from langchain.vectorstores.azure_cosmos_db import (
|
||||
AzureCosmosDBVectorSearch,
|
||||
CosmosDBSimilarityType,
|
||||
)
|
||||
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
|
||||
model_deployment = os.getenv(
|
||||
"OPENAI_EMBEDDINGS_DEPLOYMENT", "smart-agent-embedding-ada"
|
||||
)
|
||||
model_name = os.getenv("OPENAI_EMBEDDINGS_MODEL_NAME", "text-embedding-ada-002")
|
||||
|
||||
INDEX_NAME = "langchain-test-index"
|
||||
NAMESPACE = "langchain_test_db.langchain_test_collection"
|
||||
CONNECTION_STRING: str = os.environ.get("MONGODB_VCORE_URI", "")
|
||||
DB_NAME, COLLECTION_NAME = NAMESPACE.split(".")
|
||||
|
||||
num_lists = 3
|
||||
dimensions = 1536
|
||||
similarity_algorithm = CosmosDBSimilarityType.COS
|
||||
|
||||
|
||||
def prepare_collection() -> Any:
|
||||
from pymongo import MongoClient
|
||||
|
||||
test_client: MongoClient = MongoClient(CONNECTION_STRING)
|
||||
return test_client[DB_NAME][COLLECTION_NAME]
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def collection() -> Any:
|
||||
return prepare_collection()
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def azure_openai_embeddings() -> Any:
|
||||
openai_embeddings: OpenAIEmbeddings = OpenAIEmbeddings(
|
||||
deployment=model_deployment, model=model_name, chunk_size=1
|
||||
)
|
||||
return openai_embeddings
|
||||
|
||||
|
||||
"""
|
||||
This is how to run the integration tests:
|
||||
|
||||
cd libs/langchain
|
||||
pytest tests/integration_tests/vectorstores/test_azure_cosmos_db.py
|
||||
"""
|
||||
|
||||
|
||||
class TestAzureCosmosDBVectorSearch:
|
||||
@classmethod
|
||||
def setup_class(cls) -> None:
|
||||
if not os.getenv("OPENAI_API_KEY"):
|
||||
raise ValueError("OPENAI_API_KEY environment variable is not set")
|
||||
|
||||
# insure the test collection is empty
|
||||
collection = prepare_collection()
|
||||
assert collection.count_documents({}) == 0 # type: ignore[index] # noqa: E501
|
||||
|
||||
@classmethod
|
||||
def teardown_class(cls) -> None:
|
||||
collection = prepare_collection()
|
||||
# delete all the documents in the collection
|
||||
collection.delete_many({}) # type: ignore[index]
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def setup(self) -> None:
|
||||
collection = prepare_collection()
|
||||
# delete all the documents in the collection
|
||||
collection.delete_many({}) # type: ignore[index]
|
||||
|
||||
@pytest.fixture(scope="class", autouse=True)
|
||||
def cosmos_db_url(self) -> Union[str, Generator[str, None, None]]:
|
||||
"""Return the elasticsearch url."""
|
||||
return "805.555.1212"
|
||||
|
||||
def test_from_documents_cosine_distance(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
documents = [
|
||||
Document(page_content="Dogs are tough.", metadata={"a": 1}),
|
||||
Document(page_content="Cats have fluff.", metadata={"b": 1}),
|
||||
Document(page_content="What is a sandwich?", metadata={"c": 1}),
|
||||
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
|
||||
]
|
||||
|
||||
vectorstore = AzureCosmosDBVectorSearch.from_documents(
|
||||
documents,
|
||||
azure_openai_embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
sleep(1) # waits for Cosmos DB to save contents to the collection
|
||||
|
||||
# Create the IVF index that will be leveraged later for vector search
|
||||
vectorstore.create_index(num_lists, dimensions, similarity_algorithm)
|
||||
sleep(2) # waits for the index to be set up
|
||||
|
||||
output = vectorstore.similarity_search("Sandwich", k=1)
|
||||
|
||||
assert output
|
||||
assert output[0].page_content == "What is a sandwich?"
|
||||
assert output[0].metadata["c"] == 1
|
||||
vectorstore.delete_index()
|
||||
|
||||
def test_from_documents_inner_product(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
documents = [
|
||||
Document(page_content="Dogs are tough.", metadata={"a": 1}),
|
||||
Document(page_content="Cats have fluff.", metadata={"b": 1}),
|
||||
Document(page_content="What is a sandwich?", metadata={"c": 1}),
|
||||
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
|
||||
]
|
||||
|
||||
vectorstore = AzureCosmosDBVectorSearch.from_documents(
|
||||
documents,
|
||||
azure_openai_embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
sleep(1) # waits for Cosmos DB to save contents to the collection
|
||||
|
||||
# Create the IVF index that will be leveraged later for vector search
|
||||
vectorstore.create_index(num_lists, dimensions, CosmosDBSimilarityType.IP)
|
||||
sleep(2) # waits for the index to be set up
|
||||
|
||||
output = vectorstore.similarity_search("Sandwich", k=1)
|
||||
|
||||
assert output
|
||||
assert output[0].page_content == "What is a sandwich?"
|
||||
assert output[0].metadata["c"] == 1
|
||||
vectorstore.delete_index()
|
||||
|
||||
def test_from_texts_cosine_distance(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
texts = [
|
||||
"Dogs are tough.",
|
||||
"Cats have fluff.",
|
||||
"What is a sandwich?",
|
||||
"That fence is purple.",
|
||||
]
|
||||
vectorstore = AzureCosmosDBVectorSearch.from_texts(
|
||||
texts,
|
||||
azure_openai_embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
|
||||
# Create the IVF index that will be leveraged later for vector search
|
||||
vectorstore.create_index(num_lists, dimensions, similarity_algorithm)
|
||||
sleep(2) # waits for the index to be set up
|
||||
|
||||
output = vectorstore.similarity_search("Sandwich", k=1)
|
||||
|
||||
assert output[0].page_content == "What is a sandwich?"
|
||||
vectorstore.delete_index()
|
||||
|
||||
def test_from_texts_with_metadatas_cosine_distance(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
texts = [
|
||||
"Dogs are tough.",
|
||||
"Cats have fluff.",
|
||||
"What is a sandwich?",
|
||||
"The fence is purple.",
|
||||
]
|
||||
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
|
||||
vectorstore = AzureCosmosDBVectorSearch.from_texts(
|
||||
texts,
|
||||
azure_openai_embeddings,
|
||||
metadatas=metadatas,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
|
||||
# Create the IVF index that will be leveraged later for vector search
|
||||
vectorstore.create_index(num_lists, dimensions, similarity_algorithm)
|
||||
sleep(2) # waits for the index to be set up
|
||||
|
||||
output = vectorstore.similarity_search("Sandwich", k=1)
|
||||
|
||||
assert output
|
||||
assert output[0].page_content == "What is a sandwich?"
|
||||
assert output[0].metadata["c"] == 1
|
||||
|
||||
vectorstore.delete_index()
|
||||
|
||||
def test_from_texts_with_metadatas_delete_one(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
texts = [
|
||||
"Dogs are tough.",
|
||||
"Cats have fluff.",
|
||||
"What is a sandwich?",
|
||||
"The fence is purple.",
|
||||
]
|
||||
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
|
||||
vectorstore = AzureCosmosDBVectorSearch.from_texts(
|
||||
texts,
|
||||
azure_openai_embeddings,
|
||||
metadatas=metadatas,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
|
||||
# Create the IVF index that will be leveraged later for vector search
|
||||
vectorstore.create_index(num_lists, dimensions, similarity_algorithm)
|
||||
sleep(2) # waits for the index to be set up
|
||||
|
||||
output = vectorstore.similarity_search("Sandwich", k=1)
|
||||
|
||||
assert output
|
||||
assert output[0].page_content == "What is a sandwich?"
|
||||
assert output[0].metadata["c"] == 1
|
||||
|
||||
first_document_id_object = output[0].metadata["_id"]
|
||||
first_document_id = str(first_document_id_object)
|
||||
|
||||
vectorstore.delete_document_by_id(first_document_id)
|
||||
sleep(2) # waits for the index to be updated
|
||||
|
||||
output2 = vectorstore.similarity_search("Sandwich", k=1)
|
||||
assert output2
|
||||
assert output2[0].page_content != "What is a sandwich?"
|
||||
|
||||
vectorstore.delete_index()
|
||||
|
||||
def test_from_texts_with_metadatas_delete_multiple(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
texts = [
|
||||
"Dogs are tough.",
|
||||
"Cats have fluff.",
|
||||
"What is a sandwich?",
|
||||
"The fence is purple.",
|
||||
]
|
||||
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
|
||||
vectorstore = AzureCosmosDBVectorSearch.from_texts(
|
||||
texts,
|
||||
azure_openai_embeddings,
|
||||
metadatas=metadatas,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
|
||||
# Create the IVF index that will be leveraged later for vector search
|
||||
vectorstore.create_index(num_lists, dimensions, similarity_algorithm)
|
||||
sleep(2) # waits for the index to be set up
|
||||
|
||||
output = vectorstore.similarity_search("Sandwich", k=5)
|
||||
|
||||
first_document_id_object = output[0].metadata["_id"]
|
||||
first_document_id = str(first_document_id_object)
|
||||
|
||||
output[1].metadata["_id"]
|
||||
second_document_id = output[1].metadata["_id"]
|
||||
|
||||
output[2].metadata["_id"]
|
||||
third_document_id = output[2].metadata["_id"]
|
||||
|
||||
document_ids = [first_document_id, second_document_id, third_document_id]
|
||||
vectorstore.delete(document_ids)
|
||||
sleep(2) # waits for the index to be updated
|
||||
|
||||
output_2 = vectorstore.similarity_search("Sandwich", k=5)
|
||||
assert output
|
||||
assert output_2
|
||||
|
||||
assert len(output) == 4 # we should see all the four documents
|
||||
assert (
|
||||
len(output_2) == 1
|
||||
) # we should see only one document left after three have been deleted
|
||||
|
||||
vectorstore.delete_index()
|
||||
|
||||
def test_from_texts_with_metadatas_inner_product(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
texts = [
|
||||
"Dogs are tough.",
|
||||
"Cats have fluff.",
|
||||
"What is a sandwich?",
|
||||
"The fence is purple.",
|
||||
]
|
||||
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
|
||||
vectorstore = AzureCosmosDBVectorSearch.from_texts(
|
||||
texts,
|
||||
azure_openai_embeddings,
|
||||
metadatas=metadatas,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
|
||||
# Create the IVF index that will be leveraged later for vector search
|
||||
vectorstore.create_index(num_lists, dimensions, CosmosDBSimilarityType.IP)
|
||||
sleep(2) # waits for the index to be set up
|
||||
|
||||
output = vectorstore.similarity_search("Sandwich", k=1)
|
||||
|
||||
assert output
|
||||
assert output[0].page_content == "What is a sandwich?"
|
||||
assert output[0].metadata["c"] == 1
|
||||
vectorstore.delete_index()
|
||||
|
||||
def test_from_texts_with_metadatas_euclidean_distance(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
texts = [
|
||||
"Dogs are tough.",
|
||||
"Cats have fluff.",
|
||||
"What is a sandwich?",
|
||||
"The fence is purple.",
|
||||
]
|
||||
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
|
||||
vectorstore = AzureCosmosDBVectorSearch.from_texts(
|
||||
texts,
|
||||
azure_openai_embeddings,
|
||||
metadatas=metadatas,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
|
||||
# Create the IVF index that will be leveraged later for vector search
|
||||
vectorstore.create_index(num_lists, dimensions, CosmosDBSimilarityType.L2)
|
||||
sleep(2) # waits for the index to be set up
|
||||
|
||||
output = vectorstore.similarity_search("Sandwich", k=1)
|
||||
|
||||
assert output
|
||||
assert output[0].page_content == "What is a sandwich?"
|
||||
assert output[0].metadata["c"] == 1
|
||||
vectorstore.delete_index()
|
||||
|
||||
def test_max_marginal_relevance_cosine_distance(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
texts = ["foo", "foo", "fou", "foy"]
|
||||
vectorstore = AzureCosmosDBVectorSearch.from_texts(
|
||||
texts,
|
||||
azure_openai_embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
|
||||
# Create the IVF index that will be leveraged later for vector search
|
||||
vectorstore.create_index(num_lists, dimensions, CosmosDBSimilarityType.COS)
|
||||
sleep(2) # waits for the index to be set up
|
||||
|
||||
query = "foo"
|
||||
output = vectorstore.max_marginal_relevance_search(query, k=10, lambda_mult=0.1)
|
||||
|
||||
assert len(output) == len(texts)
|
||||
assert output[0].page_content == "foo"
|
||||
assert output[1].page_content != "foo"
|
||||
vectorstore.delete_index()
|
||||
|
||||
def test_max_marginal_relevance_inner_product(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
texts = ["foo", "foo", "fou", "foy"]
|
||||
vectorstore = AzureCosmosDBVectorSearch.from_texts(
|
||||
texts,
|
||||
azure_openai_embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
|
||||
# Create the IVF index that will be leveraged later for vector search
|
||||
vectorstore.create_index(num_lists, dimensions, CosmosDBSimilarityType.IP)
|
||||
sleep(2) # waits for the index to be set up
|
||||
|
||||
query = "foo"
|
||||
output = vectorstore.max_marginal_relevance_search(query, k=10, lambda_mult=0.1)
|
||||
|
||||
assert len(output) == len(texts)
|
||||
assert output[0].page_content == "foo"
|
||||
assert output[1].page_content != "foo"
|
||||
vectorstore.delete_index()
|
||||
|
||||
def invoke_delete_with_no_args(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> Optional[bool]:
|
||||
vectorstore: AzureCosmosDBVectorSearch = (
|
||||
AzureCosmosDBVectorSearch.from_connection_string(
|
||||
CONNECTION_STRING,
|
||||
NAMESPACE,
|
||||
azure_openai_embeddings,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
)
|
||||
|
||||
return vectorstore.delete()
|
||||
|
||||
def invoke_delete_by_id_with_no_args(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
vectorstore: AzureCosmosDBVectorSearch = (
|
||||
AzureCosmosDBVectorSearch.from_connection_string(
|
||||
CONNECTION_STRING,
|
||||
NAMESPACE,
|
||||
azure_openai_embeddings,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
)
|
||||
|
||||
vectorstore.delete_document_by_id()
|
||||
|
||||
def test_invalid_arguments_to_delete(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
with pytest.raises(ValueError) as exception_info:
|
||||
self.invoke_delete_with_no_args(azure_openai_embeddings, collection)
|
||||
assert str(exception_info.value) == "No document ids provided to delete."
|
||||
|
||||
def test_no_arguments_to_delete_by_id(
|
||||
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
|
||||
) -> None:
|
||||
with pytest.raises(Exception) as exception_info:
|
||||
self.invoke_delete_by_id_with_no_args(azure_openai_embeddings, collection)
|
||||
assert str(exception_info.value) == "No document id provided to delete."
|
||||
Reference in New Issue
Block a user