from __future__ import annotations
import logging
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Iterable,
List,
Optional,
Tuple,
Union,
)
import numpy as np
from langchain_core.documents import Document
from langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
from langchain_core.embeddings import Embeddings
from pymongo.collection import Collection
# Before Python 3.11 native StrEnum is not available
[docs]class CosmosDBSimilarityType(str, Enum):
"""Cosmos DB Similarity Type as enumerator."""
COS = "COS"
"""CosineSimilarity"""
IP = "IP"
"""inner - product"""
L2 = "L2"
"""Euclidean distance"""
[docs]class CosmosDBVectorSearchType(str, Enum):
"""Cosmos DB Vector Search Type as enumerator."""
VECTOR_IVF = "vector-ivf"
"""IVF vector index"""
VECTOR_HNSW = "vector-hnsw"
"""HNSW vector index"""
logger = logging.getLogger(__name__)
DEFAULT_INSERT_BATCH_SIZE = 128
[docs]class AzureCosmosDBVectorSearch(VectorStore):
"""`Azure Cosmos DB for MongoDB vCore` vector store.
To use, you should have both:
- the ``pymongo`` python package installed
- a connection string associated with a MongoDB VCore Cluster
Example:
. code-block:: python
from langchain_community.vectorstores import
AzureCosmosDBVectorSearch
from langchain_community.embeddings.openai import OpenAIEmbeddings
from pymongo import MongoClient
mongo_client = MongoClient("<YOUR-CONNECTION-STRING>")
collection = mongo_client["<db_name>"]["<collection_name>"]
embeddings = OpenAIEmbeddings()
vectorstore = AzureCosmosDBVectorSearch(collection, embeddings)
"""
[docs] def __init__(
self,
collection: Collection,
embedding: Embeddings,
*,
index_name: str = "vectorSearchIndex",
text_key: str = "textContent",
embedding_key: str = "vectorContent",
application_name: str = "LANGCHAIN_PYTHON",
):
"""Constructor for AzureCosmosDBVectorSearch
Args:
collection: MongoDB collection to add the texts to.
embedding: Text embedding model to use.
index_name: Name of the Atlas Search index.
text_key: MongoDB field that will contain the text
for each document.
embedding_key: MongoDB field that will contain the embedding
for each document.
"""
self._collection = collection
self._embedding = embedding
self._index_name = index_name
self._text_key = text_key
self._embedding_key = embedding_key
self._application_name = application_name
@property
def embeddings(self) -> Embeddings:
return self._embedding
[docs] def get_index_name(self) -> str:
"""Returns the index name
Returns:
Returns the index name
"""
return self._index_name
[docs] @classmethod
def from_connection_string(
cls,
connection_string: str,
namespace: str,
embedding: Embeddings,
application_name: str = "LANGCHAIN_PYTHON",
**kwargs: Any,
) -> AzureCosmosDBVectorSearch:
"""Creates an Instance of AzureCosmosDBVectorSearch
from a Connection String
Args:
connection_string: The MongoDB vCore instance connection string
namespace: The namespace (database.collection)
embedding: The embedding utility
**kwargs: Dynamic keyword arguments
Returns:
an instance of the vector store
"""
try:
from pymongo import MongoClient
except ImportError:
raise ImportError(
"Could not import pymongo, please install it with "
"`pip install pymongo`."
)
appname = application_name
client: MongoClient = MongoClient(connection_string, appname=appname)
db_name, collection_name = namespace.split(".")
collection = client[db_name][collection_name]
return cls(collection, embedding, **kwargs)
[docs] def index_exists(self) -> bool:
"""Verifies if the specified index name during instance
construction exists on the collection
Returns:
Returns True on success and False if no such index exists
on the collection
"""
cursor = self._collection.list_indexes()
index_name = self._index_name
for res in cursor:
current_index_name = res.pop("name")
if current_index_name == index_name:
return True
return False
[docs] def delete_index(self) -> None:
"""Deletes the index specified during instance construction if it exists"""
if self.index_exists():
self._collection.drop_index(self._index_name)
# Raises OperationFailure on an error (e.g. trying to drop
# an index that does not exist)
[docs] def create_index(
self,
num_lists: int = 100,
dimensions: int = 1536,
similarity: CosmosDBSimilarityType = CosmosDBSimilarityType.COS,
kind: str = "vector-ivf",
m: int = 16,
ef_construction: int = 64,
) -> dict[str, Any]:
"""Creates an index using the index name specified at
instance construction
Setting the numLists parameter correctly is important for achieving
good accuracy and performance.
Since the vector store uses IVF as the indexing strategy,
you should create the index only after you
have loaded a large enough sample documents to ensure that the
centroids for the respective buckets are
faily distributed.
We recommend that numLists is set to documentCount/1000 for up
to 1 million documents
and to sqrt(documentCount) for more than 1 million documents.
As the number of items in your database grows, you should
tune numLists to be larger
in order to achieve good latency performance for vector search.
If you're experimenting with a new scenario or creating a
small demo, you can start with numLists
set to 1 to perform a brute-force search across all vectors.
This should provide you with the most
accurate results from the vector search, however be aware that
the search speed and latency will be slow.
After your initial setup, you should go ahead and tune
the numLists parameter using the above guidance.
Args:
kind: Type of vector index to create.
Possible options are:
- vector-ivf
- vector-hnsw: available as a preview feature only,
to enable visit https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/preview-features
num_lists: This integer is the number of clusters that the
inverted file (IVF) index uses to group the vector data.
We recommend that numLists is set to documentCount/1000
for up to 1 million documents and to sqrt(documentCount)
for more than 1 million documents.
Using a numLists value of 1 is akin to performing
brute-force search, which has limited performance
dimensions: Number of dimensions for vector similarity.
The maximum number of supported dimensions is 2000
similarity: Similarity metric to use with the IVF index.
Possible options are:
- CosmosDBSimilarityType.COS (cosine distance),
- CosmosDBSimilarityType.L2 (Euclidean distance), and
- CosmosDBSimilarityType.IP (inner product).
m: The max number of connections per layer (16 by default, minimum
value is 2, maximum value is 100). Higher m is suitable for datasets
with high dimensionality and/or high accuracy requirements.
ef_construction: the size of the dynamic candidate list for constructing
the graph (64 by default, minimum value is 4, maximum
value is 1000). Higher ef_construction will result in
better index quality and higher accuracy, but it will
also increase the time required to build the index.
ef_construction has to be at least 2 * m
Returns:
An object describing the created index
"""
# check the kind of vector search to be performed
# prepare the command accordingly
create_index_commands = {}
if kind == CosmosDBVectorSearchType.VECTOR_IVF:
create_index_commands = self._get_vector_index_ivf(
kind, num_lists, similarity, dimensions
)
elif kind == CosmosDBVectorSearchType.VECTOR_HNSW:
create_index_commands = self._get_vector_index_hnsw(
kind, m, ef_construction, similarity, dimensions
)
# retrieve the database object
current_database = self._collection.database
# invoke the command from the database object
create_index_responses: dict[str, Any] = current_database.command(
create_index_commands
)
return create_index_responses
def _get_vector_index_ivf(
self, kind: str, num_lists: int, similarity: str, dimensions: int
) -> Dict[str, Any]:
command = {
"createIndexes": self._collection.name,
"indexes": [
{
"name": self._index_name,
"key": {self._embedding_key: "cosmosSearch"},
"cosmosSearchOptions": {
"kind": kind,
"numLists": num_lists,
"similarity": similarity,
"dimensions": dimensions,
},
}
],
}
return command
def _get_vector_index_hnsw(
self, kind: str, m: int, ef_construction: int, similarity: str, dimensions: int
) -> Dict[str, Any]:
command = {
"createIndexes": self._collection.name,
"indexes": [
{
"name": self._index_name,
"key": {self._embedding_key: "cosmosSearch"},
"cosmosSearchOptions": {
"kind": kind,
"m": m,
"efConstruction": ef_construction,
"similarity": similarity,
"dimensions": dimensions,
},
}
],
}
return command
[docs] def create_filter_index(
self,
property_to_filter: str,
index_name: str,
) -> dict[str, Any]:
command = {
"createIndexes": self._collection.name,
"indexes": [
{
"key": {property_to_filter: 1},
"name": index_name,
}
],
}
# retrieve the database object
current_database = self._collection.database
# invoke the command from the database object
create_index_responses: dict[str, Any] = current_database.command(command)
return create_index_responses
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[str, Any]]] = None,
**kwargs: Any,
) -> List:
batch_size = kwargs.get("batch_size", DEFAULT_INSERT_BATCH_SIZE)
_metadatas: Union[List, Generator] = metadatas or ({} for _ in texts)
texts_batch = []
metadatas_batch = []
result_ids = []
for i, (text, metadata) in enumerate(zip(texts, _metadatas)):
texts_batch.append(text)
metadatas_batch.append(metadata)
if (i + 1) % batch_size == 0:
result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
texts_batch = []
metadatas_batch = []
if texts_batch:
result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
return result_ids
def _insert_texts(self, texts: List[str], metadatas: List[Dict[str, Any]]) -> List:
"""Used to Load Documents into the collection
Args:
texts: The list of documents strings to load
metadatas: The list of metadata objects associated with each document
Returns:
"""
# If the text is empty, then exit early
if not texts:
return []
# Embed and create the documents
embeddings = self._embedding.embed_documents(texts)
to_insert = [
{self._text_key: t, self._embedding_key: embedding, "metadata": m}
for t, m, embedding in zip(texts, metadatas, embeddings)
]
# insert the documents in Cosmos DB
insert_result = self._collection.insert_many(to_insert) # type: ignore
return insert_result.inserted_ids
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection: Optional[Collection] = None,
**kwargs: Any,
) -> AzureCosmosDBVectorSearch:
if collection is None:
raise ValueError("Must provide 'collection' named parameter.")
vectorstore = cls(collection, embedding, **kwargs)
vectorstore.add_texts(texts, metadatas=metadatas)
return vectorstore
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
if ids is None:
raise ValueError("No document ids provided to delete.")
for document_id in ids:
self.delete_document_by_id(document_id)
return True
[docs] def delete_document_by_id(self, document_id: Optional[str] = None) -> None:
"""Removes a Specific Document by Id
Args:
document_id: The document identifier
"""
try:
from bson.objectid import ObjectId
except ImportError as e:
raise ImportError(
"Unable to import bson, please install with `pip install bson`."
) from e
if document_id is None:
raise ValueError("No document id provided to delete.")
self._collection.delete_one({"_id": ObjectId(document_id)})
def _similarity_search_with_score(
self,
embeddings: List[float],
k: int = 4,
kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF,
pre_filter: Optional[Dict] = None,
ef_search: int = 40,
score_threshold: float = 0.0,
with_embedding: bool = False,
) -> List[Tuple[Document, float]]:
"""Returns a list of documents with their scores
Args:
embeddings: The query vector
k: the number of documents to return
kind: Type of vector index to create.
Possible options are:
- vector-ivf
- vector-hnsw: available as a preview feature only,
to enable visit https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/preview-features
ef_search: The size of the dynamic candidate list for search
(40 by default). A higher value provides better
recall at the cost of speed.
score_threshold: (Optional[float], optional): Maximum vector distance
between selected documents and the query vector. Defaults to None.
Only vector-ivf search supports this for now.
Returns:
A list of documents closest to the query vector
"""
pipeline: List[dict[str, Any]] = []
if kind == CosmosDBVectorSearchType.VECTOR_IVF:
pipeline = self._get_pipeline_vector_ivf(embeddings, k, pre_filter)
elif kind == CosmosDBVectorSearchType.VECTOR_HNSW:
pipeline = self._get_pipeline_vector_hnsw(
embeddings, k, ef_search, pre_filter
)
cursor = self._collection.aggregate(pipeline)
docs = []
for res in cursor:
score = res.pop("similarityScore")
if score < score_threshold:
continue
document_object_field = res.pop("document")
text = document_object_field.pop(self._text_key)
metadata = document_object_field.pop("metadata")
if with_embedding:
metadata[self._embedding_key] = document_object_field.pop(
self._embedding_key
)
docs.append((Document(page_content=text, metadata=metadata), score))
return docs
def _get_pipeline_vector_ivf(
self, embeddings: List[float], k: int = 4, pre_filter: Optional[Dict] = None
) -> List[dict[str, Any]]:
params = {
"vector": embeddings,
"path": self._embedding_key,
"k": k,
}
if pre_filter:
params["filter"] = pre_filter
pipeline: List[dict[str, Any]] = [
{
"$search": {
"cosmosSearch": params,
"returnStoredSource": True,
}
},
{
"$project": {
"similarityScore": {"$meta": "searchScore"},
"document": "$$ROOT",
}
},
]
return pipeline
def _get_pipeline_vector_hnsw(
self,
embeddings: List[float],
k: int = 4,
ef_search: int = 40,
pre_filter: Optional[Dict] = None,
) -> List[dict[str, Any]]:
params = {
"vector": embeddings,
"path": self._embedding_key,
"k": k,
"efSearch": ef_search,
}
if pre_filter:
params["filter"] = pre_filter
pipeline: List[dict[str, Any]] = [
{
"$search": {
"cosmosSearch": params,
}
},
{
"$project": {
"similarityScore": {"$meta": "searchScore"},
"document": "$$ROOT",
}
},
]
return pipeline
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF,
pre_filter: Optional[Dict] = None,
ef_search: int = 40,
score_threshold: float = 0.0,
with_embedding: bool = False,
) -> List[Tuple[Document, float]]:
embeddings = self._embedding.embed_query(query)
docs = self._similarity_search_with_score(
embeddings=embeddings,
k=k,
kind=kind,
pre_filter=pre_filter,
ef_search=ef_search,
score_threshold=score_threshold,
with_embedding=with_embedding,
)
return docs
[docs] def similarity_search(
self,
query: str,
k: int = 4,
kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF,
pre_filter: Optional[Dict] = None,
ef_search: int = 40,
score_threshold: float = 0.0,
with_embedding: bool = False,
**kwargs: Any,
) -> List[Document]:
docs_and_scores = self.similarity_search_with_score(
query,
k=k,
kind=kind,
pre_filter=pre_filter,
ef_search=ef_search,
score_threshold=score_threshold,
with_embedding=with_embedding,
)
return [doc for doc, _ in docs_and_scores]
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF,
pre_filter: Optional[Dict] = None,
ef_search: int = 40,
score_threshold: float = 0.0,
with_embedding: bool = False,
**kwargs: Any,
) -> List[Document]:
# Retrieves the docs with similarity scores
# sorted by similarity scores in DESC order
docs = self._similarity_search_with_score(
embedding,
k=fetch_k,
kind=kind,
pre_filter=pre_filter,
ef_search=ef_search,
score_threshold=score_threshold,
with_embedding=with_embedding,
)
# Re-ranks the docs using MMR
mmr_doc_indexes = maximal_marginal_relevance(
np.array(embedding),
[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
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF,
pre_filter: Optional[Dict] = None,
ef_search: int = 40,
score_threshold: float = 0.0,
with_embedding: bool = False,
**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,
kind=kind,
pre_filter=pre_filter,
ef_search=ef_search,
score_threshold=score_threshold,
with_embedding=with_embedding,
)
return docs
[docs] def get_collection(self) -> Collection:
return self._collection
