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379 | class AzureCosmosDBMongoDBVectorSearch(BasePydanticVectorStore):
"""Azure CosmosDB MongoDB vCore Vector Store.
To use, you should have both:
- the ``pymongo`` python package installed
- a connection string associated with an Azure Cosmodb MongoDB vCore Cluster
Examples:
`pip install llama-index-vector-stores-azurecosmosmongo`
```python
import pymongo
from llama_index.vector_stores.azurecosmosmongo import AzureCosmosDBMongoDBVectorSearch
# Set up the connection string with your Azure CosmosDB MongoDB URI
connection_string = "YOUR_AZURE_COSMOSDB_MONGODB_URI"
mongodb_client = pymongo.MongoClient(connection_string)
# Create an instance of AzureCosmosDBMongoDBVectorSearch
vector_store = AzureCosmosDBMongoDBVectorSearch(
mongodb_client=mongodb_client,
db_name="demo_vectordb",
collection_name="paul_graham_essay",
)
```
"""
stores_text: bool = True
flat_metadata: bool = True
_collection: Any = PrivateAttr()
_index_name: str = PrivateAttr()
_embedding_key: str = PrivateAttr()
_id_key: str = PrivateAttr()
_text_key: str = PrivateAttr()
_metadata_key: str = PrivateAttr()
_insert_kwargs: dict = PrivateAttr()
_db_name: str = PrivateAttr()
_collection_name: str = PrivateAttr()
_cosmos_search_kwargs: dict = PrivateAttr()
_mongodb_client: Any = PrivateAttr()
def __init__(
self,
mongodb_client: Optional[Any] = None,
db_name: str = "default_db",
collection_name: str = "default_collection",
index_name: str = "default_vector_search_index",
id_key: str = "id",
embedding_key: str = "content_vector",
text_key: str = "text",
metadata_key: str = "metadata",
cosmos_search_kwargs: Optional[Dict] = None,
insert_kwargs: Optional[Dict] = None,
**kwargs: Any,
) -> None:
"""Initialize the vector store.
Args:
mongodb_client: An Azure CosmoDB MongoDB client (type: MongoClient, shown any for lazy import).
db_name: An Azure CosmosDB MongoDB database name.
collection_name: An Azure CosmosDB collection name.
index_name: An Azure CosmosDB MongoDB vCore Vector Search index name.
id_key: The data field to use as the id.
embedding_key: An Azure CosmosDB MongoDB field that will contain
the embedding for each document.
text_key: An Azure CosmosDB MongoDB field that will contain the text for each document.
metadata_key: An Azure CosmosDB MongoDB field that will contain
the metadata for each document.
cosmos_search_kwargs: An Azure CosmosDB MongoDB field that will
contain search options, such as kind, numLists, similarity, and dimensions.
insert_kwargs: The kwargs used during `insert`.
"""
super().__init__()
if mongodb_client is not None:
self._mongodb_client = cast(pymongo.MongoClient, mongodb_client)
else:
if "AZURE_COSMOSDB_MONGODB_URI" not in os.environ:
raise ValueError(
"Must specify Azure cosmodb 'AZURE_COSMOSDB_MONGODB_URI' via env variable "
"if not directly passing in client."
)
self._mongodb_client = pymongo.MongoClient(
os.environ["AZURE_COSMOSDB_MONGODB_URI"],
appname="LlamaIndex-CDBMongoVCore-VectorStore-Python",
)
self._collection = self._mongodb_client[db_name][collection_name]
self._index_name = index_name
self._embedding_key = embedding_key
self._id_key = id_key
self._text_key = text_key
self._metadata_key = metadata_key
self._insert_kwargs = insert_kwargs or {}
self._db_name = db_name
self._collection_name = collection_name
self._cosmos_search_kwargs = cosmos_search_kwargs or {}
self._create_vector_search_index()
def _create_vector_search_index(self) -> None:
db = self._mongodb_client[self._db_name]
create_index_commands = {}
kind = self._cosmos_search_kwargs.get("kind", "vector-hnsw")
if kind == "vector-ivf":
create_index_commands = self._get_vector_index_ivf(kind)
elif kind == "vector-hnsw":
create_index_commands = self._get_vector_index_hnsw(kind)
db.command(create_index_commands)
def _get_vector_index_ivf(
self,
kind: str,
) -> Dict[str, Any]:
return {
"createIndexes": self._collection_name,
"indexes": [
{
"name": self._index_name,
"key": {self._embedding_key: "cosmosSearch"},
"cosmosSearchOptions": {
"kind": kind,
"numLists": self._cosmos_search_kwargs.get("numLists", 1),
"similarity": self._cosmos_search_kwargs.get(
"similarity", "COS"
),
"dimensions": self._cosmos_search_kwargs.get(
"dimensions", 1536
),
},
}
],
}
def _get_vector_index_hnsw(
self,
kind: str,
) -> Dict[str, Any]:
return {
"createIndexes": self._collection_name,
"indexes": [
{
"name": self._index_name,
"key": {self._embedding_key: "cosmosSearch"},
"cosmosSearchOptions": {
"kind": kind,
"m": self._cosmos_search_kwargs.get("m", 2),
"efConstruction": self._cosmos_search_kwargs.get(
"efConstruction", 64
),
"similarity": self._cosmos_search_kwargs.get(
"similarity", "COS"
),
"dimensions": self._cosmos_search_kwargs.get(
"dimensions", 1536
),
},
}
],
}
def create_filter_index(
self,
property_to_filter: str,
index_name: str,
) -> dict[str, Any]:
db = self._mongodb_client[self._db_name]
command = {
"createIndexes": self._collection.name,
"indexes": [
{
"key": {property_to_filter: 1},
"name": index_name,
}
],
}
create_index_responses: dict[str, Any] = db.command(command)
return create_index_responses
def add(
self,
nodes: List[BaseNode],
**add_kwargs: Any,
) -> List[str]:
"""Add nodes to index.
Args:
nodes: List[BaseNode]: list of nodes with embeddings
Returns:
A List of ids for successfully added nodes.
"""
ids = []
data_to_insert = []
for node in nodes:
metadata = node_to_metadata_dict(
node, remove_text=True, flat_metadata=self.flat_metadata
)
entry = {
self._id_key: node.node_id,
self._embedding_key: node.get_embedding(),
self._text_key: node.get_content(metadata_mode=MetadataMode.NONE) or "",
self._metadata_key: metadata,
"timeStamp": date.today(),
}
data_to_insert.append(entry)
ids.append(node.node_id)
logger.debug("Inserting data into MongoDB: %s", data_to_insert)
insert_result = self._collection.insert_many(
data_to_insert, **self._insert_kwargs
)
logger.debug("Result of insert: %s", insert_result)
return ids
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""
Delete nodes using with ref_doc_id.
Args:
ref_doc_id (str): The doc_id of the document to delete.
"""
# delete by filtering on the doc_id metadata
self._collection.delete_one(
filter={self._metadata_key + ".ref_doc_id": ref_doc_id}, **delete_kwargs
)
@property
def client(self) -> Any:
"""Return MongoDB client."""
return self._mongodb_client
def _query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
pipeline: List[dict[str, Any]] = []
kind = self._cosmos_search_kwargs.get("kind", "vector-hnsw")
if kind == "vector-ivf":
pipeline = self._get_pipeline_vector_ivf(
query, kwargs.get("pre_filter", {})
)
elif kind == "vector-hnsw":
pipeline = self._get_pipeline_vector_hnsw(
query, kwargs.get("ef_search", 40), kwargs.get("pre_filter", {})
)
logger.debug("Running query pipeline: %s", pipeline)
cursor = self._collection.aggregate(pipeline) # type: ignore
top_k_nodes = []
top_k_ids = []
top_k_scores = []
for res in cursor:
text = res["document"].pop(self._text_key)
score = res.pop("similarityScore")
id = res["document"].pop(self._id_key)
metadata_dict = res["document"].pop(self._metadata_key)
try:
node = metadata_dict_to_node(metadata_dict)
node.set_content(text)
except Exception:
# NOTE: deprecated legacy logic for backward compatibility
metadata, node_info, relationships = legacy_metadata_dict_to_node(
metadata_dict
)
node = TextNode(
text=text,
id_=id,
metadata=metadata,
start_char_idx=node_info.get("start", None),
end_char_idx=node_info.get("end", None),
relationships=relationships,
)
top_k_ids.append(id)
top_k_nodes.append(node)
top_k_scores.append(score)
result = VectorStoreQueryResult(
nodes=top_k_nodes, similarities=top_k_scores, ids=top_k_ids
)
logger.debug("Result of query: %s", result)
return result
def _get_pipeline_vector_ivf(
self, query: VectorStoreQuery, pre_filter: Optional[Dict]
) -> List[dict[str, Any]]:
params = {
"vector": query.query_embedding,
"path": self._embedding_key,
"k": query.similarity_top_k,
}
if pre_filter:
params["filter"] = pre_filter
pipeline: List[dict[str, Any]] = [
{
"$search": {
"cosmosSearch": params,
"returnStoredSource": True,
}
},
{
"$project": {
"similarityScore": {"$meta": "searchScore"},
"document": "$$ROOT",
}
},
]
return pipeline
def _get_pipeline_vector_hnsw(
self, query: VectorStoreQuery, ef_search: int, pre_filter: Optional[Dict]
) -> List[dict[str, Any]]:
params = {
"vector": query.query_embedding,
"path": self._embedding_key,
"k": query.similarity_top_k,
"efSearch": ef_search,
}
if pre_filter:
params["filter"] = pre_filter
pipeline: List[dict[str, Any]] = [
{
"$search": {
"cosmosSearch": params,
}
},
{
"$project": {
"similarityScore": {"$meta": "searchScore"},
"document": "$$ROOT",
}
},
]
return pipeline
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
"""Query index for top k most similar nodes.
Args:
query: a VectorStoreQuery object.
Returns:
A VectorStoreQueryResult containing the results of the query.
"""
return self._query(query, **kwargs)
|