Azurecosmosmongo

AzureCosmosDBMongoDBVectorSearch

继承自: BasePydanticVectorStore

Azure CosmosDB MongoDB vCore 向量存储。

要使用它，您需要： - 安装 pymongo python 包 - 具有与 Azure Cosmodb MongoDB vCore 集群关联的连接字符串

示例

pip install llama-index-vector-stores-azurecosmosmongo

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",
)

源代码在 llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/llama_index/vector_stores/azurecosmosmongo/base.py

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_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)
        elif kind == "vector-diskann":
            create_index_commands = self._get_vector_index_diskann(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 _get_vector_index_diskann(
        self,
        kind: str,
    ) -> Dict[str, Any]:
        return {
            "createIndexes": self._collection_name,
            "indexes": [
                {
                    "name": self._index_name,
                    "key": {self._embedding_key: "cosmosSearch"},
                    "cosmosSearchOptions": {
                        "kind": kind,
                        "maxDegree": self._cosmos_search_kwargs.get("maxDegree", 32),
                        "lBuild": self._cosmos_search_kwargs.get("lBuild", 50),
                        "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,
            }
            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", {})
            )
        elif kind == "vector-diskann":
            pipeline = self._get_pipeline_vector_diskann(
                query, kwargs.get("lSearch", 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 _get_pipeline_vector_diskann(
        self, query: VectorStoreQuery, l_search: int, pre_filter: Optional[Dict]
    ) -> List[dict[str, Any]]:
        params = {
            "vector": query.query_embedding,
            "path": self._embedding_key,
            "k": query.similarity_top_k,
            "lSearch": l_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)

client 属性

client: Any

返回 MongoDB 客户端。

add

add(nodes: List[BaseNode], **add_kwargs: Any) -> List[str]

将节点添加到索引。

参数

名称	类型	描述	默认值
nodes	列表[BaseNode]	List[BaseNode]: 带有嵌入的节点列表	必需

返回值

类型	描述
列表[字符串]	成功添加节点的 ID 列表。

源代码在 llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/llama_index/vector_stores/azurecosmosmongo/base.py

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,
        }
        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

delete

delete(ref_doc_id: str, **delete_kwargs: Any) -> None

使用 ref_doc_id 删除节点。

参数

名称	类型	描述	默认值
ref_doc_id	字符串	要删除文档的 doc_id。	必需

源代码在 llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/llama_index/vector_stores/azurecosmosmongo/base.py

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
    )

query

query(query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult

查询索引以获取 top k 最相似的节点。

参数

名称	类型	描述	默认值
query	VectorStoreQuery	VectorStoreQuery 对象。	必需

返回值

类型	描述
VectorStoreQueryResult	包含查询结果的 VectorStoreQueryResult。

源代码在 llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/llama_index/vector_stores/azurecosmosmongo/base.py

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)