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Bagel

BagelVectorStore #

基础：BasePydanticVectorStore

Bagel 的向量存储。

示例

pip install llama-index-vector-stores-bagel

from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.bagel import BagelVectorStore

import bagel
from bagel import Settings

server_settings = Settings(
    bagel_api_impl="rest", bagel_server_host="api.bageldb.ai"
)

client = bagel.Client(server_settings)

collection = client.get_or_create_cluster("testing_embeddings")
vector_store = BagelVectorStore(collection=collection)
源代码位于 llama-index-integrations/vector_stores/llama-index-vector-stores-bagel/llama_index/vector_stores/bagel/base.py 
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class BagelVectorStore(BasePydanticVectorStore):
    """
    Vector store for Bagel.

    Examples:
        `pip install llama-index-vector-stores-bagel`

        ```python
        from llama_index.core import VectorStoreIndex, StorageContext
        from llama_index.vector_stores.bagel import BagelVectorStore

        import bagel
        from bagel import Settings

        server_settings = Settings(
            bagel_api_impl="rest", bagel_server_host="api.bageldb.ai"
        )

        client = bagel.Client(server_settings)

        collection = client.get_or_create_cluster("testing_embeddings")
        vector_store = BagelVectorStore(collection=collection)
        ```

    """

    # support for Bagel specific parameters
    stores_text: bool = True
    flat_metadata: bool = True

    _collection: Any = PrivateAttr()

    def __init__(self, collection: Any, **kwargs: Any) -> None:
        """
        Initialize BagelVectorStore.

        Args:
            collection: Bagel collection.
            **kwargs: Additional arguments.

        """
        super().__init__()

        try:
            from bagel.api.Cluster import Cluster
        except ImportError:
            raise ImportError("Bagel is not installed. Please install bagel.")

        if not isinstance(collection, Cluster):
            raise ValueError("Collection must be a bagel Cluster.")

        self._collection = collection

    @classmethod
    def class_name(cls) -> str:
        return "BagelVectorStore"

    def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
        """
        Add a list of nodes with embeddings to the vector store.

        Args:
            nodes: List of nodes with embeddings.
            kwargs: Additional arguments.

        Returns:
            List of document ids.

        """
        if not self._collection:
            raise ValueError("collection not set")

        ids = []
        embeddings = []
        metadatas = []
        documents = []

        for node in nodes:
            ids.append(node.node_id)
            embeddings.append(node.get_embedding())
            metadatas.append(
                node_to_metadata_dict(
                    node,
                    remove_text=True,
                    flat_metadata=self.flat_metadata,
                )
            )
            documents.append(node.get_content(metadata_mode=MetadataMode.NONE) or "")

        self._collection.add(
            ids=ids, embeddings=embeddings, metadatas=metadatas, documents=documents
        )

        return ids

    def delete(self, ref_doc_id: str, **kwargs: Any) -> None:
        """
        Delete a document from the vector store.

        Args:
            ref_doc_id: Reference document id.
            kwargs: Additional arguments.

        """
        if not self._collection:
            raise ValueError("collection not set")

        results = self._collection.get(where={"doc_id": ref_doc_id})
        if results and "ids" in results:
            self._collection.delete(ids=results["ids"])

    @property
    def client(self) -> Any:
        """
        Get the Bagel cluster.
        """
        return self._collection

    def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
        """
        Query the vector store.

        Args:
            query: Query to run.
            kwargs: Additional arguments.

        Returns:
            Query result.

        """
        if not self._collection:
            raise ValueError("collection not set")

        if query.filters is not None:
            if "where" in kwargs:
                raise ValueError("Cannot specify both filters and where")
            where = _to_bagel_filter(query.filters)
        else:
            where = kwargs.get("where", {})

        results = self._collection.find(
            query_embeddings=query.query_embedding,
            where=where,
            n_results=query.similarity_top_k,
            **kwargs,
        )

        logger.debug(f"query results: {results}")

        nodes = []
        similarities = []
        ids = []

        for node_id, text, metadata, distance in zip(
            results["ids"][0],
            results["documents"][0],
            results["metadatas"][0],
            results["distances"][0],
        ):
            try:
                node = metadata_dict_to_node(metadata)
                node.set_content(text)
            except Exception:
                # NOTE: deprecated legacy logic for backward compatibility
                metadata, node_info, relationships = legacy_metadata_dict_to_node(
                    metadata
                )

                node = TextNode(
                    text=text,
                    id_=node_id,
                    metadata=metadata,
                    start_char_idx=node_info.get("start", None),
                    end_char_idx=node_info.get("end", None),
                    relationships=relationships,
                )

            nodes.append(node)
            similarities.append(1.0 - math.exp(-distance))
            ids.append(node_id)

            logger.debug(f"node: {node}")
            logger.debug(f"similarity: {1.0 - math.exp(-distance)}")
            logger.debug(f"id: {node_id}")

        return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)

client 属性 #

client: Any

获取 Bagel 集群。

add #

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

向向量存储添加带有嵌入的节点列表。

参数

名称 类型 描述 默认值
nodes List[BaseNode]

带有嵌入的节点列表。

 必需
kwargs

额外参数。

 必需

返回值

类型 描述
List[str]

文档 ID 列表。
源代码位于 llama-index-integrations/vector_stores/llama-index-vector-stores-bagel/llama_index/vector_stores/bagel/base.py 
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def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
    """
    Add a list of nodes with embeddings to the vector store.

    Args:
        nodes: List of nodes with embeddings.
        kwargs: Additional arguments.

    Returns:
        List of document ids.

    """
    if not self._collection:
        raise ValueError("collection not set")

    ids = []
    embeddings = []
    metadatas = []
    documents = []

    for node in nodes:
        ids.append(node.node_id)
        embeddings.append(node.get_embedding())
        metadatas.append(
            node_to_metadata_dict(
                node,
                remove_text=True,
                flat_metadata=self.flat_metadata,
            )
        )
        documents.append(node.get_content(metadata_mode=MetadataMode.NONE) or "")

    self._collection.add(
        ids=ids, embeddings=embeddings, metadatas=metadatas, documents=documents
    )

    return ids

delete #

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

从向量存储删除文档。

参数

名称 类型 描述 默认值
ref_doc_id str

参考文档 ID。

 必需
kwargs Any

额外参数。

 {}
源代码位于 llama-index-integrations/vector_stores/llama-index-vector-stores-bagel/llama_index/vector_stores/bagel/base.py 
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def delete(self, ref_doc_id: str, **kwargs: Any) -> None:
    """
    Delete a document from the vector store.

    Args:
        ref_doc_id: Reference document id.
        kwargs: Additional arguments.

    """
    if not self._collection:
        raise ValueError("collection not set")

    results = self._collection.get(where={"doc_id": ref_doc_id})
    if results and "ids" in results:
        self._collection.delete(ids=results["ids"])

query #

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

查询向量存储。

参数

名称 类型 描述 默认值
query VectorStoreQuery

要运行的查询。

 必需
kwargs Any

额外参数。

 {}

返回值

类型 描述
VectorStoreQueryResult

查询结果。
源代码位于 llama-index-integrations/vector_stores/llama-index-vector-stores-bagel/llama_index/vector_stores/bagel/base.py 
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def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
    """
    Query the vector store.

    Args:
        query: Query to run.
        kwargs: Additional arguments.

    Returns:
        Query result.

    """
    if not self._collection:
        raise ValueError("collection not set")

    if query.filters is not None:
        if "where" in kwargs:
            raise ValueError("Cannot specify both filters and where")
        where = _to_bagel_filter(query.filters)
    else:
        where = kwargs.get("where", {})

    results = self._collection.find(
        query_embeddings=query.query_embedding,
        where=where,
        n_results=query.similarity_top_k,
        **kwargs,
    )

    logger.debug(f"query results: {results}")

    nodes = []
    similarities = []
    ids = []

    for node_id, text, metadata, distance in zip(
        results["ids"][0],
        results["documents"][0],
        results["metadatas"][0],
        results["distances"][0],
    ):
        try:
            node = metadata_dict_to_node(metadata)
            node.set_content(text)
        except Exception:
            # NOTE: deprecated legacy logic for backward compatibility
            metadata, node_info, relationships = legacy_metadata_dict_to_node(
                metadata
            )

            node = TextNode(
                text=text,
                id_=node_id,
                metadata=metadata,
                start_char_idx=node_info.get("start", None),
                end_char_idx=node_info.get("end", None),
                relationships=relationships,
            )

        nodes.append(node)
        similarities.append(1.0 - math.exp(-distance))
        ids.append(node_id)

        logger.debug(f"node: {node}")
        logger.debug(f"similarity: {1.0 - math.exp(-distance)}")
        logger.debug(f"id: {node_id}")

    return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)