Metal

MetalVectorStore

Bases: VectorStore

Metal Vector Store.

Examples:

pip install llama-index-vector-stores-metal

from llama_index.vector_stores.metal import MetalVectorStore

# Sign up for Metal and generate API key and client ID
api_key = "your_api_key_here"
client_id = "your_client_id_here"
index_id = "your_index_id_here"

# Initialize Metal Vector Store
vector_store = MetalVectorStore(
    api_key=api_key,
    client_id=client_id,
    index_id=index_id,
)

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-metal/llama_index/vector_stores/metal/base.py

class MetalVectorStore(VectorStore):
    """Metal Vector Store.

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

        ```python
        from llama_index.vector_stores.metal import MetalVectorStore

        # Sign up for Metal and generate API key and client ID
        api_key = "your_api_key_here"
        client_id = "your_client_id_here"
        index_id = "your_index_id_here"

        # Initialize Metal Vector Store
        vector_store = MetalVectorStore(
            api_key=api_key,
            client_id=client_id,
            index_id=index_id,
        )
        ```
    """

    def __init__(
        self,
        api_key: str,
        client_id: str,
        index_id: str,
    ):
        """Init params."""
        self.api_key = api_key
        self.client_id = client_id
        self.index_id = index_id

        self.metal_client = Metal(api_key, client_id, index_id)
        self.stores_text = True
        self.flat_metadata = False
        self.is_embedding_query = True

    def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
        if query.filters is not None:
            if "filters" in kwargs:
                raise ValueError(
                    "Cannot specify filter via both query and kwargs. "
                    "Use kwargs only for metal specific items that are "
                    "not supported via the generic query interface."
                )
            filters = _to_metal_filters(query.filters)
        else:
            filters = kwargs.get("filters", {})

        payload = {
            "embedding": query.query_embedding,  # Query Embedding
            "filters": filters,  # Metadata Filters
        }
        response = self.metal_client.search(payload, limit=query.similarity_top_k)

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

        for item in response["data"]:
            text = item["text"]
            id_ = item["id"]

            # load additional Node data
            try:
                node = metadata_dict_to_node(item["metadata"])
                node.text = text
            except Exception:
                # NOTE: deprecated legacy logic for backward compatibility
                metadata, node_info, relationships = legacy_metadata_dict_to_node(
                    item["metadata"]
                )

                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,
                )

            nodes.append(node)
            ids.append(id_)

            similarity_score = 1.0 - math.exp(-item["dist"])
            similarities.append(similarity_score)

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

    @property
    def client(self) -> Any:
        """Return Metal client."""
        return self.metal_client

    def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
        """Add nodes to index.

        Args:
            nodes: List[BaseNode]: list of nodes with embeddings.

        """
        if not self.metal_client:
            raise ValueError("metal_client not initialized")

        ids = []
        for node in nodes:
            ids.append(node.node_id)

            metadata = {}
            metadata["text"] = node.get_content(metadata_mode=MetadataMode.NONE) or ""

            additional_metadata = node_to_metadata_dict(
                node, remove_text=True, flat_metadata=self.flat_metadata
            )
            metadata.update(additional_metadata)

            payload = {
                "embedding": node.get_embedding(),
                "metadata": metadata,
                "id": node.node_id,
            }

            self.metal_client.index(payload)

        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.

        """
        if not self.metal_client:
            raise ValueError("metal_client not initialized")

        self.metal_client.deleteOne(ref_doc_id)

client property

client: Any

Return Metal client.

add

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

Add nodes to index.

Parameters:

Name	Type	Description	Default
nodes	List[BaseNode]	List[BaseNode]: list of nodes with embeddings.	required

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-metal/llama_index/vector_stores/metal/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.

    """
    if not self.metal_client:
        raise ValueError("metal_client not initialized")

    ids = []
    for node in nodes:
        ids.append(node.node_id)

        metadata = {}
        metadata["text"] = node.get_content(metadata_mode=MetadataMode.NONE) or ""

        additional_metadata = node_to_metadata_dict(
            node, remove_text=True, flat_metadata=self.flat_metadata
        )
        metadata.update(additional_metadata)

        payload = {
            "embedding": node.get_embedding(),
            "metadata": metadata,
            "id": node.node_id,
        }

        self.metal_client.index(payload)

    return ids

delete

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

Delete nodes using with ref_doc_id.

Parameters:

Name	Type	Description	Default
ref_doc_id	str	The doc_id of the document to delete.	required

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-metal/llama_index/vector_stores/metal/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.

    """
    if not self.metal_client:
        raise ValueError("metal_client not initialized")

    self.metal_client.deleteOne(ref_doc_id)