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Pgvecto rs

PGVectoRsStore #

Bases: BasePydanticVectorStore

PGVectoRs矢量存储。

示例

pip install llama-index-vector-stores-pgvecto-rs

from llama_index.vector_stores.pgvecto_rs import PGVectoRsStore

# 设置PGVectoRs客户端
from pgvecto_rs.sdk import PGVectoRs
import os

URL = "postgresql+psycopg://{username}:{password}@{host}:{port}/{db_name}".format(
    port=os.getenv("DB_PORT", "5432"),
    host=os.getenv("DB_HOST", "localhost"),
    username=os.getenv("DB_USER", "postgres"),
    password=os.getenv("DB_PASS", "mysecretpassword"),
    db_name=os.getenv("DB_NAME", "postgres"),
)

client = PGVectoRs(
    db_url=URL,
    collection_name="example",
    dimension=1536,  # 使用OpenAI的文本嵌入-ada-002
)

# 初始化PGVectoRsStore
vector_store = PGVectoRsStore(client=client)
Source code in llama_index/vector_stores/pgvecto_rs/base.py 
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class PGVectoRsStore(BasePydanticVectorStore):
    """PGVectoRs矢量存储。

    示例:
        `pip install llama-index-vector-stores-pgvecto-rs`

        ```python
        from llama_index.vector_stores.pgvecto_rs import PGVectoRsStore

        # 设置PGVectoRs客户端
        from pgvecto_rs.sdk import PGVectoRs
        import os

        URL = "postgresql+psycopg://{username}:{password}@{host}:{port}/{db_name}".format(
            port=os.getenv("DB_PORT", "5432"),
            host=os.getenv("DB_HOST", "localhost"),
            username=os.getenv("DB_USER", "postgres"),
            password=os.getenv("DB_PASS", "mysecretpassword"),
            db_name=os.getenv("DB_NAME", "postgres"),
        )

        client = PGVectoRs(
            db_url=URL,
            collection_name="example",
            dimension=1536,  # 使用OpenAI的文本嵌入-ada-002
        )

        # 初始化PGVectoRsStore
        vector_store = PGVectoRsStore(client=client)
        ```"""

    stores_text = True

    _client: "PGVectoRs" = PrivateAttr()

    def __init__(self, client: "PGVectoRs") -> None:
        self._client: PGVectoRs = client
        super().__init__()

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

    @property
    def client(self) -> Any:
        return self._client

    def add(
        self,
        nodes: List[BaseNode],
    ) -> List[str]:
        records = [
            Record(
                id=node.id_,
                text=node.get_content(metadata_mode=MetadataMode.NONE),
                meta=node_to_metadata_dict(node, remove_text=True),
                embedding=node.get_embedding(),
            )
            for node in nodes
        ]

        self._client.insert(records)
        return [node.id_ for node in nodes]

    def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
        self._client.delete(meta_contains({"ref_doc_id": ref_doc_id}))

    def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
        results = self._client.search(
            embedding=query.query_embedding,
            top_k=query.similarity_top_k,
            filter=(
                meta_contains(
                    {pair.key: pair.value for pair in query.filters.legacy_filters()}
                )
                if query.filters is not None
                else None
            ),
        )

        nodes = [
            metadata_dict_to_node(record.meta, text=record.text)
            for record, _ in results
        ]

        return VectorStoreQueryResult(
            nodes=nodes,
            similarities=[score for _, score in results],
            ids=[str(record.id) for record, _ in results],
        )