Skip to content

Milvus

MilvusReader #

Bases: BaseReader

Milvus 读取器。

Source code in llama_index/readers/milvus/base.py
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
class MilvusReader(BaseReader):
    """Milvus 读取器。"""

    def __init__(
        self,
        host: str = "localhost",
        port: int = 19530,
        user: str = "",
        password: str = "",
        use_secure: bool = False,
    ):
        """使用参数进行初始化。"""
        import_err_msg = (
            "`pymilvus` package not found, please run `pip install pymilvus`"
        )
        try:
            import pymilvus  # noqa
        except ImportError:
            raise ImportError(import_err_msg)

        from pymilvus import MilvusException

        self.host = host
        self.port = port
        self.user = user
        self.password = password
        self.use_secure = use_secure
        self.collection = None

        self.default_search_params = {
            "IVF_FLAT": {"metric_type": "IP", "params": {"nprobe": 10}},
            "IVF_SQ8": {"metric_type": "IP", "params": {"nprobe": 10}},
            "IVF_PQ": {"metric_type": "IP", "params": {"nprobe": 10}},
            "HNSW": {"metric_type": "IP", "params": {"ef": 10}},
            "RHNSW_FLAT": {"metric_type": "IP", "params": {"ef": 10}},
            "RHNSW_SQ": {"metric_type": "IP", "params": {"ef": 10}},
            "RHNSW_PQ": {"metric_type": "IP", "params": {"ef": 10}},
            "IVF_HNSW": {"metric_type": "IP", "params": {"nprobe": 10, "ef": 10}},
            "ANNOY": {"metric_type": "IP", "params": {"search_k": 10}},
            "AUTOINDEX": {"metric_type": "IP", "params": {}},
        }
        try:
            self._create_connection_alias()
        except MilvusException:
            raise

    def load_data(
        self,
        query_vector: List[float],
        collection_name: str,
        expr: Any = None,
        search_params: Optional[dict] = None,
        limit: int = 10,
    ) -> List[Document]:
        """从Milvus加载数据。

Args:
    collection_name (str): Milvus集合的名称。
    query_vector (List[float]): 查询向量。
    limit (int): 返回结果的数量。

Returns:
    List[Document]: 文档列表。
"""
        from pymilvus import Collection, MilvusException

        try:
            self.collection = Collection(collection_name, using=self.alias)
        except MilvusException:
            raise

        assert self.collection is not None
        try:
            self.collection.load()
        except MilvusException:
            raise
        if search_params is None:
            search_params = self._create_search_params()

        res = self.collection.search(
            [query_vector],
            "embedding",
            param=search_params,
            expr=expr,
            output_fields=["doc_id", "text"],
            limit=limit,
        )

        documents = []
        # TODO: In future append embedding when more efficient
        for hit in res[0]:
            document = Document(
                id_=hit.entity.get("doc_id"),
                text=hit.entity.get("text"),
            )

            documents.append(document)

        return documents

    def _create_connection_alias(self) -> None:
        from pymilvus import connections

        self.alias = None
        # Attempt to reuse an open connection
        for x in connections.list_connections():
            addr = connections.get_connection_addr(x[0])
            if (
                x[1]
                and ("address" in addr)
                and (addr["address"] == f"{self.host}:{self.port}")
            ):
                self.alias = x[0]
                break

        # Connect to the Milvus instance using the passed in Environment variables
        if self.alias is None:
            self.alias = uuid4().hex
            connections.connect(
                alias=self.alias,
                host=self.host,
                port=self.port,
                user=self.user,  # type: ignore
                password=self.password,  # type: ignore
                secure=self.use_secure,
            )

    def _create_search_params(self) -> Dict[str, Any]:
        assert self.collection is not None
        index = self.collection.indexes[0]._index_params
        search_params = self.default_search_params[index["index_type"]]
        search_params["metric_type"] = index["metric_type"]
        return search_params

load_data #

load_data(
    query_vector: List[float],
    collection_name: str,
    expr: Any = None,
    search_params: Optional[dict] = None,
    limit: int = 10,
) -> List[Document]

从Milvus加载数据。

Parameters:

Name Type Description Default
collection_name str

Milvus集合的名称。

required
query_vector List[float]

查询向量。

required
limit int

返回结果的数量。

10

Returns:

Type Description
List[Document]

List[Document]: 文档列表。

Source code in llama_index/readers/milvus/base.py
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
    def load_data(
        self,
        query_vector: List[float],
        collection_name: str,
        expr: Any = None,
        search_params: Optional[dict] = None,
        limit: int = 10,
    ) -> List[Document]:
        """从Milvus加载数据。

Args:
    collection_name (str): Milvus集合的名称。
    query_vector (List[float]): 查询向量。
    limit (int): 返回结果的数量。

Returns:
    List[Document]: 文档列表。
"""
        from pymilvus import Collection, MilvusException

        try:
            self.collection = Collection(collection_name, using=self.alias)
        except MilvusException:
            raise

        assert self.collection is not None
        try:
            self.collection.load()
        except MilvusException:
            raise
        if search_params is None:
            search_params = self._create_search_params()

        res = self.collection.search(
            [query_vector],
            "embedding",
            param=search_params,
            expr=expr,
            output_fields=["doc_id", "text"],
            limit=limit,
        )

        documents = []
        # TODO: In future append embedding when more efficient
        for hit in res[0]:
            document = Document(
                id_=hit.entity.get("doc_id"),
                text=hit.entity.get("text"),
            )

            documents.append(document)

        return documents