143 lines
4.4 KiB
Python
143 lines
4.4 KiB
Python
"""Milvus reader."""
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from typing import Any, Dict, List, Optional
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from uuid import uuid4
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from llama_index.readers.base import BaseReader
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from llama_index.schema import Document
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class MilvusReader(BaseReader):
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"""Milvus reader."""
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def __init__(
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self,
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host: str = "localhost",
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port: int = 19530,
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user: str = "",
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password: str = "",
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use_secure: bool = False,
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):
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"""Initialize with parameters."""
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import_err_msg = (
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"`pymilvus` package not found, please run `pip install pymilvus`"
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)
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try:
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import pymilvus # noqa
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except ImportError:
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raise ImportError(import_err_msg)
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from pymilvus import MilvusException
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self.host = host
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self.port = port
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self.user = user
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self.password = password
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self.use_secure = use_secure
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self.collection = None
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self.default_search_params = {
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"IVF_FLAT": {"metric_type": "IP", "params": {"nprobe": 10}},
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"IVF_SQ8": {"metric_type": "IP", "params": {"nprobe": 10}},
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"IVF_PQ": {"metric_type": "IP", "params": {"nprobe": 10}},
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"HNSW": {"metric_type": "IP", "params": {"ef": 10}},
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"RHNSW_FLAT": {"metric_type": "IP", "params": {"ef": 10}},
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"RHNSW_SQ": {"metric_type": "IP", "params": {"ef": 10}},
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"RHNSW_PQ": {"metric_type": "IP", "params": {"ef": 10}},
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"IVF_HNSW": {"metric_type": "IP", "params": {"nprobe": 10, "ef": 10}},
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"ANNOY": {"metric_type": "IP", "params": {"search_k": 10}},
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"AUTOINDEX": {"metric_type": "IP", "params": {}},
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}
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try:
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self._create_connection_alias()
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except MilvusException:
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raise
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def load_data(
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self,
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query_vector: List[float],
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collection_name: str,
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expr: Any = None,
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search_params: Optional[dict] = None,
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limit: int = 10,
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) -> List[Document]:
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"""Load data from Milvus.
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Args:
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collection_name (str): Name of the Milvus collection.
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query_vector (List[float]): Query vector.
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limit (int): Number of results to return.
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Returns:
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List[Document]: A list of documents.
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"""
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from pymilvus import Collection, MilvusException
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try:
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self.collection = Collection(collection_name, using=self.alias)
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except MilvusException:
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raise
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assert self.collection is not None
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try:
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self.collection.load()
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except MilvusException:
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raise
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if search_params is None:
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search_params = self._create_search_params()
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res = self.collection.search(
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[query_vector],
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"embedding",
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param=search_params,
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expr=expr,
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output_fields=["doc_id", "text"],
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limit=limit,
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)
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documents = []
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# TODO: In future append embedding when more efficient
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for hit in res[0]:
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document = Document(
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id_=hit.entity.get("doc_id"),
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text=hit.entity.get("text"),
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)
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documents.append(document)
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return documents
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def _create_connection_alias(self) -> None:
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from pymilvus import connections
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self.alias = None
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# Attempt to reuse an open connection
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for x in connections.list_connections():
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addr = connections.get_connection_addr(x[0])
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if (
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x[1]
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and ("address" in addr)
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and (addr["address"] == f"{self.host}:{self.port}")
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):
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self.alias = x[0]
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break
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# Connect to the Milvus instance using the passed in Environment variables
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if self.alias is None:
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self.alias = uuid4().hex
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connections.connect(
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alias=self.alias,
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host=self.host,
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port=self.port,
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user=self.user, # type: ignore
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password=self.password, # type: ignore
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secure=self.use_secure,
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)
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def _create_search_params(self) -> Dict[str, Any]:
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assert self.collection is not None
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index = self.collection.indexes[0]._index_params
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search_params = self.default_search_params[index["index_type"]]
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search_params["metric_type"] = index["metric_type"]
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return search_params
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