remove milvus due to no usage

metagpt/document_store/milvus_store.py

@@ -1,111 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/28 00:00
-@Author  : alexanderwu
-@File    : milvus_store.py
-"""
-from typing import TypedDict
-
-import numpy as np
-from pymilvus import Collection, CollectionSchema, DataType, FieldSchema, connections
-
-from metagpt.document_store.base_store import BaseStore
-
-type_mapping = {int: DataType.INT64, str: DataType.VARCHAR, float: DataType.DOUBLE, np.ndarray: DataType.FLOAT_VECTOR}
-
-
-def columns_to_milvus_schema(columns: dict, primary_col_name: str = "", desc: str = ""):
-    """Assume the structure of columns is str: regular type"""
-    fields = []
-    for col, ctype in columns.items():
-        if ctype == str:
-            mcol = FieldSchema(name=col, dtype=type_mapping[ctype], max_length=100)
-        elif ctype == np.ndarray:
-            mcol = FieldSchema(name=col, dtype=type_mapping[ctype], dim=2)
-        else:
-            mcol = FieldSchema(name=col, dtype=type_mapping[ctype], is_primary=(col == primary_col_name))
-        fields.append(mcol)
-    schema = CollectionSchema(fields, description=desc)
-    return schema
-
-
-class MilvusConnection(TypedDict):
-    alias: str
-    host: str
-    port: str
-
-
-class MilvusStore(BaseStore):
-    """
-    FIXME: ADD TESTS
-    https://milvus.io/docs/v2.0.x/create_collection.md
-    """
-
-    def __init__(self, connection):
-        connections.connect(**connection)
-        self.collection = None
-
-    def _create_collection(self, name, schema):
-        collection = Collection(name=name, schema=schema, using="default", shards_num=2, consistency_level="Strong")
-        return collection
-
-    def create_collection(self, name, columns):
-        schema = columns_to_milvus_schema(columns, "idx")
-        self.collection = self._create_collection(name, schema)
-        return self.collection
-
-    def drop(self, name):
-        Collection(name).drop()
-
-    def load_collection(self):
-        self.collection.load()
-
-    def build_index(self, field="emb"):
-        self.collection.create_index(field, {"index_type": "FLAT", "metric_type": "L2", "params": {}})
-
-    def search(self, query: list[list[float]], *args, **kwargs):
-        """
-        FIXME: ADD TESTS
-        https://milvus.io/docs/v2.0.x/search.md
-        All search and query operations within Milvus are executed in memory. Load the collection to memory before conducting a vector similarity search.
-        Note the above description, is this logic serious? This should take a long time, right?
-        """
-        search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
-        results = self.collection.search(
-            data=query,
-            anns_field=kwargs.get("field", "emb"),
-            param=search_params,
-            limit=10,
-            expr=None,
-            consistency_level="Strong",
-        )
-        # FIXME: results contain id, but to get the actual value from the id, we still need to call the query interface
-        return results
-
-    def write(self, name, schema, *args, **kwargs):
-        """
-        FIXME: ADD TESTS
-        https://milvus.io/docs/v2.0.x/create_collection.md
-        :param args:
-        :param kwargs:
-        :return:
-        """
-        raise NotImplementedError
-
-    def add(self, data, *args, **kwargs):
-        """
-        FIXME: ADD TESTS
-        https://milvus.io/docs/v2.0.x/insert_data.md
-        import random
-        data = [
-          [i for i in range(2000)],
-          [i for i in range(10000, 12000)],
-          [[random.random() for _ in range(2)] for _ in range(2000)],
-        ]
-
-        :param args:
-        :param kwargs:
-        :return:
-        """
-        self.collection.insert(data)