Milvus vs Elasticsearch and OpenSearch for vector search

# pip install -U pymilvus elasticsearch
from elasticsearch import Elasticsearch, helpers
from pymilvus import MilvusClient, DataType, Function, FunctionType

es = Elasticsearch("http://localhost:9200")
client = MilvusClient(uri="http://localhost:19530")

# ES mapping: title(text) category(keyword) tags(keyword[]) attrs(object) embedding(dense_vector, 768)
schema = client.create_schema(enable_dynamic_field=True)
schema.add_field("id", DataType.VARCHAR, is_primary=True, max_length=64)  # ES _id
schema.add_field("title", DataType.VARCHAR, max_length=8192, enable_analyzer=True, enable_match=True)
schema.add_field("title_bm25", DataType.SPARSE_FLOAT_VECTOR)  # filled by the BM25 function
schema.add_field("category", DataType.VARCHAR, max_length=128)
schema.add_field("tags", DataType.ARRAY, element_type=DataType.VARCHAR, max_capacity=64, max_length=128)
schema.add_field("attrs", DataType.JSON, nullable=True)
schema.add_field("embedding", DataType.FLOAT_VECTOR, dim=768)
schema.add_function(Function(name="title_bm25_fn", function_type=FunctionType.BM25,
                             input_field_names=["title"], output_field_names=["title_bm25"]))

idx = client.prepare_index_params()
idx.add_index(field_name="embedding", index_type="HNSW", metric_type="COSINE",
              params={"M": 16, "efConstruction": 200})
idx.add_index(field_name="title_bm25", index_type="SPARSE_INVERTED_INDEX", metric_type="BM25")
idx.add_index(field_name="category", index_type="INVERTED")
idx.add_index(field_name="attrs", index_type="INVERTED", index_name="attrs_brand",
              params={"json_path": 'attrs["brand"]', "json_cast_type": "varchar"})
client.create_collection("docs", schema=schema, index_params=idx)

batch = []
for hit in helpers.scan(es, index="docs", query={"query": {"match_all": {}}}):
    src = hit["_source"]
    batch.append({"id": hit["_id"], "title": src["title"], "category": src["category"],
                  "tags": src.get("tags", []), "attrs": src.get("attrs"),
                  "embedding": src["embedding"]})
    if len(batch) == 1000:
        client.insert("docs", batch)
        batch = []
if batch:
    client.insert("docs", batch)