.NET 原生驾驭 AI 新基建实战系列（五）：Milvus ── 大规模 AI 应用的向量数据库首选

 
 MilvusMemoryStore store = new(Host{}, vectorSize: 5, port: {Port}, consistencyLevel: ConsistencyLevel.Strong);
 

await this.store.CreateCollectionAsync("collection1");

 this.store.UpsertBatchAsync(CollectionName,
      [
          new MemoryRecord(
              new MemoryRecordMetadata(
                  isReference: true,
                  id: "Some id",
                  description: "Some description",
                  text: "Some text",
                  externalSourceName: "Some external resource name",
                  additionalMetadata: "Some additional metadata"),
              new[] { 10f, 11f, 12f, 13f, 14f },
              key: "Some key",
              timestamp: new DateTimeOffset(2023, 1, 1, 12, 0, 0, TimeSpan.Zero)),
          new MemoryRecord(
              new MemoryRecordMetadata(
                  isReference: false,
                  id: "Some other id",
                  description: "",
                  text: "",
                  externalSourceName: "",
                  additionalMetadata: ""),
              new[] { 20f, 21f, 22f, 23f, 24f },
              key: null,
              timestamp: null),
      ]);

List<(MemoryRecord Record, double SimilarityScore)> results =
  this.Store.GetNearestMatchesAsync(CollectionName, new[] { 5f, 6f, 7f, 8f, 9f }, limit: 2, withEmbeddings: withEmbeddings).ToEnumerable().ToList();
  
  Assert.True(result.Value.SimilarityScore > 0);
  MemoryRecord record = result.Value.Record;

  Assert.Equal("Some other id", record.Metadata.Id);
  Assert.Equal(
  withEmbeddings ? [20f, 21f, 22f, 23f, 24f] : [],
  record.Embedding.ToArray());

dotnet add package Milvus.Client

var milvusClient = new MilvusClient("localhost", username: "username", password: "password");

 string collectionName = "book";
 MilvusCollection collection = milvusClient.GetCollection(collectionName);

//Check if this collection exists
var hasCollection = await milvusClient.HasCollectionAsync(collectionName);

if(hasCollection){
     await collection.DropAsync();
     Console.WriteLine("Drop collection {0}",collectionName);
 }

await milvusClient.CreateCollectionAsync(
             collectionName,
             new[] {
                 FieldSchema.Create<long>("book_id", isPrimaryKey:true),
                 FieldSchema.Create<long>("word_count"),
                 FieldSchema.CreateVarchar("book_name", 256),
                 FieldSchema.CreateFloatVector("book_intro", 2L)
             }
         );

 Random ran = new ();
 List<long> bookIds = new ();
 List<long> wordCounts = new ();
 List<ReadOnlyMemory<float>> bookIntros = new ();
 List<string> bookNames = new ();
for (long i = 0L; i < 2000; ++i)
 {
     bookIds.Add(i);
     wordCounts.Add(i + 10000);
     bookNames.Add($"Book Name {i}");

     float[] vector = newfloat[2];
     for (int k = 0; k < 2; ++k)
     {
         vector[k] = ran.Next();
     }
     bookIntros.Add(vector);
 }

 MilvusCollection collection = milvusClient.GetCollection(collectionName);

 MutationResult result = await collection.InsertAsync(
     new FieldData[]
     {
         FieldData.Create("book_id", bookIds),
         FieldData.Create("word_count", wordCounts),
         FieldData.Create("book_name", bookNames),
         FieldData.CreateFloatVector("book_intro", bookIntros),
     });

// Check result
 Console.WriteLine("Insert count:{0},", result.InsertCount);

 MilvusCollection collection = milvusClient.GetCollection(collectionName);
await collection.CreateIndexAsync(
     "book_intro",
     //MilvusIndexType.IVF_FLAT,//Use MilvusIndexType.IVF_FLAT.
     IndexType.AutoIndex,//Use MilvusIndexType.AUTOINDEX when you are using zilliz cloud.
     SimilarityMetricType.L2);

// Check index status
 IList<MilvusIndexInfo> indexInfos = await collection.DescribeIndexAsync("book_intro");

foreach(var info in indexInfos){
     Console.WriteLine("FieldName:{0}, IndexName:{1}, IndexId:{2}", info.FieldName , info.IndexName,info.IndexId);
 }

// Then load it
await collection.LoadAsync();
 }

 List<string> search_output_fields = new() { "book_id" };
 List<List<float>> search_vectors = new() { new() { 0.1f, 0.2f } };
 SearchResults searchResult = await collection.SearchAsync(
     "book_intro",
     new ReadOnlyMemory<float>[] { new[] { 0.1f, 0.2f } },
     SimilarityMetricType.L2,
     limit: 2);

// Query
string expr = "book_id in [2,4,6,8]";

 QueryParameters queryParameters = new ();
 queryParameters.OutputFields.Add("book_id");
 queryParameters.OutputFields.Add("word_count");

 IReadOnlyList<FieldData> queryResult = await collection.QueryAsync(
     expr,
     queryParameters);