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Refactor(FindSimiliar): MilvusCache to use Milvus Search API #352

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Refactor(FindSimiliar): MilvusCache to use Milvus Search API #352

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155 changes: 61 additions & 94 deletions src/semantic-router/pkg/cache/milvus_cache.go
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Original file line number Diff line number Diff line change
Expand Up @@ -307,19 +307,11 @@ func (c *MilvusCache) createCollection() error {
return err
}

// Create index
indexParams := map[string]string{
"index_type": c.config.Collection.Index.Type,
"metric_type": c.config.Collection.VectorField.MetricType,
"params": fmt.Sprintf(`{"M": %d, "efConstruction": %d}`,
c.config.Collection.Index.Params.M,
c.config.Collection.Index.Params.EfConstruction),
}

observability.Debugf("MilvusCache.createCollection: creating index for %d-dimensional vectors", actualDimension)

// Create index with updated API
index := entity.NewGenericIndex(c.config.Collection.VectorField.Name, entity.IndexType(c.config.Collection.Index.Type), indexParams)
index, err := entity.NewIndexHNSW(entity.MetricType(c.config.Collection.VectorField.MetricType), c.config.Collection.Index.Params.EfConstruction, c.config.Collection.Index.Params.M)
if err != nil {
return fmt.Errorf("failed to create HNSW index: %w", err)
}
if err := c.client.CreateIndex(ctx, c.collectionName, c.config.Collection.VectorField.Name, index, false); err != nil {
return err
}
Expand Down Expand Up @@ -517,112 +509,87 @@ func (c *MilvusCache) FindSimilar(model string, query string) ([]byte, bool, err

ctx := context.Background()

// Query for completed entries with the same model
// Using Query approach for comprehensive similarity search
queryExpr := fmt.Sprintf("model == \"%s\" && response_body != \"\"", model)
observability.Debugf("MilvusCache.FindSimilar: querying with expr: %s (embedding_dim: %d)",
queryExpr, len(queryEmbedding))

// Use Query to get all matching entries, then compute similarity manually
results, err := c.client.Query(ctx, c.collectionName, []string{}, queryExpr,
[]string{"query", "response_body", c.config.Collection.VectorField.Name})
// Define search parameters
searchParam, err := entity.NewIndexHNSWSearchParam(c.config.Search.Params.Ef)
if err != nil {
return nil, false, fmt.Errorf("failed to create search parameters: %w", err)
}

// Use Milvus Search for efficient similarity search
searchResult, err := c.client.Search(
ctx,
c.collectionName,
[]string{},
fmt.Sprintf("model == \"%s\" && response_body != \"\"", model),
[]string{"response_body"},
[]entity.Vector{entity.FloatVector(queryEmbedding)},
c.config.Collection.VectorField.Name,
entity.MetricType(c.config.Collection.VectorField.MetricType),
c.config.Search.TopK,
searchParam,
)

if err != nil {
observability.Debugf("MilvusCache.FindSimilar: query failed: %v", err)
observability.Debugf("MilvusCache.FindSimilar: search failed: %v", err)
atomic.AddInt64(&c.missCount, 1)
metrics.RecordCacheOperation("milvus", "find_similar", "error", time.Since(start).Seconds())
metrics.RecordCacheMiss()
return nil, false, nil
}

if len(results) == 0 {
if len(searchResult) == 0 || searchResult[0].ResultCount == 0 {
atomic.AddInt64(&c.missCount, 1)
observability.Debugf("MilvusCache.FindSimilar: no entries found with responses")
observability.Debugf("MilvusCache.FindSimilar: no entries found")
metrics.RecordCacheOperation("milvus", "find_similar", "miss", time.Since(start).Seconds())
metrics.RecordCacheMiss()
return nil, false, nil
}

// Calculate semantic similarity for each candidate
bestSimilarity := float32(-1.0)
var bestResponse string

// Find columns by type instead of assuming order
var queryColumn *entity.ColumnVarChar
var responseColumn *entity.ColumnVarChar
var embeddingColumn *entity.ColumnFloatVector
bestScore := searchResult[0].Scores[0]
if bestScore < c.similarityThreshold {
atomic.AddInt64(&c.missCount, 1)
observability.Debugf("MilvusCache.FindSimilar: CACHE MISS - best_similarity=%.4f < threshold=%.4f",
bestScore, c.similarityThreshold)
observability.LogEvent("cache_miss", map[string]interface{}{
"backend": "milvus",
"best_similarity": bestScore,
"threshold": c.similarityThreshold,
"model": model,
"collection": c.collectionName,
})
metrics.RecordCacheOperation("milvus", "find_similar", "miss", time.Since(start).Seconds())
metrics.RecordCacheMiss()
return nil, false, nil
}

for _, col := range results {
switch typedCol := col.(type) {
case *entity.ColumnVarChar:
if typedCol.Name() == "query" {
queryColumn = typedCol
} else if typedCol.Name() == "response_body" {
responseColumn = typedCol
}
case *entity.ColumnFloatVector:
if typedCol.Name() == c.config.Collection.VectorField.Name {
embeddingColumn = typedCol
}
}
// Cache Hit
var responseBody []byte
responseBodyColumn, ok := searchResult[0].Fields[0].(*entity.ColumnVarChar)
if ok && responseBodyColumn.Len() > 0 {
responseBody = []byte(responseBodyColumn.Data()[0])
}

if queryColumn == nil || responseColumn == nil || embeddingColumn == nil {
observability.Debugf("MilvusCache.FindSimilar: missing required columns in results")
if responseBody == nil {
observability.Debugf("MilvusCache.FindSimilar: cache hit but response_body is missing or not a string")
atomic.AddInt64(&c.missCount, 1)
metrics.RecordCacheOperation("milvus", "find_similar", "error", time.Since(start).Seconds())
metrics.RecordCacheMiss()
return nil, false, nil
}

for i := 0; i < queryColumn.Len(); i++ {
storedEmbedding := embeddingColumn.Data()[i]

// Calculate dot product similarity score
var similarity float32
for j := 0; j < len(queryEmbedding) && j < len(storedEmbedding); j++ {
similarity += queryEmbedding[j] * storedEmbedding[j]
}

if similarity > bestSimilarity {
bestSimilarity = similarity
bestResponse = responseColumn.Data()[i]
}
}

observability.Debugf("MilvusCache.FindSimilar: best similarity=%.4f, threshold=%.4f (checked %d entries)",
bestSimilarity, c.similarityThreshold, queryColumn.Len())

if bestSimilarity >= c.similarityThreshold {
atomic.AddInt64(&c.hitCount, 1)
observability.Debugf("MilvusCache.FindSimilar: CACHE HIT - similarity=%.4f >= threshold=%.4f, response_size=%d bytes",
bestSimilarity, c.similarityThreshold, len(bestResponse))
observability.LogEvent("cache_hit", map[string]interface{}{
"backend": "milvus",
"similarity": bestSimilarity,
"threshold": c.similarityThreshold,
"model": model,
"collection": c.collectionName,
})
metrics.RecordCacheOperation("milvus", "find_similar", "hit", time.Since(start).Seconds())
metrics.RecordCacheHit()
return []byte(bestResponse), true, nil
}

atomic.AddInt64(&c.missCount, 1)
observability.Debugf("MilvusCache.FindSimilar: CACHE MISS - best_similarity=%.4f < threshold=%.4f",
bestSimilarity, c.similarityThreshold)
observability.LogEvent("cache_miss", map[string]interface{}{
"backend": "milvus",
"best_similarity": bestSimilarity,
"threshold": c.similarityThreshold,
"model": model,
"collection": c.collectionName,
"entries_checked": queryColumn.Len(),
atomic.AddInt64(&c.hitCount, 1)
observability.Debugf("MilvusCache.FindSimilar: CACHE HIT - similarity=%.4f >= threshold=%.4f, response_size=%d bytes",
bestScore, c.similarityThreshold, len(responseBody))
observability.LogEvent("cache_hit", map[string]interface{}{
"backend": "milvus",
"similarity": bestScore,
"threshold": c.similarityThreshold,
"model": model,
"collection": c.collectionName,
})
metrics.RecordCacheOperation("milvus", "find_similar", "miss", time.Since(start).Seconds())
metrics.RecordCacheMiss()
return nil, false, nil
metrics.RecordCacheOperation("milvus", "find_similar", "hit", time.Since(start).Seconds())
metrics.RecordCacheHit()
return responseBody, true, nil
}

// Close releases all resources held by the cache
Expand Down
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