Tutorial: Implementare la classificazione basata sul tempo in MilvusCompatible with Milvus 2.6.x

In molte applicazioni di ricerca, la freschezza dei contenuti è importante quanto la loro rilevanza. Articoli di cronaca, annunci di prodotti, post sui social media e articoli di ricerca traggono tutti vantaggio da sistemi di classificazione che bilanciano la rilevanza semantica e la ricorrenza. Questo tutorial mostra come implementare una classificazione basata sul tempo in Milvus utilizzando i ranker di decadimento.

Capire i ranker di decadimento in Milvus

I classificatori di decadimento consentono di aumentare o penalizzare i documenti in base a valori numerici (come i timestamp) rispetto a un punto di riferimento. Per i ranking basati sul tempo, ciò significa che i documenti più recenti possono ricevere punteggi più alti di quelli più vecchi, anche quando la loro rilevanza semantica è simile.

Milvus supporta tre tipi di classificatori di decadimento:

• Gaussiano (gauss): Una curva a campana che fornisce un decadimento dolce e graduale.

• Esponenziale (exp): Crea un calo iniziale più netto per enfatizzare fortemente i contenuti recenti.

• Lineare (linear): Un decadimento rettilineo, prevedibile e facile da capire.

Ogni classificatore ha caratteristiche diverse che lo rendono adatto a vari casi d'uso. Per ulteriori informazioni, consultare la sezione Panoramica dei ranker di decadimento.

Costruire un sistema di ricerca consapevole del tempo

Creeremo un sistema di ricerca di articoli giornalistici che dimostri come classificare efficacemente i contenuti in base alla rilevanza e al tempo. Iniziamo con l'implementazione:

import datetime
import matplotlib.pyplot as plt
import numpy as np
from pymilvus import (
    MilvusClient,
    DataType,
    Function,
    FunctionType,
    AnnSearchRequest,
)

# Create connection to Milvus
milvus_client = MilvusClient("http://localhost:19530")

# Define collection name
collection_name = "news_articles_tutorial"

# Clean up any existing collection with the same name
milvus_client.drop_collection(collection_name)

Passo 1: progettare lo schema

Per la ricerca basata sul tempo, dobbiamo memorizzare il timestamp di pubblicazione insieme al contenuto:

# Create schema with fields for content and temporal information
schema = milvus_client.create_schema(enable_dynamic_field=False, auto_id=True)
schema.add_field("id", DataType.INT64, is_primary=True)
schema.add_field("headline", DataType.VARCHAR, max_length=200, enable_analyzer=True)
schema.add_field("content", DataType.VARCHAR, max_length=2000, enable_analyzer=True)
schema.add_field("dense", DataType.FLOAT_VECTOR, dim=1024)  # For dense embeddings
schema.add_field("sparse_vector", DataType.SPARSE_FLOAT_VECTOR)  # For sparse (BM25) search
schema.add_field("publish_date", DataType.INT64)  # Timestamp for decay ranking

Passo 2: Configurare le funzioni di incorporamento

Configureremo funzioni di incorporamento dense (semantiche) e rade (parole chiave):

# Create embedding function for semantic search
text_embedding_function = Function(
    name="siliconflow_embedding",
    function_type=FunctionType.TEXTEMBEDDING,
    input_field_names=["content"],
    output_field_names=["dense"],
    params={
        "provider": "siliconflow",
        "model_name": "BAAI/bge-large-en-v1.5",
        "credential": "your-api-key"
    }
)
schema.add_function(text_embedding_function)

# Create BM25 function for keyword search
bm25_function = Function(
    name="bm25",
    input_field_names=["content"],
    output_field_names=["sparse_vector"],
    function_type=FunctionType.BM25,
)
schema.add_function(bm25_function)

Per maggiori dettagli sull'uso delle funzioni di incorporamento di Milvus, consultare Panoramica delle funzioni di incorporamento.

Passo 3: Configurare i parametri dell'indice

Impostiamo i parametri dell'indice appropriati per la ricerca vettoriale veloce:

# Set up indexes for fast search
index_params = milvus_client.prepare_index_params()

# Dense vector index
index_params.add_index(field_name="dense", index_type="AUTOINDEX", metric_type="L2")

# Sparse vector index
index_params.add_index(
    field_name="sparse_vector",
    index_name="sparse_inverted_index",
    index_type="AUTOINDEX",
    metric_type="BM25",
)

# Create the collection with our schema and indexes
milvus_client.create_collection(
    collection_name,
    schema=schema,
    index_params=index_params,
    consistency_level="Bounded"
)

Passo 4: Preparare i dati di esempio

Per questa esercitazione, creeremo una serie di articoli di notizie con date di pubblicazione diverse. Notate come abbiamo incluso coppie di articoli con contenuti quasi identici ma date diverse per dimostrare chiaramente l'effetto di decadimento del ranking:

# Get current time
current_time = int(datetime.datetime.now().timestamp())
current_date = datetime.datetime.fromtimestamp(current_time)
print(f"Current time: {current_date.strftime('%Y-%m-%d %H:%M:%S')}")

# Sample news articles spanning different dates
articles = [
    {
        "headline": "AI Breakthrough Enables Medical Diagnosis Advancement",
        "content": "Researchers announced a major breakthrough in AI-based medical diagnostics, enabling faster and more accurate detection of rare diseases.",
        "publish_date": int((current_date - datetime.timedelta(days=120)).timestamp())  # ~4 months ago
    },
    {
        "headline": "Tech Giants Compete in New AI Race",
        "content": "Major technology companies are investing billions in a new race to develop the most advanced artificial intelligence systems.",
        "publish_date": int((current_date - datetime.timedelta(days=60)).timestamp())  # ~2 months ago
    },
    {
        "headline": "AI Ethics Guidelines Released by International Body",
        "content": "A consortium of international organizations has released new guidelines addressing ethical concerns in artificial intelligence development and deployment.",
        "publish_date": int((current_date - datetime.timedelta(days=30)).timestamp())  # 1 month ago
    },
    {
        "headline": "Latest Deep Learning Models Show Remarkable Progress",
        "content": "The newest generation of deep learning models demonstrates unprecedented capabilities in language understanding and generation.",
        "publish_date": int((current_date - datetime.timedelta(days=15)).timestamp())  # 15 days ago
    },
    # Articles with identical content but different dates
    {
        "headline": "AI Research Advancements Published in January",
        "content": "Breakthrough research in artificial intelligence shows remarkable advancements in multiple domains.",
        "publish_date": int((current_date - datetime.timedelta(days=90)).timestamp())  # ~3 months ago
    },
    {
        "headline": "New AI Research Results Released This Week",
        "content": "Breakthrough research in artificial intelligence shows remarkable advancements in multiple domains.",
        "publish_date": int((current_date - datetime.timedelta(days=5)).timestamp())  # Very recent - 5 days ago
    },
    {
        "headline": "AI Development Updates Released Yesterday",
        "content": "Recent developments in artificial intelligence research are showing promising results across various applications.",
        "publish_date": int((current_date - datetime.timedelta(days=1)).timestamp())  # Just yesterday
    },
]

# Insert articles into the collection
milvus_client.insert(collection_name, articles)
print(f"Inserted {len(articles)} articles into the collection")

Passo 5: Configurare diversi classificatori di decadimento

Ora creiamo tre diversi classificatori di decadimento, ciascuno con parametri distinti per evidenziarne le differenze:

# Use current time as reference point
print(f"Using current time as reference point")

# Create a Gaussian decay ranker
gaussian_ranker = Function(
    name="time_decay_gaussian",
    input_field_names=["publish_date"],
    function_type=FunctionType.RERANK,
    params={
        "reranker": "decay",
        "function": "gauss",           # Gaussian/bell curve decay
        "origin": current_time,        # Current time as reference point
        "offset": 7 * 24 * 60 * 60,    # One week (full relevance)
        "decay": 0.5,                  # Articles from two weeks ago have half relevance 
        "scale": 14 * 24 * 60 * 60     # Two weeks scale parameter
    }
)

# Create an exponential decay ranker with different parameters
exponential_ranker = Function(
    name="time_decay_exponential",
    input_field_names=["publish_date"],
    function_type=FunctionType.RERANK,
    params={
        "reranker": "decay",
        "function": "exp",             # Exponential decay
        "origin": current_time,        # Current time as reference point
        "offset": 3 * 24 * 60 * 60,    # Shorter offset (3 days vs 7 days)
        "decay": 0.3,                  # Steeper decay (0.3 vs 0.5) 
        "scale": 10 * 24 * 60 * 60     # Different scale (10 days vs 14 days)
    }
)

# Create a linear decay ranker
linear_ranker = Function(
    name="time_decay_linear",
    input_field_names=["publish_date"],
    function_type=FunctionType.RERANK,
    params={
        "reranker": "decay",
        "function": "linear",          # Linear decay
        "origin": current_time,        # Current time as reference point
        "offset": 7 * 24 * 60 * 60,    # One week (full relevance)
        "decay": 0.5,                  # Articles from two weeks ago have half relevance
        "scale": 14 * 24 * 60 * 60     # Two weeks scale parameter
    }
)

Nel codice precedente:

• reranker: Impostato su decay per le funzioni di decadimento basate sul tempo

• function: Il tipo di funzione di decadimento (gauss, exp, o lineare)

• origin: Il punto di riferimento (di solito l'ora corrente)

• offset: Il periodo durante il quale i documenti mantengono la piena rilevanza

• scale: Controlla la velocità con cui la rilevanza diminuisce al di là dell'offset

• decay: Il fattore di decadimento a offset+scala (ad esempio, 0,5 significa rilevanza dimezzata)

Si noti che abbiamo configurato il ranker esponenziale con parametri diversi per dimostrare come sia possibile sintonizzare queste funzioni per ottenere comportamenti diversi.

Passo 6: visualizzare i ranker di decadimento

Prima di eseguire le ricerche, creiamo un confronto visivo del comportamento di questi decay ranker configurati in modo diverso:

# Visualize the decay functions with different parameters
days = np.linspace(0, 90, 100)
# Gaussian: offset=7, scale=14, decay=0.5
gaussian_values = [1.0 if d <= 7 else (0.5 ** ((d - 7) / 14)) for d in days]
# Exponential: offset=3, scale=10, decay=0.3
exponential_values = [1.0 if d <= 3 else (0.3 ** ((d - 3) / 10)) for d in days]
# Linear: offset=7, scale=14, decay=0.5
linear_values = [1.0 if d <= 7 else max(0, 1.0 - ((d - 7) / 14) * 0.5) for d in days]

plt.figure(figsize=(10, 6))
plt.plot(days, gaussian_values, label='Gaussian (offset=7, scale=14, decay=0.5)')
plt.plot(days, exponential_values, label='Exponential (offset=3, scale=10, decay=0.3)')
plt.plot(days, linear_values, label='Linear (offset=7, scale=14, decay=0.5)')
plt.axhline(y=0.5, color='gray', linestyle='--', alpha=0.5, label='Half relevance')
plt.xlabel('Days ago')
plt.ylabel('Relevance factor')
plt.title('Decay Functions Comparison')
plt.legend()
plt.grid(True)
plt.savefig('decay_functions.png')
plt.close()

# Print numerical representation
print("\n=== TIME DECAY EFFECT VISUALIZATION ===")
print("Days ago | Gaussian | Exponential | Linear")
print("-----------------------------------------")
for days in [0, 3, 7, 10, 14, 21, 30, 60, 90]:
    # Calculate decay factors based on the parameters in our rankers
    gaussian_decay = 1.0 if days <= 7 else (0.5 ** ((days - 7) / 14))
    exponential_decay = 1.0 if days <= 3 else (0.3 ** ((days - 3) / 10))
    linear_decay = 1.0 if days <= 7 else max(0, 1.0 - ((days - 7) / 14) * 0.5)
    
    print(f"{days:2d} days | {gaussian_decay:.4f}   | {exponential_decay:.4f}     | {linear_decay:.4f}")

Risultato atteso:

=== TIME DECAY EFFECT VISUALIZATION ===
Days ago | Gaussian | Exponential | Linear
-----------------------------------------
 0 days | 1.0000   | 1.0000     | 1.0000
 3 days | 1.0000   | 1.0000     | 1.0000
 7 days | 1.0000   | 0.6178     | 1.0000
10 days | 0.8620   | 0.4305     | 0.8929
14 days | 0.7071   | 0.2660     | 0.7500
21 days | 0.5000   | 0.1145     | 0.5000
30 days | 0.3202   | 0.0387     | 0.1786
60 days | 0.0725   | 0.0010     | 0.0000
90 days | 0.0164   | 0.0000     | 0.0000

Passo 7: Funzione di aiuto per la visualizzazione dei risultati

# Helper function to format search results with dates and scores
def print_search_results(results, title):
    print(f"\n=== {title} ===")
    for i, hit in enumerate(results[0]):
        publish_date = datetime.datetime.fromtimestamp(hit.get('publish_date'))
        days_from_now = (current_time - hit.get('publish_date')) / (24 * 60 * 60)
        
        print(f"{i+1}. {hit.get('headline')}")
        print(f"   Published: {publish_date.strftime('%Y-%m-%d')} ({int(days_from_now)} days ago)")
        print(f"   Score: {hit.score:.4f}")
        print()

Passo 8: Confronto tra ricerca standard e ricerca basata sul decadimento

Ora eseguiamo una query di ricerca e confrontiamo i risultati con e senza decay ranking:

# Define our search query
query = "artificial intelligence advancements"

# 1. Search without decay ranking (purely based on semantic relevance)
standard_results = milvus_client.search(
    collection_name,
    data=[query],
    anns_field="dense",
    limit=7,  # Get all our articles
    output_fields=["headline", "content", "publish_date"],
    consistency_level="Bounded"
)
print_search_results(standard_results, "SEARCH RESULTS WITHOUT DECAY RANKING")

# Store original scores for later comparison
original_scores = {}
for hit in standard_results[0]:
    original_scores[hit.get('headline')] = hit.score

# 2. Search with each decay function
# Gaussian decay
gaussian_results = milvus_client.search(
    collection_name,
    data=[query],
    anns_field="dense",
    limit=7,
    output_fields=["headline", "content", "publish_date"],
    ranker=gaussian_ranker,
    consistency_level="Bounded"
)
print_search_results(gaussian_results, "SEARCH RESULTS WITH GAUSSIAN DECAY RANKING")

# Exponential decay
exponential_results = milvus_client.search(
    collection_name,
    data=[query],
    anns_field="dense",
    limit=7,
    output_fields=["headline", "content", "publish_date"],
    ranker=exponential_ranker,
    consistency_level="Bounded"
)
print_search_results(exponential_results, "SEARCH RESULTS WITH EXPONENTIAL DECAY RANKING")

# Linear decay
linear_results = milvus_client.search(
    collection_name,
    data=[query],
    anns_field="dense",
    limit=7,
    output_fields=["headline", "content", "publish_date"],
    ranker=linear_ranker,
    consistency_level="Bounded"
)
print_search_results(linear_results, "SEARCH RESULTS WITH LINEAR DECAY RANKING")

Risultato atteso:

=== SEARCH RESULTS WITHOUT DECAY RANKING ===
1. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

2. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.4315

3. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

4. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.6671

5. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.6674

6. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.7279

7. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.7661

=== SEARCH RESULTS WITH GAUSSIAN DECAY RANKING ===
1. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.5322

2. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

3. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.1180

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0000

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

=== SEARCH RESULTS WITH EXPONENTIAL DECAY RANKING ===
1. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

2. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.3392

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.1574

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.0297

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0007

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

=== SEARCH RESULTS WITH LINEAR DECAY RANKING ===
1. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.4767

2. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

3. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.3831

4. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

5. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.3640

6. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.3335

7. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.2158

Passo 9: Comprendere il calcolo del punteggio

Vediamo come vengono calcolati i punteggi finali combinando la rilevanza originale con i fattori di decadimento:

# Add a detailed breakdown for the first 3 results from Gaussian decay
print("\n=== SCORE CALCULATION BREAKDOWN (GAUSSIAN DECAY) ===")
for item in gaussian_results[0][:3]:
    headline = item.get('headline')
    publish_date = datetime.datetime.fromtimestamp(item.get('publish_date'))
    days_ago = (current_time - item.get('publish_date')) / (24 * 60 * 60)
    
    # Get the original score
    original_score = original_scores.get(headline, 0)
    
    # Calculate decay factor
    decay_factor = 1.0 if days_ago <= 7 else (0.5 ** ((days_ago - 7) / 14))
    
    # Show breakdown
    print(f"Item: {headline}")
    print(f"  Published: {publish_date.strftime('%Y-%m-%d')} ({int(days_ago)} days ago)")
    print(f"  Original relevance score: {original_score:.4f}")
    print(f"  Decay factor (Gaussian): {decay_factor:.4f}")
    print(f"  Expected final score = Original × Decay: {original_score * decay_factor:.4f}")
    print(f"  Actual final score: {item.score:.4f}")
    print()

Risultato atteso:

=== SCORE CALCULATION BREAKDOWN (GAUSSIAN DECAY) ===
Item: Latest Deep Learning Models Show Remarkable Progress
  Published: 2025-04-30 (15 days ago)
  Original relevance score: 0.6674
  Decay factor (Gaussian): 0.6730
  Expected final score = Original × Decay: 0.4491
  Actual final score: 0.5322

Item: New AI Research Results Released This Week
  Published: 2025-05-10 (5 days ago)
  Original relevance score: 0.4316
  Decay factor (Gaussian): 1.0000
  Expected final score = Original × Decay: 0.4316
  Actual final score: 0.4316

Item: AI Development Updates Released Yesterday
  Published: 2025-05-14 (1 days ago)
  Original relevance score: 0.3670
  Decay factor (Gaussian): 1.0000
  Expected final score = Original × Decay: 0.3670
  Actual final score: 0.3670

Passo 10: Ricerca ibrida con decadimento temporale

Per scenari più complessi, possiamo combinare vettori densi (semantici) e radi (parole chiave) utilizzando una ricerca ibrida:

# Set up hybrid search (combining dense and sparse vectors)
dense_search = AnnSearchRequest(
    data=[query],
    anns_field="dense",  # Search dense vectors
    param={},
    limit=7
)

sparse_search = AnnSearchRequest(
    data=[query],
    anns_field="sparse_vector",  # Search sparse vectors (BM25)
    param={},
    limit=7
)

# Execute hybrid search with each decay function
# Gaussian decay
hybrid_gaussian_results = milvus_client.hybrid_search(
    collection_name,
    [dense_search, sparse_search],
    ranker=gaussian_ranker,
    limit=7,
    output_fields=["headline", "content", "publish_date"]
)
print_search_results(hybrid_gaussian_results, "HYBRID SEARCH RESULTS WITH GAUSSIAN DECAY RANKING")

# Exponential decay
hybrid_exponential_results = milvus_client.hybrid_search(
    collection_name,
    [dense_search, sparse_search],
    ranker=exponential_ranker,
    limit=7,
    output_fields=["headline", "content", "publish_date"]
)
print_search_results(hybrid_exponential_results, "HYBRID SEARCH RESULTS WITH EXPONENTIAL DECAY RANKING")

Risultato atteso:

=== HYBRID SEARCH RESULTS WITH GAUSSIAN DECAY RANKING ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 2.1467

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.7926

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.5322

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.1180

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0000

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

=== HYBRID SEARCH RESULTS WITH EXPONENTIAL DECAY RANKING ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 1.6873

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.7926

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.1574

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.0297

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0007

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0001

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

Fase 11: Esperimento con diversi valori dei parametri

Vediamo come la regolazione del parametro di scala influisce sulla funzione di decadimento gaussiano:

# Create variations of the Gaussian decay function with different scale parameters
print("\n=== PARAMETER VARIATION EXPERIMENT: SCALE ===")
for scale_days in [7, 14, 30]:
    scaled_ranker = Function(
        name=f"time_decay_gaussian_{scale_days}",
        input_field_names=["publish_date"],
        function_type=FunctionType.RERANK,
        params={
            "reranker": "decay",
            "function": "gauss",
            "origin": current_time,
            "offset": 7 * 24 * 60 * 60,  # Fixed offset of 7 days
            "decay": 0.5,                # Fixed decay of 0.5
            "scale": scale_days * 24 * 60 * 60  # Variable scale
        }
    )
    
    # Get results
    scale_results = milvus_client.search(
        collection_name,
        data=[query],
        anns_field="dense",
        limit=7,
        output_fields=["headline", "content", "publish_date"],
        ranker=scaled_ranker,
        consistency_level="Bounded"
    )
    
    print_search_results(scale_results, f"SEARCH WITH GAUSSIAN DECAY (SCALE = {scale_days} DAYS)")

Risultato atteso:

=== PARAMETER VARIATION EXPERIMENT: SCALE ===

=== SEARCH WITH GAUSSIAN DECAY (SCALE = 7 DAYS) ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.2699

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.0004

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0000

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

=== SEARCH WITH GAUSSIAN DECAY (SCALE = 14 DAYS) ===
1. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.5322

2. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

3. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.1180

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0000

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

=== SEARCH WITH GAUSSIAN DECAY (SCALE = 30 DAYS) ===
1. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.6353

2. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.5097

3. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

4. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0767

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0021

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

Fase 12: Test con diverse query

Vediamo come si comporta il ranking di decadimento con diverse query di ricerca:

# Try different queries with Gaussian decay
for test_query in ["machine learning", "neural networks", "ethics in AI"]:
    print(f"\n=== TESTING QUERY: '{test_query}' WITH GAUSSIAN DECAY ===")
    test_results = milvus_client.search(
        collection_name,
        data=[test_query],
        anns_field="dense",
        limit=4,
        output_fields=["headline", "content", "publish_date"],
        ranker=gaussian_ranker,
        consistency_level="Bounded"
    )
    print_search_results(test_results, f"TOP 4 RESULTS FOR '{test_query}'")

Risultato atteso:

=== TESTING QUERY: 'machine learning' WITH GAUSSIAN DECAY ===

=== TOP 4 RESULTS FOR 'machine learning' ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.8208

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.7287

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.6633

4. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

=== TESTING QUERY: 'neural networks' WITH GAUSSIAN DECAY ===

=== TOP 4 RESULTS FOR 'neural networks' ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.8509

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.7574

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.6364

4. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

=== TESTING QUERY: 'ethics in AI' WITH GAUSSIAN DECAY ===

=== TOP 4 RESULTS FOR 'ethics in AI' ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.7977

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.7322

3. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.0814

4. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

Conclusione

Il ranking basato sul tempo che utilizza le funzioni di decadimento in Milvus offre un modo potente per bilanciare la rilevanza semantica e la ricorrenza. Configurando la funzione di decadimento e i parametri appropriati, è possibile creare esperienze di ricerca che mettano in risalto i contenuti freschi, rispettando al contempo la rilevanza semantica.

Questo approccio è particolarmente utile per:

• Piattaforme di notizie e media

• Inserzioni di prodotti di e-commerce

• Feed di contenuti dei social media

• Basi di conoscenza e sistemi di documentazione

• Repository di documenti di ricerca

Comprendendo la matematica alla base delle funzioni di decadimento e sperimentando diversi parametri, è possibile mettere a punto il sistema di ricerca in modo da fornire l'equilibrio ottimale tra pertinenza e freschezza per il proprio caso d'uso specifico.