برنامج تعليمي: تنفيذ الترتيب المستند إلى الوقت في ميلفوسCompatible with Milvus 2.6.x

في العديد من تطبيقات البحث، لا يقل حداثة المحتوى أهمية عن أهميته. تستفيد المقالات الإخبارية وقوائم المنتجات ومنشورات وسائل التواصل الاجتماعي والأوراق البحثية من أنظمة الترتيب التي توازن بين الأهمية الدلالية والحداثة. يوضح هذا البرنامج التعليمي كيفية تنفيذ الترتيب المستند إلى الوقت في Milvus باستخدام مصنفات التضاؤل.

فهم مصنفات التضاؤل في ميلفوس

تسمح لك مصنفات التضاؤل بتعزيز أو معاقبة المستندات استنادًا إلى قيم رقمية (مثل الطوابع الزمنية) بالنسبة لنقطة مرجعية. بالنسبة للترتيب المستند إلى الوقت، هذا يعني أن المستندات الأحدث يمكن أن تحصل على درجات أعلى من المستندات الأقدم، حتى عندما تكون أهميتها الدلالية متشابهة.

يدعم ميلفوس ثلاثة أنواع من مصنفات الاضمحلال:

• غاوسي (gauss): منحنى على شكل جرس يوفر تضاؤلاً سلسًا وتدريجيًا

• أسي (exp): يخلق انخفاضًا أوليًا أكثر حدة للتأكيد بقوة على المحتوى الحديث

• خطي (linear): تضاؤل مستقيم يمكن التنبؤ به ويسهل فهمه

لكل مصنف خصائص مختلفة تجعله مناسبًا لحالات استخدام مختلفة. لمزيد من المعلومات، ارجع إلى نظرة عامة على مصنف التضاؤل.

بناء نظام بحث مدرك للوقت

سننشئ نظام بحث عن المقالات الإخبارية يوضح كيفية ترتيب المحتوى بفعالية بناءً على كل من الصلة والوقت. لنبدأ بالتنفيذ:

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)

الخطوة 1: تصميم المخطط

للبحث المستند إلى الوقت، نحتاج إلى تخزين الطابع الزمني للنشر مع المحتوى:

# 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

الخطوة 2: إعداد وظائف التضمين

سنقوم بتهيئة كل من دوال التضمين الكثيفة (الدلالية) والمتناثرة (الكلمات المفتاحية):

# 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)

للحصول على تفاصيل حول كيفية استخدام دوال التضمين في ميلفوس، راجع نظرة عامة على دوال التضمين.

الخطوة 3: تكوين معلمات الفهرس

لنقم بإعداد معلمات الفهرس المناسبة للبحث السريع عن المتجهات:

# 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"
)

الخطوة 4: إعداد عينة من البيانات

في هذا البرنامج التعليمي، سننشئ مجموعة من المقالات الإخبارية بتواريخ نشر مختلفة. لاحظ كيف أننا قمنا بتضمين أزواج من المقالات ذات المحتوى المتطابق تقريبًا ولكن بتواريخ مختلفة لتوضيح تأثير تضاؤل الترتيب:

# 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")

الخطوة 5: تكوين مصنفات اضمحلال مختلفة

الآن دعنا ننشئ ثلاثة مصنفات تضاؤل مختلفة، لكل منها مع معلمات مختلفة لإبراز الاختلافات بينها:

# 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
    }
)

في الكود السابق

• reranker: اضبط على decay لدوال الاضمحلال المستندة إلى الوقت

• function: نوع دالة الاضمحلال (غاوس، أو إكس، أو خطي)

• origin: النقطة المرجعية (عادةً الوقت الحالي)

• offset: الفترة التي تحافظ خلالها المستندات على الملاءمة الكاملة

• scale: التحكم في مدى سرعة تناقص الملاءمة بعد الإزاحة

• decay: عامل الاضمحلال عند الإزاحة + المقياس (على سبيل المثال، 0.5 يعني نصف الصلة)

لاحظ أننا قمنا بتكوين المصنف الأسي بمعلمات مختلفة لتوضيح كيف يمكنك ضبط هذه الدوال لسلوكيات مختلفة.

الخطوة 6: تصور مصنفات التضاؤل

قبل إجراء عمليات البحث، دعنا ننشئ مقارنة مرئية لكيفية تصرف مصنفات التضاؤل التي تم تكوينها بشكل مختلف:

# 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}")

المخرجات المتوقعة:

=== 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

الخطوة 7: الدالة المساعدة لعرض النتائج

# 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()

الخطوة 8: مقارنة البحث القياسي مقابل البحث القائم على التضاؤل

الآن دعنا نجري استعلام بحث ونقارن النتائج مع وبدون تصنيف التضاؤل:

# 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")

المخرجات المتوقعة:

=== 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

الخطوة 9: فهم حساب الدرجات

دعنا نفصل كيفية حساب الدرجات النهائية من خلال الجمع بين الصلة الأصلية وعوامل التضاؤل:

# 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()

المخرجات المتوقعة:

=== 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

الخطوة 10: البحث الهجين مع التضاؤل الزمني

بالنسبة للسيناريوهات الأكثر تعقيدًا، يمكننا الجمع بين المتجهات الكثيفة (الدلالية) والمتناثرة (الكلمات المفتاحية) باستخدام البحث الهجين:

# 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")

المخرجات المتوقعة:

=== 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

الخطوة 11: جرّب قيم بارامترات مختلفة

لنرى كيف يؤثر تعديل معلمة المقياس على دالة التضاؤل الغاوسي:

# 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)")

المخرجات المتوقعة:

=== 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

الخطوة 12: الاختبار باستخدام استعلامات مختلفة

لنرى كيف يعمل ترتيب التضاؤل مع استعلامات بحث مختلفة:

# 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}'")

المخرجات المتوقعة:

=== 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

الخاتمة

يوفر الترتيب المستند إلى الوقت باستخدام دوال التضاؤل في ميلفوس طريقة فعالة لتحقيق التوازن بين الأهمية الدلالية والحداثة. من خلال تكوين دالة الاضمحلال المناسبة والمعلمات المناسبة، يمكنك إنشاء تجارب بحث تبرز المحتوى الجديد مع احترام الصلة الدلالية.

هذا النهج ذو قيمة خاصة لـ

• الأخبار والمنصات الإعلامية

• قوائم منتجات التجارة الإلكترونية

• موجزات محتوى الوسائط الاجتماعية

• قواعد المعرفة وأنظمة التوثيق

• مستودعات الأوراق البحثية

من خلال فهم الرياضيات الكامنة وراء وظائف الاضمحلال وتجربة معلمات مختلفة، يمكنك ضبط نظام البحث الخاص بك لتوفير التوازن الأمثل بين الملاءمة والتحديث لحالة الاستخدام الخاصة بك.