Filter your Valyu DeepSearch results by publication date to find exactly the content you need, when you need it. Whether you’re researching recent breakthroughs or analysing historical trends, date filtering your DeepSearch results are relevant.
Date filters use the ISO 8601 standard format: YYYY-MM-DD. Both start_date and end_date are optional parameters that work independently or together.

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Why Use Date Filtering?

Date and time filters provide precise temporal control over your search results, enabling you to:
  • 🎯 Find recent developments - Get the latest research, news, or market data
  • 📚 Access historical content - Research how topics evolved over time
  • ⚡ Improve search efficiency - Reduce noise by focusing on relevant time periods
  • 🔍 Enable trend analysis - Compare content across different eras

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Parameters Reference

start_date

Format: YYYY-MM-DDOnly include content published on or after this date.Example: "2024-01-01"

end_date

Format: YYYY-MM-DDOnly include content published on or before this date.Example: "2024-12-31"

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Date Format Requirements

Strict Format Required: Dates must match the YYYY-MM-DD format exactly (ISO 8601 standard).
✅ Correct Format❌ Incorrect Format
"2024-03-15""3/15/2024"
"2024-01-01""March 15, 2024"
"2024-12-31""15-03-2024"

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Quick Start Examples

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Recent Content Only

Perfect for getting the latest information on rapidly evolving topics: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
    "quantum computing breakthroughs",
    start_date="2024-06-01"  # Only content from June 2024 onwards
)

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Specific Date Range

Ideal for research within defined time periods: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
    "artificial intelligence ethics",
    start_date="2023-01-01",
    end_date="2023-12-31"  # All of 2023
)

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Historical Research

Great for studying foundational work or past trends: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
    "machine learning foundations",
    end_date="2015-12-31"  # Pre-deep learning era
)

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Use Case Examples

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Market Intelligence

Track market trends, earnings reports, and economic indicators within specific periods: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
    "cryptocurrency adoption trends Q4 2024",
    start_date="2024-10-01",
    search_type="all",
    max_num_results=10
)

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Academic Research

Find recent papers, compare research across eras, or track scientific progress: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
    "CRISPR gene editing therapeutic applications",
    search_type="proprietary",  # Academic sources only
    start_date="2023-06-01",
    max_num_results=15
)

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Trend Analysis

Study how topics, opinions, or technologies evolved over different time periods: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")

# Compare AI discourse: early era vs modern era
early_ai = valyu.search(
    "artificial intelligence capabilities",
    start_date="1980-01-01",
    end_date="2000-12-31"
)

modern_ai = valyu.search(
    "artificial intelligence capabilities", 
    start_date="2020-01-01",
    end_date="2024-12-31"
)

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Event-Based Research

Research content around specific events, crises, or breakthrough moments: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
    "remote work productivity studies",
    start_date="2020-03-01",
    end_date="2022-12-31",  # Pandemic era
    search_type="all",
    max_num_results=20
)

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Best Practices

Performance Tip: More specific date ranges typically improve response times and result relevance by reducing the search space.

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Strategic Date Range Selection

Content TypeRecommended RangeReasoning
Academic Papers6-12 monthsLonger publication cycles
News & Current EventsDays to weeksFast-moving information
Technical Documentation1-3 monthsRegular updates, best practices
Financial ReportsQuarters or yearsReporting cycles and trends