The Answer API combines intelligent search with AI processing to generate comprehensive, factual answers to questions. It automatically searches relevant sources and uses advanced AI to synthesize accurate responses.

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Basic Usage

from valyu import Valyu

valyu = Valyu()

response = valyu.answer(
    "What are the latest developments in quantum computing?"
)

if response.success:
    print("Answer:", response.contents)
    print("Sources used:", response.search_metadata.number_of_results)

​
Parameters

​
Query (Required)

ParameterTypeDescription
querystrThe question or query to answer

​
Options (Optional)

ParameterTypeDescriptionDefault
structured_outputdictJSON schema for structured response formatNone
system_instructionsstrCustom instructions for AI processing (max 2000 characters)None
search_type"web" | "proprietary" | "all"Type of search to perform before generating answer"all"
data_max_pricefloatMaximum cost in USD for data retrieval (search costs only)30.0
country_codestr2-letter ISO country code to bias search resultsNone
included_sourcesList[str]List of specific sources to search withinNone
excluded_sourcesList[str]List of sources to exclude from searchNone
start_datestrStart date for time filtering (YYYY-MM-DD format)None
end_datestrEnd date for time filtering (YYYY-MM-DD format)None
fast_modeboolEnable fast mode for reduced latencyFalse

​
Response Format

# Union type - either success or error response
AnswerResponse = Union[AnswerSuccessResponse, AnswerErrorResponse]

class AnswerSuccessResponse:
    success: bool = True
    ai_tx_id: str
    original_query: str
    contents: Union[str, Dict[str, Any]]  # string or structured object
    data_type: str  # "unstructured" | "structured"
    search_results: List[SearchResult]
    search_metadata: SearchMetadata
    ai_usage: AIUsage
    cost: Cost

class AnswerErrorResponse:
    success: bool = False
    error: str

​
Parameter Examples

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Basic Question Answering

Get a comprehensive answer to any question: 
response = valyu.answer(
    "How do neural networks learn?"
)

if response.success:
    print(response.contents)

​
System Instructions

Customize the AI’s response style and focus: 
response = valyu.answer(
    "Explain quantum computing",
    system_instructions="You are a computer science professor. Explain concepts clearly with practical examples and avoid overly technical jargon."
)

​
Structured Output Response

Get answers in a specific JSON format: 
response = valyu.answer(
    "What is the impact of AI on software development?",
    structured_output={
        "type": "object",
        "properties": {
            "summary": {
                "type": "string",
                "description": "Brief summary of AI's impact"
            },
            "key_impacts": {
                "type": "array",
                "items": {"type": "string"},
                "description": "List of key impacts"
            },
            "benefits": {
                "type": "array",
                "items": {"type": "string"},
                "maxItems": 5
            },
            "challenges": {
                "type": "array", 
                "items": {"type": "string"},
                "maxItems": 5
            },
            "future_outlook": {
                "type": "string",
                "description": "Future implications and trends"
            }
        },
        "required": ["summary", "key_impacts", "future_outlook"]
    }
)

if response.success and response.data_type == "structured":
    structured_answer = response.contents
    print("Summary:", structured_answer["summary"])
    print("Key impacts:", structured_answer["key_impacts"])

​
Source Filtering

Get answers from specific, authoritative sources: 
response = valyu.answer(
    "What are the best practices for React performance optimization?",
    search_type="web",
    included_sources=[
        "react.dev", 
        "developer.mozilla.org",
        "web.dev"
    ],
    system_instructions="Focus on official recommendations and proven techniques"
)

​
Geographic Filtering

Get location-specific information: 
response = valyu.answer(
    "What are the current renewable energy policies and incentives?",
    country_code="US",
    start_date="2024-01-01",
    system_instructions="Focus on federal and state-level policies with specific program details"
)

​
Date Range Filtering

Get answers about recent events or developments: 
response = valyu.answer(
    "What are the latest breakthroughs in AI model architectures?",
    start_date="2024-06-01",
    end_date="2024-12-01",
    search_type="proprietary",
    included_sources=["valyu/valyu-arxiv"],
    system_instructions="Focus on novel architectures and their performance improvements"
)

​
Fast Mode

For time-sensitive applications, enable fast mode for quicker responses: 
response = valyu.answer(
    "What's the current status of the stock market?",
    fast_mode=True
)

​
Use Case Examples

​
Financial Analysis

Build an AI-powered financial research assistant that provides comprehensive market analysis: 
response = valyu.answer(
    "How has Tesla's stock performance compared to other EV companies in 2024?",
    search_type="proprietary",
    included_sources=["valyu/valyu-stocks-US"],
    structured_output={
        "type": "object",
        "properties": {
            "tesla_performance": {
                "type": "object",
                "properties": {
                    "ytd_return": {"type": "string"},
                    "current_price": {"type": "string"},
                    "key_events": { 
                        "type": "array", 
                        "items": {"type": "string"} 
                    }
                }
            },
            "competitor_comparison": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "company": {"type": "string"},
                        "ytd_return": {"type": "string"},
                        "relative_performance": {"type": "string"}
                    }
                }
            },
            "market_analysis": {"type": "string"}
        }
    },
    system_instructions="Provide detailed financial analysis with specific metrics, key events, and market context. Include percentage changes and dollar values where applicable."
)

if response.success and response.data_type == "structured":
    analysis = response.contents
    
    # Display Tesla performance
    print("Tesla Performance:")
    print(f"YTD Return: {analysis['tesla_performance']['ytd_return']}")
    print(f"Current Price: {analysis['tesla_performance']['current_price']}")
    
    # Compare with competitors
    print("\nCompetitor Analysis:")
    for comp in analysis['competitor_comparison']:
        print(f"{comp['company']}: {comp['ytd_return']} ({comp['relative_performance']})")
    
    print(f"\nMarket Analysis: {analysis['market_analysis']}")

​
Technical Documentation Assistant

Create an AI assistant that provides comprehensive technical guidance with implementation details: 
def get_technical_guide(topic: str):
    response = valyu.answer(
        f"How do I implement {topic} in a Node.js application?",
        system_instructions="Provide step-by-step implementation guidance with code examples, security best practices, and common pitfalls to avoid. Structure the response as a comprehensive tutorial.",
        included_sources=[
            "nodejs.org",
            "developer.mozilla.org",
            "github.com"
        ],
        structured_output={
            "type": "object",
            "properties": {
                "overview": {
                    "type": "string",
                    "description": "Brief overview of the implementation"
                },
                "prerequisites": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Required dependencies and setup"
                },
                "implementation_steps": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "step": {"type": "string"},
                            "description": {"type": "string"},
                            "code_example": {"type": "string"}
                        }
                    }
                },
                "security_considerations": {
                    "type": "array",
                    "items": {"type": "string"}
                },
                "common_pitfalls": {
                    "type": "array",
                    "items": {"type": "string"}
                },
                "additional_resources": {
                    "type": "array",
                    "items": {"type": "string"}
                }
            },
            "required": ["overview", "implementation_steps", "security_considerations"]
        }
    )

    if response.success and response.data_type == "structured":
        guide = response.contents
        
        print("=== Technical Implementation Guide ===")
        print(f"\nOverview: {guide['overview']}")
        
        print("\nPrerequisites:")
        for i, req in enumerate(guide.get('prerequisites', []), 1):
            print(f"{i}. {req}")
        
        print("\nImplementation Steps:")
        for i, step in enumerate(guide['implementation_steps'], 1):
            print(f"\n{i}. {step['step']}")
            print(f"   {step['description']}")
            if step.get('code_example'):
                print(f"   Code: {step['code_example']}")
        
        return guide
    
    return None

# Usage example
oauth_guide = get_technical_guide("OAuth 2.0 authentication")

​
Comparative Analysis Tool

Build a decision-making assistant that provides structured comparisons of different options: 
def compare_options(topic: str, options: List[str]):
    response = valyu.answer(
        f"Compare the pros and cons of {', '.join(options)} for {topic}",
        system_instructions="Provide a comprehensive comparison with objective analysis. Include specific examples, metrics, and real-world considerations for each option.",
        structured_output={
            "type": "object",
            "properties": {
                "comparison_summary": {
                    "type": "string",
                    "description": "Brief overview of the comparison"
                },
                "options": {
                    "type": "array",
                    "items": {
                        "type": "object", 
                        "properties": {
                            "name": {"type": "string"},
                            "pros": { 
                                "type": "array", 
                                "items": {"type": "string"} 
                            },
                            "cons": { 
                                "type": "array", 
                                "items": {"type": "string"} 
                            },
                            "best_for": {"type": "string"},
                            "learning_curve": {"type": "string"},
                            "market_adoption": {"type": "string"},
                            "performance_rating": {"type": "string"}
                        }
                    }
                },
                "decision_matrix": {
                    "type": "object",
                    "properties": {
                        "criteria": {
                            "type": "array",
                            "items": {"type": "string"}
                        },
                        "scores": {
                            "type": "object",
                            "description": "Scores for each option on each criteria"
                        }
                    }
                },
                "recommendation": { 
                    "type": "string",
                    "description": "Final recommendation with reasoning"
                }
            },
            "required": ["comparison_summary", "options", "recommendation"]
        }
    )

    if response.success and response.data_type == "structured":
        comparison = response.contents
        
        print("=== Comparative Analysis ===")
        print(f"\n{comparison['comparison_summary']}")
        
        print("\n=== Option Analysis ===")
        for option in comparison['options']:
            print(f"\n{option['name'].upper()}")
            print(f"Best for: {option['best_for']}")
            print(f"Learning curve: {option['learning_curve']}")
            print(f"Market adoption: {option['market_adoption']}")
            
            print("Pros:")
            for pro in option['pros']:
                print(f"  ✓ {pro}")
            
            print("Cons:")
            for con in option['cons']:
                print(f"  ✗ {con}")
        
        print(f"\n=== Final Recommendation ===")
        print(comparison['recommendation'])
        
        return comparison
    
    return None

# Usage examples
framework_comparison = compare_options(
    "enterprise web applications", 
    ["React", "Vue", "Angular"]
)

database_comparison = compare_options(
    "scalable microservices architecture",
    ["PostgreSQL", "MongoDB", "Cassandra"]
)

​
Error Handling

response = valyu.answer(query, **options)

if not response.success:
    print("Answer generation failed:", response.error)
    
    # Handle specific error cases
    if "insufficient credits" in response.error:
        print("Please add more credits to your account")
    elif "invalid" in response.error:
        print("Check your query and options")
    
    return

# Process successful response
print(f"AI Transaction ID: {response.ai_tx_id}")
print(f"Data type: {response.data_type}")
print(f"Sources processed: {response.search_metadata.number_of_results}")
print(f"Answer: {response.contents}")

​
Best Practices

​
Improve Answer Quality

response = valyu.answer(
    query,
    system_instructions="Provide detailed, evidence-based answers with specific examples",
    search_type="proprietary",  # Use high-quality academic/professional sources
    included_sources=["authoritative-domain.com"],
    start_date="2024-01-01"  # Focus on recent information
)

​
Handle Structured Responses

response = valyu.answer(query, structured_output=schema)

if response.success:
    if response.data_type == "structured":
        structured_data = response.contents  # Dict[str, Any]
        # Process structured data
    else:
        text_answer = response.contents  # str
        # Process text answer