# Deep Research

NeuraAI's deep research capabilities enable comprehensive analysis and investigation of complex topics. These specialized models can conduct multi-source research, synthesize information, and provide detailed analytical reports.

## Overview

Deep research models are designed for tasks that require:

* Comprehensive information gathering from multiple sources
* Critical analysis and synthesis of data
* Detailed, evidence-based conclusions
* Citation of sources and supporting evidence

## Basic Usage

```python
from openai import OpenAI

client = OpenAI(
    base_url="https://api.neura-ai.app/v1",
    timeout=3600  # Research tasks may take longer
)

input_text = """
Research the economic impact of semaglutide on global healthcare systems.
Do:
- Include specific figures, trends, statistics, and measurable outcomes.
- Prioritize reliable, up-to-date sources: peer-reviewed research, health
  organizations (e.g., WHO, CDC), regulatory agencies, or pharmaceutical
  earnings reports.
- Include inline citations and return all source metadata.

Be analytical, avoid generalities, and ensure that each section supports
data-backed reasoning that could inform healthcare policy or financial modeling.
"""

response = client.responses.create(
    model="sonar-deep-research",
    input=input_text,
    tools=[
        {"type": "web_search_preview"},
        {"type": "code_interpreter", "container": {"type": "auto"}},
    ],
)

print(response.output_text)
```

## Available Tools

### Web Search

Enables the model to search for current information online:

```python
tools=[
    {"type": "web_search_preview"}
]
```

### Code Interpreter

Allows the model to execute code for data analysis:

```python
tools=[
    {"type": "code_interpreter", "container": {"type": "auto"}}
]
```

### Combined Approach

Use multiple tools for comprehensive research:

```python
tools=[
    {"type": "web_search_preview"},
    {"type": "code_interpreter", "container": {"type": "auto"}}
]
```

## Example Use Cases

### Market Research

```python
response = client.responses.create(
    model="sonar-deep-research",
    input="""
    Analyze the current state of the electric vehicle market in Europe.
    Include:
    - Market size and growth projections for 2024-2026
    - Leading manufacturers and their market share
    - Government policies affecting adoption
    - Infrastructure development trends
    - Consumer sentiment data
    
    Provide specific figures and cite all sources.
    """,
    tools=[{"type": "web_search_preview"}]
)
```

### Scientific Literature Review

```python
response = client.responses.create(
    model="sonar-deep-research",
    input="""
    Conduct a comprehensive review of recent advances in CRISPR gene editing 
    technology (2023-2024). Focus on:
    - Clinical trial results
    - New applications and methodologies
    - Ethical considerations and regulations
    - Future research directions
    
    Reference peer-reviewed publications and clinical data.
    """,
    tools=[{"type": "web_search_preview"}]
)
```

### Competitive Analysis

```python
response = client.responses.create(
    model="sonar-deep-research",
    input="""
    Compare the business models, pricing strategies, and market positioning 
    of the top 5 cloud infrastructure providers.
    
    Analyze:
    - Service offerings and differentiation
    - Pricing structures and total cost of ownership
    - Target customer segments
    - Recent financial performance
    - Strategic partnerships and acquisitions
    """,
    tools=[
        {"type": "web_search_preview"},
        {"type": "code_interpreter", "container": {"type": "auto"}}
    ]
)
```

### Data-Driven Analysis

```python
response = client.responses.create(
    model="sonar-deep-research",
    input="""
    Investigate the correlation between remote work adoption and 
    commercial real estate values in major US cities (2020-2024).
    
    Use statistical analysis to:
    - Identify trends in office space vacancy rates
    - Correlate with property values
    - Compare across different cities
    - Project future implications
    
    Include data visualizations where appropriate.
    """,
    tools=[
        {"type": "web_search_preview"},
        {"type": "code_interpreter", "container": {"type": "auto"}}
    ]
)
```

## Best Practices

### Clear Research Objectives

Define what you want to learn and what constitutes a complete answer:

```python
input_text = """
Research Question: How has AI impacted software development productivity?

Scope:
- Time period: 2022-2024
- Focus on: Code completion tools, automated testing, and bug detection
- Metrics: Developer productivity, code quality, time savings

Required:
- Quantitative data from studies or surveys
- Case studies from companies implementing these tools
- Expert opinions from industry leaders
"""
```

### Specify Source Quality

Guide the model toward reliable sources:

```python
input_text = """
Research renewable energy adoption in developing nations.

Source Requirements:
- Prefer: Academic journals, international organizations (UN, World Bank), 
  government reports
- Include: Industry reports from reputable firms
- Avoid: Blog posts, opinion pieces, unverified claims
"""
```

### Request Structure

Ask for organized, actionable output:

```python
input_text = """
Analyze smartphone market trends in Asia.

Output Structure:
1. Executive Summary (key findings)
2. Market Size and Growth
3. Competitive Landscape
4. Consumer Preferences
5. Future Outlook
6. Sources and References
"""
```

## Timeout Considerations

Research tasks can take significant time. Set appropriate timeouts:

```python
client = OpenAI(
    base_url="https://api.neura-ai.app/v1",
    timeout=3600  # 1 hour for complex research
)
```

## Tips for Effective Research

* Be specific about the scope and depth required
* Request citations and source verification
* Ask for data visualizations when analyzing numbers
* Specify the format you need (report, summary, bullet points)
* Set realistic timeouts for complex queries
* Review and verify critical findings independently