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Skillsbyted-deepsearch

byted-deepsearch

Data & analytics

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Updated 10-15 17:30

About this skill

BytedDeepsearch

Overview

Deep Research Agent System Community Edition is a workflow-based deep research system capable of performing multi-round iterative web searches and comprehensive analysis on complex topics. The system generates detailed research reports by combining intelligent LLM analysis and real-time web search.

Dependent Skills

Workflow Invocation Logic

System Architecture

This system is a workflow description document and has no execution scripts. It depends on the following external skills:

  • byted-web-search skill: Execute web search`
  • LLM Skill: Used for intelligent analysis and reasoning

Workflow

1. Input Reception Phase

  • User Input: Research topic (sys.query) and research depth (depth)
  • Default Depth: 3 (configurable)

2. Initialization Phase

  • Create an iteration array with a depth equal to the specified depth value
  • Array format:[0, 1, ..., depth-1]

3. Iterative Search Phase (multi-round execution)

Each iteration performs the following steps:

a) LLM Intelligent Analysis
  • Use the LLM to analyze the current research state
  • Input: user query, collected findings, searched topics
  • Output: JSON format, containing:
    • nextSearchTopic: Next search topic (string or None)
    • shouldContinue: Whether to continue searching (boolean)
b) JSON Parsing
  • ExtractnextSearchTopicandshouldContinuefields
  • Update conversation variables
c) Conditional Judgment
  • IfshouldContinueis True:
    • Execute byted-web-search:python scripts/web_search.py "<nextSearchTopic>"
    • Append search results to the findings array
    • Update search progress display
    • Continue to the next iteration
  • IfshouldContinueis False:
    • End the current iteration
    • Output intermediate results
d) Variable Management
  • UpdatenextSearchTopicandshouldContinuevariables
  • AppendnextSearchTopictotopicsarray (record searched topics)
  • Avoid searching the same topic repeatedly

4. Comprehensive Analysis Phase

  • After all iterations are complete, use the LLM to comprehensively analyze all collected findings
  • Generate a detailed comprehensive analysis report
  • Output format: detailed report in Markdown format

5. Report Generation Phase

  • Output the final research analysis results
  • Include important insights, conclusions, and remaining uncertainties
  • Cite sources appropriately

Variable Description

The system maintains the following conversation variables:

Variable Name Type Description
topics array[string] List of searched topics
nextSearchTopic string The next topic to search
findings array[string] List of collected search results
shouldContinue string Flag indicating whether to continue searching

Web Search Integration

When a web search is needed:

  1. UsenextSearchTopicas the query parameter
  2. Run the byted-web-search skill:python scripts/web_search.py "<query>"
  3. Organize the answer based on the returned summary list, without adding or fabricating content
  4. Append the search results to thefindingsarray

Note: Do not use any search parameter configurations (such as search_depth, topic, max_results, country, time_range, days, etc.); keep only the core input query.

Progress Tracking

The system displays search progress in real time:

  • Format:{index + 1}/{depth}th search executed.
  • For example:1/3th search executed.

Use Cases

Applicable Scenarios

  1. Complex Topic Research: Requires in-depth and comprehensive research on a specific topic
  2. Latest Information Analysis: Requires generating a detailed analysis report based on the latest web information
  3. Multi-Perspective Exploration: Requires exploring a topic from different angles and dimensions
  4. Systematic Investigation: Requires systematic investigation and evidence collection

Typical Use Cases

  • Market Trend Analysis
  • Technology Development Research
  • Competitor Analysis
  • Academic Literature Review
  • Product Research

Technical Features

Intelligent Features

  1. Adaptive Search: After each round of search, the LLM analyzes the results and intelligently determines the next search direction
  2. Avoid Duplication: The system records searched topics to avoid repeatedly searching the same content
  3. Deep Reasoning: Use a dedicated reasoning model for comprehensive analysis

System Features

  1. Multi-Round Iteration: Supports multi-round search with a specified depth
  2. Parallel Capability: Supports up to 10 parallel searches
  3. State Management: Complete variable management and state tracking
  4. Visualized Progress: Displays search progress and status in real time

Integration Features

  1. LLM Integration: Combines GPT-4o for intelligent analysis and deepseek-reasoner for deep reasoning
  2. Web Search: Integrates the byted-web-search skill to obtain real-time web information
  3. JSON Processing: Uses JSON parsing tools to process structured data

Workflow Example

Input Example

用户查询: "人工智能在医疗领域的最新发展"
研究深度: 3

Execution Flow

  1. Round 1:

    • LLM Analysis: Decides to search "Latest Advances in AI Medical Diagnosis"
    • Web Search: Executes the search and collects results
    • Status Update: Records the topic and decides to continue searching

  2. Round 2:

    • LLM Analysis: Based on the Round 1 results, decides to search "Breakthroughs in Medical Imaging AI Technology"
    • Web Search: Executes the search and collects results
    • Status Update: Records the topic and decides to continue searching

  3. Round 3:

    • LLM Analysis: Based on the results of the first two rounds, decides to search "Applications of AI in Drug Development"
    • Web Search: Executes the search and collects results
    • Status Update: Records the topic and decides to end the search

  4. Comprehensive Analysis:

    • LLM comprehensively analyzes all collected findings
    • Generate a detailed report on "The Latest Developments of Artificial Intelligence in the Medical Field"

Output Example

# 人工智能在医疗领域的最新发展研究报告

## 执行摘要
[基于三轮搜索的综合分析...]

## 主要发现
1. AI在医疗诊断方面的最新进展
   - [具体发现1]
   - [具体发现2]

2. 医疗影像AI技术突破
   - [具体发现3]
   - [具体发现4]

3. AI在药物研发中的应用
   - [具体发现5]
   - [具体发现6]

## 结论与建议
[综合分析结论...]

## 未来研究方向
[基于研究发现提出的未来研究方向...]

Notes

Workflow Limitations

  1. No Executable Script: This skill is a workflow description document and does not contain an executable script
  2. External Dependencies: Depends on the byted-web-search skill to perform actual searches
  3. Parameter Simplification: When searching, only the query parameter is used, and other search configurations are ignored

Usage Recommendations

  1. Depth Setting: Set an appropriate depth value according to the research complexity
  2. Query Optimization: Provide a clear and specific research topic
  3. Result Verification: Perform necessary fact-checking on the generated report

Best Practices

  1. Progressive Research: Start with a broad topic and gradually go deeper into specific directions
  2. Multi-source Verification: Cross-verify information from multiple sources
  3. Timely Updates: For rapidly developing topics, it is recommended to re-research regularly

Troubleshooting

Common Issues

  1. No Search Results: Check whether the query is too specific or specialized, and try more general search terms
  2. Iteration Ends Too Early: Adjust the LLM's temperature parameter or provide more context
  3. Duplicate Results: The system has a built-in mechanism to avoid duplication; if it still occurs, manual intervention is possible

Performance Optimization

  1. Parallel Search: Make full use of the system's parallel capability (up to 10 in parallel)
  2. Cache Utilization: For multiple studies on the same topic, consider caching the results
  3. Incremental Updates: For ongoing research, an incremental update strategy can be adopted

Important Note: This skill describes a deep research workflow, and actual execution depends on external skills and configurations. Please ensure that the byted-web-search skill and related LLM services are correctly configured.