orchestrator/examples/basic/text_analysis.yaml at main · ContextLab/orchestrator · GitHub

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id: text-analysis-basic
name: "Basic Text Analysis"
description: |
  Analyze a piece of text for sentiment, key themes, and word count.
  Demonstrates basic text processing and structured output.

parameters:
  text:
    type: string
    required: true
    description: "Text to analyze"
  analysis_type:
    type: string
    default: "comprehensive"
    choices: ["sentiment", "themes", "comprehensive"]
    description: "Type of analysis to perform"

steps:
  - id: analyze_text
    action: analyze_text
    parameters:
      text: "{{ text }}"
      prompt: |
        Analyze the following text:
        "{{ text }}"

        {% if analysis_type == 'sentiment' %}
        Provide sentiment analysis with:
        - Overall sentiment (positive/negative/neutral)
        - Confidence score (0-1)
        - Key emotional indicators
        {% elif analysis_type == 'themes' %}
        Identify key themes:
        - Main topics (3-5 themes)
        - Supporting evidence for each theme
        - Theme importance ranking
        {% else %}
        Provide comprehensive analysis including:
        - Sentiment analysis
        - Key themes and topics
        - Writing style and tone
        - Key phrases or terminology
        - Overall summary
        {% endif %}

        Return structured JSON format.
      model: <AUTO task="analysis">Select best model for text analysis</AUTO>
      response_format: "json_object"

  - id: word_count
    action: count_words
    parameters:
      text: "{{ text }}"

  - id: generate_summary
    action: generate_text
    parameters:
      prompt: |
        Based on this analysis: {{ analyze_text.result | from_json }}
        And word count: {{ word_count.count }}

        Create a brief summary of the analysis findings in plain English.
        Focus on the most important insights.
      model: <AUTO task="summarize">Select model optimized for summarization</AUTO>
      max_tokens: 200
    dependencies:
      - analyze_text
      - word_count

outputs:
  analysis: "{{ analyze_text.result }}"
  word_count: "{{ word_count.count }}"
  summary: "{{ generate_summary.result }}"

metadata:
  category: "basic"
  complexity: "beginner"
  estimated_runtime: "1-2 minutes"
  requirements: ["text generation model"]
  use_cases:
    - "Content analysis"
    - "Document review"
    - "Social media monitoring"
    - "Feedback analysis"