# Simple Graph Actor - Sequential Processing
# Demonstrates a simple 3-node graph with linear execution

name: workflows/document_processor
type: graph
description: Simple document processing workflow (extract → analyze → summarize)
version: "1.0"

# LLM model
provider: openai
model: gpt-3.5-turbo

# Graph topology
route:
  nodes:
    # Node 1: Extract text from document
    - id: extractor
      type: tool
      name: Text Extractor
      description: Extracts text from various document formats
      config:
        tool_name: documents/extract_text
        parameters:
          formats:
            - pdf
            - docx
            - txt

    # Node 2: Analyze content
    - id: analyzer
      type: agent
      name: Content Analyzer
      description: Analyzes document structure and content
      config:
        model: gpt-3.5-turbo
        prompt: |
          Analyze the document content and identify:
          - Main topics and themes
          - Key entities (people, places, organizations)
          - Sentiment and tone
          - Document structure
        tools:
          - analysis/extract_entities
          - analysis/sentiment_analysis

    # Node 3: Generate summary
    - id: summarizer
      type: agent
      name: Summarizer
      description: Creates concise summary of document
      config:
        model: gpt-3.5-turbo
        prompt: |
          Create a concise summary of the document including:
          - Main points (3-5 bullet points)
          - Key findings
          - Actionable insights
          Keep it under 200 words.

  # Linear edges
  edges:
    - from_node: extractor
      to_node: analyzer
    - from_node: analyzer
      to_node: summarizer

  # Entry and exit
  entry_node: extractor
  exit_nodes:
    - summarizer

# Context settings
context_view: executor
memory:
  enabled: true
  max_messages: 10

context:
  max_context_tokens: 4000