config.yaml

# Configuration for Production Quant Paper Agent Example
# Uses Gemini 2.5 Pro & Flash for production-level performance

# Source configuration
source:
  type: "arxiv"
  config:
    max_results: 1
    retry_delay: 1.0

# Parser configuration
parser:
  type: "llama"
  config:
    api_key: ${LLAMA_CLOUD_API_KEY}
    parsing_instructions: "Extract comprehensive quantitative finance content including methodologies, mathematical models, empirical findings, and practical implications."

# Storage configuration
storage:
  type: "local"
  config:
    base_path: "./data"
    auto_create_dirs: true
    enable_indexing: true

# Flow configurations
flows:
  # Summary flow using Gemini models
  summary_flow:
    type: "summary"
    config:
      name: "summary_flow"
      prompt_templates_path: "flows/summary_flow/prompts.yaml"
      use_chunking: true
      chunk_size: 4000
      chunk_strategy: "by_size"
      llm_blocks:
        cheap_summarizer:
          model: "gemini/gemini-2.5-flash"
          temperature: 0.3
          max_tokens: 8192
          api_key: ${GOOGLE_API_KEY}
        powerful_combiner:
          model: "gemini/gemini-2.5-pro"
          temperature: 0.2
          max_tokens: 8192
          api_key: ${GOOGLE_API_KEY}

  # QA flow using Gemini models for production
  qa_flow:
    type: "qa"
    config:
      name: "qa_flow"
      prompt_templates_path: "flows/qa_flow/prompts.yaml"
      max_questions: 7
      question_depth: "deep"
      focus_areas: ["methodology", "findings", "implications", "applications", "limitations"]
      llm_blocks:
        question_generator:
          model: "gemini/gemini-2.5-pro"
          temperature: 0.8
          max_tokens: 8192
          api_key: ${GOOGLE_API_KEY}
        question_refiner:
          model: "gemini/gemini-2.5-pro"
          temperature: 0.3
          max_tokens: 8192
          api_key: ${GOOGLE_API_KEY}