# Reference config for the `t2_finqa` pre-built evaluation database.
# T²-RAGBench (FinQA) financial QA, scored by exact numeric match.
# No `evaluations.judge` block: the spec sets `NumberMatchEvaluator`, which
# replaces the evaluator list, so no LLM judge is constructed for this dataset.
# Run: evaluations run t2_finqa --skip-db --target analysis-capability --config configs/t2_finqa.yaml
# base_url uses the `vllm` host serving each model over an OpenAI-compatible API.

environment: development

storage:
  auto_vacuum: false

embeddings:
  model:
    provider: openai
    name: qwen3-embedding-4b
    vector_dim: 2560
    base_url: http://vllm:11431/v1

processing:
  converter: docling-local
  pictures: none  # ~4% of pages carry a figure/chart; dropped as non-essential to the numeric QA
  chunking_use_markdown_tables: true

reranking:
  model:
    provider: vllm
    name: Qwen/Qwen3-Reranker-4B
    base_url: http://vllm:11455

qa:
  model:
    provider: openai
    name: RedHatAI/Qwen3.6-35B-A3B-NVFP4
    base_url: http://vllm:11430/v1
    temperature: 0.3
    max_tokens: 16384
    extra_body:
      chat_template_kwargs:
        enable_thinking: true

prompts:
  domain_preamble: |
    Use search() to find the relevant documents. Do not iterate over all of
    /documents or read every document's content — that will time out.

    For questions with a numeric answer, end your response with a final line
    formatted exactly as `ANSWER: <number>`, containing a single number. Keep a
    percent sign if the answer is a percentage.