# Project: LangGraph HF Agent for GAIA
# config.yaml
data: "data/metadata.jsonl"  # Path to the GAIA documents dataset
retrievers:
  enable_vector_search: true # Enable vector-based document retrieval
  enable_keyword_search: true # Enable keyword-based document retrieval
  final_rrf_k: 3          # Number of top documents to consider after reciprocal rank fusion
  vector_store:
    table: "gaia_documents"               # Type of vector store (e.g., faiss, chroma)
    query: "match_documents"              # Method to query the vector store
    k: 10                                 # Number of top documents to retrieve
    threshold: 0.5                       # Similarity threshold for document retrieval
  bm25:
    k: 5                                  # Number of top documents to retrieve using keyword search
models:
  cache_folder: "./models/hf_cache"  # Directory to cache Hugging Face models
  embeddings:
    model_name: "Alibaba-NLP/gte-modernbert-base"  # Hugging Face embedding model ID
  reranker:
    model_name: "Alibaba-NLP/gte-reranker-modernbert-base"  # Hugging Face model ID for reranking
  llm:
    model_name: "Qwen/Qwen3-32B"  # Hugging Face model ID
    parameters:
      temperature: 0.6
      repetition_penalty: 1.3
      provider: "auto"
      thinking_enabled: false
      timeout: 300                          # Read timeout (s) for the HF Inference call. Default 120 is too short under load.
      max_new_tokens: 4096                  # Output cap. Default 512 truncates long responses and breaks tool calls.
  vlm:
    model_name: "Qwen/Qwen3-VL-32B-Instruct"  # Hugging Face model ID
  asr:
    model_name: "openai/whisper-large-v3"  # Hugging Face model ID — must have a provider on HF Inference Providers
graph:
  recursion_limit: 40                     # Max graph-node visits before bailing.

api:
  base_url: "https://agents-course-unit4-scoring.hf.space"
  files_dir: "./data/task_files"          # Local directory for downloaded task files