vectordb:
  - name: chroma_dragonkue2
    db_type: chroma
    client_type: persistent
    embedding_model: huggingface_drangonku-v2-ko
    collection_name: huggingface_drangonku-v2-ko
    path: ${PROJECT_DIR}/resources/chroma

node_lines:
- node_line_name: retrieve_node_line  # Arbitrary node line name
  nodes:
    - node_type: retrieval
      strategy:
        metrics: [ retrieval_f1, retrieval_recall, retrieval_precision,
                   retrieval_ndcg, retrieval_map, retrieval_mrr ]
        speed_threshold: 10
      top_k: 10
      modules:
        - module_type: bm25
          bm25_tokenizer: [ ko_kiwi, ko_okt ]
        - module_type: vectordb
          vectordb: chroma_dragonkue2 # chromadb
        - module_type: hybrid_cc
          normalize_method: [ mm, tmm, z, dbsf ]
          target_modules: ('bm25', 'vectordb')
          weight_range: (0.6, 0.4)
          test_weight_size: 101
    - node_type: passage_reranker # re-ranker
      strategy:
        metrics:
        - retrieval_recall
        - retrieval_precision
        - retrieval_map
      modules:
      - module_type: dragonkue2
        top_k: 5

- node_line_name: post_retrieve_node_line # 생성노드
  nodes:
  - node_type: prompt_maker
    strategy:
      metrics:
      - metric_name: bleu
      - metric_name: meteor
      - metric_name: rouge
      - metric_name: sem_score
        embedding_model: huggingface_drangonku-v2-ko  # raise ValueError("Only one embedding model is supported")
        lang: ko
      generator_modules:
      - module_type: llama_index_llm
        llm: ollama
        model: [ gemma3:12b, phi4, deepseek-r1:14b, aya-expanse:8b ]
        request_timeout: 3000.0
    modules:
      - module_type: fstring
        prompt:
        - |
          ### Task:
          Respond to the user query using the provided context.

          ### Guidelines:
          - If you don't know the answer, clearly state that.
          - If uncertain, ask the user for clarification.
          - Respond in the same language as the user's query.
          - If the context is unreadable or of poor quality, inform the user and provide the best possible answer.
          - If the answer isn't present in the context but you possess the knowledge, explain this to the user and provide the answer using your own understanding.
          - Do not use XML tags in your response.

          ### Output:
          Provide a clear and direct response to the user's query.

          <context>
          {retrieved_contents}
          </context>

          <user_query>
          {query}
          </user_query>
  - node_type: generator # Gen-LLM
    strategy: 
      metrics:
      - metric_name: bleu
      - metric_name: meteor
      - metric_name: rouge
      - metric_name: sem_score
    modules:
    - module_type: llama_index_llm
      llm: ollama
      model: gemma3:12b  # phi4, deepseek-r1:14b, aya-expanse:8b
      temperature: 0.0 
      request_timeout: 30000.0
      batch: 4