config.example.zh.yaml

# Heta 配置示例（国内版）
#
# 使用步骤：
#   1. cp config.example.zh.yaml config.yaml
#   2. 在 providers 中填入你的 API 密钥
#   3. docker-compose up -d
#
# 持久化服务地址已与 docker-compose.yml 对齐，直接启动无需修改。
# 若在宿主机直接运行（不走 Docker），将各 host 改为 127.0.0.1。

# ─────────────────────────────────────────────────────────────────────────────
# Provider anchors — define API credentials once, referenced below.
# ─────────────────────────────────────────────────────────────────────────────
providers:

  dashscope: &dashscope
    api_key: "YOUR_DASHSCOPE_API_KEY"
    base_url: "https://dashscope.aliyuncs.com/compatible-mode/v1"

  siliconflow: &siliconflow
    api_key: "YOUR_SILICONFLOW_API_KEY"
    base_url: "https://api.siliconflow.cn/v1"


# ─────────────────────────────────────────────────────────────────────────────
# Persistence — aligned with docker-compose service names and default credentials.
# ─────────────────────────────────────────────────────────────────────────────
persistence:
  postgresql:
    host: postgres
    port: 5432
    user: postgres
    password: postgres
    database: postgres
    options: "-c search_path=public"

  milvus:
    host: "milvus"
    port: 19530
    url: "http://milvus:19530"

  neo4j:
    url: "bolt://neo4j:7687"
    username: "neo4j"
    password: "heta_password"
    database: "neo4j"


# ─────────────────────────────────────────────────────────────────────────────
heta:
  title: "Heta API"
  version: "0.1.0"

  fastapi:
    host: "0.0.0.0"
    port: 8000
    reload: false
    log_level: "info"


# ─────────────────────────────────────────────────────────────────────────────
hetadb:
  title: "HetaDB API"
  version: "0.1.0"
  workspace: "workspace"

  fastapi:
    host: "0.0.0.0"
    port: 8001
    reload: false
    log_level: "info"

  llm:
    <<: *dashscope
    model: "Qwen/Qwen3-32B"
    max_concurrent_requests: 10
    max_retries: 3
    timeout: 120

  vlm:
    <<: *dashscope
    model: "qwen3.5-plus"
    max_concurrent_requests: 10
    max_retries: 5
    timeout: 120

  embedding_api:
    <<: *siliconflow
    model: "BAAI/bge-m3"
    dim: 1024
    timeout: 30
    batch_size: 2000
    num_threads: 8
    max_file_size_bytes: 3221225472
    max_retries: 5
    retry_delay: 2

  # File storage — uses MinIO from docker-compose (S3-compatible)
  s3:
    ak: "minioadmin"
    sk: "minioadmin"
    endpoint: "http://minio:9000"           # 后端连接 MinIO 的地址（Docker 内网）
    public_endpoint: "http://localhost:9000" # 返回给客户端的下载链接中使用的地址
                                             # 公网部署请改为服务器 IP 或域名
    bucket: "hetadb-raw-files"
    config:
      signature_version: "s3v4"
      retries:
        max_attempts: 3
        mode: "adaptive"
      read_timeout: 300
      connect_timeout: 60

  # postgresql/milvus injected from persistence at loader level
  milvus:
    db_name: "hetadb"
    sentence_mode: false

  # Max concurrent dataset parse tasks.  Each task spawns graph.max_workers
  # LLM threads, so effective LLM concurrency = parse_max_workers × llm.max_concurrent_requests.
  parse_max_workers: 2

  query_defaults:
    top_k: 10
    threshold: 0.0
    similarity_weight: 1.5
    occur_weight: 1.0
    reranker_url: ""    # Optional: fill in your reranker service URL to enable reranking

  search_params:
    ef_multiplier: 10


# ─────────────────────────────────────────────────────────────────────────────
hetamem:
  title: "HetaMem API"
  version: "0.1.0"

  fastapi:
    host: "0.0.0.0"
    port: 8003
    reload: false
    log_level: "info"

  memorykb:
    llm:
      <<: *dashscope
      model: "qwen-plus"

    embedding:
      <<: *dashscope
      model: "text-embedding-v4"
      dim: 1536

  memoryvg:
    llm:
      provider: "openai"
      config:
        <<: *dashscope
        model: "qwen3-max"

    embedder:
      provider: "openai"
      config:
        <<: *siliconflow
        model: "BAAI/bge-m3"
        embedding_dims: 1024

    vector_store:
      provider: "milvus"
      config:
        collection_name: "memoryvg"
        embedding_model_dims: 1024
        metric_type: "IP"
        db_name: "hetamem"

    graph_store:
      provider: "neo4j"

    version: "v1.1"


# ─────────────────────────────────────────────────────────────────────────────
hetagen:
  fastapi:
    host: "0.0.0.0"
    port: 8002
    reload: false
    log_level: "info"

  llm:
    <<: *dashscope
    model: "Qwen/Qwen3-32B"
    max_concurrent_requests: 10
    max_retries: 5
    timeout: 120

  vlm:
    <<: *siliconflow
    model: "Qwen/Qwen3-VL-32B-Instruct"
    max_concurrent_requests: 10
    max_retries: 5
    timeout: 120

  embedding_api:
    <<: *siliconflow
    model: "BAAI/bge-m3"
    dim: 1024
    timeout: 30

  milvus:
    # host/port injected from persistence.milvus at loader level
    db_name: "hetagen"
    collection_name: "top_100_embeddings"
    embedding_dim: 1024
    index_type: "IVF_FLAT"
    metric_type: "IP"
    nlist: 128
    batch_size: 100