docker-compose.yaml

include:
  - path:
    - ../../local_deploy/docker-compose-vectordb.yaml
    - ../../local_deploy/docker-compose-nim-ms.yaml

services:
  chain-server:
    container_name: chain-server
    image: chain-server:${TAG:-latest}
    build:
      # Set context to repo's root directory
      context: ../../../../
      dockerfile: RAG/src/chain_server/Dockerfile
      args:
        # Build args, used to copy relevant directory inside the container relative to GenerativeAIExamples/RAG/examples
        EXAMPLE_PATH: 'basic_rag/langchain'
    volumes:
      - ./prompt.yaml:/prompt.yaml
    # start the server on port 8081
    command: --port 8081 --host 0.0.0.0
    environment:
      # Path to example directory relative to GenerativeAIExamples/RAG/examples
      EXAMPLE_PATH: 'basic_rag/langchain'
      # URL on which vectorstore is hosted
      APP_VECTORSTORE_URL: "http://milvus:19530"
      # Type of vectordb used to store embedding supported type milvus, pgvector
      APP_VECTORSTORE_NAME: "milvus"
      # url on which llm model is hosted. If "", Nvidia hosted API is used
      APP_LLM_MODELNAME: ${APP_LLM_MODELNAME:-"meta/llama3-70b-instruct"}
      # embedding model engine used for inference, supported type nvidia-ai-endpoints, huggingface
      APP_LLM_MODELENGINE: nvidia-ai-endpoints
      APP_LLM_SERVERURL: ${APP_LLM_SERVERURL:-""}
      APP_EMBEDDINGS_MODELNAME: ${APP_EMBEDDINGS_MODELNAME:-nvidia/nv-embedqa-e5-v5}
      # embedding model engine used for inference, supported type nvidia-ai-endpoints
      APP_EMBEDDINGS_MODELENGINE: ${APP_EMBEDDINGS_MODELENGINE:-nvidia-ai-endpoints}
      # url on which embedding model is hosted. If "", Nvidia hosted API is used
      APP_EMBEDDINGS_SERVERURL: ${APP_EMBEDDINGS_SERVERURL:-""}
      # text splitter model name, it's fetched from huggingface
      APP_TEXTSPLITTER_MODELNAME: Snowflake/snowflake-arctic-embed-l
      APP_TEXTSPLITTER_CHUNKSIZE: 506
      APP_TEXTSPLITTER_CHUNKOVERLAP: 200
      NVIDIA_API_KEY: ${NVIDIA_API_KEY}
      # vectorstore collection name to store embeddings 
      COLLECTION_NAME: ${COLLECTION_NAME:-nvidia_api_catalog}
      APP_RETRIEVER_TOPK: 4
      APP_RETRIEVER_SCORETHRESHOLD: 0.25
      # observability server url
      OTEL_EXPORTER_OTLP_ENDPOINT: http://otel-collector:4317
      OTEL_EXPORTER_OTLP_PROTOCOL: grpc
      # enable observability in chain server
      ENABLE_TRACING: false
      # Log level for server, supported level NOTSET, DEBUG, INFO, WARN, ERROR, CRITICAL
      LOGLEVEL: ${LOGLEVEL:-INFO}
    ports:
    - "8081:8081"
    expose:
    - "8081"
    shm_size: 5gb
    depends_on:
      nemollm-embedding:
        condition: service_healthy
        required: false
      nemollm-inference:
        condition: service_healthy
        required: false

  rag-playground:
    container_name: rag-playground
    image: rag-playground:${TAG:-latest}
    build:
      # Set context to repo's root directory
      context: ../../../../RAG/src/rag_playground/
      dockerfile: Dockerfile
      args:
        # select UI type, supported model default, speech
        PLAYGROUND_MODE: ${PLAYGROUND_MODE:-default}
    command: --port 8090
    environment:
      # URL or chain server container
      APP_SERVERURL: http://chain-server
      APP_SERVERPORT: 8081
      # model name displayed on UI
      APP_MODELNAME: ${APP_LLM_MODELNAME:-"meta/llama3-70b-instruct"}
      OTEL_EXPORTER_OTLP_ENDPOINT: http://otel-collector:4317
      OTEL_EXPORTER_OTLP_PROTOCOL: grpc
      # enable observability in rag playground
      ENABLE_TRACING: false
    ports:
    - "8090:8090"
    expose:
    - "8090"
    depends_on:
    - chain-server

networks:
  default:
    name: nvidia-rag