Livrare LOT 1 - Didi
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43
ai_platform/modules/embeddings/deploy/Dockerfile
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43
ai_platform/modules/embeddings/deploy/Dockerfile
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# Embeddings API Server
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# Multi-stage build for smaller final image
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FROM python:3.11.12-slim AS builder
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# Install uv
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COPY --from=ghcr.io/astral-sh/uv:0.10 /uv /usr/local/bin/uv
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WORKDIR /app
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# Copy project files
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COPY pyproject.toml README.md ./
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COPY src/ ./src/
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# Install dependencies (with optional extras for backends)
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RUN uv sync --frozen --no-dev --all-extras || uv sync --no-dev --all-extras
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# Production image
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FROM python:3.11.12-slim
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WORKDIR /app
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# Copy virtual environment from builder
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COPY --from=builder /app/.venv /app/.venv
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# Copy source code
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COPY src/ ./src/
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# Set environment variables
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ENV PATH="/app/.venv/bin:$PATH"
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ENV PYTHONUNBUFFERED=1
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ENV EMB_HOST=0.0.0.0
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ENV EMB_PORT=14100
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# Health check (using Python since curl not available in slim image)
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HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
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CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:14100/health')" || exit 1
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# Expose port
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EXPOSE 14100
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# Run the server
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CMD ["python", "-m", "embeddings.cli"]
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142
ai_platform/modules/embeddings/deploy/deploy.sh
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142
ai_platform/modules/embeddings/deploy/deploy.sh
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#!/usr/bin/env bash
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#
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# Docker Compose Startup Script for Embeddings
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#
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# Usage: ./deploy/deploy.sh [OPTIONS]
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#
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# Options:
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# --profile <api|vllm|llamacpp> Docker compose profile
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# --detach Run in detached mode
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# --down Stop and remove containers
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# --logs Show logs
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# --help Show this help message
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#
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# Required: Set environment variables in .env file or export them before running.
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# See .env.example for the full list of required variables.
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set -euo pipefail
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# Script directory
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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# Load .env file if it exists (check deploy/ first, then module root)
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ENV_FILE=""
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if [[ -f "$SCRIPT_DIR/.env" ]]; then
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ENV_FILE="$SCRIPT_DIR/.env"
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elif [[ -f "$SCRIPT_DIR/../.env" ]]; then
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ENV_FILE="$SCRIPT_DIR/../.env"
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fi
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if [[ -n "$ENV_FILE" ]]; then
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echo "Loading environment from: $ENV_FILE"
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set -a
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source "$ENV_FILE"
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set +a
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fi
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PROFILE=""
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DETACH=""
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ACTION="up"
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show_help() {
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sed -n '2,14p' "$0" | sed 's/^# //' | sed 's/^#//'
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exit 0
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}
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check_required_var() {
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local var_name="$1"
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if [[ -z "${!var_name:-}" ]]; then
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echo "ERROR: Required environment variable $var_name is not set"
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echo "Set it in .env file or export it before running this script"
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exit 1
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fi
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}
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# Parse arguments
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while [[ $# -gt 0 ]]; do
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case $1 in
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--profile)
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PROFILE="$2"
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shift 2
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;;
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--detach|-d)
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DETACH="-d"
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shift
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;;
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--down)
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ACTION="down"
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shift
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;;
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--logs)
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ACTION="logs"
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shift
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;;
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--help|-h)
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show_help
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;;
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*)
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echo "Unknown option: $1"
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echo "Use --help for usage information"
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exit 1
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;;
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esac
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done
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# Require profile to be specified
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if [[ -z "$PROFILE" && "$ACTION" == "up" ]]; then
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echo "ERROR: --profile is required"
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echo "Options: api, vllm, llamacpp"
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exit 1
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fi
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# Check required variables
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check_required_var "EMB_DEFAULT_BACKEND"
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check_required_var "EMB_ENABLE_VLLM"
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check_required_var "EMB_ENABLE_LLAMACPP"
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check_required_var "EMB_EXTERNAL_URL"
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# Check profile-specific variables
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case $PROFILE in
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vllm)
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check_required_var "EMB_VLLM_MODEL"
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;;
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llamacpp)
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check_required_var "MODELS_DIR"
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check_required_var "EMB_LLAMACPP_MODEL"
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;;
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esac
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cd "$SCRIPT_DIR"
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case $ACTION in
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up)
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echo "Starting Embeddings with profile: $PROFILE"
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echo " Default backend: $EMB_DEFAULT_BACKEND"
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echo " vLLM enabled: $EMB_ENABLE_VLLM"
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echo " llama.cpp enabled: $EMB_ENABLE_LLAMACPP"
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if [[ "$PROFILE" == "vllm" ]]; then
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echo " vLLM model: ${EMB_VLLM_MODEL:-BAAI/bge-m3}"
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fi
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if [[ "$PROFILE" == "llamacpp" ]]; then
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echo " llama.cpp model: ${EMB_LLAMACPP_MODEL:-bge-m3-q4_k_m.gguf}"
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fi
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echo ""
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# shellcheck disable=SC2086
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exec docker compose --profile "$PROFILE" up $DETACH
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;;
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down)
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if [[ -z "$PROFILE" ]]; then
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echo "ERROR: --profile is required with --down"
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exit 1
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fi
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echo "Stopping Embeddings containers..."
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exec docker compose --profile "$PROFILE" down
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;;
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logs)
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if [[ -z "$PROFILE" ]]; then
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echo "ERROR: --profile is required with --logs"
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exit 1
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fi
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exec docker compose --profile "$PROFILE" logs -f
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;;
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esac
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131
ai_platform/modules/embeddings/deploy/docker-compose.yml
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131
ai_platform/modules/embeddings/deploy/docker-compose.yml
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# Embeddings Module - Docker Compose Configuration
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#
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# Port Allocation (x41xx = Embeddings):
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# 14100 - Embeddings API (Prod)
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# 54100 - Embeddings API (Dev)
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# 14101/54101 - vLLM Embedding Server
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# 14110/54110 - llama.cpp Embedding Server
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#
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# Profiles:
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# api - API server only (uses external embedding servers)
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# vllm - API + vLLM server (GPU required)
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# llamacpp - API + llama.cpp server
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#
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# Naming Convention: didiAI-{module}-{service}
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#
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# Network:
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# Uses deploy_default network (shared with other modules)
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networks:
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deploy_default:
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external: true
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services:
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# ==========================================================================
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# Embeddings API Server
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# ==========================================================================
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embeddings-api:
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container_name: didiAI-embeddings-api
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image: didiai-embeddings-api
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build:
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context: ..
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dockerfile: deploy/Dockerfile
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ports:
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- "${EMB_PORT:-14100}:${EMB_PORT:-14100}"
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networks:
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- deploy_default
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environment:
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- EMB_PORT=${EMB_PORT:-14100}
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- EMB_EXTERNAL_URL=${EMB_EXTERNAL_URL}
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- EMB_DEFAULT_BACKEND=${EMB_DEFAULT_BACKEND}
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- EMB_ENABLE_VLLM=${EMB_ENABLE_VLLM}
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- EMB_ENABLE_LLAMACPP=${EMB_ENABLE_LLAMACPP}
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- EMB_VLLM_BASE_URL=http://didiAI-embeddings-vllm:14101
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- EMB_LLAMACPP_BASE_URL=http://didiAI-embeddings-llamacpp:8080
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- EMB_API_TOKENS=${EMB_API_TOKENS:-}
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- EMB_DASHBOARD_URL=${EMB_DASHBOARD_URL:-http://didiAI-dashboard:51300}
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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:${EMB_PORT:-14100}/health')"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 30s
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restart: unless-stopped
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profiles:
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- api
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- vllm
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- llamacpp
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# ==========================================================================
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# vLLM Embedding Server
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# ==========================================================================
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# Embedding model using vLLM with --task embed (v0.8.x syntax)
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vllm-embed:
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container_name: didiAI-embeddings-vllm
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image: vllm/vllm-openai:v0.8.5
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ports:
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- "${EMB_VLLM_PORT:-14101}:14101"
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networks:
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- deploy_default
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volumes:
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- ${HF_CACHE_DIR:-/cai2_ds_storage/hf_cache}:/root/.cache/huggingface
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environment:
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- HF_HOME=/root/.cache/huggingface
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- HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
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- CUDA_VISIBLE_DEVICES=${EMB_VLLM_GPU:-0}
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command: >
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--model ${EMB_VLLM_MODEL:-BAAI/bge-m3}
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--host 0.0.0.0
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--port 14101
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--task embed
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--trust-remote-code
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--max-model-len ${EMB_VLLM_MAX_LEN:-8192}
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--gpu-memory-utilization ${EMB_VLLM_GPU_UTIL:-0.50}
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--disable-log-requests
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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device_ids: ['${EMB_VLLM_GPU:-0}']
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capabilities: [gpu]
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:14101/health"]
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interval: 30s
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timeout: 10s
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retries: 10
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start_period: 300s
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restart: unless-stopped
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profiles:
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- vllm
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# ==========================================================================
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# llama.cpp Embedding Server
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# ==========================================================================
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# Embedding model using llama.cpp with --embedding
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llamacpp-embed:
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container_name: didiAI-embeddings-llamacpp
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image: ghcr.io/ggml-org/llama.cpp:server-b4769
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ports:
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- "${EMB_LLAMACPP_PORT:-14110}:8080"
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networks:
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- deploy_default
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volumes:
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- ${MODELS_DIR:-/cai2_ds_storage/models}:/models:ro
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command: >
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--model /models/${EMB_LLAMACPP_MODEL:-bge-m3-q4_k_m.gguf}
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--host 0.0.0.0
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--port 8080
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--embedding
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--ctx-size ${EMB_LLAMACPP_CTX:-8192}
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--threads ${EMB_LLAMACPP_THREADS:-4}
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--parallel ${EMB_LLAMACPP_PARALLEL:-4}
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 60s
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restart: unless-stopped
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profiles:
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- llamacpp
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