Livrare LOT 1 - Didi

This commit is contained in:
Dezvoltari Evotech 2026-06-25 14:13:25 -07:00
commit 5380c3fc63
990 changed files with 133308 additions and 0 deletions

View file

@ -0,0 +1,43 @@
# Embeddings API Server
# Multi-stage build for smaller final image
FROM python:3.11.12-slim AS builder
# Install uv
COPY --from=ghcr.io/astral-sh/uv:0.10 /uv /usr/local/bin/uv
WORKDIR /app
# Copy project files
COPY pyproject.toml README.md ./
COPY src/ ./src/
# Install dependencies (with optional extras for backends)
RUN uv sync --frozen --no-dev --all-extras || uv sync --no-dev --all-extras
# Production image
FROM python:3.11.12-slim
WORKDIR /app
# Copy virtual environment from builder
COPY --from=builder /app/.venv /app/.venv
# Copy source code
COPY src/ ./src/
# Set environment variables
ENV PATH="/app/.venv/bin:$PATH"
ENV PYTHONUNBUFFERED=1
ENV EMB_HOST=0.0.0.0
ENV EMB_PORT=14100
# Health check (using Python since curl not available in slim image)
HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:14100/health')" || exit 1
# Expose port
EXPOSE 14100
# Run the server
CMD ["python", "-m", "embeddings.cli"]

View file

@ -0,0 +1,142 @@
#!/usr/bin/env bash
#
# Docker Compose Startup Script for Embeddings
#
# Usage: ./deploy/deploy.sh [OPTIONS]
#
# Options:
# --profile <api|vllm|llamacpp> Docker compose profile
# --detach Run in detached mode
# --down Stop and remove containers
# --logs Show logs
# --help Show this help message
#
# Required: Set environment variables in .env file or export them before running.
# See .env.example for the full list of required variables.
set -euo pipefail
# Script directory
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
# Load .env file if it exists (check deploy/ first, then module root)
ENV_FILE=""
if [[ -f "$SCRIPT_DIR/.env" ]]; then
ENV_FILE="$SCRIPT_DIR/.env"
elif [[ -f "$SCRIPT_DIR/../.env" ]]; then
ENV_FILE="$SCRIPT_DIR/../.env"
fi
if [[ -n "$ENV_FILE" ]]; then
echo "Loading environment from: $ENV_FILE"
set -a
source "$ENV_FILE"
set +a
fi
PROFILE=""
DETACH=""
ACTION="up"
show_help() {
sed -n '2,14p' "$0" | sed 's/^# //' | sed 's/^#//'
exit 0
}
check_required_var() {
local var_name="$1"
if [[ -z "${!var_name:-}" ]]; then
echo "ERROR: Required environment variable $var_name is not set"
echo "Set it in .env file or export it before running this script"
exit 1
fi
}
# Parse arguments
while [[ $# -gt 0 ]]; do
case $1 in
--profile)
PROFILE="$2"
shift 2
;;
--detach|-d)
DETACH="-d"
shift
;;
--down)
ACTION="down"
shift
;;
--logs)
ACTION="logs"
shift
;;
--help|-h)
show_help
;;
*)
echo "Unknown option: $1"
echo "Use --help for usage information"
exit 1
;;
esac
done
# Require profile to be specified
if [[ -z "$PROFILE" && "$ACTION" == "up" ]]; then
echo "ERROR: --profile is required"
echo "Options: api, vllm, llamacpp"
exit 1
fi
# Check required variables
check_required_var "EMB_DEFAULT_BACKEND"
check_required_var "EMB_ENABLE_VLLM"
check_required_var "EMB_ENABLE_LLAMACPP"
check_required_var "EMB_EXTERNAL_URL"
# Check profile-specific variables
case $PROFILE in
vllm)
check_required_var "EMB_VLLM_MODEL"
;;
llamacpp)
check_required_var "MODELS_DIR"
check_required_var "EMB_LLAMACPP_MODEL"
;;
esac
cd "$SCRIPT_DIR"
case $ACTION in
up)
echo "Starting Embeddings with profile: $PROFILE"
echo " Default backend: $EMB_DEFAULT_BACKEND"
echo " vLLM enabled: $EMB_ENABLE_VLLM"
echo " llama.cpp enabled: $EMB_ENABLE_LLAMACPP"
if [[ "$PROFILE" == "vllm" ]]; then
echo " vLLM model: ${EMB_VLLM_MODEL:-BAAI/bge-m3}"
fi
if [[ "$PROFILE" == "llamacpp" ]]; then
echo " llama.cpp model: ${EMB_LLAMACPP_MODEL:-bge-m3-q4_k_m.gguf}"
fi
echo ""
# shellcheck disable=SC2086
exec docker compose --profile "$PROFILE" up $DETACH
;;
down)
if [[ -z "$PROFILE" ]]; then
echo "ERROR: --profile is required with --down"
exit 1
fi
echo "Stopping Embeddings containers..."
exec docker compose --profile "$PROFILE" down
;;
logs)
if [[ -z "$PROFILE" ]]; then
echo "ERROR: --profile is required with --logs"
exit 1
fi
exec docker compose --profile "$PROFILE" logs -f
;;
esac

View file

@ -0,0 +1,131 @@
# Embeddings Module - Docker Compose Configuration
#
# Port Allocation (x41xx = Embeddings):
# 14100 - Embeddings API (Prod)
# 54100 - Embeddings API (Dev)
# 14101/54101 - vLLM Embedding Server
# 14110/54110 - llama.cpp Embedding Server
#
# Profiles:
# api - API server only (uses external embedding servers)
# vllm - API + vLLM server (GPU required)
# llamacpp - API + llama.cpp server
#
# Naming Convention: didiAI-{module}-{service}
#
# Network:
# Uses deploy_default network (shared with other modules)
networks:
deploy_default:
external: true
services:
# ==========================================================================
# Embeddings API Server
# ==========================================================================
embeddings-api:
container_name: didiAI-embeddings-api
image: didiai-embeddings-api
build:
context: ..
dockerfile: deploy/Dockerfile
ports:
- "${EMB_PORT:-14100}:${EMB_PORT:-14100}"
networks:
- deploy_default
environment:
- EMB_PORT=${EMB_PORT:-14100}
- EMB_EXTERNAL_URL=${EMB_EXTERNAL_URL}
- EMB_DEFAULT_BACKEND=${EMB_DEFAULT_BACKEND}
- EMB_ENABLE_VLLM=${EMB_ENABLE_VLLM}
- EMB_ENABLE_LLAMACPP=${EMB_ENABLE_LLAMACPP}
- EMB_VLLM_BASE_URL=http://didiAI-embeddings-vllm:14101
- EMB_LLAMACPP_BASE_URL=http://didiAI-embeddings-llamacpp:8080
- EMB_API_TOKENS=${EMB_API_TOKENS:-}
- EMB_DASHBOARD_URL=${EMB_DASHBOARD_URL:-http://didiAI-dashboard:51300}
healthcheck:
test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:${EMB_PORT:-14100}/health')"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
restart: unless-stopped
profiles:
- api
- vllm
- llamacpp
# ==========================================================================
# vLLM Embedding Server
# ==========================================================================
# Embedding model using vLLM with --task embed (v0.8.x syntax)
vllm-embed:
container_name: didiAI-embeddings-vllm
image: vllm/vllm-openai:v0.8.5
ports:
- "${EMB_VLLM_PORT:-14101}:14101"
networks:
- deploy_default
volumes:
- ${HF_CACHE_DIR:-/cai2_ds_storage/hf_cache}:/root/.cache/huggingface
environment:
- HF_HOME=/root/.cache/huggingface
- HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
- CUDA_VISIBLE_DEVICES=${EMB_VLLM_GPU:-0}
command: >
--model ${EMB_VLLM_MODEL:-BAAI/bge-m3}
--host 0.0.0.0
--port 14101
--task embed
--trust-remote-code
--max-model-len ${EMB_VLLM_MAX_LEN:-8192}
--gpu-memory-utilization ${EMB_VLLM_GPU_UTIL:-0.50}
--disable-log-requests
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ['${EMB_VLLM_GPU:-0}']
capabilities: [gpu]
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:14101/health"]
interval: 30s
timeout: 10s
retries: 10
start_period: 300s
restart: unless-stopped
profiles:
- vllm
# ==========================================================================
# llama.cpp Embedding Server
# ==========================================================================
# Embedding model using llama.cpp with --embedding
llamacpp-embed:
container_name: didiAI-embeddings-llamacpp
image: ghcr.io/ggml-org/llama.cpp:server-b4769
ports:
- "${EMB_LLAMACPP_PORT:-14110}:8080"
networks:
- deploy_default
volumes:
- ${MODELS_DIR:-/cai2_ds_storage/models}:/models:ro
command: >
--model /models/${EMB_LLAMACPP_MODEL:-bge-m3-q4_k_m.gguf}
--host 0.0.0.0
--port 8080
--embedding
--ctx-size ${EMB_LLAMACPP_CTX:-8192}
--threads ${EMB_LLAMACPP_THREADS:-4}
--parallel ${EMB_LLAMACPP_PARALLEL:-4}
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
interval: 30s
timeout: 10s
retries: 3
start_period: 60s
restart: unless-stopped
profiles:
- llamacpp