toolserver/Dockerfile

# ===== Base met CUDA11.8 + cuDNN + conda =====
FROM pytorch/pytorch:2.3.1-cuda11.8-cudnn8-runtime

WORKDIR /app

# Zorg dat conda libs altijd eerst gevonden worden
ENV LD_LIBRARY_PATH=/opt/conda/lib:${LD_LIBRARY_PATH}

# P5000 = Pascal SM 6.1; handig voor (eventueel) on-the-fly builds
ENV TORCH_CUDA_ARCH_LIST="6.1"

# ===== Model caches op vaste paden =====
ENV HF_HOME=/opt/hf \
    HUGGINGFACE_HUB_CACHE=/opt/hf \
    TRANSFORMERS_CACHE=/opt/hf \
    SENTENCE_TRANSFORMERS_HOME=/opt/sentence-transformers \
    XDG_CACHE_HOME=/opt/cache \
    STT_MODEL=small

ARG RAG_EMBEDDINGS=gte-multilingual
ARG STT_MODEL_ARG=small
ENV RAG_EMBEDDINGS=${RAG_EMBEDDINGS}
ENV STT_MODEL=${STT_MODEL_ARG}

# directories
RUN mkdir -p /opt/hf /opt/cache /opt/sentence-transformers /opt/whisper && \
    chmod -R a+rX /opt/hf /opt/cache /opt/sentence-transformers /opt/whisper

# ===== Alleen minimale apt utils (géén multimedia libs!) =====
RUN apt-get update && DEBIAN_FRONTEND=noninteractive \
    apt-get install -y --no-install-recommends \
      git curl build-essential ca-certificates \
    && rm -rf /var/lib/apt/lists/*

# ===== Multimedia via conda-forge (alles uit één ecosysteem) =====
# - av 10 + ffmpeg<7 (past goed bij pyAV) 
# - cairo/pango/gdk-pixbuf/pixman voor cairosvg stack
# VERVANG de vorige conda multimedia regel door deze:
# Tooling voor PyAV build
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
    pkg-config git curl ffmpeg libcairo2 libpango-1.0-0 libgdk-pixbuf2.0-0 apt-utils pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libavfilter-dev libswscale-dev libswresample-dev build-essential && rm -rf /var/lib/apt/lists/*

# FFmpeg via conda-forge (zodat je recente headers/libs hebt)
RUN conda config --system --set channel_priority flexible \
 && conda install -y -c conda-forge "ffmpeg>=6,<8" \
 && conda clean -afy

# Later in je pip stap:
# ... faster-whisper==1.0.0 zal av==11.* trekken en nu WEL kunnen bouwen tegen conda’s FFmpeg 6

# ===== Python deps =====
COPY requirements.txt .
RUN pip install --upgrade pip
# jouw requirements
RUN pip install --no-cache-dir -r requirements.txt
# losse extras (let op: av via conda, niet via pip!)
RUN pip install --no-cache-dir \
      PyPDF2 python-multipart gitpython chromadb httpx meilisearch \
      pandas openpyxl python-pptx faster-whisper==1.0.0 \
      cairosvg sentence-transformers rank-bm25 pymupdf

# ===== Prefetch modellen =====

# 1) SentenceTransformers
RUN python - <<'PY'
import os
from sentence_transformers import SentenceTransformer
mapping = {
    "gte-multilingual": "Alibaba-NLP/gte-multilingual-base",
    "bge-small": "BAAI/bge-small-en-v1.5",
    "e5-small": "intfloat/e5-small-v2",
    "gte-base-en": "thenlper/gte-base",
}
choice = os.environ.get("RAG_EMBEDDINGS","gte-multilingual").lower()
hf_id = mapping.get(choice, "BAAI/bge-small-en-v1.5")
cache_root = os.environ.get("SENTENCE_TRANSFORMERS_HOME", "/opt/sentence-transformers")
local_dir = os.path.join(cache_root, "embedder")
os.makedirs(cache_root, exist_ok=True)
print("Downloading SentenceTransformer:", hf_id)
model = SentenceTransformer(hf_id, cache_folder=cache_root, device="cpu")  # download only
model.save(local_dir)
print("Prefetched SentenceTransformer:", hf_id)
PY

# 2) faster-whisper (prefetch CPU-kant; runtime kan je device kiezen)
RUN python - <<'PY'
import os
from faster_whisper import WhisperModel
name = os.environ.get("STT_MODEL","small")
cache_root = os.path.join(os.environ.get("XDG_CACHE_HOME","/opt/cache"), "whisper")
os.makedirs(cache_root, exist_ok=True)
_ = WhisperModel(name, device="cpu", compute_type="int8", download_root=cache_root)
print("Prefetched faster-whisper:", name, "->", cache_root)
PY

# (optioneel) piper skip ik hier; kan later

# ===== App code =====
COPY app.py .
COPY queue_helper.py .
COPY agent_repo.py .
COPY windowing_utils.py .
COPY smart_rag.py .
COPY llm_client.py .
COPY web_search.py .

EXPOSE 8080
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8080"]