Updated

Dockerfile

@@ -1,38 +1,46 @@
-# Use lightweight Python image
+# ------------------------------
+# Base image
+# ------------------------------
 FROM python:3.10-slim
 
+# ------------------------------
+# Create a non-root user
+# ------------------------------
+RUN useradd -m appuser
+WORKDIR /app
+USER appuser
+
+# ------------------------------
 # Install system dependencies
+# ------------------------------
 RUN apt-get update && apt-get install -y \
     build-essential \
-    curl \
     git \
+    curl \
     && rm -rf /var/lib/apt/lists/*
 
-# Set environment variables for HF cache
-ENV HF_HOME=/app/huggingface_cache
-ENV TRANSFORMERS_CACHE=/app/huggingface_cache
-ENV TORCH_HOME=/app/huggingface_cache
-
-# Create cache and DB folders with proper permissions
-RUN mkdir -p /app/huggingface_cache /app/Dev_Assist_SME_Builder_DB \
-    && useradd -m appuser \
-    && chown -R appuser:appuser /app/huggingface_cache /app/Dev_Assist_SME_Builder_DB
-
-# Switch to non-root user
-USER appuser
-WORKDIR /app
-
-# Copy requirements first for caching
-COPY requirements.txt .
-
-# Install Python dependencies
+# ------------------------------
+# Copy requirements and install
+# ------------------------------
+COPY --chown=appuser:appuser requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy project files
+# ------------------------------
+# Create cache & DB folders (writable by appuser)
+# ------------------------------
+RUN mkdir -p /app/huggingface_cache /app/Dev_Assist_SME_Builder_DB
+
+# ------------------------------
+# Copy app source code
+# ------------------------------
 COPY --chown=appuser:appuser . .
 
-# Expose FastAPI default port
+# ------------------------------
+# Expose FastAPI port
+# ------------------------------
 EXPOSE 7860
 
-# Command to run FastAPI with Uvicorn
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+# ------------------------------
+# CMD to run FastAPI
+# ------------------------------
+CMD ["python", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

smebuilder_vector.py

@@ -1,3 +1,5 @@
+# smebuilder_vector.py
+
 import os
 import pandas as pd
 from langchain_huggingface import HuggingFaceEmbeddings
@@ -6,10 +8,10 @@ from langchain_core.documents import Document
 
 # ----------------- CONFIG -----------------
 DATASET_PATH = "sme_builder_dataset.csv"
-DB_LOCATION = "/app/Dev_Assist_SME_Builder_DB"       # absolute path inside container
-HF_CACHE = "/app/huggingface_cache"                  # absolute path for HF cache
+DB_LOCATION = "/app/Dev_Assist_SME_Builder_DB"  # absolute path for container
 COLLECTION_NAME = "landing_page_generation_examples"
 EMBEDDING_MODEL = "intfloat/e5-base-v2"
+HF_CACHE = "/app/huggingface_cache"  # writable cache folder
 
 # ----------------- LOAD DATASET -----------------
 if not os.path.exists(DATASET_PATH):
@@ -20,10 +22,10 @@ df = pd.read_csv(DATASET_PATH)
 # ----------------- EMBEDDINGS -----------------
 embeddings = HuggingFaceEmbeddings(
     model_name=EMBEDDING_MODEL,
-    cache_dir=HF_CACHE  # ensures HF uses a container-safe writable folder
+    model_kwargs={"cache_dir": HF_CACHE}  # pass writable cache
 )
 
-# Check if vector store exists
+# ----------------- CHECK VECTOR STORE -----------------
 add_documents = not os.path.exists(DB_LOCATION)
 
 # ----------------- CREATE DOCUMENTS -----------------
@@ -46,7 +48,7 @@ if add_documents:
 # ----------------- VECTOR STORE -----------------
 vector_store = Chroma(
     collection_name=COLLECTION_NAME,
-    persist_directory=DB_LOCATION,  # absolute path
+    persist_directory=DB_LOCATION,
     embedding_function=embeddings,
 )