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AI Insight/rag_service.py

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+"""
+RAG Microservice untuk CP 707 Knowledge Base
+Berjalan di port 5002, dipanggil oleh backend Node.js
+Menggunakan ChromaDB + SentenceTransformers (offline mode)
+"""
+
+import os
+import sys
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Set offline agar tidak download dari HuggingFace
+os.environ["HF_HUB_OFFLINE"] = "1"
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+
+# Load .env dari direktori yang sama dengan script ini
+script_dir = Path(__file__).parent
+load_dotenv(script_dir / ".env")
+
+CHROMA_DB_DIR = str(script_dir / os.getenv("CHROMA_DB_DIR", "chroma_db"))
+EMBEDDING_MODEL_NAME = os.getenv("EMBEDDING_MODEL_NAME", "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
+RAG_PORT = int(os.getenv("RAG_PORT", "5002"))
+COLLECTION_NAME = "company_sop"
+
+print(f"[RAG Service] ChromaDB path: {CHROMA_DB_DIR}")
+print(f"[RAG Service] Embedding model: {EMBEDDING_MODEL_NAME}")
+
+# Import setelah env set
+import chromadb
+from sentence_transformers import SentenceTransformer
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+import uvicorn
+
+# ─── Init ChromaDB & Embedding Model ─────────────────────────────────────────
+print("[RAG Service] Memuat ChromaDB...")
+try:
+    chroma_client = chromadb.PersistentClient(path=CHROMA_DB_DIR)
+    collection = chroma_client.get_collection(name=COLLECTION_NAME)
+    total_chunks = collection.count()
+    print(f"[RAG Service] ChromaDB loaded. Total chunks: {total_chunks}")
+except Exception as e:
+    print(f"[RAG Service] ERROR: Gagal load ChromaDB: {e}")
+    sys.exit(1)
+
+print(f"[RAG Service] Memuat embedding model '{EMBEDDING_MODEL_NAME}'...")
+try:
+    embedding_model = SentenceTransformer(EMBEDDING_MODEL_NAME)
+    print("[RAG Service] Embedding model berhasil dimuat.")
+except Exception as e:
+    print(f"[RAG Service] ERROR: Gagal load embedding model: {e}")
+    print("[RAG Service] Pastikan model sudah didownload. Jalankan populate_db.py terlebih dahulu.")
+    sys.exit(1)
+
+# ─── FastAPI App ──────────────────────────────────────────────────────────────
+app = FastAPI(
+    title="CP707 RAG Service",
+    description="Retrieval-Augmented Generation service untuk buku Manajemen Broiler CP 707",
+    version="1.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:3000", "http://localhost:5001", "http://127.0.0.1:5001"],
+    allow_methods=["GET", "POST"],
+    allow_headers=["*"],
+)
+
+# ─── Request/Response Models ─────────────────────────────────────────────────
+class QueryRequest(BaseModel):
+    query: str = Field(..., min_length=1, description="Query text untuk mencari chunk CP707 relevan")
+    n_results: int = Field(default=4, ge=1, le=10, description="Jumlah chunk yang dikembalikan")
+    topic: str = Field(default="", description="Topic insight: berat_ayam, fcr, iot_panel, dll")
+
+class QueryResponse(BaseModel):
+    success: bool
+    chunks: list
+    sources: list
+    total_found: int
+    query_used: str
+
+class HealthResponse(BaseModel):
+    status: str
+    total_chunks: int
+    embedding_model: str
+
+# ─── Topic → Query enhancement mapping ──────────────────────────────────────
+# Tambahkan keyword relevan per topic agar embedding search lebih tepat sasaran
+TOPIC_QUERY_HINTS = {
+    "berat_ayam":    "berat badan target bobot ADG pertumbuhan standar mingguan ayam broiler",
+    "fcr":           "FCR feed conversion ratio konsumsi pakan efisiensi standar broiler",
+    "iot_panel":     "suhu kandang kelembapan amonia CO2 ventilasi lingkungan pemeliharaan broiler",
+    "eef":           "EEF indeks performa IP efisiensi produksi siklus broiler",
+    "hitung_ayam":   "mortalitas deplesi kematian afkir populasi standar toleransi broiler",
+    "hitung_karung": "pakan karung konsumsi harian feed intake standar broiler",
+}
+
+# ─── Endpoints ───────────────────────────────────────────────────────────────
+@app.get("/health", response_model=HealthResponse)
+def health_check():
+    return HealthResponse(
+        status="ok",
+        total_chunks=collection.count(),
+        embedding_model=EMBEDDING_MODEL_NAME,
+    )
+
+@app.post("/query", response_model=QueryResponse)
+def query_cp707(req: QueryRequest):
+    """
+    Cari chunk CP707 yang relevan berdasarkan query.
+    Jika topic disediakan, tambahkan hint keyword agar hasil lebih relevan.
+    """
+    enhanced_query = req.query
+    if req.topic and req.topic in TOPIC_QUERY_HINTS:
+        enhanced_query = f"{req.query} {TOPIC_QUERY_HINTS[req.topic]}"
+
+    try:
+        query_embedding = embedding_model.encode([enhanced_query])[0].tolist()
+
+        results = collection.query(
+            query_embeddings=[query_embedding],
+            n_results=min(req.n_results, collection.count()),
+        )
+
+        chunks = results["documents"][0] if results["documents"] else []
+        metadatas = results["metadatas"][0] if results["metadatas"] else []
+
+        sources = [
+            f"{m.get('source', 'unknown')} (chunk {m.get('chunk_index', '?')})"
+            for m in metadatas
+        ]
+
+        return QueryResponse(
+            success=True,
+            chunks=chunks,
+            sources=sources,
+            total_found=len(chunks),
+            query_used=enhanced_query,
+        )
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"RAG query error: {str(e)}")
+
+
+if __name__ == "__main__":
+    print(f"[RAG Service] Starting on http://0.0.0.0:{RAG_PORT}")
+    uvicorn.run(app, host="0.0.0.0", port=RAG_PORT, log_level="warning")