e

backend/rag.py

@@ -39,6 +39,30 @@ from functools import lru_cache
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+class Settings(BaseSettings):
+    # Parquet + Whoosh/FAISS
+    parquet_path: str = "gs://mda_eu_project/data/consolidated_clean_pred.parquet"
+    whoosh_dir:    str = "gs://mda_eu_project/whoosh_index"
+    vectorstore_path: str = "gs://mda_eu_project/vectorstore_index"
+    # Models
+    embedding_model:     str = "sentence-transformers/LaBSE"
+    llm_model:           str = "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16"
+    cross_encoder_model: str = "cross-encoder/mmarco-mMiniLMv2-L12-H384-v1"
+    # RAG parameters
+    chunk_size:    int = 750
+    chunk_overlap: int = 100
+    hybrid_k:      int = 50
+    assistant_role: str = (
+        "You are a concise, factual assistant. Cite Document [ID] for each claim."
+    )
+    skip_warmup: bool = False
+    allowed_origins: List[str] = ["*"]
+
+    class Config:
+        env_file = ".env"
+
+settings = Settings()
+
 # === Global Embeddings & Cache ===
 EMBEDDING = HuggingFaceEmbeddings(model_name=settings.embedding_model)
 

frontend/src/hooks/useAppState.ts

@@ -86,10 +86,10 @@ export const useAppState = () => {
     setQuestion("");
 
     try {
-      const res = await fetch("/api/chat/query", {
+      const res = await fetch("/rag", {
         method: "POST",
         headers: { "Content-Type": "application/json" },
-        body: JSON.stringify({ question })
+        body: JSON.stringify({ query: question })
       });
 
       const data: { answer: string } = await res.json();