add linear regression model initial version

supporting/RAGAS_Service/regression_service.py

@@ -0,0 +1,61 @@
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+from fastapi import FastAPI
+import torch
+import torch.nn as nn
+
+app = FastAPI()
+
+MODEL_PATH = "logreg_classifier.pt"
+
+class LogisticNet(nn.Module):
+    def __init__(self, input_dim: int, hidden_dim: int, num_classes: int, dropout: float):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.BatchNorm1d(hidden_dim),
+            nn.ReLU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, num_classes),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+checkpoint = torch.load(MODEL_PATH, map_location="cpu")
+
+encoder = SentenceTransformer(checkpoint["embedding_model"])
+
+model = LogisticNet(
+    input_dim   = checkpoint["input_dim"],
+    hidden_dim  = checkpoint["hidden_dim"],
+    num_classes = checkpoint["num_classes"],
+    dropout     = checkpoint["dropout"],
+)
+model.load_state_dict(checkpoint["model_state"])
+model.eval()
+
+
+class EvalRequest(BaseModel):
+    answer: str
+
+
+@app.post("/evaluate")
+def evaluate(req: EvalRequest):
+    embedding = encoder.encode(
+        [req.answer],
+        normalize_embeddings=True,
+        show_progress_bar=False,
+    )
+
+    x = torch.tensor(embedding, dtype=torch.float32)
+
+    with torch.no_grad():
+        logits = model(x)
+
+    probs = torch.softmax(logits, dim=1)
+
+    return {
+        "probabilities": probs.cpu().numpy().tolist()
+    }