fixed the RAg in test pipeline issue

app/services/llm.py

@@ -1,9 +1,43 @@
 from app.config import settings, MODEL_PROVIDERS, PLAN_LIMITS
 from typing import List, Dict, Any, Optional, AsyncGenerator
 import logging
+import re
 
 logger = logging.getLogger(__name__)
 
+# Ordered fallback chain — tried in sequence when the primary model fails.
+# Fireworks models are used for free/starter plans so they must always be available.
+# llama-v3p3-70b-instruct is the guaranteed last resort (confirmed working).
+_FIREWORKS_FALLBACKS = [
+    "accounts/fireworks/models/kimi-k2p5-instruct",
+    "accounts/fireworks/models/deepseek-v3p2",
+    "accounts/fireworks/models/llama-v3p3-70b-instruct",
+]
+
+
+def _normalize_model(model: str) -> str:
+    """Strip date-based version suffixes from Fireworks model IDs.
+    e.g. 'accounts/fireworks/models/kimi-k2-instruct-0905' → 'accounts/fireworks/models/kimi-k2-instruct'
+    Matches only purely-numeric suffixes (4–8 digits) so names like 'llama-v3p3-70b' are untouched."""
+    if model.startswith("accounts/fireworks/") or model.startswith("fireworks/"):
+        model = re.sub(r"-\d{4,8}$", "", model)
+    return model
+
+
+def _infer_provider(model: str) -> str:
+    """Infer the LLM provider from the model ID when it's not in MODEL_PROVIDERS.
+    Handles versioned variants like 'accounts/fireworks/models/kimi-k2-instruct-0905'."""
+    if model.startswith("accounts/fireworks/") or model.startswith("fireworks/"):
+        return "fireworks"
+    if model.startswith("gpt-") or model.startswith("o1") or model.startswith("o3"):
+        return "openai"
+    if model.startswith("claude-"):
+        return "anthropic"
+    if model.startswith("gemini-"):
+        return "google"
+    logger.warning(f"[LLM] Unknown model '{model}', defaulting to fireworks")
+    return "fireworks"
+
 
 class LLMService:
     """Routes requests to appropriate LLM provider"""
@@ -16,7 +50,8 @@ class LLMService:
         temperature: float = 0.7,
     ) -> Dict[str, Any]:
         """Generate a response from the LLM"""
-        provider = MODEL_PROVIDERS.get(model, "openai")
+        model = _normalize_model(model)
+        provider = MODEL_PROVIDERS.get(model) or _infer_provider(model)
 
         try:
             if provider == "fireworks":
@@ -31,9 +66,16 @@ class LLMService:
                 return await self._call_openai(messages, model, max_tokens, temperature)
         except Exception as e:
             logger.error(f"LLM error ({provider}/{model}): {e}")
-            fallback = "accounts/fireworks/models/llama-v3p3-70b-instruct"
-            if model != fallback and settings.fireworks_api_key:
-                return await self._call_fireworks(messages, fallback, max_tokens, temperature)
+            if not settings.fireworks_api_key:
+                raise
+            for fallback in _FIREWORKS_FALLBACKS:
+                if model == fallback:
+                    continue
+                try:
+                    logger.warning(f"[LLM] Falling back to {fallback}")
+                    return await self._call_fireworks(messages, fallback, max_tokens, temperature)
+                except Exception as fe:
+                    logger.error(f"[LLM] Fallback {fallback} also failed: {fe}")
             raise
 
     async def _call_fireworks(