added logging and fixed dspY error

ai/groq_setup.py

@@ -8,27 +8,33 @@ import dspy
 import config
 
 
+# Configure DSPy ONCE at import time, on the main thread, with Groq as default.
+_default_lm = dspy.LM(
+    model=f"groq/{config.GROQ_MODEL}",
+    api_key=config.GROQ_API_KEY,
+    max_tokens=4096,
+    temperature=0.2,
+)
+dspy.configure(lm=_default_lm)
+
+
 def get_lm(provider: str = "groq") -> dspy.LM:
-    """Return a configured DSPy language-model instance.
+    """Return a DSPy language-model instance for the requested provider.
+
+    This does NOT call dspy.configure to avoid thread-safety issues;
+    the global settings are configured once at import using Groq.
 
     Parameters
     ----------
     provider : "groq" | "openai"
     """
     if provider == "openai":
-        lm = dspy.LM(
+        return dspy.LM(
             model=f"openai/{config.OPENAI_MODEL}",
             api_key=config.OPENAI_API_KEY,
             max_tokens=4096,
             temperature=0.2,
         )
-    else:  # default: groq
-        lm = dspy.LM(
-            model=f"groq/{config.GROQ_MODEL}",
-            api_key=config.GROQ_API_KEY,
-            max_tokens=4096,
-            temperature=0.2,
-        )
 
-    dspy.configure(lm=lm)
-    return lm
+    # Default / Groq: reuse the global LM so we share configuration.
+    return _default_lm

ai/pipeline.py

@@ -39,11 +39,11 @@ class SQLAnalystPipeline:
         self.provider = provider
         self._lm = get_lm(provider)
 
-        # DSPy predict modules
-        self.analyze = dspy.Predict(AnalyzeAndPlan)
-        self.generate_sql = dspy.Predict(SQLGeneration)
-        self.interpret = dspy.Predict(InterpretAndInsight)
-        self.repair = dspy.Predict(SQLRepair)
+        # DSPy predict modules — each bound to the chosen LM instance
+        self.analyze = dspy.Predict(AnalyzeAndPlan, lm=self._lm)
+        self.generate_sql = dspy.Predict(SQLGeneration, lm=self._lm)
+        self.interpret = dspy.Predict(InterpretAndInsight, lm=self._lm)
+        self.repair = dspy.Predict(SQLRepair, lm=self._lm)
 
     # ── public API ──────────────────────────────────────────────────────
 

app.py

@@ -10,6 +10,7 @@ from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s  %(name)s  %(message)s")
+logger = logging.getLogger("api")
 
 app = FastAPI(title="AI SQL Analyst", version="1.0.0")
 
@@ -57,12 +58,24 @@ def chat_endpoint(req: QuestionRequest):
     from ai.pipeline import SQLAnalystPipeline
     from db.memory import get_recent_history, add_turn
 
+    logger.info(
+        "CHAT request | provider=%s | conversation_id=%s | question=%s",
+        req.provider,
+        req.conversation_id or "default",
+        req.question,
+    )
+
     conversation_id = req.conversation_id or "default"
 
     history = get_recent_history(conversation_id, limit=5)
 
     # Augment the question with recent conversation context
     if history:
+        logger.info(
+            "CHAT context | conversation_id=%s | history_turns=%d",
+            conversation_id,
+            len(history),
+        )
         history_lines: list[str] = ["You are in a multi-turn conversation. Here are the recent exchanges:"]
         for turn in history:
             history_lines.append(f"User: {turn['question']}")
@@ -70,11 +83,22 @@ def chat_endpoint(req: QuestionRequest):
         history_lines.append(f"Now the user asks: {req.question}")
         question_with_context = "\n".join(history_lines)
     else:
+        logger.info(
+            "CHAT context | conversation_id=%s | history_turns=0 (no prior context used)",
+            conversation_id,
+        )
         question_with_context = req.question
 
     pipeline = SQLAnalystPipeline(provider=req.provider)
     result = pipeline.run(question_with_context)
 
+    logger.info(
+        "CHAT result | conversation_id=%s | used_context=%s | sql_preview=%s",
+        conversation_id,
+        "yes" if history else "no",
+        (result.get("sql") or "").replace("\n", " ")[:200],
+    )
+
     # Persist this turn for future context
     add_turn(conversation_id, req.question, result["answer"], result["sql"])