feat(backend): add LLM monitoring with step names, timing, and prompt logging

- LLMClient.complete() now accepts step_name parameter to identify processing step

- Logs prompt preview (first 100 + last 100 chars) at INFO level

- Logs processing time in milliseconds with token usage stats

- Updated QueryDecomposer, RelevanceFilter, and RAGService to pass step names

Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)

Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>

backend/app/services/llm_client.py

@@ -1,4 +1,5 @@
 import logging
+import time
 from typing import Optional
 
 import httpx
@@ -28,11 +29,30 @@ class LLMClient:
             ),
         )
 
-    async def complete(self, prompt: str, temperature: float = 0.7) -> str:
-        """Send a chat completion request with optional thinking control."""
+    def _truncate_prompt_for_log(self, prompt: str, first_chars: int = 100, last_chars: int = 100) -> str:
+        """Truncate prompt for logging: show first N and last N chars with ellipsis."""
+        if len(prompt) <= first_chars + last_chars:
+            return prompt
+        return f"{prompt[:first_chars]}...({len(prompt) - first_chars - last_chars} chars omitted)...{prompt[-last_chars:]}"
+
+    async def complete(self, prompt: str, temperature: float = 0.7, step_name: str = "LLM") -> str:
+        """Send a chat completion request with optional thinking control.
+
+        Args:
+            prompt: The prompt to send to the LLM.
+            temperature: Sampling temperature.
+            step_name: Identifier for the processing step (e.g., "QueryDecomposer").
+
+        Returns:
+            The LLM response text.
+        """
         messages = [{"role": "user", "content": prompt}]
         extra_body = self._build_extra_body()
 
+        prompt_preview = self._truncate_prompt_for_log(prompt)
+        self.logger.info("[%s] LLM request started. Prompt: %s", step_name, prompt_preview)
+        start_time = time.perf_counter()
+
         try:
             response = await self._client.chat.completions.create(
                 model=self.model,
@@ -40,12 +60,23 @@ class LLMClient:
                 temperature=temperature,
                 extra_body=extra_body if extra_body else None,
             )
-            return response.choices[0].message.content or ""
+            content = response.choices[0].message.content or ""
+            elapsed_ms = (time.perf_counter() - start_time) * 1000
+            self.logger.info(
+                "[%s] LLM request completed in %.2fms (prompt_tokens=%s, completion_tokens=%s)",
+                step_name,
+                elapsed_ms,
+                response.usage.prompt_tokens if response.usage else "?",
+                response.usage.completion_tokens if response.usage else "?",
+            )
+            return content
         except (APITimeoutError, APIError) as exc:
-            self.logger.error("LLM API error: %s", exc)
+            elapsed_ms = (time.perf_counter() - start_time) * 1000
+            self.logger.error("[%s] LLM API error after %.2fms: %s", step_name, elapsed_ms, exc)
             raise LLMClientError from exc
         except Exception as exc:
-            self.logger.error("Unexpected LLM error: %s", exc)
+            elapsed_ms = (time.perf_counter() - start_time) * 1000
+            self.logger.error("[%s] Unexpected LLM error after %.2fms: %s", step_name, elapsed_ms, exc)
             raise LLMClientError from exc
 
     def _build_extra_body(self) -> dict:

backend/app/services/query_decomposer.py

@@ -43,7 +43,7 @@ class QueryDecomposer:
         prompt = f"Given question: '{question}', extract key search keywords as JSON array"
 
         try:
-            response = await self.llm_client.complete(prompt)
+            response = await self.llm_client.complete(prompt, step_name="QueryDecomposer")
         except Exception as exc:
             logger.warning("LLM decomposition failed: %s", exc)
             return []

backend/app/services/rag.py

@@ -112,4 +112,4 @@ class RAGService:
             f"Answer:"
         )
 
-        return await self.llm_client.complete(prompt=prompt, temperature=0.3)
+        return await self.llm_client.complete(prompt=prompt, temperature=0.3, step_name="ResponseGeneration")

backend/app/services/relevance_filter.py

@@ -36,7 +36,7 @@ class RelevanceFilter:
 
         prompt = self._build_prompt(question, chunks)
         try:
-            response = await self.llm_client.complete(prompt, temperature=0.0)
+            response = await self.llm_client.complete(prompt, temperature=0.0, step_name="RelevanceFilter")
         except Exception as exc:
             logger.error("RelevanceFilter LLM call failed: %s", exc)
             return []