fix(advisor): fix LLM API call, wire advice_markdown to webapp, update .env.example timeouts

- llm_analyzer.py: use llm.langchain_llm.ainvoke() (correct RAGAS 0.4.3 API)
- webapp/models.py: add advice_markdown field to ReportData
- webapp/services/run_reader.py: add read_advice_markdown() reading optimization_advice.md
- webapp/services/report_builder.py: pass advice_markdown into ReportData
- .env.example: OPENAI_TIMEOUT_SECONDS 30→180, RAGAS_METRIC_TIMEOUT_SECONDS 45→300

Co-Authored-By: Claude <noreply@anthropic.com>

rag_eval/advisor/llm_analyzer.py

@@ -87,8 +87,9 @@ async def analyze(
     try:
         logger.info("[advisor] calling LLM for optimization analysis  scenario=%s", scenario_name)
         from langchain_core.messages import HumanMessage
-        result = await llm.agenerate(texts=[[HumanMessage(content=prompt)]])
-        text = result.generations[0][0].text.strip()
+        # Use the underlying langchain chat model directly (RAGAS LangchainLLMWrapper wraps BaseChatModel)
+        response = await llm.langchain_llm.ainvoke([HumanMessage(content=prompt)])
+        text = response.content.strip()
         logger.info("[advisor] LLM analysis complete  chars=%d", len(text))
         return text
     except Exception as exc: