♻️(summary) extract LLMService class into dedicated module

Move LLMService class from existing file into separate dedicated
module to improve code organization.

src/summary/summary/core/celery_worker.py

@@ -7,7 +7,7 @@ import os
 import tempfile
 import time
 from pathlib import Path
-from typing import Any, Mapping, Optional
+from typing import Optional
 
 import openai
 import sentry_sdk
@@ -21,6 +21,7 @@ from urllib3.util import Retry
 
 from summary.core.analytics import MetadataManager, get_analytics
 from summary.core.config import get_settings
+from summary.core.llm_service import LLMException, LLMService
 from summary.core.prompt import (
     FORMAT_NEXT_STEPS,
     FORMAT_PLAN,
@@ -83,51 +84,6 @@ def create_retry_session():
     return session
 
 
-class LLMException(Exception):
-    """LLM call failed."""
-
-
-class LLMService:
-    """Service for performing calls to the LLM configured in the settings."""
-
-    def __init__(self):
-        """Init the LLMService once."""
-        self._client = openai.OpenAI(
-            base_url=settings.llm_base_url,
-            api_key=settings.llm_api_key.get_secret_value(),
-        )
-
-    def call(
-        self,
-        system_prompt: str,
-        user_prompt: str,
-        response_format: Optional[Mapping[str, Any]] = None,
-    ):
-        """Call the LLM service.
-
-        Takes a system prompt and a user prompt, and returns the LLM's response
-        Returns None if the call fails.
-        """
-        try:
-            params: dict[str, Any] = {
-                "model": settings.llm_model,
-                "messages": [
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": user_prompt},
-                ],
-            }
-            if response_format is not None:
-                params["response_format"] = response_format
-
-            response = self._client.chat.completions.create(**params)
-
-            return response.choices[0].message.content
-
-        except Exception as e:
-            logger.exception("LLM call failed: %s", e)
-            raise LLMException("LLM call failed: {e}") from e
-
-
 def format_actions(llm_output: dict) -> str:
     """Format the actions from the LLM output into a markdown list.
 

src/summary/summary/core/llm_service.py

@@ -0,0 +1,57 @@
+"""LLM service to encapsulate LLM's calls."""
+
+import logging
+from typing import Any, Mapping, Optional
+
+import openai
+
+from summary.core.config import get_settings
+
+settings = get_settings()
+
+logger = logging.getLogger(__name__)
+
+
+class LLMException(Exception):
+    """LLM call failed."""
+
+
+class LLMService:
+    """Service for performing calls to the LLM configured in the settings."""
+
+    def __init__(self):
+        """Init the LLMService once."""
+        self._client = openai.OpenAI(
+            base_url=settings.llm_base_url,
+            api_key=settings.llm_api_key.get_secret_value(),
+        )
+
+    def call(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        response_format: Optional[Mapping[str, Any]] = None,
+    ):
+        """Call the LLM service.
+
+        Takes a system prompt and a user prompt, and returns the LLM's response
+        Returns None if the call fails.
+        """
+        try:
+            params: dict[str, Any] = {
+                "model": settings.llm_model,
+                "messages": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt},
+                ],
+            }
+            if response_format is not None:
+                params["response_format"] = response_format
+
+            response = self._client.chat.completions.create(**params)
+
+            return response.choices[0].message.content
+
+        except Exception as e:
+            logger.exception("LLM call failed: %s", e)
+            raise LLMException("LLM call failed: {e}") from e