Refactor summarize_to_words to accept text and title.

Updated the `summarize_to_words` function to take `text` and `title` as separate parameters instead of a single `article` dictionary. Adjusted payload and function calls accordingly for better clarity and flexibility.

src/phoenix_technologies/smd/server.py

@@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
 # Initialize FastMCP server
 mcp = FastMCP("SMD Researcher", host="0.0.0.0", port=8000, timeout_keep_alive=720)
 
-async def summarize_to_words(article: dict, target_word_count: int = 1000) -> str:
+async def summarize_to_words(text: str, title: str, target_word_count: int = 1000) -> str:
     url = f"https://maas.ai-2.kvant.cloud/engines/{os.getenv('SWISSDOX_SUMMARIZING_MODEL', '')}/chat/completions"
     headers = {
         "x-litellm-api-key": f"Bearer {os.getenv('SWISSDOX_SUMMARIZING_MODEL_APIKEY', '')}",
@@ -41,7 +41,7 @@ async def summarize_to_words(article: dict, target_word_count: int = 1000) -> st
             },
             {
                 "role": "user",
-                "content": f"{str(article)}"
+                "content": f"{title} - {text}"
             }
         ]
     }
@@ -65,7 +65,7 @@ async def smd_detail_article(article_id):
         async with session.post(url, headers=headers, json=payload) as response:
             if response.status == 200:
                 data = await response.json()
-                summarized_content = await summarize_to_words(data, target_word_count=10000)
+                summarized_content = await summarize_to_words(title=data.get("title"), text=data.get("text"), target_word_count=10000)
                 return summarized_content
             else:
                 return {