Remove test file

core/llm.py

@@ -0,0 +1,101 @@
+"""
+LLM communication module
+"""
+import os
+import json
+from pathlib import Path
+from typing import Optional
+from dotenv import load_dotenv
+from openai import OpenAI
+
+
+class LLMClient:
+    def __init__(self, prompts_dir: Path):
+        load_dotenv()
+        
+        self.api_url = os.getenv('OPENAI_API_URL', 'https://api.openai.com/v1')
+        self.model = os.getenv('OPENAI_MODEL', 'gpt-4')
+        self.api_key = os.getenv('OPENAI_API_KEY', '')
+        
+        self.client = OpenAI(
+            base_url=self.api_url,
+            api_key=self.api_key
+        ) if self.api_key else None
+        
+        self.prompts_dir = prompts_dir
+        if not prompts_dir.exists():
+            prompts_dir.mkdir(parents=True)
+            self._create_default_prompt()
+    
+    def _create_default_prompt(self):
+        """Create default system prompt"""
+        default_prompt = """You are a metadata extraction assistant for markdown documents.
+
+Your task is to analyze the document content and suggest appropriate frontmatter metadata.
+
+Guidelines:
+- For tags: prefer selecting from the provided existing tags list when appropriate
+- You may create new tags if they better fit the content
+- New tags must follow the rules: lowercase letters, numbers, and underscores only
+- Be concise and accurate
+- Preserve important existing metadata when present"""
+        
+        with open(self.prompts_dir / 'default.txt', 'w', encoding='utf-8') as f:
+            f.write(default_prompt)
+    
+    def load_prompt(self, name: str = 'default') -> str:
+        """Load prompt template"""
+        prompt_path = self.prompts_dir / f'{name}.txt'
+        
+        if not prompt_path.exists():
+            prompt_path = self.prompts_dir / 'default.txt'
+            if not prompt_path.exists():
+                self._create_default_prompt()
+        
+        with open(prompt_path, 'r', encoding='utf-8') as f:
+            return f.read()
+    
+    def infer_metadata(self, field: str, prompt_name: str, original_fm: dict, 
+                      current_fm: dict, document_body: str, 
+                      all_tags: list, schema: dict) -> Optional[str]:
+        """Use LLM to infer metadata field value"""
+        if not self.client:
+            return None
+        
+        system_prompt = self.load_prompt(prompt_name)
+        
+        user_message = f"""Document content:
+{document_body[:2000]}
+
+Original frontmatter:
+{json.dumps(original_fm, indent=2)}
+
+Current frontmatter being built:
+{json.dumps(current_fm, indent=2)}
+
+Available tags in repository:
+{', '.join(all_tags[:100])}
+
+Please provide appropriate metadata for this document following the schema."""
+        
+        try:
+            response = self.client.chat.completions.create(
+                model=self.model,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_message}
+                ],
+                response_format={"type": "json_schema", "json_schema": {
+                    "name": "frontmatter_response",
+                    "strict": True,
+                    "schema": schema
+                }},
+                temperature=0.3
+            )
+            
+            result = json.loads(response.choices[0].message.content)
+            return result.get(field)
+            
+        except Exception as e:
+            print(f"  LLM error: {e}")
+            return None