Initial commit

obsidian_rag/llm_client.py

@@ -0,0 +1,74 @@
+from typing import Dict
+
+import openai
+
+
+class LLMClient:
+  """
+  Minimalist client for interacting with Clovis LLM via OpenAI SDK.
+
+  Attributes:
+      api_key (str): API key for Clovis.
+      base_url (str): Base URL for Clovis LLM gateway.
+      model (str): Model name to use. Defaults to 'ClovisLLM'.
+  """
+  
+  def __init__(self, api_key: str, base_url: str, model: str = "ClovisLLM") -> None:
+    if not api_key:
+      raise ValueError("API key is required for LLMClient.")
+    if not base_url:
+      raise ValueError("Base URL is required for LLMClient.")
+    
+    self.api_key = api_key
+    self.base_url = base_url
+    self.model = model
+    self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
+  
+  def generate(self, system_prompt: str, user_prompt: str, context: str) -> Dict[str, object]:
+    """
+    Generate a response from the LLM given a system prompt, user prompt, and context.
+
+    Args:
+        system_prompt (str): Instructions for the assistant.
+        user_prompt (str): The user's query.
+        context (str): Concatenated chunks from RAG search.
+
+    Returns:
+        Dict[str, object]: Contains:
+            - "answer" (str): Text generated by the LLM.
+            - "usage" (int): Total tokens used in the completion.
+    """
+    # Construct user message with explicit CONTEXT / QUESTION separation
+    user_message_content = f"CONTEXT:\n{context}\n\nQUESTION:\n{user_prompt}"
+    
+    try:
+      response = self.client.chat.completions.create(model=self.model,
+                                                     messages=[
+                                                         {"role": "system", "content": system_prompt},
+                                                         {"role": "user", "content": user_message_content}
+                                                     ],
+                                                     temperature=0.7,
+                                                     max_tokens=2000,
+                                                     top_p=1.0,
+                                                     n=1,
+                                                     # stream=False,
+                                                     # presence_penalty=0.0,
+                                                     # frequency_penalty=0.0,
+                                                     # stop=None,
+                                                     # logit_bias={},
+                                                     user="obsidian_rag",
+                                                     )
+    except Exception as e:
+      # For now, propagate exceptions (C1 minimal)
+      raise e
+    
+    # Extract text and usage
+    try:
+      answer_text = response.choices[0].message.content
+      total_tokens = response.usage.total_tokens
+    except AttributeError:
+      # Fallback if response structure is unexpected
+      answer_text = ""
+      total_tokens = 0
+    
+    return {"answer": answer_text, "usage": total_tokens}