API: Implement echo + logprobs for /v1/completions endpoint

modules/llama_cpp_server.py

@@ -310,8 +310,45 @@ class LlamaServer:
         else:
             raise Exception(f"Unexpected response format: 'completion_probabilities' not found in {result}")
 
+    def get_prompt_logprob_entries(self, token_ids, n_probs=5, prompt=""):
+        """Get logprob entries for prompt tokens via a single n_predict=0 request.
+
+        Requires llama.cpp server with prompt_logprobs support.
+        Returns entries in the standard format for format_completion_logprobs().
+        """
+        token_ids_list = token_ids.tolist() if hasattr(token_ids, 'tolist') else list(token_ids)
+
+        url = f"http://127.0.0.1:{self.port}/completion"
+        payload = {
+            "prompt": token_ids_list,
+            "n_predict": 0,
+            "n_probs": n_probs,
+            "prompt_logprobs": True,
+            "stream": False,
+            "cache_prompt": False,
+        }
+
+        response = self.session.post(url, json=payload)
+        result = response.json()
+
+        prompt_probs = result.get("prompt_probabilities", [])
+        if not prompt_probs:
+            return []
+
+        # Null first token (no conditioning context); use empty string for BOS
+        # or tokens that don't appear at the start of the prompt text.
+        first_token_str = self.decode([token_ids_list[0]])
+        if self.bos_token and first_token_str == self.bos_token:
+            first_token_str = ""
+        elif not prompt.startswith(first_token_str):
+            first_token_str = ""
+
+        entries = [{"token": first_token_str, "null_logprob": True}]
+        entries.extend(prompt_probs)
+        return entries
+
     def _get_vocabulary_size(self):
-        """Get and store the model's maximum context length."""
+        """Get and store the model's vocabulary size."""
         url = f"http://127.0.0.1:{self.port}/v1/models"
         response = self.session.get(url).json()