refactor: Refactor for core SDK (#1092)

dbgpt/model/utils/llm_utils.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+from pathlib import Path
+from typing import Dict, List
+
+import cachetools
+
+from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, LLM_MODEL_CONFIG
+from dbgpt.model.base import SupportedModel
+from dbgpt.util.parameter_utils import _get_parameter_descriptions
+
+
+def is_sentence_complete(output: str):
+    """Check whether the output is a complete sentence."""
+    end_symbols = (".", "?", "!", "...", "。", "？", "！", "…", '"', "'", "”")
+    return output.endswith(end_symbols)
+
+
+def is_partial_stop(output: str, stop_str: str):
+    """Check whether the output contains a partial stop str."""
+    for i in range(0, min(len(output), len(stop_str))):
+        if stop_str.startswith(output[-i:]):
+            return True
+    return False
+
+
+@cachetools.cached(cachetools.TTLCache(maxsize=100, ttl=60 * 5))
+def list_supported_models():
+    from dbgpt.model.parameter import WorkerType
+
+    models = _list_supported_models(WorkerType.LLM.value, LLM_MODEL_CONFIG)
+    models += _list_supported_models(WorkerType.TEXT2VEC.value, EMBEDDING_MODEL_CONFIG)
+    return models
+
+
+def _list_supported_models(
+    worker_type: str, model_config: Dict[str, str]
+) -> List[SupportedModel]:
+    from dbgpt.model.adapter.loader import _get_model_real_path
+    from dbgpt.model.adapter.model_adapter import get_llm_model_adapter
+
+    ret = []
+    for model_name, model_path in model_config.items():
+        model_path = _get_model_real_path(model_name, model_path)
+        model = SupportedModel(
+            model=model_name,
+            path=model_path,
+            worker_type=worker_type,
+            path_exist=False,
+            proxy=False,
+            enabled=False,
+            params=None,
+        )
+        if "proxyllm" in model_name:
+            model.proxy = True
+        else:
+            path = Path(model_path)
+            model.path_exist = path.exists()
+        param_cls = None
+        try:
+            llm_adapter = get_llm_model_adapter(model_name, model_path)
+            param_cls = llm_adapter.model_param_class()
+            model.enabled = True
+            params = _get_parameter_descriptions(
+                param_cls, model_name=model_name, model_path=model_path
+            )
+            model.params = params
+        except Exception:
+            pass
+        ret.append(model)
+    return ret