Merge pull request #421 from devchat-ai/add_max_tokens_for_llm_api

Add max_tokens configuration for LLM API

devchat/llm/openai.py

@@ -7,12 +7,15 @@
 import json
 import os
 import re
-from typing import Dict, List
+from pathlib import Path
+from typing import Any, Dict, List
 
 import httpx
 import openai
+import oyaml as yaml
 
 from devchat.ide import IDEService
+from devchat.workflow.path import CHAT_CONFIG_FILENAME, CHAT_DIR
 
 from .pipeline import (
     RetryException,  # Import RetryException class
@@ -42,6 +45,35 @@ def _try_remove_markdown_block_flag(content):
     return content
 
 
+# 模块级变量用于缓存配置
+_chat_config: Dict[str, Any] = {}
+
+
+def _load_chat_config() -> None:
+    """加载聊天配置到全局变量"""
+    global _chat_config
+    chat_config_path = Path(CHAT_DIR) / CHAT_CONFIG_FILENAME
+    with open(chat_config_path, "r", encoding="utf-8") as file:
+        _chat_config = yaml.safe_load(file)
+
+
+def get_maxtokens_by_model(model: str) -> int:
+    # 如果配置还没有加载，则加载配置
+    if not _chat_config:
+        _load_chat_config()
+
+    # 默认值设置为1024
+    default_max_tokens = 1024
+
+    # 检查模型是否在配置中
+    if model in _chat_config.get("models", {}):
+        # 如果模型存在，尝试获取max_tokens，如果不存在则返回默认值
+        return _chat_config["models"][model].get("max_tokens", default_max_tokens)
+    else:
+        # 如果模型不在配置中，返回默认值
+        return default_max_tokens
+
+
 def chat_completion_stream_commit(
     messages: List[Dict],  # [{"role": "user", "content": "hello"}]
     llm_config: Dict,  # {"model": "...", ...}
@@ -62,6 +94,7 @@ def chat_completion_stream_commit(
     # Update llm_config dictionary
     llm_config["stream"] = True
     llm_config["timeout"] = 60
+    llm_config["max_tokens"] = get_maxtokens_by_model(llm_config["model"])
     # Return chat completions
     return client.chat.completions.create(messages=messages, **llm_config)
 
@@ -83,6 +116,7 @@ def chat_completion_stream_raw(**kwargs):
     # Update kwargs dictionary
     kwargs["stream"] = True
     kwargs["timeout"] = 60
+    kwargs["max_tokens"] = get_maxtokens_by_model(kwargs["model"])
     # Return chat completions
     return client.chat.completions.create(**kwargs)