Merge pull request #2750 from chatchat-space/dev

配置文件修改

configs/model_config.py.example

@@ -8,7 +8,7 @@ MODEL_ROOT_PATH = ""
 # 选用的 Embedding 名称
 EMBEDDING_MODEL = "bge-large-zh-v1.5"
 
-# Embedding 模型运行设备。设为"auto"会自动检测(会有警告)，也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。
+# Embedding 模型运行设备。设为 "auto" 会自动检测(会有警告)，也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。
 EMBEDDING_DEVICE = "auto"
 
 # 选用的reranker模型
@@ -26,11 +26,11 @@ EMBEDDING_MODEL_OUTPUT_PATH = "output"
 # 在这里，我们使用目前主流的两个离线模型，其中，chatglm3-6b 为默认加载模型。
 # 如果你的显存不足，可使用 Qwen-1_8B-Chat, 该模型 FP16 仅需 3.8G显存。
 
-LLM_MODELS = ["zhipu-api"]
+LLM_MODELS = ["chatglm3-6b", "zhipu-api", "openai-api"]
 Agent_MODEL = None
 
 # LLM 模型运行设备。设为"auto"会自动检测(会有警告)，也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。
-LLM_DEVICE = "cuda"
+LLM_DEVICE = "auto"
 
 HISTORY_LEN = 3
 
@@ -66,7 +66,7 @@ ONLINE_LLM_MODEL = {
         "APPID": "",
         "APISecret": "",
         "api_key": "",
-        "version": "v1.5",  # 你使用的讯飞星火大模型版本，可选包括 "v3.0", "v1.5", "v2.0"
+        "version": "v3.0", # 你使用的讯飞星火大模型版本，可选包括 "v3.0", "v2.0", "v1.5"
         "provider": "XingHuoWorker",
     },
 
@@ -120,7 +120,7 @@ ONLINE_LLM_MODEL = {
         "secret_key": "",
         "provider": "TianGongWorker",
     },
-    # Gemini API (开发组未测试，由社群提供，只支持pro）https://makersuite.google.com/或者google cloud，使用前先确认网络正常，使用代理请在项目启动（python startup.py -a)环境内设置https_proxy环境变量
+    # Gemini API (开发组未测试，由社群提供，只支持pro）
     "gemini-api": {
         "api_key": "",
         "provider": "GeminiWorker",
@@ -155,7 +155,7 @@ MODEL_PATH = {
         "bge-large-zh": "BAAI/bge-large-zh",
         "bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
         "bge-base-zh-v1.5": "BAAI/bge-base-zh-v1.5",
-        "bge-large-zh-v1.5": "/share/home/zyx/Models/bge-large-zh-v1.5",
+        "bge-large-zh-v1.5": "BAAI/bge-large-zh-v1.5",
         "piccolo-base-zh": "sensenova/piccolo-base-zh",
         "piccolo-large-zh": "sensenova/piccolo-large-zh",
         "nlp_gte_sentence-embedding_chinese-large": "damo/nlp_gte_sentence-embedding_chinese-large",
@@ -168,11 +168,15 @@ MODEL_PATH = {
         "chatglm3-6b": "THUDM/chatglm3-6b",
         "chatglm3-6b-32k": "THUDM/chatglm3-6b-32k",
 
+        "Orion-14B-Chat": "OrionStarAI/Orion-14B-Chat",
+        "Orion-14B-Chat-Plugin": "OrionStarAI/Orion-14B-Chat-Plugin",
+        "Orion-14B-LongChat": "OrionStarAI/Orion-14B-LongChat",
+
         "Llama-2-7b-chat-hf": "meta-llama/Llama-2-7b-chat-hf",
         "Llama-2-13b-chat-hf": "meta-llama/Llama-2-13b-chat-hf",
         "Llama-2-70b-chat-hf": "meta-llama/Llama-2-70b-chat-hf",
 
-        "Qwen-1_8B-Chat": "/media/checkpoint/Qwen-1_8B-Chat",
+        "Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat",
         "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
         "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
         "Qwen-72B-Chat": "Qwen/Qwen-72B-Chat",
@@ -197,7 +201,7 @@ MODEL_PATH = {
         "agentlm-70b": "THUDM/agentlm-70b",
 
         "falcon-7b": "tiiuae/falcon-7b",
-        "falcon-40b": "tiiuae/falcon-40b",
+        "falcon-40b": "tiiuae/falcon-40,b",
         "falcon-rw-7b": "tiiuae/falcon-rw-7b",
 
         "aquila-7b": "BAAI/Aquila-7B",
@@ -287,9 +291,11 @@ VLLM_MODEL_DICT = {
 }
 
 SUPPORT_AGENT_MODEL = [
-    "azure-api",
+    "openai-api",  # GPT4 模型
-    "openai-api",
+    "qwen-api",  # Qwen Max模型
-    "qwen-api",
+    "zhipu-api",  # 智谱AI GLM4模型
-    "Qwen",
+    "Qwen",  # 所有Qwen系列本地模型
-    "chatglm3",
+    "chatglm3-6b",
+    "internlm2-chat-20b",
+    "Orion-14B-Chat-Plugin",
 ]

requirements.txt

@@ -40,6 +40,9 @@ einops>=0.7.0
 transformers_stream_generator==0.0.4
 vllm==0.2.7; sys_platform == "linux"
 
+# flash-attn>=2.4.3 # For Orion-14B-Chat and Qwen-14B-Chat
+
+
 # optional document loaders
 
 #rapidocr_paddle[gpu]>=1.3.0.post5 # gpu accelleration for ocr of pdf and image files

requirements_api.txt

@@ -39,6 +39,7 @@ transformers_stream_generator==0.0.4
 vllm==0.2.7; sys_platform == "linux"
 httpx==0.26.0
 llama-index
+# flash-attn>=2.4.3 # For Orion-14B-Chat and Qwen-14B-Chat
 
 # optional document loaders
 

server/chat/agent_chat.py

@@ -19,6 +19,7 @@ from server.chat.utils import History
 from server.agent import model_container
 from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
 
+
 async def agent_chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
                      history: List[History] = Body([],
                                                    description="历史对话",
@@ -82,8 +83,7 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
                 memory.chat_memory.add_user_message(message.content)
             else:
                 memory.chat_memory.add_ai_message(message.content)
-
+        if "chatglm3" in model_container.MODEL.model_name or "zhipu-api" in model_container.MODEL.model_name:
-        if "chatglm3" in model_container.MODEL.model_name:
             agent_executor = initialize_glm3_agent(
                 llm=model,
                 tools=tools,

server/model_workers/zhipu.py

@@ -57,21 +57,24 @@ class ChatGLMWorker(ApiModelWorker):
             "messages": params.messages,
             "max_tokens": params.max_tokens,
             "temperature": params.temperature,
-            "stream": True
+            "stream": False
         }
         url = "https://open.bigmodel.cn/api/paas/v4/chat/completions"
-        response = requests.post(url, headers=headers, json=data, stream=True)
+        response = requests.post(url, headers=headers, json=data)
-        for chunk in response.iter_lines():
+        # for chunk in response.iter_lines():
-            if chunk:
+        #     if chunk:
-                chunk_str = chunk.decode('utf-8')
+        #         chunk_str = chunk.decode('utf-8')
-                json_start_pos = chunk_str.find('{"id"')
+        #         json_start_pos = chunk_str.find('{"id"')
-                if json_start_pos != -1:
+        #         if json_start_pos != -1:
-                    json_str = chunk_str[json_start_pos:]
+        #             json_str = chunk_str[json_start_pos:]
-                    json_data = json.loads(json_str)
+        #             json_data = json.loads(json_str)
-                    for choice in json_data.get('choices', []):
+        #             for choice in json_data.get('choices', []):
-                        delta = choice.get('delta', {})
+        #                 delta = choice.get('delta', {})
-                        content = delta.get('content', '')
+        #                 content = delta.get('content', '')
-                        yield {"error_code": 0, "text": content}
+        #                 yield {"error_code": 0, "text": content}
+        ans = response.json()
+        content = ans["choices"][0]["message"]["content"]
+        yield {"error_code": 0, "text": content}
 
     def get_embeddings(self, params):
         # 临时解决方案，不支持embedding