更新一些内容

requirements.txt

@@ -1,6 +1,6 @@
 # API requirements
 
-langchain>=0.0.346
+langchain>=0.0.348
 langchain-experimental>=0.0.42
 pydantic==1.10.13
 fschat==0.2.35

server/api.py

@@ -127,7 +127,6 @@ def mount_app_routes(app: FastAPI, run_mode: str = None):
 
 
 def mount_knowledge_routes(app: FastAPI):
-    from server.chat.knowledge_base_chat import knowledge_base_chat
     from server.chat.file_chat import upload_temp_docs, file_chat
     from server.knowledge_base.kb_api import list_kbs, create_kb, delete_kb
     from server.knowledge_base.kb_doc_api import (list_files, upload_docs, delete_docs,
@@ -135,10 +134,6 @@ def mount_knowledge_routes(app: FastAPI):
                                                 search_docs, DocumentWithVSId, update_info,
                                                 update_docs_by_id,)
 
-    app.post("/chat/knowledge_base_chat",
-             tags=["Chat"],
-             summary="与知识库对话")(knowledge_base_chat)
-
     app.post("/chat/file_chat",
              tags=["Knowledge Base Management"],
              summary="文件对话"

server/chat/chat.py

@@ -83,11 +83,15 @@ def create_models_chains(history, history_len, prompts, models, tools, callbacks
             memory=memory,
             verbose=True,
         )
-    branch = RunnableBranch(
+    agent_use = False
-        (lambda x: "1" in x["topic"].lower(), agent_executor),
+    if agent_use:
-        chain
+        branch = RunnableBranch(
-    )
+            (lambda x: "1" in x["topic"].lower(), agent_executor),
-    full_chain = ({"topic": classifier_chain, "input": lambda x: x["input"]} | branch)
+            chain
+        )
+        full_chain = ({"topic": classifier_chain, "input": lambda x: x["input"]} | branch)
+    else:
+        full_chain = ({"input": lambda x: x["input"]} | chain)
     return full_chain
 
 
@@ -107,15 +111,11 @@ async def chat(query: str = Body(..., description="用户输入", examples=["恼
                tool_config: Dict = Body({}, description="工具配置"),
                ):
     async def chat_iterator() -> AsyncIterable[str]:
-        message_id = add_message_to_db(chat_type="llm_chat", query=query,
+        message_id = add_message_to_db(chat_type="llm_chat", query=query, conversation_id=conversation_id) if conversation_id else  None
-                                       conversation_id=conversation_id) if conversation_id else None
-
         callback = CustomAsyncIteratorCallbackHandler()
         callbacks = [callback]
         models, prompts = create_models_from_config(callbacks=callbacks, configs=model_config)
-
         tools = [tool for tool in all_tools if tool.name in tool_config]
-
         # 构建完整的Chain
         full_chain = create_models_chains(prompts=prompts,
                                           models=models,

server/utils.py

@@ -363,7 +363,8 @@ def get_model_worker_config(model_name: str = None) -> dict:
     config = FSCHAT_MODEL_WORKERS.get("default", {}).copy()
     config.update(ONLINE_LLM_MODEL.get(model_name, {}).copy())
     config.update(FSCHAT_MODEL_WORKERS.get(model_name, {}).copy())
-
+    print(config, "*******")
+    breakpoint()
     if model_name in ONLINE_LLM_MODEL:
         config["online_api"] = True
         if provider := config.get("provider"):
@@ -380,6 +381,7 @@ def get_model_worker_config(model_name: str = None) -> dict:
         if path and os.path.isdir(path):
             config["model_path_exists"] = True
         config["device"] = llm_device(config.get("device"))
+    breakpoint()
     return config
 
 
@@ -523,6 +525,8 @@ def detect_device() -> Literal["cuda", "mps", "cpu"]:
 
 def llm_device(device: str = None) -> Literal["cuda", "mps", "cpu"]:
     device = device or LLM_DEVICE
+    # if device.isdigit():
+    #     return "cuda:" + device
     if device not in ["cuda", "mps", "cpu"]:
         device = detect_device()
     return device

test.py

@@ -1,32 +0,0 @@
-from transformers import FuyuProcessor, FuyuForCausalLM
-from PIL import Image
-import requests
-import torch
-
-# 加载模型和处理器
-model_id = "/data/models/fuyu-8b"
-processor = FuyuProcessor.from_pretrained(model_id)
-model = FuyuForCausalLM.from_pretrained(model_id, device_map="cuda:0", torch_dtype=torch.float16)
-
-# 将模型转换为 bf16
-model = model.to(dtype=torch.bfloat16)
-
-# 准备模型的输入
-# text_prompt = "According to this chart, which model performs best?\n"
-
-text_prompt = "Generate a coco-style caption.\n"
-image = Image.open("1.png").convert("RGB")
-
-while True:
-    # 获取用户输入的文本提示
-    text_prompt = input("请输入文本提示: ")
-    if text_prompt.lower() == 'exit':
-        break
-    inputs = processor(text=text_prompt, images=image, return_tensors="pt").to("cuda:0")
-
-    # 生成输出
-    generation_output = model.generate(**inputs, max_new_tokens=7)
-    generation_text = processor.batch_decode(generation_output[:, -7:], skip_special_tokens=True)
-
-    # 打印生成的文本
-    print(generation_text)

webui_pages/dialogue/dialogue.py

@@ -175,7 +175,7 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
         for k, v in config_models.get("online", {}).items():
             if not v.get("provider") and k not in running_models and k in LLM_MODELS:
                 available_models.append(k)
-        llm_models = running_models + available_models
+        llm_models = running_models + available_models + ["openai-api"]
         cur_llm_model = st.session_state.get("cur_llm_model", default_model)
         if cur_llm_model in llm_models:
             index = llm_models.index(cur_llm_model)