添加configs/prompt_config.py，允许用户自定义prompt模板： (#1504)

1、 默认包含2个模板，分别用于LLM对话，知识库和搜索引擎对话
2、 server/utils.py提供函数get_prompt_template，获取指定的prompt模板内容（支持热加载）
3、 api.py中chat/knowledge_base_chat/search_engine_chat接口支持prompt_name参数

configs/__init__.py

@@ -2,6 +2,7 @@ from .basic_config import *
 from .model_config import *
 from .kb_config import *
 from .server_config import *
+from .prompt_config import *
 
 
 VERSION = "v0.2.5-preview"

configs/kb_config.py.exmaple

@@ -23,14 +23,6 @@ SCORE_THRESHOLD = 1
 SEARCH_ENGINE_TOP_K = 3
 
 
-# 基于本地知识问答的提示词模版（使用Jinja2语法，简单点就是用双大括号代替f-string的单大括号
-PROMPT_TEMPLATE = """<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题”，不允许在答案中添加编造成分，答案请使用中文。 </指令>
-
-<已知信息>{{ context }}</已知信息>
-
-<问题>{{ question }}</问题>"""
-
-
 # Bing 搜索必备变量
 # 使用 Bing 搜索需要使用 Bing Subscription Key,需要在azure port中申请试用bing search
 # 具体申请方式请见

configs/prompt_config.py.example

@@ -0,0 +1,23 @@
+# prompt模板使用Jinja2语法，简单点就是用双大括号代替f-string的单大括号
+# 本配置文件支持热加载，修改prompt模板后无需重启服务。
+
+
+# LLM对话支持的变量：
+#   - input: 用户输入内容
+
+# 知识库和搜索引擎对话支持的变量：
+#   - context: 从检索结果拼接的知识文本
+#   - question: 用户提出的问题
+
+
+PROMPT_TEMPLATES = {
+    # LLM对话模板
+    "llm_chat": "{{ input }}",
+
+    # 基于本地知识问答的提示词模
+    "knowledge_base_chat": """<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题”，不允许在答案中添加编造成分，答案请使用中文。 </指令>
+
+<已知信息>{{ context }}</已知信息>
+
+<问题>{{ question }}</问题>""",
+}

configs/server_config.py.example

@@ -76,6 +76,9 @@ FSCHAT_MODEL_WORKERS = {
     "qianfan-api": {
         "port": 21004,
     },
+    "fangzhou-api": {
+        "port": 21005,
+    },
 }
 
 # fastchat multi model worker server

server/chat/chat.py

@@ -1,6 +1,6 @@
 from fastapi import Body
 from fastapi.responses import StreamingResponse
-from configs.model_config import LLM_MODEL, TEMPERATURE
+from configs import LLM_MODEL, TEMPERATURE
 from server.chat.utils import wrap_done, get_ChatOpenAI
 from langchain import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
@@ -9,6 +9,7 @@ import asyncio
 from langchain.prompts.chat import ChatPromptTemplate
 from typing import List
 from server.chat.utils import History
+from server.utils import get_prompt_template
 
 
 async def chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
@@ -22,12 +23,14 @@ async def chat(query: str = Body(..., description="用户输入", examples=["恼
                 model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
                 temperature: float = Body(TEMPERATURE, description="LLM 采样温度", gt=0.0, le=1.0),
                 # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0),
+                prompt_name: str = Body("llm_chat", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
          ):
     history = [History.from_data(h) for h in history]
 
     async def chat_iterator(query: str,
                             history: List[History] = [],
                             model_name: str = LLM_MODEL,
+                            prompt_name: str = prompt_name,
                             ) -> AsyncIterable[str]:
         callback = AsyncIteratorCallbackHandler()
         model = get_ChatOpenAI(
@@ -35,7 +38,9 @@ async def chat(query: str = Body(..., description="用户输入", examples=["恼
             temperature=temperature,
             callbacks=[callback],
         )
-        input_msg = History(role="user", content="{{ input }}").to_msg_template(False)
+
+        prompt_template = get_prompt_template(prompt_name)
+        input_msg = History(role="user", content=prompt_template).to_msg_template(False)
         chat_prompt = ChatPromptTemplate.from_messages(
             [i.to_msg_template() for i in history] + [input_msg])
         chain = LLMChain(prompt=chat_prompt, llm=model)
@@ -58,5 +63,8 @@ async def chat(query: str = Body(..., description="用户输入", examples=["恼
 
         await task
 
-    return StreamingResponse(chat_iterator(query, history, model_name),
+    return StreamingResponse(chat_iterator(query=query,
+                                           history=history,
+                                           model_name=model_name,
+                                           prompt_name=prompt_name),
                              media_type="text/event-stream")

server/chat/knowledge_base_chat.py

@@ -1,10 +1,8 @@
 from fastapi import Body, Request
 from fastapi.responses import StreamingResponse
-from configs import (LLM_MODEL, PROMPT_TEMPLATE,
+from configs import (LLM_MODEL, VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD, TEMPERATURE)
-                    VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD,
-                    TEMPERATURE)
 from server.chat.utils import wrap_done, get_ChatOpenAI
-from server.utils import BaseResponse
+from server.utils import BaseResponse, get_prompt_template
 from langchain import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from typing import AsyncIterable, List, Optional
@@ -33,6 +31,7 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
                             stream: bool = Body(False, description="流式输出"),
                             model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
                             temperature: float = Body(TEMPERATURE, description="LLM 采样温度", gt=0.0, le=1.0),
+                            prompt_name: str = Body("knowledge_base_chat", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
                             local_doc_url: bool = Body(False, description="知识文件返回本地路径(true)或URL(false)"),
                             request: Request = None,
                         ):
@@ -43,10 +42,10 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
     history = [History.from_data(h) for h in history]
 
     async def knowledge_base_chat_iterator(query: str,
-                                           kb: KBService,
                                            top_k: int,
                                            history: Optional[List[History]],
                                            model_name: str = LLM_MODEL,
+                                           prompt_name: str = prompt_name,
                                            ) -> AsyncIterable[str]:
         callback = AsyncIteratorCallbackHandler()
         model = get_ChatOpenAI(
@@ -57,7 +56,8 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
         docs = search_docs(query, knowledge_base_name, top_k, score_threshold)
         context = "\n".join([doc.page_content for doc in docs])
 
-        input_msg = History(role="user", content=PROMPT_TEMPLATE).to_msg_template(False)
+        prompt_template = get_prompt_template(prompt_name)
+        input_msg = History(role="user", content=prompt_template).to_msg_template(False)
         # 用户最后一个问题会进入PROMPT_TEMPLATE，不用再作为history 了
         if len(history) >= 1:
             history.pop()
@@ -98,5 +98,9 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
 
         await task
 
-    return StreamingResponse(knowledge_base_chat_iterator(query, kb, top_k, history, model_name),
+    return StreamingResponse(knowledge_base_chat_iterator(query=query,
+                                                          top_k=top_k,
+                                                          history=history,
+                                                          model_name=model_name,
+                                                          prompt_name=prompt_name),
                              media_type="text/event-stream")

server/chat/search_engine_chat.py

@@ -1,12 +1,11 @@
 from langchain.utilities import BingSearchAPIWrapper, DuckDuckGoSearchAPIWrapper
 from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY, 
-                     LLM_MODEL, SEARCH_ENGINE_TOP_K,
+                     LLM_MODEL, SEARCH_ENGINE_TOP_K, TEMPERATURE)
-                    PROMPT_TEMPLATE, TEMPERATURE)
 from fastapi import Body
 from fastapi.responses import StreamingResponse
 from fastapi.concurrency import run_in_threadpool
 from server.chat.utils import wrap_done, get_ChatOpenAI
-from server.utils import BaseResponse
+from server.utils import BaseResponse, get_prompt_template
 from langchain import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from typing import AsyncIterable
@@ -73,6 +72,7 @@ async def search_engine_chat(query: str = Body(..., description="用户输入",
                             stream: bool = Body(False, description="流式输出"),
                             model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
                             temperature: float = Body(TEMPERATURE, description="LLM 采样温度", gt=0.0, le=1.0),
+                            prompt_name: str = Body("knowledge_base_chat", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
                        ):
     if search_engine_name not in SEARCH_ENGINES.keys():
         return BaseResponse(code=404, msg=f"未支持搜索引擎 {search_engine_name}")
@@ -87,6 +87,7 @@ async def search_engine_chat(query: str = Body(..., description="用户输入",
                                           top_k: int,
                                           history: Optional[List[History]],
                                           model_name: str = LLM_MODEL,
+                                          prompt_name: str = prompt_name,
                                           ) -> AsyncIterable[str]:
         callback = AsyncIteratorCallbackHandler()
         model = get_ChatOpenAI(
@@ -98,7 +99,8 @@ async def search_engine_chat(query: str = Body(..., description="用户输入",
         docs = await lookup_search_engine(query, search_engine_name, top_k)
         context = "\n".join([doc.page_content for doc in docs])
 
-        input_msg = History(role="user", content=PROMPT_TEMPLATE).to_msg_template(False)
+        prompt_template = get_prompt_template(prompt_name)
+        input_msg = History(role="user", content=prompt_template).to_msg_template(False)
         chat_prompt = ChatPromptTemplate.from_messages(
             [i.to_msg_template() for i in history] + [input_msg])
 
@@ -129,5 +131,10 @@ async def search_engine_chat(query: str = Body(..., description="用户输入",
                              ensure_ascii=False)
         await task
 
-    return StreamingResponse(search_engine_chat_iterator(query, search_engine_name, top_k, history, model_name),
+    return StreamingResponse(search_engine_chat_iterator(query=query,
+                                                         search_engine_name=search_engine_name,
+                                                         top_k=top_k,
+                                                         history=history,
+                                                         model_name=model_name,
+                                                         prompt_name=prompt_name),
                              media_type="text/event-stream")

server/utils.py

@@ -327,6 +327,17 @@ def webui_address() -> str:
     return f"http://{host}:{port}"
 
 
+def get_prompt_template(name: str) -> Optional[str]:
+    '''
+    从prompt_config中加载模板内容
+    '''
+    from configs import prompt_config
+    import importlib
+    importlib.reload(prompt_config) # TODO: 检查configs/prompt_config.py文件有修改再重新加载
+
+    return prompt_config.PROMPT_TEMPLATES.get(name)
+
+
 def set_httpx_timeout(timeout: float = None):
     '''
     设置httpx默认timeout。

tests/api/test_stream_chat_api.py

@@ -114,7 +114,7 @@ def test_search_engine_chat(api="/chat/search_engine_chat"):
             assert data["msg"] == f"要使用Bing搜索引擎，需要设置 `BING_SUBSCRIPTION_KEY`"
 
         print("\n")
-        print("=" * 30 + api + " by {se}  output" + "="*30)
+        print("=" * 30 + api + f" by {se}  output" + "="*30)
         for line in response.iter_content(None, decode_unicode=True):
             data = json.loads(line)
             if "answer" in data:

webui_pages/utils.py

@@ -315,6 +315,7 @@ class ApiRequest:
         stream: bool = True,
         model: str = LLM_MODEL,
         temperature: float = TEMPERATURE,
+        prompt_name: str = "llm_chat",
         no_remote_api: bool = None,
     ):
         '''
@@ -329,6 +330,7 @@ class ApiRequest:
             "stream": stream,
             "model_name": model,
             "temperature": temperature,
+            "prompt_name": prompt_name,
         }
 
         print(f"received input message:")
@@ -385,6 +387,7 @@ class ApiRequest:
         stream: bool = True,
         model: str = LLM_MODEL,
         temperature: float = TEMPERATURE,
+        prompt_name: str = "knowledge_base_chat",
         no_remote_api: bool = None,
     ):
         '''
@@ -403,6 +406,7 @@ class ApiRequest:
             "model_name": model,
             "temperature": temperature,
             "local_doc_url": no_remote_api,
+            "prompt_name": prompt_name,
         }
 
         print(f"received input message:")
@@ -428,6 +432,7 @@ class ApiRequest:
         stream: bool = True,
         model: str = LLM_MODEL,
         temperature: float = TEMPERATURE,
+        prompt_name: str = "knowledge_base_chat",
         no_remote_api: bool = None,
     ):
         '''
@@ -443,6 +448,7 @@ class ApiRequest:
             "stream": stream,
             "model_name": model,
             "temperature": temperature,
+            "prompt_name": prompt_name,
         }
 
         print(f"received input message:")