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update file format
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@imClumsyPanda
imClumsyPanda committed Nov 30, 2023
1 parent f2d917d commit c34b413
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Showing 2 changed files with 44 additions and 31 deletions.
2 changes: 1 addition & 1 deletion configs/prompt_config.py.example
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ PROMPT_TEMPLATES = {
'<已知信息>{{ context }}</已知信息>\n'
'<问题>{{ question }}</问题>\n',

"Empty": # 搜不到知识库的时候使用
"empty": # 搜不到知识库的时候使用
'请你回答我的问题:\n'
'{{ question }}\n\n',
},
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73 changes: 43 additions & 30 deletions server/chat/knowledge_base_chat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,36 +18,49 @@


async def knowledge_base_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
score_threshold: float = Body(SCORE_THRESHOLD, description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右", ge=0, le=2),
history: List[History] = Body([],
description="历史对话",
examples=[[
{"role": "user",
"content": "我们来玩成语接龙，我先来，生龙活虎"},
{"role": "assistant",
"content": "虎头虎脑"}]]
),
stream: bool = Body(False, description="流式输出"),
model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
request: Request = None,
):
knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
score_threshold: float = Body(
SCORE_THRESHOLD,
description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右",
ge=0,
le=2
),
history: List[History] = Body(
[],
description="历史对话",
examples=[[
{"role": "user",
"content": "我们来玩成语接龙，我先来，生龙活虎"},
{"role": "assistant",
"content": "虎头虎脑"}]]
),
stream: bool = Body(False, description="流式输出"),
model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
max_tokens: Optional[int] = Body(
None,
description="限制LLM生成Token数量，默认None代表模型最大值"
),
prompt_name: str = Body(
"default",
description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
),
request: Request = None,
):
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
if kb is None:
return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")

history = [History.from_data(h) for h in history]

async def knowledge_base_chat_iterator(query: str,
top_k: int,
history: Optional[List[History]],
model_name: str = LLM_MODELS[0],
prompt_name: str = prompt_name,
) -> AsyncIterable[str]:
async def knowledge_base_chat_iterator(
query: str,
top_k: int,
history: Optional[List[History]],
model_name: str = LLM_MODELS[0],
prompt_name: str = prompt_name,
) -> AsyncIterable[str]:
nonlocal max_tokens
callback = AsyncIteratorCallbackHandler()
if isinstance(max_tokens, int) and max_tokens <= 0:
Expand All @@ -61,8 +74,8 @@ async def knowledge_base_chat_iterator(query: str,
)
docs = search_docs(query, knowledge_base_name, top_k, score_threshold)
context = "\n".join([doc.page_content for doc in docs])
if len(docs) == 0: ## 如果没有找到相关文档，使用Empty模板
prompt_template = get_prompt_template("knowledge_base_chat", "Empty")
if len(docs) == 0: # 如果没有找到相关文档，使用empty模板
prompt_template = get_prompt_template("knowledge_base_chat", "empty")
else:
prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
input_msg = History(role="user", content=prompt_template).to_msg_template(False)
Expand All @@ -80,14 +93,14 @@ async def knowledge_base_chat_iterator(query: str,
source_documents = []
for inum, doc in enumerate(docs):
filename = doc.metadata.get("source")
parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name":filename})
parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name": filename})
base_url = request.base_url
url = f"{base_url}knowledge_base/download_doc?" + parameters
text = f"""出处 [{inum + 1}] [{filename}]({url}) \n\n{doc.page_content}\n\n"""
source_documents.append(text)

if len(source_documents) == 0: # 没有找到相关文档
source_documents.append(f"""<span style='color:red'>未找到相关文档,该回答为大模型自身能力解答！</span>""")
if len(source_documents) == 0: # 没有找到相关文档
source_documents.append(f"<span style='color:red'>未找到相关文档,该回答为大模型自身能力解答！</span>")

if stream:
async for token in callback.aiter():
Expand All @@ -108,4 +121,4 @@ async def knowledge_base_chat_iterator(query: str,
history=history,
model_name=model_name,
prompt_name=prompt_name),
media_type="text/event-stream")
media_type="text/event-stream")

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