194 lines
9.4 KiB
Python
194 lines
9.4 KiB
Python
import asyncio
|
||
import json
|
||
from typing import AsyncIterable, List, Optional
|
||
from urllib.parse import urlencode
|
||
|
||
from fastapi import Body, Request
|
||
from fastapi.concurrency import run_in_threadpool
|
||
from langchain.callbacks import AsyncIteratorCallbackHandler
|
||
from langchain.chains import LLMChain
|
||
from langchain.prompts import PromptTemplate
|
||
from langchain.prompts.chat import ChatPromptTemplate
|
||
from sse_starlette.sse import EventSourceResponse
|
||
|
||
from configs import (TEMPERATURE,
|
||
USE_RERANKER,
|
||
RERANKER_MODEL,
|
||
RERANKER_MAX_LENGTH,
|
||
LLM_MODELS,
|
||
MODEL_PATH,
|
||
MAX_TOKENS)
|
||
from server.chat.utils import History
|
||
from server.knowledge_base.kb_service.base import KBServiceFactory
|
||
from server.reranker.reranker import LangchainReranker
|
||
from server.utils import BaseResponse, get_prompt_template
|
||
from server.utils import embedding_device
|
||
from server.utils import wrap_done, get_ChatOpenAI
|
||
|
||
|
||
async def article_overview(query: str = Body("你好", description="用户输入", examples=["你好"]),
|
||
knowledge_base_name: str = Body(..., description="知识库名称", examples=["t_policy_total_bce_v1"]),
|
||
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(
|
||
MAX_TOKENS,
|
||
description="限制LLM生成Token数量，默认None代表模型最大值"
|
||
),
|
||
prompt_name: str = Body(
|
||
"Article Overview",
|
||
description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
|
||
),
|
||
source_name_list: List[str] = Body([], description="资源列表"),
|
||
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}")
|
||
query = "帮我对以下文件进行总结 ：" + ",".join(source_name_list)
|
||
if len(source_name_list) > 1:
|
||
prompt_name = "Article Overview2"
|
||
else:
|
||
prompt_name = "Article Overview"
|
||
async def article_overview_iterator(
|
||
query: str,
|
||
model_name: str = model_name,
|
||
prompt_name: str = prompt_name,
|
||
) -> AsyncIterable[str]:
|
||
nonlocal max_tokens
|
||
callback = AsyncIteratorCallbackHandler()
|
||
if isinstance(max_tokens, int) and max_tokens <= 0:
|
||
max_tokens = None
|
||
|
||
model = get_ChatOpenAI(
|
||
model_name=model_name,
|
||
temperature=temperature,
|
||
max_tokens=max_tokens,
|
||
callbacks=[callback],
|
||
)
|
||
docs = []
|
||
docs = await run_in_threadpool(kb.get_doc_by_sources_name,
|
||
source_name_list=source_name_list)
|
||
|
||
# 加入reranker
|
||
if USE_RERANKER:
|
||
reranker_model_path = MODEL_PATH["reranker"].get(RERANKER_MODEL, "BAAI/bge-reranker-large")
|
||
print("-----------------model path------------------")
|
||
print(reranker_model_path)
|
||
reranker_model = LangchainReranker(top_n=3,
|
||
device=embedding_device(),
|
||
max_length=RERANKER_MAX_LENGTH,
|
||
model_name_or_path=reranker_model_path
|
||
)
|
||
print("---------before rerank------------------")
|
||
print(docs)
|
||
docs = reranker_model.compress_documents(documents=docs,
|
||
query=query)
|
||
print("---------after rerank------------------")
|
||
print(docs)
|
||
# context = "\n".join([doc.page_content for doc in docs])
|
||
# 相关信息把标题和内容进行整合
|
||
if knowledge_base_name == 't_policy_total_bce_v1':
|
||
knowledge = []
|
||
for doc in docs:
|
||
if doc.metadata["_type"] == "title":
|
||
knowledge.append(doc.page_content + "\n" + doc.metadata['content'])
|
||
if doc.metadata["_type"] == "content":
|
||
knowledge.append(doc.metadata['title'] + "\n" + doc.page_content)
|
||
context = "\n\n".join(knowledge)
|
||
# 非政策知识库
|
||
else:
|
||
context = "\n".join([doc.page_content for doc in docs])
|
||
print(f"context:{context}\n")
|
||
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)
|
||
chat_prompt = ChatPromptTemplate.from_messages([input_msg])
|
||
print(f"chat_prompt:{chat_prompt}\n")
|
||
chain = LLMChain(prompt=chat_prompt, llm=model)
|
||
|
||
# Begin a task that runs in the background.
|
||
task = asyncio.create_task(wrap_done(
|
||
chain.acall({"context": context, "question": query}),
|
||
callback.done),
|
||
)
|
||
|
||
source_documents = []
|
||
# 政策知识库
|
||
if knowledge_base_name == 't_policy_total_bce_v1':
|
||
for inum, doc in enumerate(docs):
|
||
# 获取标题以及详情地址（url）
|
||
filename = doc.metadata.get("title")
|
||
detail_url = doc.metadata.get("source")
|
||
# 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"""
|
||
if filename:
|
||
text = f"""出处: [{filename}]({detail_url}) \n\n"""
|
||
else:
|
||
text = f"""出处: [{"原文地址"}]({detail_url}) \n\n"""
|
||
source_documents.append(text)
|
||
# 非政策知识库
|
||
else:
|
||
for inum, doc in enumerate(docs):
|
||
filename = doc.metadata.get("source")
|
||
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
|
||
if filename:
|
||
text = f"""出处: [{filename}]({url}) \n\n"""
|
||
else:
|
||
text = f"""出处: [{"原文地址"}]({url}) \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 stream:
|
||
async for token in callback.aiter():
|
||
# Use server-sent-events to stream the response
|
||
yield json.dumps({"answer": token}, ensure_ascii=False)
|
||
yield json.dumps({"docs": source_documents}, ensure_ascii=False)
|
||
else:
|
||
answer = ""
|
||
async for token in callback.aiter():
|
||
answer += token
|
||
yield json.dumps({"answer": answer,
|
||
"docs": source_documents},
|
||
ensure_ascii=False)
|
||
await task
|
||
|
||
return EventSourceResponse(article_overview_iterator(query, model_name=model_name, prompt_name=prompt_name))
|
||
|
||
|
||
class ArticleOverview:
|
||
query = "请给我对文件进行一下总结"
|
||
|
||
def __init__(self):
|
||
self._PROMPT_TEMPLATE = """
|
||
'<角色> 你是由浪潮开发的知冶大模型中所选定的文件综述助手。</角色> \n\n'
|
||
'Your task is to write a detailed summary of the provided {{context}} file. Ensure that your summary is '
|
||
'longer than 300 words and captures the essence of the content. Focus on the main points, '
|
||
'key findings, and any important implications or conclusions. Maintain an unbiased tone and avoid relying '
|
||
'on stereotypes. Organize the summary in a clear and coherent manner, using appropriate headings or '
|
||
'bullet points if necessary. Remember to keep the summary concise while preserving the core information. '
|
||
'Let\'s start with a brief overview of the file\'s main topic and then delve into the specifics.'
|
||
'PLEASE ALWAYS RESPOND IN CHINESE!\n'
|
||
'<已知信息>{{ context }}</已知信息>\n'
|
||
'<问题>{{ question }}</问题>\n',
|
||
"""
|
||
self.PROMPT = PromptTemplate(
|
||
input_variables=["question", "database_names"],
|
||
template=self._PROMPT_TEMPLATE,
|
||
)
|
||
|
||
def query_out(self, knowledge_base_name: str, source_name_list: list):
|
||
self.query = "帮我对以下文件进行总结 ：" + ",".join(source_name_list)
|
||
return article_overview(self.query,
|
||
knowledge_base_name=knowledge_base_name,
|
||
source_name_list=source_name_list
|
||
)
|