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23 Commits

Author SHA1 Message Date
weiweiw b577a3ad18 搜索api 换成 zhipu-search并提升agent 提示词 2025-04-24 16:26:20 +08:00
GuanYuankai 122033131b 更新api的记忆功能 2025-04-22 10:44:59 +08:00
GuanYuankai 468b3116cd 更新web-search的api接口 2025-04-18 16:11:37 +08:00
GuanYuankai 43c82ee797 Merge branch 'main' of http://192.168.0.75:3000/BNSTechnologyDevelopment/Langchain-Chatchat 2025-04-18 10:42:12 +08:00
weiweiw 5c7566bc69 知识库查询对溯源文件不网络超连接---回滚修改 2025-04-18 10:36:55 +08:00
GuanYuankai 20228503a6 控制互联网搜索api喂给大数据的内容数量 2025-04-18 09:38:57 +08:00
GuanYuankai cc8564ccca 互联网搜索api修改 2025-04-18 09:12:52 +08:00
GuanYuankai 8cd80dcd9c 智谱搜索接口加入 2025-04-16 16:55:41 +08:00
GuanYuankai 718766abb8 Merge remote-tracking branch 'origin/main' 2025-04-16 16:36:31 +08:00
weiweiw c6bb3b4ffd 知识库查询对溯源文件不网络超连接 2025-04-11 17:05:12 +08:00
weiweiw c897a1722f 将知识库文件分块文件保存,但是不显示 2025-04-07 07:55:31 +08:00
weiweiw 28c87ac7a7 优化天气查询时的健壮性 2025-03-19 08:18:06 +08:00
weiweiw bad876fc98 优化天气查询时的健壮性 2025-03-09 16:34:22 +08:00
GuanYuankai 0e3a9d8dd0 Update .gitignore 2025-03-06 13:52:06 +08:00
GuanYuankai c49b048976 文档名称添加搜索 2025-03-04 18:16:44 +08:00
GuanYuankai 34dc4f2c7f dev:search_result2doc接口根据引擎名称自动配置 2025-03-04 16:15:09 +08:00
guanyuankai 04db85f02d Merge pull request 'dev/search_internet_tavily' (#1) from dev/search_internet_tavily into main
Reviewed-on: #1
2025-03-04 15:55:49 +08:00
GuanYuankai 42315ab3da Revert "更新model_settings:重新启用xinterface"
This reverts commit ac368b3814.
2025-03-04 15:55:12 +08:00
GuanYuankai dfa583d344 Create .gitignore 2025-03-04 15:49:52 +08:00
GuanYuankai 1ac1ab0ced 使用tavily作为互联网搜索引擎 2025-03-04 15:49:31 +08:00
GuanYuankai ac368b3814 更新model_settings:重新启用xinterface 2025-03-04 09:19:37 +08:00
GuanYuankai 0bff316575 更新gitignore 2025-03-03 17:57:33 +08:00
weiweiw 00f56aa23c 互联网查询 2025-03-03 17:53:32 +08:00
13 changed files with 273 additions and 119 deletions

40
.gitignore vendored Normal file
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@ -0,0 +1,40 @@
*.csv
*.yaml
*.xlsx
*.pdf
*.txt
*.log
*.pyc
/chatchat_data.bak
/chatchat_data/data/knowledge_base/samples
/chatchat_data
.idea/inspectionProfiles/profiles_settings.xml
.idea/Langchain-Chatchat.iml
.idea/misc.xml
.idea/modules.xml
.idea/prettier.xml
.idea/vcs.xml
.idea/inspectionProfiles/profiles_settings.xml
.idea/Langchain-Chatchat.iml
.idea/modules.xml
.idea/prettier.xml
.idea/vcs.xml
/.idea
/test_tool
chatchat_data/tool_settings.yaml
chatchat_data/prompt_settings.yaml
chatchat_data/model_settings.yaml
chatchat_data/basic_settings.yaml
localconfig/data/knowledge_base/samples/content/分布式训练技术原理.md
localconfig/data/knowledge_base/samples/content/大模型应用技术原理.md
localconfig/data/knowledge_base/samples/content/大模型技术栈-实战与应用.md
localconfig/data/knowledge_base/samples/content/大模型技术栈-算法与原理.md
localconfig/data/knowledge_base/samples/content/大模型指令对齐训练原理.md
localconfig/data/knowledge_base/samples/content/大模型推理优化策略.md
localconfig/data/knowledge_base/samples/vector_store/bge-large-zh-v1.5/index.faiss
localconfig/data/knowledge_base/samples/vector_store/bge-large-zh-v1.5/index.pkl
localconfig/data/knowledge_base/info.db
chatchat_data/basic_settings.yaml
chatchat_data/model_settings.yaml
chatchat_data/prompt_settings.yaml
chatchat_data/tool_settings.yaml

8
.idea/.gitignore vendored Normal file
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@ -0,0 +1,8 @@
# 默认忽略的文件
/shelf/
/workspace.xml
# 基于编辑器的 HTTP 客户端请求
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml

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@ -0,0 +1,7 @@
<component name="ProjectDictionaryState">
<dictionary name="Guan">
<words>
<w>aggrid</w>
</words>
</dictionary>
</component>

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@ -2,7 +2,7 @@
# 默认选用的 LLM 名称
DEFAULT_LLM_MODEL: qwen2-instruct
DEFAULT_LLM_MODEL: qwen2.5-instruct
# 默认选用的 Embedding 名称
DEFAULT_EMBEDDING_MODEL: bge-large-zh-v1.5
@ -112,78 +112,78 @@ LLM_MODEL_CONFIG:
MODEL_PLATFORMS:
- platform_name: xinference
platform_type: xinference
api_base_url: http://127.0.0.1:9997/v1
api_base_url: http://192.168.0.21:9997/v1
api_key: EMPTY
api_proxy: ''
api_concurrencies: 5
auto_detect_model: true
llm_models: []
embed_models: []
text2image_models: []
image2text_models: []
rerank_models: [bge-reranker-large]
speech2text_models: []
text2speech_models: []
- platform_name: ollama
platform_type: ollama
api_base_url: http://127.0.0.1:11434/v1
api_key: EMPTY
api_proxy: ''
api_concurrencies: 5
auto_detect_model: false
llm_models:
- qwen:7b
- qwen2:7b
embed_models:
- quentinz/bge-large-zh-v1.5
text2image_models: []
image2text_models: []
rerank_models: []
speech2text_models: []
text2speech_models: []
- platform_name: oneapi
platform_type: oneapi
api_base_url: http://127.0.0.1:3000/v1
api_key: sk-
api_proxy: ''
api_concurrencies: 5
auto_detect_model: false
llm_models:
- chatglm_pro
- chatglm_turbo
- chatglm_std
- chatglm_lite
- qwen-turbo
- qwen-plus
- qwen-max
- qwen-max-longcontext
- ERNIE-Bot
- ERNIE-Bot-turbo
- ERNIE-Bot-4
- SparkDesk
embed_models:
- text-embedding-v1
- Embedding-V1
text2image_models: []
image2text_models: []
rerank_models: []
speech2text_models: []
text2speech_models: []
- platform_name: openai
platform_type: openai
api_base_url: https://api.openai.com/v1
api_key: sk-proj-
api_proxy: ''
api_concurrencies: 5
auto_detect_model: false
llm_models:
- gpt-4o
- gpt-3.5-turbo
embed_models:
- text-embedding-3-small
- text-embedding-3-large
llm_models: [qwen2.5-instruct]
embed_models: [bge-large-zh-v1.5]
text2image_models: []
image2text_models: []
rerank_models: []
speech2text_models: []
text2speech_models: []
# - platform_name: ollama
# platform_type: ollama
# api_base_url: http://127.0.0.1:11434/v1
# api_key: EMPTY
# api_proxy: ''
# api_concurrencies: 5
# auto_detect_model: false
# llm_models:
# - qwen:7b
# - qwen2:7b
# embed_models:
# - quentinz/bge-large-zh-v1.5
# text2image_models: []
# image2text_models: []
# rerank_models: []
# speech2text_models: []
# text2speech_models: []
# - platform_name: oneapi
# platform_type: oneapi
# api_base_url: http://127.0.0.1:3000/v1
# api_key: sk-
# api_proxy: ''
# api_concurrencies: 5
# auto_detect_model: false
# llm_models:
# - chatglm_pro
# - chatglm_turbo
# - chatglm_std
# - chatglm_lite
# - qwen-turbo
# - qwen-plus
# - qwen-max
# - qwen-max-longcontext
# - ERNIE-Bot
# - ERNIE-Bot-turbo
# - ERNIE-Bot-4
# - SparkDesk
# embed_models:
# - text-embedding-v1
# - Embedding-V1
# text2image_models: []
# image2text_models: []
# rerank_models: []
# speech2text_models: []
# text2speech_models: []
# - platform_name: openai
# platform_type: openai
# api_base_url: https://api.openai.com/v1
# api_key: sk-proj-
# api_proxy: ''
# api_concurrencies: 5
# auto_detect_model: false
# llm_models:
# - gpt-4o
# - gpt-3.5-turbo
# embed_models:
# - text-embedding-3-small
# - text-embedding-3-large
# text2image_models: []
# image2text_models: []
# rerank_models: []
# speech2text_models: []
# text2speech_models: []

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@ -14,7 +14,7 @@ search_local_knowledgebase:
# 搜索引擎工具配置项。推荐自己部署 searx 搜索引擎,国内使用最方便。
search_internet:
use: false
search_engine_name: searx
search_engine_name: zhipu_search
search_engine_config:
bing:
bing_search_url: https://api.bing.microsoft.com/v7.0/search
@ -30,6 +30,14 @@ search_internet:
engines: []
categories: []
language: zh-CN
tavily:
tavily_api_key: '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'
include_answer: true
search_depth: advanced
include_raw_content: True
max_results: 1
zhipu_search:
zhipu_api_key: 'e2bdc39618624fd782ebcd721185645c.pcvcrTPFT69Jda8B'
top_k: 5
verbose: Origin
conclude_prompt: "<指令>这是搜索到的互联网信息,请你根据这些信息进行提取并有调理,简洁的回答问题。如果无法从中得到答案,请说 “无法搜索到能回答问题的内容”。

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@ -13,6 +13,6 @@ print(f"cuDNN 版本: {cudnn_version}")
# 检查是否可以访问 CUDA
if torch.cuda.is_available():
print("CUDA is available. GPU name:", torch.cuda.get_device_name(0))
print("pip install sentence-transformers -i https://pypi.mirrors.ustc.edu.cn/simpleCUDA is available. GPU name:", torch.cuda.get_device_name(0))
else:
print("CUDA is not available. Please check your installation.")

View File

@ -1,5 +1,8 @@
import json
import uuid
from typing import Dict, List
import requests
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.utilities.bing_search import BingSearchAPIWrapper
@ -7,15 +10,21 @@ from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from langchain.utilities.searx_search import SearxSearchWrapper
from markdownify import markdownify
from strsimpy.normalized_levenshtein import NormalizedLevenshtein
from langchain_community.tools.tavily_search import TavilySearchResults
import os
from chatchat.settings import Settings
from chatchat.server.pydantic_v1 import Field
from chatchat.server.utils import get_tool_config
from chatchat.utils import build_logger
# from tavily import TavilyClient
from .tools_registry import BaseToolOutput, regist_tool, format_context
logger = build_logger()
def searx_search(text ,config, top_k: int):
def searx_search(text, config, top_k: int):
print(f"searx_search: text: {text},config:{config},top_k:{top_k}")
search = SearxSearchWrapper(
searx_host=config["host"],
@ -26,7 +35,7 @@ def searx_search(text ,config, top_k: int):
return search.results(text, top_k)
def bing_search(text, config, top_k:int):
def bing_search(text, config, top_k: int):
search = BingSearchAPIWrapper(
bing_subscription_key=config["bing_key"],
bing_search_url=config["bing_search_url"],
@ -34,15 +43,15 @@ def bing_search(text, config, top_k:int):
return search.results(text, top_k)
def duckduckgo_search(text, config, top_k:int):
def duckduckgo_search(text, config, top_k: int):
search = DuckDuckGoSearchAPIWrapper()
return search.results(text, top_k)
def metaphor_search(
text: str,
config: dict,
top_k:int
text: str,
config: dict,
top_k: int
) -> List[Dict]:
from metaphor_python import Metaphor
@ -85,21 +94,77 @@ def metaphor_search(
return docs
def tavily_search(text, config, top_k):
# 配置tavily api key
os.environ["TAVILY_API_KEY"] = config["tavily_api_key"]
# 初始化工具(配置参数)
tavily_tool = TavilySearchResults(
include_answer=config["include_answer"], # 关键参数:启用答案生成
search_depth=config["search_depth"], # 必须使用高级搜索模式
include_raw_content=config["include_raw_content"],
max_results=config["max_results"]
)
# 直接执行搜索
raw_results = tavily_tool.run(text)
search_results = [{k: v for k, v in item.items() if k != 'url'} for item in raw_results]
# print("=== 完整搜索返回值 ===")
# print(search_results)
return search_results
def zhipu_search(text, config, top_k):
api_key = config["zhipu_api_key"]
endpoint = "https://open.bigmodel.cn/api/paas/v4/web_search"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
payload = {
"search_engine": "Search-Pro", # 指定Web搜索专用模型
"search_query": text
}
response = requests.post(endpoint, headers=headers, json=payload)
result = response.json()
print(f"================!! result: {result}")
return result
SEARCH_ENGINES = {
"bing": bing_search,
"duckduckgo": duckduckgo_search,
"metaphor": metaphor_search,
"searx": searx_search,
"tavily": tavily_search,
"zhipu_search": zhipu_search
}
def search_result2docs(search_results) -> List[Document]:
def search_result2docs(search_results, engine_name, top_k) -> List[Document]:
docs = []
if engine_name == "zhipu_search":
try:
# search_results_json = json.loads(search_results)
results = search_results["search_result"]
except (KeyError, IndexError) as e:
print(f"结构异常: {e}")
results = []
# 遍历并处理每个结果
for item in results[:top_k]:
doc = Document(
page_content=item['content'],
metadata={"link": item['link'], "title": item['title']}
)
docs.append(doc)
return docs
page_contents_key = "snippet" if engine_name != "tavily" else "content"
metadata_key = "link" if engine_name != "tavily" else "url"
for result in search_results:
doc = Document(
page_content=result["snippet"] if "snippet" in result.keys() else "",
page_content=result[page_contents_key] if page_contents_key in result.keys() else "",
metadata={
"source": result["link"] if "link" in result.keys() else "",
"source": result[metadata_key] if metadata_key in result.keys() else "",
"filename": result["title"] if "title" in result.keys() else "",
},
)
@ -107,7 +172,7 @@ def search_result2docs(search_results) -> List[Document]:
return docs
def search_engine(query: str, top_k:int=0, engine_name: str="", config: dict={}):
def search_engine(query: str, top_k: int = 0, engine_name: str = "", config: dict = {}):
config = config or get_tool_config("search_internet")
if top_k <= 0:
top_k = config.get("top_k", Settings.kb_settings.SEARCH_ENGINE_TOP_K)
@ -117,12 +182,20 @@ def search_engine(query: str, top_k:int=0, engine_name: str="", config: dict={})
results = search_engine_use(
text=query, config=config["search_engine_config"][engine_name], top_k=top_k
)
docs = [x for x in search_result2docs(results) if x.page_content and x.page_content.strip()]
docs = [x for x in search_result2docs(results, engine_name, top_k) if x.page_content and x.page_content.strip()]
print(f"len(docs): {len(docs)}")
# print(f"docs:{docs}")
# # print(f"docs: {docs[:150]}")
return {"docs": docs, "search_engine": engine_name}
@regist_tool(title="互联网搜索")
def search_internet(query: str = Field(description="query for Internet search")):
"""用这个工具实现获取世界、历史、实时新闻、或除电力系统之外的信息查询"""
print(f"search_internet: query: {query}")
return BaseToolOutput(search_engine(query=query), format=format_context)
try:
print(f"search_internet: query: {query}")
return BaseToolOutput(data=search_engine(query=query), format=format_context)
except Exception as e:
logger.error(f"未知错误: {str(e)}")
return BaseToolOutput(f"搜索过程中发生未知错误,{str(e)}", format=format_context)

View File

@ -176,7 +176,7 @@ def format_context(self: BaseToolOutput) -> str:
doc = DocumentWithVSId.parse_obj(doc)
source_documents.append(doc.page_content)
print(f"format_context: doc.page_content: {doc.page_content}")
# print(f"format_context: doc.page_content: {doc.page_content}")
if len(source_documents) == 0:
context = "没有找到相关文档,请更换关键词重试"
else:

View File

@ -12,15 +12,15 @@ from .tools_registry import BaseToolOutput, regist_tool
@regist_tool(title="天气查询")
def weather_check(
city: str = Field(description="城市名称,包括市和县,例如 '厦门'"),
date: str = Field(
default=None,
description="日期参数,支持以下格式:\n"
"- '今天':获取当前实时天气\n"
"- '明天'/'后天'获取未来24/48小时预报\n"
"- '未来X天'获取最多X天预报'未来3天',X的抽取要符合客户意图\n"
"- 不支持其他参数如果是其他参数则时间参数为None"
)
city: str = Field(description="城市名称,包括市和县,例如 '厦门'"),
date: str = Field(
default=None,
description="日期参数,支持以下格式:\n"
"- '今天':获取当前实时天气\n"
"- '明天'/'后天'获取未来24/48小时预报\n"
"- '未来X天'获取最多X天预报'未来3天',X的抽取要符合客户意图\n"
"- 不支持其他参数如果是其他参数则时间参数为None\n"
)
):
"""用这个工具获取指定地点和指定时间的天气"""
@ -32,23 +32,22 @@ def weather_check(
missing_params.append("日期参数")
if missing_params:
return BaseToolOutput(
error_message=f"缺少必要参数:{', '.join(missing_params)},请补充完整查询信息",
require_additional_input=True
)
return BaseToolOutput(data={"error_message": f"缺少必要参数:{', '.join(missing_params)},请补充完整查询信息"},
require_additional_input=True
)
print(f"city:{city}, date:{date}")
try:
weather_type, number = parse_date_parameter(date)
except ValueError as e:
logging.error(f"日期参数解析失败: {str(e)}")
return BaseToolOutput(str(e))
return BaseToolOutput(data={"error_message": str(e)})
# 获取API配置
tool_config = get_tool_config("weather_check")
api_key = tool_config.get("api_key")
if not api_key:
return BaseToolOutput("API密钥未配置请联系管理员")
return BaseToolOutput(data={"error_message": "API密钥未配置请联系管理员"})
# 根据天气类型调用API
if weather_type == "daily":
@ -56,7 +55,8 @@ def weather_check(
elif weather_type == "future":
return _get_future_weather(city, api_key, number)
else:
return BaseToolOutput("不支持的天气类型")
return BaseToolOutput(data={"error_message": "不支持的天气类型"})
def _get_current_weather(city: str, api_key: str) -> BaseToolOutput:
"""获取当前实时天气"""
@ -66,14 +66,15 @@ def _get_current_weather(city: str, api_key: str) -> BaseToolOutput:
if response.status_code != 200:
logging.error(f"天气查询失败: {response.status_code}")
return BaseToolOutput("天气查询API请求失败")
return BaseToolOutput(data={"error_message": "天气查询API请求失败"})
data = response.json()
weather = {
"temperature": data["results"][0]["now"]["temperature"],
"description": data["results"][0]["now"]["text"],
}
return BaseToolOutput(weather)
return BaseToolOutput(data=weather)
def _get_future_weather(city: str, api_key: str, days: int) -> BaseToolOutput:
"""获取未来天气预报"""
@ -115,9 +116,10 @@ def _get_future_weather(city: str, api_key: str, days: int) -> BaseToolOutput:
"后天最高温度": daily_data[2]["high"],
}
else:
return BaseToolOutput("不支持的天数参数")
return BaseToolOutput(data={"error_message": "不支持的天数参数"})
return BaseToolOutput(data=weather)
return BaseToolOutput(weather)
def parse_date_parameter(date: str) -> tuple:
"""解析日期参数,返回天气类型和天数"""
@ -136,5 +138,6 @@ def parse_date_parameter(date: str) -> tuple:
else:
raise ValueError("不支持的日期参数")
if __name__ == "__main__":
weather_check("合肥","明天")
weather_check("合肥", "明天")

View File

@ -65,7 +65,7 @@ async def chat_completions(
# import rich
# rich.print(body)
# 当调用本接口且 body 中没有传入 "max_tokens" 参数时, 默认使用配置中定义的值
logger.info(f"body.model_config:{body.model_config},body.tools: {body.tools},body.messages:{body.messages}")
# logger.info(f"body.model_config:{body.model_config},body.tools: {body.tools},body.messages:{body.messages}")
if body.max_tokens in [None, 0]:
body.max_tokens = Settings.model_settings.MAX_TOKENS

View File

@ -70,6 +70,9 @@ def list_files_from_folder(kb_name: str):
for x in ["temp", "tmp", ".", "~$"]:
if tail.startswith(x):
return True
if "_source.txt" in tail.lower() or "_split.txt" in tail.lower():
return True
return False
def process_entry(entry):
@ -422,15 +425,15 @@ class KnowledgeFile:
docs = zh_first_title_enhance(docs)
docs = customize_zh_title_enhance(docs)
# i = 1
# outputfile = file_name_without_extension + "_split.txt"
# # 打开文件以写入模式
# with open(outputfile, 'w') as file:
# for doc in docs:
# #print(f"**********切分段{i}{doc}")
# file.write(f"\n**********切分段{i}")
# file.write(doc.page_content)
# i = i+1
i = 1
outputfile = file_name_without_extension + "_split.txt"
# 打开文件以写入模式
with open(outputfile, 'w') as file:
for doc in docs:
#print(f"**********切分段{i}{doc}")
file.write(f"\n**********切分段{i}")
file.write(doc.page_content)
i = i+1
self.splited_docs = docs
return self.splited_docs

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@ -488,7 +488,7 @@ class ToolSettings(BaseFileSettings):
search_internet: dict = {
"use": False,
"search_engine_name": "duckduckgo",
"search_engine_name": "zhipu_search",
"search_engine_config": {
"bing": {
"bing_search_url": "https://api.bing.microsoft.com/v7.0/search",
@ -506,11 +506,21 @@ class ToolSettings(BaseFileSettings):
"engines": [],
"categories": [],
"language": "zh-CN",
},
"tavily":{
"tavily_api_key": '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',
"include_answer": True,
"search_depth": "advanced",
"include_raw_content": True,
"max_results": 1
},
"zhipu_search":{
"zhipu_api_key": ""
}
},
"top_k": 5,
"top_k": 1,
"verbose": "Origin",
"conclude_prompt": "<指令>这是搜索到的互联网信息,请你根据这些信息进行提取并有调理,简洁的回答问题。如果无法从中得到答案,请说 “无法搜索到能回答问题的内容”。 "
"conclude_prompt": "<指令>这是搜索到的互联网信息,请你根据这些信息进行提取并有调理,简洁的回答问题,不得包含有重复的词汇或句子。如果无法从中得到答案,请说 “无法搜索到能回答问题的内容”。 "
"</指令>\n<已知信息>{{ context }}</已知信息>\n"
"<问题>\n"
"{{ question }}\n"
@ -650,7 +660,7 @@ class PromptSettings(BaseFileSettings):
rag: dict = {
"default": (
"【指令】根据已知信息,简洁和专业的来回答问题"
"【指令】根据已知信息,简洁和专业的来回答问题,不得包含有重复的词汇或句子"
"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。\n\n"
"【已知信息】{{context}}\n\n"
"【问题】{{question}}\n"
@ -741,6 +751,8 @@ class PromptSettings(BaseFileSettings):
"Begin!\n\n"
"Question: {input}\n\n"
"{agent_scratchpad}\n\n"
"Important: After the last Observation, you must always add a Final Answer "
"summarizing the result. Do not skip this step."
),
"structured-chat-agent": (
"Respond to the human as helpfully and accurately as possible. You have access to the following tools:\n\n"

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@ -238,7 +238,7 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
doc_details,
{
("No", "序号"): {},
("file_name", "文档名称"): {},
("file_name", "文档名称"): {"filter": "agTextColumnFilter"},
# ("file_ext", "文档类型"): {},
# ("file_version", "文档版本"): {},
("document_loader", "文档加载器"): {},