Compare commits
2 Commits
| Author | SHA1 | Date |
|---|---|---|
weiweiw
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22c666b4bf | |
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weiweiw
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b78edb72a1 |
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@ -1,40 +0,0 @@
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*.csv
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*.yaml
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*.xlsx
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*.pdf
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*.txt
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*.log
|
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*.pyc
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/chatchat_data.bak
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/chatchat_data/data/knowledge_base/samples
|
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/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
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||||
.idea/prettier.xml
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||||
.idea/vcs.xml
|
||||
/.idea
|
||||
/test_tool
|
||||
chatchat_data/tool_settings.yaml
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chatchat_data/prompt_settings.yaml
|
||||
chatchat_data/model_settings.yaml
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||||
chatchat_data/basic_settings.yaml
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localconfig/data/knowledge_base/samples/content/分布式训练技术原理.md
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||||
localconfig/data/knowledge_base/samples/content/大模型应用技术原理.md
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||||
localconfig/data/knowledge_base/samples/content/大模型技术栈-实战与应用.md
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localconfig/data/knowledge_base/samples/content/大模型技术栈-算法与原理.md
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localconfig/data/knowledge_base/samples/content/大模型指令对齐训练原理.md
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||||
localconfig/data/knowledge_base/samples/content/大模型推理优化策略.md
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||||
localconfig/data/knowledge_base/samples/vector_store/bge-large-zh-v1.5/index.faiss
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||||
localconfig/data/knowledge_base/samples/vector_store/bge-large-zh-v1.5/index.pkl
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localconfig/data/knowledge_base/info.db
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chatchat_data/basic_settings.yaml
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chatchat_data/model_settings.yaml
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chatchat_data/prompt_settings.yaml
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chatchat_data/tool_settings.yaml
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|
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@ -1,8 +0,0 @@
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# 默认忽略的文件
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/shelf/
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/workspace.xml
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# 基于编辑器的 HTTP 客户端请求
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/httpRequests/
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# Datasource local storage ignored files
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/dataSources/
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/dataSources.local.xml
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|
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@ -1,7 +0,0 @@
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<component name="ProjectDictionaryState">
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<dictionary name="Guan">
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<words>
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<w>aggrid</w>
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</words>
|
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</dictionary>
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</component>
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@ -2,7 +2,7 @@
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# 默认选用的 LLM 名称
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DEFAULT_LLM_MODEL: qwen2.5-instruct
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DEFAULT_LLM_MODEL: qwen2-instruct
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|
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# 默认选用的 Embedding 名称
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DEFAULT_EMBEDDING_MODEL: bge-large-zh-v1.5
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|
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@ -112,78 +112,78 @@ LLM_MODEL_CONFIG:
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MODEL_PLATFORMS:
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- platform_name: xinference
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platform_type: xinference
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api_base_url: http://192.168.0.21:9997/v1
|
||||
api_base_url: http://127.0.0.1:9997/v1
|
||||
api_key: EMPTY
|
||||
api_proxy: ''
|
||||
api_concurrencies: 5
|
||||
auto_detect_model: true
|
||||
llm_models: [qwen2.5-instruct]
|
||||
embed_models: [bge-large-zh-v1.5]
|
||||
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:
|
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- quentinz/bge-large-zh-v1.5
|
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text2image_models: []
|
||||
image2text_models: []
|
||||
rerank_models: []
|
||||
speech2text_models: []
|
||||
text2speech_models: []
|
||||
- platform_name: oneapi
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||||
platform_type: oneapi
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||||
api_base_url: http://127.0.0.1:3000/v1
|
||||
api_key: sk-
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||||
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
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||||
llm_models:
|
||||
- gpt-4o
|
||||
- gpt-3.5-turbo
|
||||
embed_models:
|
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- text-embedding-3-small
|
||||
- text-embedding-3-large
|
||||
text2image_models: []
|
||||
image2text_models: []
|
||||
rerank_models: []
|
||||
speech2text_models: []
|
||||
text2speech_models: []
|
||||
# - platform_name: ollama
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||||
# 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: []
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ search_local_knowledgebase:
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# 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: zhipu_search
|
||||
search_engine_name: searx
|
||||
search_engine_config:
|
||||
bing:
|
||||
bing_search_url: https://api.bing.microsoft.com/v7.0/search
|
||||
|
|
@ -30,14 +30,6 @@ search_internet:
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|||
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”。
|
||||
|
|
|
|||
|
|
@ -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("pip install sentence-transformers -i https://pypi.mirrors.ustc.edu.cn/simpleCUDA is available. GPU name:", torch.cuda.get_device_name(0))
|
||||
print("CUDA is available. GPU name:", torch.cuda.get_device_name(0))
|
||||
else:
|
||||
print("CUDA is not available. Please check your installation.")
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
你是一名意图识别专家，任务是根据用户输入提取意图并提取相关的参数信息。意图分为以下六类：
|
||||
|
||||
1.日计划数量
|
||||
2.日计划作业内容
|
||||
3.周计划数量
|
||||
4.周计划作业内容
|
||||
5.页面操作
|
||||
6.其他
|
||||
|
||||
模版结构与提取要求
|
||||
1. 意图 1 和 2：日计划相关
|
||||
1)提取参数的要求如下：
|
||||
a.时间 (time)：必须字段，缺失时提示用户输入时间。
|
||||
b.工程名称 (project)：去除 "工程" 后的内容。
|
||||
c.公司名称 (company)：去除 "公司" 后的内容。
|
||||
d.项目(部)名称 (program)：去除 "项目" 或 "项目部" 后的内容。
|
||||
e.项目经理名称 (manager)：去除 "项目经理" 后的内容。
|
||||
f.班组名称 (class)：去除 "班组" 后的内容。
|
||||
g.风险等级 (risk)：限定为 "一"、"二"、"三"、"四"、"五"、"六"。
|
||||
2)返回格式：
|
||||
{
|
||||
"intention": "日计划数量",
|
||||
"time": "时间",
|
||||
"program": "项目(部)名称",
|
||||
"company": "公司名称",
|
||||
"project": "工程名称",
|
||||
"manager": "项目经理名称",
|
||||
"class": "班组名称",
|
||||
"risk": "风险等级"
|
||||
}
|
||||
3)未提取到的字段：不包含在结果中。
|
||||
4)时间缺失时：提示用户输入特定时间。
|
||||
5)风险等级无效时：提示用户提供有效风险等级（"一" 到 "六"）。
|
||||
|
||||
2. 意图 3 和 4：周计划相关
|
||||
1)提取参数的要求如下：
|
||||
a.与日计划相同，增加施工状态 (status)，限定为："未开始","进行中"和"已结束"
|
||||
2)返回格式：
|
||||
{
|
||||
"intention": "周计划数量",
|
||||
"time": "时间",
|
||||
"program": "项目(部)名称",
|
||||
"company": "公司名称",
|
||||
"project": "工程名称",
|
||||
"manager": "项目经理名称",
|
||||
"class": "班组名称",
|
||||
"risk": "风险等级",
|
||||
"status": "施工状态"
|
||||
}
|
||||
3）时间缺失时：提示用户输入特定时间。
|
||||
4）风险等级无效或施工状态不匹配时：提示用户提供有效值。
|
||||
|
||||
3. 意图 5：页面操作
|
||||
1）提取参数的要求如下：
|
||||
操作类型 (action)：存储 "打开" 或 "切换"。若用户输入单一名词，默认为 "切换"。
|
||||
模块名称 (module)：去除 "页面"、"模块"、"菜单" 后的部分内容。
|
||||
2）返回格式：
|
||||
{
|
||||
"intention": "页面操作",
|
||||
"action": "操作类型",
|
||||
"module": "模块名称"
|
||||
}
|
||||
4. 意图 6：其他
|
||||
1）提取参数不需要有任务要求。
|
||||
2）返回格式：
|
||||
{
|
||||
"intention": "其他",
|
||||
"content": "用户输入的原始内容"
|
||||
}
|
||||
|
||||
5.示例
|
||||
1）示例 1：
|
||||
用户输入'今天送变电一公司1号工程B项目5号班组有多少项二级风险作业计划',
|
||||
返回：
|
||||
{
|
||||
'intention': '日计划数量',
|
||||
'time': '今天',
|
||||
'company': '变电一',‘
|
||||
'project': '1号',
|
||||
'program': 'B',
|
||||
'class': '5号,
|
||||
'risk': '二'
|
||||
}
|
||||
2）示例 2：
|
||||
本周1号项目部多少项一级风险作业计划正在施工
|
||||
返回：
|
||||
{
|
||||
"intention": "周计划数量",
|
||||
"time": "本周",
|
||||
"program": "1号",
|
||||
"risk": "一",
|
||||
"status": "进行中"
|
||||
}
|
||||
|
||||
3）示例 3：
|
||||
用户输入：
|
||||
切换到首页
|
||||
返回：
|
||||
{
|
||||
"intention": "页面操作",
|
||||
"action": "切换",
|
||||
"module": "首页"
|
||||
}
|
||||
4）示例 4：
|
||||
用户输入：
|
||||
你好，请帮我查一下
|
||||
返回：
|
||||
{
|
||||
"intention": "其他",
|
||||
"content": "你好，请帮我查一下"
|
||||
}
|
||||
|
|
@ -0,0 +1,144 @@
|
|||
你是一名意图识别专家，任务是根据用户输入提取意图并提取相关的参数信息，意图分为以下9类：
|
||||
1.日计划数量 - 用户询问日计划的数量相关。
|
||||
2.日计划作业内容 - 用户询问日计划的作业内容相关。
|
||||
3.周计划数量 - 用户询问周计划的数量相关。
|
||||
4.周计划作业内容 - 用户询问周计划的作业内容相关。
|
||||
5.页面操作 - 用户希望打开或跳转具体页面。
|
||||
6.联网查询 - 用户要求获取世界、历史、实时新闻、或除电力系统之外的信息。
|
||||
7.天气查询 - 用户要求查某地方某时间的天气。
|
||||
8.知识库查询 - 用户寻找特定的信息或知识，如国家电网各部门规章制度、安徽送变电规章制度等相关的问题，需要通过知识库来回答。
|
||||
9.其他 - 无法匹配到以上的几个意图，要求用户根据补充问题。
|
||||
|
||||
模版结构与提取要求
|
||||
1. 意图 1 和 2：日计划相关
|
||||
1)提取参数的要求如下：
|
||||
a.时间 (time)：必须字段，缺失时提示用户输入时间。
|
||||
b.工程名称 (project)：去除 '工程' 后的内容。
|
||||
c.公司名称 (company)：去除 '公司' 后的内容。
|
||||
d.项目(部)名称 (program)：去除 '项目' 或 '项目部' 后的内容。
|
||||
e.项目经理名称 (manager)：去除 '项目经理' 后的内容。
|
||||
f.班组名称 (class)：去除 '班组' 后的内容。
|
||||
g.风险等级 (risk)：限定为 '一'、'二'、'三'、'四'、'五'、'六'。
|
||||
2)返回格式：
|
||||
{
|
||||
'intention': '日计划数量',
|
||||
'time': '时间',
|
||||
'program': '项目(部)名称',
|
||||
'company': '公司名称',
|
||||
'project': '工程名称',
|
||||
'manager': '项目经理名称',
|
||||
'class': '班组名称',
|
||||
'risk': '风险等级'
|
||||
}
|
||||
3)未提取到的字段：不包含在结果中。
|
||||
4)时间缺失时：提示用户输入特定时间。
|
||||
5)风险等级无效时：提示用户提供有效风险等级（'一' 到 '六'）。
|
||||
|
||||
2. 意图 3 和 4：周计划相关
|
||||
1)提取参数的要求如下：
|
||||
a.与日计划相同，增加施工状态 (status)，限定为：'未开始','进行中'和'已结束'
|
||||
2)返回格式：
|
||||
{
|
||||
'intention': '周计划数量',
|
||||
'time': '时间',
|
||||
'program': '项目(部)名称',
|
||||
'company': '公司名称',
|
||||
'project': '工程名称',
|
||||
'manager': '项目经理名称',
|
||||
'class': '班组名称',
|
||||
'risk': '风险等级',
|
||||
'status': '施工状态'
|
||||
}
|
||||
3）时间缺失时：提示用户输入特定时间。
|
||||
4）风险等级无效或施工状态不匹配时：提示用户提供有效值。
|
||||
|
||||
3. 意图 5：页面操作
|
||||
1）提取参数的要求如下：
|
||||
操作类型 (action)：存储 '打开' 或 '切换'。若用户输入单一名词，默认为 '切换'。
|
||||
模块名称 (module)：去除 '页面'、'模块'、'菜单' 后的部分内容。
|
||||
2）返回格式：
|
||||
{
|
||||
'intention': '页面操作',
|
||||
'action': '操作类型',
|
||||
'module': '模块名称'
|
||||
}
|
||||
|
||||
4.意图 6：联网查询
|
||||
1）不需要提取任何参数。
|
||||
2）返回格式：
|
||||
{
|
||||
'intention': '联网查询',
|
||||
}
|
||||
|
||||
5. 意图7：天气查询
|
||||
1）不需要提取任何参数。
|
||||
2）返回格式：
|
||||
{
|
||||
'intention': '天气查询'
|
||||
}
|
||||
|
||||
6.意图8：知识库查询
|
||||
1）不需要提取任何参数。
|
||||
2）返回格式：
|
||||
{
|
||||
'intention': '知识库查询',
|
||||
}
|
||||
|
||||
7. 意图 9：其他
|
||||
1）不需要提取任何参数。
|
||||
2）返回格式：
|
||||
{
|
||||
'intention': '其他',
|
||||
'content': '用户输入的原始内容'
|
||||
}
|
||||
|
||||
5.示例
|
||||
1）示例 1：
|
||||
用户输入'今天送变电一公司1号工程B项目5号班组有多少项二级风险作业计划',
|
||||
返回：
|
||||
{
|
||||
'intention': '日计划数量',
|
||||
'time': '今天',
|
||||
'company': '变电一',‘
|
||||
'project': '1号',
|
||||
'program': 'B',
|
||||
'class': '5号,
|
||||
'risk': '二'
|
||||
}
|
||||
2）示例 2：
|
||||
本周1号项目部多少项一级风险作业计划正在施工
|
||||
返回：
|
||||
{
|
||||
'intention': '周计划数量',
|
||||
'time': '本周',
|
||||
'program': '1号',
|
||||
'risk': '一',
|
||||
'status': '进行中'
|
||||
}
|
||||
|
||||
3）示例 3：
|
||||
用户输入：
|
||||
切换到首页
|
||||
返回：
|
||||
{
|
||||
'intention': '页面操作',
|
||||
'action': '切换',
|
||||
'module': '首页'
|
||||
}
|
||||
|
||||
4）示例 4：
|
||||
用户输入：
|
||||
本周合肥会有降雨吗？
|
||||
返回：
|
||||
{
|
||||
'intention': '天气查询'
|
||||
}
|
||||
|
||||
5）示例 5：
|
||||
用户输入：
|
||||
你好，请帮我查一下
|
||||
返回：
|
||||
{
|
||||
'intention': '其他',
|
||||
'content': '你好，请帮我查一下'
|
||||
}
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
你是一名意图识别专家，任务是根据用户输入提取意图。意图分为以下六类：
|
||||
|
||||
1.日计划数量
|
||||
2.日计划作业内容
|
||||
3.周计划数量
|
||||
4.周计划作业内容
|
||||
5.页面操作
|
||||
6.联网查询
|
||||
7.天气
|
||||
8.知识库查询
|
||||
|
||||
|
||||
你是一名意图识别专家，任务是根据下面提供的用户输入，确定其对应的意图类别。意图类别包括：
|
||||
1. 日计划数量 - 用户询问日计划的数量相关。
|
||||
2. 日计划作业内容 - 用户询问日计划的作业内容相关。
|
||||
3. 周计划数量 - 用户询问周计划的数量相关。
|
||||
4. 周计划作业内容 - 用户询问周计划的作业内容相关。
|
||||
5. 页面操作 - 用户希望打开或跳转具体页面。
|
||||
6. 联网查询或天气 - 用户要求获取世界、历史、实时新闻、天气或除电力系统之外的信息。
|
||||
7. 知识库查询 - 用户寻找特定的信息或知识，如国家电网各部门规章制度、安徽送变电规章制度等相关的问题，需要通过知识库来回答。
|
||||
|
||||
规则：
|
||||
- 对于每个输入，请明确指出它属于类别的序号1，2，3，4，，6，7，一定不要有其他多余描述。
|
||||
- 尽可能从用户输入中确定以上类别中的一个，如果无法确定，请用户补充更多信息。
|
||||
|
|
@ -0,0 +1,72 @@
|
|||
你是一名意图识别专家，任务是根据用户输入提取意图并提取相关的参数信息，意图分为以下9类：
|
||||
1.日计划数量 - 用户询问日计划的数量相关。
|
||||
2.日计划作业内容 - 用户询问日计划的作业内容相关。
|
||||
3.周计划数量 - 用户询问周计划的数量相关。
|
||||
4.周计划作业内容 - 用户询问周计划的作业内容相关。
|
||||
|
||||
模版结构与提取要求
|
||||
1. 意图 1 和 2：日计划相关
|
||||
1)提取参数的要求如下：
|
||||
a.时间 (time)：必须字段，缺失时提示用户输入时间。
|
||||
b.工程名称 (project)：去除 '工程' 后的内容。
|
||||
c.公司名称 (company)：去除 '公司' 后的内容。
|
||||
d.项目(部)名称 (program)：去除 '项目' 或 '项目部' 后的内容。
|
||||
e.项目经理名称 (manager)：去除 '项目经理' 后的内容。
|
||||
f.班组名称 (class)：去除 '班组' 后的内容。
|
||||
g.风险等级 (risk)：限定为 '一'、'二'、'三'、'四'、'五'、'六'。
|
||||
2)返回格式：
|
||||
{
|
||||
'intention': '日计划数量',
|
||||
'time': '时间',
|
||||
'program': '项目(部)名称',
|
||||
'company': '公司名称',
|
||||
'project': '工程名称',
|
||||
'manager': '项目经理名称',
|
||||
'class': '班组名称',
|
||||
'risk': '风险等级'
|
||||
}
|
||||
3)未提取到的字段：不包含在结果中。
|
||||
4)时间缺失时：提示用户输入特定时间。
|
||||
5)风险等级无效时：提示用户提供有效风险等级（'一' 到 '六'）。
|
||||
|
||||
2. 意图 3 和 4：周计划相关
|
||||
1)提取参数的要求如下：
|
||||
a.与日计划相同，增加施工状态 (status)，限定为：'未开始','进行中'和'已结束'
|
||||
2)返回格式：
|
||||
{
|
||||
'intention': '周计划数量',
|
||||
'time': '时间',
|
||||
'program': '项目(部)名称',
|
||||
'company': '公司名称',
|
||||
'project': '工程名称',
|
||||
'manager': '项目经理名称',
|
||||
'class': '班组名称',
|
||||
'risk': '风险等级',
|
||||
'status': '施工状态'
|
||||
}
|
||||
3）时间缺失时：提示用户输入特定时间。
|
||||
4）风险等级无效或施工状态不匹配时：提示用户提供有效值。
|
||||
|
||||
5.示例
|
||||
1）示例 1：
|
||||
用户输入'今天送变电一公司1号工程B项目5号班组有多少项二级风险作业计划',
|
||||
返回：
|
||||
{
|
||||
'intention': '日计划数量',
|
||||
'time': '今天',
|
||||
'company': '变电一',‘
|
||||
'project': '1号',
|
||||
'program': 'B',
|
||||
'class': '5号,
|
||||
'risk': '二'
|
||||
}
|
||||
2）示例 2：
|
||||
本周1号项目部多少项一级风险作业计划正在施工
|
||||
返回：
|
||||
{
|
||||
'intention': '周计划数量',
|
||||
'time': '本周',
|
||||
'program': '1号',
|
||||
'risk': '一',
|
||||
'status': '进行中'
|
||||
}
|
||||
|
|
@ -20,7 +20,7 @@ def amap_poi_search_engine(keywords: str,types: str,config: dict):
|
|||
|
||||
|
||||
|
||||
#@regist_tool(title="高德地图POI搜索")
|
||||
# @regist_tool(title="高德地图POI搜索")
|
||||
def amap_poi_search(location: str = Field(description="'实际地名'或者'具体的地址',不能使用简称或者别称"),
|
||||
types: str = Field(description="POI类型，比如商场、学校、医院等等")):
|
||||
""" A wrapper that uses Amap to search."""
|
||||
|
|
|
|||
|
|
@ -36,7 +36,7 @@ def get_weather(adcode: str, config: dict) -> dict:
|
|||
else:
|
||||
return {"error": "API request failed"}
|
||||
|
||||
#@regist_tool(title="高德地图天气查询")
|
||||
# @regist_tool(title="高德地图天气查询")
|
||||
def amap_weather(city: str = Field(description="城市名")):
|
||||
"""A wrapper that uses Amap to get weather information."""
|
||||
tool_config = get_tool_config("amap")
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from chatchat.server.pydantic_v1 import Field
|
|||
from .tools_registry import BaseToolOutput, regist_tool
|
||||
|
||||
|
||||
#@regist_tool(title="ARXIV论文")
|
||||
# @regist_tool(title="ARXIV论文")
|
||||
def arxiv(query: str = Field(description="The search query title")):
|
||||
"""A wrapper around Arxiv.org for searching and retrieving scientific articles in various fields."""
|
||||
from langchain.tools.arxiv.tool import ArxivQueryRun
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from chatchat.server.pydantic_v1 import Field
|
|||
from .tools_registry import BaseToolOutput, regist_tool
|
||||
|
||||
|
||||
#@regist_tool(title="数学计算器")
|
||||
# @regist_tool(title="数学计算器")
|
||||
def calculate(text: str = Field(description="a math expression")) -> float:
|
||||
"""
|
||||
Useful to answer questions about simple calculations.
|
||||
|
|
|
|||
|
|
@ -1,8 +1,5 @@
|
|||
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
|
||||
|
|
@ -10,21 +7,15 @@ 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"],
|
||||
|
|
@ -35,7 +26,7 @@ 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"],
|
||||
|
|
@ -43,15 +34,15 @@ 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
|
||||
|
||||
|
|
@ -94,77 +85,21 @@ 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, engine_name, top_k) -> List[Document]:
|
||||
def search_result2docs(search_results) -> 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[page_contents_key] if page_contents_key in result.keys() else "",
|
||||
page_content=result["snippet"] if "snippet" in result.keys() else "",
|
||||
metadata={
|
||||
"source": result[metadata_key] if metadata_key in result.keys() else "",
|
||||
"source": result["link"] if "link" in result.keys() else "",
|
||||
"filename": result["title"] if "title" in result.keys() else "",
|
||||
},
|
||||
)
|
||||
|
|
@ -172,7 +107,7 @@ def search_result2docs(search_results, engine_name, top_k) -> 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)
|
||||
|
|
@ -182,20 +117,12 @@ def search_engine(query: str, top_k: int = 0, engine_name: str = "", config: dic
|
|||
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, 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]}")
|
||||
docs = [x for x in search_result2docs(results) if x.page_content and x.page_content.strip()]
|
||||
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"""
|
||||
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)
|
||||
print(f"search_internet: query: {query}")
|
||||
return BaseToolOutput(search_engine(query=query), format=format_context)
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from chatchat.server.pydantic_v1 import Field
|
|||
from .tools_registry import BaseToolOutput, regist_tool
|
||||
|
||||
|
||||
#@regist_tool(title="油管视频")
|
||||
# @regist_tool(title="油管视频")
|
||||
def search_youtube(query: str = Field(description="Query for Videos search")):
|
||||
"""use this tools_factory to search youtube videos"""
|
||||
from langchain_community.tools import YouTubeSearchTool
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from chatchat.server.pydantic_v1 import Field
|
|||
from .tools_registry import BaseToolOutput, regist_tool
|
||||
|
||||
|
||||
#@regist_tool(title="系统命令")
|
||||
# @regist_tool(title="系统命令")
|
||||
def shell(query: str = Field(description="The command to execute")):
|
||||
"""Use Shell to execute system shell commands"""
|
||||
tool = ShellTool()
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ from chatchat.server.utils import MsgType, get_tool_config, get_model_info
|
|||
from .tools_registry import BaseToolOutput, regist_tool
|
||||
|
||||
|
||||
#@regist_tool(title="文生图", return_direct=True)
|
||||
# @regist_tool(title="文生图", return_direct=True)
|
||||
def text2images(
|
||||
prompt: str,
|
||||
n: int = Field(1, description="需生成图片的数量"),
|
||||
|
|
|
|||
|
|
@ -108,7 +108,7 @@ def query_prometheus(query: str, config: dict) -> str:
|
|||
return content
|
||||
|
||||
|
||||
#@regist_tool(title="Prometheus对话")
|
||||
# @regist_tool(title="Prometheus对话")
|
||||
def text2promql(
|
||||
query: str = Field(
|
||||
description="Tool for querying a Prometheus server, No need for PromQL statements, "
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@ def query_database(query: str, config: dict):
|
|||
return context
|
||||
|
||||
|
||||
#@regist_tool(title="数据库对话")
|
||||
# @regist_tool(title="数据库对话")
|
||||
def text2sql(
|
||||
query: str = Field(
|
||||
description="No need for SQL statements,just input the natural language that you want to chat with database"
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ from chatchat.server.agent.tools_factory.tools_registry import format_context
|
|||
from .tools_registry import BaseToolOutput, regist_tool
|
||||
|
||||
|
||||
#@regist_tool(title="URL内容阅读")
|
||||
# @regist_tool(title="URL内容阅读")
|
||||
def url_reader(
|
||||
url: str = Field(
|
||||
description="The URL to be processed, so that its web content can be made more clear to read. Then provide a detailed description of the content in about 500 words. As structured as possible. ONLY THE LINK SHOULD BE PASSED IN."),
|
||||
|
|
|
|||
|
|
@ -12,132 +12,26 @@ 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（如'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"
|
||||
)
|
||||
city: str = Field(description="City name,include city and county,like '厦门'"),
|
||||
):
|
||||
"""用这个工具获取指定地点和指定时间的天气"""
|
||||
# """Use this tool to check the weather at a specific city"""
|
||||
|
||||
# 参数校验
|
||||
missing_params = []
|
||||
if not city:
|
||||
missing_params.append("城市名称")
|
||||
if not date:
|
||||
missing_params.append("日期参数")
|
||||
|
||||
if missing_params:
|
||||
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(data={"error_message": str(e)})
|
||||
|
||||
# 获取API配置
|
||||
print(f"weather_check tool内部调用，city{city}")
|
||||
tool_config = get_tool_config("weather_check")
|
||||
api_key = tool_config.get("api_key")
|
||||
if not api_key:
|
||||
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":
|
||||
return _get_current_weather(city, api_key)
|
||||
elif weather_type == "future":
|
||||
return _get_future_weather(city, api_key, number)
|
||||
else:
|
||||
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"""
|
||||
url = f"http://api.seniverse.com/v3/weather/now.json?key={api_key}&location={city}&language=zh-Hans&unit=c"
|
||||
logging.info(f"请求URL: {url}")
|
||||
logging.info(f"url:{url}")
|
||||
response = requests.get(url)
|
||||
|
||||
if response.status_code != 200:
|
||||
logging.error(f"天气查询失败: {response.status_code}")
|
||||
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(data=weather)
|
||||
|
||||
|
||||
def _get_future_weather(city: str, api_key: str, days: int) -> BaseToolOutput:
|
||||
"""获取未来天气预报"""
|
||||
url = f"http://api.seniverse.com/v3/weather/daily.json?key={api_key}&location={city}&language=zh-Hans&unit=c"
|
||||
logging.info(f"请求URL: {url}")
|
||||
response = requests.get(url)
|
||||
|
||||
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")
|
||||
|
||||
data = response.json()
|
||||
daily_data = data["results"][0]["daily"]
|
||||
|
||||
if days == 1:
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
logging.info(f"response.json():{data}")
|
||||
weather = {
|
||||
"date": "明天",
|
||||
"low_temperature": daily_data[1]["low"],
|
||||
"high_temperature": daily_data[1]["high"],
|
||||
"description": daily_data[1]["text_day"],
|
||||
}
|
||||
elif days == 2:
|
||||
weather = {
|
||||
"date": "后天",
|
||||
"low_temperature": daily_data[2]["low"],
|
||||
"high_temperature": daily_data[2]["high"],
|
||||
"description": daily_data[2]["text_day"],
|
||||
}
|
||||
elif days == 3:
|
||||
weather = {
|
||||
"今天天气": daily_data[0]["text_day"],
|
||||
"今天最低温度": daily_data[0]["low"],
|
||||
"今天最高温度": daily_data[0]["high"],
|
||||
"明天天气": daily_data[1]["text_day"],
|
||||
"明天最低温度": daily_data[1]["low"],
|
||||
"明天最高温度": daily_data[1]["high"],
|
||||
"后天天气": daily_data[2]["text_day"],
|
||||
"后天最低温度": daily_data[2]["low"],
|
||||
"后天最高温度": daily_data[2]["high"],
|
||||
"temperature": data["results"][0]["now"]["temperature"],
|
||||
"description": data["results"][0]["now"]["text"],
|
||||
}
|
||||
return BaseToolOutput(weather)
|
||||
else:
|
||||
return BaseToolOutput(data={"error_message": "不支持的天数参数"})
|
||||
logging.error(f"Failed to retrieve weather: {response.status_code}")
|
||||
raise Exception(f"Failed to retrieve weather: {response.status_code}")
|
||||
|
||||
return BaseToolOutput(data=weather)
|
||||
|
||||
|
||||
def parse_date_parameter(date: str) -> tuple:
|
||||
"""解析日期参数，返回天气类型和天数"""
|
||||
if date == "今天":
|
||||
return "daily", 1
|
||||
elif date == "明天":
|
||||
return "future", 1
|
||||
elif date == "后天":
|
||||
return "future", 2
|
||||
elif date.startswith("未来") and date.endswith("天"):
|
||||
days = int(date[2:-1])
|
||||
if 1 <= days <= 3:
|
||||
return "future", days
|
||||
else:
|
||||
raise ValueError("未来预报仅支持1-3天")
|
||||
else:
|
||||
raise ValueError("不支持的日期参数")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
weather_check("合肥", "明天")
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ from chatchat.server.pydantic_v1 import Field
|
|||
from .tools_registry import BaseToolOutput, regist_tool
|
||||
|
||||
|
||||
#@regist_tool(title="维基百科搜索")
|
||||
# @regist_tool(title="维基百科搜索")
|
||||
def wikipedia_search(query: str = Field(description="The search query")):
|
||||
""" A wrapper that uses Wikipedia to search."""
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from chatchat.server.utils import get_tool_config
|
|||
from .tools_registry import BaseToolOutput, regist_tool
|
||||
|
||||
|
||||
#@regist_tool
|
||||
# @regist_tool
|
||||
def wolfram(query: str = Field(description="The formula to be calculated")):
|
||||
"""Useful for when you need to calculate difficult formulas"""
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ from chatchat.server.utils import (wrap_done, get_ChatOpenAI, get_default_llm,
|
|||
BaseResponse, get_prompt_template, build_logger,
|
||||
check_embed_model, api_address
|
||||
)
|
||||
import time
|
||||
|
||||
|
||||
logger = build_logger()
|
||||
|
||||
|
|
@ -60,8 +60,6 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=["
|
|||
return_direct: bool = Body(False, description="直接返回检索结果，不送入 LLM"),
|
||||
request: Request = None,
|
||||
):
|
||||
logger.info(f"kb_chat:,mode {mode}")
|
||||
start_time = time.time()
|
||||
if mode == "local_kb":
|
||||
kb = KBServiceFactory.get_service_by_name(kb_name)
|
||||
if kb is None:
|
||||
|
|
@ -69,8 +67,6 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=["
|
|||
|
||||
async def knowledge_base_chat_iterator() -> AsyncIterable[str]:
|
||||
try:
|
||||
logger.info(f"***********************************knowledge_base_chat_iterator:,mode {mode}")
|
||||
start_time1 = time.time()
|
||||
nonlocal history, prompt_name, max_tokens
|
||||
|
||||
history = [History.from_data(h) for h in history]
|
||||
|
|
@ -78,10 +74,8 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=["
|
|||
if mode == "local_kb":
|
||||
kb = KBServiceFactory.get_service_by_name(kb_name)
|
||||
ok, msg = kb.check_embed_model()
|
||||
logger.info(f"***********************************knowledge_base_chat_iterator:,mode {mode}，kb_name：{kb_name}")
|
||||
if not ok:
|
||||
raise ValueError(msg)
|
||||
# docs = search_docs( query = query,knowledge_base_name = kb_name,top_k = top_k, score_threshold = score_threshold,)
|
||||
docs = await run_in_threadpool(search_docs,
|
||||
query=query,
|
||||
knowledge_base_name=kb_name,
|
||||
|
|
@ -89,13 +83,7 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=["
|
|||
score_threshold=score_threshold,
|
||||
file_name="",
|
||||
metadata={})
|
||||
|
||||
source_documents = format_reference(kb_name, docs, api_address(is_public=True))
|
||||
# logger.info(
|
||||
# f"***********************************knowledge_base_chat_iterator:,after format_reference:{docs}")
|
||||
end_time1 = time.time()
|
||||
execution_time1 = end_time1 - start_time1
|
||||
logger.info(f"kb_chat Execution time检索完成: {execution_time1:.6f} seconds")
|
||||
elif mode == "temp_kb":
|
||||
ok, msg = check_embed_model()
|
||||
if not ok:
|
||||
|
|
@ -151,7 +139,6 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=["
|
|||
if max_tokens in [None, 0]:
|
||||
max_tokens = Settings.model_settings.MAX_TOKENS
|
||||
|
||||
start_time1 = time.time()
|
||||
llm = get_ChatOpenAI(
|
||||
model_name=model,
|
||||
temperature=temperature,
|
||||
|
|
@ -236,12 +223,6 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=["
|
|||
return
|
||||
|
||||
if stream:
|
||||
eventSource = EventSourceResponse(knowledge_base_chat_iterator())
|
||||
# 记录结束时间
|
||||
end_time = time.time()
|
||||
# 计算执行时间
|
||||
execution_time = end_time - start_time
|
||||
logger.info(f"final kb_chat Execution time: {execution_time:.6f} seconds")
|
||||
return eventSource
|
||||
return EventSourceResponse(knowledge_base_chat_iterator())
|
||||
else:
|
||||
return await knowledge_base_chat_iterator().__anext__()
|
||||
|
|
|
|||
|
|
@ -32,7 +32,6 @@ from chatchat.server.utils import (
|
|||
get_default_embedding,
|
||||
)
|
||||
from chatchat.utils import build_logger
|
||||
from typing import List, Dict,Tuple
|
||||
|
||||
logger = build_logger()
|
||||
|
||||
|
|
@ -72,15 +71,8 @@ def search_docs(
|
|||
if kb is not None:
|
||||
if query:
|
||||
docs = kb.search_docs(query, top_k, score_threshold)
|
||||
if docs is not None:
|
||||
logger.info(f"search_docs, query:{query},top_k:{top_k},score_threshold:{score_threshold},len(docs):{len(docs)}")
|
||||
|
||||
docs_key = kb.search_content_internal(query,2)
|
||||
if docs_key is not None:
|
||||
logger.info(f"before merge_and_deduplicate ,len(docs_key):{len(docs_key)}")
|
||||
docs = merge_and_deduplicate(docs, docs_key)
|
||||
if docs is not None:
|
||||
logger.info(f"after merge_and_deduplicate len(docs):{len(docs)}")
|
||||
logger.info(f"search_docs, query:{query},top_k:{top_k},score_threshold:{score_threshold}")
|
||||
# data = [DocumentWithVSId(**x[0].dict(), score=x[1], id=x[0].metadata.get("id")) for x in docs]
|
||||
data = [DocumentWithVSId(**{"id": x.metadata.get("id"), **x.dict()}) for x in docs]
|
||||
elif file_name or metadata:
|
||||
data = kb.list_docs(file_name=file_name, metadata=metadata)
|
||||
|
|
@ -89,20 +81,6 @@ def search_docs(
|
|||
del d.metadata["vector"]
|
||||
return [x.dict() for x in data]
|
||||
|
||||
def merge_and_deduplicate(list1: List[Document], list2: List[Document]) -> List[Document]:
|
||||
# 使用字典存储唯一的 Document
|
||||
merged_dict = {}
|
||||
if list1 is not None:
|
||||
merged_dict = {doc.page_content: doc for doc in list1}
|
||||
|
||||
# 遍历 list2，将新的 Document 添加到字典
|
||||
if list2 is not None:
|
||||
for doc in list2:
|
||||
if doc.page_content not in merged_dict:
|
||||
merged_dict[doc.page_content] = doc
|
||||
|
||||
# 返回去重后的列表
|
||||
return list(merged_dict.values())
|
||||
|
||||
def list_files(knowledge_base_name: str) -> ListResponse:
|
||||
if not validate_kb_name(knowledge_base_name):
|
||||
|
|
|
|||
|
|
@ -210,12 +210,6 @@ class KBService(ABC):
|
|||
docs = self.do_search(query, top_k, score_threshold)
|
||||
return docs
|
||||
|
||||
def search_content_internal(self,
|
||||
query: str,
|
||||
top_k: int,
|
||||
)->List[Document]:
|
||||
docs = self.searchbyContentInternal(query,top_k)
|
||||
return docs
|
||||
def get_doc_by_ids(self, ids: List[str]) -> List[Document]:
|
||||
return []
|
||||
|
||||
|
|
@ -325,16 +319,6 @@ class KBService(ABC):
|
|||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def searchbyContentInternal(self,
|
||||
query: str,
|
||||
top_k: int,
|
||||
)->List[Tuple[Document, float]]:
|
||||
"""
|
||||
搜索知识库子类实自己逻辑
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def do_add_doc(
|
||||
self,
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ from chatchat.server.knowledge_base.kb_service.base import KBService, SupportedV
|
|||
from chatchat.server.knowledge_base.utils import KnowledgeFile
|
||||
from chatchat.server.utils import get_Embeddings
|
||||
from chatchat.utils import build_logger
|
||||
from chatchat.server.knowledge_base.model.kb_document_model import DocumentWithVSId
|
||||
|
||||
|
||||
logger = build_logger()
|
||||
|
||||
|
|
@ -37,12 +37,9 @@ class ESKBService(KBService):
|
|||
self.client_cert = kb_config.get("client_cert", None)
|
||||
self.dims_length = kb_config.get("dims_length", None)
|
||||
self.embeddings_model = get_Embeddings(self.embed_model)
|
||||
logger.info(f"self.kb_path:{self.kb_path },self.index_name:{self.index_name}, self.scheme:{self.scheme},self.IP:{self.IP},"
|
||||
f"self.PORT:{self.PORT},self.user:{self.user},self.password:{self.password},self.verify_certs:{self.verify_certs},"
|
||||
f"self.client_cert:{self.client_cert},self.client_key:{self.client_key},self.dims_length:{self.dims_length}")
|
||||
try:
|
||||
connection_info = dict(
|
||||
hosts=f"{self.scheme}://{self.IP}:{self.PORT}"
|
||||
host=f"{self.scheme}://{self.IP}:{self.PORT}"
|
||||
)
|
||||
if self.user != "" and self.password != "":
|
||||
connection_info.update(basic_auth=(self.user, self.password))
|
||||
|
|
@ -56,9 +53,7 @@ class ESKBService(KBService):
|
|||
connection_info.update(client_key=self.client_key)
|
||||
connection_info.update(client_cert=self.client_cert)
|
||||
# ES python客户端连接（仅连接）
|
||||
logger.info(f"connection_info:{connection_info}")
|
||||
self.es_client_python = Elasticsearch(**connection_info)
|
||||
logger.info(f"after Elasticsearch connection_info:{connection_info}")
|
||||
except ConnectionError:
|
||||
logger.error("连接到 Elasticsearch 失败！")
|
||||
raise ConnectionError
|
||||
|
|
@ -89,10 +84,9 @@ class ESKBService(KBService):
|
|||
es_url=f"{self.scheme}://{self.IP}:{self.PORT}",
|
||||
index_name=self.index_name,
|
||||
query_field="context",
|
||||
distance_strategy="COSINE",
|
||||
vector_query_field="dense_vector",
|
||||
embedding=self.embeddings_model,
|
||||
# strategy=ApproxRetrievalStrategy(),
|
||||
strategy=ApproxRetrievalStrategy(),
|
||||
es_params={
|
||||
"timeout": 60,
|
||||
},
|
||||
|
|
@ -107,7 +101,6 @@ class ESKBService(KBService):
|
|||
params["es_params"].update(client_key=self.client_key)
|
||||
params["es_params"].update(client_cert=self.client_cert)
|
||||
self.db = ElasticsearchStore(**params)
|
||||
logger.info(f"after ElasticsearchStore create params:{params}")
|
||||
except ConnectionError:
|
||||
logger.error("### 初始化 Elasticsearch 失败！")
|
||||
raise ConnectionError
|
||||
|
|
@ -140,72 +133,16 @@ class ESKBService(KBService):
|
|||
def vs_type(self) -> str:
|
||||
return SupportedVSType.ES
|
||||
|
||||
def do_search(self, query: str, top_k: int, score_threshold: float)->List[Document]:
|
||||
# 确保 ElasticsearchStore 正确初始化
|
||||
if not hasattr(self, "db") or self.db is None:
|
||||
raise ValueError("ElasticsearchStore (db) not initialized.")
|
||||
|
||||
def do_search(self, query: str, top_k: int, score_threshold: float):
|
||||
# 文本相似性检索
|
||||
retriever = get_Retriever("vectorstore").from_vectorstore(
|
||||
self.db,
|
||||
top_k=top_k,
|
||||
score_threshold=score_threshold,
|
||||
)
|
||||
|
||||
docs = retriever.get_relevant_documents(query)
|
||||
|
||||
return docs
|
||||
|
||||
def searchbyContent(self, query:str, top_k: int = 2):
|
||||
if self.es_client_python.indices.exists(index=self.index_name):
|
||||
logger.info(f"******ESKBService searchByContent {self.index_name},query:{query}")
|
||||
tem_query = {
|
||||
"query": {"match": {
|
||||
"context": "*" + query + "*"
|
||||
}},
|
||||
"highlight":{"fields":{
|
||||
"context":{}
|
||||
}}
|
||||
}
|
||||
search_results = self.es_client_python.search(index=self.index_name, body=tem_query, size=top_k)
|
||||
hits = [hit for hit in search_results["hits"]["hits"]]
|
||||
|
||||
docs_and_scores = []
|
||||
for hit in hits:
|
||||
highlighted_contexts = ""
|
||||
if 'highlight' in hit:
|
||||
highlighted_contexts = " ".join(hit['highlight']['context'])
|
||||
#print(f"******searchByContent highlighted_contexts:{highlighted_contexts}")
|
||||
docs_and_scores.append(DocumentWithVSId(
|
||||
page_content=highlighted_contexts,
|
||||
metadata=hit["_source"]["metadata"],
|
||||
id = hit["_id"],
|
||||
))
|
||||
return docs_and_scores
|
||||
|
||||
def searchbyContentInternal(self, query:str, top_k: int = 2):
|
||||
if self.es_client_python.indices.exists(index=self.index_name):
|
||||
logger.info(f"******ESKBService searchbyContentInternal {self.index_name},query:{query}")
|
||||
tem_query = {
|
||||
"query": {"match": {
|
||||
"context": "*" + query + "*"
|
||||
}}
|
||||
}
|
||||
search_results = self.es_client_python.search(index=self.index_name, body=tem_query, size=top_k)
|
||||
hits = [hit for hit in search_results["hits"]["hits"]]
|
||||
docs_and_scores = [
|
||||
# (
|
||||
Document(
|
||||
page_content=hit["_source"]["context"],
|
||||
metadata=hit["_source"]["metadata"],
|
||||
)
|
||||
# ,
|
||||
# 1.3,
|
||||
# )
|
||||
for hit in hits
|
||||
]
|
||||
# logger.info(f"docs_and_scores:{docs_and_scores}")
|
||||
return docs_and_scores
|
||||
def get_doc_by_ids(self, ids: List[str]) -> List[Document]:
|
||||
results = []
|
||||
for doc_id in ids:
|
||||
|
|
@ -242,13 +179,10 @@ class ESKBService(KBService):
|
|||
},
|
||||
"track_total_hits": True,
|
||||
}
|
||||
print(f"***do_delete_doc: kb_file.filepath:{kb_file.filepath}, kb.filename:{kb_file.filename}")
|
||||
print(f"***do_delete_doc: kb.filename:{kb_file.filename}")
|
||||
# 注意设置size，默认返回10个。
|
||||
search_results = self.es_client_python.search(index=self.index_name, body=query,size=200)
|
||||
delete_list = [hit["_id"] for hit in search_results['hits']['hits']]
|
||||
size = len(delete_list)
|
||||
#print(f"***do_delete_doc: 删除的size:{size}, {delete_list}")
|
||||
# 注意设置size，默认返回10个，es检索设置track_total_hits为True返回数据库中真实的size。
|
||||
size = self.es_client_python.search(body=query)["hits"]["total"]["value"]
|
||||
search_results = self.es_client_python.search(body=query, size=size)
|
||||
delete_list = [hit["_id"] for hit in search_results["hits"]["hits"]]
|
||||
if len(delete_list) == 0:
|
||||
return None
|
||||
else:
|
||||
|
|
@ -278,34 +212,20 @@ class ESKBService(KBService):
|
|||
|
||||
if self.es_client_python.indices.exists(index=self.index_name):
|
||||
file_path = docs[0].metadata.get("source")
|
||||
print(f"****************do_add_doc, file_path:{file_path}")
|
||||
# enhanced by weiweiwang 2025/2/24 to specific index name
|
||||
# query = {
|
||||
# "query": {
|
||||
# "term": {"metadata.source.keyword": file_path},
|
||||
# # "term": {"_index": self.index_name},
|
||||
# }
|
||||
# }
|
||||
query = {
|
||||
query = {
|
||||
"query": {
|
||||
"bool": {
|
||||
"must": [
|
||||
{ "term": { "metadata.source.keyword": file_path } },
|
||||
{ "term": { "_index": self.index_name } }
|
||||
]
|
||||
}
|
||||
"term": {"metadata.source.keyword": file_path},
|
||||
"term": {"_index": self.index_name},
|
||||
}
|
||||
}
|
||||
# 注意设置size，默认返回10个。
|
||||
search_results = self.es_client_python.search(body=query, size=200)
|
||||
search_results = self.es_client_python.search(body=query, size=50)
|
||||
if len(search_results["hits"]["hits"]) == 0:
|
||||
raise ValueError("召回元素个数为0")
|
||||
info_docs = [
|
||||
{"id": hit["_id"], "metadata": hit["_source"]["metadata"]}
|
||||
for hit in search_results["hits"]["hits"]
|
||||
]
|
||||
# size = len(info_docs)
|
||||
# print(f"do_add_doc 召回元素个数：{size}")
|
||||
return info_docs
|
||||
|
||||
def do_clear_vs(self):
|
||||
|
|
|
|||
|
|
@ -78,12 +78,6 @@ class FaissKBService(KBService):
|
|||
docs = retriever.get_relevant_documents(query)
|
||||
return docs
|
||||
|
||||
def searchbyContent(self, query:str, top_k: int = 2):
|
||||
pass
|
||||
|
||||
def searchbyContentInternal(self, query:str, top_k: int = 2):
|
||||
return None
|
||||
|
||||
def do_add_doc(
|
||||
self,
|
||||
docs: List[Document],
|
||||
|
|
|
|||
|
|
@ -88,12 +88,6 @@ class MilvusKBService(KBService):
|
|||
docs = retriever.get_relevant_documents(query)
|
||||
return docs
|
||||
|
||||
def searchbyContent(self, query:str, top_k: int = 2):
|
||||
pass
|
||||
|
||||
def searchbyContentInternal(self, query:str, top_k: int = 2):
|
||||
pass
|
||||
|
||||
def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
|
||||
for doc in docs:
|
||||
for k, v in doc.metadata.items():
|
||||
|
|
|
|||
|
|
@ -84,12 +84,6 @@ class PGKBService(KBService):
|
|||
docs = retriever.get_relevant_documents(query)
|
||||
return docs
|
||||
|
||||
def searchbyContent(self, query:str, top_k: int = 2):
|
||||
pass
|
||||
|
||||
def searchbyContentInternal(self, query:str, top_k: int = 2):
|
||||
pass
|
||||
|
||||
def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
|
||||
ids = self.pg_vector.add_documents(docs)
|
||||
doc_infos = [{"id": id, "metadata": doc.metadata} for id, doc in zip(ids, docs)]
|
||||
|
|
|
|||
|
|
@ -94,13 +94,6 @@ class RelytKBService(KBService):
|
|||
docs = self.relyt.similarity_search_with_score(query, top_k)
|
||||
return score_threshold_process(score_threshold, top_k, docs)
|
||||
|
||||
|
||||
def searchbyContent(self, query:str, top_k: int = 2):
|
||||
pass
|
||||
|
||||
def searchbyContentInternal(self, query:str, top_k: int = 2):
|
||||
pass
|
||||
|
||||
def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
|
||||
print(docs)
|
||||
ids = self.relyt.add_documents(docs)
|
||||
|
|
|
|||
|
|
@ -79,12 +79,6 @@ class ZillizKBService(KBService):
|
|||
docs = retriever.get_relevant_documents(query)
|
||||
return docs
|
||||
|
||||
def searchbyContent(self, query:str, top_k: int = 2):
|
||||
pass
|
||||
|
||||
def searchbyContentInternal(self, query:str, top_k: int = 2):
|
||||
pass
|
||||
|
||||
def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
|
||||
for doc in docs:
|
||||
for k, v in doc.metadata.items():
|
||||
|
|
|
|||
|
|
@ -70,9 +70,6 @@ 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):
|
||||
|
|
@ -425,15 +422,15 @@ 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
|
||||
|
|
|
|||
|
|
@ -488,7 +488,7 @@ class ToolSettings(BaseFileSettings):
|
|||
|
||||
search_internet: dict = {
|
||||
"use": False,
|
||||
"search_engine_name": "zhipu_search",
|
||||
"search_engine_name": "duckduckgo",
|
||||
"search_engine_config": {
|
||||
"bing": {
|
||||
"bing_search_url": "https://api.bing.microsoft.com/v7.0/search",
|
||||
|
|
@ -506,21 +506,11 @@ 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": 1,
|
||||
"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,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”。 "
|
||||
"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”。 "
|
||||
"</指令>\n<已知信息>{{ context }}</已知信息>\n"
|
||||
"<问题>\n"
|
||||
"{{ question }}\n"
|
||||
|
|
@ -660,7 +650,7 @@ 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,不得包含有重复的词汇或句子。"
|
||||
"【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。\n\n"
|
||||
"【已知信息】{{context}}\n\n"
|
||||
"【问题】{{question}}\n"
|
||||
|
|
@ -751,8 +741,6 @@ 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"
|
||||
|
|
|
|||
|
|
@ -46,7 +46,7 @@ if __name__ == "__main__":
|
|||
|
||||
with st.sidebar:
|
||||
st.image(
|
||||
get_img_base64("logo-long-chatchat-trans-v2.png"), use_column_width=True
|
||||
get_img_base64("logo-long-chatchat-trans-v2.png"), use_container_width=True
|
||||
)
|
||||
st.caption(
|
||||
f"""<p align="right">当前版本：{__version__}</p>""",
|
||||
|
|
|
|||
|
|
@ -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"): {"filter": "agTextColumnFilter"},
|
||||
("file_name", "文档名称"): {},
|
||||
# ("file_ext", "文档类型"): {},
|
||||
# ("file_version", "文档版本"): {},
|
||||
("document_loader", "文档加载器"): {},
|
||||
|
|
@ -398,7 +398,7 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
|
|||
cellEditor="agLargeTextCellEditor",
|
||||
cellEditorPopup=True,
|
||||
autoWidth=True,
|
||||
cellEditorParams= { "maxLength": 1500}
|
||||
cellEditorParams= { "maxLength": 1000}
|
||||
)
|
||||
gb.configure_column(
|
||||
"to_del",
|
||||
|
|
@ -406,8 +406,8 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
|
|||
editable=True,
|
||||
width=50,
|
||||
wrapHeaderText=True,
|
||||
cellEditor="agTextCellEditor",
|
||||
cellRender="agTextCellRenderer",
|
||||
cellEditor="agCheckboxCellEditor",
|
||||
cellRender="agCheckboxCellRenderer",
|
||||
)
|
||||
# 启用分页
|
||||
gb.configure_pagination(
|
||||
|
|
@ -428,15 +428,15 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
|
|||
changed_docs = []
|
||||
for index, row in edit_docs.data.iterrows():
|
||||
origin_doc = origin_docs[row["id"]]
|
||||
# if row["page_content"] != origin_doc["page_content"]:
|
||||
if row["to_del"] not in ["Y", "y", 1]:
|
||||
changed_docs.append(
|
||||
{
|
||||
"page_content": row["page_content"],
|
||||
"type": row["type"],
|
||||
"metadata": json.loads(row["metadata"]),
|
||||
}
|
||||
)
|
||||
if row["page_content"] != origin_doc["page_content"]:
|
||||
if row["to_del"] not in ["Y", "y", 1]:
|
||||
changed_docs.append(
|
||||
{
|
||||
"page_content": row["page_content"],
|
||||
"type": row["type"],
|
||||
"metadata": json.loads(row["metadata"]),
|
||||
}
|
||||
)
|
||||
|
||||
if changed_docs:
|
||||
if api.update_kb_docs(
|
||||
|
|
|
|||
Loading…
Reference in New Issue