Langchain-Chatchat/tests/api/test_kb_api_request.py
liunux4odoo 9ce328fea9
实现Api和WEBUI的前后端分离 (#1772)
* update ApiRequest: 删除no_remote_api本地调用模式;支持同步/异步调用
* 实现API和WEBUI的分离:
- API运行服务器上的配置通过/llm_model/get_model_config、/server/configs接口提供,WEBUI运行机器上的配置项仅作为代码内部默认值使用
- 服务器可用的搜索引擎通过/server/list_search_engines提供
- WEBUI可选LLM列表中只列出在FSCHAT_MODEL_WORKERS中配置的模型
- 修改WEBUI中默认LLM_MODEL获取方式,改为从api端读取
- 删除knowledge_base_chat中`local_doc_url`参数

其它修改:
- 删除多余的kb_config.py.exmaple(名称错误)
- server_config中默认关闭vllm
- server_config中默认注释除智谱AI之外的在线模型
- 修改requests从系统获取的代理,避免model worker注册错误

* 修正:
- api.list_config_models返回模型原始配置
- api.list_config_models和api.get_model_config中过滤online api模型的敏感信息
- 将GPT等直接访问的模型列入WEBUI可选模型列表

其它:
- 指定langchain==0.3.313, fschat==0.2.30, langchain-experimental==0.0.30
2023-10-17 16:52:07 +08:00

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import requests
import json
import sys
from pathlib import Path
root_path = Path(__file__).parent.parent.parent
sys.path.append(str(root_path))
from server.utils import api_address
from configs import VECTOR_SEARCH_TOP_K
from server.knowledge_base.utils import get_kb_path, get_file_path
from webui_pages.utils import ApiRequest
from pprint import pprint
api_base_url = api_address()
api: ApiRequest = ApiRequest(api_base_url)
kb = "kb_for_api_test"
test_files = {
"FAQ.MD": str(root_path / "docs" / "FAQ.MD"),
"README.MD": str(root_path / "README.MD"),
"test.txt": get_file_path("samples", "test.txt"),
}
print("\n\nApiRquest调用\n")
def test_delete_kb_before():
if not Path(get_kb_path(kb)).exists():
return
data = api.delete_knowledge_base(kb)
pprint(data)
assert data["code"] == 200
assert isinstance(data["data"], list) and len(data["data"]) > 0
assert kb not in data["data"]
def test_create_kb():
print(f"\n尝试用空名称创建知识库:")
data = api.create_knowledge_base(" ")
pprint(data)
assert data["code"] == 404
assert data["msg"] == "知识库名称不能为空,请重新填写知识库名称"
print(f"\n创建新知识库: {kb}")
data = api.create_knowledge_base(kb)
pprint(data)
assert data["code"] == 200
assert data["msg"] == f"已新增知识库 {kb}"
print(f"\n尝试创建同名知识库: {kb}")
data = api.create_knowledge_base(kb)
pprint(data)
assert data["code"] == 404
assert data["msg"] == f"已存在同名知识库 {kb}"
def test_list_kbs():
data = api.list_knowledge_bases()
pprint(data)
assert isinstance(data, list) and len(data) > 0
assert kb in data
def test_upload_docs():
files = list(test_files.values())
print(f"\n上传知识文件")
data = {"knowledge_base_name": kb, "override": True}
data = api.upload_kb_docs(files, **data)
pprint(data)
assert data["code"] == 200
assert len(data["data"]["failed_files"]) == 0
print(f"\n尝试重新上传知识文件, 不覆盖")
data = {"knowledge_base_name": kb, "override": False}
data = api.upload_kb_docs(files, **data)
pprint(data)
assert data["code"] == 200
assert len(data["data"]["failed_files"]) == len(test_files)
print(f"\n尝试重新上传知识文件, 覆盖自定义docs")
docs = {"FAQ.MD": [{"page_content": "custom docs", "metadata": {}}]}
data = {"knowledge_base_name": kb, "override": True, "docs": docs}
data = api.upload_kb_docs(files, **data)
pprint(data)
assert data["code"] == 200
assert len(data["data"]["failed_files"]) == 0
def test_list_files():
print("\n获取知识库中文件列表:")
data = api.list_kb_docs(knowledge_base_name=kb)
pprint(data)
assert isinstance(data, list)
for name in test_files:
assert name in data
def test_search_docs():
query = "介绍一下langchain-chatchat项目"
print("\n检索知识库:")
print(query)
data = api.search_kb_docs(query, kb)
pprint(data)
assert isinstance(data, list) and len(data) == VECTOR_SEARCH_TOP_K
def test_update_docs():
print(f"\n更新知识文件")
data = api.update_kb_docs(knowledge_base_name=kb, file_names=list(test_files))
pprint(data)
assert data["code"] == 200
assert len(data["data"]["failed_files"]) == 0
def test_delete_docs():
print(f"\n删除知识文件")
data = api.delete_kb_docs(knowledge_base_name=kb, file_names=list(test_files))
pprint(data)
assert data["code"] == 200
assert len(data["data"]["failed_files"]) == 0
query = "介绍一下langchain-chatchat项目"
print("\n尝试检索删除后的检索知识库:")
print(query)
data = api.search_kb_docs(query, kb)
pprint(data)
assert isinstance(data, list) and len(data) == 0
def test_recreate_vs():
print("\n重建知识库:")
r = api.recreate_vector_store(kb)
for data in r:
assert isinstance(data, dict)
assert data["code"] == 200
print(data["msg"])
query = "本项目支持哪些文件格式?"
print("\n尝试检索重建后的检索知识库:")
print(query)
data = api.search_kb_docs(query, kb)
pprint(data)
assert isinstance(data, list) and len(data) == VECTOR_SEARCH_TOP_K
def test_delete_kb_after():
print("\n删除知识库")
data = api.delete_knowledge_base(kb)
pprint(data)
# check kb not exists anymore
print("\n获取知识库列表:")
data = api.list_knowledge_bases()
pprint(data)
assert isinstance(data, list) and len(data) > 0
assert kb not in data