Langchain-Chatchat/tests/api/test_kb_api_request.py
liunux4odoo d316efe8d3
release 0.2.6 (#1815)
## 🛠 新增功能

- 支持百川在线模型 (@hzg0601 @liunux4odoo in #1623)
- 支持 Azure OpenAI 与 claude 等 Langchain 自带模型 (@zRzRzRzRzRzRzR in #1808)
- Agent 功能大量更新,支持更多的工具、更换提示词、检索知识库 (@zRzRzRzRzRzRzR in #1626 #1666 #1785)
- 加长 32k 模型的历史记录 (@zRzRzRzRzRzRzR in #1629 #1630)
- *_chat 接口支持 max_tokens 参数 (@liunux4odoo in #1744)
- 实现 API 和 WebUI 的前后端分离 (@liunux4odoo in #1772)
- 支持 zlilliz 向量库 (@zRzRzRzRzRzRzR in #1785)
- 支持 metaphor 搜索引擎 (@liunux4odoo in #1792)
- 支持 p-tuning 模型 (@hzg0601 in #1810)
- 更新完善文档和 Wiki (@imClumsyPanda @zRzRzRzRzRzRzR @glide-the in #1680 #1811)

## 🐞 问题修复

- 修复 bge-* 模型匹配超过 1 的问题 (@zRzRzRzRzRzRzR in #1652)
- 修复系统代理为空的问题 (@glide-the in #1654)
- 修复重建知识库时 `d == self.d assert error` (@liunux4odoo in #1766)
- 修复对话历史消息错误 (@liunux4odoo in #1801)
- 修复 OpenAI 无法调用的 bug (@zRzRzRzRzRzRzR in #1808)
- 修复 windows下 BIND_HOST=0.0.0.0 时对话出错的问题 (@hzg0601 in #1810)
2023-10-20 23:16:06 +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