知识库组件（KnowledgeBase）

简介

知识库组件（KnowledgeBase）是对线上知识库操作的组件，可以通过SDK实现创建知识库、添加知识文档、查询知识库文档、删除知识文档等操作，可在平台console中查看结果。

功能介绍

对console端知识库进行操作，可以通过SDK实现创建知识库、添加知识文档、查询知识库文档、删除知识文档等操作，可在平台console中查看结果。

特色优势

和console端知识库操作一致，可实现快速创建、查询、删除等操作。

应用场景

通过SDK代码实现console端知识库操作。

Python基本用法

1、新建知识库`KnowledgeBase().create_knowledge_base(name: str, description: str, type: str, clusterId: str, esUserName: str, esPassword: str, location: str) -> KnowledgeBaseDetailResponse`

方法参数

参数名称	参数类型	是否必传	描述	示例值
name	string	是	希望创建的知识库名称	"我的知识库"
description	string	否	知识库描述	"我的知识库"
type	string	是	知识库索引存储配置 (public、bes、vdb)	"public"
clusterId	string	type=bes 和 vdb 时填写	集群/实例 ID 请在bes、vdb控制台中查看。	"clusterId"
esUserName	string	type=bes 和 vdb 时填写	用户名/账号请在bes、vdb控制台中查看	"username"
esPassword	string	type=bes 和 vdb 时填写	密码/API密钥请在bes、vdb控制台中查看	"password"
location	string	type=bes 和 vdb 时填写	托管资源的区域，type填vdb时填写可选值： - bj：北京 - bd：保定 - sz：苏州 - gz：广州	"bj"
pathPrefix	string	否	创建知识库的指定目录，最大层级为5，默认为根目录下	"/全部群组/##/##"

方法返回值

KnowledgeBaseDetailResponse 类定义如下：

class KnowledgeBaseDetailResponse(BaseModel):
    id: str = Field(..., description="知识库ID")
    name: str = Field(..., description="知识库名称")
    description: Optional[str] = Field(None, description="知识库描述")
    config: Optional[KnowledgeBaseConfig] = Field(..., description="知识库配置")

衍生类KnowledgeBaseConfig定义如下：

class KnowledgeBaseConfig(BaseModel):
    index: Optional[KnowledgeBaseConfigIndex] = Field(..., description="索引配置")
    catalogue: Optional[KnowledgeBaseConfigCatalogue] = Field(None, description="知识库目录配置")

衍生类KnowledgeBaseConfigIndex定义如下：

class KnowledgeBaseConfigIndex(BaseModel):
    type: str = Field(..., description="索引类型", enum=["public", "bes", "vdb"])
    clusterId: Optional[str] = Field(None, description="集群/实例 ID")
    username: Optional[str] = Field(None, description="bes用户名")
    password: Optional[str] = Field(None, description="bes密码")
    location: Optional[str] = Field(None, description="托管资源的区域", enum=["bj", "bd", "sz", "gz"])

衍生类KnowledgeBaseConfigCatalogue

class KnowledgeBaseConfigCatalogue(BaseModel):
    pathPrefix: Optional[str] = Field(None, description="知识库所属目录绝对路径")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

knowledge = appbuilder.KnowledgeBase()
resp = knowledge.create_knowledge_base(
        name="my_knowledge",
        description="my_knowledge",
        type="public",
    )
print("新建的知识库ID: ", resp.id)
print("新建的知识库名称: ", resp.name)

# 新建的知识库ID:  da51a988-cbe7-4b24-aa5b-768985e8xxxx
# 新建的知识库名称:  my_knowledge

2、实例化已创建的知识库 `KnowledgeBase(knowledge_id: str)`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledge_id	string	是	线上知识库ID	"正确的知识库ID"

方法返回值

参数名称	参数类型	必然存在	描述	示例值
KnowledgeBase	class KnowledgeBase	是	实例化的知识库类	-

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

my_knowledge_base_id = "your_knowledge_base_id"
my_knowledge = appbuilder.KnowledgeBase(my_knowledge_base_id)
print("知识库ID: ", my_knowledge.knowledge_id)

# 知识库ID:  your_knowledge_base_id

3、获取知识库详情`get_knowledge_base_detail(knowledge_base_id: Optional[str] = None) -> KnowledgeBaseDetailResponse`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledge_id	string	是	线上知识库ID	"正确的知识库ID"

方法返回值

KnowledgeBaseDetailResponse 类定义如下：

class KnowledgeBaseDetailResponse(BaseModel):
    id: str = Field(..., description="知识库ID")
    name: str = Field(..., description="知识库名称")
    description: Optional[str] = Field(None, description="知识库描述")
    config: Optional[KnowledgeBaseConfig] = Field(..., description="知识库配置")

衍生类KnowledgeBaseConfig定义如下：

class KnowledgeBaseConfig(BaseModel):
    index: Optional[KnowledgeBaseConfigIndex] = Field(..., description="索引配置")
    catalogue: Optional[KnowledgeBaseConfigCatalogue] = Field(None, description="知识库目录配置")

衍生类KnowledgeBaseConfigIndex定义如下：

class KnowledgeBaseConfigIndex(BaseModel):
    type: str = Field(..., description="索引类型", enum=["public", "bes", "vdb"])
    esUrl: Optional[str] = Field(..., description="ES地址")
    username: Optional[str] = Field(None, description="ES用户名")
    password: Optional[str] = Field(None, description="ES密码")

衍生类KnowledgeBaseConfigCatalogue

class KnowledgeBaseConfigCatalogue(BaseModel):
    pathPrefix: Optional[str] = Field(None, description="知识库所属目录绝对路径")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

knowledge = appbuilder.KnowledgeBase()
resp = knowledge.get_knowledge_base_detail("da51a988-cbe7-4b24-aa5b-768985e8xxxx")
print("新建的知识库ID: ", resp.id)
print("新建的知识库名称: ", resp.name)

# 新建的知识库ID:  da51a988-cbe7-4b24-aa5b-768985e8xxxx
# 新建的知识库名称:  my_knowledge

4、获取知识库列表`get_knowledge_base_list(knowledge_base_id: Optional[str] = None, maxKeys: int = 10, keyword: Optional[str] = None) -> KnowledgeBaseGetListResponse`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledge_base_id	string	否	起始位置，知识库id	"正确的知识库ID"
maxKeys	int	否	数据大小，默认10，最大值100	10
keyword	string	否	搜索关键字	"测试"

方法返回值

KnowledgeBaseGetListResponse 类定义如下：

class KnowledgeBaseGetListResponse(BaseModel):
    requestId: str = Field(..., description="请求ID")
    data: list[KnowledgeBaseDetailResponse] = Field([], description="知识库详情列表")
    marker: str = Field(..., description="起始位置")
    nextMarker: str = Field(..., description="下一页起始位置")
    maxKeys: int = Field(10, description="返回文档数量大小，默认10，最大值100")
    isTruncated: bool = Field(..., description="是否有更多结果")

衍生类KnowledgeBaseDetailResponse 定义如下：

class KnowledgeBaseDetailResponse(BaseModel):
    id: str = Field(..., description="知识库ID")
    name: str = Field(..., description="知识库名称")
    description: Optional[str] = Field(None, description="知识库描述")
    config: Optional[KnowledgeBaseConfig] = Field(..., description="知识库配置")

衍生类KnowledgeBaseConfig定义如下：

class KnowledgeBaseConfig(BaseModel):
    index: Optional[KnowledgeBaseConfigIndex] = Field(..., description="索引配置")
    catalogue: Optional[KnowledgeBaseConfigCatalogue] = Field(None, description="知识库目录配置")

衍生类KnowledgeBaseConfigIndex定义如下：

class KnowledgeBaseConfigIndex(BaseModel):
    type: str = Field(..., description="索引类型", enum=["public", "bes", "vdb"])
    esUrl: Optional[str] = Field(..., description="ES地址")
    username: Optional[str] = Field(None, description="ES用户名")
    password: Optional[str] = Field(None, description="ES密码")

衍生类KnowledgeBaseConfigCatalogue

class KnowledgeBaseConfigCatalogue(BaseModel):
    pathPrefix: Optional[str] = Field(None, description="知识库所属目录绝对路径")

方法示例：

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

knowledge = appbuilder.KnowledgeBase()
resp = knowledge.get_knowledge_base_list("da51a988-cbe7-4b24-aa5b-768985e8xxxx",10)
print("获取到的知识库列表: ", resp)

5、修改知识库`modify_knowledge_base(knowledge_base_id: Optional[str] = None, name: Optional[str] = None, description: Optional[str] = None)`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledge_base_id	string	是	起始位置，知识库id	"正确的知识库ID"
name	string	否	修改后的知识库名称	"new_name"
description	string	否	修改后的知识库描述	"测试"
pathPrefix	string	否	知识库的指定目录，最大层级为5，默认为根目录下

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

knowledge = appbuilder.KnowledgeBase()
knowledge.modify_knowledge_base("da51a988-cbe7-4b24-aa5b-768985e8xxxx", name="new_name", description="测试")

6、删除知识库`delete_knowledge_base(knowledge_base_id: Optional[str] = None)`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledge_base_id	string	是	知识库的id	"正确的知识库ID"

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

knowledge = appbuilder.KnowledgeBase()
knowledge.delete_knowledge_base("da51a988-cbe7-4b24-aa5b-768985e8xxxx")

7、导入知识库`create_documents(id: Optional[str] = None, contentFormat: str = "", source: DocumentSource = None, processOption: DocumentProcessOption = None)`

方法参数

参数名称	参数类型	是否必传	描述	示例值
id	string	是	知识库id	"正确的知识库ID"
contentFormat	string	是	文档格式：rawText (允许配置后续分割策略)	"rawText"
source	DocumentSource	是	数据来源
processOption	DocumentProcessOption	当contentFormat参数配置为rawText时必传	文档处理策略

DocumentSource类定义如下：

class DocumentSourceUrlConfig(BaseModel):
    frequency: int = Field(
        ...,
        description="更新频率，目前支持的更新频率为-1(不自动更新),1（每天）,3（每3天）,7（每7天）,30（每30天）。",
    )

class DocumentSource(BaseModel):
    type: str = Field(..., description="数据来源类型", enum=["bos", "web"])
    urls: list[str] = Field(None, description="文档URL")
    urlDepth: int = Field(None, description="url下钻深度，1时不下钻")
    urlConfigs: Optional[list[DocumentSourceUrlConfig]] = Field(None, description="该字段的长度需要和source、urls字段长度保持一致。")

DocumentProcessOption类及衍生类定义如下：

class DocumentProcessOption(BaseModel):
    template: str = Field(
        ...,
        description="模板类型，ppt：模版配置—ppt幻灯片, resume：模版配置—简历文档, paper：模版配置—论文文档, custom：自定义配置—自定义切片, default：自定义配置—默认切分",
        enum=["ppt", "paper", "qaPair", "resume", " custom", "default"],
    )
    parser: Optional[DocumentChoices] = Field(None, description="解析方法(文字提取默认启动，参数不体现，layoutAnalysis版面分析，ocr光学字符识别，pageImageAnalysis文档图片解析，chartAnalysis图表解析，tableAnalysis表格深度解析，按需增加)")
    knowledgeAugmentation: Optional[DocumentChoices] = Field(
        None, description="知识增强，faq、spokenQuery、spo、shortSummary按需增加。问题生成:faq、spokenQuery，段落摘要:shortSummary，三元组知识抽取:spo"
    )
    chunker: Optional[DocumentChunker] = Field(None, description="分段器类型")

class DocumentChoices(BaseModel):
    choices: list[str] = Field(..., description="选择项")

class DocumentChunker(BaseModel):
    choices: list[str] = Field(..., description="使用哪些chunker方法 (separator | pattern | onePage)，separator：自定义切片—标识符，pattern：自定义切片—标识符中选择正则表达式，onePage：整文件切片")
    prependInfo: list[str] = Field(
        None,
        description="chunker关联元数据，可选值为title (增加标题), filename(增加文件名)",
    )
    separator: Optional[DocumentSeparator] = Field(None, description="分隔符配置")
    pattern: Optional[DocumentPattern] = Field(None, description="正则表达式")

class DocumentSeparator(BaseModel):
    separators: list[str] = Field(..., description="分隔符列表，可以使用分页符")
    targetLength: int = Field(..., description="分段最大长度")
    overlapRate: float = Field(..., description="分段重叠最大字数占比，推荐值0.25")

class DocumentPattern(BaseModel):
    markPosition: str = Field(
        ..., description="命中内容放置策略, head：前序切片, tail：后序切片, drop：匹配后丢弃", enum=["head", "tail", "drop"]
    )
    regex: str = Field(..., description="正则表达式")
    targetLength: int = Field(..., description="分段最大长度")
    overlapRate: float = Field(..., description="分段重叠最大字数占比，推荐值0.25")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

knowledge_base_id = "your_knowledge_base_id"
knowledge = appbuilder.KnowledgeBase()
knowledge.create_documents(
	id=knowledge_base_id,
	contentFormat="rawText",
	source=appbuilder.DocumentSource(
		type="web",
		urls=["https://baijiahao.baidu.com/s?id=1802527379394162441"],
    urlDepth=1,
    urlConfigs=[appbuilder.DocumentSourceUrlConfig(frequency=1)]
  ),
	processOption=appbuilder.DocumentProcessOption(
		template="custom",
		parser=appbuilder.DocumentChoices(
			choices=["layoutAnalysis", "ocr"]
		),
		chunker=appbuilder.DocumentChunker(
			choices=["separator"],
			separator=appbuilder.DocumentSeparator(
				separators=["。"],
				targetLength=300,
				overlapRate=0.25,
      ),
			prependInfo=["title", "filename"],
		),
		knowledgeAugmentation=appbuilder.DocumentChoices(choices=["faq"]),
	),
)

8、上传文档到知识库`upload_documents(file_path: str, content_format: str = "rawText", id: Optional[str] = None, processOption: DocumentProcessOption = None)`

方法参数

参数名称	参数类型	是否必传	描述	示例值
file_path	string	是	文件路径	"正确的文件路径"
content_format	string	否	文档格式：rawText (允许配置后续分割策略)	"rawText"
id	string	是	知识库ID	"正确的知识库ID"
processOption	DocumentProcessOption	是	文档处理策略

DocumentProcessOption类及衍生类定义如下：

class DocumentProcessOption(BaseModel):
    template: str = Field(
        ...,
        description="模板类型，ppt: 模版配置—ppt幻灯片, resume：模版配置—简历文档, paper：模版配置—论文文档, custom：自定义配置—自定义切片, default：自定义配置—默认切分",
        enum=["ppt", "paper", "qaPair", "resume", " custom", "default"],
    )
    parser: Optional[DocumentChoices] = Field(None, description="解析器类型")
    knowledgeAugmentation: Optional[DocumentChoices] = Field(
        None, description="知识增强，faq、spokenQuery、spo、shortSummary按需增加。问题生成:faq、spokenQuery，段落摘要:shortSummary，三元组知识抽取:spo"
    )
    chunker: Optional[DocumentChunker] = Field(None, description="分段器类型")

class DocumentChoices(BaseModel):
    choices: list[str] = Field(..., description="选择项")

class DocumentChunker(BaseModel):
    choices: list[str] = Field(..., description="使用哪些chunker方法 (separator | pattern | onePage), separator：自定义切片—标识符，pattern：自定义切片—标识符中选择正则表达式，onePage：整文件切片")
    prependInfo: list[str] = Field(
        ...,
        description="chunker关联元数据，可选值为title (增加标题), filename(增加文件名)",
    )
    separator: Optional[DocumentSeparator] = Field(..., description="分段符号")
    pattern: Optional[DocumentPattern] = Field(None, description="正则表达式")

class DocumentSeparator(BaseModel):
    separators: list[str] = Field(..., description="分段符号")
    targetLength: int = Field(..., description="分段最大长度")
    overlapRate: float = Field(..., description="分段重叠最大字数占比，推荐值0.25")

class DocumentPattern(BaseModel):
    markPosition: str = Field(
        ..., description="命中内容放置策略, head：前序切片, tail：后序切片, drop：匹配后丢弃", enum=["head", "tail", "drop"]
    )
    regex: str = Field(..., description="正则表达式")
    targetLength: int = Field(..., description="分段最大长度")
    overlapRate: float = Field(..., description="分段重叠最大字数占比，推荐值0.25")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

knowledge_base_id = "your_knowledge_base_id"
knowledge = appbuilder.KnowledgeBase()
knowledge.upload_documents(
	id=knowledge_base_id,
	content_format="rawText",
	file_path="./python/tests/data/qa_appbuilder_client_demo.pdf",
	processOption=appbuilder.DocumentProcessOption(
		template="custom",
		parser=appbuilder.DocumentChoices(
			choices=["layoutAnalysis", "ocr"]
		),
		chunker=appbuilder.DocumentChunker(
			choices=["separator"],
			separator=appbuilder.DocumentSeparator(
				separators=["。"],
				targetLength=300,
				overlapRate=0.25,
      ),
			prependInfo=["title", "filename"],
		),
		knowledgeAugmentation=appbuilder.DocumentChoices(choices=["faq"]),
	),
)

9、从知识库删除文档 `KnowledgeBase().delete_document()->KnowledgeBaseDeleteDocumentResponse`

方法参数

参数名称	参数类型	是否必传	描述	示例值
document_id	string	是	待删除的文档ID	'5e0eb279-7688-4100-95d1-241f3d19xxxx'

方法返回值

方法返回 KnowledgeBaseDeleteDocumentResponse, 该类的定义是

class KnowledgeBaseDeleteDocumentResponse(BaseModel):
    request_id: str = Field(..., description="请求ID")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

my_knowledge_base_id = "your_knowledge_base_id"
my_knowledge = appbuilder.KnowledgeBase(my_knowledge_base_id)
print("知识库ID: ", my_knowledge.knowledge_id)

upload_res = my_knowledge.upload_file("./test.txt")
print("文件上传结果: ",upload_res)

add_res = my_knowledge.add_document(content_type='raw_text', file_ids=[upload_res.id])
print("添加文档结果: ",add_res)

delete_res = my_knowledge.delete_document(document_id=add_res.document_ids[0])
print("删除文档结果: ",delete_res)

# 知识库ID:  da51a988-cbe7-4b24-aa5b-768985e8xxxx
# 文件上传结果: request_id='255eec22-ec87-4564-bdeb-3e5623eaxxxx' id='ef12119b-d5be-492a-997c-77f8e84axxxx' name='test.txt'
# 添加文档结果: request_id='412e1630-b570-47c9-a042-caf3cd9dxxxx' knowledge_base_id='da51a988-cbe7-4b24-aa5b-768985e8xxxx' document_ids=['5e0eb279-7688-4100-95d1-241f3d19xxxx']
# 删除文档结果: request_id='ba0e8bc0-b799-45b5-bdac-0d4c50e2xxxx'

10、获取知识库的文档列表`KnowledgeBase().get_documents_list()->KnowledgeBaseGetDocumentsListResponse`

方法参数

参数名称	参数类型	是否必传	描述	示例值
limit	int	否	单次请求列表获得的文档数量,最大100	10
after	str	否	用于分页的游标。after 是一个文档的id，它定义了在列表中的位置。例如，如果你发出一个列表请求并收到 10个对象，以 app_id_123 结束，那么你后续的调用可以包含 after=app_id_123 以获取列表的下一页数据。默认为空	'5e0eb279-7688-4100-95d1-241f3d19xxxx'
before	str	否	用于分页的游标。与after相反，填写它将获取前一页数据,如果和after都传，两个参数都会起到分页效果，默认为空	'5e0eb279-7688-4100-95d1-241f3d19xxxx'

方法返回值

方法返回类KnowledgeBaseGetDocumentsListResponse，定义为

class KnowledgeBaseGetDocumentsListResponse(BaseModel):
    request_id: str = Field(..., description="请求ID")
    data: list[Document] = Field([], description="文档信息列表")

衍生类Document以及DocumentMeta定义为：

class Document(BaseModel):
    id: str = Field(..., description="文档ID")
    name: str = Field(..., description="文档名称")
    created_at: str = Field(..., description="文档创建时间")
    word_count: int = Field(..., description="文档字数")
    enabled: bool = Field(True, description="文档是否可用")
    meta: Optional[DocumentMeta] = Field(..., description="文档元信息，包括source、file_id")

class DocumentMeta(BaseModel):
    source: str = Field("", description="文档来源")
    file_id: str = Field("", description="文档对应的文件ID")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

my_knowledge_base_id = "your_knowledge_base_id"
my_knowledge = appbuilder.KnowledgeBase(my_knowledge_base_id)
print("知识库ID: ", my_knowledge.knowledge_id)

list_res = my_knowledge.get_documents_list()
print("文档列表: ", list_res)

# 知识库ID:  da51a988-cbe7-4b24-aa5b-768985e8xxxx
# 文档列表: request_id='f66c2193-6035-4022-811b-c4cd7743xxxx' data=[{'id': '8f388b10-5e6a-423f-8acc-dd5fdc2fxxxx', 'name': 'test.txt', 'created_at': 1719988868, 'word_count': 16886, 'enabled': True, 'meta': {'source': 'upload_file', 'file_id': '0ebb03fb-ea48-4c49-b494-cf0cec11xxxx'}}, {'id': '5e0eb279-7688-4100-95d1-241f3d19xxxx', 'name': 'test.txt', 'created_at': 1719987921, 'word_count': 16886, 'enabled': True, 'meta': {'source': 'upload_file', 'file_id': '059e2ae2-1e3c-43ea-8b42-5d988f93xxxx'}}]

11、获取知识库全部文档`KnowledgeBase().get_all_documents()->list`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledge_base_id	string	是	线上知识库ID	"正确的知识库ID"

方法返回值

list 数据类型list[Document]

衍生类Document以及DocumentMeta定义为：

class Document(BaseModel):
    id: str = Field(..., description="文档ID")
    name: str = Field(..., description="文档名称")
    created_at: str = Field(..., description="文档创建时间")
    word_count: int = Field(..., description="文档字数")
    enabled: bool = Field(True, description="文档是否可用")
    meta: Optional[DocumentMeta] = Field(..., description="文档元信息，包括source、file_id")

class DocumentMeta(BaseModel):
    source: str = Field("", description="文档来源")
    file_id: str = Field("", description="文档对应的文件ID")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

my_knowledge_base_id = "your_knowledge_base_id"
my_knowledge = appbuilder.KnowledgeBase(my_knowledge_base_id)
print("知识库ID: ", my_knowledge.knowledge_id)

doc_list = my_knowledge.get_all_documents(my_knowledge_base_id)
for message in doc_list:
    print(message)

12. 创建切片`create_chunk(documentId: str, content: str) -> CreateChunkResponse`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledgeBaseId	string	是	知识库ID
documentId	string	是	文档ID	"正确的文档ID"
content	string	是	切片内容	"内容"

方法返回值

CreateChunkResponse类定义如下：

class CreateChunkResponse(BaseModel):
    id: str = Field(..., description="切片ID")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

my_knowledge_base_id = "your_knowledge_base_id"
my_knowledge = appbuilder.KnowledgeBase(my_knowledge_base_id)
print("知识库ID: ", my_knowledge.knowledge_id)
resp = my_knowledge.create_chunk("your_document_id", "content", knowledgebase_id=knowledge_base_id)
print("切片ID: ", resp.id)
chunk_id = resp.id

13. 修改切片信息`modify_chunk(chunkId: str, content: str, enable: bool)`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledgeBaseId	string	是	知识库ID
chunkId	string	是	文档ID	"正确的切片ID"
content	string	是	切片内容	"内容"
enable	bool	是	是否用该切片	True

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

my_knowledge_base_id = "your_knowledge_base_id"
my_knowledge = appbuilder.KnowledgeBase(my_knowledge_base_id)
print("知识库ID: ", my_knowledge.knowledge_id)
my_knowledge.modify_chunk("your_chunk_id", "content", True, knowledgebase_id=my_knowledge_base_id)

14. 删除切片`delete_chunk(chunkId: str)`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledgeBaseId	string	是	知识库ID
chunkId	string	是	文档ID	"正确的切片ID"

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

my_knowledge_base_id = "your_knowledge_base_id"
my_knowledge = appbuilder.KnowledgeBase(my_knowledge_base_id)
print("知识库ID: ", my_knowledge.knowledge_id)
my_knowledge.delete_chunk("your_chunk_id", knowledgebase_id=my_knowledge_base_id)

15. 获取切片信息`describe_chunk(chunkId: str)`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledgeBaseId	string	是	知识库ID
chunkId	string	是	文档ID	"正确的切片ID"

方法返回值

DescribeChunkResponse类定义如下:

class DescribeChunkResponse(BaseModel):
    id: str = Field(..., description="切片ID")
    type: str = Field(..., description="切片类型")
    knowledgeBaseId: str = Field(..., description="知识库ID")
    documentId: str = Field(..., description="文档ID")
    content: str = Field(..., description="文档内容")
    enabled: bool = Field(..., description="是否启用")
    wordCount: int = Field(..., description="切片内字符数量")
    tokenCount: int = Field(..., description="切片内token数量")
    status: str = Field(..., description="切片状态")
    statusMessage: str = Field(..., description="切片状态信息")
    imageUrls: list[str] = Field(..., description="图片地址")
    createTime: int = Field(..., description="创建时间")
    updateTime: int = Field(None, description="更新时间")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

my_knowledge_base_id = "your_knowledge_base_id"
my_knowledge = appbuilder.KnowledgeBase(my_knowledge_base_id)
print("知识库ID: ", my_knowledge.knowledge_id)
resp = my_knowledge.describe_chunk("your_chunk_id", knowledgebase_id=my_knowledge_base_id)
print("切片详情：")
print(resp)

16. 获取切片列表`describe_chunks(documentId: str, marker: str = None, maxKeys: int = None, type: str = None) -> DescribeChunksResponse`

方法参数

参数名称	参数类型	是否必传	描述	示例值
knowledgeBaseId	string	是	知识库ID
documentId	string	是	文档ID	"正确的文档ID"
marker	string	否	起始位置，切片ID	"正确的切片ID"
maxKeys	string	否	返回文档数量大小，默认10，最大值100	10
type	string	否	根据类型获取切片列表(RAW、NEW、COPY)，RAW：原文切片，NEW：新增切片，COPY：复制切片	"RAW"
keyword	string	否	根据关键字模糊匹配切片，最大长度2000字符。	"test"

方法返回值

DescribeChunksResponse 类定义如下：

class DescribeChunksResponse(BaseModel):
    data: list[DescribeChunkResponse] = Field(..., description="切片列表")
    marker: str = Field(..., description="起始位置")
    isTruncated: bool = Field(
        ..., description="true表示后面还有数据，false表示已经是最后一页"
    )
    nextMarker: str = Field(..., description="下一页起始位置")
    maxKeys: int = Field(..., description="本次查询包含的最大结果集数量")

衍生类DescribeChunkResponse定义如下：

class DescribeChunkResponse(BaseModel):
    id: str = Field(..., description="切片ID")
    type: str = Field(..., description="切片类型")
    knowledgeBaseId: str = Field(..., description="知识库ID")
    documentId: str = Field(..., description="文档ID")
    content: str = Field(..., description="文档内容")
    enabled: bool = Field(..., description="是否启用")
    wordCount: int = Field(..., description="切片内字符数量")
    tokenCount: int = Field(..., description="切片内token数量")
    status: str = Field(..., description="切片状态")
    statusMessage: str = Field(..., description="切片状态信息")
    imageUrls: list[str] = Field(..., description="图片地址")
    createTime: int = Field(..., description="创建时间")
    updateTime: int = Field(None, description="更新时间")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

my_knowledge_base_id = "your_knowledge_base_id"
my_knowledge = appbuilder.KnowledgeBase(my_knowledge_base_id)
print("知识库ID: ", my_knowledge.knowledge_id)
resp = my_knowledge.describe_chunks("your_document_id", knowledgebase_id=my_knowledge_base_id)
print("切片列表：")
print(resp)

17. 知识库检索`query_knowledge_base(query: str, knowledgebase_ids: list[str], type: Optional[data_class.QueryType] = None, metadata_filters: data_class.MetadataFilter = None,pipeline_config: data_class.QueryPipelineConfig = None, rank_score_threshold: Optional[float] = 0.4, top: int = 6, skip: int = None) -> data_class.QueryKnowledgeBaseResponse`

方法参数

参数名称	参数类型	是否必传	描述	示例值
query	string	是	检索query，最长为1024字符，超过自动截断	民法典第三条
knowledgebase_ids	list[str]	是	指定知识库的id集合	["kb-1", "kb-2"]
type	string	否	检索策略。可选值： * fulltext，全文检索 * semantic，语义检索 * hybird，混合检索	"fulltext"
metadata_filters	data_class.MetadataFilters	否	元数据过滤条件，详细见MetadataFilters	-
pipeline_config	data_class.QueryPipelineConfig	否	检索配置，详细见QueryPipelineConfig	-
rank_score_threshold	float	否	重排序匹配分阈值，只有rank_score大于等于该分值的切片重排序时才会被筛选出来。当且仅当，pipeline_config中配置了ranking节点时，该过滤条件生效。取值范围： [0, 1]。默认0.4	0.4
top	int	否	返回前多少的条目。默认值6。如果检索结果的数量未达到top值，则按实际检索到的结果数量返回	6
skip	int	否	跳过条目数（通过top和skip可以实现类似分页的效果，比如top 10 skip 0，取第一页的10个，top 10 skip 10，取第二页的10个）	0

data_class.MetadataFilters 类定义如下：

class MetadataFilters(BaseModel):
    filters: list[MetadataFilter] = Field(..., description="过滤条件")
    condition: str = Field(..., description="文档组合条件。and:与，or:或")

data_class.MetadataFilter 类定义如下：

class MetadataFilter(BaseModel):
    operator: str = Field(..., description="操作符名称。==:等于，in:在数组中，not_in:不在数组中")
    field: str = Field(None, description="字段名，目前支持doc_id")
    value: Union[str, list[str]] = Field(
        ..., description="字段值，如果是in操作符，value为数组"
    )

data_class.QueryPipelineConfig 类定义如下：

class QueryPipelineConfig(BaseModel):
    id: str = Field(
        None, description="配置唯一标识，如果用这个id，则引用已经配置好的QueryPipeline"
    )
    pipeline: list[Union[ElasticSearchRetrieveConfig, RankingConfig, VectorDBRetrieveConfig, SmallToBigConfig]] = Field(
        None, description="配置的Pipeline，如果没有用id，可以用这个对象指定一个新的配置"
    )

data_class.ElasticSearchRetrieveConfig 类定义如下：

class ElasticSearchRetrieveConfig(BaseModel): # 托管资源为共享资源 或 BES资源时使用该配置
    name: str = Field(..., description="配置名称")
    type: str = Field(None, description="elastic_search标志，该节点为es全文检索")
    threshold: float = Field(None, description="得分阈值，默认0.1")
    top: int = Field(None, description="召回数量，默认400")

data_class.RankingConfig 类定义如下：

class RankingConfig(BaseModel):
    name: str = Field(..., description="配置名称")
    type: str = Field(None, description="ranking标志，该节点为ranking节点")
    inputs: list[str] = Field(
        ...,
        description='输入的节点名，如es检索配置的名称为pipeline_001，则该inputs为["pipeline_001"]',
    )
    model_name: str = Field(None, description="ranking模型名（当前仅一种，暂不生效）")
    top: int = Field(None, description="取切片top进行排序，默认20，最大400")

data_class.VectorDBRetrieveConfig 类定义如下：

class VectorDBRetrieveConfig(BaseModel):
    name: str = Field(..., description="该节点的自定义名称。")
    type: str = Field("vector_db", description="该节点的类型，默认为vector_db。")
    threshold: Optional[float] = Field(0.1, description="得分阈值。取值范围：[0, 1]", ge=0.0, le=1.0)
    top: Optional[int] = Field(400, description="召回数量。取值范围：[0, 800]", ge=0, le=800)
    pre_ranking: Optional[PreRankingConfig] = Field(None, description="粗排配置")

data_class.PreRankingConfig 类定义如下：

class PreRankingConfig(BaseModel):
    bm25_weight: float = Field(
        None, description="粗排bm25比重，取值范围在 [0, 1]，默认0.75"
    )
    vec_weight: float = Field(
        None, description="粗排向量余弦分比重，取值范围在 [0, 1]，默认0.25"
    )
    bm25_b: float = Field(
        None, description="控制文档长度对评分影响的参数，取值范围在 [0, 1]，默认0.75"
    )
    bm25_k1: float = Field(
        None,
        description="词频饱和因子，控制词频（TF）对评分的影响，常取值范围在 [1.2, 2.0]，默认1.5",
    )
    bm25_max_score: float = Field(
        None, description="得分归一化参数，不建议修改，默认50"
    )

方法返回值

data_class.QueryKnowledgeBaseResponse 类定义如下：

class QueryKnowledgeBaseResponse(BaseModel):
    requestId: str = Field(None, description="请求ID")
    code: str = Field(None, description="状态码")
    message: str = Field(None, description="状态信息")
    chunks: list[Chunk] = Field(..., description="切片列表")
    total_count: int = Field(..., description="切片总数")

衍生类Chunk定义如下：

class Chunk(BaseModel):
    chunk_id: str = Field(..., description="切片ID")
    knowledgebase_id: str = Field(..., description="知识库ID")
    document_id: str = Field(..., description="文档ID")
    document_name: str = Field(None, description="文档名称")
    meta: dict = Field(None, description="文档元数据")
    chunk_type: str = Field(..., description="切片类型")
    content: str = Field(..., description="切片内容")
    create_time: datetime = Field(..., description="创建时间")
    update_time: datetime = Field(..., description="更新时间")
    retrieval_score: float = Field(..., description="粗检索得分")
    rank_score: float = Field(..., description="rerank得分")
    locations: ChunkLocation = Field(None, description="切片位置")
    children: List[Chunk] = Field(None, description="子切片")

方法示例

import os
import appbuilder
os.environ["APPBUILDER_TOKEN"] = "your_appbuilder_token"

knowledge = appbuilder.KnowledgeBase()
client = appbuilder.KnowledgeBase()
res = client.query_knowledge_base(
    query="民法典第三条",
    type="fulltext",
    knowledgebase_ids=["70c6375a-1595-41f2-9a3b-e81bc9060b7f"],
    top=5,
    skip=0,
    metadata_filters=data_class.MetadataFilters(filters=[], condition="or"),
    pipeline_config=data_class.QueryPipelineConfig(
        id="pipeline_001",
        pipeline=[
            {
                "name": "step1",
                "type": "elastic_search",
                "threshold": 0.1,
                "top": 400,
                "pre_ranking": {
                    "bm25_weight": 0.25,
                    "vec_weight": 0.75,
                    "bm25_b": 0.75,
                    "bm25_k1": 1.5,
                    "bm25_max_score": 50,
                },
            },
            {
                "name": "step2",
                "type": "ranking",
                "inputs": ["step1"],
                "model_name": "ranker-v1",
                "top": 20,
            },
        ],
    ),
)
chunk_id = res.chunks[0].chunk_id
for chunk in res.chunks:
    print(chunk.content)

Java基本用法

方法及各方法入参/出参

参考 python KnowledgeBase接口文档

示例代码

public class KnowledgebaseTest {
    @Before
    public void setUp() {
        System.setProperty("APPBUILDER_TOKEN","");
        System.setProperty("APPBUILDER_LOGLEVEL", "DEBUG");
    }
    
    @Test
    public void testDocument() throws IOException, AppBuilderServerException {
        // 实例化Knowledgebase
        String knowledgeBaseId  = "";
        Knowledgebase knowledgebase = new Knowledgebase();

        // 获取知识库文档列表
        DocumentListRequest listRequest = new DocumentListRequest(); 
        listRequest.setKonwledgeBaseId(knowledgeBaseId);
        listRequest.setLimit(10);
        Document[] documents = knowledgebase.getDocumentList(listRequest);

        // 从知识库删除文档
        DocumentDeleteRequest deleteRequest = new DocumentDeleteRequest();
        deleteRequest.setKonwledgeBaseId(knowledgeBaseId);
        deleteRequest.setDocumentId("期望删除的DocumentId");
        knowledgebase.deleteDocument(deleteRequest);
    }
  
    @Test
    public void testCreateKnowledgebase() throws IOException, AppBuilderServerException {
        Knowledgebase knowledgebase = new Knowledgebase();
        KnowledgeBaseDetail request = new KnowledgeBaseDetail();
        request.setName("test_knowledgebase");
        request.setDescription("test_knowledgebase");

        // 创建知识库
        KnowledgeBaseConfig.Index index = new KnowledgeBaseConfig.Index("public",
                "", "", "", "");
        KnowledgeBaseConfig config = new KnowledgeBaseConfig(index);
        request.setConfig(config);
        KnowledgeBaseDetail response = knowledgebase.createKnowledgeBase(request);
        String knowledgeBaseId = response.getId();
        System.out.println(knowledgeBaseId);
        assertNotNull(response.getId());

        // 获取知识库详情
        KnowledgeBaseDetail detail = knowledgebase.getKnowledgeBaseDetail(knowledgeBaseId);
        System.out.println(detail.getId());
        assertNotNull(detail.getId());

        // 获取知识库列表
        KnowledgeBaseListRequest listRequest =
                new KnowledgeBaseListRequest(knowledgeBaseId, 10, null);
        KnowledgeBaseListResponse knowledgeBases = knowledgebase.getKnowledgeBaseList(listRequest);
        System.out.println(knowledgeBases.getMarker());
        assertNotNull(knowledgeBases.getMarker());

        // 更新知识库
        KnowledgeBaseModifyRequest modifyRequest = new KnowledgeBaseModifyRequest();
        modifyRequest.setKnowledgeBaseId(knowledgeBaseId);
        modifyRequest.setName("test_knowledgebase2");
        modifyRequest.setDescription(knowledgeBaseId);
        knowledgebase.modifyKnowledgeBase(modifyRequest);

        // 导入知识库
        DocumentsCreateRequest.Source source = new DocumentsCreateRequest.Source("web",
                new String[] {"https://baijiahao.baidu.com/s?id=1802527379394162441"}, 1);
        DocumentsCreateRequest.ProcessOption.Parser parser =
                new DocumentsCreateRequest.ProcessOption.Parser(
                        new String[] {"layoutAnalysis", "ocr"});
        DocumentsCreateRequest.ProcessOption.Chunker.Separator separator =
                new DocumentsCreateRequest.ProcessOption.Chunker.Separator(new String[] {"。"}, 300,
                        0.25);
        DocumentsCreateRequest.ProcessOption.Chunker chunker =
                new DocumentsCreateRequest.ProcessOption.Chunker(new String[] {"separator"},
                        separator, null, new String[] {"title", "filename"});
        DocumentsCreateRequest.ProcessOption.KnowledgeAugmentation knowledgeAugmentation =
                new DocumentsCreateRequest.ProcessOption.KnowledgeAugmentation(
                        new String[] {"faq"});
        DocumentsCreateRequest.ProcessOption processOption =
                new DocumentsCreateRequest.ProcessOption("custom", parser, chunker,
                        knowledgeAugmentation);
        DocumentsCreateRequest documentsCreateRequest =
                new DocumentsCreateRequest(knowledgeBaseId, "rawText", source, processOption);
        knowledgebase.createDocuments(documentsCreateRequest);

        // 上传文档
        String filePath = "src/test/java/com/baidubce/appbuilder/files/test.pdf";
        DocumentsCreateRequest.Source source2 =
                new DocumentsCreateRequest.Source("file", null, null);
        DocumentsCreateRequest documentsCreateRequest2 =
                new DocumentsCreateRequest(knowledgeBaseId, "rawText", source2, processOption);
        knowledgebase.uploadDocuments(filePath, documentsCreateRequest2);

        // 删除知识库
        knowledgebase.deleteKnowledgeBase(knowledgeBaseId);
    }
  
    @Test
    public void testCreateChunk() throws IOException, AppBuilderServerException {
        String documentId = "";
        // 知识库ID
        String knowledgeBaseId = "";
        // Appbuilder Token
        String secretKey = "";
        Knowledgebase knowledgebase = new Knowledgebase(knowledgeBaseID, secretKey);
        // 创建切片
        String chunkId = knowledgebase.createChunk(documentId, "test");
        // 修改切片
        knowledgebase.modifyChunk(chunkId, "new test", true);
        // 获取切片详情
        knowledgebase.describeChunk(chunkId);
        // 获取切片列表
        knowledgebase.describeChunks(documentId, chunkId, 10, null);
        // 删除切片
        knowledgebase.deleteChunk(chunkId);
    }
}

Go基本用法

方法及各方法入参/出参

参考 python KnowledgeBase接口文档

示例代码

package appbuilder

import (
	"fmt"
	"os"
	"testing"
)

func TestKnowledgeBase(t *testing.T) {
	os.Setenv("APPBUILDER_LOGLEVEL", "DEBUG")
	os.Setenv("APPBUILDER_LOGFILE", "")

    // 实例化KnowledgeBase
	knowledgeBaseID := ""
	config, err := NewSDKConfig("", "")
	if err != nil {
		t.Fatalf("new http client config failed: %v", err)
	}

	client, err := NewKnowledgeBase(config)
	if err != nil {
		t.Fatalf("new Knowledge base instance failed")
	}

    // 获取知识库中的文档列表
	documentsRes, err := client.GetDocumentList(GetDocumentListRequest{
		KnowledgeBaseID: knowledgeBaseID,
	})
	if err != nil {
		t.Fatalf("create document failed: %v", err)
	}
	fmt.Println(documentsRes)

    // 从知识库中删除文档
	err = client.DeleteDocument(DeleteDocumentRequest{
		KnowledgeBaseID: knowledgeBaseID,
		DocumentID:      "期望删除的DocumentID"})
	if err != nil {
		t.Fatalf("delete document failed: %v", err)
	}
}

func TestCreateKnowledgeBase(t *testing.T) {
	os.Setenv("APPBUILDER_LOGLEVEL", "DEBUG")
	os.Setenv("APPBUILDER_TOKEN", "")
	config, err := NewSDKConfig("", "")
	if err != nil {
		t.Fatalf("new http client config failed: %v", err)
	}

	client, err := NewKnowledgeBase(config)
	if err != nil {
		t.Fatalf("new Knowledge base instance failed")
	}

	// 创建知识库
	createKnowledgeBaseRes, err := client.CreateKnowledgeBase(KnowledgeBaseDetail{
		Name:        "test-go",
		Description: "test-go",
		Config: &KnowlegeBaseConfig{
			Index: KnowledgeBaseConfigIndex{
				Type:     "public",
			},
		},
	})
	if err != nil {
		t.Fatalf("create knowledge base failed: %v", err)
	}
	knowledgeBaseID := createKnowledgeBaseRes.ID
	fmt.Println(knowledgeBaseID)

	// 获取知识库详情
	getKnowledgeBaseRes, err := client.GetKnowledgeBaseDetail(knowledgeBaseID)
	if err != nil {
		t.Fatalf("get knowledge base failed: %v", err)
	}
	fmt.Println(getKnowledgeBaseRes)

	// 获取知识库列表
	knowledgeBaseListRes, err := client.GetKnowledgeBaseList(
		GetKnowledgeBaseListRequest{
			Marker: knowledgeBaseID,
		},
	)
	if err != nil {
		t.Fatalf("get knowledge base list failed: %v", err)
	}
	fmt.Println(knowledgeBaseListRes)

	// 导入知识库
	err = client.CreateDocuments(CreateDocumentsRequest{
		ID:            knowledgeBaseID,
		ContentFormat: "rawText",
		Source: DocumentsSource{
			Type:     "web",
			Urls:     []string{"https://baijiahao.baidu.com/s?id=1802527379394162441"},
			UrlDepth: 1,
		},
		ProcessOption: &DocumentsProcessOption{
			Template: "custom",
			Parser: &DocumentsProcessOptionParser{
				Choices: []string{"layoutAnalysis", "ocr"},
			},
			Chunker: &DocumentsProcessOptionChunker{
				Choices: []string{"separator"},
				Separator: &DocumentsProcessOptionChunkerSeparator{
					Separators:   []string{"。"},
					TargetLength: 300,
					OverlapRate:  0.25,
				},
				PrependInfo: []string{"title", "filename"},
			},
			KnowledgeAugmentation: &DocumentsProcessOptionKnowledgeAugmentation{
				Choices: []string{"faq"},
			},
		},
	})
	if err != nil {
		t.Fatalf("create documents failed: %v", err)
	}

	// 上传知识库文档
	err = client.UploadDocuments("./files/test.pdf", CreateDocumentsRequest{
		ID:            knowledgeBaseID,
		ContentFormat: "rawText",
		Source: DocumentsSource{
			Type: "file",
		},
		ProcessOption: &DocumentsProcessOption{
			Template: "custom",
			Parser: &DocumentsProcessOptionParser{
				Choices: []string{"layoutAnalysis", "ocr"},
			},
			Chunker: &DocumentsProcessOptionChunker{
				Choices: []string{"separator"},
				Separator: &DocumentsProcessOptionChunkerSeparator{
					Separators:   []string{"。"},
					TargetLength: 300,
					OverlapRate:  0.25,
				},
				PrependInfo: []string{"title", "filename"},
			},
			KnowledgeAugmentation: &DocumentsProcessOptionKnowledgeAugmentation{
				Choices: []string{"faq"},
			},
		},
	})
	if err != nil {
		t.Fatalf("upload documents failed: %v", err)
	}

	// 修改知识库
	name := "test-go"
	description := "22"
	err = client.ModifyKnowledgeBase(ModifyKnowlegeBaseRequest{
		ID:          knowledgeBaseID,
		Name:        &name,
		Description: &description,
	})
	if err != nil {
		t.Fatalf("modify knowledge base failed: %v", err)
	}

	// 删除知识库
	err = client.DeleteKnowledgeBase(knowledgeBaseID)
	if err != nil {
		t.Fatalf("delete knowledge base failed: %v", err)
	}
}

func TestChunk(t *testing.T) {
	os.Setenv("APPBUILDER_LOGLEVEL", "DEBUG")
	os.Setenv("APPBUILDER_TOKEN", "")
	documentID := ""
  knowledgeBaseID := "";
	config, err := NewSDKConfig("", "")
	if err != nil {
		t.Fatalf("new http client config failed: %v", err)
	}

	client, err := NewKnowledgeBaseWithKnowledgeBaseID(knowledgeBaseID, config)
	if err != nil {
		t.Fatalf("new Knowledge base instance failed")
	}
	// 创建切片
	chunkID, err := client.CreateChunk(CreateChunkRequest{
		DocumentID: documentID,
		Content:    "test",
	})
	if err != nil {
		t.Fatalf("create chunk failed: %v", err)
	}
	fmt.Println(chunkID)

	// 修改切片
	err = client.ModifyChunk(ModifyChunkRequest{
		ChunkID: chunkID,
		Content: "new test",
		Enable:  true,
	})
	if err != nil {
		t.Fatalf("modify chunk failed: %v", err)
	}

	// 获取切片详情
	describeChunkRes, err := client.DescribeChunk(chunkID)
	if err != nil {
		t.Fatalf("describe chunk failed: %v", err)
	}
	fmt.Println(describeChunkRes)

	// 获取切片列表
	describeChunksRes, err := client.DescribeChunks(DescribeChunksRequest{
		DocumnetID: documentID,
		Marker:     chunkID,
		MaxKeys:    10,
	})
	if err != nil {
		t.Fatalf("describe chunks failed: %v", err)
	}
	fmt.Println(describeChunksRes)

	// 删除切片
	err = client.DeleteChunk(chunkID)
	if err != nil {
		t.Fatalf("delete chunk failed: %v", err)
	}
}

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知识库组件（KnowledgeBase）

简介

功能介绍

特色优势

应用场景

Python基本用法

1、新建知识库KnowledgeBase().create_knowledge_base(name: str, description: str, type: str, clusterId: str, esUserName: str, esPassword: str, location: str) -> KnowledgeBaseDetailResponse

方法参数

方法返回值

方法示例

2、实例化已创建的知识库 KnowledgeBase(knowledge_id: str)

方法参数

方法返回值

方法示例

3、获取知识库详情get_knowledge_base_detail(knowledge_base_id: Optional[str] = None) -> KnowledgeBaseDetailResponse

方法参数

方法返回值

方法示例

4、 获取知识库列表get_knowledge_base_list(knowledge_base_id: Optional[str] = None, maxKeys: int = 10, keyword: Optional[str] = None) -> KnowledgeBaseGetListResponse

方法参数

方法返回值

方法示例：

5、修改知识库modify_knowledge_base(knowledge_base_id: Optional[str] = None, name: Optional[str] = None, description: Optional[str] = None)

方法参数

方法示例

6、 删除知识库delete_knowledge_base(knowledge_base_id: Optional[str] = None)

方法参数

7、 导入知识库create_documents(id: Optional[str] = None, contentFormat: str = "", source: DocumentSource = None, processOption: DocumentProcessOption = None)

方法参数

方法示例

8、 上传文档到知识库upload_documents(file_path: str, content_format: str = "rawText", id: Optional[str] = None, processOption: DocumentProcessOption = None)

方法参数

方法示例

9、从知识库删除文档 KnowledgeBase().delete_document()->KnowledgeBaseDeleteDocumentResponse

方法参数

方法返回值

方法示例

10、获取知识库的文档列表KnowledgeBase().get_documents_list()->KnowledgeBaseGetDocumentsListResponse

方法参数

方法返回值

方法示例

11、获取知识库全部文档KnowledgeBase().get_all_documents()->list

方法参数

方法返回值

方法示例

12. 创建切片create_chunk(documentId: str, content: str) -> CreateChunkResponse

方法参数

方法返回值

方法示例

13. 修改切片信息modify_chunk(chunkId: str, content: str, enable: bool)

方法参数

方法示例

14. 删除切片delete_chunk(chunkId: str)

方法参数

方法示例

15. 获取切片信息describe_chunk(chunkId: str)

方法参数

方法返回值

方法示例

16. 获取切片列表describe_chunks(documentId: str, marker: str = None, maxKeys: int = None, type: str = None) -> DescribeChunksResponse

方法参数

方法返回值

方法示例

方法参数

方法返回值

方法示例

Java基本用法

方法及各方法入参/出参

示例代码

Go基本用法

方法及各方法入参/出参

示例代码

1、新建知识库`KnowledgeBase().create_knowledge_base(name: str, description: str, type: str, clusterId: str, esUserName: str, esPassword: str, location: str) -> KnowledgeBaseDetailResponse`

2、实例化已创建的知识库 `KnowledgeBase(knowledge_id: str)`

3、获取知识库详情`get_knowledge_base_detail(knowledge_base_id: Optional[str] = None) -> KnowledgeBaseDetailResponse`

4、获取知识库列表`get_knowledge_base_list(knowledge_base_id: Optional[str] = None, maxKeys: int = 10, keyword: Optional[str] = None) -> KnowledgeBaseGetListResponse`

5、修改知识库`modify_knowledge_base(knowledge_base_id: Optional[str] = None, name: Optional[str] = None, description: Optional[str] = None)`

6、删除知识库`delete_knowledge_base(knowledge_base_id: Optional[str] = None)`

7、导入知识库`create_documents(id: Optional[str] = None, contentFormat: str = "", source: DocumentSource = None, processOption: DocumentProcessOption = None)`

8、上传文档到知识库`upload_documents(file_path: str, content_format: str = "rawText", id: Optional[str] = None, processOption: DocumentProcessOption = None)`

9、从知识库删除文档 `KnowledgeBase().delete_document()->KnowledgeBaseDeleteDocumentResponse`

10、获取知识库的文档列表`KnowledgeBase().get_documents_list()->KnowledgeBaseGetDocumentsListResponse`

11、获取知识库全部文档`KnowledgeBase().get_all_documents()->list`

12. 创建切片`create_chunk(documentId: str, content: str) -> CreateChunkResponse`

13. 修改切片信息`modify_chunk(chunkId: str, content: str, enable: bool)`

14. 删除切片`delete_chunk(chunkId: str)`

15. 获取切片信息`describe_chunk(chunkId: str)`

16. 获取切片列表`describe_chunks(documentId: str, marker: str = None, maxKeys: int = None, type: str = None) -> DescribeChunksResponse`