374 lines
11 KiB
Markdown
374 lines
11 KiB
Markdown
本项目采用了和ragflow一致的api接口，python的api接口如下。
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http接口可参考原文档：https://github.com/infiniflow/ragflow/blob/main/docs/references/http_api_reference.md
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# 目录
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- 1. 依赖安装/密钥准备
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- 2. 创建聊天(Create chat completion)
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- 3. 知识库管理(DATASET MANAGEMENT)
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* 3.1 创建知识库(Create dataset)
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* 3.2 查询知识库(List datasets)
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* 3.3 删除知识库(Delete datasets)
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* 3.4 更新知识库配置(Update dataset)
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- 4. 文件管理 (FILE MANAGEMENT WITHIN DATASET)
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* 4.1 上传文件(Upload documents)
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* 4.2 更新文件配置(Upload documents)
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* 4.3 删除文件(Delete documents)
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- 5. 块管理(CHUNK MANAGEMENT WITHIN DATASET)
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* 5.1 添加块(Add chunk)
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* 5.2 查询块(List chunks)
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- 6. 聊天助手管理(CHAT ASSISTANT MANAGEMENT)
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* 6.1 创建聊天助手(Create chat assistant)
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* 6.2 更新聊天助手配置(Update chat assistant)
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* 6.3 删除聊天助手(Delete chat assistants)
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* 6.4 查询聊天助手(List chat assistants)
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- 7. 会话管理(SESSION MANAGEMENT)
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* 7.1 创建会话(Create session with chat assistant)
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* 7.2 更新会话信息(Update chat assistant's session)
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* 7.3 查询会话历史记录(List chat assistant's sessions)
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* 7.4 删除会话(Delete chat assistant's sessions)
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* 7.5 交互会话(Converse with chat assistant)
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# 1. 依赖安装/密钥准备
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使用python调用API接口，需要安装`ragflow-sdk`依赖，可用pip进行安装：
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```python
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pip install ragflow-sdk
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```
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之后，需要在API菜单中，创建一个`API KEY`，复制该值，后续要用到。
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# 2. 创建聊天(Create chat completion)
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通过OpenAI的API为选择助理进行聊天。
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这里的示例需要修改三个值：
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- model：模型名称
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- api_key：替换成自己的api_key，后文同理
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- base_url：最后面一串为具体助手的`dialogId`，可直接从url中查看获取
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可选参数：stream，用于指定是否采用流式输出
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```python
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from openai import OpenAI
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model = "deepseek-r1:1.5b"
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client = OpenAI(api_key="ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm", base_url=f"http://localhost/api/v1/chats_openai/ec69b3f4fbeb11ef862c0242ac120002")
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completion = client.chat.completions.create(
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model=model,
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messages=[
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{"role": "system", "content": "你是一个乐于助人的助手"},
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{"role": "user", "content": "你是谁？"},
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],
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stream=True
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)
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stream = True
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if stream:
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for chunk in completion:
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print(chunk)
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else:
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print(completion.choices[0].message.content)
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```
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# 3. 知识库管理(DATASET MANAGEMENT)
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## 3.1 创建知识库(Create dataset)
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创建一个名称为`kb_1`的知识库
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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dataset = rag_object.create_dataset(name="kb_1")
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```
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## 3.2 查询知识库(List datasets)
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根据名称，查询知识库信息
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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# 查询所有知识库
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for dataset in rag_object.list_datasets():
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print(dataset)
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# 根据name查询某一知识库
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dataset = rag_object.list_datasets(name = "kb_1")
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print(dataset[0])
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```
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## 3.3 删除知识库(Delete datasets)
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删除指定知识库
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只能根据知识库id进行删除，无name接口，id通过上一步查询得到
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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rag_object.delete_datasets(ids = ["50f80d7c099111f0ad0e0242ac120006"])
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```
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## 3.4 更新知识库配置(Update dataset)
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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dataset = rag_object.list_datasets(name="kb_1")
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dataset = dataset[0]
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dataset.update({"chunk_method":"manual"})
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```
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# 4. 文件管理 (FILE MANAGEMENT WITHIN DATASET)
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## 4.1 上传文件(Upload documents)
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上传文件进入到`kb_1`的知识库
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两个主要参数：
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- display_name：文件名
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- blob：文件的二进制内容
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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dataset = rag_object.list_datasets(name="kb_1")
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dataset = dataset[0]
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dataset.upload_documents([{"display_name": "1.txt", "blob": open('1.txt',"rb").read()}])
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```
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## 4.2 更新文件配置(Upload documents)
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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dataset = rag_object.list_datasets(name="kb_1")
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dataset = dataset[0]
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doc = dataset.list_documents(id="7c5ea41409f811f0b9270242ac120006")
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doc = doc[0]
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doc.update({"parser_config": {"chunk_token_count": 256}})
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```
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## 4.3 删除文件(Delete documents)
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根据documents id，删除文件
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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dataset = rag_object.list_datasets(name="kb_1")
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dataset = dataset[0]
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dataset.delete_documents(ids=["7c5ea41409f811f0b9270242ac120006"])
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```
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# 5. 块管理(CHUNK MANAGEMENT WITHIN DATASET)
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## 5.1 添加块(Add chunk)
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增加一个分块，content为具体chunk的内容
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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dataset = rag_object.list_datasets(name="kb_1")
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dataset = dataset[0]
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doc = dataset.list_documents(id="91bd7c5e0a0711f08a730242ac120006")
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doc = doc[0]
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chunk = doc.add_chunk(content="xxxxxxx")
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```
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## 5.2 查询块(List chunks)
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查询`kb_1`中所有块的具体信息
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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dataset = rag_object.list_datasets(name="kb_1")
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dataset = dataset[0]
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docs = dataset.list_documents(keywords="1", page=1, page_size=12)
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for chunk in docs[0].list_chunks(keywords="", page=1, page_size=12):
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print(chunk)
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```
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# 6. 聊天助手管理(CHAT ASSISTANT MANAGEMENT)
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## 6.1 创建聊天助手(Create chat assistant)
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创建一个名为`"Miss R"`的聊天助手
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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datasets = rag_object.list_datasets(name="kb_1")
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dataset_ids = []
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for dataset in datasets:
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dataset_ids.append(dataset.id)
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assistant = rag_object.create_chat("Miss R", dataset_ids=dataset_ids)
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```
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## 6.2 更新聊天助手配置(Update chat assistant)
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更新聊天助手的各种配置，可选项参考原文档
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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datasets = rag_object.list_datasets(name="kb_1")
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dataset_id = datasets[0].id
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assistant = rag_object.create_chat("Miss R2", dataset_ids=[dataset_id])
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assistant.update({"name": "Stefan", "llm": {"temperature": 0.8}, "prompt": {"top_n": 8}})
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```
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## 6.3 删除聊天助手(Delete chat assistants)
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根据dialogId，删除指定助手
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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rag_object.delete_chats(ids=["a39fa5480a2d11f082850242ac120006"])
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```
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## 6.4 查询聊天助手(List chat assistants)
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查询所有聊天助手信息
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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for assistant in rag_object.list_chats():
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print(assistant)
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```
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# 7. 会话管理(SESSION MANAGEMENT)
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## 7.1 创建会话(Create session with chat assistant)
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选择`Miss R`助理，创建新会话
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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assistant = rag_object.list_chats(name="Miss R")
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assistant = assistant[0]
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session = assistant.create_session()
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```
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## 7.2 更新会话信息(Update chat assistant's session)
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创建完会话，并更新了会话名称
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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assistant = rag_object.list_chats(name="Miss R")
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assistant = assistant[0]
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session = assistant.create_session("session_name")
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session.update({"name": "updated_name"})
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```
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## 7.3 查询会话历史记录(List chat assistant's sessions)
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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assistant = rag_object.list_chats(name="Miss R")
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assistant = assistant[0]
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for session in assistant.list_sessions():
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print(session)
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```
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## 7.4 删除会话(Delete chat assistant's sessions)
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根据conversationId，删除某一会话
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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assistant = rag_object.list_chats(name="Miss R")
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assistant = assistant[0]
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assistant.delete_sessions(ids=["0ed10bce0a3111f0a3240242ac120006"])
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```
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## 7.5 交互会话(Converse with chat assistant)
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指定某一助手，进行交互提问
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```python
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from ragflow_sdk import RAGFlow
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api_key = "ragflow-I0NmRjMWNhMDk3ZDExZjA5NTA5MDI0Mm"
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base_url = "http://localhost:9380"
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rag_object = RAGFlow(api_key=api_key, base_url=base_url)
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assistant = rag_object.list_chats(name="Miss R")
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assistant = assistant[0]
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session = assistant.create_session()
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print("\n==================== Miss R =====================\n")
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print("Hello. What can I do for you?")
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while True:
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question = input("\n==================== User =====================\n> ")
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print("\n==================== Miss R =====================\n")
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cont = ""
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for ans in session.ask(question, stream=True):
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print(ans.content[len(cont):], end='', flush=True)
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cont = ans.content
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```
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