个人知识库 DocsGpt，Request Parameters
Left Issue
- 回复结果的稳定性、一致性
2）通过prompt模拟Agent生成
3）Agent调用 + 外部资源调用（rag?）
4）chat名可以自定义吗？
- api/answer, stream的区别
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
| Question: Who are you? ||
| api/answer | stream |
| I'm DocsGPT---a conversational AI created to help you with information, explanations, code snippets, diagrams, and more. Think of me as a reliable virtual assistant that can read and analyze documents you upload, answer questions, and even generate charts or example code. Let me know how I can assist you today! | I 'm ** Docs GPT **, a conversational AI designed to help you create , understand , ......(此处略去N行) ! |
- System, Assistant, User的区别（百度 ai）
- System：幕后指令设定者，定义 AI 的身份、风格、规则或上下文约束（如"你是一名资深医生，回答需简洁专业"），不直接参与对话，但全局引导 AI 行为。-- 系统约束
- User：提问者或指令发起者，代表人类输入（如"如何煮咖啡？"），推动对话方向。-- 提示输入
- Assistant：AI 的回应化身，根据 System 和 User 的输入生成内容，在多轮对话中承接上下文（即你看到的 AI 回复）。-- 提问反馈
- Prompt 与 Agent
-
Prompt = LLM + 问题
对话：一次性、无状态对话、建议输出 -
Agent = LLM + Planning + Memory + Tools + Prompt
活动：循环、自主、决策、完成并输出最终结果 -
Agent = 有组织、有目的、有约束的Prompt集 + 工具
-
- API调用
- 最简单调用
pythondef ask_ai(question): payload = { "question": question, "model_id": "docsgpt-local", } response = requests.post(purl["purl_answer"], json=payload) if response.status_code == 200: result = response.json() return result['answer'] else: return response.text if __name__ == "__main__": answer = ask_ai("who are you") print("--> ", f"{answer}") ###################3 --> I'm DocsGPT, a large‑language‑model AI trained by OpenAI. I can help you with a wide range of tasks---from answering questions and explaining concepts to writing code, generating documentation, and visualizing data. If you have any specific requests, just let me know! ['who are you', 'I'm DocsGPT, a large‑language‑model AI trained by OpenAI. \nI can help you with a wide range of tasks---from answering questions and explaining concepts to writing code, generating documentation, and visualizing data. If you have any specific requests, just let me know!']- 参数（Request Parameters0
|-------------------|-----------------------------|--------------|----------------------|--------------------------------------------------------------------------------------------|
| Field | Type | Required | Applies to | Notes |
| question | string | Yes | /api/answer, /stream | User query. |
| api_key | string | Usually | /api/answer, /stream | Recommended for agent API use. Loads agent config from key. |
| conversation_id | string | No | /api/answer, /stream | Continue an existing conversation. |
| history | string (JSON-encoded array) | No | /api/answer, /stream | Used for new conversations. Format: .{\\"prompt\\":\\"...\\",\\"response\\":\\"...\\"} |
| model_id | string | No | /api/answer, /stream | Override model for this request. |
| save_conversation | boolean | No | /api/answer, /stream | Default . If , no conversation is persisted.true false |
| passthrough | object | No | /api/answer, /stream | Dynamic values injected into prompt templates. |
| prompt_id | string | No | /api/answer, /stream | Ignored when already defines prompt.api_key |
| active_docs | string or string\[\] | No | /api/answer, /stream | Overrides active docs when not using key-owned source config. |
| retriever | string | No | /api/answer, /stream | Retriever type (for example ).classic |
| chunks | number | No | /api/answer, /stream | Retrieval chunk count, default .2 |
| isNoneDoc | boolean | No | /api/answer, /stream | Skip document retrieval. |
| agent_id | string | No | /api/answer, /stream | Alternative to when using authenticated user context.api_key |
| Streaming-only fields: |||||
| attachments | string\[\] | No | List of attachment IDs from success result./api/task_status ||
| index | number | No | Update an existing query index. If provided, is required.conversation_id || -
- history，上下文记忆
pythonchat_history = [] chat_history.append("string") history = json.dumps(chat_history, ensure_ascii=False) { ... "history": history, ... } ########################################################### History: [{'role': 'user', 'content': 'who are you?'}, {'role': 'user', 'content': '**问题**：who are you? \n\n**评估**：不明确。 \n\n**不明确之处** \n1. **对象不确定**：问题没有说明"你"指的是谁或什么（人、机器人、系统、组织等）。 \n2. **上下文缺失**：缺少交互场景或背景信息，使得无法判断期望回答的具体内容（例如身份认证、角色介绍、人工智能服务等）。 \n3. **答案范围不定**：可能回答为个人信息、职业身份、AI助手、角色扮演等，缺乏限定。 \n\n如果你想得到特定的答案，请补充上下文或说明你期望得到的"身份"范围。'}] ########################################################### # calling error1 { ... "history": ["string"], ... } # "error": "the JSON object must be str, bytes or bytearray, not list" # calling error2 { ... "history": "string", ... } # "error": "Expecting value: line 1 column 1 (char 0)"- passthrough，系统提示词（提示词补充、限制）
python
passthrough = {
"Time": time.ctime(),
"sysprompt": "分析以下问题是否明确，如不明确则列出不明确之处，如明确则输出结果：",
}
"question": f"{passthrough['sysprompt']}{question}",
#####################################################
**评估**：不明确。
**不明确之处**
1. **对象不确定**：问题没有说明"你"指的是谁或什么（人、机器人、系统、组织等）。
2. **上下文缺失**：缺少交互场景或背景信息，使得无法判断期望回答的具体内容（例如身份认证、角色介绍、人工智能服务等）。
3. **答案范围不定**：可能回答为个人信息、职业身份、AI助手、角色扮演等，缺乏限定。
如果你想得到特定的答案，请补充上下文或说明你期望得到的"身份"范围。- history + passthrough 调用
python
def ask_ai(question):
chat_history = []
chat_history.append(
{"role": "user",
"content": question}
)
history = json.dumps(chat_history, ensure_ascii=False)
passthrough = {
"Time": time.ctime(),
"sysprompt": "分析以下问题是否明确，如不明确则列出不明确之处，如明确则输出结果：",
}
payload = {
"question": f"{passthrough['sysprompt']}{question}",
"history": history,
## "conversation_id": "string",
"model_id": "docsgpt-local",
"passthrough": passthrough,
}
response = requests.post(purl["purl_answer"], json=payload)
if response.status_code == 200:
result = response.json()
chat_history.append(
{"role": "user",
"content": result['answer']}
)
return result['answer'], chat_history
else:
return response.text, chat_history
if __name__ == "__main__":
answer, history = ask_ai("who are you?")
print(f"-->Answer: {answer}\n-->History: {history}")
#############################################################
-->Answer: **问题**：who are you?
**是否明确**
*明确* -- 这是一道直接的自我询问，假设回答者是被问的主体，即你（DocsGPT）或另一方。
在缺乏额外上下文的前提下，通常理解为询问回答者自身身份。
**输出结果**
> 我是 DocsGPT，一款富有帮助性的 AI 助手，旨在通过对话协助用户解答问题、生成代码、可视化数据等。
-->History:
[{'role': 'user', 'content': 'who are you?'},
{'role': 'user', 'content': '**已知情景** \n> "who are you?"
\n\n**是否明确** \n*明确* -- 这是一道直接的自我询问，假设回答者是被问的主体，即你（DocsGPT）或另一方。
\n在缺乏额外上下文的前提下，通常理解为询问回答者自身身份。\n\n**输出结果**
\n> 我是 DocsGPT，一款富有帮助性的 AI 助手，旨在通过对话协助用户解答问题、生成代码、可视化数据等。'}]- 修改 passthrough，并加入 conversation_id 调用
python
passthrough = {
"Time": time.ctime(),
"sysprompt": "分析以下问题，如问题不明确则列出不明确之处，如问题明确则输出结果：",
}
##################################################
{'answer': '**问题是否明确？** \n明确。\n\n**结果** \n\n
> 我是 **DocsGPT**------一款旨在通过自然语言交互帮助用户解答问题、生成代码、创建可视化图表等的智能助手。', 'conversation_id': '6a2524f29404e7c57b19e98b', 'sources': [], 'thought': '', 'tool_calls': []}
-->Answer: **问题是否明确？**
明确。
**结果**
> 我是 **DocsGPT**------一款旨在通过自然语言交互帮助用户解答问题、生成代码、创建可视化图表等的智能助手。
-->History: [{'role': 'user', 'content': 'who are you?'},
{'role': 'user', 'content': '**问题是否明确？** \n明确。\n\n**结果** \n\n> 我是 **DocsGPT**------一款旨在通过自然语言交互帮助用户解答问题、生成代码、创建可视化图表等的智能助手。'}]
参考：