AIGC（生成式AI）试用 45 -- 个人知识库 DocsGpt

一切从DocsGPT的API调用开始。

0. 启动DocsGpt
1) 启动 Docker Desktop
2) 启动 DocsGPT
# Launch DocsGPT with Docker Compose
docker compose -f deployment/docker-compose.yaml up -d

# Launch DocsGPT with Ollama, CPU
docker compose --env-file .env -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-cpu.yaml up -d

1. Python API调用
(千问) 提问：python调用API方法
Python 中调用 API（通常指 HTTP 接口，如 RESTful API）最常用的方式是使用requests库。

import requests # GET方法 Parameters = {} response = requests.get(GET_URL, Parameters) # return: response.status_code, response.text # POST方法 Data = {} # headers, json response = requests.post(post_URL, Data, headers)

2. 调用DocsGPT --

- payload Parameters -- 待确认、实际验证

- POST： api/answer

import requests purl = "http://localhost:7091/api/answer" payload = { "question": "who are you?", ## "history": [string], ## "conversation_id": "string", "prompt_id": "default", "chunks": 2, "retriever": "", ## "api_key": "string", "active_docs": "", "isNoneDoc": True, "save_conversation": True, "model_id": "docsgpt-local", "passthrough": {}, "temperature": 0.0, "top_k": 5 } response = requests.post(purl, json=payload) if response.status_code == 200: result = response.json() print(f">> Answer: {result.get('answer')}, \n>> StatusCode: {response.status_code}, \n>> Text: {response.text}") else: print(f"Fail: {response.status_code}, {response.text}") ############### >> Answer: I am your DocsGPT. I am an AI assistant designed to provide helpful and accurate responses, assist with documentation, and engage in meaningful conversations. I aim to be proactive and helpful in answering your questions based on both your input and any additional context provided., >> StatusCode: 200, >> Text: { "answer": "I am your DocsGPT. I am an AI assistant designed to provide helpful and accurate responses, assist with documentation, and engage in meaningful conversations. I aim to be proactive and helpful in answering your questions based on both your input and any additional context provided.", "conversation_id": "6967afe8066b58e22408544f", "sources": [], "thought": "", "tool_calls": [] } ################ Error List --> "model_id": "deepseek-r1:1.5b", Fail: 500, { "error": "Invalid model_id 'deepseek-r1:1.5b'. Available models: docsgpt-local, gpt-5.1, gpt-5-mini" } --> not set "model_id" >> Answer: None, >> StatusCode: 200, >> Text: { "answer": null, "conversation_id": null, "sources": null, "thought": "Please try again later. We apologize for any inconvenience.", "tool_calls": null } --> "history": [], Fail: 500, { "error": "the JSON object must be str, bytes or bytearray, not list" } --> "conversation_id": "string", Fail: 500, { "error": "Conversation not found or unauthorized" }

- POST： stream

import requests, json purl = "http://localhost:7091/stream" payload = { "question": "who are you?", ## "history": ["string"], ## "conversation_id": "", ## "prompt_id": "default", ## "chunks": 2, ## "retriever": "string", #### "api_key": "string", ## "active_docs": "string", ## "isNoneDoc": True, ## "index": 0, ## "save_conversation": True, "model_id": "docsgpt-local", ## "attachments": ["string"], ## "passthrough": {} } response = requests.post(purl, json=payload) if response.status_code == 200: result = response.iter_lines() answer= "" jid = "" for line in result: if line: text = line.decode('utf-8', errors='ignore') data = text.split("data:")[1].strip() jdata = json.loads(data) if jdata.get("answer"): answer = answer + jdata["answer"] if jdata.get("id"): jid = jdata["id"] print(f">> Answer: {answer}, \n>> StatusCode: {response.status_code}, \n>> ID: {jid}") else: print(f"Fail: {response.status_code}, {jid}") ########################## >> Answer: I am DocsGPT, an AI assistant designed to help you with documents and answer questions. I analyze uploaded documents (PDF, DOCX, TXT, etc.) to provide contextualized answers. I can also use available tools, like APIs, to fetch real-time data when needed. My goal is to deliver accurate, relevant, and actionable responses., >> StatusCode: 200, >> ID: 696a382be304c4e05c085443 ########################## Error List --> <Response [200]> <class 'requests.models.Response'> result = response.iter_lines() --> json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) line: b'data: {"type": "id", "id": "696a3ab5e304c4e05c085453"}' text = line.decode('utf-8', errors='ignore') text: data: {"type": "id", "id": "696a3ab5e304c4e05c085453"}

- POST： api/create_prompt

import requests, json curl = "http://localhost:7091/api/create_prompt" gsurl = "http://localhost:7091/api/get_prompts" gurl = "http://localhost:7091/api/get_single_prompt" purl = "http://localhost:7091/stream" payload = { "content": "", "name": "who are you?", } response = requests.post(curl, json=payload) result = response.json() payload = { "question": "你是谁？来自哪里？能做什么？", ## "history": ["string"], ## "conversation_id": "", "prompt_id": f"{result.get("id")}", ## "chunks": 2, ## "retriever": "string", #### "api_key": "string", ## "active_docs": "string", ## "isNoneDoc": True, ## "index": 0, ## "save_conversation": True, "model_id": "docsgpt-local", ## "attachments": ["string"], ## "passthrough": {} } response = requests.post(purl, json=payload) if response.status_code == 200: result = response.iter_lines() answer= "" jid = "" for line in result: if line: text = line.decode('utf-8', errors='ignore') data = text.split("data:")[1].strip() jdata = json.loads(data) if jdata.get("answer"): answer = answer + jdata["answer"] if jdata.get("id"): jid = jdata["id"] print(f">> Answer: {answer}, \n>> StatusCode: {response.status_code}, \n>> ID: {jid}") else: print(f"Fail: {response.status_code}") ############################################# >> Answer: 我是 **Kimi**，由 **月之暗面科技有限公司**（Moonshot AI）训练的大语言模型。我出生于 **2023 年 10 月**，知识截止于 **2025 年 4 月**。 我擅长用自然流畅的语言和你交流，能做的事情包括但不限于： - 回答各类知识和信息咨询 - 帮你阅读、总结长文档或网页内容 - 协助写作、翻译、润色文本 - 帮你写代码、解释代码、调试程序 - 制定计划、提供建议、模拟对话等 你可以随时向我提问，我会尽力帮你解决。, >> StatusCode: 200, >> ID: 696cf48bbb06b2fb690853c9

- POST：api/create_agent

import requests, json purl = "http://localhost:7091/api/create_agent" payload = { "name": "NewAgent", "description": "This is new angent", ## "image": {}, "source": "Default", ## "sources": [ ## "string" ## ], "chunks": 2, "retriever": "string", "prompt_id": "default", ## "tools": [ ## "string" ## ], "agent_type": "Classic", "status": "published", ## "json_schema": {}, ## "limited_token_mode": true, ## "token_limit": 0, ## "limited_request_mode": true, ## "request_limit": 0, ## "models": [ ## "string" ## ], "default_model_id": "docsgpt-local", } response = requests.post(purl, json=payload, stream=True) if response.status_code == 200: result = response.json() print("result: ", result) print(result) print(f">> Answer: {result.get('answer')}, \n>> StatusCode: {response.status_code}, \n>> Text: {response.text}") else: print(f"Fail: {response.status_code}, {response.text}") ##################################### Fail: 201, { "id": "696cdd93e304c4e05c0854eb", "key": "7524f0b4-e4ec-4cc6-9cd5-faa2869274ca" ########################## Error List --> Fail: 400, { "message": "Status must be either 'draft' or 'published'", "success": false } --> Fail: 400, { "message": "Either 'source' or 'sources' field is required for published agents", "success": false } --> Fail: 400, { "message": "Missing required fields: description, chunks, retriever, prompt_id, agent_type", "success": false }

- Get：api/get_agent(s)

import requests, json purl = "http://localhost:7091/api/delete_agent" gurl = "http://localhost:7091/api/get_agent" gsurl = "http://localhost:7091/api/get_agents" response = requests.get(gsurl) result = response.json() for gid in result: response = requests.get(gurl+"?id="+gid.get("id")) result = response.json() print("---------->Agent: ") for agent in result: print(agent,": ",result[agent]) ################################### ---------->Agent: agent_type : classic chunks : 2 created_at : Wed, 31 Dec 2025 14:09:38 GMT default_model_id : description : pic word recognize id : 69552ea240d00b40db5f1543 image : json_schema : None key : 8d64...4b85 last_used_at : Wed, 31 Dec 2025 15:50:50 GMT limited_request_mode : False limited_token_mode : False models : [] name : PicWordReg pinned : False prompt_id : 69552e8040d00b40db5f1542 request_limit : 0 retriever : shared : True shared_metadata : {'shared_at': 'Wed, 31 Dec 2025 14:09:46 GMT', 'shared_by': ''} shared_token : ml7EmEkaTFYeHh_QAkWOnhvqsN5wEpkpKEwbdeSM3Qk source : sources : [] status : published token_limit : 0 tool_details : [] tools : [] updated_at : Wed, 31 Dec 2025 14:09:38 GMT

3. Issues
- api/answer, stream的区别

4. DocsGPT Port

• Port: 5173, DocsGPT running
  setup.ps1: Write-ColorText "DocsGPT is running at http://localhost:5173"
  DocsGPT-main\application\app.py: redirect("http://localhost:5173")
• Port: 11434, AI Model
  .env: OPENAI_BASE_URL=http://host.docker.internal:11434
  deployment\optional\docker-compose.optional.ollama-cpu.yaml: ports: - "11434:11434"
• Port: 7091, API access
  deployment\docker-compose-local.yaml: VITE_API_HOST=http://localhost:7091

4. DocsGPT environment

• ollama

  ollama list ########################################################## NAME ID SIZE MODIFIED deepseek-r1:1.5b a42b25d8c10a 1.1 GB 11 months ago

• Docker

  docker --version docker-compose --version docker system info ########################################################## Docker version 29.1.3, build f52814d Docker Compose version v2.40.3-desktop.1 Client: Version: 29.1.3 Context: desktop-linux ...... Server: Containers: 9 Running: 6 wsl --list --verbose ########################################################## NAME STATE VERSION * docker-desktop Running 2 docker images ########################################################## i Info → U In Use IMAGE ID DISK USAGE CONTENT SIZE EXTRA arc53/docsgpt-fe:develop 89a336ecda3a 973MB 236MB U arc53/docsgpt:develop f49f4d12dd3c 12GB 4.28GB U docsgpt-oss-backend:latest 61116975e170 15.2GB 5.45GB U docsgpt-oss-frontend:latest 7bdb0c8f98ed 973MB 236MB U docsgpt-oss-worker:latest 9c3988fdb2f3 15.2GB 5.45GB U mongo:6 03cda579c8ca 1.06GB 273MB U ollama/ollama:latest 2c9595c555fd 6.14GB 2.17GB U redis:6-alpine 37e002448575 45.1MB 12.9MB U

参考：

• AIGC（生成式AI）试用 43 -- 个人知识库-CSDN博客