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# nanobot 图片识别修复补丁
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> 适用对象:`nanobot`(uv 工具安装,位于 `~/.local/share/uv/tools/nanobot-ai/`)
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> 修复日期:2026-08-21
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> 备份目录:本目录 `/home/ubuntu/nanocode/nanobot-patches/`
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---
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## 一、问题现象
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使用支持视觉的模型(如 `deepseek-v4-flash-vision-exp`)时,通过 `read_file` 读取图片后,模型**看不到图片**,只收到类似 `(Image file: xxx.png)` 的占位符文本,导致无法识别图片内容。
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## 二、根因
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`nanobot/providers/openai_compat_provider.py` 的 `_sanitize_messages()` 中有一行逻辑:
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```python
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force_string_content = bool(self._spec and self._spec.name == "deepseek")
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```
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只要 provider 名字是 `deepseek`,它就把**所有**消息的 `content` 强制转为纯字符串。而 `content` 一旦是字符串,里面 `image_url` / `input_image` / `image` / `file` 等视觉块就会被剥离,图片信息丢失。
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> 该逻辑本意是兼容 DeepSeek 纯文本模型的"只收字符串 content",但**一刀切**地也作用到了视觉模型上,导致视觉内容被丢弃。
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## 三、修复方法
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在 `openai_compat_provider.py` 中:
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1. 新增一个静态方法,判断 `content` 是否包含视觉块:
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```python
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@staticmethod
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def _content_has_vision_blocks(content: Any) -> bool:
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"""Return True when content is a block list containing image/file blocks.
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Used to keep vision content intact for multimodal models (e.g.
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``deepseek-v4-flash-vision-exp``) instead of coercing it to plain text.
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"""
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if not isinstance(content, list):
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return False
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for item in content:
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mapping = OpenAICompatProvider._maybe_mapping(item)
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if not mapping:
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continue
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if mapping.get("type") in ("image_url", "input_image", "image", "file"):
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return True
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return False
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```
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2. 在 `_sanitize_messages()` 的强制转字符串条件里,增加"含视觉块则跳过"的判断:
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```python
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if (
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force_string_content
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and not (clean.get("role") == "assistant" and clean.get("tool_calls"))
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and not OpenAICompatProvider._content_has_vision_blocks(clean.get("content"))
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):
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clean["content"] = self._coerce_content_to_string(clean.get("content"))
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```
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> 关键点:`_content_has_vision_blocks()` 判断在 `_coerce_content_to_string()` **之前**生效,视觉块因此被保留。纯文本消息仍会走原字符串转换逻辑,不受影响。
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## 四、涉及文件与路径
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- 被修改文件:
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`/home/ubuntu/.local/share/uv/tools/nanobot-ai/lib/python3.11/site-packages/nanobot/providers/openai_compat_provider.py`
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- 本目录备份:
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- `openai_compat_provider.py.before_patch` —— 修复**前**(旧)文件
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- `openai_compat_provider.py.after_patch` —— 修复**后**(当前)文件
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## 五、⚠️ 重要:uv 升级会覆盖补丁
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`nanobot-ai` 是通过 `uv tool` 安装的。一旦执行升级(例如 `uv tool upgrade nanobot-ai` 或重新安装),`site-packages` 下的文件会被**覆盖**,补丁随之丢失。
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**重新打补丁的方法**(升级后执行):
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```bash
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# 1. 确认当前文件与 before_patch 是否一致(若一致说明补丁已丢失)
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diff /home/ubuntu/nanocode/nanobot-patches/openai_compat_provider.py.before_patch \
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/home/ubuntu/.local/share/uv/tools/nanobot-ai/lib/python3.11/site-packages/nanobot/providers/openai_compat_provider.py
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# 2. 若补丁丢失,用 after_patch 覆盖回去
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cp /home/ubuntu/nanocode/nanobot-patches/openai_compat_provider.py.after_patch \
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/home/ubuntu/.local/share/uv/tools/nanobot-ai/lib/python3.11/site-packages/nanobot/providers/openai_compat_provider.py
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# 3. 重启 nanobot 服务使补丁生效
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sudo systemctl restart nanobot
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```
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## 六、验证方法
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用 nanobot 自身的 venv Python 跑一次完整链路(构造图片块 → `_sanitize_messages` → `chat`),确认 `content` 仍为 `list` 且保留 `image_url` 块,并能返回图片描述:
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```bash
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PY=/home/ubuntu/.local/share/uv/tools/nanobot-ai/bin/python
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$PY -c "
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import asyncio, base64
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from nanobot.providers.registry import PROVIDERS
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from nanobot.providers.openai_compat_provider import OpenAICompatProvider
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spec = next(s for s in PROVIDERS if s.name=='deepseek')
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key = 'sk-xxxx' # 替换为 DeepSeek API Key
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model = 'deepseek-v4-flash-vision-exp'
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b64 = base64.b64encode(open('/path/to/img.png','rb').read()).decode()
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messages = [{'role':'user','content':[
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{'type':'image_url','image_url':{'url':'data:image/png;base64,'+b64}},
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{'type':'text','text':'请描述这张图片'}
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]}]
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prov = OpenAICompatProvider(api_key=key, spec=spec, default_model=model)
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async def main():
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s = prov._sanitize_messages(messages)
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print('blocks:', [b.get('type') for b in s[0]['content']]) # 期望 ['image_url','text']
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resp = await prov.chat(messages, model=model)
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print(resp.text)
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asyncio.run(main())
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"
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```
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## 七、关于"识别能力"的说明
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- **图片识别能力本身**是模型自带的(如 `deepseek-v4-flash-vision-exp`、Kimi 等视觉模型都支持),**不需要**记忆到 nanobot 的长期记忆里。
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- **需要防丢失的**是"代码补丁"这一工程事实:它属于 `site-packages` 的源码改动,会随 `uv` 升级被覆盖。因此用**文档 + 前后备份**来保护,而不是当作模型能力去记忆。
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## 八、关联历史
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- 修改前备份文件:`/home/ubuntu/backup_openai_compat_provider.py.1787316174`
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- 历史记忆(memory)中的补丁位置与"uv 升级会覆盖、需重新打"的记录与此文档一致。
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