[LLM-Agents]浅析Agent工具使用框架：MM-ReAct

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上文[LLM-Agents]详解Agent中工具使用Workflow提到MM-ReAct框架，通过结合ChatGPT 与视觉专家模型来解决复杂的视觉理解任务的框架。通过设计文本提示（prompt design），使得语言模型能够接受、关联和处理多模态信息，如图像和视频。展示了 MM-REACT 在不同场景下处理高级视觉理解任务的有效性，如多图像推理、多跳文档理解、视频摘要和事件定位等。今天我们尝试安装使用一下，了解一下在LLM中如何使用工具。

1. 安装

1.1下载工程

git clone https://github.com/microsoft/MM-REACT

1.2 安装依赖

MM-ReAct是使用Poetry解决依赖包，所以除了安装poetry，还需要额外安装pillow、imagesize 和openai。其中openai需要限制版本为0.28，否则会有兼容性问题。

curl -sSL https://install.python-poetry.org | python3 -
subl ~/.zshrc
export PATH="/Users/xxxx/.local/bin:$PATH"
source ~/.zshrc
pip install pillow imagesize
pip install openai==0.28

1.3 设置环境变量

因为该Repo使用了大量的Microsoft的云端API，需要注册运行，此处为了了解运行过程，就不注册了。但为了能够基本运行，依然需要设置一些无效的环境变量。

BING_SEARCH_URL="https://api.bing.microsoft.com/v7.0/search";
BING_SUBSCRIPTION_KEY=xxxx;
IMUN_CELEB_PARAMS=xxxx;
IMUN_CELEB_URL="https://yourazureendpoint.cognitiveservices.azure.com/vision/v3.2/models/celebrities/analyze";
IMUN_OCR_BC_URL="https://yourazureendpoint.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-businessCard:analyze";
IMUN_OCR_INVOICE_URL="https://yourazureendpoint.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-invoice:analyze";
IMUN_OCR_LAYOUT_URL="https://yourazureendpoint.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-layout:analyze";
IMUN_OCR_PARAMS="api-version\=2022-08-31";
IMUN_OCR_READ_URL="https://yourazureendpoint.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-read:analyze";
IMUN_OCR_RECEIPT_URL="https://yourazureendpoint.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-receipt:analyze";
IMUN_OCR_SUBSCRIPTION_KEY=xxx;
IMUN_PARAMS="visualFeatures\=Tags,Objects,Faces";
IMUN_PARAMS2="api-version\=2023-02-01-preview&model-version\=latest&features\=denseCaptions";
IMUN_SUBSCRIPTION_KEY=xxxx;
IMUN_SUBSCRIPTION_KEY2=xxxx;
IMUN_URL="https://yourazureendpoint.cognitiveservices.azure.com/vision/v3.2/analyze";
IMUN_URL2="https://yourazureendpoint.cognitiveservices.azure.com/computervision/imageanalysis:analyze"

2. 运行

为了使用本地安装的大模型，需要修改两个文件。

  • langchain/llms/openai.py
  • sample.py

2.1 修改sample.py

替换代码中的AzureOpenAI为OpenAI，包括import。

llm = OpenAI(model_name="gpt-3.5-turbo", chat_completion=True,
             openai_api_base="http://localhost:11434/v1",
             openai_api_key="sk", temperature=0, max_tokens=MAX_TOKENS,
             openai_log="debug")

2.2 修改langchain/llms/openai.py

由于自带的langchain中，可能版本比较老，不支持设置openai_api_base ，因此需要增加一点配置代码。

加一点配置代码。
diff --git a/langchain/llms/openai.py b/langchain/llms/openai.py
index 4180165..70711c1 100644
--- a/langchain/llms/openai.py
+++ b/langchain/llms/openai.py
@@ -115,6 +115,8 @@ class BaseOpenAI(BaseLLM, BaseModel):
     """Whether to stream the results or not."""
     chat_completion: bool = False
     """Whether to use the chat client"""
+    openai_api_base: str = ""
+    openai_log: str = "debug"
 
     class Config:
         """Configuration for this pydantic object."""
@@ -146,7 +148,9 @@ class BaseOpenAI(BaseLLM, BaseModel):
         openai_api_key = get_from_dict_or_env(
             values, "openai_api_key", "OPENAI_API_KEY"
         )
-        openai_api_version = values.get("openai_api_version") or os.environ.get("OPENAI_API_VERSION") 
+        openai_api_version = values.get("openai_api_version") or os.environ.get("OPENAI_API_VERSION")
+        openai_api_base = values.get("openai_api_base") or os.environ.get("OPENAI_API_BASE")
+        openai_log = values.get("openai_log") or os.environ.get("OPENAI_LOG")
         chat_completion = values.get("chat_completion") or False
         values["chat_completion"] = chat_completion
         try:
@@ -155,6 +159,10 @@ class BaseOpenAI(BaseLLM, BaseModel):
             openai.api_key = openai_api_key
             if openai_api_version:
                 openai.api_version = openai_api_version
+            if openai_api_base:
+                openai.api_base = openai_api_base
+            if openai_log:
+                openai.log = openai_log
             if chat_completion:
                 values["client"] = openai.ChatCompletion
             else:

2.3 运行

代码运行入口为sample.py本身较为简单，初始化OpenAI，Tool，Agent和开始对话。可以看到除了定义一堆Azure Cloud的工具之外，还自定义了一个edit_photo。

def edit_photo(query: str) -> str:
    ....
    return "Here is the edited image " + endpoint + response.json()["edited_image"]

# these tools should not step on each other's toes
tools = [
    ...
    Tool(
        name = "Photo Editing",
        func=edit_photo,
        description=(
        "A wrapper around photo editing. "
        "Useful to edit an image with a given instruction."
        "Input should be an image url, or path to an image file (e.g. .jpg, .png)."
        )
    ),
]

默认输入图像为一个表格，我们将图像改为科比。 开始运行 python sample.py 输出，为了阅读体验，删除中间的一些输出。

> Entering new AgentExecutor chain...
message='Request to OpenAI API' method=post path=http://localhost:11434/v1/chat/completions
...

1. There is a new image in the input
 Assistant, please detect objects in this image: https://microsoft-cognitive-service-mm-react.hf.space/file=/tmp/b008c4062adec3b7295dc10fc04305813b2dec9e/celebrity.png
python-BaseException
xxx
...无法连接到Microsoft...

由于无法连接Microsoft云端服务，因此没法继续运行下去，如果连接上了会输出

AI: 1. There is an image in the input
AI: 1. This is an image of a basketball player in a yellow jersey holding a basketball
2. There are two faces of men detected in this image.
3. Facial recognition can detect celebrity names for these faces
AI: 1. The celebrities detected are Paul Pierce and Kobe Bryant
2. They are likely the basketball players in the image
To summerize, this is an image of basketball players Paul Pierce and Kobe Bryant in a game. Paul Pierce is in a yellow jersey holding a basketball.

总结

总的来说这篇文章中对工具的使用有点过时，收获不是很大，有点浪费时间，尤其是Prompt设计没有啥亮点，并且代码有点绕。要是现在使用Function Calling ，那么就是将函数描述给到LLM，然后设计ReAct的Few Shot ，外加一个For Loop串起整个流程。 后面分析了HuggingGPT，它对于工具使用好多了。

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