有人使用过Langchain-Chatchat + stable diffusion的方式，在Agent里面生成图片并返回吗

[DeepAichemist]

1、Langchain-Chatchat需要返回的数据格式是什么，生成的图片的path还是直接可以展示图片
2、需要重新写prompt模板吗
3、使用ChatGLM3-6B在server/chat/agent_chat/怎么增加判定呢，每次进行询问都没有调用tool

[dosubot[bot]]

To integrate Langchain-Chatchat with Stable Diffusion for generating images within an Agent, you'll need to follow a few steps:

1. Data Format: Langchain-Chatchat currently uses JSON to return responses. While it primarily focuses on text-based responses, JSON can include image data or paths. If you want to return the image directly, you can encode the image as a base64 string and include it in the JSON response. If you prefer to return the path of the generated image, you can include the path as a string in the JSON response. However, you'll need to ensure that the client-side can access the image at the provided path.

2. Prompt Template: Depending on how you integrate Stable Diffusion, you might need to rewrite the prompt template. If you create a new tool for Stable Diffusion (as described below), you'll need to modify the prompt creation to include this new tool and handle its output appropriately.

3. ChatGLM3-6B Condition: To use ChatGLM3-6B conditionally, you can modify the agent's execution logic to choose between different models based on certain conditions. This might involve checking the prompt or other context to decide which model to use.

Here's a more detailed guide:

1. Create a New Tool for Stable Diffusion:
   You can create a new tool that wraps around Stable Diffusion. This tool will be responsible for generating images based on prompts provided by the agent. Here's a sample code snippet:

from langchain.tools import BaseTool

class StableDiffusionTool(BaseTool):
    name = "stable_diffusion"
    description = "Generates images using Stable Diffusion model."
    
    def run(self, prompt: str) -> str:
        # Code to generate an image using Stable Diffusion with the given prompt
        image_path = generate_image_with_stable_diffusion(prompt)
        # Convert the image to a suitable format (e.g., base64) for returning
        image_data = convert_image_to_base64(image_path)
        return image_data

def generate_image_with_stable_diffusion(prompt: str) -> str:
    # Implement the integration with Stable Diffusion here
    pass

def convert_image_to_base64(image_path: str) -> str:
    # Convert the image at image_path to base64 encoded string
    pass

2. Modify the Agent to Use the New Tool:
   You need to modify the agent's prompt creation to include the new StableDiffusionTool and handle its output appropriately. This might involve rewriting the prompt template to include instructions for using the image generation tool and parsing the tool's output.

# In the StructuredGLM3ChatAgent or a similar class where you define the agent's behavior

@classmethod
def create_prompt(cls, tools: Sequence[BaseTool], prompt: str = None, input_variables: Optional[List[str]] = None, memory_prompts: Optional[List[BasePromptTemplate]] = None) -> BasePromptTemplate:
    # Add the StableDiffusionTool to the list of tools if not already included
    if 'stable_diffusion' not in [tool.name for tool in tools]:
        tools = list(tools) + [StableDiffusionTool()]
    # Proceed with the rest of the method as before

3. Conditionally Use ChatGLM3-6B:
   To conditionally use ChatGLM3-6B, you can modify the agent's execution logic to choose between different models based on certain conditions. This might involve checking the prompt or other context to decide which model to use.

# In the method where you handle the agent's execution, add a condition to choose the model
if should_use_chat_glm3_6b(prompt):
    # Use ChatGLM3-6B
    llm = load_chat_glm3_6b_model()
else:
    # Use another model or default behavior
    llm = load_default_model()

4. Return Image Data in the Required Format:
   Ensure that the StableDiffusionTool returns the image data in the format expected by the rest of your system. If you're using base64 encoded strings, you might need to adjust the client-side to decode and display the images properly.

Remember to test the integration thoroughly to ensure that the agent can generate and return images as expected and that the conditional logic for using ChatGLM3-6B works correctly.

For more details, you can refer to the following files in the Langchain-Chatchat repository:

• server/agent/custom_agent/ChatGLM3Agent.py
• server/chat/file_chat.py
• server/agent/tools_select.py

I hope this helps! If you have any further questions, feel free to ask.

Sources

• server/agent/custom_agent/ChatGLM3Agent.py
• server/chat/file_chat.py
• server/agent/tools_select.py

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[zhkuo24]

@DeepAichemist 请问生成图片返回并在客户端显示有好的办法吗

[DeepAichemist]

我也不知道，因为我不知道他的tool需要返回什么数据，webui展示图片应该很简单，但是就是不知道怎么得到生成的图片的数据

[zRzRzRzRzRzRzR]

你可生成一个url返回就可以在UI显示了