def create_image_message(self, image: str, save_as: str = '') -> ToolInvokeMessage:
"""
create an image message
:param image: the url of the image
:return: the image message
"""
链接
如果你需要返回一个链接，可以使用以下接口。
def create_link_message(self, link: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a link message
:param link: the url of the link
:return: the link message
"""
文本
如果你需要返回一个文本消息，可以使用以下接口。
def create_text_message(self, text: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a text message
:param text: the text of the message
:return: the text message
"""
def summary(self, user_id: str, content: str) -> str:
"""
summary the content
:param user_id: the user id
:param content: the content
:return: the summary
"""
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_Parameters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
工具调用，图片变量名需要从这里传递进来，从而我们就可以从变量池中获取到图片
"""
def get_runtime_parameters(self) -> List[ToolParameter]:
"""
重写工具参数列表，我们可以根据当前变量池里的实际情况来动态生成参数列表，从而 LLM 可以根据参数列表来生成表单
"""
def is_tool_available(self) -> bool:
"""
当前工具是否可用，如果当前变量池中没有图片，那么我们就不需要展示这个工具，这里返回 False 即可
"""
接下来我们来实现这三个函数
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_Parameters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
api_key_name = self.runtime.credentials.get('api_key_name', None)
api_key_value = self.runtime.credentials.get('api_key_value', None)
if not api_key_name or not api_key_value:
raise ToolProviderCredentialValidationError('Please input api key name and value')
# 获取 image_id，image_id 的定义可以在 get_runtime_parameters 中找到
image_id = tool_Parameters.get('image_id', '')
if not image_id:
return self.create_text_message('Please input image id')
# 从变量池中获取到之前 DallE 生成的图片
image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)
if not image_binary:
return self.create_text_message('Image not found, please request user to generate image firstly.')
# 生成矢量图
response = post(
'https://vectorizer.ai/api/v1/vectorize',
files={ 'image': image_binary },
data={ 'mode': 'test' },
auth=(api_key_name, api_key_value),
timeout=30
)
if response.status_code != 200:
raise Exception(response.text)
return [
self.create_text_message('the vectorized svg is saved as an image.'),
self.create_blob_message(blob=response.content,
meta={'mime_type': 'image/svg+xml'})
]
def get_runtime_parameters(self) -> List[ToolParameter]:
"""
override the runtime parameters
"""
# 这里，我们重写了工具参数列表，定义了 image_id，并设置了它的选项列表为当前变量池中的所有图片，这里的配置与 yaml 中的配置是一致的
return [
ToolParameter.get_simple_instance(
name='image_id',
llm_description=f'the image id that you want to vectorize, \
and the image id should be specified in \
{[i.name for i in self.list_default_image_variables()]}',
type=ToolParameter.ToolParameterType.SELECT,
required=True,
options=[i.name for i in self.list_default_image_variables()]
)
]
def is_tool_available(self) -> bool:
# 只有当变量池中有图片时，LLM 才需要使用这个工具
return len(self.list_default_image_variables()) > 0
