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To create an agent that uses an API, extracts details, and provides a response based on the API response, you can use the DashScopeAgent class from the LlamaIndex framework. Here's a basic outline of how you can achieve this:
Install the Required Package: If you are using a platform like Google Colab, you may need to install the llama-index-agent-dashscope package first.
%pipinstallllama-index-agent-dashscope
Initialize the Agent: You can create a new agent by providing necessary parameters like the app_id, api_key, and optionally, a workspace if your application is in a non-default workspace.
fromllama_index.agent.dashscopeimportDashScopeAgentagent=DashScopeAgent(
app_id="your_app_id", # The id of the app that you createdchat_session=True, # Enable chat session to auto-save and pass chat history to LLMverbose=False, # Set to True if you need more detailed output
)
You can set the API key either by environment variables or directly in the parameters:
# Set API key by environment variables%envDASHSCOPE_API_KEY=your_api_key# Or set API key by parameters# agent = DashScopeAgent(api_key="your_api_key", app_id="your_app_id")
Interact with the Agent: Use the chat method to send a message to the agent and get a response.
resp=agent.chat("Fetch data from the API and respond accordingly.")
print(resp.response)
Streaming Chat: If you want to receive responses as a stream, you can use the stream_chat method.
response=agent.stream_chat("What is the area of Seattle?")
forrespinresponse.chat_stream:
print(resp.message.content)
Reset the Agent: If you need to start a new conversation, you can reset the agent.
agent.reset()
This setup allows you to create an agent that can interact with APIs, extract details, and provide responses based on the API's output [1][2].
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how to create an agent to use api and extract the details and givr response according to api response.
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