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51 lines
1.2 KiB
51 lines
1.2 KiB
# Agent with DeepSeek
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- Add your `DEEPSEEK_API_KEY` in the `.env` file
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- Select your model_name like `deepseek/deepseek-chat` follows [LiteLLM conventions](https://docs.litellm.ai/docs/providers/deepseek)
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- Execute your agent!
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```python
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from swarms import Agent
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import os
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from dotenv import load_dotenv
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load_dotenv()
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# Initialize the agent with ChromaDB memory
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agent = Agent(
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agent_name="Financial-Analysis-Agent",
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model_name="deepseek/deepseek-chat",
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system_prompt="Agent system prompt here",
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agent_description="Agent performs financial analysis.",
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)
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# Run a query
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agent.run("What are the components of a startup's stock incentive equity plan?")
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```
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## R1
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This is a simple example of how to use the DeepSeek Reasoner model otherwise known as R1.
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```python
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import os
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from swarms import Agent
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from dotenv import load_dotenv
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load_dotenv()
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# Initialize the agent with ChromaDB memory
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agent = Agent(
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agent_name="Financial-Analysis-Agent",
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model_name="deepseek/deepseek-reasoner",
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system_prompt="Agent system prompt here",
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agent_description="Agent performs financial analysis.",
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)
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# Run a query
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agent.run("What are the components of a startup's stock incentive equity plan?")
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``` |