In contemporary AI applications, especially those involving Large Language Models (LLMs), efficient data handling and model management are paramount. The Swarmauri SDK provides robust support for various LLM integrations using Pydantic, which powers seamless serialization and deserialization of models and configurations.

We will walk you through using Pydantic deserialization and reserialization with three different LLM types within Swarmauri: GroqModel, OpenAIModel, and PerplexityModel.

Prerequisites

Before diving into the examples, ensure you have the Swarmauri SDK installed in your Python environment. Please note, the SDK is still in beta.

pip install swarmauri[full]==0.4.1

GroqModel with SimpleConversationAgent

Let’s begin with how you might use the GroqModel and verify the model’s integrity through serialization and deserialization using Pydantic.

import os
from swarmauri.standard.llms.concrete.GroqModel import GroqModel
from swarmauri.standard.agents.concrete.SimpleConversationAgent import SimpleConversationAgent

# Initialize the GroqModel
API_KEY = os.getenv('GROQ_API_KEY')
llm = GroqModel(api_key=API_KEY)

# Create a SimpleConversationAgent with the GroqModel
agent = SimpleConversationAgent(llm=llm)

# Execute a query
result = agent.exec(input_str='hello')
print(result)

# Validate the model using Pydantic serialization and deserialization
assert agent.id == SimpleConversationAgent.model_validate_json(agent.model_dump_json()).id

OpenAIModel with SimpleConversationAgent

Now, let’s see a similar example using OpenAIModel.

import os
from swarmauri.standard.llms.concrete.OpenAIModel import OpenAIModel
from swarmauri.standard.agents.concrete.SimpleConversationAgent import SimpleConversationAgent

# Initialize the OpenAIModel
API_KEY = os.getenv('OPENAI_API_KEY')
llm = OpenAIModel(api_key=API_KEY)

# Create a SimpleConversationAgent with the OpenAIModel
agent = SimpleConversationAgent(llm=llm)

# Execute a query
result = agent.exec(input_str='hello')
print(result)

# Validate the model using Pydantic serialization and deserialization
assert agent.id == SimpleConversationAgent.model_validate_json(agent.model_dump_json()).id

PerplexityModel with SimpleConversationAgent

Finally, let’s leverage the PerplexityModel.

import os
from swarmauri.standard.llms.concrete.PerplexityModel import PerplexityModel
from swarmauri.standard.agents.concrete.SimpleConversationAgent import SimpleConversationAgent

# Initialize the PerplexityModel
API_KEY = os.getenv('PERPLEXITY_API_KEY')
llm = PerplexityModel(api_key=API_KEY)

# Create a SimpleConversationAgent with the PerplexityModel
agent = SimpleConversationAgent(llm=llm)

# Execute a query
result = agent.exec(input_str='hello')
print(result)

# Validate the model using Pydantic serialization and deserialization
assert agent.id == SimpleConversationAgent.model_validate_json(agent.model_dump_json()).id

Conclusion

In this article, we’ve demonstrated how to use the Swarmauri SDK to handle various LLMs through Pydantic deserialization and reserialization. With examples using GroqModel, OpenAIModel, and PerplexityModel, you can observe the efficiency and robustness that Pydantic brings to the table in managing data integrity and model consistency.

The SimpleConversationAgent serves as a versatile agent class capable of integrating with different LLMs, making the Swarmauri framework highly adaptable for your AI application needs. The use of Pydantic serialization and deserialization ensures that data remains validated and consistent across various operations, facilitating reliable integrations and extensibility within your projects.

Join Us

We are continually working to make Swarmauri a powerful toolset for developers and data scientists. Your contributions, feedback, and engagement are what make this project thrive.

• GitHub Repository
• Community Discord

Thank you for being part of the Swarmauri community. Together, let’s make text processing and machine learning more accessible and powerful than ever!

Happy Coding! 🚀