25. LangChain Integration

Date: 2025-02-22

Status

Accepted

Context

We want to refactor LLM microservice code to be more maintainable, scalable, and follows best practices for working with LLMs. The use of prompt templates, in particular, is a significant improvement for future development.

Decision

We will refactor code based on Langchain.

Consequences

1. Langchain Integration: The code now uses langchain.llms.Gemini to interact with the Gemini API. This provides a more structured and consistent way to work with LLMs.

2. Prompt Templates: A PromptTemplate is introduced. While not strictly necessary for this simple example, it’s highly recommended for any real-world application. Prompt templates make it much easier to manage and modify prompts, especially as they become more complex. They also prevent prompt injection vulnerabilities by clearly separating instructions from user input.

3. Simplified API Call: Langchain handles the details of the API call, so we no longer need to manually call model.generate_content() and response.resolve(). We simply call the LLM instance (llm) with the formatted prompt.

4. Error Handling: The try…except block remains for robust error handling. The error messages are slightly improved to be more informative. The code now explicitly checks if the Gemini response is empty and returns a specific message in that case. This helps in debugging and understanding potential issues.

5. Logging: The logging configuration is included. Robust logging is vital for debugging and monitoring your application.