Can gemma be used to create custom app

[rahul7514]

Hi 

Can Gemma LLM be used to create a custom chatbot that can assist me in creating custom parsers for Google Chronicle. We are seeing numerous client requirements to build custom parsers for custom app that doesn't have a parsers currently in Chronicle. 

[dawnberdan]

Hi @rahul7514,

Welcome to Google Cloud Community!

Gemma LLM can be a powerful asset in your custom parser development journey for Google Chronicle. While it won't directly generate parsers, it can assist with tasks like understanding log formats and providing guidance on parser design.

Challenges with Gemma LLM for Direct Parser Creation: 

• While Gemma LLM is a valuable tool, crafting highly precise parsers for Google Chronicle requires a deep understanding of log structures and syntax, which may exceed Gemma's current capabilities.
• Custom parsers often involve application-specific logic that might be difficult for Gemma LLM to capture without extensive training.

Here are the recommendations for Effective Gemma LLM Usage:

• Log Analysis and Structure Understanding: Train Gemma LLM on your log data to identify key fields and patterns, and use it to summarize the log format.
• Code Snippet Generation: Use Gemma LLM to generate basic code snippets for your parsers.
• Parser Documentation: Have Gemma LLM help you write documentation for your parsers.
• Parser Validation and Testing: Use Gemma LLM to test your parsers and ensure they produce accurate results.

For a deeper understanding about Google Chronicle, please refer to this video.

I hope the above information is helpful.

[rahul7514]

Thanks @dawnberdan for the explanation. Is there any other model which can assist me in this task. 

Also can you guide me with an example if this is feasible on paid version of Gemini how i can accomplish this task? 

[dawnberdan]

Hi @rahul7514,

Glad to help. 

For your task of creating custom parsers for Google Chronicle, besides Gemma LLM, you might also explore other advanced AI models or tools that offer specialized capabilities in log analysis and parser creation. While specific models might not be tailored exclusively for this purpose.

Here are a few options to consider:

• Google Cloud AI Tools: Platforms like AutoML and Vertex AI offer machine learning capabilities that could be adapted to custom model training for log parsing.

• Custom Models: Training a custom model using platforms supporting sequence-to-sequence tasks can provide tailored solutions based on specific log requirements.

• OpenAI Models: Models like GPT-4 excel in code generation and understanding complex formats. They can assist with generating initial parser code templates and understanding log structures.

Using Paid AI Models (e.g., Gemini):

1. Data Collection: Collect a diverse set of log data samples and clearly outline the requirements for your parsers. This includes defining expected fields, data types, and any recurring patterns.

2. Model Training: Train the paid AI model (such as Gemini) with your log data. Fine-tuning the model is essential to adapt it to your specific log formats. You can refer to this document for more details.
3. Code Generation: Utilize the model to create code snippets that efficiently parse your logs. This involves extracting fields, defining regular expressions, and accommodating various log types.
4. Validation and Iteration: Test the parsers with real log data to check their accuracy. Make adjustments as needed based on the results, and keep refining them with the AI model.

I hope the above information is helpful.