finetuning with my own data at postgresql using azure openai gpt 3.5 16k model

[PradiptaAtByteIQMind-Graph2023]

Hi

is there any methodologies for finetuning the azure open ai with my own data for improve the accuracy of the RAG application i.e. NL to SQL using NLSQLTableQueryEngine?

Thanks

[dosubot[bot]]

Hey there, @PradiptaAtByteIQMind-Graph2023! Great to see you diving into another intriguing project. 😊

To fine-tune the Azure OpenAI GPT-3.5 16k model with your own data to improve the accuracy of the RAG application, specifically for NL to SQL using NLSQLTableQueryEngine, you can follow these general steps:

1. Data Preparation: Prepare a dataset that consists of pairs of natural language queries and their corresponding SQL queries. This dataset should be representative of the types of queries your application will handle.

2. Prompt Design: Design effective prompt templates that guide the model to generate SQL queries from natural language inputs. The code provided shows examples of prompt templates (DEFAULT_TEXT_TO_SQL_PROMPT, DEFAULT_RESPONSE_SYNTHESIS_PROMPT, etc.) that you can use as a starting point. Customize these prompts to better fit your specific use case and data.

3. Model Selection and Configuration: Choose the appropriate model configuration for your task. While the code does not explicitly mention fine-tuning, Azure OpenAI Service provides options to use pre-trained models like GPT-3.5. You might need to select a model variant that best suits your balance of performance and cost.

4. Fine-Tuning (if available): As of my last update, Azure Cognitive Services for Language (including OpenAI models) primarily offers pre-trained models. If Azure introduces fine-tuning capabilities, you would use your prepared dataset to fine-tune the model, adjusting its weights to better understand and generate SQL queries from your specific natural language inputs.

5. Integration and Iteration: Integrate the fine-tuned model or the best-suited pre-trained model into your application. Use the NLSQLTableQueryEngine or a similar custom engine to process natural language queries, generate SQL, and optionally synthesize responses based on SQL query results. Continuously collect feedback and data from real-world usage, and iterate on your prompts, model selection, and fine-tuning dataset to improve performance over time.

6. Monitoring and Evaluation: Implement logging and monitoring to evaluate the model's performance in production. Use metrics such as accuracy, precision, recall, and user satisfaction to assess how well the model is performing and identify areas for improvement.

Remember, the specific capabilities and options for fine-tuning may vary based on Azure's offerings and updates to their service. Always refer to the latest Azure OpenAI documentation for the most current information on model training and customization options.

Regarding your question about the LlamaIndex repository, it does provide specific methods and utilities for preparing a dataset of natural language queries and corresponding SQL queries. This is evident from the presence of classes like NLStructStoreQueryEngine and NLSQLTableQueryEngine in the provided context. These engines are designed to handle natural language queries and translate them into SQL queries or directly execute them against a structured store or SQL database, respectively.

However, as of my last update, I couldn't find any specific methods or utilities in the LlamaIndex repository for fine-tuning the Azure OpenAI GPT-3.5 16k model with user data. The capabilities and options for fine-tuning may vary based on Azure's offerings and updates to their service. Always refer to the latest Azure OpenAI documentation for the most current information on model training and customization options.

I hope this information is helpful. If you have any further questions or need more specific guidance, please don't hesitate to ask.

Sources

• llama-index-core/llama_index/core/indices/struct_store/sql_query.py
• llama-index-legacy/tests/indices/struct_store/test_sql_query.py

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