Optimizing Multi-Agent Postgress Data Analytics for Efficient Operations

Introduction

In this blog, we delve into the strategies for optimizing multi-agent postgress data analytics for efficient operations. From refactoring the code to improving database query efficiency, we explore key tactics to enhance performance and cost-effectiveness.

Refactoring the Code for Multi-Agent Postgress Data Analytics Tool

• The multi-agent postgress data analytics tool has a limitation of reading only two postgress tables, which is inadequate for production databases with potentially hundreds of tables and millions of rows.

• To address this limitation, the focus is on enabling the system to specifically target tables relevant to natural language queries, while also incorporating token count and price estimation to track costs over the system’s lifecycle.

• The code has been reorganized and refactored into two new modules – agent configuration and agents. These changes have streamlined the codebase, making it more manageable and easier to read.

• The agent configuration now includes all custom agent functions and helper functions for creating function maps. Additionally, the orchestration process has been simplified to enable the building of appropriate teams based on the team name, supporting data engineering and data visualization teams.

• The updates aim to overcome the token explosion issue by enhancing the system’s ability to handle a larger volume of data effectively and efficiently.

Optimizing Database Queries and Embeddings for Natural Language Processing

• A key challenge in the given scenario is to filter out the relevant tables for natural language queries to optimize database performance.

• The solution involves creating a new database embedder class to compute embeddings for each table’s SQL create statement, which details all the columns of the table.

• These embeddings are then used to compare the natural language query to the table definitions, returning the most relevant tables based on similarity.

• The process also includes a fallback mechanism where if the table name is specified in the query, the relevant table is immediately selected without going through the embeddings comparison.

Improving Database Query Efficiency

• The team has developed a new function that combines word embeddings and word match results into a single table, updating the database module.

• The new get similar tables function runs both word matching and embeddings, allowing for more efficient table queries.

• The team has also added a function to the database module that retrieves create statements for a given list of tables, enhancing the application flow.

• The implementation of a token counting system in the orchestrator provides better cost understanding for running postgress data analytics, ensuring efficient resource allocation.

• The token counting system utilizes the llm estimate price and tokens function to estimate costs based on message conversations and token encoding, allowing for precise cost calculations.

• The multi-agent system’s total cost can now be calculated, providing insights into the resource consumption of different teams within the organization.

• The importance of understanding costs and building sustainable modules is emphasized to avoid unnecessary expenditure on open AI’s services.

• The need for writing better prompts with essential information is highlighted to optimize cost-effective usage of the system.

Optimizing Data Operations and Cost Reduction

• The speaker mentions the need to make cost cuts and downsize the team due to high expenses for the data visualization team.

• They emphasize the use of techniques like random sampling to reduce costs and tap into the SQL response efficiently.

• After downsizing the team, the data engineering team takes on more responsibility, such as reporting successful delivery to the product manager.

• The speaker demonstrates running a larger query and analyzing the cost and results to showcase the effectiveness of the trimmed-down team.

• They highlight the capability to return, modify, and rerun queries for further analysis, showcasing the flexibility and efficiency of the system.

• Despite firing the data visualization team, the speaker assures that the option to rehire or use them in the future still exists.

• The application’s progress towards becoming a full-scale, production-ready product is discussed, and the speaker expresses plans to integrate it behind an API for broader usability.

Building Powerful Agentic Software

• The speaker discusses the potential for a user interface where natural language inputs generate SQL queries and results in varying formats based on the data size.

• The focus is on developing efficient systems without overwhelming the context window, emphasizing the importance of monitoring costs and memory limitations.

• The speaker advocates for using the best technology available, specifically praising GPT-4 from OpenAI for its superior performance.

• The channel aims to provide unique, valuable software development insights, steering away from rehashed examples and focusing on real, powerful applications.

• The concept of agentic software, which can operate parallel to engineers, is highlighted for its potential to create immense value.

• The speaker expresses gratitude for the support and engagement from the audience, encouraging them to stay updated for more insightful content.

Conclusion

By implementing the discussed tactics, organizations can significantly enhance the performance and cost-effectiveness of multi-agent postgress data analytics. These strategies pave the way for streamlined operations and improved resource allocation, ultimately leading to a more sustainable and efficient system.

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