Coralogix is a full-stack observability platform that effortlessly processes logs, metrics, traces, and security data. More specifically, log monitoring in Coralogix are processed in larger volumes…
We are pleased to share a sneak peek of Query Assistant, our latest innovation that bridges the world of declarative querying with Generative AI.
Leveraging our large language models (LLMs), Coralogix’s Query Assistant translates your natural language request for insights into data queries. This delivers deep visibility into all your data for everyone in your organization.
In this post we will explore the challenges facing both novice and experienced users of querying languages and how Query Assistant makes data easily accessible to all.
The art of querying data is a fundamental skill for observability, yet it can be challenging for all experience levels. From beginners grappling with the basics to experts navigating intricate datasets, the journey of mastering, say for example Lucene’s data querying language, is filled with visits to Stack Overflow, Reddit’s r/Lucene and YouTube.
Even Coralogix’s powerful DataPrime querying language is not immune to the inherent challenges of mastering a new querying system.
Today, every engineer needs to occasionally troubleshoot and dig into logs, metrics and traces from complex and often disparate systems. As querying is not part of their core focus, figuring out how to find what they need can be a heavy load. Even experts may waste time trying to figure out why a query with a typo or incorrect command didn’t generate the expected results.
This pain point is exactly where Coralogix’s AI-powered Query Assistant enters the picture.
Generative AI models, like OpenAI’s Codex and tools based on it such as GitHub Copilot have already improved developer productivity. Capabilities such as writing code, generating code snippets, and providing coding assistance, helps developers write more efficient code faster and reduces the time spent on basic coding tasks.
Natural Language Processing (NLP) tools can also automatically generate documentation for code. By analyzing the code structure and comments, these tools can produce readable and informative descriptions of what the code does, its functions, classes, methods, and the relationships between them. This makes it easier to keep documentation up-to-date and reduces the workload on developers.
AI-powered slackbots are becoming popular for DevOps self-serve, helping developers access and execute any number of workflows. From rolling out a Kubernetes deployment to triggering lambda functions, Generative AI helps bridge the gap between Devs and Ops.
Today we are excited to share a preview of Coralogix’s Query Assistant, an AI-powered search feature that allows you to input your query request in natural language.
Now you no longer need to worry about remembering commands, proper syntax or typos. Just enter in what you are looking for and the Query Assistant will translate your request into Coralogix’s DataPrime query language. You can then easily modify your query, either in natural language or by changing the generated DataPrime query itself.
The Coralogix Query Assistant is just the beginning of our Generative AI offerings. We are already working on AI-powered analysis where users can interact with data on a semantic level. For example, you will be able to ask questions such as “which resources are being monitored” or “what kind of metrics do I have on each Redis instance” or “what is the unit of measurement of each metric”, etc. and instantly receive dashboards and other visualizations that address your query. This will allow you to explore data on your own without prior knowledge of the intricate details of the data being investigated and truly democratize troubleshooting, root cause analysis and other data analysis activities for all Coralogix users.