Low code LLM Agents with Pre-build RAG Pipeline - Introducing Lyzr
Rohan Paul
I build & write AI stuff. → Join 34K others on my X / Twitter. AI Engineer and Entrepreneur (Ex Investment Banking).
?? In contrast to Gen AI, "agentic" AI is where the business value is.
We are at a stage where Large Language Models are surely competent, but without agents, LLMs are always on zero-shot mode and we are not using their full capability at all.
?? And with agents the same LLM can go over the task multiple times, fine-tuning the results each time, use extra tools and smarter decision-making to really leverage what LLMs can do.
? Standalone LLMs are not agentic because no matter what tokens they output the training data is the same.
And that's where agentic workflows come. ??
?? Agentic workflows are all about making LLMs smarter by integrating them into structured processes. This helps the AI deliver higher-quality results. Without Agents, Large Language Models operate on a zero-shot mode, and can not leverage their full potential.
Introducing Lyzr?
Lyzr is a low-code agent framework with an agentic approach to building generative AI applications. Its fully integrated agents come with pre-built RAG pipelines, allowing you to build and launch in minutes.
With Lyzr, you can build chatbots, knowledge search, data analysis, RAG-powered apps, and multi-agent workflow automation with minimal effort.
?? Checkout detailed step by step video on my YouTube channel
The best part is that you can get locally deployable SDKs and private APIs to run the agents on your cloud, thus eliminating all concerns related to data privacy, compliance, and latency.
?? Specific Advantages of Lyzr:
?? A. Pre-Built Agents and Automation agents that are available like Chat, Search, Jazon as well as pre-built agents for each industry.https://www.lyzr.ai/industries/
?? B. Lyzr’s Chat Agent is powered by a state-of-the-art chatbot architecture that super abstracts all the complexity of building an advanced LLM-powered chatbot. This enables developers to focus more on data quality, prompt quality, and the application use case instead of spending countless hours stitching together various building blocks and indexes to build the backend RAG pipeline.
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?? C. Lyzr offers SDKs, so its flat pricing irrespective of number of users, number of queries. At scale Lyzr is at least ~2000 times cheaper.
?? D. Data privacy. Data never leaves your cloud with Lyzr as SDKs sit in your cloud
An example usecase of Lyzr
?? Problem: HR departments frequently encounter queries from employees regarding company policies, procedures, and other related documents. :page_with_curl: Responding to these inquiries promptly and accurately is crucial for maintaining a productive work environment.
? Solution: To address this challenge, with Lyzr SDK you can develop a solution that automates answering employee questions using RAG - The Employee-HR Q&A
This QA bot helps the employee to resolve queries at the organizational level. If the employee has any kind of question, this QA bot will work like HR and provide answers to his/her questions. Cool, isn't it?
?? Read more on this in the full blogpost : AI in HR: Building an Employee-HR Q&A with Lyzr
That's a wrap and here are all the important links for Lyzr ??
?? Join the Lyzr Community on Slack (Anybody can AI) and connect with developers who are building innovative solutions: https://join.slack.com/t/anybodycanai/shared_invite/zt-2ezcdy0o4-zOJDTmEVw5KkSx4AIhXCsg
?? Access their Open Source SDKs Documentation: https://docs.lyzr.ai/lyzr-sdk/opensource/
?? Explore example apps built with Lyzr SDKs: https://www.lyzr.ai/examples/
?? Discover more about Lyzr on their website: https://www.lyzr.ai/
?? Book a demo to see their solutions in action: https://www.lyzr.ai/book-demo/