Retrieval Augmented Generation (RAG) is a very important technique that leverages external knowledge bases to enhance the standard of enormous language model (LLM) outputs and provides transparency into model sources that could be cross-checked by humans.
But based on Jerry Liu, co-founder and CEO of LamaIndexeasy RAG systems can have primitive interfaces and poor understanding and planning, lack function calls or tool usage, and are stateless (with no memory). Data silos only exacerbate this problem. Liu spoke yesterday during VB Transform in San Francisco.
This could make it difficult to provide LLM apps at scale as a consequence of issues with accuracy, scaling, and too many required parameters (which require deep technical expertise).
Therefore, there are numerous inquiries to which RAG has no answer.
“RAG was really just the start,” Liu said on stage at VB Transform this week. Many of the core concepts of the naive RAG were “sort of silly” and led to “very suboptimal decisions.”
LlamaIndex goals to beat these challenges by providing a platform that helps developers quickly and simply construct next-generation LLM-based apps. The framework offers data extraction, which transforms unstructured and semi-structured data into unified, programmatically accessible formats; RAG, which answers queries on internal data via question-answering systems and chatbots; and autonomous agents, Liu explained.
Synchronize data so it’s all the time up so far
It is crucial to bring together all the differing types of knowledge inside a corporation, whether unstructured or structured, Liu noted. Multi-agent systems can then tap into “the wealth of heterogeneous data” that exists inside corporations.
“Any LLM application is barely pretty much as good as your data,” Liu said. “If your data quality shouldn’t be good, you won’t get good results.”
LamaCloud – now available via waitlist – offers advanced ETL (Extract, Transform Load) capabilities. This allows developers to “synchronize data over time so it's all the time up so far,” Liu explained. “When you ask a matter, you're guaranteed to have the relevant context, irrespective of how complex or difficult the query is.”
LlamaIndex's interface can handle each easy and sophisticated questions in addition to sophisticated research tasks, and the outcomes can include short answers, structured results and even research reports, he said.
The companys CallParse is a complicated document parser specifically geared toward reducing LLM hallucinations. Liu said it has 500,000 downloads a month, 14,000 unique users, and has processed greater than 13 million pages.
“LlamaParse is currently the very best technology I even have seen for parsing complex document structures for corporate RAG pipelines,” said Dean Barr, head of applied AI at a world investment firm The Carlyle Group. “The ability to keep up nested tables and extract sophisticated spatial layouts and pictures is essential to maintaining data integrity when constructing advanced RAG and agent-based models.”
Liu explained that LlamaIndex's platform is used to support financial analysts, centralized web search, sensor data analytics dashboards, and internal LLM application development platforms, in addition to in industries comparable to technology, consulting, financial services and healthcare.
From easy agents to advanced multi-agents
Importantly, LlamaIndex is built on agent-based considering to enable higher query understanding, planning and gear usage across different data interfaces, Liu explained. It also includes multiple agents that provide specialization and parallelization, helping to optimize costs and reduce latency.
The problem with single-agent systems is that “the more you are attempting to cram into them, the less reliable they grow to be, even when the general theoretical complexity is higher,” says Liu. In addition, single agents cannot solve an infinite variety of tasks. “If you are attempting to present an agent 10,000 tools, it's not going to perform thoroughly.”
Multi-agents help each agent concentrate on a selected task, he explained. This has system-level advantages, comparable to parallelization costs and latency.
“The idea is that through collaboration and communication, you’ll be able to solve even more difficult tasks,” Liu said.
