When large language models (LLMs) emerged, corporations quickly integrated them into their workflows. They developed LLM applications using Retrieval-Augmented Generation (RAG), a way that leveraged internal data sets to make sure models provide answers with relevant business context and fewer hallucinations. The approach worked like a charm and led to the emergence of functional chatbots and search products that helped users immediately find the knowledge they needed, be it a selected clause in a policy or questions on an ongoing project.
However, although RAG continues to achieve success in several areas, corporations have encountered cases where it doesn’t deliver the expected results. This is the case with agent RAG, where a series of AI agents improve the RAG pipeline. It's still recent and should cause occasional problems, however it guarantees to fundamentally change the way in which LLM-based applications process and retrieve data to handle complex user requests.
“Agentic RAG… integrates AI agents into the RAG pipeline to orchestrate its components and perform additional actions beyond easy information retrieval and generation to beat the restrictions of the non-agent pipeline,” based on the Vector database company Weaviate's Technology partner manager Erika Cardenas and ML engineer Leonie Monigatti wrote in a joint Blog post Description of the potential of the energetic ingredient RAG.
The problem of the “vanilla” RAG
Although traditional RAG is utilized in many applications, it is commonly compromised on account of its inherent functionality.
At its core, a vanilla RAG pipeline consists of two primary components – a retriever and a generator. The Retriever component uses a vector database and an embedding model to receive the user query and perform a similarity search over the indexed documents to retrieve the documents which might be most just like the query. Meanwhile, the generator grounds the connected LLM with the retrieved data to generate answers with relevant business context.
The architecture helps organizations provide fairly accurate answers, but the issue begins when there’s a must transcend a knowledge source (vector database). Traditional pipelines simply cannot ground LLMs with two or more sources, limiting the performance of downstream products and limiting them to pick applications only.
Additionally, there might also make sure complex cases where apps built using traditional RAG may suffer from reliability issues on account of lack of follow-up reasoning or validation of the retrieved data. Whatever the Retriever component pulls without delay ultimately forms the premise of the model's response.
Agentic RAG to the rescue
As corporations proceed to enhance their RAG applications, these issues change into more outstanding and force users to try additional features. One such capability is agent AI, where LLM-driven AI agents with memory and reasoning skills plan a series of steps and take actions across various external tools to perform a task. It is especially used to be used cases reminiscent of customer support, but may also orchestrate various components of the RAG pipeline, starting with the Retriever component.
According to the Weaviate team, AI agents can access a wide selection of tools – reminiscent of web search, calculators, or a software API (like Slack/Gmail/CRM) – to retrieve data beyond pulling information from only one source of data.
This allows the reasoning and memory-enabled AI agent to choose, depending on the user request, whether to retrieve information, which is probably the most appropriate tool to retrieve the required information, and whether the retrieved context is relevant (and whether it must be relevant). -retrieve) before passing the retrieved data to the generator component to create a response.
The approach expands the knowledge base for downstream LLM applications, enabling them to offer more accurate, informed and validated answers to complex user queries.
For example, if a user has a vector database filled with support tickets and the query is, “What was probably the most often addressed issue today?” The agent experience would have the opportunity to perform an internet search to find out the day of the request and match that with the Combine information from the vector database to offer an entire answer.
“By adding agents with access to tool usage, the retrieval agent can route queries to specialized knowledge sources. Additionally, the agent's reasoning capabilities enable a level of validation of the retrieved context before using it for further processing. This allows agent RAG pipelines to lead to more robust and accurate responses,” the Weaviate team noted.
Easy to implement, but challenges remain
Thanks to the wide availability of enormous language models with function call capabilities, corporations have already began moving from vanilla RAG pipelines to Agent RAG. There can be an increase in agent frameworks reminiscent of DSPy, LangChain, CrewAI, LlamaIndex and Letta, which simplify constructing agentic RAG systems by stitching together pre-built templates.
There are two primary ways to establish these pipelines. One is to integrate a single agent system that leverages multiple knowledge sources to retrieve and validate data. The other is a multi-agent system, where a set of specialised agents controlled by a master agent work across their respective sources to retrieve data. The master agent then processes the retrieved information to forward it to the generator.
However, whatever the approach used, it is vital to notice that agent RAG remains to be recent and occasional issues may arise, including latency on account of multi-stage processing and unreliability.
“Depending on the reasoning capabilities of the underlying LLM, an agent may not complete a task sufficiently (or in any respect). It is essential to include appropriate failure modes to assist an AI agent disengage when it’s unable to finish a task,” the Weaviate team emphasized.
The company's CEO, Bob van Luijt, also told VentureBeat that the agent RAG pipeline is also expensive since the more requests the LLM agent makes, the upper the computational cost. However, he also identified that the way in which all the architecture is built could make a price difference in the long term.
“Agent architectures are critical to the subsequent wave of AI applications that may “do” tasks quite than simply retrieve information. As teams move the primary wave of RAG applications into production and change into acquainted with LLMs, they need to search for educational resources on recent techniques reminiscent of agentic RAG or generative feedback loops, an agentic architecture for tasks reminiscent of data cleansing and enrichment,” added he added.
