Agentic RAG: Make Chatting with Docs Smarter

Prompt Engineering
16 Jul 202416:11

TLDRThis video introduces Agentic RAG, an enhancement to the traditional Retrieval Augmented Generation (RAG) model, which improves information retrieval by incorporating agents. These agents can analyze and reformulate user queries, ensuring more accurate information retrieval even when the initial query is poorly formulated. The video demonstrates how Agentic RAG works by refining queries and retrying searches with different arguments until it retrieves relevant documents from a knowledge base. It also compares Agentic RAG's performance with traditional RAG, showing the former's ability to provide more detailed and accurate responses. The tutorial includes a step-by-step guide on implementing Agentic RAG using tools like Transformers agents and sentence Transformers, making it easier to build smarter chatting applications.

Takeaways

  • 🤖 The video discusses 'Agentic RAG', an enhancement to the traditional Retrieval Augmented Generation (RAG) model that can improve information retrieval by introducing agentic capabilities.
  • 🔍 Traditional RAG can struggle with poorly formulated user queries, potentially leading to incorrect or incomplete information retrieval.
  • 🛠 Agentic RAG introduces agents that can analyze and reformulate user queries, as well as evaluate the responses generated by the RAG pipeline, enhancing the accuracy of information retrieval.
  • 🔄 The agentic loop involves the agent refining the query and repeating the retrieval process if necessary, aiming to achieve a better match between the query and the knowledge base.
  • 🧩 The video outlines the use of frameworks like Cere AI, Auto or Lang chain, and Transformers agents to implement agentic capabilities within the RAG pipeline.
  • 💻 The speaker demonstrates coding examples using Google Colab and local installations, highlighting the use of packages such as pandas, Lang chain, sentence Transformers, and F for vector storage.
  • 📚 The example dataset used in the video consists of Hugging Face documentation, which is processed into chunks and embedded using the GPT-E small model.
  • 🔗 The creation of a retrieval tool within the agentic RAG framework is discussed, which uses semantic similarity to retrieve relevant documents from the knowledge base.
  • 📈 The agentic RAG is shown to provide more detailed and accurate responses compared to the standard RAG pipeline, especially when dealing with complex or ambiguous queries.
  • 🔗 A comparison between standard RAG and agentic RAG is presented, demonstrating the agentic RAG's ability to generate more comprehensive and helpful answers.

Q & A

  • What is the main issue with traditional Retrieval-Augmented Generation (RAG) when the user query is not well-formulated?

    -In traditional RAG, if the user query is not well-formulated, the retrieval of information can be difficult, even if the information is present in the knowledge base. This can lead to the system either hallucinating answers or informing the user that it couldn't find the information.

  • How does Agentic RAG address the problem of poorly formulated user queries?

    -Agentic RAG introduces agents into the RAG pipeline that can analyze the initial query and the responses generated by the RAG pipeline. These agents can reformulate the query and refine the search process, ensuring more accurate retrieval of information even from poorly formulated queries.

  • What are the key components of the Agentic RAG process described in the transcript?

    -The key components include an agent that reformulates the initial query, a knowledge base for semantic-based similarity search, retrieval of relevant documents, analysis and refinement of the retrieved documents by the agent, and an iterative process to improve query and context before generating a final answer with the help of an LLM.

  • Which frameworks and tools are mentioned for implementing Agentic RAG?

    -The transcript mentions frameworks like Cere AI, Auto or Lang graph from Lang chain, and the use of Transformers agents within the Transformer package for creating custom agents.

  • What is the role of the 'retrieval' tool in the Agentic RAG setup?

    -The 'retrieval' tool is used to perform semantic similarity searches on the knowledge base to retrieve the most relevant documents based on the user query. It is a crucial part of the agent's ability to reformulate queries and retrieve context for the LLM to generate responses.

  • How does the agent refine the query in the Agentic RAG process?

    -The agent refines the query by analyzing the initial query and the responses generated by the RAG pipeline. If the retrieved documents do not adequately answer the question, the agent reformulates the query and repeats the retrieval process until it is satisfied with the context and the reformulated query.

  • What is the significance of using embeddings in the Agentic RAG pipeline?

    -Embeddings are used to represent text in a vector space, allowing for efficient semantic similarity searches. In the Agentic RAG pipeline, they help in identifying the most relevant chunks of information from the knowledge base that can be used by the LLM to generate responses.

  • How does the agent ensure that the final response is relevant and concise?

    -The agent ensures relevance and conciseness by iteratively refining the query and analyzing the retrieved context. It follows a system prompt that instructs it to respond only to the question asked and to be concise and relevant, retrying with different arguments if necessary.

  • What is the difference between the responses generated by the standard RAG and Agentic RAG as illustrated in the transcript?

    -The responses generated by Agentic RAG are more detailed and informative compared to those from standard RAG. The agent's ability to reformulate queries and refine the search process leads to more comprehensive and accurate answers.

  • How does the agent handle the situation when it cannot find information in the knowledge base?

    -If the agent cannot find information, it is instructed to not give up and to try calling the retriever again with different arguments. This allows the agent to reformulate the query semantically and attempt to retrieve relevant documents multiple times.

Outlines

00:00

🔍 Enhancing Information Retrieval with Agentic RAG

The paragraph discusses the challenges in information retrieval within Retrieval Augmented Generation (RAG) systems, which are heavily dependent on the formulation of user queries. It highlights how traditional RAG can struggle with poorly formulated queries, leading to 'hallucination' or failure to retrieve relevant information even when it's present. The solution proposed is the introduction of 'agentic RAG,' where agents analyze and refine queries, improving the retrieval process. The paragraph outlines the traditional RAG setup and how agents can iteratively reformulate queries and analyze retrieved documents to enhance information retrieval, ultimately leading to more accurate responses.

05:02

🛠 Building the Agentic RAG Pipeline

This paragraph delves into the technical aspects of constructing an agentic RAG pipeline. It starts with the installation of necessary packages like pandas, LangChain, Sentence Transformers, and others. The process involves importing these packages and modules, setting up a dataset, and splitting it into chunks. The use of embeddings and a vector store for similarity search is discussed, with a focus on the cosine similarity measure. The paragraph also introduces the concept of agents that can utilize tools and the creation of a 'retrieval tool' for semantic similarity-based document retrieval. The setup for the language model (LLM) is also covered, including the use of the Hugging Face engine and the configuration of the agent with tools and maximum iterations for the agentic loop.

10:03

🤖 Implementing the Agent in RAG

The paragraph explains the implementation of an agent in the RAG pipeline, emphasizing the agent's ability to use tools and perform multiple iterations to refine user queries and retrieve context. It details the creation of a retrieval tool, the configuration of the LLM, and the setting up of the agent with access to these tools and a system prompt. The system prompt guides the agent to use the knowledge base effectively. The paragraph illustrates the agent's internal thought process and how it iteratively retrieves information and refines queries to generate a comprehensive response. It also contrasts the agentic RAG's detailed responses with those from a standard RAG pipeline, showcasing the benefits of the agentic approach.

15:03

📚 Comparing Standard and Agentic RAG

The final paragraph compares the output of a standard RAG pipeline with that of an agentic RAG pipeline. It presents an example question and shows how both systems handle it, highlighting the differences in the level of detail and relevance in the responses. The agentic RAG provides more comprehensive and detailed answers, demonstrating the effectiveness of incorporating agents in the RAG process. The paragraph concludes with a call to action for viewers to subscribe for more content on agents and to explore advanced RAG techniques through the provided course link.

Mindmap

Keywords

💡Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is a machine learning technique that combines retrieval and generation to enhance the quality of responses from AI systems. In the context of the video, RAG is used to improve the accuracy and relevance of information retrieval. The video explains that traditional RAG can struggle with poorly formulated user queries, leading to inaccurate or incomplete responses. Agentic RAG, as discussed, introduces an agent that can reformulate queries and analyze responses to improve the retrieval process.

💡Information Retrieval

Information retrieval is the process of accessing and obtaining information from a repository or database. In the video, information retrieval is a critical step in the RAG pipeline, where the system searches for relevant documents based on user queries. The effectiveness of RAG hinges on how well the information retrieval step can identify and fetch the most pertinent data to answer a user's question.

💡Semantic Similarity Search

Semantic similarity search is a method used to find documents or chunks of text that are semantically close to a given query. The video describes how this search method is employed in RAG to locate the most relevant information in the knowledge base. It's crucial for the system to accurately interpret the user's intent and retrieve documents that can effectively address the query.

💡Agentic RAG

Agentic RAG refers to an enhanced version of RAG that includes an agent capable of analyzing and reformulating user queries, as well as evaluating the responses generated by the RAG pipeline. The video emphasizes how agentic RAG can address the limitations of traditional RAG by introducing an agent that can iteratively refine queries and responses, leading to more accurate and detailed answers.

💡Knowledge Base

A knowledge base is a collection of information that an AI system can draw upon to provide responses. In the video, the knowledge base contains documentation from Hugging Face, which the RAG system uses to retrieve relevant information. The knowledge base is crucial for the RAG system to have a comprehensive dataset to perform effective information retrieval.

💡Vector Store

A vector store is a database that stores and manages vector representations of data, which can be used for similarity searches. In the context of the video, the vector store is used to hold the embeddings of the documents, allowing the RAG system to perform efficient semantic similarity searches to retrieve relevant information.

💡Embeddings

Embeddings are the numerical representations of words, phrases, or documents in a continuous vector space. They capture semantic meanings and are used in the video to convert text into a format that can be compared for similarity. The embeddings are generated using models like the GTE small, which is used in the video to represent the chunks of documents.

💡Chunking

Chunking refers to the process of breaking down a document into smaller, manageable pieces or chunks. In the video, chunking is used to divide the documents into segments that can be individually processed and indexed. This is essential for the RAG system to be able to search and retrieve specific parts of the documents that are relevant to a user's query.

💡LLM (Large Language Model)

A Large Language Model (LLM) is a type of AI model that has been trained on vast amounts of text data and can generate human-like text. In the video, an LLM is used to generate responses based on the retrieved context. The agentic RAG framework leverages the LLM's capabilities to produce detailed and accurate answers to user queries.

💡Hugging Face

Hugging Face is a company that provides a platform for developers to build, train, and deploy machine learning models, particularly in the field of natural language processing. In the video, Hugging Face is mentioned as the source of the documentation used in the knowledge base, and their tools and models are used to implement the RAG system.

Highlights

Agentic RAG enhances information retrieval by introducing agents into the RAG pipeline.

Traditional RAG struggles with poorly formulated user queries, leading to inaccurate information retrieval.

Agentic RAG allows for the analysis and refinement of both the initial query and the generated responses.

Agents in Agentic RAG can reformulate queries to improve the retrieval of relevant information.

The agentic loop in RAG involves planning, analyzing, and executing to ensure accurate information retrieval.

Frameworks like Crew AI, Auto, or Lang graph from Lang chain can be used to build agents for RAG.

Transformers agents within the Transformer package offer a clear and modular approach to creating agents.

The process involves installing necessary packages such as pandas, Lang chain, sentence Transformers, and more.

The agent uses tools like the hugging face engine to select specific LLMs for response generation.

A dataset of 2,647 documents from hugging face is used to demonstrate the Agentic RAG setup.

Documents are split into chunks and embeddings are created using the GTE small model.

The recursive character text splitter is used to split documents into 200 tokens with a 20-token overlap.

Duplicate chunks are removed to ensure unique chunks are used in the vector store.

The vector store uses cosine similarity for similarity search, a widely used strategy.

Agents have the ability to use different tools, such as the retrieval tool created for semantic similarity.

The LLM used in the agentic workflow is set up using the message rle and GL get C message list functions.

The agent is created with access to tools, an LLM, and a maximum number of iterations for the agentic loop.

A system prompt is provided to the agent to guide the information retrieval process.

The agentic RAG pipeline is demonstrated with a question about pushing a model to the hugging face hub.

Comparisons show Agentic RAG provides more detailed and concise answers compared to standard RAG.

The video concludes with a call to action for viewers interested in learning more about advanced RAG techniques.

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