Building Agentic RAG, Step by Step: From Static Retrieval to Reasoning Pipelines

Introduction to Agentic RAG

Retrieval-augmented generation (RAG) is an approach that helps artificial intelligence (AI) models generate text based on facts they have never been trained on. However, the classical version of RAG has its limitations. It performs a single search and relies on the assumption that the first few results contain the correct answer. Agentic RAG addresses these limitations by incorporating an agent, controlled by an LLM (large language model), which decides what and when to search for, evaluates the quality of retrieved contexts, and combines them to generate an answer.

What is Agentic RAG?

Agentic RAG is a process where an agent makes decisions based on the current state of the model and the task context. This agent can request additional data if the initial results do not meet the required level of accuracy or completeness.

Steps to Create Agentic RAG

Step 1: Data Preparation

The first step involves preparing data for the model. This includes gathering, cleaning, and structuring information that will be used to train the agent and the model.

import pandas as pd

def load_data(file_path):
    return pd.read_csv(file_path)

def clean_data(df):
    df.dropna(inplace=True)
    df.drop_duplicates(inplace=True)
    return df

data = load_data('data.csv')
cleaned_data = clean_data(data)

Step 2: Creating the Agent

The agent must be capable of making decisions about which data to request and how to use the obtained data for generating answers.

class Agent:
    def __init__(self, llm_model):
        self.llm_model = llm_model

    def decide_to_retrieve(self, context):
        # Here you can add logic for decision-making
        return True

    def retrieve_data(self, query):
        # Using LLM to request data
        response = self.llm_model.generate(query)
        return response

Step 3: Integrating the Agent with the Model

Create a function that integrates the agent with the RAG model, ensuring interaction between them.

def integrate_agent_with_rag(agent, rag_model, question):
    if agent.decide_to_retrieve(question):
        additional_data = agent.retrieve_data(question)
        return rag_model.generate(question, additional_data=additional_data)
    else:
        return rag_model.generate(question)

Step 4: Evaluating the Quality of the Response

Add functionality for evaluating the quality of the model's response after each step.

def evaluate_response(response, expected_answer):
    # Here you can add logic for evaluating the quality of the response
    pass

Step 5: Repeating the Cycle

If the response does not meet the requirements, repeat the cycle until a satisfactory result is achieved.

def run_agentic_rag(agent, rag_model, question):
    while True:
        response = integrate_agent_with_rag(agent, rag_model, question)
        if evaluate_response(response, expected_answer):
            break
    return response

Practical Tips

• Use Powerful LLM Models: Ensure that your LLM model is powerful enough for analyzing and generating content.
• Optimize Decision-Making Algorithms: Develop efficient algorithms for deciding whether to request additional data.
• Conduct Thorough Quality Evaluation: Use various metrics to evaluate the quality of responses and optimize the process.

Conclusion

Creating Agentic RAG significantly enhances the accuracy and reliability of AI models, providing a more flexible and adaptive approach to solving problems. The application of this method can lead to significant improvements in the quality of generated content and increased user trust in AI systems.