Building an Agentic RAG Solution: Enhancing AI Agents with Intelligent Knowledge Retrieval

Retrieval Augmented Generation (RAG) has been the go-to method for incorporating external knowledge into Large Language Models (LLMs) since the early days of generative AI. It's the classic approach for turning an LLM into an expert on a specific domain, like your favorite agent framework or e-commerce store. However, if you've tried implementing RAG before, you're likely familiar with its common pitfalls - issues that can make you want to pull your hair out, such as irrelevant text being returned from searches or the LLM completely ignoring the extra context provided.

These challenges often lead to RAG falling apart in practice, even when it seems logically sound in theory. You're not alone if you've experienced these frustrations. That's why there's so much ongoing research in the industry focused on improving RAG implementations.

While there are many strategies out there for enhancing RAG, such as reranking, query expansion, and rank normalization (topics for future videos), agentic RAG stands out as one of the most promising and effective approaches. In this article, we'll explore how to transform standard RAG into an agentic approach that actually delivers results, so you won't feel like throwing your computer out the window when nothing seems to work for your agent.

What is Agentic RAG?

Before diving into the implementation details, it's crucial to understand what agentic RAG is and why it's so powerful. Agentic RAG is an evolution of the standard RAG approach that gives the AI agent more control and reasoning capabilities when interacting with the knowledge base.

In standard RAG:

1. A knowledge base is created from documents split into chunks.
2. These chunks are converted into vector representations (embeddings) and stored in a vector database.
3. When a query comes in, it's also converted to a vector representation.
4. The most relevant chunks are retrieved based on vector similarity.
5. The retrieved context is added to the prompt, and the LLM generates a response.

The downside to this approach is that it's a one-shot process. The agent can't reason about the retrieved information or decide to search again if the initial results are insufficient.

Agentic RAG, on the other hand:

1. Treats RAG as a set of tools for the agent to interact with.
2. Allows the agent to reason about where and how to find knowledge.
3. Enables the agent to use multiple search strategies or knowledge bases.
4. Gives the agent the ability to refine its search based on initial results.

This approach unlocks much more powerful and flexible knowledge retrieval, allowing the agent to explore data intelligently and not just work with what it's given in the first shot.

Building an Agentic RAG Solution

Now that we understand the concept, let's walk through the process of building an agentic RAG solution. We'll use the Pantic AI documentation as our knowledge base and create an agent that can answer questions about it.

Step 1: Creating the Knowledge Base

The first step is to create our knowledge base by scraping the Pantic AI documentation and storing it in a database. We'll use Superbase as our database solution.

Here's an overview of the process:

1. Use a web crawler (like crawl-for-ai) to scrape the Pantic AI documentation.
2. Process the scraped content by splitting it into chunks.
3. Generate embeddings for each chunk using OpenAI's embedding model.
4. Store the chunks, along with metadata and embeddings, in a Superbase table.

Here's a simplified version of the Python script to accomplish this:

import os
from dotenv import load_dotenv
from supabase import create_client
from openai import OpenAI
from crawl_for_ai import crawl

load_dotenv()

# Initialize clients
supabase = create_client(os.getenv("SUPABASE_URL"), os.getenv("SUPABASE_KEY"))
openai_client = OpenAI()

def process_and_store_document(url, content):
    chunks = chunk_text(content)
    for i, chunk in enumerate(chunks):
        embedding = get_embedding(chunk)
        title, summary = get_title_and_summary(chunk)
        metadata = {"source": "pantic_ai_docs", "url": url, "chunk_number": i}
        
        supabase.table("site_pages").insert({
            "url": url,
            "chunk_number": i,
            "title": title,
            "summary": summary,
            "content": chunk,
            "metadata": metadata,
            "embedding": embedding
        }).execute()

# Implement chunk_text, get_embedding, and get_title_and_summary functions

# Main execution
urls = get_pantic_ai_doc_urls()
for url in urls:
    content = crawl(url)
    process_and_store_document(url, content)

This script will create a knowledge base in Superbase with all the necessary information for our agentic RAG solution.

Step 2: Setting Up the AI Agent

Now that we have our knowledge base, we can create our AI agent using Pantic AI. We'll start with a basic RAG implementation and then extend it to support agentic RAG.

Here's the basic structure of our agent:

from pydantic import BaseModel, Field
from panticai import Agent, tool

class Dependencies(BaseModel):
    openai_client: OpenAI = Field(...)
    supabase: Client = Field(...)

system_prompt = """
You are an AI assistant specializing in Pantic AI documentation.
You have access to tools that can help you retrieve information from the documentation.
Always use the most appropriate tool to answer user questions accurately.
"""

agent = Agent(
    llm=openai_client,
    system_prompt=system_prompt,
    dependencies=Dependencies
)

@agent.tool()
def retrieve_relevant_documentation(query: str, openai_client: OpenAI, supabase: Client) -> str:
    """Retrieve relevant documentation for the given query using RAG."""
    embedding = get_embedding(query, openai_client)
    results = supabase.rpc("match_site_pages", {
        "query_embedding": embedding,
        "match_count": 5,
        "filter": {"source": "pantic_ai_docs"}
    }).execute()
    
    if not results.data:
        return "No relevant documentation found."
    
    return format_results(results.data)

# Implement get_embedding and format_results functions

This basic implementation allows the agent to perform RAG queries, but it's still limited in its ability to reason about the knowledge base.

Step 3: Implementing Agentic RAG

To transform our basic RAG into an agentic RAG solution, we'll add more tools that allow the agent to explore the knowledge base more intelligently. Here are two key tools we'll add:

@agent.tool()
def list_documentation_pages(supabase: Client) -> List[str]:
    """List all available documentation page URLs."""
    result = supabase.table("site_pages").select("url").filter("metadata->source", "eq", "pantic_ai_docs").execute()
    return list(set([page['url'] for page in result.data]))

@agent.tool()
def get_page_content(url: str, supabase: Client) -> str:
    """Retrieve the full content of a specific documentation page."""
    result = supabase.table("site_pages").select("title", "content", "chunk_number").filter("url", "eq", url).execute()
    if not result.data:
        return f"No content found for URL: {url}"
    
    return format_page_content(result.data)

# Implement format_page_content function

With these additional tools, our agent can now:

1. Get a list of all available documentation pages.
2. Retrieve the full content of specific pages when needed.
3. Reason about which pages might be most relevant to a user's question.

This allows the agent to make more informed decisions about where to look for information, rather than relying solely on embedding-based similarity searches.

Step 4: Enhancing the System Prompt

To take full advantage of our new agentic RAG capabilities, we need to update our system prompt to instruct the agent on how to use these new tools:

system_prompt = """
You are an AI assistant specializing in Pantic AI documentation.
You have access to the following tools to help you answer questions:
1. retrieve_relevant_documentation: Use this for quick lookups on specific topics.
2. list_documentation_pages: Use this to see all available documentation pages.
3. get_page_content: Use this to read the full content of a specific page.

When answering questions:
1. Start by using retrieve_relevant_documentation for a quick search.
2. If the initial search doesn't provide enough information, use list_documentation_pages to find relevant pages.
3. Use get_page_content to read full pages that seem most relevant to the question.
4. Combine information from multiple sources if necessary to provide comprehensive answers.
5. Always cite the specific documentation pages you used in your answer.

Prioritize accuracy and completeness in your responses.
"""

This updated system prompt guides the agent on how to use its tools effectively, enabling more intelligent exploration of the knowledge base.

Benefits of Agentic RAG

Implementing agentic RAG offers several advantages over standard RAG:

1. Improved accuracy: By allowing the agent to reason about where to look for information, it can find more relevant content, especially for complex queries.

2. Better handling of edge cases: When initial searches don't yield good results, the agent can try alternative strategies.

3. More comprehensive answers: The agent can combine information from multiple sources, providing more thorough and nuanced responses.

4. Transparency: The agent can explain its reasoning process and cite specific sources, increasing user trust.

5. Flexibility: It's easier to add new knowledge sources or search strategies without major changes to the agent's core logic.

Potential Enhancements

While our implementation provides a solid foundation for agentic RAG, there are several ways to further enhance it:

1. Dedicated knowledge bases: Create separate knowledge bases for different types of content (e.g., examples, API references, tutorials) and give the agent tools to search these specifically.

2. Query refinement: Implement tools that allow the agent to refine its queries based on initial results.

3. Contextual memory: Add a short-term memory component so the agent can remember and reference information from earlier in the conversation.

4. Multi-step reasoning: Implement more complex reasoning chains, allowing the agent to break down complex queries into sub-questions.

5. User feedback incorporation: Create mechanisms for the agent to learn from user feedback and improve its search strategies over time.

Conclusion

Agentic RAG represents a significant advancement in how AI agents interact with knowledge bases. By giving agents the ability to reason about where and how to retrieve information, we can create more robust, accurate, and flexible systems.

The implementation we've explored in this article provides a strong starting point for building agentic RAG solutions. However, the true power of this approach lies in its extensibility. As you build on this foundation, you can create increasingly sophisticated agents capable of handling complex queries and providing nuanced, well-researched responses.

As the field of AI continues to evolve, techniques like agentic RAG will play a crucial role in creating more intelligent and capable AI assistants. By understanding and implementing these advanced approaches, developers can stay at the forefront of AI technology and create solutions that truly push the boundaries of what's possible with artificial intelligence.

Remember, the key to success with agentic RAG is continuous experimentation and refinement. As you implement this approach in your own projects, pay close attention to how your agent performs and look for opportunities to enhance its capabilities. With each iteration, you'll be one step closer to creating an AI assistant that can rival human experts in its depth of knowledge and ability to provide insightful, accurate information.

Article created from: https://youtu.be/_R-ff4ZMLC8?si=j1JuIZyun8DIKJI7