✨ Mastering Agentic RAG ✨ | Agents | Phidata
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Introduction
In today's article, we're diving into a powerful technique known as Retrieval-Augmented Generation (RAG), specifically focusing on agentic RAG. This innovative approach equips agents with domain-specific information sourced from knowledge bases populated with various types of documents, including PDFs, websites, and texts. By employing RAG, agents can enhance their ability to answer queries based on structured information from their knowledge bases.
Setting Up Agentic RAG
We will be working from the Fidata repository, which contains the necessary codes and resources to implement agentic RAG. Begin by cloning the repository and navigating to the cookbook/rag folder. Here, you'll find recipes for both traditional RAG and agentic RAG, our focus for this article.
For our demonstration, we will be using a cookbook PDF filled with diverse content, from images to multiple languages. This comprehensive document will serve as an excellent testing ground for our RAG implementation.
Loading the Knowledge Base
The first step involves running a Postgres container with PGVector to use for storage. Afterward, we will install the required dependencies and run the traditional RAG PGVector file. As we initiate this process, the system will begin reading the PDF URL, chunking the content, and embedding it into the knowledge base.
Now that the knowledge base is being populated, let’s discuss the setup of our agent. We will use a PDF URL knowledge base, although we could also utilize other formats such as DOCX or standard text files. With PGVector as our vector database, the embeddings will be stored in an AI recipes table.
Implementing Traditional RAG
It's important to comment out the code that loads the knowledge base after the first run, as the knowledge base will already be populated. In traditional RAG, we set the add context flag to true, indicating that the system should search the vector database for the user’s query and insert this context into the agent's prompt. However, since our agents already include the features of agentic RAG, we will explicitly set agentic rag to false and search knowledge to false.
Once everything is set up, we can ask the agent questions derived from our knowledge base. The traditional RAG approach is effective for straightforward queries, but it has limitations for complex requests, such as asking for a three-course meal.
Transitioning to Agentic RAG
To tackle the limitations of traditional RAG, we will now switch to agentic RAG. We will comment out the knowledge loading code and set search knowledge to true, allowing the agent to actively look for needed information in the knowledge base.
In our example, we might ask the agent to recommend recipes for a three-course meal, and the agent will conduct multiple searches in the knowledge base to ensure that relevant information is retrieved. This showcases the superior capabilities of agentic RAG for complex tasks compared to its traditional counterpart.
Exploring the Playground
Next, we will delve into a hands-on experience using the Playground. By running the agent in a local environment with the ability to read chat history, we can issue queries regarding available recipes and how to make specific dishes. The system can search through the knowledge base effectively to deliver precise responses.
Additionally, we will incorporate a feature to summarize conversations, highlighting the power of agentic RAG by retrieving chat history for context-aware dialog. This ensures users receive coherent, relevant, and enriching conversational experiences.
In conclusion, agentic RAG stands out as a robust solution for agents wanting to enhance their functionality by leveraging a well-structured knowledge base. From answering simple questions to navigating more complex culinary inquiries, agentic RAG displays promising results.
If you have any questions or seek further assistance, feel free to reach out on Discord.
Keywords
- Agentic RAG
- Retrieval-Augmented Generation
- Knowledge Base
- PGVector
- Traditional RAG
- Hybrid Search
- Cooking Recipes
- Chat History
- Document Embedding
FAQ
What is agentic RAG?
Agentic RAG is an approach that enhances agents' capabilities to retrieve and generate responses based on a structured knowledge base.
What types of documents can be used in the knowledge base?
You can load various types of documents, including PDFs, DOCX files, plain text, and websites.
How does traditional RAG differ from agentic RAG?
Traditional RAG simply searches a knowledge base for the context to include in the agent’s responses. In contrast, agentic RAG allows the agent to actively query the knowledge base for information whenever it’s needed.
What role does hybrid search play in RAG?
Hybrid search combines vector search and full-text search, which can provide better results, especially for non-English language documents.
Can I run agentic RAG locally?
Yes, you can run agentic RAG locally by setting up the necessary components, like PGVector, to store both the knowledge and conversation history.