It's GENIUS How This Agentic RAG Blogging System Thinks (n8n)

Key Concepts

Agentic RAG, Traditional RAG, Vector Database, Embedding Model, Data Ingestion, Querying, Reasoning Model, Autonomy, No-Code DB, Web Scraping, Data Sources, Cost Analysis, Speed, Query Complexity, Accuracy, Reliability, Autonomy.

Agentic RAG: Enhancing LLM Responses with Real-World Knowledge

The video discusses Agentic Retrieval Augmented Generation (RAG) as an advanced approach to grounding Large Language Model (LLM) responses in real-world knowledge, addressing the limitations of traditional RAG. Traditional RAG struggles with complex queries, typically queries a single data source, and can produce poor-quality results leading to hallucinations. Agentic RAG empowers the AI to reason, choose data sources, decompose complex queries, trigger multiple calls, and validate retrieved information.

Demo: Agentic RAG Blogger

The video demonstrates an Agentic RAG system integrated into a blogging system on N8N, focusing on article research and outline generation. The example centers around a local news website in Columbus, Ohio.

• System Overview: The Agentic RAG blogger uses a retrieval agent to gather information from curated datasets (Pinecone vector store) and publicly available data sources. It also accesses information on Columbus's capital projects stored in a No-Code DB. External data sources include deep research tools like Perplexity and Gina.
• Workflow:
  1. An article title (e.g., "Update on progress of capital projects in Columbus") is input into the system.
  2. The retrieval agent first queries the No-Code DB for capital project data.
  3. It then queries the Pinecone vector database multiple times to fetch relevant information from the company's knowledge base.
  4. Next, it performs a public search to find external information.
  5. Finally, it uses Perplexity for deep research on the topic.
• Chat Model Interactions: The AI agent demonstrates reasoning by:
  • Rewriting queries for the vector database.
  • Clarifying queries based on initial results.
  • Iterating on searches to obtain more specific information.
• Output: The system generates a detailed article outline with statistics, citations, and research from various data sources. This outline is then used to generate the full article, including:
  • Wordpress integration for internal links.
  • Image prompt generation.
  • YouTube video fetching.
  • Social media post creation.
• Example Articles: The video showcases articles generated using the system, highlighting the inclusion of local knowledge, statistics, and external data sources. One example is an opinion piece on Columbus capital projects, while another focuses on trash disposal guidelines based on Columbus City Council requirements.

Traditional RAG vs. Agentic RAG: A Theoretical Comparison

The video contrasts traditional RAG with Agentic RAG, outlining the key differences in data ingestion and querying.

• Traditional RAG:
  1. Data Ingestion: Documents (web pages, PDFs) are chunked and converted into vector embeddings using an embedding model. These vectors are stored in a vector database (e.g., Pinecone, Quadrant).
  2. Querying: A user query is converted into a vector embedding and compared to the vectors in the database. The most semantically relevant vectors are retrieved and used by the LLM to generate a response.
• Agentic RAG:
  1. Data Ingestion: Similar to traditional RAG, but Agentic RAG can utilize multiple data sources, including vector databases and structured databases.
  2. Querying: The AI agent receives a user message and uses a menu of tools to query different data sources. It can rewrite queries, create specific search terms, generate JSON for APIs, and formulate SQL queries for structured databases.
• Benefits of Agentic RAG:
  • Planning: The agent can plan an approach to answer a query.
  • Tool Selection: It can choose the appropriate tools based on the query.
  • Iteration: It can iterate based on feedback from the tools.
  • Memory: It can track its progress and use memory to inform its decisions.
  • Autonomy: It can autonomously work towards an outcome.

Data Ingestion Pipelines

The video details the data ingestion pipelines used in the Agentic RAG system.

• Web Scraping:
  1. Websites (City of Columbus, Experience Columbus) are crawled using SpiderCloud on a scheduled basis.
  2. SpiderCloud is highlighted as a cost-effective web scraping platform.
  3. The scraped data is stored in a No-Code DB (Nood DB), which is favored over Airtable due to its scalability and cost-effectiveness.
  4. The system checks for changes on the scraped pages using a hash function and updates the database accordingly.
• Document Ingestion (PDFs, Google Docs):
  1. Documents are uploaded to a designated folder.
  2. The system extracts text from PDFs or converts Google Docs to Markdown.
  3. The text is chunked and converted into vector embeddings.
  4. The system checks for existing vectors in Pinecone and deletes duplicates.
  5. The new vectors are upserted into the Pinecone vector store.
• Structured Data (Capital Projects):
  1. Data on Columbus capital projects is imported into a No-Code DB table.
  2. The AI agent can query this data using structured queries generated from natural language.
  3. The system prompt includes the data schema and examples of No-Code DB operators to guide the AI in creating the queries.
• External Data Sources:
  • Perplexity: Used for deep research via its API.
  • Gina Deep Search: Used for searching, reading, and reasoning about specific questions.

Traditional RAG vs. Agentic RAG: A Detailed Comparison

The video provides a detailed comparison of traditional RAG and Agentic RAG across eight key aspects.

1. Cost: Agentic RAG is significantly more expensive due to the multiple LLM calls required for reasoning and validation. The example article research cost around $0.30 in LLM calls alone.
2. Speed: Agentic RAG can be slower due to the latency of multiple tool calls.
3. Query Complexity: Agentic RAG can handle more complex queries by breaking them down into multiple steps.
4. Accuracy: Agentic RAG has the potential for higher accuracy due to its ability to validate and refine results, but it also relies on the accuracy of external data sources.
5. Data Sources: Agentic RAG can access a wider range of data sources, including vector databases, APIs, and SQL databases.
6. Quality: The quality of retrieved information in traditional RAG can be inconsistent due to independent chunking.
7. Reliability: Traditional RAG is more reliable due to its simpler architecture. Agentic RAG relies on the availability and responsiveness of multiple services.
8. Autonomy: Agentic RAG is autonomous, allowing it to independently retrieve information and generate results. Traditional RAG is typically integrated into a predefined workflow.

Conclusion

Agentic RAG represents a significant advancement in retrieval-augmented generation, offering enhanced capabilities for handling complex queries, integrating diverse data sources, and improving the accuracy of LLM responses. While it comes with increased cost and complexity, its potential for autonomous research and knowledge grounding makes it a promising approach for various applications, particularly in content creation and information retrieval. The video emphasizes the importance of reasoning models like Claude 3.7 Sonnet for effective Agentic RAG implementations.