Don't do RAG - This method is way faster & accurate...

Key Concepts

• Retrieval Augmented Generation (RAG): A method for enhancing large language models (LLMs) by retrieving relevant information from an external knowledge base and incorporating it into the model's context.
• Cage Augmented Generation (CAG): An alternative approach to RAG where the entire knowledge base is pre-loaded into the LLM's context window.
• Context Window: The amount of text an LLM can process at once.
• Vector Database: A database that stores data as vectors, allowing for efficient similarity searches.
• Embedding: A numerical representation of text that captures its semantic meaning.
• Needle in a Haystack: A benchmark for evaluating an LLM's ability to retrieve specific information from a large context.
• Hallucination Rate: The frequency with which an LLM generates incorrect or nonsensical information.
• Headon: An open-source platform for logging, monitoring, and debugging LLM applications.
• Filec: A service used for scraping web content.
• Contestation: A feature (currently unavailable for Gemini 2.0) that allows for caching large contexts to speed up future generations.

RAG vs. CAG: A Detailed Comparison

The video contrasts two primary methods for augmenting LLMs with external knowledge: RAG and CAG.

Retrieval Augmented Generation (RAG):

• Process:
  1. Data Preparation: Convert the knowledge base into a vector database containing small chunks of data.
  2. Query Embedding: Transform the user's question into an embedding.
  3. Retrieval: Search the vector database using the query embedding to retrieve the most relevant chunks (top-k results).
  4. Prompting: Send both the retrieved chunks and the user's question to the LLM.
• Challenges:
  • Complexity: Requires setting up and maintaining a vector database and retrieval pipeline.
  • Latency: Introduces delays due to embedding generation and database searching.
  • Retrieval Accuracy: Ensuring the retrieved chunks contain all the necessary information for the LLM to answer the question.
  • Chunking Issues: Incomplete information if code examples are split across chunks.
  • Irrelevant Information: Retrieval of outdated or irrelevant information that can confuse the LLM.
• Mitigation Techniques: Metadata filtering, query transformation, reranking.

Cage Augmented Generation (CAG):

• Process: Pre-load the entire knowledge base into the LLM's context window.
• Advantages:
  • Simplicity: Eliminates the need for a vector database and retrieval pipeline.
  • Guaranteed Information Access: Ensures the LLM has access to all relevant information.
• Feasibility: Made possible by the dramatic increase in LLM context window sizes (e.g., Google's Gemini model supports up to 2 million tokens).

The Impact of Large Context Windows

The video emphasizes the significance of the increasing context window sizes of LLMs.

• Historical Context: 24 months ago, the state-of-the-art context window was only 4,000 tokens.
• Current Capabilities: Flagship models now support 100,000-200,000 tokens, and Google's Gemini model supports up to 2 million tokens.
• Real-World Analogy: 2 million tokens is roughly equivalent to 1.5 million words, exceeding the length of most novels (e.g., "War and Peace" is 587,589 words).

Performance and Cost Considerations with Gemini

The video highlights the performance and cost benefits of using Google's Gemini model for CAG.

• Retrieval Accuracy: Gemini 1.5 Pro demonstrates near-perfect recall of specific information in vast contexts (up to 1 million tokens).
• Hallucination Rate: Gemini 2.0 achieves an extraordinary low hallucination rate.
• Cost: Gemini 2.0 Flash offers a significantly lower input price ($0.10 per million tokens) compared to models like GPT-4 ($2.50 per million input tokens).
• Speed: The video creator was able to feed the entire FireC developer documentation to Gemini 2.0 and get results within 3.4 seconds at a cost of $0.006.

Headon: Monitoring and Debugging LLM Applications

The video promotes Headon as a valuable tool for managing LLM applications.

• Functionality:
  • Logging and tracking user interactions.
  • Monitoring cost, errors, and latencies.
  • Optimizing performance.
  • Auto-caching responses to save cost and improve speed.
  • Setting up custom properties to segment requests.
• Ease of Use: Simple integration by adding HTTP options to existing Gemini calls.

Building an External Doc MCP with CAG: A Step-by-Step Example

The video provides a practical example of building an MCP (Minimum Competent Product) using CAG to enable Cursor or Wingman to work with external documentation.

Use Case: Retrieve the most relevant code example from a documentation website based on a user's request.

Steps:

1. Install Packages: Install necessary Python packages (e.g., google-generativeai, filec).
2. Set Up Environment Keys: Obtain an API key from Google AI Studio and set up Filec and Headon.
3. Basic Chat with PDF Example: Demonstrate a simple chat with PDF use case using Gemini 2.0.
4. External Doc MCP Pipeline:
   • Initial Test: Call Gemini directly with a prompt to generate a request to scrape a website using Filec.
   • Filter Relevant URLs: Use Gemini to filter out URLs that are relevant to the API reference.
   • Batch Scraping: Use Filec's batch script URL endpoint to download the markdown of each relevant page.
   • Feed Markdown to Gemini: Pass the downloaded markdown content and the user's prompt to Gemini to generate the desired code example.
   • Save Docs Locally: Save the scraped markdown files locally to avoid re-scraping in the future.
5. MCP Server: Turn the pipeline into an MCP server with a function called retrieve_api_doc that handles the retrieval of code examples from documentation.

When to Use RAG vs. CAG

• CAG (Preloading): Preferred when the dataset can fit within the LLM's context window.
• RAG (Retrieval): Still relevant for very large and diverse datasets that cannot fit into the context window.

Alternative CAG Approaches for Large Datasets

• Traditional Search + CAG: Use traditional search methods (metadata, filename) to identify relevant data and then feed only that data to the LLM.
• Parallel LLM Calls + Summarization: Perform parallel LLM calls on different data subsets and then use another LLM call to summarize the results.

Conclusion

The video advocates for CAG as a simpler and more effective approach for augmenting LLMs with external knowledge, especially with the increasing context window sizes and improved performance of models like Google's Gemini. While RAG remains relevant for extremely large datasets, CAG offers a compelling alternative for many use cases. The video also highlights the importance of monitoring and debugging LLM applications using tools like Headon.