Hello everyone, welcome to a new video on N8N workflows. In this video, we'll explore context-based chunking in RAG Geek and how it can enhance retrieval accuracy. Let's dive in.
Let's start by understanding what retrieval augmented generation is. Retrieval augmented generation is a technique that improves the accuracy and reliability of generative AI models by incorporating information from specific and relevant data sources. In simple terms, we provide an LLM with a data source, allowing it to retrieve and use relevant information before generating a response.
This makes the output more precise and contextually accurate. Here's a simple diagram illustrating the traditional retrieval augmented generation system. Now, there's a challenge with the traditional retrieval augmented generation approach. It often struggles to retrieve highly relevant data based on the current context.
This can lead to inaccurate or incomplete answers. To improve retrieval accuracy, we can use different chunking strategies such as recursive text splitting with overlap, which helps retain context across chunks. Let's take a closer look. But in this video, we'll be implementing context-based chunking, as described by Anthropic Contextual Retrieval.
In context-based chunking, each chunk is attached to a broader context from the entire document. This helps maintain better coherence. and improves retrieval accuracy, ensuring the model understands the bigger picture when generating responses. Let's see how we can set this up in N8n. Our workflow starts by retrieving the source document from Google Drive.
In the next step, we extract text data from the document. Now, here's the interesting part. We've added clear boundary lines in the document to mark different sections.
These boundaries will help us split the text into meaningful chunks. Next, we use a code node in N8n, where we'll write a script to divide the document into sections. Or in other words, create structured chunks.
This ensures that each chunk retains its context properly. Once the chunks are extracted from the document, we loop through each chunk using a loop node in N8n. Next, we use an agent node, which generates contextual information for each chunk by referencing the entire document.
This step ensures that every chunk maintains a strong connection to the overall context, improving retrieval accuracy. In this node, we are using OpenAI's GPT 4.0 Mini, provided by OpenRouter, as our LLM model. Once the chunk's context is generated, we prepend that context to the related chunk. This enriched chunk is then sent to the next node, where we create embeddings for storage in a vector database. For our vector store, We are using Pinecone, and for text-to-vector conversion, we're leveraging Google's Gemini text-embedding Oak 04 model.
We've also added a recursive text splitter, but since we've set a large chunk size, it won't have much effect in this case. That's it. Now we can connect our vector store to a RAG setup and see the magic in action.
With context-enriched chunks and optimized retrieval, our system is now much more accurate and efficient. In this video, We explored how context-based chunking enhances retrieval accuracy in a RAG setup. We walked through an N8n workflow covering retrieving a document from Google Drive, extracting text and structuring it into meaningful chunks, using an agent node to generate context for each chunk, creating embeddings with Google Gemini Text Embedding 04 and storing them in Pinecone.
With this setup, our Our RAG system is now more accurate and context aware. You can try it out yourself. The workflow link is in the description. Thanks for watching. If you found this helpful, don't forget to like, share, and subscribe for more N8n tutorials.
See you in the next one.