Pegasus Reranker (Fusion) for Agentic Chatbots
Get ready to powerup your chatflows and unlock their full potential with N8N and this custom reranker. This is a powerful reranking automation that delivers lightning fast results for your Chatbot responses and AI applications.
Imagine having your own reranker to get more accurate results from your RAG documents and web searches. What makes the Pegasus Reranker so flexible are the options it gives you in ranking your documents.
With this automation, you’ll not only get a plug and play system that works with your existing AI Chatbot, you’ll also get a project that shows you how you can use N8N’s features to build almost anything you can imagine.
The Power of Ranking: Why You Need a Reranker
Rerankers are the secret sauce that can enhance your chatbot’s performance. By comparing your question to the documents returned by your vector store or web search results, the Pegasus Reranker calculates a relevancy score for each document and then reranks them based on their similarity to your question.
As a result, rerankers help you prioritize the most important documents first and filter out irrelevant information. This gives your chatbot the ability to provide more accurate and relevant responses, leading to a better user experience. This is especially helpful for RAG applications as well as AI powered web searches.
How to Customize and Roll Your Own Lightning Fast Reranker
Building a reranker is not easy. The most important element is the embedding model that allows you to do all the calculations needed to rank documents. To deliver answers quickly, the embedding model needs to be fast and offer a high level of performance.
For this project, we used the Nomic text embedding model which outperforms OpenAI’s Ada 002 model as well as their text-embed-3 small model on MTEB benchmarks. This ensured that we could consistently get the best results for our ranking algorithm.
Easily Rank And Filter Documents By Relevancy Score
Two limitations that come with some rerankers are that you either don’t have full control over the ranking process or their pricing is based on searches rather than more flexible token-based usage.
With the Pegasus Reranker, you get complete freedom to rank documents as you need based on your application. In addition to ranking you can also filter out documents with a max relevancy score at the same time and fine tune your results.
You can also self-host embedding models like Nomic on Ollama or choose production ready providers such as Fireworks AI and OpenAI’s embedding models for more flexible and developer friendly token-based pricing. This means you can easily switch providers at any time and even upgrade to the latest embedding models as they become available.
What’s Included in the Project Files
Includes two agents to visualize data for SQL databases and CSV files.
Includes (4) Database Schema Retrieval and SQL Query Execution workflows
Includes all the CSV files you'll need to recreate the postgres database as demonstrated in the video.
Includes all the CSV files you'll need to recreate the postgres database as demonstrated in the video.
Includes all the CSV files you'll need to recreate the postgres database as demonstrated in the video.
Includes all the CSV files you'll need to recreate the postgres database as demonstrated in the video.
Get Access to this Complete N8N Automation Project
If you had to build a similar Reranker, it would take you nearly 40+ hours from researching the best embedding models, building the JSON data integrations, developing code to testing and deploying the N8N automation. However, with this workflow, you can significantly reduce the time spent building your own Reranker.
Get instant access to this project for just $97. After you place your order you will be taken the members area where you’ll be able to download the entire project and start customizing your reranker.
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