Don't fall behind the LLM revolution, I can help integrate machine learning/AI into your company.
AI Freelancing: mosleh587084.typeform.com/to/...
Code: github.com/mosh98/RAG_With_Mo...
When building RAG (Retrieval-Augmented Generation) applications, choosing the right retrieval parameters and strategies is crucial. Options range from chunk size to vector search, keyword search, hybrid search, and using embeddings.
But what if you could test multiple strategies simultaneously and have an AI/reranker/LLM (Large Language Model) refine the results?
This method offers two key benefits:
1. Enhanced retrieved results by pooling multiple strategies, assuming the reranker is effective, although it might incur higher costs.
2. Benchmarking different retrieval strategies against each other with respect to the reranker’s performance.
Explore how this innovative approach can elevate your RAG applications and optimize retrieval strategies. Don’t miss out on understanding the future of AI-driven search optimization and retrieval techniques.
Llama_index: docs.llamaindex.ai/en/stable/...
Langchain: python.langchain.com/v0.1/doc...
Ensemble retriever is based upon a paper called (Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods)
Reciprocal Rank Fusion or Ensemble Retrieval
The research paper introduces Reciprocal Rank Fusion (RRF) as an advanced technique for combining document rankings from multiple information retrieval systems. This method consistently outperforms individual systems and the Condorcet Fuse method, showcasing significant improvements in ranking accuracy. Tested across various TREC experiments and the LETOR 3 dataset, RRF's effectiveness is attributed to its simplicity and ability to harness the diversity within individual rankings. Discover how RRF can enhance the performance of your information retrieval systems with superior accuracy and reliability.