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Crafting your own RAG system: Leveraging 30+ LLMs for enhanced performance by Stephan Janssen
Learn how to craft your own RAG system using 30+ LLMs, enhance performance through careful selection and implementation, and explore re-ranking, query expansion, and answer generation techniques.
- Crafting your own RAG (Relevance Aware Retrieval) system is possible using 30+ LLMs (Large Language Models).
- Leveraging LLMs can enhance performance, but requires careful selection and implementation.
- LLMs can be used for re-ranking, query expansion, and answer generation.
- Choosing the right embedding is crucial for semantic search, with options including local, open source, and proprietary models.
- Embeddings can be used to convert text into numerical representations, allowing for efficient querying and ranking.
- RAG systems can be trained using various techniques, including supervised learning, unsupervised learning, and reinforcement learning.
- Local models can be used to generate responses, with options including Ollama, LM Studio, and GPT.
- Deploying a RAG system requires consideration of scalability, latency, and cost.
- Using local models can reduce latency and cost, but may require more development and maintenance effort.
- Visualizing embeddings can help with understanding and debugging the system.
- Embeddings can be used to improve search relevance, with options including BM25, DSSM, and Co-Attention.
- Quantizing embeddings can reduce storage requirements and improve inference speed.
- Open source libraries like LangChain4j can be used to simplify implementation and development.