Best of RAG — June 2024
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freeCodeCamp·2y
How to Build a RAG Chatbot with Agent Cloud and Google Sheets
Learn how to build a Retrieval-Augmented Generation (RAG) chatbot using Agent Cloud and Google Sheets. Understand the complexities of setting up RAG chat applications and how Agent Cloud simplifies the process through automated data retrieval, natural language processing, and scalable infrastructure. This guide covers everything from setting up Agent Cloud via Docker, adding models, creating a Google Cloud Platform service account key, enabling the Google Sheets API, and building an interactive chat application to communicate with data sourced from Google Sheets.
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neo4j·2y
Get Started With GraphRAG: Neo4j’s Ecosystem Tools
Neo4j’s GraphRAG Ecosystem Tools provide open-source resources to enhance GenAI applications using knowledge graphs. GraphRAG addresses issues like hallucination and lack of domain-specific context by combining retrieval-augmented generation with structured and semi-structured data. The tools include the LLM Knowledge Graph Builder for transforming unstructured text into knowledge graphs, and NeoConverse for generating Cypher graph queries from natural language questions. These tools integrate seamlessly with various programming languages and frameworks, making it easier to build and optimize GenAI applications.
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GoPenAI·2y
Introduction to Retrieval-Augmented Generation (RAG): A Beginner’s Guide
Introduction to Retrieval-Augmented Generation (RAG): A Beginner's Guide. RAG combines retrieval and generative AI techniques to ensure accurate and meaningful responses. The RAG process involves document ingestion, retrieval, and response generation. RAG systems provide precise and top-notch text responses, elevating the performance of AI applications.
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The New Stack·2y
RAG vs. Fine-Tuning Models: What’s the Right Approach?
Retrieval-Augmented Generation (RAG) retrieves relevant documents to generate contextually accurate responses, ideal for dynamic environments like enterprise search and customer support. Fine-tuning involves training a model on specific datasets for specialized tasks, ensuring consistency and improved performance for targeted applications. Choosing between RAG and fine-tuning depends on the need for adaptability or task-specific expertise.
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Can 2 LLM calls boost your RAG’s performance?
Building a real-world Retrieval Augmented Generation (RAG) system for handling company reports presents unique challenges and solutions. Initially struggling with generating accurate responses from unstructured data, the author experimented with different models and retrieval methods. Ultimately, using a smaller in-house LLM, Mistral 7B, for both generating metadata and crafting responses, outperformed even a powerful LLM like GPT-4. The key takeaway is the effective use of metadata filters and strategic application of smaller LLMs for enhanced performance.
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Medium·2y
Architect scalable LLM & RAG inference pipelines
This post discusses the architecting of scalable and cost-effective LLM and RAG inference pipelines. It explains the difference between monolithic and microservice architectures, and showcases the implementation of the RAG business module and the LLM microservice. The post also provides details on deploying and running the inference pipeline on the Qwak AI platform.
