Tech Events & Workshops
Discover and join virtual tech events, workshops, and meetups from the Ubuntu community worldwide. Connect with developers, engineers, and tech enthusiasts.
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Deep Dive into Retrieval Augmented Generation (RAG) with LangChain / Agents
🚀 Join us for an enlightening Ubuntu TechHive meetup where we'll take a deep dive into Retrieval Augmented Generation (RAG) with LangChain / Agents! Implementing advanced retrieval-augmented generation involves combining techniques from natural language processing (NLP), information retrieval (IR), and machine learning (ML) to generate coherent and contextually relevant responses. 🎯 **Topics**: 1. **Problem Definition** 2. **Data Collection and Preprocessing** 3. **Retrieval Model** 4. **Generation Model** 5. **Integration and Architecture** 6. **Fine-tuning and Evaluation** 7. **Deployment and Monitoring** 8. **Continuous Improvement** 9. **Ethical Considerations During Implementation** We will utilize Vector-store, Python, and LangChain modules such as Model I/O, Retrieval, Agents, Chains, and many more. We will explore Naïve RAG and ways of optimizing it for retrieval quality.
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Deep Dive into Retrieval Augmented Generation (RAG) with LangChain / Agents
🚀 Join us for an enlightening Ubuntu TechHive meetup where we'll take a deep dive into Retrieval Augmented Generation (RAG) with LangChain / Agents! Implementing advanced retrieval-augmented generation involves combining techniques from natural language processing (NLP), information retrieval (IR), and machine learning (ML) to generate coherent and contextually relevant responses. 🎯 **Topics**: 1. **Problem Definition** 2. **Data Collection and Preprocessing** 3. **Retrieval Model** 4. **Generation Model** 5. **Integration and Architecture** 6. **Fine-tuning and Evaluation** 7. **Deployment and Monitoring** 8. **Continuous Improvement** 9. **Ethical Considerations During Implementation** We will utilize Vector-store, Python, and LangChain modules such as Model I/O, Retrieval, Agents, Chains, and many more. We will explore Naïve RAG and ways of optimizing it for retrieval quality.
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