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Update README.md #191

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6 changes: 3 additions & 3 deletions workshops/README.md
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Expand Up @@ -4,9 +4,9 @@ We have designed a series of hands-on workshops to take you from zero to hero wi

|Workshop name | Links | Description | Language | Tools used |
|------------|-------------|----------------|-------------|-------------|
| Vector Search: Beginner to Pro | [Slides](https://docs.google.com/presentation/d/e/2PACX-1vR4lPTcr2ZXPTkQLPq3HtTn4vSLG4VrFD3jkOjXmEDrrvyLEElTaz-6JC5KZN4__VJZ2h13aTabGXhG/pub), [Hands-on Lab](https://mongodb-developer.github.io/vector-search-lab/) | Learn vector search concepts such as embeddings, how vector search works in MongoDB, and also advanced concepts such as pre-filtering, vector quantization etc. Gain hands-on experience by building a multimodal vector search application for an online library. | Python | MongoDB Atlas, Voyage AI|
| Building RAG Applications with MongoDB | [Slides](https://docs.google.com/presentation/d/e/2PACX-1vSN_7zZTqpXSmtUyDalox2kAoealuO4V_aVGqLuTuDKa3I3aJ9nQUdViQKasBNnu2zQVOpT5cubnyFd/pub), [Hands-on Lab](https://mongodb-developer.github.io/ai-rag-lab/) | Learn RAG concepts such as embedding, chunking and vector search. Gain hands-on experience by building a RAG-based chatbot for a technical documentation website. | Python | MongoDB Atlas, Voyage AI, Anthropic/Azure OpenAI/Gemini |
| The A to Z of Building AI Agents | [Slides](https://docs.google.com/presentation/d/e/2PACX-1vRMH-7DLejrxrEgrReZTy4p9sKzN35uTaiDRZ8JAM9xtyLFz-utjJzk97FG8mGI96VEuLPnLZWzq10Q/pub), [Hands-on Lab](https://mongodb-developer.github.io/ai-agents-lab/) | Learn the core concepts of AI agents, such as reasoning, tools, and memory. Gain hands-on experience by buiding a technical documentation AI agent. | Python | MongoDB Atlas, Voyage AI, Anthropic/Azure OpenAI/Gemini, LangGraph |
| Vector Search: Beginner to Pro | [Slides + Self-paced lab](https://docs.google.com/presentation/d/e/2PACX-1vR4lPTcr2ZXPTkQLPq3HtTn4vSLG4VrFD3jkOjXmEDrrvyLEElTaz-6JC5KZN4__VJZ2h13aTabGXhG/pub) | Learn vector search concepts such as embeddings, how vector search works in MongoDB, and also advanced concepts such as pre-filtering, vector quantization etc. Gain hands-on experience by building a multimodal vector search application for an online library. | Python | MongoDB Atlas, Voyage AI|
| Building RAG Applications with MongoDB | [Slides + Self-paced lab](https://docs.google.com/presentation/d/e/2PACX-1vSN_7zZTqpXSmtUyDalox2kAoealuO4V_aVGqLuTuDKa3I3aJ9nQUdViQKasBNnu2zQVOpT5cubnyFd/pub) | Learn RAG concepts such as embedding, chunking and vector search. Gain hands-on experience by building a RAG-based chatbot for a technical documentation website. | Python | MongoDB Atlas, Voyage AI, Anthropic/Azure OpenAI/Gemini |
| The A to Z of Building AI Agents | [Slides + Self-paced lab](https://docs.google.com/presentation/d/e/2PACX-1vRMH-7DLejrxrEgrReZTy4p9sKzN35uTaiDRZ8JAM9xtyLFz-utjJzk97FG8mGI96VEuLPnLZWzq10Q/pub) | Learn the core concepts of AI agents, such as reasoning, tools, and memory. Gain hands-on experience by buiding a technical documentation AI agent. | Python | MongoDB Atlas, Voyage AI, Anthropic/Azure OpenAI/Gemini, LangGraph |
| Pragmatic LLM Application Development: From RAG Pipelines to AI Agents | [Hands-on Lab](https://github.com/mongodb-developer/GenAI-Showcase/blob/main/notebooks/agents/Pragmatic_LLM_Application_Introduction_From_RAG_to_Agents_with_MongoDB.ipynb) | Build RAG pipelines and AI agents with and without abstraction frameworks such as LangChain. Also introduces techniques such as prompt compression for optimizing LLM apps. | Python | MongoDB Atlas, OpenAI, LangChain, LLMLingua |
| Building Multimodal AI Agents from Scratch | [Video](https://www.youtube.com/watch?v=640KMYtxCeI), [Hands-on Lab](https://github.com/mongodb-developer/multimodal-agents-lab) | Learn techniques to process multimodal data and build a multimodal AI agent from scratch, no abstractions involved. | Python | Voyage AI, MongoDB Atlas, Gemini |

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