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GenAI-Showcase discussion
GenAI-Showcase reviews and mentions
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Persistent multi-agent conversations with the OpenAI Agents SDK and MongoDB
Python 3.10+, an OpenAI API key, and either a local mongod or a MongoDB Atlas cluster. Nothing in the demo requires Atlas-only features — a 27017 on localhost is fine.
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Top-K queries with MongoDB search indexes (BM25)
You can use BM25 indexes on MongoDB in several environments: the cloud-managed service (MongoDB Atlas), its local deployment (Atlas Local), on-premises MongoDB Enterprise Server, and the open-source MongoDB Community edition. The mongot engine that powers MongoDB Search is in public preview, with its source available at github.com/mongodb/mongot.
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Building Persistent Memory for Voice AI Agents with MongoDB
Demo code: Check out the complete working demo at GitHub: voice-memory-demo.
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LangGraph With MongoDB: Building Conversational Long-Term Memory for Intelligent AI Agents
Ready to give your AI agents a memory? Start building with the complete code example in our interactive notebook and see how persistent memory can transform your user experiences today.
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Building Multimodal AI Applications With MongoDB, Voyage AI, and Gemini
Refer to the steps under the title Step 10: Evaluating retrieval and generation in the Jupyter Notebook accompanying this tutorial to see the code that defines the metrics above and does the evaluation, but let’s discuss the results here.
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Building an HR Team-Matching Agent With MongoDB Vector Search, Voyage AI, & Vercel AI SDK
First, let's create a utility function to generate embeddings (GitHub):
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Grab a Pint With Django-MongoDB-Backend, Voyage AI, and LangChain
Clone the GitHub repo for this project from our Gen AI Showcase!
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A note from our sponsor - SaaSHub
www.saashub.com | 12 Aug 2026
Stats
mongodb-developer/GenAI-Showcase is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of GenAI-Showcase is Jupyter Notebook.
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