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Quickstart
Building RAG Pipelines With Haystack and MongoDB Atlas
Explore the integration of Haystack with MongoDB Atlas to build robust retrieval-augmented generation (RAG) pipelines.Sep 18, 2024
Quickstart
Quickstart Guide to RAG Application Using LangChain and LlamaIndex
In this tutorial, explore the capabilities of LangChain, LlamaIndex, and PyMongo with step-by-step instructions to use their methods for effective searching..png&w=3840&q=75)
Sep 18, 2024
Tutorial
Build Smart Applications With Atlas Vector Search and Google Vertex AI
Leverage Atlas Vector Search to perform semantic search, Google Vertex AI for AI capabilities, and LangChain for integration to build smart applications.
Sep 18, 2024
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Tutorial
Leveraging MongoDB Atlas Vector Search With LangChain
Discover the integration of MongoDB Atlas Vector Search with LangChain, in Python. Learn how semantic search and embeddings revolutionize data retrieval.
Sep 18, 2024
Tutorial
Interactive RAG With MongoDB Atlas + Function Calling API
Learn how dynamic retrieval strategies, enhanced LLM performance, and real-time data integration can revolutionize your digital investigations.
Sep 18, 2024
Tutorial
Boosting AI: Build Your Chatbot Over Your Data With MongoDB Atlas Vector Search and LangChain Templates Using the RAG Pattern
Learn how to enhance your AI chatbot's accuracy with MongoDB Atlas Vector Search and LangChain Templates using the RAG pattern in our guide.
Sep 18, 2024
Article
Multi-agent Systems With AutoGen and MongoDB
Discover how to build powerful multi-agent AI systems using AutoGen and MongoDB. This guide explores the integration of Microsoft's AutoGen framework with MongoDB's Atlas Vector Search, enabling efficient retrieval-augmented generation (RAG) and collaborative AI agents. Learn step-by-step implementation, from environment setup to agent configuration, and unlock the potential of scalable, context-aware AI solutions for complex data-driven tasks.
Sep 18, 2024
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Quickstart
Best Practices for Using Flask and MongoDB
Learn some of the best practices for using Flask and MongoDB so you can set yourself up for success.Sep 16, 2024
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Tutorial
Building an Advanced RAG System With Self-Querying Retrieval
In this tutorial, we will see how to build an advanced RAG system with self-query retrieval.
Sep 12, 2024
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Article
Implementing Robust RAG Pipelines: Integrating Google's Gemma 2 (2B) Open Model, MongoDB, and LLM Evaluation Techniques
This tutorial explores building a retrieval-augmented generation (RAG) pipeline by integrating Googleās Gemma 2 (2B) model, MongoDB, and LLM evaluation techniques. Gemma 2, a lightweight model with two billion parameters, is used for efficient response generation, while MongoDB acts as the vector database, enabling semantic search for relevant documents. The tutorial demonstrates how to create an asset management assistant that analyzes market reports stored in MongoDB. It covers embedding generation, vector search, and the use of the DeepEval library to assess the relevance and faithfulness of LLM-generated responses. By combining these tools, the tutorial highlights an efficient approach to building AI-driven solutions with robust performance evaluation in a RAG pipeline.
Sep 12, 2024