Vector search mongodb tutorial The query engine in LlamaIndex is an interface to ask questions about your data and configure query settings. This course will provide you with an introduction to artificial intelligence and vector search. If you're trying to develop an AI based product with noSQL, their Atlas Vector Search is super convenient Apr 25, 2025 · Dimension Matching: The vector dimensions in MongoDB must match your model’s output. 11, or v7. Dense Vector Tutorial: A walkthrough of building your own dense vector feature extraction engine. The sample data loaded into your Atlas cluster. By leveraging MongoDB Atlas, developers can integrate AI-powered search without complex infrastructure. 0+ Atlas cluster). Aug 29, 2024 · MongoDB’s Atlas platform offers a fully managed vector search feature, integrating the operational database and a vector store. To demonstrate this, it takes you through the following steps: Create an Atlas Vector Search index on the numeric field named plot_embedding in the sample_mflix. mongosh or a supported MongoDB Driver to run queries on your cluster. What Undercode Say. Harshad Dhavale is a Staff Technical Services Engineer, who has been with MongoDB for over six years. This tutorial walks you through how to create an Atlas Vector Search index programmatically with a supported MongoDB Driver or using the Atlas CLI. . Atlas Vector Search Tutorials Create a Vector Search Index To create a vector search index, use createSearchIndex() method, which expects the name, type, and definition of the index. Vector search revolutionizes how we retrieve semantically similar data. Nov 21, 2023 · (Optional) Alternatively, we can use pymongo driver to create these vector search indexes programatically The python command given in the cell below will create the index (this only works for the most recent version of the Python Driver for MongoDB and MongoDB server version 7. Instead of sifting through complex queries and extensive code, Atlas Vector Search provides an intuitive and straightforward way to implement vector-based search functionality. May 6, 2024 · Note the score In addition to movie attributes (title, year, plot, etc. This tutorial covers step-by-step instructions to integrate advanced search capabilities into Kubernetes clusters, enabling scalable, high-performance workloads with MongoDB Atlas. 4: Atlas Vector Search Engine: Guides that showcase MongoDB Atlas' vector search implementation. Learn how to deploy MongoDB Atlas Vector Search, Atlas Search, and Search Nodes using the Atlas Kubernetes Operator. He is a subject matter expert in Atlas Search and Atlas Vector Search, and has made significant contributions in these domains over his tenure. In order to use OpenAIEmbeddings , we need to set up our OpenAI API key. 5: Vector Search Comparisons: A comparison of the most popular vector search engines. A one-stop-shop for MongoDB users to learn about Vector Search. Hybrid Search: Combine keyword and vector search for best results. 2. To complete these tutorials, you must have the following: An Atlas cluster with MongoDB version v6. embedded_movies collection that indexes the plot_embedding field as the vector type. MongoDB recently released their vector db offering. Sep 18, 2024 · MongoDB Atlas Vector Search is a game-changer for developers like us who appreciate the power of simplicity and efficiency in database operations. Converts the vector store index created in Step 4 into a query engine. Oct 2, 2024 · The search mode can be text_search for full-text search, default for vector search, and hybrid for hybrid search. Feb 14, 2024 · Here is a quick tutorial on how to use MongoDB’s Atlas vector search with RAG architecture to build your Q&A app. For information on other ways to create an Atlas Vector Search index, see How to Index Fields for Vector Search. Then, you'll learn how to generate embeddings for your data, store your embeddings in MongoDB Atlas, and index and search your embeddings to perform a semantic search. We've gathered the most helpful guides, docs, videos, courses and more - all to help you master Vector Search on MongoDB. In this quick start, you complete the following steps: Create an index definition for the sample_mflix. Atlas Vector Search. This unified approach supports quick integrations into LLMs, facilitating the development of semantic search and AI-powered applications using MongoDB-stored data. This tutorial describes how to perform an ANN search on a vector in the plot_embedding field in the sample_mflix. Project Data Access Admin access to the project to create Atlas Vector Search indexes. When using vector search, you can query using a question or a phrase rather than just a word. In this example, we use the createSearchIndex() method to create an index named vectorPlotIndex , which is a vectorSearch index. embedded_movies collection on your Atlas cluster. 2 or later. This is a meta attribute — not really part of the movies collection but generated as a result of the vector search. embedded_movies Sep 18, 2024 · (Spoiler: It’s a game-changer!) 02:35 - MongoDB + LangChain setup: Chunking strategies & metadata tips 10:06 - Async processing: Ingest 25K docs WITHOUT crashing your system 15:04 - Vector search indexes: Optimize for speed & accuracy 20:12 - AI Agent demo: Answer complex questions with context expansion 25:56 - Pro tips: Avoid “tool loops Aug 30, 2024 · Let’s first understand exactly what vector search is: Vector search is the way to search based on meaning rather than specific words. ), we are also displaying search_score. This comes in handy when querying using similarities rather than searching based on keywords. 0. For a hands-on experience creating Atlas Vector Search indexes and running Atlas Vector Search queries against sample data, try the Atlas Vector Search Course on MongoDB University and the tutorials in the following pages: Atlas Vector Search Quick Start. sdwbxgt fbevfjcb aqly kzazx ddjk ynuacu epjfsm ifca yhs pvbqd