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The video is a tutorial on how to enable semantic search on user-specific data using Lang chain and MongoDB Atlas. The process involves loading, transforming, embedding, and storing data before it can be queried. The tutorial uses the AT&T Wikipedia page as a data source and demonstrates how to use libraries from Lang chain to load, transform, embed, and store vectors. The video also explains how to retrieve and query relevant data using Vector search.
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⏱️ Timestamps ⏱️
Introduction to Semantic Search [00:00:00 - 00:02:35]
The video begins with an explanation of the multi-step process of enabling semantic search on user-specific data. This process includes loading, transforming, embedding, and storing data before it can be queried. The speaker mentions the team at Lang chain, whose goal is to simplify this process. The tutorial will walk through each of these steps using MongoDB Atlas as the vector store and the AT&T Wikipedia page as the data source.
Setting Up the Environment [00:02:36 - 00:05:11]
In this chapter, the speaker guides the viewer through the process of setting up the environment. This includes creating a free MongoDB Atlas account, obtaining an OpenAI API key, and cloning a repository with all the necessary code. The speaker also explains how to add the OpenAI API key and MongoDB connection string to the project, create a new Python environment, and install all the required dependencies.
Vectorizing and Querying Data [00:05:12 - 00:07:47]
The final chapter focuses on vectorizing and querying data. The speaker demonstrates how to load, transform, embed, and store data using the AT&T Wikipedia page as the data source. The process of creating vector representations of data using an LLM is explained, as well as how to store these vectors in a MongoDB database. The speaker then shows how to query this data using vector search, and how to set up a vector search index on the vector embeddings field. The video concludes with a demonstration of a CLI-based query and the results of a semantic search.
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