Vector Search RAG Tutorial – Combine Your Data with LLMs with Advanced Search

The paragraph introduces the course's focus on using vector search and embeddings to integrate data with large language models like GPT-4. It outlines three projects: building a semantic search feature for movies, creating a question-answering app using the RAG architecture, and modifying a chatbot to answer questions about contributing to a curriculum based on official documentation. The course will cover concepts, development, and use of Python and JavaScript, with a focus on vector embeddings for semantic similarity searches and their integration with AI applications.

This section details the process of creating a MongoDB Atlas account and setting up a new project. It guides through the steps of creating a deployment, selecting the free tier options, and setting up authentication. The paragraph also discusses loading sample data related to movies into the MongoDB instance and preparing for the next phase of connecting the local environment to the database.

The paragraph explains the process of creating embeddings using the Hugging Face inference API for text data. It covers generating a token for authentication, setting up the API call, and testing the function with a sample text to produce an embedding vector. The section also discusses handling the API's rate limits and the potential need for a paid plan for larger-scale operations.

This part describes the process of creating and storing vector embeddings based on the plot field of movie documents in the database. It explains the use of machine learning models for generating embeddings necessary for similarity searches based on intent. The paragraph also touches on updating the code to create embeddings for a subset of documents due to rate limits and the possibility of extending it to the entire database for better search results.

The paragraph outlines the steps for creating a vector search index on MongoDB Atlas. It includes selecting the database and collection, naming the index, and specifying the field and dimensionality for indexing. The section also explains the choice of similarity metric (dot product) and the creation of a KNN vector field for efficient similarity searches. The paragraph concludes with testing the index and preparing for the next steps in the project.

This section details the implementation of vector search within the application. It covers the process of using the aggregation pipeline stage for vector search, setting parameters like 'num candidates' for optimization, and limiting the results. The paragraph also discusses the results obtained from the search, highlighting the semantic relevance of the returned documents to the query and the potential for more accurate results with a complete set of embeddings.

The paragraph discusses the limitations of Large Language Models (LLMs) and how the Retrieval-Augmented Generation (RAG) architecture can address them. It explains how RAG uses vector search to retrieve relevant documents and provides them as context for the LLM to generate more informed responses. The section also introduces the concept of using external databases and knowledge bases to enhance LLMs and mentions the upcoming project that will utilize RAG, Atlas Vector Search, and the Lang Chain framework for a real-world application.

This section provides an overview of the technologies used for the next project, including the Lang Chain framework for creating LLM applications and Grideo for building a web interface. It explains the process of installing necessary packages, creating an OpenAI API key, and setting up the environment with API keys and MongoDB URI. The paragraph also outlines the structure of the code, mentioning the use of different models and libraries for creating embeddings and building the question-answering application.

The paragraph describes the process of loading text documents and creating embeddings for them using the directory loader and OpenAI's embedding model. It covers the initialization of the vector store, the vectorization of text from documents, and the insertion of these embeddings into the MongoDB collection. The section also includes the creation of a search index in MongoDB Atlas and the preparation for the next steps in the application development.

This section details the development of a question-answering application that uses Atlas vector search and the retrieval augmented generation (RAG) architecture. It explains the process of defining the OpenAI embedding model, accessing the vector store, and creating a function to process user queries. The paragraph also discusses the integration of the RAG architecture with Lang Chain and OpenAI's language models to efficiently process and answer complex queries. The section concludes with the creation of a web interface for the application using Gradio.

The paragraph outlines the process of enhancing a chatbot to answer questions using the Free Code Camp documentation. It describes the steps of creating embeddings for the documentation, storing them in MongoDB, and updating the API routes to utilize these embeddings. The section also explains the creation of a vector search index and the integration of vector search with the chatbot's functionality to provide more accurate and context-specific responses based on the official documentation.

The final paragraph demonstrates the testing of the enhanced chatbot with the ability to answer questions based on the Free Code Camp documentation. It shows the process of asking questions related to contributing to the platform and receiving answers that are directly pulled from the relevant sections of the documentation. The section highlights the chatbot's capability to provide precise and helpful information to users seeking guidance on contributing to Free Code Camp.