Vector Search RAG Tutorial – Combine Your Data with LLMs with Advanced Search
TLDRThis tutorial demonstrates how to integrate vector search with large language models (LLMs) for advanced data combination and semantic search. It covers three projects: building a semantic search for movies, creating a question-answering app using RAG architecture, and modifying a chatbot to answer queries based on documentation. The course explains vector embeddings, Atlas Vector Search, and how to develop AI-powered applications using MongoDB Atlas and Hugging Face or OpenAI APIs.
Takeaways
- 📚 Learn how to combine your data with large language models (LLMs) like GPT-4 using vector search and embeddings.
- 🔍 Understand the concept of vector embeddings as a digital way of sorting and describing items, turning words, images, or any other data into numerical vectors.
- 🌐 Explore the MongoDB Atlas Vector Search, which allows semantic similarity searches on data, integrating with LLMs for AI-powered applications.
- 🎬 Develop three projects: a semantic search feature for movies, a question-answering app using the RAG architecture, and a modified chatbot for specific documentation-based queries.
- 💡 Discover how similar items have similar vectors, aiding in tasks like information searching, language translation, and AI sharing.
- 🔑 Create and store vector embeddings in MongoDB using the Hugging Face inference API and the OpenAI API for semantic search capabilities.
- 🛠️ Utilize the aggregation pipeline in MongoDB for fast semantic similarity searches using an approximate nearest neighbors algorithm.
- 📈 Address limitations of LLMs, such as factual inaccuracy and lack of access to personal databases, with the RAG architecture, enhancing the model's responses with factual information.
- 🔗 Integrate the Lang Chain framework for simplifying the creation of LLM applications, providing a standard interface for chaining components to process language tasks.
- 📊 Create a question-answering app that can answer questions from custom data using Atlas vector search as a vector store and the RAG architecture with Lang Chain and OpenAI.
- 🚀 Enhance a chat GPT clone to answer questions about contributing to a specific curriculum based on official documentation by incorporating vector search with custom data.
Q & A
What is the primary focus of the Vector Search RAG Tutorial?
-The primary focus of the Vector Search RAG Tutorial is to teach users how to combine their data with large language models like GPT-4 using vector search and embeddings, through the development of three projects including semantic search, a question answering app, and a modified chatbot.
What are vector embeddings, and how do they help in semantic search?
-Vector embeddings are numerical representations of words, images, or any other data that capture their semantic meaning. They help in semantic search by allowing the comparison of vectors to find items that are similar in meaning, thus enabling the search for relevant results based on context rather than exact matches.
How does MongoDB Atlas Vector Search integrate with large language models (LLMs)?
-MongoDB Atlas Vector Search performs semantic similarity searches on data, which can be integrated with LLMs to build AI-powered applications. It stores vector embeddings alongside source data and metadata, and uses an approximate nearest neighbors algorithm to perform fast semantic similarity searches.
What is the significance of the RAG (Retrieval-Augmented Generation) architecture in the context of this tutorial?
-The RAG architecture addresses limitations of LLMs by using vector search to retrieve relevant documents based on the input query. It provides these documents as context to the LLM, helping generate more informed and accurate responses, thus minimizing hallucinations and ensuring up-to-date information is reflected in the responses.
How does the tutorial's first project utilize Python, machine learning models, and Atlas Vector Search?
-The first project builds a semantic search feature to find movies using natural language queries. It utilizes Python for coding, machine learning models for generating vector embeddings, and Atlas Vector Search for performing semantic similarity searches on a database of movie documents to find the most relevant results.
What are the main components of the second project in the tutorial?
-The second project creates a simple question answering app that uses the RAG architecture and Atlas Vector Search to answer questions using the user's own data. It involves using the Lang Chain framework, OpenAI models, and a web interface built with Grideo.
How does the third project modify a chat GPT clone to answer questions about contributing to a curriculum?
-The third project modifies a chat GPT clone so it can answer questions about contributing to the FricoCamp.org curriculum based on the official documentation. It involves creating embeddings for the documentation and using vector search to find relevant sections of the documentation to provide accurate answers.
What is the role of the Hugging Face inference API in the tutorial's examples?
-The Hugging Face inference API is used to generate vector embeddings for text data. In the examples, it is used to create embeddings for movie plots and other text documents, which are then used in semantic search and question answering applications.
What are the limitations of LLMs that the RAG architecture helps to overcome?
-The RAG architecture helps overcome limitations of LLMs such as generating factually inaccurate information (hallucinations), lack of access to up-to-date information beyond the training data, and inability to access user's local data. RAG improves these by grounding responses in factual information from retrieved documents.
How does the Lang Chain framework simplify the creation of LLM applications?
-The Lang Chain framework simplifies the creation of LLM applications by providing a standard interface for chaining lots of integrations with other tools and end-to-end chains for common applications. It uses a modular approach where different components or modules are chained together to process language tasks, making complex application development, debugging, and maintenance easier.
Outlines
📚 Introduction to Vector Search and Embeddings
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.
🚀 Setting Up MongoDB Atlas Account and Project
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.
🔍 Creating and Testing Embeddings with Hugging Face API
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.
🧠 Utilizing Vector Embeddings for Semantic Search
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.
🔎 Building a Vector Search Index on MongoDB Atlas
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.
🤖 Implementing Vector Search in the Application
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.
🛠️ Leveraging RAG Architecture and Atlas Vector Search
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.
🔄 Integrating OpenAI and MongoDB with Lang Chain
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.
📄 Loading Documents and Creating Embeddings
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.
🔍 Developing a Question Answering Application
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.
📖 Enhancing Chatbot with Free Code Camp Documentation
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.
🏁 Testing the Enhanced Chatbot
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.
Mindmap
Keywords
💡Vector Search
💡Embeddings
💡Large Language Models (LLMs)
💡RAG (Retrieval-Augmented Generation)
💡MongoDB Atlas
💡Semantic Search
💡Hugging Face
💡JavaScript
💡OpenAI
💡Question Answering App
Highlights
The tutorial introduces the concept of vector search and embeddings, explaining how they can be used to enhance data combination with large language models like GPT-4.
Three projects are outlined for the tutorial, including building a semantic search feature for movies, creating a question answering app using the RAG architecture, and modifying a chatbot to answer questions based on official documentation.
Vector embeddings are digital representations that transform items like words or images into numerical vectors, allowing for the comparison of similarity through mathematical operations.
Vector search enables semantic similarity searches by understanding the meaning or context of a query, rather than just looking for exact matches like traditional search engines.
MongoDB Atlas Vector Search is highlighted as a powerful tool for performing semantic similarity searches on data, integrating with large language models to build AI-powered applications.
The tutorial demonstrates the process of creating a MongoDB Atlas account and setting up a deployment for the projects, including the use of sample data sets like the movie data set.
The use of Python and JavaScript in the projects is mentioned, with Python being used for the first two examples and JavaScript for the final project.
The process of generating vector embeddings for movie plots using the Hugging Face inference API is detailed, showcasing how to transform text into numerical vectors.
Creating a vector search index in MongoDB Atlas is explained, including the selection of the database, collection, and specifying the vector field and dimensionality for the index.
The tutorial shows how to perform a vector search using the aggregation pipeline in MongoDB, with the aim of finding semantically similar movie plots based on a natural language query.
The limitations of large language models (LLMs) are discussed, such as the potential for generating inaccurate information and the inability to access up-to-date or personalized data.
The Retrieval-Augmented Generation (RAG) architecture is introduced as a solution to LLM limitations, using vector search to retrieve relevant documents and provide context for more accurate responses.
A question answering application is demonstrated, leveraging the RAG architecture with Atlas Vector Search and the Lang Chain framework to answer questions using custom data.
The tutorial covers the integration of the OpenAI API for creating embeddings and generating text responses, as well as the use of Grideo for building a web interface for the application.
The final project involves modifying a chatbot to interact with the Free Code Camp documentation, showcasing the potential for similar applications to connect with and answer questions based on private data.
The process of creating embeddings for text documents and storing them in MongoDB Atlas, as well as creating and utilizing vector search indexes, is emphasized as a crucial part of building AI-powered applications.