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 processing. It covers three projects: building a semantic search for movies, creating a question-answering app using the RAG architecture, and modifying a chatbot to answer queries based on official documentation. The use of vector embeddings and MongoDB Atlas Vector Search is highlighted, showing how they can enhance AI applications by providing semantic similarity searches and leveraging external databases for more informed responses.
Takeaways
- 🌟 Vector search and embeddings can be used to combine data with large language models (LLMs) like GPT-4 for advanced search functionalities.
- 🔍 The course introduces vector embeddings as a digital way of sorting and describing items, turning them into numerical vectors for easier mathematical processing.
- 📈 Vector search is a method that understands the meaning or context of a query, different from traditional search engines that look for exact matches.
- 🧠 LLMs have limitations such as generating inaccurate information, not having access to local data, and a limit on the text they can process in one interaction.
- 💡 The Retrieval-Augmented Generation (RAG) architecture addresses LLM limitations by using vector search to retrieve relevant documents and provide context for more informed responses.
- 🛠️ The tutorial demonstrates creating a semantic search feature for movie recommendations using Python, machine learning models, and Atlas Vector Search.
- 📚 A question-answering app is built using RAG, Atlas Vector Search, and the Lang Chain framework, which can answer questions using your own data.
- 🔗 The course shows how to modify a chatbot to answer questions about contributing to a curriculum based on official documentation, using vector search and LLMs.
- 🎯 The use of MongoDB Atlas Vector Search allows for semantic similarity searches on data, integrating with LLMs to build AI-powered applications.
- 📊 The tutorial covers the process of creating vector embeddings for documents, creating a vector search index, and using these for semantic search and information retrieval.
- 🔧 The course provides practical guidance on developing AI applications that leverage both machine learning models and vector databases to enhance search and data processing capabilities.
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.
What are the three projects included in the tutorial?
-The three projects include building a semantic search feature for finding movies using natural language queries, creating a simple question answering app using the RAG architecture, and modifying a chat GPT clone to answer questions about contributing to the FricoCamp.org curriculum based on official documentation.
What is the significance of vector embeddings in the context of the tutorial?
-Vector embeddings are significant as they allow for the digital sorting or describing of items. They turn words, images, or any other data into a list of numbers (vector) that can be processed mathematically to understand and find similarities, which is crucial for tasks like semantic search and AI interaction.
How does MongoDB Atlas Vector Search integrate with LLMs?
-MongoDB Atlas Vector Search integrates with LLMs by allowing semantic similarity searches on data, which can then be used to build AI-powered applications. It stores vector embeddings alongside source data and metadata, enabling fast semantic similarity searches using an approximate nearest neighbors algorithm.
What is the role of the Hugging Face inference API in the tutorial?
-The Hugging Face inference API is used to generate vector embeddings for text data. It provides an open-source platform for building, training, and deploying machine learning models, making it easy to use machine learning models via API for tasks like semantic search.
What is the Retrieval-Augmented Generation (RAG) architecture?
-The Retrieval-Augmented Generation (RAG) architecture is designed to address limitations of LLMs by retrieving relevant documents based on the input query and using these documents as context to generate more informed and accurate responses. It minimizes 'hallucinations' and ensures responses reflect the most current and accurate information available.
How does the RAG architecture improve upon the limitations of LLMs?
-The RAG architecture improves upon LLMs by grounding the model's responses in factual information from retrieved documents, ensuring responses are up-to-date and accurate. It also makes the model's use of tokens more efficient by retrieving only the most relevant documents for generating a response.
What is the purpose of creating a vector search index in MongoDB Atlas?
-Creating a vector search index in MongoDB Atlas allows for efficient similarity searches on the data. It enables the comparison of vectors to find the best matches, providing a powerful tool for searching through large and complex data sets based on semantic similarity.
How does the tutorial demonstrate the use of vector search in a real-world project?
-The tutorial demonstrates the use of vector search in a real-world project by building a question-answering app that uses RAG architecture and Atlas Vector Search to answer questions using the user's own data. It shows how to combine the capabilities of open AI's language models, MongoDB Atlas vector search, and Lang Chain to process and answer complex queries.
What are the benefits of using vector search for semantic search?
-Using vector search for semantic search allows for a more natural language understanding and processing of queries. It can capture the intent behind the query and find results that are semantically similar, rather than just matching exact keywords, leading to more relevant and meaningful search results.
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 RAG architecture, and modifying a chatbot to answer questions about contributing to a curriculum based on official documentation. The course will cover the basics of vector embeddings, their role in semantic similarity searches, and their integration with MongoDB Atlas Vector Search for AI-powered applications.
🚀 Setting Up MongoDB Atlas Account and Project
This paragraph 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 speaker also discusses loading sample data related to movies into the MongoDB instance and using this data for the first project, which involves implementing semantic search for movie recommendations.
🔍 Creating and Testing Embeddings with Hugging Face API
The speaker explains the process of creating embeddings using the Hugging Face inference API, which is a free way to generate embeddings. The paragraph covers the steps of setting up the API, generating an embedding for a given text, and testing the function to ensure it works correctly. The speaker also discusses the limitations of the free API and the need for a paid plan if rate limits are exceeded.
🧠 Generating and Storing Embeddings for Movie Plots
This section describes the process of generating vector embeddings for the plot summaries of movie documents in the MongoDB database. It explains how to execute an operation to create embeddings for a subset of the data and store these embeddings in the database. The speaker also discusses the potential of using natural language queries to find movies with similar plots based on these embeddings.
🔎 Building and Using a Vector Search Index
The paragraph explains the creation of a vector search index in MongoDB Atlas to enable semantic searches based on the embeddings. It details the steps of selecting the database and collection, naming the index, and specifying the field and dimensionality for indexing. The speaker also discusses the use of a similarity metric and the creation of a K nearest neighbor vector type for efficient similarity searches.
🤖 Integrating Vector Search with Natural Language Queries
This section demonstrates how to perform a vector search using the aggregation pipeline stage in MongoDB to find documents semantically similar to a provided natural language query. It explains the process of generating an embedding for the query, setting up the aggregation pipeline, and optimizing parameters for the search. The speaker also discusses the limitations of searching only a subset of the data and the potential results of searching the entire database.
🛠️ Utilizing RAG Architecture and Atlas Vector Search for QA
The paragraph discusses the limitations of large language models (LLMs) and how the retrieval-augmented generation (RAG) architecture can address these issues. It explains how RAG uses vector search to retrieve relevant documents and provides these as context for the LLM to generate more informed responses. The speaker also introduces the concept of using RAG with Atlas Vector Search to build a question-answering application using custom data.
🔧 Building a Question Answering App with Custom Data
This section outlines the process of building a question-answering application using custom data. It introduces the technologies used, including the LANG Chain framework, OpenAI API, and Grideo library. The speaker explains the steps of installing necessary packages, creating API keys, and setting up the environment for the application. The paragraph also covers the process of loading documents and ingesting text and vector embeddings into a MongoDB collection.
📄 Preprocessing Documents for Vector Embeddings
The paragraph details the process of preprocessing documents for vector embeddings. It explains how to access the database, initialize the directory loader, and define the OpenAI embedding model. The speaker also discusses the steps of vectorizing text from documents and inserting the embeddings into the specified MongoDB collection. The paragraph provides a brief overview of the code used in the load data and extract information files.
🔍 Enhancing Search with Atlas Vector Search and RAG
This section describes the process of enhancing search capabilities with Atlas Vector Search and the RAG architecture. It explains how to create a search index in MongoDB Atlas, define a query data function that converts input queries into vectors, and perform a similarity search to retrieve the most relevant document. The speaker also discusses the integration of OpenAI's language models, MongoDB vector search, and LANG Chain to efficiently process and answer complex queries.
🌐 Testing the Question Answering Application
The paragraph presents a test of the question-answering application developed in the tutorial. It demonstrates how the application can retrieve and process information from custom data using vector search and RAG. The speaker provides examples of different types of queries, such as retrieving specific information, summarizing conversations, and performing sentiment analysis. The results show how the application can provide more refined and context-specific answers by leveraging custom data and advanced AI technologies.
📚 Final Project: Chatbot for Free Code Camp Documentation
The final project involves creating a chatbot that can answer questions about contributing to Free Code Camp using the official documentation. The paragraph explains the process of updating the chatbot application to access custom data from the Free Code Camp documentation. It covers the steps of creating embeddings for the documentation, setting up a vector search index, and updating the API routes to utilize these embeddings. The speaker also demonstrates how the chatbot can provide answers based on the official documentation, enhancing the user experience with relevant and accurate information.
Mindmap
Keywords
💡Vector Search
💡Embeddings
💡Large Language Models (LLMs)
💡RAG (Retrieval-Augmented Generation)
💡Atlas Vector Search
💡Semantic Search
💡Hugging Face
💡MongoDB Atlas
💡JavaScript
💡OpenAI
Highlights
This tutorial teaches how to combine data with large language models like GPT-4 using vector search and embeddings.
Three projects will be developed, including a semantic search feature for movies, a question answering app using RAG architecture, and a modified chatbot for FricoCamp.org curriculum.
Vector embeddings are used to organize and describe objects in a digital way, turning items into a list of numbers that can be processed mathematically.
Vector search enables semantic similarity searches, understanding the meaning or context of a query to find relevant results.
MongoDB Atlas Vector Search integrates with LLMs to build AI-powered applications, allowing for semantic searches on data.
The tutorial demonstrates how to use Atlas Vector Search in applications and the benefits of its basic free tier.
The first project involves creating a semantic search for movie recommendations using a sample movie dataset and the HuggingFace LM L6 V2 model.
The process of setting up a MongoDB Atlas account and deploying a new project is outlined, including the provisioning and authentication steps.
The tutorial covers the creation of vector embeddings for movie plots and storing them in MongoDB using the HuggingFace inference API.
The creation and use of a vector search index in MongoDB Atlas is detailed, allowing for efficient semantic similarity searches.
The limitations of LLMs, such as factual inaccuracy and lack of access to local data, are discussed as motivation for using RAG architecture.
RAG uses vector search to retrieve relevant documents and provides them as context to LLMs for generating more informed responses.
The second project demonstrates building a question answering app using RAG, Atlas Vector Search, and the Lang Chain framework with OpenAI models.
The tutorial also shows how to use the OpenAI embedding model and API for creating embeddings and generating text responses.
A chatbot is modified to answer questions about contributing to the FricoCamp.org curriculum based on official documentation.
The final project illustrates the potential of combining advanced AI models with database technologies to create powerful, customized information retrieval systems.