Vector Search RAG Tutorial β Combine Your Data with LLMs with Advanced Search
TLDRThis tutorial demonstrates how to integrate vector search with large language models (LLMs) for advanced data querying. It covers building a semantic movie search, creating a question-answering app using the RAG architecture, and modifying a chatbot to answer questions based on documentation. The course utilizes tools like Python, MongoDB Atlas Vector Search, and the Hugging Face API to enhance AI applications with vector embeddings and semantic understanding.
Takeaways
- π Vector search and embeddings can be used to combine data with large language models (LLMs) for advanced search capabilities.
- π The course introduces vector embeddings as a digital way of sorting and describing items, turning them into numerical vectors for easier mathematical processing.
- π Vector search is a method that understands the meaning or context of a query, different from traditional search engines that look for exact matches.
- π§ 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.
- π‘ The Retrieval-Augmented Generation (RAG) architecture addresses LLM limitations by using vector search to retrieve relevant documents and providing them as context for the LLM to generate more informed responses.
- π οΈ The tutorial demonstrates creating a semantic search feature for movie recommendations using Python, machine learning models, and Atlas Vector Search.
- π₯ A question-answering app is built using RAG, Atlas Vector Search, and the Lang Chain framework, showing how to answer questions with context from custom data.
- π The final project modifies a chatbot to answer questions about contributing to a curriculum based on official documentation, using vector search and RAG.
- π MongoDB Atlas Vector Search is highlighted as a powerful tool for performing semantic similarity searches, allowing data from various sources to be represented numerically as vector embeddings.
- π The process of creating vector embeddings for documents is shown, along with creating a vector search index for efficient retrieval of similar documents based on a query vector.
- π€ The integration of advanced AI models with database technologies like MongoDB Atlas Vector Search demonstrates the potential for building powerful AI-powered applications.
Q & A
What is the primary focus of the tutorial?
-The primary focus of the tutorial is to teach users how to combine their data with large language models (LLMs) like GPT-4 using vector search and embeddings.
What are the three projects outlined in the tutorial?
-The three projects outlined are: 1) Building a semantic search feature to find movies using natural language queries, 2) Creating a simple question answering app using the RAG architecture and vector search, and 3) Modifying a chat GPT clone to answer questions about contributing to the FricoCamp.org curriculum based on official documentation.
What is a vector embedding?
-A vector embedding is a digital representation that describes objects, such as words or images, as a list of numbers (vector). Similar items will have similar vectors, which can be used for semantic searches and machine learning tasks.
How does MongoDB Atlas Vector Search work?
-MongoDB Atlas Vector Search allows for semantic similarity searches on data by storing vector embeddings alongside source data and metadata, and then using an approximate nearest neighbors algorithm to perform fast semantic similarity searches.
What is the Retrieval-Augmented Generation (RAG) architecture?
-The Retrieval-Augmented Generation (RAG) architecture is a method that uses vector search to retrieve relevant documents based on the input query and provides these documents as context to the LLM to generate more informed and accurate responses.
How does the tutorial handle the limitations of LLMs?
-The tutorial addresses LLM limitations by using the RAG architecture to ground model responses in factual information, retrieving up-to-date sources for current data, and utilizing external databases or knowledge bases for personalized responses.
What is the role of the Hugging Face inference API in the tutorial?
-The Hugging Face inference API is used to generate embeddings for the search terms and documents, which are then used in the semantic search feature to find and rank relevant results.
How does the tutorial demonstrate the creation of a question-answering app?
-The tutorial demonstrates the creation of a question-answering app by using the RAG architecture with Atlas Vector Search and the Lang Chain framework, along with OpenAI models, to develop a real-world project that uses these technologies and concepts.
What are the benefits of using vector embeddings in semantic searches?
-Using vector embeddings in semantic searches allows for a more accurate understanding of the meaning or context of the query, leading to more relevant results. It also enables the use of natural language queries to find information, translating languages, or sharing with AI.
What is the significance of the vector search index in the MongoDB Atlas?
-The vector search index in MongoDB Atlas is crucial for performing semantic similarity searches. It stores the vector embeddings and uses them to efficiently retrieve documents with vectors similar to a query vector, enabling powerful semantic search capabilities.
Outlines
π Introduction to Vector Search and Embeddings
The paragraph introduces the course that teaches how to use vector search and embeddings with large language models like GPT-4. It outlines the 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 begins with an explanation of vector embeddings, their importance in understanding similarity between items, and how they are used in semantic search to find relevant results by comparing vectors. It also introduces MongoDB Atlas Vector Search and its role in performing semantic similarity searches.
π Setting Up MongoDB Atlas Account and Project
This paragraph walks through the process of creating a MongoDB Atlas account and setting up a new project. It explains how to create a deployment, set up authentication, and connect to the database. The speaker also discusses loading sample data related to movies into the database and provides a brief overview of the database's structure and content. It then transitions into discussing the use of the `pi Mongo` package for connecting to the MongoDB instance from a local environment and preparing for the next steps of the project.
π 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 details how to set up the API, create an access token, and use the API to generate embeddings for text. It also covers testing the embeddings by printing the generated vector for a sample text. The speaker emphasizes the importance of embeddings for performing similarity searches and sets up the function to generate embeddings for the next steps.
π§ Utilizing Embeddings for Semantic Movie Search
This section describes the process of creating and storing vector embeddings based on the plot field of movie documents in the database. The speaker explains how to execute operations to create embeddings for a subset of the data and store these embeddings in the database. The goal is to enable semantic search based on the plots of movies, allowing users to find movies with similar themes or narratives using natural language queries. The speaker also discusses the limitations of working with a sample of the data due to rate limits and the potential need for a paid inference endpoint for larger datasets.
π Building a Vector Search Index on MongoDB Atlas
The paragraph explains the next step in setting up the semantic search feature: creating a vector search index on MongoDB Atlas. The speaker guides through the process of selecting the database and collection, naming the index, and defining the index specifications, such as the field to be indexed and the dimensionality of the vectors. The importance of choosing the right similarity metric for the vector field is highlighted, and the speaker demonstrates how to create the index and wait for the indexing process to complete.
π€ Performing Vector Search and Aggregation
In this paragraph, the speaker demonstrates how to perform a vector search using the aggregation pipeline stage in MongoDB. The focus is on finding documents in the collection whose plot embeddings are semantically similar to a provided query. The speaker explains the process of generating an embedding for the query, setting up the aggregation pipeline, and defining parameters such as the number of candidate matches and the final number of results to return. The results of the search are then extracted and presented, showcasing the power of vector search in understanding and responding to natural language queries.
π Integrating OpenAI Embeddings for Enhanced Search
The speaker discusses the process of integrating OpenAI embeddings for an enhanced search experience. It covers creating a search index tailored for the embeddings generated with the OpenAI API and modifying the code to use these embeddings. The speaker emphasizes the difference in the field path and the similarity metric used for the OpenAI embeddings compared to the Hugging Face API. The results of the search using OpenAI embeddings are presented, demonstrating how the search outcomes become more relevant when querying against the entire database with embeddings created by the same API.
π Building a Question Answering App with Custom Data
The paragraph introduces the process of building a question answering application using custom data. It outlines the technologies used, such as the LANG Chain framework, OpenAI API, and Grideo library. The speaker explains how to install necessary packages and set up API keys for OpenAI. The process of loading documents and ingesting text and vector embeddings into a MongoDB collection is detailed. The speaker also discusses the creation of a user interface for the application, which will allow for question answering against custom data using vector search and large language models.
π οΈ Preparing Data and Embeddings for the App
This section details the preparation of data and embeddings for the question answering app. The speaker demonstrates how to load text files into the app, create embeddings for these documents using the OpenAI API, and store them in a MongoDB collection. The process of defining the OpenAI embedding model and initializing the vector store is explained. The speaker also shows how to create a search index in MongoDB Atlas for the embeddings, which is crucial for the vector search functionality in the app.
π Combining Vector Search with Retrieval QA for Enhanced Responses
The paragraph explains how to enhance the question answering app by combining vector search with retrieval QA. The speaker outlines the process of defining a function that takes a query, converts it into a vector, performs a vector search, and retrieves the most similar document. The app then uses the retrieved document and the nature of the query to generate a response using a large language model. The integration of OpenAI's language models, MongoDB vector search, and LANG Chain is showcased to efficiently process and answer complex queries. The speaker also discusses creating a web interface for the app using Gradio and demonstrates the different outputs from using only vector search versus the combined approach.
π Modifying a Chatbot to Interact with Custom Documentation
The speaker describes the process of modifying a chatbot to interact with and answer questions based on custom documentation. The steps involve configuring the application with OpenAI and MongoDB Atlas credentials, creating embeddings for the documentation, and building a vector search index. The chatbot is then updated to utilize these embeddings to provide context-specific answers. The speaker also demonstrates how to test the application and how it can be used to extract relevant information from the documentation to answer user queries.
π Conclusion and Final Thoughts
The speaker concludes the tutorial by summarizing the key points covered and the skills learned. The focus is on the ability to implement vector search in personal projects and the potential applications of the knowledge gained. The speaker thanks the audience for their engagement and encourages them to explore further with the newly acquired skills.
Mindmap
Keywords
π‘Vector Search
π‘Embeddings
π‘Large Language Models (LLMs)
π‘Atlas Vector Search
π‘Semantic Search
π‘RAG (Retrieval-Augmented Generation)
π‘Hugging Face
π‘JavaScript
π‘OpenAI
π‘MongoDB
Highlights
This course teaches how to combine data with large language models using vector search and embeddings.
Three projects are covered: semantic search for movies, a question answering app using RAG architecture, and a 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 representing their similarity.
Vector search enables semantic similarity searches, understanding the meaning or context of a query rather than just exact matches.
MongoDB Atlas Vector Search integrates with large language models to build AI-powered applications, allowing for semantic searches on data.
The course demonstrates using Atlas Vector Search with Python and JavaScript, and working with MongoDB to store and retrieve movie data.
Creating embeddings involves complex math and large datasets, where the computer learns to turn words into vectors based on sentence usage.
The tutorial includes a step-by-step guide on setting up a MongoDB Atlas account and deploying a new project with a free tier cluster.
Loading sample data and connecting to a MongoDB instance is crucial for implementing semantic search in the movie recommendation project.
The course covers using the Hugging Face inference API to generate embeddings for natural language queries, such as finding movies by plot.
Vector embeddings are stored alongside source data and metadata in MongoDB, allowing for fast semantic similarity searches using an aggregation pipeline.
The RAG architecture, combined with vector search, helps overcome limitations of LLMs by grounding responses in factual information and using retrieved documents for context.
LANG Chain framework simplifies the creation of LLM applications by providing a standard interface for chaining components to process language tasks.
The final project modifies a chat GPT clone to answer questions about contributing to FricoCamp.org based on its official documentation, showcasing the practical application of vector search.
The tutorial also discusses the limitations of LLMs, such as generating inaccurate information and not having access to user-specific data, which vector search helps mitigate.
By using vector search with LLMs, developers can build powerful AI applications that provide more informed, accurate, and personalized responses.