Build a RAG app in Python with Ollama in minutes
TLDRThe video tutorial guides viewers through the process of building a Retrieval-Augmented Generation (RAG) application using Python and Ollama. It emphasizes the importance of embedding, which is key for creating a database to answer questions about various documents like markdown, text, web pages, and PDFs. The video explains the components of a RAG system, including a model for question answering and a database for storing documents. It recommends using Chroma DB for vector embeddings and similarity search, and suggests using the nltk tokenize package for chunking documents into sentences. The tutorial also covers embedding models, with a preference for 'namc embed text' for its efficiency. The process involves importing text, creating embeddings, and populating the database. Finally, it demonstrates how to perform searches using the command line, retrieve relevant documents, and generate responses using the model. The video concludes by encouraging viewers to explore further enhancements and customizations for their RAG applications.
Takeaways
- 📚 **Embedding Importance**: Embedding is crucial for setting up a Retrieval-Augmented Generation (RAG) system, which is effective for creating databases to ask questions about various documents.
- 📈 **PDF Challenge**: PDFs are a common but challenging file type for RAG systems due to their design, which often makes text extraction difficult.
- 🔍 **Database Requirements**: For a RAG system, a database supporting vector embeddings and similarity search is necessary, with Chroma DB being recommended for its simplicity and speed.
- ✂️ **Text Chunking**: The best approach for chunking documents is based on the number of sentences, which is simple, fast, and effective in Python using the `nltk.tokenize` package.
- 🧮 **Embedding Models**: Using an embedding model is essential for generating mathematical representations of text; among the options, `namc embed text` and `mxB AI embed large` are highlighted, with the former being faster.
- 🚀 **Building the App**: The process of building the RAG app involves initializing a Chroma DB instance, connecting to the database, and populating it with embedded text chunks from source documents.
- 🔗 **Source Data Import**: The source data for embedding includes articles from a website, with a focus on Mac Rumors, and involves a method to download and process these files.
- 🔑 **Metadata and IDs**: Each embedded document chunk in the database requires a unique ID, often derived from the source file name and the chunk's index.
- 🔎 **Search Functionality**: The RAG system performs searches using the query from the command-line arguments, returning a specified number of top results based on similarity.
- 📝 **Prompting the Model**: The original query and relevant documents are combined into a prompt for the model, which then generates a response that is streamed and printed out token by token.
- 🌟 **Model Flexibility**: The system allows for experimentation with different embedding and main models, such as `dolphin mistl` and `Gemma colon 2B`, to find the best fit for the task.
- 📝 **Further Development**: There's potential for further development, such as incorporating article dates for sorting or filtering search results, or integrating web search results for more comprehensive queries.
Q & A
What is a RAG (Retrieval-Augmented Generation) system?
-A RAG system is a type of artificial intelligence model that combines retrieval mechanisms with text generation. It creates a database where you can ask questions, and the system retrieves relevant documents or document fragments to assist in generating an answer.
Why is PDF considered a poor format for text extraction?
-PDF is often used to make it difficult to extract text because it is not designed for easy text extraction. It is a common file type but can be challenging to process for RAG systems due to its structure and encoding.
What is the role of a vector database in a RAG system?
-A vector database is crucial in a RAG system as it supports vector embeddings and similarity search. It allows the system to find and retrieve relevant document fragments based on the query.
Why is chunking based on the number of sentences a good approach for RAG systems?
-Chunking based on the number of sentences is simple, fast, and effective in Python. It helps to break down documents into manageable pieces that can be more easily processed by the RAG system without overwhelming the model with too much information.
What is embedding in the context of RAG systems?
-Embedding is the process of generating a mathematical representation of text in the form of an array of numbers. It is used to convert text into a format that can be efficiently processed and compared for similarity within the RAG system.
Which embedding models are mentioned in the transcript?
-The transcript mentions three embedding models: Namc Embed Text, MXB AI Embed Large, and All-Mini LM. Namc and Mix Bread performed well in quick testing, with Mix Bread taking longer to generate embeddings.
What is the purpose of the 'sentore tokenize' package in the RAG system?
-The 'sentore tokenize' package is used for breaking down text into sentences. It is part of the nltk.tokenize package and is crucial for the chunking process in preparing documents for embedding.
How does the Chroma DB function in the context of the RAG system?
-Chroma DB is used as the vector database in the RAG system. It stores the embeddings of document chunks and metadata, allowing for efficient similarity searches to retrieve relevant information based on user queries.
What is the significance of the 'config file' in the RAG system?
-The config file is used to set the names of the embedding model and the main model. It provides an easy way to change these settings without altering the code, allowing for testing and comparison of different models.
How does the RAG system handle queries from the user?
-The RAG system takes a query from the user, creates an embedding for it, and then performs a search in the Chroma DB to find the most relevant document chunks. These are then used to form a prompt that is sent to the model to generate a response.
What are some potential enhancements to the RAG system mentioned in the transcript?
-The transcript suggests enhancements such as adding the date of the article to metadata for sorting results by date, filtering searches by specified dates, and importing and embedding top search results from web page facilities for more accurate answers.
How can users provide feedback or suggest ideas for future videos?
-Users can provide feedback or suggest ideas for future videos by leaving comments on the video or joining the Discord community at discord.gg/ollama.
Outlines
📚 Introduction to Building a RAG System
The first paragraph introduces the concept of embedding, which is crucial for setting up a Retrieval-Augmented Generation (RAG) system. The RAG system is designed to create a database that allows users to ask questions about various documents, including markdown, text, web pages, and PDFs. Despite PDFs being a less preferable format due to their complexity in extracting text, the speaker plans to build a functional RAG system using Python. The paragraph also mentions the upcoming TypeScript video and the decision to avoid using PDFs in this instance. The core components of a RAG application are discussed: a model for asking questions and a database for storing documents. It's emphasized that only relevant document fragments should be provided to the model, not entire documents, to avoid confusion. The need for a database that supports vector embeddings and similarity search is highlighted, with Chroma DB being chosen for its simplicity and efficiency. The process of splitting documents into chunks, preferably by sentences using the NLTK package, is also covered. Finally, the paragraph touches on the embedding process using models like Namc, MXB AI, and All-Mini LM, with a preference for Namc due to its balance of speed and performance.
🔍 Embedding Text and Searching with Chroma DB
The second paragraph delves into the process of embedding text and performing searches using Chroma DB. It starts with the deletion process, which is used to reset the database for each run of the example. The paragraph explains how articles are pulled from a website and how the speaker uses a file named 'source docs.txt' to list URLs or file paths for embedding. The process of downloading and extracting text from these sources is briefly mentioned. The text is then chunked into sentences using the `chunk_text_by_sentence` function from the `MattsOlamaTools` module, which utilizes the `sentore_tokenize` method. Each text chunk is embedded using a chosen model, with a configuration file allowing easy switching between models for testing purposes. The embedded values are stored in the database along with source text and metadata, including a unique ID generated from the source file name and chunk index. With the database populated, searches can be performed. The paragraph outlines the steps for initializing the model, connecting to Chroma DB, and performing searches using command-line arguments. It describes how to construct a prompt from the query and relevant documents, and how to generate a response using a specified model. The speaker provides examples of queries and their corresponding results, demonstrating the system's functionality. The paragraph concludes with suggestions for future improvements and invites questions and ideas for new videos.
Mindmap
Keywords
💡Embedding
💡RAG (Retrieval-Augmented Generation)
💡Chroma DB
💡nltk tokenize
💡Vector Embeddings
💡Semantic Chunking
💡Model
💡CLI Args
💡Vector Database
💡Ollama
💡Sentore Tokenize
Highlights
Building a Retrieval-Augmented Generation (RAG) system with Python and Ollama.
RAG is useful for creating a database to ask questions about various document types such as markdown, text, web pages, and PDFs.
Despite PDFs being a difficult format to work with, they are commonly used files in RAG systems.
A basic RAG application includes a model for asking questions and a database for storing source documents.
Chroma DB is used as the vector database for its simplicity and ease of setup.
Document chunking is essential for RAG systems, with sentence-based chunking being the most effective method.
The nltk.tokenize package and sentore are used for efficient sentence tokenization in Python.
Embedding is the process of converting text into a mathematical representation for the RAG system.
Ollama offers three embedding models as of April 2024: namc, embed-text, mxb, and AI-embed-large.
In testing, namic and mix bread performed better than nomic, with mix bread being slightly slower.
The app development process involves setting up a Chroma DB instance, importing text data, and performing embeddings.
Source text and metadata are added to the vector database for efficient searching and retrieval.
Searching the database returns the top results which are then used to form a prompt for the RAG model.
Ollama's generate function can be used to get a stream of responses based on the prompt.
The RAG application can be further enhanced by adding article dates to the metadata and sorting results by relevance or date.
The video provides a comprehensive guide on creating a basic RAG application, showcasing its potential for practical applications.
Join the Discord community for further discussions, questions, and sharing ideas on RAG applications.