Build a RAG app in Python with Ollama in minutes
TLDRThis video tutorial demonstrates how to build a Retrieval-Augmented Generation (RAG) system using Python and Ollama. The host explains that RAG is ideal for creating databases to answer questions about various document types, particularly PDFs, despite their complexity. The process involves using a model to ask questions and a database to store documents. The video emphasizes the importance of providing relevant document fragments to the model rather than full documents to avoid confusion. Chroma DB is chosen for its simplicity and speed in handling vector embeddings and similarity searches. The tutorial covers document chunking, embedding using the 'namc embed text' model, and populating the database with metadata. The search functionality is showcased, along with the integration of the query into a prompt for the model to generate responses. The video concludes with a live demonstration of the system answering questions about recent events and products, highlighting the potential for customization and further development of RAG applications.
Takeaways
- 📚 **Embedding is crucial**: Embedding is a key part of setting up a Retrieval-Augmented Generation (RAG) system, which is good for creating a database to ask questions about various documents.
- 📈 **Document Types**: RAG can handle documents in markdown, text, web pages, or PDFs, with PDFs being the most common but challenging due to their design.
- 🚫 **Avoid PDFs**: The speaker chooses to initially avoid using PDFs in the demonstration due to their complexity, but acknowledges their importance and the need for a robust PDF-to-text workflow.
- 🔍 **Database Requirements**: A RAG system requires a database that supports vector embeddings and similarity search, with Chroma DB being chosen for its simplicity and speed.
- ✂️ **Chunking Documents**: The best approach for splitting documents is based on the number of sentences, using the `nltk.tokenize` package.
- 🔢 **Embedding Process**: Embedding generates a mathematical representation of text in the form of a numerical array, with specific models recommended for efficiency and performance.
- 🚀 **Ollama Models**: As of April 2024, the preferred embedding models in Ollama are `namc embed text`, `mxb AI embed large`, and `all- mini LM`, with `namc embed text` being the fastest.
- 🛠️ **Building the App**: The process involves initializing a Chroma DB instance, connecting to the database, and populating it with embedded document chunks.
- 🔑 **Unique Identifiers**: Each item in the vector database requires a unique ID, often derived from the source file name and the chunk index.
- 🔎 **Search Functionality**: The app can perform searches using the Chroma DB, returning a specified number of top results, which are then used to construct a prompt for the model.
- 📝 **Prompt and Generate**: The original query and relevant documents are used to create a prompt for the model, which then generates a response that is streamed and printed out token by token.
- 🔄 **Model Flexibility**: The app allows for switching between different main models and embedding models to find the best combination for specific queries or document types.
Q & A
What is a RAG (Retrieval-Augmented Generation) system?
-A RAG system is a type of AI model that combines retrieval mechanisms with generative models. It creates a database where you can ask questions about any documents, such as text, markdown, web pages, or PDFs. The system retrieves relevant document fragments and uses them to inform the generation of responses.
Why is PDF considered a difficult format to work with?
-PDF is considered difficult because it's not designed to make it easy to extract text. It's often used to make it hard to get intelligible text out of the file, which can be a challenge for text processing applications.
What is the role of a vector database in a RAG system?
-A vector database is crucial in a RAG system because it supports vector embeddings and similarity search. This allows the system to efficiently find and retrieve relevant document fragments based on their semantic similarity to the query.
Which vector database is used in the video?
-The video uses Chroma DB as the vector database. It's chosen for its simplicity, speed, and ease of setup.
How does the document chunking process work in the RAG system?
-The document is chunked into smaller parts, often based on the number of sentences. The nltk.tokenize package is used to split the text into sentences, which are then used as chunks for embedding.
What is embedding in the context of a RAG system?
-Embedding is the process of converting text into a mathematical representation, typically an array of numbers. This representation allows for efficient similarity comparisons and is crucial for the retrieval part of the RAG system.
Which embedding models are mentioned in the video?
-The video mentions three embedding models: Namc Embed Text, MXB AI Embed Large, and All-Mini LM. Namc Embed Text and MXB AI Embed Large performed the best in the presenter's quick testing.
How does the video demonstrate the process of building a RAG app?
-The video demonstrates building a RAG app by first setting up a Chroma DB instance, importing documents, chunking the text, creating embeddings, and populating the database. It then shows how to perform searches, retrieve relevant documents, and use these to generate responses using a model.
What is the purpose of the 'source_docs.txt' file in the video?
-The 'source_docs.txt' file lists each URL or file path that the system will embed. It's used to specify the articles or documents that the RAG system will include in its database.
How does the video handle the retrieval of articles for embedding?
-The video doesn't focus on the process of downloading the articles but mentions that the code in the repo demonstrates how to do it. The output of the retext function is the text of the article, which is then chunked and embedded.
What are some possible extensions to the basic RAG application?
-Extensions could include adding the date of the article to the metadata for sorting or filtering results by date, or using web search facilities to find relevant documents, importing the top results, and performing a similarity search to get answers from the model.
How can viewers get more information or ask questions about the RAG system?
-Viewers can ask questions in the comments section below the video or join the Discord community at discord.gg/ollama for further discussions and support.
Outlines
🚀 Introduction to RAG and Embedding
The first paragraph introduces the concept of Retrieval-Augmented Generation (RAG) and its importance in creating a system that can answer questions based on documents. The speaker discusses the challenges of working with PDFs and outlines the components of a basic RAG application, which include a model for asking questions and a database for storing documents. The paragraph also touches on the process of embedding, which is essential for generating a mathematical representation of text for the model to understand. The speaker plans to use Python for the demonstration and mentions the use of Chroma DB as a vector database for storing and searching documents based on their embeddings.
📚 Building a RAG Application with Python
The second paragraph delves into the process of building a RAG application using Python. It discusses the steps involved in setting up the application, including initializing a Chroma DB instance, connecting to the database, and creating a new collection. The paragraph also covers the process of importing documents into the system, which involves downloading articles, chunking the text into sentences, and embedding the text using a chosen model. The speaker provides a detailed explanation of how to perform a search using the database and retrieve relevant documents based on a query. The paragraph concludes with a demonstration of how to use the application to answer questions about specific topics, such as recent events in Taiwan or details about a product called Vision Pro.
Mindmap
Keywords
💡Embedding
💡Retrieval-Augmented Generation (RAG)
💡Chroma DB
💡Chunking
💡Sentore Tokenize
💡Vector Embeddings
💡Namic Embed Text
💡Mixed Breed
💡Dolphin Mistl
💡Gemma Colon 2B
💡CLI Args
💡Ollama
Highlights
Building a Retrieval-Augmented Generation (RAG) system using Python and Ollama.
Embedding is a key part of setting up a RAG system for creating a searchable database of documents.
PDFs are commonly used but are not the most user-friendly format for text extraction.
Chroma DB is chosen as the vector database for its simplicity, speed, and ease of use.
The nltk.tokenize package and the sentore function are used for chunking text into sentences.
Embedding models generate a mathematical representation of text for efficient processing.
Namic and Mix Bread are two embedding models mentioned, with Namic being faster.
A GitHub repo named 'techno evangelist video projects' contains the code for the project.
The process involves importing articles from a website and embedding them into the database.
The retext function extracts the text of articles for embedding.
A config file is used to easily switch between different embedding and main models.
The embedding value is saved and associated with source text and metadata in the vector database.
Chroma DB requires a unique ID for each stored item, created from the source file name and chunk index.
The search functionality of Chroma DB is used to return top results based on the query.
Ollama generate is used to pass the model name, prompt, and streaming response for generation.
The streamed response is printed out token by token to form the final output.
The embed model 'nomic embed text' and the main model 'dolphin mistl' are used to demonstrate the system.
Different models and embedding models can be tested for improved results.
Suggestions for future enhancements include adding date metadata for sorting and filtering.
The potential for importing and embedding top search results from web pages is also mentioned.
Join the Discord at discord.gg/ollama for questions and future video ideas.
