Build a RAG app in Python with Ollama in minutes

The video provides a step-by-step guide to building a Retrieval-Augmented Generation (RAG) system using Python and Ollama. The process involves creating a database for storing documents, which can be in various formats like markdown, text, web pages, or PDFs. The system uses a model to answer questions based on these documents. The video emphasizes the importance of using a database that supports vector embeddings and similarity search, opting for ChromaDB for its simplicity and speed. The script discusses techniques for chunking documents into sentences using nltk's sentore tokenizer and generating embeddings with the Namc Embed Text model for efficiency and performance. The app is demonstrated with a live example, importing articles from a website, embedding them into the database, and performing searches to answer queries. The video concludes with suggestions for further enhancements and invites viewers to join a Discord community for more discussions.

Takeaways

• 📚 **RAG Overview**: RAG (Retrieval-Augmented Generation) is useful for creating databases that allow querying documents like text, markdown, web pages, and PDFs.
• 🚫 **PDF Challenge**: PDFs are not ideal for text extraction, but the speaker aims to find a better PDF-to-text workflow beyond common tools.
• 🔍 **Database Choice**: Chroma DB is chosen for its simplicity, speed, and ease of use, despite having fewer features compared to other vector databases.
• ✂️ **Text Chunking**: The best method for chunking documents is by sentence count, using the `nltk.tokenize` package in Python.
• 🧮 **Embedding Process**: Embedding involves generating a numerical representation of text, and using a specialized model like `namc embed text` or `mxbi AI embed` is recommended for efficiency and performance.
• 🏗️ **Building the App**: The app is constructed by importing text, chunking it, embedding it, and storing it in a vector database like Chroma DB.
• 🔗 **Data Import**: Articles from a website are imported, chunked into sentences, and then embedded before being stored in the database.
• 🔑 **Unique ID**: Each item in the vector database is assigned a unique ID, often derived from the source file name and chunk index.
• 🔎 **Search Functionality**: The app can perform searches using the vector database's similarity search feature, returning a specified number of top results.
• ⏱️ **Performance Note**: The embedding process can be time-consuming, with some models like `mix bread` taking significantly longer than others.
• 📈 **Potential Enhancements**: The app could be improved by incorporating article dates for sorting or filtering results, or by integrating web search capabilities for more relevant document retrieval.
• 🤖 **Model Flexibility**: The app allows for switching between different main models and embedding models to find the best combination for a given task.

Q & A

What is the key part of setting up a Retrieval-Augmented Generation (RAG) system?

The key part of setting up a RAG system is embedding, which involves creating a database where you can ask questions to any documents, such as markdown, text, web pages, or PDFs.

Why is PDF considered a less ideal format for RAG systems?

PDF is considered less ideal because it is not designed to make it easy to extract text. It's often used to make it difficult to get intelligible text out of the file.

What is the main component of a basic RAG application?

The main components of a basic RAG application are a model that you can ask questions to and a database that stores all the source documents.

Why is it better to provide fragments rather than full documents to the model?

Providing full documents can confuse the model, whereas providing relevant fragments helps the model answer the question more effectively.

What type of database is recommended for a RAG system?

A database that supports vector embeddings and some sort of similarity search is recommended. In the script, Chroma DB is used as an example.

How is the document chunking best done according to the script?

The best approach for document chunking, as mentioned in the script, is based on the number of sentences using the sentore tokenize from the nltk tokenize package.

What is embedding in the context of RAG?

Embedding is a process that generates a mathematical representation of the text in the form of an array of numbers.

Which embedding models are mentioned in the script?

The script mentions three embedding models: namc embed text, mxb AI embed large, and all- mini LM.

How does the script handle the process of importing text and creating a RAG database?

The script handles this by downloading files from a list of URLs, chunking the text by sentences, embedding the chunks, and then adding the embeddings, source text, and metadata to the vector database.

What is the purpose of the unique ID for each item stored in the vector database?

The unique ID is necessary for the vector database to identify and reference each stored item, often created from the source file name and the index of the chunk.

How does the search functionality in the RAG system work?

The search functionality involves creating an embedding from the query, running the query against the database to return the top results, and then using those results to form a prompt for the model to generate an answer.

What are some potential enhancements to the basic RAG system discussed in the script?

Potential enhancements include adding the date of the article to metadata for sorting or filtering results, using web search facilities to find relevant documents, and importing and embedding the top search results before performing a similarity search.

Outlines

00:00

🚀 Introduction to Building a Retrieval-Augmented Generation (RAG) System

This paragraph introduces the concept of embedding as a critical component in setting up a RAG system. The speaker discusses the utility of RAG for creating a database that can answer questions about various document types, with a particular focus on PDFs despite their complexity. The paragraph outlines the intention to build a RAG system using Python and mentions the upcoming TypeScript version. It also touches on the importance of using a database that supports vector embeddings and similarity search, choosing Chroma DB for its simplicity and efficiency. The process of document chunking based on sentences is highlighted as the preferred method, utilizing the `nltk.tokenize` package. Finally, the paragraph discusses the embedding process, emphasizing the use of specific models for optimal performance, with a comparison between 'namc embed text', 'mxb AI embed large', and 'all-Mini LM' models.

05:01

📚 Detailed Walkthrough of RAG Application Development

The second paragraph delves into the specifics of developing a RAG application. It starts with the setup of a fresh Chroma DB instance, including the deletion and creation of a new collection. The process of importing articles from a website into the database is outlined, with a focus on embedding text chunks using the `nltk.tokenize` and a custom `chunk_text_by_sentence` function. The paragraph explains the embedding process using the `ama.embed` library and the configuration of model names through a config file. The application of the embedding values to the vector database is detailed, along with the need for a unique ID for each stored item. The search functionality of the Chroma DB is utilized to perform queries, with the results used to form prompts for model responses. The paragraph concludes with an interactive demonstration of the application, showcasing how different models and embedding techniques can be tested and the types of questions the system can answer effectively.

Keywords

Embedding

Embedding is a process that involves creating a mathematical representation of text in the form of an array of numbers. It is a key component in the setup of a Retrieval-Augmented Generation (RAG) system, which is used to create a database that can answer questions about various documents. In the context of the video, embedding is crucial for generating vector representations of text that can be efficiently searched and compared for similarity.

RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is a system that combines a retrieval system with a generative model. It is used for creating databases that can answer questions about a variety of documents, such as text files, web pages, and PDFs. The RAG system retrieves relevant information from the database and uses it to inform the generation of answers. In the video, the creator discusses building a RAG system using Python and various tools to handle document embedding and retrieval.

Chroma DB

Chroma DB is a vector database mentioned in the video that is used for storing and managing embeddings. It supports vector embeddings and similarity search, which are essential for the RAG system to function effectively. The simplicity and speed of Chroma DB make it an ideal choice for the project described in the video, as it allows for quick setup and efficient operation.

PDF

PDF stands for Portable Document Format, which is a common file type for sharing documents. However, the video script mentions that PDFs are not the ideal format for text extraction due to their design, which often makes it difficult to obtain clear text from them. Despite this, PDFs are frequently used and the video hints at the need for a robust PDF-to-text workflow for better integration into the RAG system.

Vector Embeddings

Vector embeddings are numerical representations of words, phrases, sentences, or documents in a multi-dimensional space. These embeddings capture the semantic meaning of the text and are used to determine the similarity between different pieces of text. In the context of the video, vector embeddings are generated for text chunks and stored in Chroma DB to enable efficient similarity searches.

Sentiment Analysis

Sentiment analysis, while not explicitly mentioned in the transcript, is a technique used in natural language processing to determine the sentiment or emotional tone of a piece of text. It could be relevant in the context of analyzing the content of documents within a RAG system to understand the context better, although it is not a direct focus of the video.

nltk (Natural Language Toolkit)

nltk, or the Natural Language Toolkit, is a Python library used for working with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources and a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. In the video, nltk is used with the 'sentore' package to tokenize text into sentences, which is a step in preparing the text for embedding.

Model

In the context of the video, a 'model' refers to a machine learning or artificial intelligence model that can process and generate text based on input data. The video discusses using different embedding models like 'namc embed text' and 'mxb AI embed large' for creating vector representations of text. Additionally, the term 'model' is used to refer to the main text generation models like 'dolphin mistl' and 'Gemma colon 2B' that generate responses based on the retrieved information.

CLI (Command Line Interface)

The CLI, or Command Line Interface, is a text-based interface used for interacting with a computer or application. In the video, the CLI is used to input queries into the RAG system, which then processes these queries to generate responses. The CLI is a common tool for executing commands and interacting with systems in a more direct and automated way.

Metadata

Metadata refers to data that provides information about other data. In the context of the video, metadata is used to store additional information about the text chunks, such as the source file name and the index of the chunk. This information can be useful for organizing and searching the database more effectively, and it can also be used to add context to the search results.

Ollama

Ollama is a term used in the video, likely referring to a specific tool or framework that the creator is using to facilitate the process of building the RAG application. Although not a widely recognized term outside the context of the video, it seems to be a part of the workflow for generating responses from the RAG system.

Highlights

Building a Retrieval-Augmented Generation (RAG) system is useful for creating a database to ask questions about various documents.

PDFs are commonly used but are not the best format for text extraction due to their design.

A basic RAG application includes a model for asking questions and a database for storing source documents.

Chroma DB is used for its simplicity, speed, and ease of setup as a vector database supporting vector embeddings and similarity search.

The nltk.tokenize package's sentore.tokenize function is recommended for chunking text into sentences.

Embedding models are crucial for generating mathematical representations of text for efficient and effective RAG systems.

Namic and Mix Bread embedding models performed well in tests, with Namic being faster.

The GitHub repo 'techno-evangelist/video-projects' contains the code for the RAG app.

A working Chroma DB instance is required, which can be set up by running a specific command.

The source documents are chunked into sentences using a function from the 'mattasollamatools' module.

Embedding in Olama is straightforward using the python library, with the ability to specify the model name.

The embedding value is saved and used to add the source text and metadata to the vector database.

Chroma DB requires a unique ID for each item, which is created from the source file name and chunk index.

The query from the CLI args is used to create an embedding and run a search in the Chroma DB.

The top search results can be specified and joined into one string for the model prompt.

Olama generate is used to run the model with the prompt, streaming the response.

The streamed response is printed out token by token to provide the final answer.

Different embedding and main models can be tested for various questions to improve the RAG system.

Further enhancements could include adding date information to the metadata for sorting or filtering results.

The potential for importing and embedding top web search results for a query before performing a similarity search is discussed.

Join the Discord at discord.gg/ollama for questions and future video ideas.