Ollama Embedding: How to Feed Data to AI for Better Response?

Discover the innovative Ollama Embedding technology that enhances AI response quality by ingesting data from URLs, converting it into embeddings, and storing it in a Vector database. Utilizing the powerful Nomic Embed Text model, this method surpasses Open AI's embedding models in context length. The video demonstrates how to create a user interface with Gradio, enabling users to run large language models locally on their machines using the Ollama local AI model server, supporting MacOS, Linux, and Windows, all at no cost.

Takeaways

• 📈 **Ollama Embedding**: A method to enhance AI response quality by creating embeddings from ingested data.
• 🌐 **Data Ingestion**: Data is taken from URLs, converted into embeddings, and stored in a Vector database for efficient retrieval.
• 🔍 **Relevance**: The system aims to provide more relevant answers by using the Ollama language model with the ingested data.
• 📚 **Chroma DB & Nomic Embedding**: Utilizes Chroma DB and the Nomic Embed Text model for higher context length and performance over OpenAI models.
• 📈 **Performance Chart**: A chart is used to demonstrate the superior performance of the Nomic Embedding model.
• 🛠️ **User Interface**: A user interface is created using Gradio for easier interaction with the AI system.
• 📝 **LangChain**: The script outlines the use of LangChain for assembling the components necessary for the AI application.
• 🔗 **URL Processing**: The process involves splitting data from URLs into chunks with a specific overlap for better context.
• 🔑 **Model Initialization**: The Nomic Ember Text model is initialized for creating embeddings from the documents.
• 🤖 **RAG Application**: A Retrieval-Augmented Generation (RAG) application is built to run locally, leveraging a large language model.
• ⏱️ **Speed & Efficiency**: The embedding process is noted for its speed, taking approximately 219 milliseconds.
• 📱 **Local AI Model Server**: Ollama is described as a local AI model server that allows users to run large language models on their own machines.
• 🌟 **Zero Cost & Local Execution**: The RAG application can be run completely locally with no cost, providing a cost-effective solution.
• 📱 **Enhanced Interface**: The addition of Gradio to the code allows for a more user-friendly interface for the AI application.
• 🔄 **Process Steps**: The script details a step-by-step process for data extraction, chunking, embedding, and RAG application creation.

Q & A

What is the main purpose of using Ollama Embedding?

The main purpose of using Ollama Embedding is to create a more relevant and higher performance application by converting data from URLs into embeddings and storing them in a Vector database, which allows for more relevant data to be sent to the language model when a question is asked.

Why is the Nomic Embed Text model chosen for embedding?

The Nomic Embed Text model is chosen because it has a higher context length and surpasses other embedding models like Open AI's, providing better performance in terms of capturing the context and meaning of the text.

What is the role of Chroma DB in this process?

Chroma DB is used to store the embeddings created from the data. It serves as a Vector database that holds the converted data, allowing for efficient retrieval and use in the application.

How does the process of creating a user interface using Gradio work?

The user interface is created by adding Gradio to the code, defining a function to process input which includes a list of URLs and a question. The function splits the URLs, processes the data, and then provides a way for users to input URLs and ask questions, with the interface displaying the results.

What is the significance of the RAG (Retrieval-Augmented Generation) application created?

The RAG application is significant because it allows for the entire process to run locally on a user's machine, providing a zero-cost solution for running large language models with support for various operating systems like Mac OS, Linux, and Windows.

What is the first step in the process described in the transcript?

The first step is to retrieve data from the URL, split the data into chunks, and combine them for further processing.

How is the data split and what is the purpose of the overlap?

The data is split into chunks using a character text splitter, with a specified chunk size and an overlap of 100 characters between different chunks. The overlap helps in maintaining context when converting the chunks into embeddings.

What is the role of the Prompt Template in the RAG process?

The Prompt Template is used to structure the input to the language model. It defines the format in which the question is asked and the context is provided, ensuring that the language model can generate a relevant and coherent response.

How is the performance of the embeddings measured in the transcript?

The performance is measured by the time it takes to create the embeddings, which is approximately 219 milliseconds in the example provided, indicating a fast processing time.

What are the steps involved in running the code for the RAG application?

The steps include installing necessary packages like Lang Chain, creating an app.py file, defining the model, splitting the data, converting it to embeddings, storing them in Chroma DB, and then running the code using the command 'Python app.py'.

How does adding a user interface with Gradio enhance the RAG application?

Adding a user interface with Gradio makes the RAG application more user-friendly and accessible. It allows users to input URLs and questions directly, and receive responses without needing to interact with the code or command line.

What is the final step in the process after running the code?

The final step is to launch the user interface by running the 'ui.py' file in the terminal, which opens a web-based interface where users can interact with the RAG application.

Outlines

00:00

🚀 Introduction to OLLAMA Embedding and Application Development

The video begins with an introduction to OLLAMA embedding, a technology that enhances application performance. The speaker explains that they will ingest data from URLs, convert it into embeddings, and store it in a Vector database. This process is designed to provide more relevant answers when questions are asked, utilizing a large language model. The chosen embedding model is Nomic Embed Text, which is noted for its higher context length surpassing OpenAI's models. The video outlines the steps to create this application, starting from installing necessary packages like Lang Chain, to defining the model and processing data chunks. It concludes with running the code to demonstrate the application's functionality and speed, highlighting the local AI model server capabilities of OLLAMA.

05:01

💻 User Interface Integration and OLLAMA Application Testing

The second paragraph focuses on integrating a user interface into the OLLAMA application using Gradio. The speaker modifies the code to include a function that processes input from a list of URLs and a question. The process involves splitting the URLs, converting documents into embeddings, and storing them in the Chroma DB. The video demonstrates the application's user interface, where the speaker inputs a URL, asks a question about OLLAMA, and submits the request. The application processes the request and provides a response based on the context, explaining that OLLAMA is a platform that allows users to run large language models locally across various operating systems. The speaker expresses excitement about the technology and encourages viewers to stay tuned for more similar content, ending with a call to like, share, and subscribe.

Keywords

💡Ollama Embedding

Ollama Embedding refers to a process where data is converted into a format that can be better understood and utilized by AI systems. In the context of the video, it is used to enhance the performance of a Rag (Retrieval-Augmented Generation) application by creating embeddings from data ingested from URLs and storing them in a Vector database. This allows for more relevant answers to be retrieved when a question is asked of the language model.

💡Vector Database

A Vector Database is a type of database designed to store and manage data in the form of vectors, which are essentially numerical arrays representing complex information in a simplified, high-dimensional space. In the video, embeddings created from the ingested data are stored in a Vector Database called Chroma DB, which facilitates quick retrieval of relevant data for the language model.

💡Nomic Embed Text

Nomic Embed Text is a model for embedding text into a vector format, which is mentioned to have a higher context length and surpasses other embedding models like OpenAI's. It is used in the video to create better embeddings for the data from URLs, which are then used to improve the performance of the AI's responses.

💡Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique in natural language processing that combines retrieval mechanisms with generative models to produce more relevant and accurate responses. In the video, RAG is used to compare the performance before and after its application, demonstrating how it can enhance the output of the language model when answering questions.

💡Chroma DB

Chroma DB is the specific Vector Database mentioned in the video that is used to store the embeddings of the ingested data. It plays a crucial role in the process as it allows for the efficient retrieval of data that can be used by the language model to generate responses.

💡Language Model

A Language Model is a type of AI model that is trained on a large dataset of human language, allowing it to understand and generate human-like text. In the video, the language model named 'Mistal' is used to process prompts and generate outputs based on the embeddings retrieved from the Vector Database.

💡Web-based Loader

A Web-based Loader is a tool or function used to extract data from URLs. In the video, it is used to ingest data from provided URLs, which is then processed and converted into embeddings, contributing to the overall performance of the AI application.

💡Character Text Splitter

A Character Text Splitter is a tool that divides text into chunks, which can be useful for processing large documents. In the video, it is used to split the ingested data into chunks with a specified overlap, preparing it for conversion into embeddings.

💡Gradio

Gradio is a Python library used for quickly creating interactive web-based interfaces for machine learning models. In the video, Gradio is used to add a user interface to the AI application, allowing users to input URLs and questions to receive responses from the language model.

💡Local AI Model Server

A Local AI Model Server is a server that runs on a user's own machine, enabling them to execute large language models locally. The term 'Ollama' is mentioned as a local AI model server in the video, emphasizing the ability to run AI applications without relying on external servers or incurring costs.

💡Prompt Template

A Prompt Template is a predefined structure or format for inputting data into a language model. In the video, it is used to standardize the input to the language model, ensuring that the model receives the necessary information to generate a relevant response.

Highlights

Ollama Embedding allows for the creation of more efficient and performance-driven rag applications.

Data from URLs is ingested, converted to embeddings, and stored in a Vector database for quick retrieval.

Chroma DB and Nomic Embed Text are utilized for their high context length and superior performance over Open AI models.

A user interface is created using Gradio to interact with the language model.

The Nomic Embed Text model is chosen for its higher context length capabilities.

The process involves splitting data into chunks with a specified overlap for better context.

Chroma DB is initialized for storing embeddings with a defined collection name.

The retrieval process involves using a web-based loader and character text splitter for data extraction and organization.

The RAG (Retrieval-Augmented Generation) process is demonstrated before and after the application for comparison.

The Prompt template and RAG chain are created for interaction with the language model, Mistal.

Ollama is a local AI model server enabling users to run large language models on their own machines.

The entire process, from data extraction to embedding storage and RAG application, is done locally with zero cost.

The user interface allows for input of URLs and questions, processing requests and providing responses based on context.

Performance metrics, such as embedding time, are logged for optimization and analysis.

The final application is capable of running completely locally, supporting MacOS, Linux, and Windows.

The tutorial provides a step-by-step guide on implementing Ollama Embedding with a user interface.

The video concludes with a demonstration of the application's ability to answer questions based on provided context.

The presenter expresses excitement about the potential for creating more videos on similar topics.