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

Mervin Praison
23 Feb 202405:39

TLDRThe video introduces a novel approach to creating Rag (Retrieval-Augmented Generation) applications using the OLA (Ollama) embedding technique for enhanced performance. It highlights the use of the Nomic Embed Text model for its superior context length and surpassing Open AI's models. The process involves ingesting data from URLs, converting it into embeddings, and storing it in a Vector database. The video demonstrates how to build a user interface with Gradio, enabling users to run large language models locally on their machines, showcasing the setup through a step-by-step guide and emphasizing the ease of implementation with just a few lines of code.

Takeaways

  • 🚀 The presentation introduces 'Ollama', a technology for creating applications with better performance using embeddings.
  • 🌐 Ollama involves ingesting data from URLs, converting it into embeddings, and storing it in a Vector database for efficient retrieval.
  • 📈 The 'Nomic' embed text model is highlighted for its higher context length and superior performance compared to OpenAI's models.
  • 🛠️ The process starts with data retrieval from URLs, followed by splitting the data, converting it to embeddings, and storing it in the database.
  • 🔍 The Retriever component is used to find relevant documents when a question is asked, enhancing the relevance of the answers.
  • 🔗 The use of 'Lang chain' is mentioned for putting all pieces together, with specific packages to be installed for the implementation.
  • 📝 A step-by-step guide is provided on creating an application using the 'Nomic' model and the 'Ollama' server for local AI model execution.
  • 📊 The script includes a demonstration of the application's code and its execution, showcasing the speed and output of the embedding process.
  • 🖥️ The application is further enhanced with a user interface using 'Gradio', allowing for a more interactive experience.
  • 🎥 The presenter also invites viewers to subscribe to their YouTube channel for more content on Artificial Intelligence.
  • 🌟 The script concludes with a summary of the benefits of using 'Ollama', emphasizing its ability to run large language models locally at no cost.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about using OLLama embedding to create a RAg (Retrieval-Augmented Generation) application with better performance.

  • What is the purpose of ingesting data from URLs and converting them to embeddings?

    -The purpose is to store the data in a Vector database, which allows for the retrieval of relevant data when a question is asked, leading to more accurate and contextually relevant answers.

  • Which embedding model is being used in the video and why was it chosen?

    -The video is using the Nomic Embed Text model because it has a higher context length and surpasses OpenAI embedding models in performance.

  • What is the first step in creating the RAg application?

    -The first step is to retrieve data from URLs, split the data, and prepare it for embedding.

  • How is the data combined and split for processing?

    -The data from URLs is combined, and then it is split into chunks with an overlap of 100 characters to ensure continuity and context.

  • What is the role of the Character Text Splitter in the process?

    -The Character Text Splitter is used to divide the data chunks, maintaining a specified overlap for effective embedding and retrieval.

  • How is the Vector database utilized in the RAg application?

    -The Vector database, specifically Chroma DB, is used to store the embeddings of the documents, which can then be retrieved when needed for generating responses.

  • What is the significance of the Prompt Template in the RAg process?

    -The Prompt Template is crucial as it structures the input to the language model, ensuring that the generated responses are relevant and contextually appropriate.

  • How does the video demonstrate the execution of the RAg application?

    -The video demonstrates the execution by running a Python script that goes through the steps of data retrieval, embedding, storing in the database, and finally generating a response using the RAg process.

  • What is the user interface added to the RAg application in the video?

    -The user interface added to the RAg application is Gradio, which allows users to interact with the application more conveniently.

  • What is the final outcome of the video?

    -The final outcome is a functioning RAg application with a user interface that can run locally on a machine, providing information based on the context provided by the user.

Outlines

00:00

🚀 Introduction to OLA and Embedding Technology

The paragraph introduces OLA, a technology for creating AI applications with better performance through embedding. It explains the process of ingesting data from URLs, converting it into embeddings, and storing them in a Vector database. The goal is to provide relevant data to a language model to generate more accurate answers. The script also mentions the use of the nomic embed text model for its superior context length and performance over OpenAI models. The creation of a user interface with gradio is also discussed, along with a brief introduction to the video's content and the importance of subscribing to the YouTube channel for more AI-related content.

05:01

📝 Step-by-Step Guide to Building a Rag Application

This paragraph provides a detailed guide on building a Rag (Retrieval-Augmented Generation) application using the nomic embed text model. It outlines the steps involved, starting with data retrieval from URLs, splitting the data, converting it into embeddings, and storing them in a Vector database. The paragraph then explains the Rag process, which involves comparing before and after Rag templates, and using a language model to generate outputs. The script also includes instructions on running the code, downloading necessary models, and the expected performance metrics. Finally, it discusses the addition of a user interface with gradio and the functionality of the application.

Mindmap

Keywords

💡embedding

In the context of the video, 'embedding' refers to a technique in machine learning and artificial intelligence where textual data is transformed into numerical vectors that capture semantic meaning. This process is crucial for creating applications that can understand and process human language. The video discusses using embeddings to improve the performance of a rag (retrieve-and-generate) application by converting data from URLs into embeddings and storing them in a vector database for efficient retrieval and processing.

💡rag application

A 'rag application' is a type of artificial intelligence system that combines the processes of retrieval (finding relevant information) and generation (creating new content based on that information). In the video, the creator aims to enhance a rag application's performance by using embeddings and a local AI model server, which allows for better handling of data and more relevant answers to user queries.

💡ollama

Although 'ollama' is not a widely recognized term in the field of AI, within the context of the video, it appears to refer to a local AI model server that enables users to run and create large language models on their own machines. This concept is significant as it suggests a move towards more accessible and localized AI technologies.

💡nomic embed text model

The 'nomic embed text model' is mentioned as a specific type of embedding model that has a higher context length, surpassing other models like Open AI's. In the context of the video, this model is chosen for its ability to better capture and represent the nuances of language, which is essential for creating a more effective rag application.

💡vector database

A 'vector database' is a type of database designed to store and manage vector representations of data, such as embeddings of textual data. This database is optimized for handling the numerical vectors that result from the embedding process, allowing for efficient retrieval and comparison of these vectors, which is crucial for the functionality of the rag application described in the video.

💡user interface

The 'user interface' refers to the means by which users interact with a computer system or application. In the context of the video, a user interface is being added to the rag application using a tool called 'gradio', which allows users to input URLs and questions and receive answers from the system in a more interactive and user-friendly manner.

💡gradio

Gradio is a Python library used for creating user interfaces for machine learning models. It allows developers to quickly build web interfaces that can be used to interact with models without the need for extensive web development skills. In the video, gradio is used to add a user interface to the rag application, making it more accessible and easy to use.

💡language model

A 'language model' is an AI model that is trained to understand and generate human language. It is a fundamental component in natural language processing and is used in various applications, such as text generation, translation, and understanding. In the video, a language model named 'mistal' is used as part of the rag application to generate relevant answers based on the embeddings and user input.

💡retriever

In the context of AI and natural language processing, a 'retriever' is a component that retrieves relevant information or documents from a database in response to a query. In the video, the retriever is part of the rag application's process, used to find relevant documents from the vector database to answer user questions.

💡prompt template

A 'prompt template' is a predefined structure or set of instructions used to guide the generation of responses by a language model. It typically includes placeholders for user input and is designed to elicit specific types of responses from the model. In the video, prompt templates are used to format user questions and context in a way that the language model can understand and respond to effectively.

💡local AI model server

A 'local AI model server' refers to a server or system that is hosted on-site, within the user's own environment, rather than being accessed remotely. This allows for greater control over data privacy and processing, and can reduce reliance on external services. The video discusses the use of a local AI model server, specifically 'ollama', to run large language models on the user's machine.

Highlights

Introducing the innovative use of OLLAMA embedding for creating RAG applications with enhanced performance.

Ingest data from URLs and convert it into embeddings, storing them in a Vector database for efficient retrieval.

Utilizing the Nomic embed text model for its superior context length and surpassing OpenAI's embedding models.

Demonstration of the Nomic embed text model's performance through a comparative chart.

Creating a user-friendly interface with Gradio to streamline the RAG application's interaction.

Installation of necessary packages like Lang chain, web-based loader, and others for the application setup.

Defining the model structure and preparing the groundwork for the RAG application with detailed steps.

Retrieving data from URLs, splitting it, and storing it in a Vector DB for efficient data handling.

Explanation of the RAG process and its role in enhancing the relevance of the answers obtained from the language model.

Showcasing the prompt template and its importance in structuring the input for the language model.

Running the code to demonstrate the practical application and performance of the RAG application locally.

Clarification and correction regarding OLLAMA, highlighting its role as a local AI model server.

Integration of the Gradio library to add a user interface for a more accessible and interactive experience.

Providing a step-by-step guide on modifying the code to include Gradio for a seamless user experience.

Demonstration of the final product, showcasing the RAG application's ability to process user input and provide relevant answers.

The video aims to educate and inspire viewers on the potential of RAG applications and local AI model servers like OLLAMA.

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