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

Mervin Praison
23 Feb 202405:39

TLDRThe video introduces the concept of 'Ollama Embedding,' a method for enhancing the performance of language models by converting data from URLs into embeddings and storing them in a Vector database like Chroma DB. The process involves using the Nomic Embed Text model, which is noted for its higher context length and superior performance compared to Open AI embeddings. The video demonstrates how to set up a local AI model server that can run large language models on a user's machine, without incurring costs. The steps include splitting data into chunks, converting documents into embeddings, and performing retrieval-augmented generation (RAG) to obtain more relevant answers. The final application is showcased with a user interface created using Gradio, allowing users to input URLs and questions to receive context-based responses. The video concludes with a call to action for viewers to subscribe and stay updated for more content on Artificial Intelligence.

Takeaways

  • 📈 **Ollama Embedding**: A method to enhance AI responses by creating applications with better performance through embedding technology.
  • 🌐 **Data Ingestion**: The process involves ingesting data from URLs, converting it into embeddings, and storing it in a Vector database for efficient retrieval.
  • 🔍 **Chroma DB**: Utilizes Chroma DB for storing embeddings and Nomic Embed Text model for higher context length, surpassing OpenAI's models.
  • 📚 **Document Chunking**: Splits data into chunks with an overlap of 100 characters to manage the size and continuity of the data.
  • 🔧 **RAG Application**: Develops a Retrieval-Augmented Generation (RAG) application that can run locally on a user's machine.
  • 💻 **Local AI Model Server**: Ollama is described as a local AI model server allowing users to run large language models on their own machines.
  • 🚀 **Performance**: The embedding process is noted to be fast, taking approximately 219 milliseconds.
  • 📝 **User Interface**: The application includes a user interface built with Gradio, enabling users to input URLs and questions for the AI to process.
  • 📺 **YouTube Channel**: The presenter regularly creates videos about Artificial Intelligence and encourages viewers to subscribe and stay updated.
  • 🔗 **LangChain**: Utilizes LangChain for putting together the application components, including web-based loaders and chat prompt templates.
  • 📝 **Code Execution**: Instructions are provided for running the application using Python, including the installation of necessary modules and execution of the `app.py` script.
  • 🌟 **Local Deployment**: Emphasizes the excitement around creating an application that can run completely locally with zero 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 more performant language application by converting data from URLs into embeddings and storing them in a Vector database.

  • What is the purpose of using a Vector database?

    -The purpose of using a Vector database is to store embeddings of data, which allows for efficient retrieval and processing of relevant data when a question is asked.

  • Which embedding model is mentioned in the video as superior to OpenAI's embedding models?

    -The video mentions 'Nomic Embed Text' as an embedding model that has a higher context length and surpasses OpenAI's embedding models.

  • What is the role of the 'web-based loader' in the process?

    -The 'web-based loader' is used to extract data from a list of URLs provided for the application.

  • How does the 'Character Text Splitter' contribute to the process?

    -The 'Character Text Splitter' divides the data into chunks with a specified overlap, which is essential for creating embeddings that capture the context of the data.

  • What is the name of the local AI model server mentioned in the video?

    -The local AI model server mentioned in the video is called 'Ollama'.

  • What is the purpose of the 'Prompt Template' in the RAG application?

    -The 'Prompt Template' is used to structure the input to the language model, ensuring that the model receives the necessary context and question to generate a relevant answer.

  • How does the RAG (Retrieval-Augmented Generation) process improve the response?

    -The RAG process improves the response by first retrieving relevant documents based on the question and then using those documents to generate a more contextually relevant answer.

  • What is the significance of the 'mLess Large Language Model' in the video?

    -The 'mLess Large Language Model' is used to process the prompt and retrieve relevant information, contributing to the generation of a more accurate and contextually rich response.

  • What is the user interface tool used to interact with the application?

    -The user interface tool used to interact with the application is Gradio.

  • How can users run the created RAG application?

    -Users can run the RAG application locally on their machine by typing 'Python app.py' in the terminal, after ensuring that all necessary models and dependencies are installed.

  • What is the benefit of using the 'Ollama' platform?

    -The 'Ollama' platform allows users to run and create large language models locally, providing a cost-effective solution that supports various operating systems like Mac OS, Linux, and Windows.

Outlines

00:00

🚀 Building a Local AI Application with Ollama Embeddings

The first paragraph introduces the concept of using Ollama embeddings to create a locally running AI application with improved performance. The process involves ingesting data from URLs, converting it into embeddings, and storing these in a Vector database. This setup allows for more relevant answers to be generated when questions are asked, leveraging a large language model. The speaker emphasizes the use of the Nomic embed text model for its higher context length and superior performance over OpenAI's models. The tutorial outlines steps to achieve this, including installing necessary packages, creating an app.py file, and defining the model and steps for data retrieval, splitting, conversion to embeddings, and storing in the database. The process concludes with invoking a chain for the Retrieval-Augmented Generation (RAG) application, which is claimed to run locally with zero cost.

05:01

💻 Adding a User Interface to the Local AI Application

The second paragraph demonstrates how to add a user interface to the previously discussed AI application using Gradio. The speaker modifies the code to include Gradio at the top and encapsulates the process within a function named 'process_input'. This function takes a list of URLs and a question as inputs, splits the URLs, and repeats the process of data handling as before. The interface is then launched, allowing the user to input URLs and questions to receive processed responses. The speaker provides a live example by entering a URL, asking a question about 'Ollama', and receiving an answer based on the provided context. The video concludes with an invitation for viewers to stay tuned for more similar content, and a call to like, share, and subscribe to the channel.

Mindmap

Keywords

💡Ollama Embedding

Ollama Embedding refers to a method of encoding data into a format that can be better understood and utilized by artificial intelligence (AI) systems. In the context of the video, it is used to improve the performance of a 'rag application' (possibly a typo for 'rag' as in 'retrieve and generate'), which is an application that uses AI to retrieve and generate responses. The term 'Ollama' seems to be a local or proprietary term, possibly a server or platform for running large language models locally.

💡Vector Database

A Vector Database is a type of database designed to store, search, and manage large volumes of vector data, which can be used for machine learning and AI applications. In the video, embeddings (data representations) are stored in a Vector Database called Chroma DB, which allows for efficient retrieval of relevant data when generating responses.

💡Nomic Embed Text

Nomic Embed Text is a model for embedding text into a format that can be used by AI systems. It is mentioned in the video as having a higher context length and surpassing other embedding models in performance. It is used to convert text data into embeddings for storage in the Vector Database.

💡Large Language Model

A Large Language Model is an AI system that is trained on vast amounts of text data to understand and generate human-like language. In the video, a model named 'Mistal' is used as a large language model to process prompts and generate responses.

💡Gradio

Gradio is an open-source Python library used to quickly create interactive web interfaces for machine learning models. In the video, Gradio is used to add a user interface to the application, allowing users to input URLs and questions to receive responses from the AI system.

💡Web Based Loader

A Web Based Loader is a tool or method used to load or extract data from the web. In the video, it is used to extract data from a list of URLs, which is then processed and converted into embeddings for the AI system.

💡Character Text Splitter

A Character Text Splitter is a tool that divides text into smaller chunks, often with an overlap to maintain context. In the video, it is used to split the extracted data into chunks with a specified overlap, which are then converted into embeddings.

💡Retriever

A Retriever in the context of AI is a component that retrieves relevant documents or data based on a query. In the video, a retriever is used to find relevant documents when a question is asked, which is part of the process to generate a response.

💡RAG (Retrieve and Generate)

Retrieve and Generate (RAG) is a method used in AI where the system first retrieves relevant information and then generates a response based on that information. In the video, RAG is used to compare the performance before and after its implementation in the application.

💡Prompt Template

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

💡Local AI Model Server

A Local AI Model Server is a server that runs on a user's own machine and allows them to run large language models without relying on external servers. In the video, 'Ollama' is described as a local AI model server that enables users to run large language models locally.

Highlights

Ollama Embedding is a method to improve AI response by creating rag applications with better performance.

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

Chroma DB and Nomic Embed Text model are used for their higher context length and surpassing performance over Open AI embeddings.

A user interface is created using Gradio to interact with the AI system.

Nomic Embed Text is chosen for its superior performance in handling context.

LangChain is used to put all the pieces together for the AI application.

The process involves splitting data into chunks with an overlap of 100 characters.

Chroma DB is initialized to store the embeddings derived from the documents.

The model name 'Nomic Embed Text' is defined for the embeddings.

A retriever is used to fetch relevant documents when a question is asked.

Before and after RAG (Retrieval-Augmented Generation) comparisons are made to evaluate performance.

The Prompt is sent to the LLM (Large Language Model) for topic identification.

The RAG chain is created by combining the Prompt template and invoking the LLM with the question.

Ollama is a local AI model server that allows running large language models on personal machines.

The entire RAG application can run locally with zero cost.

The user interface is added to the application to make it more accessible.

Performance is monitored, with embedding taking approximately 219 milliseconds.

Ollama enables users to run and create large language models locally, supporting Mac OS, Linux, and Windows.

The video demonstrates the creation of a RAG application from data extraction to user interface integration.

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