Ollama 0.1.26 Makes Embedding 100x Better

Matt Williams
22 Feb 202408:16

TLDRThe video discusses the significant release of Olama with a focus on its new foundational feature—support for BERT and dynamic BERT embedding models. This enhancement allows for more extensive use of Olama and improves the model's ability to provide relevant content through semantic embeddings. The video highlights the efficiency of the new model, demonstrating its speed in processing text chunks from 'War and Peace' and comparing it favorably to previous models. The release also includes support for Google's Gemma model and Windows improvements.

Takeaways

  • 🚀 The release of Olama is considered significant, potentially being one of the top five new features.
  • 🌐 The new model, Gemma from Google, is a headline feature, but the support for BERT and dynamic BERT embedding models is seen as a foundational and impactful addition.
  • 📈 Embeddings are about creating vectors that represent the semantic meaning of data, which are crucial for applications like RAG search to provide relevant content.
  • 🔍 RAG search is important for keeping the model on point and for speeding up the process, despite some criticisms about its effectiveness.
  • 🛠️ The speaker created a code sample using the Chicago Institute of Art's collection, demonstrating the use of embeddings for RAG in September.
  • 📚 Vector databases store embedded content and source text, allowing for quick mathematical comparisons to find the most similar embeddings to a given question.
  • 🏎️ The new version of Olama (0.126) offers faster and more reliable embeddings, with a significant speed improvement over previous models like Llama 2.
  • 📂 The script mentions strategies for splitting documents into chunks for embedding, focusing on providing relevant parts of the document to the model.
  • 💻 A Python code example is provided to demonstrate how to split a text file into 500-word chunks and embed them using the new Olama model.
  • 🤖 Google's involvement with the team is highlighted, as they continue to work on making the Gemma model more reliable in its responses.
  • 🛠️ Windows support improvements and environment variable setup recommendations are mentioned, showing ongoing efforts to refine the user experience.

Q & A

  • What is the most significant release for Olama mentioned in the transcript?

    -The most significant release for Olama mentioned is the one found at ama.com, which the speaker believes may be one of the top five most significant new features.

  • What is the new model from Google discussed in the transcript?

    -The new model from Google discussed is Gemma, which is highlighted as the top item in the release.

  • What feature does the speaker find more exciting than the new Google model?

    -The speaker finds the support for Bert and dynamic Bert embedding models more exciting, as it is a foundational feature that greatly expands the usability of Olama.

  • What is the purpose of embedding in the context of AI models?

    -Embedding creates a vector that represents the semantic meaning of data provided to the model, which is commonly used for tasks like rag search to find relevant content and keep the model on point.

  • How does the speaker view the current state of rag in terms of effectiveness?

    -The speaker believes that while rag is still important, it misses nuances, themes, and the general hierarchy of knowledge, and that improvements can be made.

  • What is the process for using Olama's embedding feature?

    -To use Olama's embedding feature, one should split the document into chunks, embed each chunk, and supply only the relevant parts to the model for a specific question.

  • How does the speed of the new embedding feature in Olama compare to the previous version?

    -The new embedding feature in Olama (version 0.126) is significantly faster than the previous version; it can process chunks in about 40 milliseconds compared to 1.4 seconds for the older model.

  • What is the recommended approach for setting up environment variables on Windows according to the transcript?

    -The team recommends using system variables instead of the method shown in an old video.

  • How does the speaker plan to structure their video content moving forward?

    -The speaker plans to experiment with creating a video every Monday and Thursday, and filling the other days with shorter content based on the main videos.

  • What is the speaker's stance on updating their content when mistakes are found?

    -The speaker is open to updating their content when mistakes are found, as long as the corrections are credible and repeatable.

  • What is the speaker's view on the recent level of engagement in the comments section?

    -The speaker is very excited about the increased level of engagement and activity in the comments section recently.

Outlines

00:00

🚀 Introducing New Features and Embedding Models in Olama

The paragraph discusses an upcoming significant release for Olama, highlighting the importance of embedding models like BERT and dynamic BERT embeddings. It emphasizes the foundational role of these features in expanding the application of Olama and improving its ability to provide relevant content through RAg search. The speaker shares their experience with the AMA repository and the advantages of the new model over traditional RAg methods, including speed and semantic understanding. Additionally, the paragraph touches on the technical aspects of embedding and vector databases, comparing different options and their use cases.

05:01

📈 Performance Comparison and Code Examples for Olama Embeddings

This paragraph presents a performance comparison between the new Olama version 0.126 and the previous Llama 2 model, demonstrating the significant speed improvement in embedding large texts like 'War and Peace'. It delves into the practical implementation of text chunking for embedding and provides a code example for splitting text files into manageable pieces. The speaker shares their excitement about the potential applications of fast and reliable embeddings in Olama and briefly mentions other features in the 0.126 update, such as support for Google's Gemma model and improved Windows support.

Mindmap

Keywords

💡Olama

Olama is a technology or platform mentioned in the transcript that seems to be related to AI or machine learning models. The significance of the release for olama suggests it is a major update or a new feature that could greatly improve its functionality or user experience. The context indicates that this release might be one of the top five most significant updates, highlighting its importance.

💡Discord

Discord is a communication platform used by communities, including tech and gaming enthusiasts. In the context of the transcript, it is where the speaker observes the reactions and discussions of users regarding the new features of olama. It serves as a channel for the speaker to gauge what aspects of the update are generating excitement or interest among the community.

💡Google's new model Gemma

Google's new model Gemma is mentioned as the headline feature in the olama update. It is likely a machine learning model developed by Google that is being integrated into olama, suggesting a collaboration or use of Google's technology to enhance olama's capabilities. The mention of Gemma as the top item suggests it is a prominent and marketable aspect of the update.

💡Bert and dynamic Bert embedding models

Bert (Bidirectional Encoder Representations from Transformers) and dynamic Bert embedding models are AI technologies that are used for understanding the semantic meaning of data. In the context of the transcript, these models are a foundational feature for olama, allowing it to be used in more places and for a wider range of applications. The excitement around this feature suggests it could greatly expand the capabilities and utility of olama.

💡Rag search

Rag search, or Retrieval-Augmented Generation, is a method that combines retrieval (finding relevant content) with generation (creating new content) to provide more accurate and relevant responses to queries. In the transcript, rag search is discussed as an important use case for embeddings, allowing the model to find relevant content to provide the right inputs for a good answer. The speaker also discusses the limitations of rag search in terms of missing nuances and themes, suggesting room for improvement with the new olama features.

💡Embedding

Embedding, in the context of AI and machine learning, refers to the process of converting data into a numerical representation, or vector, that captures the semantic meaning of the input. In the transcript, embedding is described as a foundational feature for olama, allowing it to understand and process data more effectively. The speaker emphasizes the importance of embedding for improving olama's ability to stay on point and respond quickly to queries.

💡Vector database

A vector database is a type of database that stores and manages vectors, which are numerical representations of data points. In the context of the transcript, vector databases are used to store embeddings of content, allowing for quick and efficient searching and comparison of these embeddings to find the most relevant information in response to a query. The speaker mentions that there are many vector databases available, differing mainly in their ease of use and hosting options.

💡AMA.com

AMA.com is mentioned as the website where more information about the olama release can be found. It is likely the official source for updates, features, and technical details related to olama. The speaker encourages viewers to visit the site to learn more about the significant release, indicating that it is a trusted and authoritative source.

💡Command line

The command line is a text-based interface for interacting with a computer or software. In the transcript, the speaker uses the command line to demonstrate how to run olama's new embedding feature, showing that it can be executed through simple commands. This suggests that the new version of olama is designed to be user-friendly and accessible through various interfaces, including the command line.

💡Code sample

A code sample is a small piece of code that is used to illustrate a programming concept or demonstrate a particular functionality. In the transcript, the speaker refers to a code sample in the AMA repo that shows how to use rag search with a collection from the Chicago Institute of Art. This indicates that the speaker provides practical examples to help others understand and implement the technology discussed.

💡Windows support

Windows support refers to the compatibility and functionality of software or applications on the Windows operating system. In the transcript, the speaker discusses issues related to setting up environment variables on Windows and the team's recommendations for resolving them. This shows that the developers of olama are actively working on improving the user experience across different platforms, including Windows.

Highlights

The release of a significant update for olama, potentially one of the top five most significant new features.

The introduction of a new model, Gemma from Google, as the headline feature.

The real excitement lies in the support for Bert and dynamic Bert embedding models, marking a foundational feature for olama.

Embedding allows olama to be used in more places than ever before, enhancing its versatility and utility.

The feature was highly anticipated and requested by the team since August.

Embedding involves creating a vector that represents the semantic meaning of data, crucial for understanding and responding to user inputs.

The most common use case for embedding is rag search, which helps find relevant content for the model to provide accurate answers.

Rag search remains important for keeping the model on point and for speedy responses.

The new model is expected to perform better than rag in terms of understanding nuances, themes, and knowledge hierarchy.

AMA has supported embedding for a long time but with less accurate and slower regular models.

The new version 0.126 of AMA offers a significant improvement in embedding speed and accuracy.

A demonstration of how to use the new AMA version for embedding, using command line and Python code.

The process of splitting a text file into chunks for embedding, to provide only relevant parts of the document to the model.

An example of embedding chunks of 'War and Peace' and the impressive speed of processing each chunk.

A comparison of the new AMA version's speed with the previous llama 2 model, showing a significant time reduction.

The other features in version 0.126 include further support for Gemma from Google and improvements in Windows support.

The video creator's openness to updating content when credible and repeatable corrections are provided.

The impact of the new AMA release on the creator's content production and the reaction of the audience.

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