No Connection, No Problem: AI Solutions with GPT4All and KNIME

In a world increasingly driven by technology and artificial intelligence, tools like the KNIME Analytics Platform and the revolutionary GPT4All initiative are playing a crucial role in enabling people and businesses to take full advantage of powerful next-generation language models. KNIME Analytics Platform is an open-source data analytics platform that allows users to manipulate, analyze and visualize data efficiently and effectively. On the other hand, GPT4All is an open-source and innovative ecosystem that seeks to train and deploy custom LLMs that run locally without depending on an Internet connection. In this article, we will explore how these two technologies can be combined to achieve amazing results.

Connecting KNIME to GPT4All: A Glance at the process

To start working locally with models like ChatGPT in KNIME, the process is simpler than you might think.

• We download GPT4All desktop version on our computer.
• Once we have it downloaded, we choose the model we want to use according to the work we are going to do. There are many models available and some perform better than others in a specific task. GPT4All provides a few facts about each of the available models and list the system requirements.

• Once the model is downloaded to our computer, we copy the path where it is located and paste it into the GPT4All LLM Connector node. That’s it — now we have a powerful artificial intelligence model integrated into our KNIME workflow.

• At this point, to start using the power of LLMs for human-like text generation, we only need to connect this node to the LLM Prompter node and our model is ready to take on the indications (prompts ) that we deem appropriate, with the freedom of forms and formats that KNIME allows.

For a practical example of how to use these nodes in KNIME, check the workflow that I created for Just KNIME It , Season 2, Challenge 20. In the workflow, we asked the model to generate a representative word from a list of words describing a topic for a corpus of hotel reviews.

Find the workflow below and learn more about it in a previous data story: “The Power of Artificial Intelligence to Analyze Opinions in Hotel Reviews ”.

Advantages of working with LLMs locally

The ability to work with advanced LLMs locally offers several notable advantages:

1. Improved Performance: By running the models on your own machine, you can take full advantage of your CPU/GPU power without depending on your Internet connection speed.
2. Data Privacy: Not requiring an Internet connection means that your data remains in your local environment, which can be especially important when handling sensitive information.
3. Flexibility and Customization: By running models locally, you have more control over how they are configured and used. You can customize the models according to your specific needs.
4. Price: Most models that can be downloaded (!= installed) and run locally without are FREE, which implies eliminating utilization costs.
5. Lower Latency: The response of the models is faster due to the elimination of network latency.

Currently available models

As of today, GPT4All offers several valuable models that can be used locally, including:

1. Wizard v1.1
2. GPTALL Falcon
3. Llama-2 7B-Chat
4. Hermes
5. Snoozy
6. Mini Orca
7. Wizard Uncensored
8. Calla-2–7B Chat

Customization using Vector Stores (Advanced users)

In the field of artificial intelligence, vectors serve as dense numeric representations of single words to entire documents. They are like “secret” numerical encodings that reflect the essence of what they are representing. For example, related words would have close numbers in this representation.

Vector Stores are a sort of library of numeric vectors that describe different objects. This is very useful for searching for information and performing similarity searches: if the numerical encodings look alike, it means that the objects they represent also look alike.

Imagine searching for a song similar to the one you like. Vector Stores do the same thing but, instead of songs, they search data. When we look up or compare data, we are actually comparing stored numeric representations. If the numbers are similar, it implies that the data is also similar in some respect. It’s like saying “Find things similar to this”, and the vector store uses those numeric representations to find matches.

Furthermore, the use of vector stores in combination with artificial intelligence models offers significant benefits. For example, it allows organizations to adapt models to solve organization-specific problems. Creating vectors that represent business concepts allows you to feed the model with specific knowledge and query them to generate relevant content in a business context.

Working with vector stores in combination with local LLMs means using your own data and having full control over how models handle information, which is useful for keeping sensitive data private. Efficiency and improved results are other positive aspects. By adjusting the vectors as needed, you get more accurate and relevant results, and working locally reduces latency and improves efficiency by not relying on external connections.

Creating Vector Stores. The workflow below shows how to create a vector store from a KNIME table using the OpenAI Embeddings Connector and FAISS Vector Store Creator nodes.

To run the workflow, you need an OpenAI API key.

Reading existing Vector Stores. The workflow below shows how to read an existing vector store with the FAISS Vector Store Reader node in KNIME.

To run the workflow, you need an OpenAI API key.

Conclusion: Harnessing the Power of KNIME and GPT4All

In short, the combination of the KNIME Analytics Platform and GPT4All opens new doors for collaboration between advanced data analytics and powerful and open-source LLMs. Working with these models on your own computer, without the need to connect to the Internet, provides considerable advantages in terms of cost, performance, privacy and flexibility. With the variety of models available, applications are endless: from generating text to automating complex tasks. So go ahead, experiment with it and discover how this combination can elevate your projects and analyses to a whole new level.

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