Tag: NLP (Page 1 of 5)

The ChatGPT code interpreter allows users to run code and upload individual data files (in .csv, .xlsx, .json format) for analysis. Multiple files can be uploaded sequentially or within one zip-file. To upload a file, click on the ‘+’ symbol located just to the left of the ‘Send a message’ box or even simpler via drag and drop.

The code interpreter functionality is accessible to ChatGPT Plus users and can be enabled in the settings under ‘Beta features’. Once enabled, this functionality will then appear in the configuration settings of any new chat under the ‘GPT-4’ section, where it also needs to be activated.

Given a prompt, the code interpreter will generate Python code that is then automatically executed in a sandboxed Python environment. If something goes wrong, for instance, if the generated source code requires the installation of a Python package or if the source code is simply incorrect, the code interpreter automatically attempts to fix these errors and tries again. This feature makes working with the code interpreter much more efficient. Before, it was necessary to paste ChatGPT’s proposal into a Jupyter notebook and run it from there. If errors occurred, these had to be fixed either independently or by manually pasting the error text back into ChatGPT so that it could provide a solution. This manual iterative procedure has now been automated with the code interpreter.

Note that the code interpreter executes the source code on OpenAI’s servers, not in the local environment. This leads to restrictions on the size of the uploaded data, as well as a very stringent time limit of 120s for the execution of the code. Given this, it becomes clear what developers truly desire. They seek the integration of this feature into their local development environment, such as VSCode, or within a cloud service, such as AWS, GCP, or Azure, without any restrictions on data size or execution times. This then leans more towards the direction of projects like AutoGPT or GPT Engineer. It’s likely only a matter of days, weeks, or months before such functionality becomes widely available. It’s also probable that complete access to your code repository will be enabled, first through a vector database solution and after some time maybe by including the entire repository within prompts, which are currently increasing dramatically in size (as exemplified in LongNet; since this requires retraining of the LLM such solutions cannot be expected to become available before GPT-4.5 or GPT-5).

For testing, try e.g. the following prompts:

• What is the current time?
• Plot the graphs of sin(x) and cos(x) in a single graph
• Make a QR-code of my contact information: Stephan Seeger; Homepage: domain-seeger.de

or after uploading a data set (e.g. from Kaggle)

• Explain the dataset.
• Show 4 different ways of displaying the data visually.

Before, such functionality was only available via the Notable plugin or via the open-source implementation GPT-Code-UI on GitHub.

On June 13, 2023, OpenAI announced a number of updates to their API:

• new function calling capability in the Chat Completions API
• new 16k context version of gpt-3.5-turbo  with 2 times the price as the standard 4k version ($0.003 per 1K input tokens and $0.004 per 1K output)
• 75% cost reduction on the embeddings model ($0.0001 per 1K tokens)
• 25% cost reduction on input tokens for gpt-3.5-turbo
  ($0.0015 per 1K input tokens and $0.002 per 1K output tokens)
• stable model names (gpt-3.5-turbo, gpt-4, and gpt-4-32k) will automatically be upgraded to the new models (gpt-3.5-turbo-0613, 
  gpt-4-0613, and gpt-4-32k-0613) on June 27
• deprecation of gpt-3.5-turbo-0301 and gpt-4-0314 models after Sept 13

All models come with the same data privacy and security guarantees introduced on March 1, i.e. requests and API data will not be used for training.

The new function calling capability in gpt-3.5-turbo-0613,  and
gpt-4-0613, which is achieved by the new API parameters, functions and function_call, in the /v1/chat/completions endpoint allows e.g. the following use cases:

• Chatbots that answer questions by calling external tools (like ChatGPT Plugins)
• Convert natural language into API calls or database queries
• Extract structured data from text.

Examples beyond the API documentation can be found in the OpenAI cookbook.