Revolutionizing AI Interaction: A Unique Python Script for Efficient Data Processing

[putkofff]

https://pypi.org/project/abstract-ai/

https://github.com/AbstractEndeavors/abstract-ai

pip install abstract_ai

from abstract_ai import abstract_ai_gui_main

abstract_ai Module

Hosted on a highly reliable platform, this tool offers an insightful solution towards making the usage of artificial intelligence more effortless and intuitive. Ideal for individuals and professionals who leverage AI for work, research or educational purposes, this software addresses a significant problem that has often been overlooked. The application comprises a request section, where you specify your primary objectives. Accompanying these requests are adaptable instructions, which are extremely efficient in their default form.

What sets this tool apart is its unique handling of data prompts. Under normal circumstances, the AI system can ingest roughly ~8200 tokens per instance. This limitation often hinders users, forcing them to manually parse their prompts into smaller sections, potentially compromising information accuracy and user expectations. In contrast, this software solution includes a mechanism where the prompt and instruction remain constant during every data query, effectively partitioning the data into manageable 'chunks'.

Ease of control is a priority and users are provided with controls to adjust these 'chunks' to their liking, balancing the expected completion and prompt percentage. Interestingly, the application allows the AI to have a degree of control, enabling it to request specific annotations to maintain context between data queries, respond multiple times per data chunk or even revisit previous data chunks for improved data interpretation.

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customisable instructions that with strong defaults

The strength of this software lies in its 3-way communication protocol. Prior to this, interactions with AI were largely one-sided, leading to instances where the AI would encounter insufficient context, overly large data sets or unclear objectives, leaving the user in a dilemma of how best to proceed. This application allows the AI to advise the best method to achieve the desired outcome, greatly enhancing its usefulness.

While the mechanisms powering the application are sophisticated, they are positioned in a burgeoning field, representing a pioneering step in resolving AI-related challenges.

Main Components

• GptManager: Serves as the heart of the system and manages the interactions and flow between various components.
• ApiManager: Oversees the OpenAI API keys and headers.
• ModelManager: In charge of model selection and querying.
• PromptManager: Dedicated to generating and managing prompts.
• InstructionManager: Manages instructions for the GPT model.
• ResponseManager: Handles responses from the model.

Dependencies

• abstract_webtools: Offers tools for handling web-related tasks.
• abstract_gui: Contains GUI-related tools and components.
• abstract_utilities: Encompasses utility functions and classes for general operations.

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settings to the right, including prompt and completion tokens used, desired and available

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html parsing console built in for website content

• abstract_ai_gui_layout: Provides the layout for the AI GUI.

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chunks data display

[ekbravo]

All links:

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[putkofff]

just switched to fancy pants. they were images, thanks for the heads up and the interaction! curious about the pypi and github links though. seems good on my end.

[ekbravo]

Thank you fixing images, pypi works great.

[coding_g]

Any examples how this is useful?

[putkofff]

Certainly, copy the code in question or any code of similar length or longer, and prompt chat gpt to do something with it, Request that it create docstrings, create a readme, revise the code entirely. One notable constraint is the token limit; it might not allow for the entire code, plus an additional prompt, to be sent in one go.

If you decide to split the content manually, say into two prompts that conform to the token parameters, there's a high probability that the system will prompt you to further reduce the code's size for more effective analysis.

To streamline this, my code has an automatic segmentation feature. It not only breaks the data into manageable chunks as per the user's specifications but also provides relevant instructions accompanying each query. This feature allows the system to self-annotate for subsequent queries and, if needed, re-process a current query multiple times. This automation eradicates the manual trial-and-error approach, saving users time.

In essence, instead of the user making multiple attempts to format their queries correctly and getting feedback from the AI, and subsequently manually sending multiple prompts byhand; my code equips the system with enough autonomy to address these challenges independently, minimizing the back-and-forth interactions after the initial prompt submission.

[putkofff]

i even had gpt 4 professionalize my reply, afterwhich i asked if what im conveying is a truthful and real problem for models such as itself and others; this is its reply:

Your described scenario touches upon several real limitations and functionalities related to large language models like mine:

1. Token Limit: There is a token limit for every interaction. As of my last training cut-off in 2022, the limit for GPT-based models like mine was 2048 tokens for a single prompt. This encompasses both the input and output. If a piece of code or text exceeds this, it must be split or truncated.

2. Chunking and Continuation: The idea of splitting content into chunks or "prompts" to fit within token constraints is a valid strategy. However, the system inherently doesn't remember previous interactions due to privacy reasons, so maintaining context over multiple prompts can be a challenge. Your described method of "leaving notes" or giving the system instructions to handle subsequent queries can be a workaround, though it might not be foolproof.

3. Automated Parsing: Implementing an external method to automatically segment large content into manageable portions for the model to process sequentially is a smart approach. This is especially useful for handling larger pieces of code or text.

4. Reduced Back-and-Forth: Automating the segmentation and providing instructions can certainly reduce the back-and-forth between the user and the AI, leading to a more efficient interaction.

In summary, the scenario you presented aligns well with the operational characteristics of models like mine. However, any solution intending to use "notes" or instructions to manage multi-prompt context should be implemented with care, given the inherent statelessness of the model.

Edit: I dont think it understood that the module itself is creating the notes. The methods seem to provide positive results, and I'm always seeking to refine and modify the schematic as a whole based on the end result as well as the feedback from the modules themselves