Enhancing GPT's External Data Lookup Capacity: A Walkthrough

Accessing external information and blending it with AI-generated text is a capability that would significantly enhance AI applications. For instance, the combination of OpenAI's GPT and external data lookup, when executed efficiently, can lead to more comprehensive and contextually accurate output.

One promising approach is to leverage the LangChain API to extract and split text, embed it, and create a vectorstore which can be queried for relevant context to add to a prompt template. A helpful demonstration of how LangChain works can be found on YouTube.

The unique aspect lies in how you query and write your prompt to the API. An innovative idea is to prompt for an outline using a research question or topic statement as the query, have GPT format the outline in a specific way for easy split and query, and then feed individual statements while keeping the overall context intact. If you don't have access to the API, the prompt generation can be done manually in ChatGPT.

Another workaround to enhance data lookup can be executed using Adobe Acrobat to combine PDFs into a single file and then uploading it to chatpdf. While this might not provide a citation to the specific PDF page, it's still an efficient way to pool resources.

There are other tools available for this purpose too. Vault.pash.city and ChatGPT Splitter are worth exploring. Bing Assistant also has an interesting feature where it can answer questions about documents opened in Edge.

It's important to remember that these methods are experimental and under development. They offer interesting possibilities and are worth exploring if you're interested in enhancing AI's interaction with external data.

Tags: GPT, External Data Lookup, LangChain, API, Vectorstore, ChatGPT, PDF, Bing Assistant, Vault.pash.city, ChatGPT Splitter

GPT-4 API Integration and Similar Websites for Text Analysis

Users of GPT Plus may wonder how to access the GPT-4 model API for use on vault.pash.city, as well as if there are other websites where they can upload documents for their GPT-4 API to use when generating responses. Other users have tried the tool on sizable documents and found it successful in answering questions about the document. However, it may not perform as well when given context outside of the uploaded document.

Some users have suggested potential applications … click here to read

Unlocking GPU Inferencing Power with GGUF, GPTQ/AWQ, and EXL2

If you are into the fascinating world of GPU inference and exploring the capabilities of different models, you might have encountered the tweet by turboderp_ showcasing some 3090 inference on EXL2. The discussion that followed revealed intriguing insights into GGUF, GPTQ/AWQ, and the efficient GPU inferencing powerhouse - EXL2.

GGUF, described as the container of LLMs (Large Language Models), resembles the .AVI or .MKV of the inference world. Inside this container, it supports various quants, including traditional ones (4_0, 4_1, 6_0, … click here to read

Using Langchain and GPT-4 to Create a PDF Chatbot

Users discussed how to create a PDF chatbot using the GPT-4 language model and Langchain. They shared a step-by-step guide on setting up the ChatGPT API and using Langchain's Documentreader `PyPDFLoader` to convert PDF files into a format that can be fed to ChatGPT. The users also provided a link to a GitHub repository that demonstrates this process: https://github.com/mayooear/gpt4-pdf-chatbot-langchain .

One user mentioned using GPT-4 for writing a novel and pointed out the model's limitations in referencing data from conversations that … click here to read

Exploring the Capabilities of ChatGPT: A Summary

ChatGPT is an AI language model that can process large amounts of text data, including code examples, and can provide insights and answer questions based on the text input provided to it within its token limit of 4k tokens. However, it cannot browse the internet or access external links or files outside of its platform, except for a select few with plugin access.

Users have reported that ChatGPT can start to hallucinate data after a certain point due to its token … click here to read

Engaging with AI: Harnessing the Power of GPT-4

As Artificial Intelligence (AI) becomes increasingly sophisticated, it’s fascinating to explore the potential that cutting-edge models such as GPT-4 offer. This version of OpenAI's Generative Pretrained Transformer surpasses its predecessor, GPT-3.5, in addressing complex problems and providing well-articulated solutions.

Consider a scenario where multiple experts - each possessing unique skills and insights - collaborate to solve a problem. Now imagine that these "experts" are facets of the same AI, working synchronously to tackle a hypothetical … click here to read

LLAMA-style LLMs and LangChain: A Solution to Long-Term Memory Problem

LLAMA-style Long-Form Memory (LLM) models are gaining popularity in solving long-term memory (LTM) problems. However, the creation of LLMs requires a fully manual process. Users may wonder whether any existing GPT-powered applications perform similar tasks. A project called gpt-llama.cpp, which uses llama.cpp and mocks an OpenAI endpoint, has been proposed to support GPT-powered applications with llama.cpp, which supports Vicuna.

LangChain, a framework for building agents, provides a solution to the LTM problem by combining LLMs, tools, and memory. … click here to read

Open Source Projects: Hyena Hierarchy, Griptape, and TruthGPT

Hyena Hierarchy is a new subquadratic-time layer in AI that combines long convolutions and gating, reducing compute requirements significantly. This technology has the potential to increase context length in sequence models, making them faster and more efficient. It could pave the way for revolutionary models like GPT4 that could run much faster and use 100x less compute, leading to exponential improvements in speed and performance. Check out Hyena on GitHub for more information.

Elon Musk has been building his own … click here to read

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