ntroduction to Langchain Javascript Documentation. How to Create GPT-3 GPT-4 Chatbots that can contextually reference your data (txt, JSON, webpages, PDF) with embeddings . Brief Introduction into embeddings, vectorstorage options such as Pinecone, Chroma.
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Large language models like GPT-3 have spawned a new emerging field called Prompt Engineering. People are coming up with clever techniques to prompt these models to perform different tasks, such as:
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OpenAI APIkey
Zapier Natural Language API Key
Zapier NLA Project ID (create a demo task in their UI according to website instructions listed below in resources)
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In this introduction to Quantum Natural Language Processing (QNLP), we will see how words’ meaning is classically encoded into computers. We will then discuss the challenges representing the meaning of sentences, and present a framework to achieve it (DisCoCat). After seeing that this method benefits a quantum speedup, we will present three examples of use cases.
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OpenChatKit provides a powerful, open-source base to create both specialized and general purpose chatbots for various applications. We collaborated with LAION and Ontocord to create the training dataset. Much more than a model release, this is the beginning of an open source project. We are releasing a set of tools and processes for ongoing improvement with community contributions.
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Two weeks ago, Cohere.ai announced their new dedicated summarization endpoint! For someone currently doing their PhD on text summarization, this is both worrying, but obviously also a rather intriguing development: while recent advancements have been focusing on rather broadly applicable models (think, chatGPT), providing more task-specific alternatives seems to be the niche that Cohere is carving out for themselves.
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GPT-4 is pretty incredible, but like its predecessors, it has problems. Hallucinations and the lack of up-to-date information are typical of Large Language Models (LLMs), from PaLM and LLaMa, to ChatGPT (gpt-3.5-turbo) and GPT 4.
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