staplechain
Promptify
| staplechain | Promptify | |
|---|---|---|
| 1 | 29 | |
| 10 | 3,046 | |
| - | 2.3% | |
| 3.5 | 8.5 | |
| about 1 year ago | 2 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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staplechain
Promptify
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Promptify 2.0: More Structured, More Powerful LLMs with Prompt-Optimization, Prompt-Engineering, and Structured Json Parsing with GPT-n Models! 🚀
First up, a huge Thank You for making Promptify a hit with over 2.3k+ stars on Github ! 🌟
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A minimal design pattern for LLM-powered microservices with FastAPI & LangChain
You're absolutely correct, and I agree that there's potentially a risk of quality loss. But likewise, since these are all intrinsically linked, it may be possible to leverage strength by combining these tasks. I'm unaware of a paper reviewing the reliability and/or performance of LLMs in this specific scenario. If you find any, do share :) With regards to generating JSON responses - there are simple ways to nudge the model and even validate it, using libraries such as https://github.com/promptslab/Promptify, https://github.com/eyurtsev/kor and https://github.com/ShreyaR/guardrails
- Promptify: Prompt Engineering Library
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A python module to generate optimized prompts, Prompt-engineering & solve different NLP problems using GPT-n (GPT-3, ChatGPT) based models and return structured python object for easy parsing
Examples: https://github.com/promptslab/Promptify/tree/main/examples
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Promptify - Prompt Engineering for Named Entity Recognition(NER)
In this blog, we are going to try to understand how promptify is going to be used along with LLMs(Large Language Models) to perform named entity recognition(NER).
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[D] What ML dev tools do you wish you'd discovered earlier?
Check Promptify for LLM https://github.com/promptslab/Promptify
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[P] Extracting Causal Chains from Text Using Language Models
Awesome project! I am working on something similar using Promptify (extending this PR -> https://github.com/promptslab/Promptify/issues/3)
- Classification using prompt or fine tuning?
What are some alternatives?
Azure-Cognitive-Search-Azure-OpenAI-Accelerator - Virtual Assistant - GPT Smart Search Engine - Bot Framework + Azure OpenAI + Azure AI Search + Azure SQL + Bing API + Azure Document Intelligence + LangChain + CosmosDB
finetuner - :dart: Task-oriented embedding tuning for BERT, CLIP, etc.
Get-Things-Done-with-Prompt-Engineering-and-LangChain - LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading and indexing data, creating prompt templates, CSV agents, and using retrieval QA chains to query the custom data. Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment analysis.
causal-chains - Library for creating causal chains using language models.
openai-cookbook - Examples and guides for using the OpenAI API
kor - LLM(😽)
langforge - A Toolkit for Creating and Deploying LangChain Apps
llm-api-starterkit - Beginner-friendly repository for launching your first LLM API with Python, LangChain and FastAPI, using local models or the OpenAI API.
chameleon-llm - Codes for "Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models".
guardrails - Adding guardrails to large language models.