guardrails
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| guardrails | browser-friend | |
|---|---|---|
| 13 | 1 | |
| 3,284 | 88 | |
| 9.8% | - | |
| 9.9 | 3.7 | |
| 5 days ago | 7 months ago | |
| Python | TypeScript | |
| Apache License 2.0 | MIT License |
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guardrails
- Guardrails AI
- Does anyone have an example of a langchain based customer facing agent like a cashier/waitress?
- Is there a UI that can limit LLM tokens to a preset list?
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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
- Ask HN: People who were laid off or quit recently, how are you doing?
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Ask HN: AI to study my DSL and then output it?
There are a couple different approaches:
- Use multi-shot prompting with something like guardrails to try prompting a commercial model until it works. [1]
- Use a local model with something with a final layer that steers token selection towards syntactically valid tokens [2]
[1] https://github.com/ShreyaR/guardrails
[2] "Structural Alignment: Modifying Transformers (like GPT) to Follow a JSON Schema" @ https://github.com/newhouseb/clownfish.
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Introducing :🤖 Megabots - State-of-the-art, production ready full-stack LLM apps made mega-easy with LangChain and FastAPI
👍 validate and correct the outputs of LLMs using guardrails
- For consistent output from vicuna 13b
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[D] Is all the talk about what GPT can do on Twitter and Reddit exaggerated or fairly accurate?
not vouching for it, but I know this is at least a thing that exists and I like the general idea: https://github.com/shreyar/guardrails
- Introducing Agents in Haystack: Make LLMs resolve complex tasks
browser-friend
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Introducing Agents in Haystack: Make LLMs resolve complex tasks
includes a TypeScript interface that GPT4 is asked to follow. Works very well and reliably uses tools like Calculate, RequestDOM, etc.
https://github.com/cantino/browser-friend
What are some alternatives?
lmql - A language for constraint-guided and efficient LLM programming.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
GPTCache - Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
openai-translator - 基于 ChatGPT API 的划词翻译浏览器插件和跨平台桌面端应用 - Browser extension and cross-platform desktop application for translation based on ChatGPT API.
JARVIS - JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
qdrant-haystack - An integration of Qdrant ANN vector database backend with Haystack
dynamic-gpt-ui - Dynamic UI generation with GPT-3 (OpenAI)
haltt4llm - This project is an attempt to create a common metric to test LLM's for progress in eliminating hallucinations which is the most serious current problem in widespread adoption of LLM's for many real purposes.
truss - Assertions micro-library for Clojure/Script
quivr - Your GenAI Second Brain 🧠 A personal productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic, VertexAI, Ollama, LLMs, Groq that you can share with users ! Local & Private alternative to OpenAI GPTs & ChatGPT powered by retrieval-augmented generation.
ghostwheel - Hassle-free inline clojure.spec with semi-automatic generative testing and side effect detection
empirical-philosophy - A collection of empirical experiments using large language models and other neural network architectures to test the usefulness of metaphysical constructs.