megabots
guardrails
| megabots | guardrails | |
|---|---|---|
| 16 | 13 | |
| 335 | 3,361 | |
| - | 6.4% | |
| 6.9 | 9.9 | |
| 11 months ago | 4 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
megabots
- 🤖 Release 0.0.11 in Megabots | Memory and Vectorstores are live!
- Introducing :🤖 Megabots - State-of-the-art, production ready full-stack LLM apps made mega-easy with LangChain and FastAPI
- Introducing: 🤖 Megabots - State-of-the-art, production ready full-stack LLM apps made mega-easy with LangChain and FastAPI
- 🤖 Megabots - Version 0.0.9 released
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?
-
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?
-
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.
-
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
-
[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
What are some alternatives?
GPTCache - Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
lmql - A language for constraint-guided and efficient LLM programming.
Baichuan-7B - A large-scale 7B pretraining language model developed by BaiChuan-Inc.
llmware - Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
JARVIS - JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
SHREC2023-ANIMAR - Source codes of team TikTorch (1st place solution) for track 2 and 3 of the SHREC2023 Challenge
truss - Assertions micro-library for Clojure/Script
snowChat - Chat snowflake database - Text to SQL
dynamic-gpt-ui - Dynamic UI generation with GPT-3 (OpenAI)
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
ghostwheel - Hassle-free inline clojure.spec with semi-automatic generative testing and side effect detection