EdgeChains VS babyagi

We build a WASM compiler to compile our prompts and chains into webassembly. Honojs was a critical part of it.

https://github.com/arakoodev/EdgeChains/

EdgeChains: github.com/arakoodev/EdgeChains

would love your thoughts on this as well - https://github.com/arakoodev/edgechains

got frustrated in the same way with "Black Box Prompting - every library hides prompts/chains in layers of libraries...while it should have been declarative.

EdgeChains - allows u to specify ur prompt and chain in jsonnet. This why i think Generative AI needs declarative orchestration and not previous generations. https://github.com/arakoodev/edgechains#why-do-you-need-decl...

when will you have pgvector as a destination ? we (https://github.com/arakoodev/edgechains) work with a lot of enterprises and they would not move away from using redis or pgvector even as their vector store. Is there a way where we can leverage that ?

Second, for a LOT of enterprises, they want to use non-openai embedding models (minilm, GTE, BGE), will you support that. For e.g. in Edgechains we natively support BGE and minilm. Would you be able to support that ?

oh really ? Thats awfully kind. I'll take that in for EdgeChains as well.

https://github.com/arakoodev/EdgeChains/issues/172

Promptfile is written in markdown, which is unsuited for templates and config management.

I have an attempt in the same domain, would love feedback

We didnt invent a new markup - we used jsonnet which is used in large scale kubernetes and has a grammar that has been well tested for config mgmt.

https://github.com/arakoodev/EdgeChains/blob/main/Examples/r...

Prompts live outside the code.

Define agency. Does AutoGPT or BabyAGI fit the definition?

BabyAGI: github.com/yoheinakajima/babyagi

In my opinion the most interesting Agents: Auto-GPT Github: https://github.com/Significant-Gravitas/Auto-GPT BabyAGI Github: https://github.com/yoheinakajima/babyagi Voyager Github: https://github.com/MineDojo/Voyager / Paper: https://arxiv.org/abs/2305.16291 I would also add: ChemCrow: Augmenting large-language models with chemistry tools Github: https://github.com/ur-whitelab/chemcrow-public/ Paper: https://arxiv.org/abs/2304.05376

People also have been trying to build multi-agent and task-planning systems. MS research in Asia seems to produce decent results with Task Matrix and HuggingGPT. Similar things have been tried in the form of Auto-GPT and BabyAGI , but both projects are setting their goal so high that they may not achieve the at all, and they are likely to see a complete rework when multi-modal solutions become widespread.

Joke's on you, /u/ILoveThisPlace is actually just a bot responding using the BabyAGI script, we've all been had!

BabyAGI AI-Powered Task Management for OpenAI + Pinecone or Llama.cpp

Yes, we haven't seen anything like that yet. But we do see the people trying to build these things (see AutoGPT, babyagi, ChaosGPT, etc) today, and with the last few years of advancement in LLMs they now have the fundamental building blocks to succeed in the near term (say the next 5 years) rather than in some imaginary far future.

You should check out some of the projects that combine LangChain with LLMs to automate this process like BabyAGI (https://github.com/yoheinakajima/babyagi) and AutoGPT (https://github.com/Significant-Gravitas/Auto-GPT). They were originally designed around ChatGPT models but have expanded to include llamacpp as an alternative. These provide your language models with the ability to save long term memory, a goal-oriented task list and extra functionality like surfing the web and, in some cases, creating and modifying files on disk.