EdgeChains
tree-of-thought-llm
EdgeChains | tree-of-thought-llm | |
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12 | 41 | |
289 | 4,200 | |
4.8% | 3.6% | |
9.4 | 7.2 | |
4 days ago | 3 months ago | |
JavaScript | Python | |
MIT License | MIT License |
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EdgeChains
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HonoJS: Small, simple, and ultrafast web framework for the Edges
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/
- looking for someone to codereview an opensource Typescript+webassembly framework for Generative AI apps
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Overview: AI Assembly Architectures
EdgeChains: github.com/arakoodev/EdgeChains
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Stanford DSPy: The framework for programming with foundation models
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...
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Show HN: Chat with your data using LangChain, Pinecone, and Airbyte
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 ?
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Chunking 2M+ files a day for Code Search using Syntax Trees
oh really ? Thats awfully kind. I'll take that in for EdgeChains as well.
https://github.com/arakoodev/EdgeChains/issues/172
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Langchain Is Pointless
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.
- Calling ChatGPT API from Spring Boot
tree-of-thought-llm
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AI Chat Applications with the Metacognition Approach: Tree of Thoughts (ToT)
[2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large Language Models (arxiv.org)
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Last night /u/ alesneolith posted a very serious writeup claiming to have worked in one of the projects. The writeup is more elaborate than expected and got surprisingly little attention. His account has been since deleted.
Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, “Tree of Thoughts” (ToT), which generalizes over the popular “Chain of Thought” approach to prompting language models, and enables exploration over coherent units of text (“thoughts”) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models’ problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm.
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Ultra Fast Bert
GPU utilization should be down when using this technique. I’m hoping this could allow for more efficient batch inference on GPUs. If you can predict 10 tokens for the price of 1 it should allow you to do tree of thought much more efficiently.
https://github.com/princeton-nlp/tree-of-thought-llm
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Is it best to not pay attention to AI news and/or find ways to delude ourselves into believing better outcomes?
For those familiar with Daniel Kahneman's Thinking Fast and Slow, the current LLMs (such as GPT-4 via ChatGPT) seem to resemble System 1 thinking (near-instantaneous, automatic, intuitive processes like next-word prediction). However, they lack System 2 thinking (slow, effortful, logical, planning, reasoning). What I learned today is that Google's Gemini (an LLM in training now) not only has more modalities (I think all Youtube Video and audio??), more compute, and almost twice the training data, but they're building in AlphaGo-type learning, which resembles tree of thoughts and looks a LOT like the missing puzzle piece of System 2 thinking. Will it be AGI? Maybe, and it's coming this winter.
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Langchain Is Pointless
Tree of thoughts: https://arxiv.org/abs/2305.10601
Good video on "Tree of thoughts" which also reviews / puts it in the context of other methods: https://www.youtube.com/watch?v=ut5kp56wW_4
Completion vs conversational interface is something you can read about in the OpenAI API documentation.
For the remaining things I don't have single specific pointer at hand.
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To all skeptics with a background in AI/CS : what is your realistic timeline for AGI/ASI ?
What do you think about the combination of Tree of Thoughts: Deliberate Problem Solving with Large Language Models LongNet: Scaling Transformers to 1,000,000,000 Tokens Textbooks Are All You Need Attention Is All You Need Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
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Why do language models appear to work left-to-right?
You are right. Tree of Thoughts: Deliberate Problem Solving with Large Language Models proposes to solve this via MCTS-style generation (similar to how AlphaGo worked, and a lot of planning & control problems are executed).
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Munk Debate on Artificial Intelligence
The transformer was developed in 2017 and it powers all modern LLMs. If you're familiar with Daniel Kahneman's work from Thinking Fast and Slow, you could easily summarize LLMs as excellent System 1 thinking: our fast, automatic, unconscious responses (e.g. autocomplete). I'd argue that we're one development (similar to the transformer) away from creating System 2 thinking: deliberate and strategic thinking. In fact, with merely GPT-4 and some clever architectures, researchers have developed chain-of-thought prompting and, more recently, tree-of-thoughts reasoning. While external to the LLM architecture, embedding these concepts into a LLM could very likely solve the creation of System 2 thinking and produce the first real AGI. Adding more modalities (e.g. audio, images, video, topography, etc.) will simply add more nuance in the weights and biases of a complete system.
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Question regarding model compatibility for Alpaca Turbo
There are a bunch of other methods to improve quality and performance like tree-of-thought-llm, connecting a LLM to a database or have it review its own output.
- Tree of thoughts build in open-source model
What are some alternatives?
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
Voyager - An Open-Ended Embodied Agent with Large Language Models
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
AgentVerse - 🤖 AgentVerse 🪐 is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation
tree-of-thoughts - Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
Neurite - Fractal Graph Desktop for Ai-Agents, Web-Browsing, Note-Taking, and Code.
awesome-ai-agents - A list of AI autonomous agents
hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
langchain - 🦜🔗 Build context-aware reasoning applications
Mr.-Ranedeer-AI-Tutor - A GPT-4 AI Tutor Prompt for customizable personalized learning experiences.