dspy
EdgeChains
dspy | EdgeChains | |
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22 | 12 | |
10,820 | 289 | |
17.5% | 4.8% | |
9.9 | 9.4 | |
7 days ago | 2 days ago | |
Python | JavaScript | |
MIT License | MIT License |
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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.
dspy
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Computer Vision Meetup: Develop a Legal Search Application from Scratch using Milvus and DSPy!
Legal practitioners often need to find specific cases and clauses across thousands of dense documents. While traditional keyword-based search techniques are useful, they fail to fully capture semantic content of queries and case files. Vector search engines and large language models provide an intriguing alternative. In this talk, I will show you how to build a legal search application using the DSPy framework and the Milvus vector search engine.
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Pydantic Logfire
I’ve observed that Pydantic - which we’ve used for years in our API stack - has become very popular in LLM applications, for its type-adjacent features. It serves as a foundational technology for prompting libraries like [DSPy](https://github.com/stanfordnlp/dspy) which are abstracting “up the stack” of LLM apps. (some opinions there)
Operating AI apps reveals a big challenge, in that debugging probabilistic code paths requires more than the usual introspective abilities, and in an environment where function calls can have very real monetary impact we have to be able to see what’s happening in the runtime. See LangChain’s hosted solution (can’t recall the name) that allows an operator to see prompts and responses “on the wire”. (It just occurred to me that Langchain and Pydantic have a lot in common here, in approach.)
Having a coupling between Pydantic - which is *just about* the data layer itself - and an observability tool seems very interesting to me, and having this come from the folks who built it does not seem unreasonable. WRT open source and monetization, I would be lying if I said I wasn’t a little worried - given the recent few months - but I am choosing to see this in a positive light, given this team’s “believability weight” (to overuse Dalio) and history of delivering solid and really useful tooling.
- Ask HN: Most efficient way to fine-tune an LLM in 2024?
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Princeton group open sources "SWE-agent", with 12.3% fix rate for GitHub issues
DSPy is the best tool for optimizing prompts [0]: https://github.com/stanfordnlp/dspy
Think of it as a meta-prompt optimizer, it uses a LLM to optimize your prompts, to optimize your LLM.
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Winner of the SF Mistral AI Hackathon: Automated Test Driven Prompting
Isn’t this just a very naive implementation of what DsPY does?
https://github.com/stanfordnlp/dspy
I don’t understand what is exceptional here.
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Show HN: Fructose, LLM calls as strongly typed functions
Have you done any comparison with DSPy ? (https://github.com/stanfordnlp/dspy)
Feels very similiar to DSPy except you dont have optimizations yet. But I like your API and the programming model your are enforcing through this.
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AI Prompt Engineering Is Dead
I'm interested in hearing if anyone has used DSPy (https://github.com/stanfordnlp/dspy) just for prompt optimization for GPT-3.5 or GPT-4. Was it worth the effort and much better than manual prompt iteration? Was the optimized prompt some weird incantation? Any other insights?
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Ask HN: Are you using a GPT to prompt-engineer another GPT?
You should check out x.com/lateinteraction's DSPy — which is like an optimizer for prompts — https://github.com/stanfordnlp/dspy
- SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
- FLaNK Stack Weekly for 12 September 2023
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
What are some alternatives?
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
open-interpreter - A natural language interface for computers
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
playground - Play with neural networks!
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
MLflow - Open source platform for the machine learning lifecycle
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
FastMJPG - FastMJPG is a command line tool for capturing, sending, receiving, rendering, piping, and recording MJPG video with extremely low latency. It is optimized for running on constrained hardware and battery powered devices.
awesome-ai-agents - A list of AI autonomous agents
prompt-engine-py - A utility library for creating and maintaining prompts for Large Language Models
langchain - 🦜🔗 Build context-aware reasoning applications