embedchain
core
| embedchain | core | |
|---|---|---|
| 6 | 12 | |
| 8,541 | 1,990 | |
| 2.3% | 5.4% | |
| 9.8 | 9.8 | |
| 6 days ago | about 12 hours ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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.
embedchain
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
You can use embedchain[1] to connect various data sources and then get a RAG application running on your local and production very easily. Embedchain is an open source RAG framework and It follows a conventional but configurable approach.
The conventional approach is suitable for software engineer where they may not be less familiar with AI. The configurable approach is suitable for ML engineer where they have sophisticated uses and would want to configure chunking, indexing and retrieval strategies.
[1]: https://github.com/embedchain/embedchain
- Embedchain
- Framework to easily create LLM powered bots over any dataset
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[D] Hardest thing about building with LLMs?
Langchain is a big wrapper in itself and people can't be bothered to even use that to write 10 lines of code. Look at the traction this project is getting https://github.com/embedchain/embedchain, at it's heart it's just using few modules from langchain. The whole thing, chunking+embedding+retrieval+promoting can be done in 100 lines without langchain and embedchain.
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AI — weekly megathread!
Embedchain: a framework to easily create LLM powered bots over any dataset [Link].
- EmbedChain: Framework to easily create LLM powered bots over any dataset.
core
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
I haven't personally tried this for anything serious yet, but to get the thread started:
Cheshire Cat [0] looks promising. It's a framework for building AI assistants by providing it with documents that it stores as "memories" that can be retrieved later. I'm not sure how well it works yet, but it has an active community on Discord and seems to be developing rapidly.
[0] https://github.com/cheshire-cat-ai/core
- Cheshire cat AI: open-source and customizable AI architecture
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[Chat Gpt] Cheshire Cat - Livello open source sopra qualsiasi modello di lingua (estendibile tramite plugin)
[https://github.com/pieroit/cheshire-cat lasting(https://github.com/pieroit/cheshire-cat)
- Composable, customisable, self-hosted AI architecture: meet the Cheshire Cat
- Cheshire Cat - Open source layer on top of any language model (extendible via plugins)
What are some alternatives?
trulens - Evaluation and Tracking for LLM Experiments
anything-llm - The all-in-one Desktop & Docker AI application with full RAG and AI Agent capabilities.
HeimdaLLM - Constrain LLM output
SecureAI-Tools - Private and secure AI tools for everyone's productivity.
WebGLM - WebGLM: An Efficient Web-enhanced Question Answering System (KDD 2023)
h2ogpt - Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
openchat - OpenChat: Advancing Open-source Language Models with Imperfect Data
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
gpt-migrate - Easily migrate your codebase from one framework or language to another.
gpt4all - gpt4all: run open-source LLMs anywhere
searchGPT - Grounded search engine (i.e. with source reference) based on LLM / ChatGPT / OpenAI API. It supports web search, file content search etc.
gpt-researcher - GPT based autonomous agent that does online comprehensive research on any given topic