anything-llm
langroid
anything-llm | langroid | |
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21 | 15 | |
12,782 | 1,594 | |
24.4% | 16.2% | |
9.8 | 9.8 | |
6 days ago | 7 days ago | |
JavaScript | Python | |
MIT License | MIT License |
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anything-llm
- AnythingLLM: Chat with your documents using any LLM
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
anything-llm looks pretty interesting and easy to use https://github.com/Mintplex-Labs/anything-llm
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local/private llm based chatbot using free/open source tools.
You can just fork AnythingLLM for a very advanced starting point or just straight rip the code ive already written to build yours 🚀
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Some solutions that work on older intel macs
AnythingLLM also works on an Intel Mac (i develop it on an intel mac) and can use any GGUF model to do local inferencing. Includes document embedding + local vector database so i can do chatting with documents and even coding inside of it. Pretty much a ChatGPT equilivent i can run locally via the repo or docker.
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What tools or programs have you made or are working on?
If you want a UI you can leverage https://github.com/Mintplex-Labs/anything-llm and do all your coding in localhost with a locally running model.
- Web interface for Azure Open Ai
- DIY custom AI chatbot trained on your company data
langroid
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OpenAI: Streaming is now available in the Assistants API
This was indeed true in the beginning, and I don’t know if this has changed. Inserting messages with Assistant role is crucial for many reasons, such as if you want to implement caching, or otherwise edit/compress a previous assistant response for cost or other reason.
At the time I implemented a work-around in Langroid[1]: since you can only insert a “user” role message, prepend the content with ASSISTANT: whenever you want it to be treated as an assistant role. This actually works as expected and I was able to do caching. I explained it in this forum:
https://community.openai.com/t/add-custom-roles-to-messages-...
[1] the Langroid code that adds a message with a given role, using this above “assistant spoofing trick”:
https://github.com/langroid/langroid/blob/main/langroid/agen...
- FLaNK Stack 29 Jan 2024
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Ollama Python and JavaScript Libraries
Same question here. Ollama is fantastic as it makes it very easy to run models locally, But if you already have a lot of code that processes OpenAI API responses (with retry, streaming, async, caching etc), it would be nice to be able to simply switch the API client to Ollama, without having to have a whole other branch of code that handles Alama API responses. One way to do an easy switch is using the litellm library as a go-between but it’s not ideal (and I also recently found issues with their chat formatting for mistral models).
For an OpenAI compatible API my current favorite method is to spin up models using oobabooga TGW. Your OpenAI API code then works seamlessly by simply switching out the api_base to the ooba endpoint. Regarding chat formatting, even ooba’s Mistral formatting has issues[1] so I am doing my own in Langroid using HuggingFace tokenizer.apply_chat_template [2]
[1] https://github.com/oobabooga/text-generation-webui/issues/53...
[2] https://github.com/langroid/langroid/blob/main/langroid/lang...
Related question - I assume ollama auto detects and applies the right chat formatting template for a model?
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Pushing ChatGPT's Structured Data Support to Its Limits
we (like simpleaichat from OP) leverage Pydantic to specify the desired structured output, and under the hood Langroid translates it to either the OpenAI function-calling params or (for LLMs that don’t natively support fn-calling), auto-insert appropriate instructions into tje system-prompt. We call this mechanism a ToolMessage:
https://github.com/langroid/langroid/blob/main/langroid/agen...
We take this idea much further — you can define a method in a ChatAgent to “handle” the tool and attach the tool to the agent. For stateless tools you can define a “handle” method in the tool itself and it gets patched into the ChatAgent as the handler for the tool.
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
Many services/platforms are careless/disingenuous when they claim they “train” on your documents, where they actually mean they do RAG.
An under-appreciate benefit of RAG is the ability to have the LLM cite sources for its answers (which are in principle automatically/manually verifiable). You lose this citation ability when you finetune on your documents.
In Langroid (the Multi-Agent framework from ex-CMU/UW-Madison researchers) https://github.com/langroid/langroid
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Build a search engine, not a vector DB
This resonates with the approach we’ve taken in Langroid (the Multi-Agent framework from ex-CMU/UW-Madison researchers): our DocChatAgent uses a combination of lexical and semantic retrieval, reranking and relevance extraction to improve precision and recall:
https://github.com/langroid/langroid/blob/main/langroid/agen...
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HuggingChat – ChatGPT alternative with open source models
In the Langroid library (a multi-agent framework from ex-CMU/UW-Madison researchers) we have these and more. For example here’s a script that combines web search and RAG:
https://github.com/langroid/langroid/blob/main/examples/docq...
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SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
Thanks, also found Langdroid: https://github.com/langroid/langroid/blob/main/README.md
- memory in ConversationalRetrievalChain removed
- [D] github repositories for ai web search agents
What are some alternatives?
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
privateGPT - Interact with your documents using the power of GPT, 100% privately, no data leaks [Moved to: https://github.com/zylon-ai/private-gpt]
modelfusion - The TypeScript library for building AI applications.
LLMStack - No-code platform to build LLM Agents, workflows and applications with your data
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
gpt4all - gpt4all: run open-source LLMs anywhere
vectordb - A minimal Python package for storing and retrieving text using chunking, embeddings, and vector search.
awesome-ml - Curated list of useful LLM / Analytics / Datascience resources
Adala - Adala: Autonomous DAta (Labeling) Agent framework
CSharp-ChatBot-GPT - This repository contains a simple C# chatbot powered by OpenAI’s ChatGPT. The chatbot utilizes the RestSharp and Newtonsoft.Json libraries to interact with the ChatGPT API and process user input.
chidori - A reactive runtime for building durable AI agents