langroid
gpt4all
langroid | gpt4all | |
---|---|---|
15 | 139 | |
1,698 | 65,076 | |
21.4% | 3.3% | |
9.8 | 9.8 | |
1 day ago | 7 days ago | |
Python | C++ | |
MIT License | MIT License |
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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
gpt4all
- Show HN: I made an app to use local AI as daily driver
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Ollama Python and JavaScript Libraries
I don’t know if Ollama can do this but https://gpt4all.io/ can.
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
Gpt4all is a local desktop app with a Python API that can be trained on your documents: https://gpt4all.io/
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WyGPT: Minimal mature GPT model in C++
The readme page is cryptic. What does 'mature' mean in this context? What is the sample text a continuation of?
Hving a gif the thing in use would be great, similar to the gpt4all readme page. (https://github.com/nomic-ai/gpt4all)
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LibreChat
Check https://github.com/nomic-ai/gpt4all instead.
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OpenAI Negotiations to Reinstate Altman Hit Snag over Board Role
"I ran performance tests on two systems, here's the results of system 1, and heres the results of system 2. Summarize the results, and build a markdown table containing x,y,z rows."
"extract the reusable functions out of this bash script"
"write me a cfssl command to generate a intermediate CA"
"What is the regex for _____"
"Here are my accomplishments over the last 6 months, summarize them into a 1 page performance report."
etc etc etc
If you're not using GPT4 or some LLM as part of your daily flow you're working too hard.
Get GPT4All (https://gpt4all.io), log into OpenAI, drop $20 on your account, get a API key, and start using GPT4.
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Darbe uzdraude naudotis CHATGPT: ar cia normalu?
offline versija, nors ir ne tokia pažengus - https://github.com/nomic-ai/gpt4all ; https://gpt4all.io/index.html
- GPT4All: An ecosystem of open-source on-edge large language models - by Nomic AI
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Why use OpenAI's ChatGPT3.5 online service, if you can instead host your own local llama?
Take a look at https://gpt4all.io, their docs are pretty awesome
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Ask HN: Are you using a local LLM? If yes, what for?
I run one. I built an iMessage-like frontend to it using plain JS and a Python websocket backend. I mostly just use it for curiosity and playing with different prompts. I only have 16GB of RAM to dedicate to it, so I use an 8B parameter model which is enough for fun and chitchat, but I don't find it good enough to replace ChatGPT.
https://github.com/nomic-ai/gpt4all
What are some alternatives?
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
llama.cpp - LLM inference in C/C++
modelfusion - The TypeScript library for building AI applications.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
vectordb - A minimal Python package for storing and retrieving text using chunking, embeddings, and vector search.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Adala - Adala: Autonomous DAta (Labeling) Agent framework
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
chidori - A reactive runtime for building durable AI agents
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)