langroid
khoj
langroid | khoj | |
---|---|---|
15 | 50 | |
1,698 | 4,912 | |
21.4% | 5.3% | |
9.8 | 9.9 | |
1 day ago | 6 days ago | |
Python | Python | |
MIT License | GNU Affero General Public License v3.0 |
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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
khoj
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Show HN: I made an app to use local AI as daily driver
There are already several RAG chat open source solutions available. Two that immediately come to mind are:
Danswer
https://github.com/danswer-ai/danswer
Khoj
https://github.com/khoj-ai/khoj
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
I'm a fan of Khoj. Been using it for months. https://github.com/khoj-ai/khoj
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You probably don’t need to fine-tune LLMs
https://github.com/khoj-ai/khoj
This is the easiest I found, on here too.
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Show HN: Khoj – Chat Offline with Your Second Brain Using Llama 2
Thanks for the feedback. Does your machine have a GPU? 32GB CPU RAM should be enough but GPU speeds up response time.
We have fixes for the seg fault[1] and improvement to the query speed[2] that should be released by end of day today[3].
Update khoj to version 0.10.1 with pip install --upgrade khoj-assistant to see if that improves your experience.
The number of documents/pages/entries doesn't scale memory utilization as quickly and doesn't affect the search, chat response time as much
[1]: The seg fault would occur when folks sent multiple chat queries at the same time. A lock and some UX improvements fixed that
[2]: The query time improvements are done by increasing batch size, to trade-off increased memory utilization for more speed
[3]: The relevant pull request for reference: https://github.com/khoj-ai/khoj/pull/393
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A Review: Using Llama 2 to Chat with Notes on Consumer Hardware
We recently integrated Llama 2 into Khoj. I wanted to share a short real-world evaluation of using Llama 2 for the chat with docs use-cases and hear which models have worked best for you all. The standard benchmarks (ARC, HellaSwag, MMLU etc.) are not tuned for evaluating this
- FLaNK Stack Weekly for 17 July 2023
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An open source AI search + chat assistant for your Notion workspace
Self-host your Notion assistant using the instructions here. You'll need Python >= 3.8 to get started.
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When will we get JARVIS?
Here's an early example: https://github.com/khoj-ai/khoj
What are some alternatives?
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
obsidian-smart-connections - Chat with your notes & see links to related content with AI embeddings. Use local models or 100+ via APIs like Claude, Gemini, ChatGPT & Llama 3
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
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
vectordb - A minimal Python package for storing and retrieving text using chunking, embeddings, and vector search.
llama-cpp-python - Python bindings for llama.cpp
Adala - Adala: Autonomous DAta (Labeling) Agent framework
obsidian-ava - Quickly format your notes with ChatGPT in Obsidian
chidori - A reactive runtime for building durable AI agents
logseq-plugin-gpt3-openai - A plugin for GPT-3 AI assisted note taking in Logseq