khoj
private-gpt
khoj | private-gpt | |
---|---|---|
50 | 131 | |
4,858 | 51,882 | |
2.8% | 2.3% | |
9.9 | 9.2 | |
about 17 hours ago | 1 day ago | |
Python | Python | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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.
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
private-gpt
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
PrivateGPT is a nice tool for this. It's not exactly what you're asking for, but it gets part of the way there.
https://github.com/zylon-ai/private-gpt
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PrivateGPT exploring the Documentation
Further details available at: https://docs.privategpt.dev/api-reference/api-reference/ingestion
- Show HN: I made an app to use local AI as daily driver
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privateGPT VS quivr - a user suggested alternative
2 projects | 12 Jan 2024
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
Run https://github.com/imartinez/privateGPT
Then
make ingest /path/to/folder/with/files
Then chat to the LLM.
Done.
Docs: https://docs.privategpt.dev/overview/welcome/quickstart
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Mozilla "MemoryCache" Local AI
PrivateGPT repository in case anyone's interested: https://github.com/imartinez/privateGPT . It doesn't seem to be linked from their official website.
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What Is Retrieval-Augmented Generation a.k.a. RAG
I’m preparing a small internal tool for my work to search documents and provide answers (with references), I’m thinking of using GPT4All [0], Danswer [1] and/or privateGPT [2].
The RAG technique is very close to what I have in mind, but I don’t want the LLM to “hallucinate” and generate answers on its own by synthesizing the source documents. As stated by many others, we’re living in interesting times.
[0] https://gpt4all.io/index.html
[1] https://www.danswer.ai/
[2] https://github.com/imartinez/privateGPT
- LM Studio – Discover, download, and run local LLMs
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Ask HN: Local LLM Recommendation?
https://www.reddit.com/r/LocalLLaMA/comments/14niv66/using_a...
https://github.com/imartinez/privateGPT
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Run ChatGPT-like LLMs on your laptop in 3 lines of code
I've been playing around with https://github.com/imartinez/privateGPT and https://github.com/simonw/llm and wanted to create a simple Python package that made it easier to run ChatGPT-like LLMs on your own machine, use them with non-public data, and integrate them into practical applications.
This resulted in Python package I call OnPrem.LLM.
In the documentation, there are examples for how to use it for information extraction, text generation, retrieval-augmented generation (i.e., chatting with documents on your computer), and text-to-code generation: https://amaiya.github.io/onprem/
Enjoy!
What are some alternatives?
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
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
gpt4all - gpt4all: run open-source LLMs anywhere
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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/
llama-cpp-python - Python bindings for llama.cpp
obsidian-ava - Quickly format your notes with ChatGPT in Obsidian
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
logseq-plugin-gpt3-openai - A plugin for GPT-3 AI assisted note taking in Logseq
llama.cpp - LLM inference in C/C++