obsidian-copilot
private-gpt
obsidian-copilot | private-gpt | |
---|---|---|
5 | 131 | |
445 | 52,175 | |
- | 3.2% | |
7.3 | 9.2 | |
3 months ago | 10 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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obsidian-copilot
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
hadn't seen your repo yet [1] - adding it to my list right now.
Your blog post is really neat on top - thanks for sharing
https://github.com/eugeneyan/obsidian-copilot
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Obsidian-Copilot: A Prototype Assistant for Writing and Thinking
Um... can someone explain what this actually does?
In the video the user chooses the 'Copilot: Draft' action, and wow, it generates code...
...but, the 'draft' action [1] calls `/get_chunks` and then runs 'queryLLM' [2] which then just invokes 'https://api.openai.com/v1/chat/completions' directly.
So, generating text this way is 100% not interesting or relevant.
What's interesting here is how it's building the prompt to send to the openai-api.
So... can anyone shed some light on what the actual code [3] in get_chunks() does, and why you would... hm... I guess, do a lookup and pass the results to the openai api, instead of just the raw text?
The repo says: "You write a section header and the copilot retrieves relevant notes & docs to draft that section for you.", and you can see in the linked post [4], this is basically what the OP is trying to implement here; you write 'I want X', and the plugin (a bit like copilot) does a lookup of related documents, crafts a meta-prompt and passes the prompt to the openai api.
...but, it doesn't seem to do that. It seems to ignore your actual prompt, lookup related documents by embedding similarity... and then... pass those documents in as the prompt?
I'm pretty confused as to why you would want that.
It basically requires that you write your prompt separately before hand, so you can invoke it magically with a one-line prompt later. Did I misunderstand how this works?
[1] - https://github.com/eugeneyan/obsidian-copilot/blob/bdabdc422...
[2] - https://github.com/eugeneyan/obsidian-copilot/blob/bdabdc422...
[3] - https://github.com/eugeneyan/obsidian-copilot/blob/main/src/...
[4] - https://eugeneyan.com/writing/llm-experiments/#shortcomings-...
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.
llmware - Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
gpt4all - gpt4all: run open-source LLMs anywhere
tonic_validate - Metrics to evaluate the quality of responses of your Retrieval Augmented Generation (RAG) applications.
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/
chroma-langchain
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
ResuLLMe - Enhance your résumé with Large Language Models
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
markdown-embeddings-search - Obisidan notes to pinecone embeddings plus other files in effor to learn llama_index
llama.cpp - LLM inference in C/C++