ResuLLMe
obsidian-copilot
| ResuLLMe | obsidian-copilot | |
|---|---|---|
| 4 | 5 | |
| 235 | 445 | |
| - | - | |
| 7.9 | 7.3 | |
| 4 months ago | 3 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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ResuLLMe
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Sunday Daily Thread: What's everyone working on this week?
These past weeks I have been working on ResuLLMe. It is a tool to enhance your CV using Large Language Models. It takes your old CV in PDF or Word Document format, feeds it to LLMs to improve it, converts it to a JSON Resume format, and then renders it to PDF using Jinja and LaTeX. You can see how it was built at https://github.com/IvanIsCoding/ResuLLMe and try it at https://resullme.streamlit.app/
- ResuLLMe: Enhance your CV with Large Language Models
- Show HN: ResuLLMe – Enhance Your CV with LLMs
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-...
What are some alternatives?
Rezume - An awesome LaTeX resume template to jumpstart your job search!
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
resume - Resume written in LaTeX
llmware - Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
arasgungore-CV - My curriculum vitae (CV) written using LaTeX.
tonic_validate - Metrics to evaluate the quality of responses of your Retrieval Augmented Generation (RAG) applications.
llm-client-sdk - SDK for using LLM
chroma-langchain
SecondBrain - SecondBrain is your second brain in the cloud, designed to easily store and retrieve unstructured information. It's like Obsidian but powered by generative AI.
markdown-embeddings-search - Obisidan notes to pinecone embeddings plus other files in effor to learn llama_index
prisma-client-py - Prisma Client Python is an auto-generated and fully type-safe database client designed for ease of use
autollm - Ship RAG based LLM web apps in seconds.