vecsync
A fast command-line utility for synchronizing journals and papers to OpenAI vector storage for chat interaction. Vecsync helps you research topics by simpliyfing your workflow.
- 📄 Synchronize a local collection of PDFs to a remote vector store
- ✅ Automatically manage OpenAI files, vector store, and assistant
- 💬 Quickly chat with documents from command line or local Gradio UI
- 👀 Connect to a local Zotero collection
Sync and chat
vs sync && vs chat
Chat with Gradio
vs chat --ui
Getting Started
OpenAI API Requirements
Currently vecsync only supports OpenAI for remote operations and requires a valid OpenAI key with credits. Visit https://openai.com/api/ for more information.
Costs
Vecsync uses OpenAI gpt-4o-mini which is Input: $0.15/million tokens and Output: $0.60/million tokens. These costs are tied to your OpenAI API account. See pricing for details.
Installation
Install vecsync from PyPI.
pip install vecsync
Set your OpenAI API key environment.
export OPENAI_API_KEY=...
You can also define the key via .env file in the working directory.
echo "OPENAI_API_KEY=…" > .env
Usage
Syncing Collections
Use the vs sync command for all synching operations.
Sync from local file path.
cd path/to/pdfs && vs sync
Synching 2 files from local to OpenAI
Uploading 2 files to OpenAI file storage
Attaching 2 files to OpenAI vector store
🏁 Sync results:
Saved: 2 | Deleted: 0 | Skipped: 0
Remote count: 2
Duration: 8.93 seconds
Sync from a Zotero collection. Interactive selections are remembered for future sessions.
vs sync -source zotero
Enter the path to your Zotero directory (Default: /Users/jbencina/Zotero):
Available collections:
[1]: My research
Enter the collection ID to sync (Default: 1):
Synching 15 files from local to OpenAI
Uploading 15 files to OpenAI file storage
Attaching 15 files to OpenAI vector store
🏁 Sync results:
Saved: 15 | Deleted: 0 | Skipped: 0
Remote count: 15
Duration: 57.99 seconds
Chat Interactions
Use vs chat to chat with uploaded documents via the command line. The responding assistant is automatically linked to your
vector store. Alternatively, you can use vs chat --ui to spawn a local Gradio instance.
vs chat
✅ Assistant found: asst_123456789
Type "exit" to quit at any time.
> Give a one sentence summary of your vector store collection contents.
💬 Conversation started: thread_123456789
The contents of the vector store collection primarily focus on machine learning techniques for causal effect inference,particularly through adversarial representation learning methods that address challenges in treatment selection bias and information loss in observational data
Conversations are remembered across sessions.
vs chat
✅ Assistant found: asst_123456789
✅ Thread found: thread_123456789
Type "exit" to quit at any time.
> What was my last question to you?
Your last question to me was asking for a one sentence summary of the contents of my vector store collection.