| Name | kion-vectorstore JSON |
| Version |
0.1.7
JSON |
| download |
| home_page | https://github.com/Thoriso-Molefe/kion-vectorstore |
| Summary | Kion Consulting: Postgres (pgvector) vector database file management library and web GUI for LangChain. |
| upload_time | 2025-09-08 07:14:24 |
| maintainer | None |
| docs_url | None |
| author | Kion Consulting |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 Kion Consulting
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
langchain
pgvector
postgresql
rag
vectorstore
flask
openai
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
python-dotenv
flask
flask-cors
langchain
langchain_community
langchain_text_splitters
langchain-openai
psycopg2-binary
pypdf
pgvector
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
kion_vectorstore
License: MIT
Overview
kion_vectorstore is a Python library and GUI application for managing vector stores in PostgreSQL (with pgvector) using LangChain.
It lets you upload PDFs or .txt files, organize them into collections, perform semantic search, and query them via an OpenAI-powered chat UI.
You can also delete files or whole collections from a simple web interface.
Features
- Upload PDFs and text files
- Organize documents into named collections
- OpenAI-powered semantic search across selected collections
- Delete individual files or entire empty collections
- Use functions programmatically in Python
- Simple Flask-based web UI
Prerequisites
- Python 3.8+
- PostgreSQL installed locally or reachable on your network
- pgvector extension enabled in your database
See: https://github.com/pgvector/pgvector
Quick Start
1) Install the package
pip install kion-vectorstore
2) Create a .env file (once per project) using the CLI
env-init --path "<-your folder name->" - Enter *ONLY* the name of your folder (*DO NOT ENTER A PATH*)
# Add --force to overwrite an existing .env:
# e.g. env-init --path "env" --force
3) Fill in your .env
OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4o-mini
OPENAI_EMBEDDING_MODEL=text-embedding-3-small
PGHOST=localhost
PGUSER=postgres
PGPASSWORD=yourpassword
PGDATABASE=yourdb
PGPORT=5432
4) Launch the web app
Option A: Use the CLI
kion-vectorstore-web
Option B: From Python
python -m kion_vectorstore.app
The app will open http://127.0.0.1:5000/ in your browser.
Using the Web UI
- File Loader tab: upload .txt or .pdf files to a collection (set chunk size/overlap)
- Remove Files tab: select a collection, list files, and delete
- Chat tab: pick collections and ask questions; the assistant answers using only your documents
Programmatic Use
Initialize config once in your Python script, then use the plugin:
from kion_vectorstore import initialize_config, PGVectorPlugin
from langchain_openai import OpenAIEmbeddings
initialize_config(".env")
embeddings = OpenAIEmbeddings() # uses OPENAI_API_KEY from env
db = PGVectorPlugin(embedding_model=embeddings)
print(db.list_collections())
Notes
- This package ships a .env template inside the package. The env-init CLI copies it to your project.
- Static HTML files are served from within the installed package; you do not need to copy them.
Troubleshooting
- If you see "Configuration has not been initialized", ensure your .env exists and initialize_config has been called (the web app does this automatically).
- Ensure the pgvector extension is installed in your database, and the required LangChain tables exist (they are created on first insert by langchain_community.vectorstores.PGVector).
License
MIT © 2025 Kion Consulting
Raw data
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