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https://github.com/banjtheman/grakn-codex
Create Grakn Databases with ease
https://github.com/banjtheman/grakn-codex
Last synced: 16 days ago
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Create Grakn Databases with ease
- Host: GitHub
- URL: https://github.com/banjtheman/grakn-codex
- Owner: banjtheman
- License: apache-2.0
- Created: 2020-10-07T22:54:39.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-02-01T17:36:28.000Z (almost 4 years ago)
- Last Synced: 2024-10-12T04:54:09.325Z (about 1 month ago)
- Language: Python
- Size: 471 KB
- Stars: 16
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Codex - Programmatic Knowledge Graph Framework
## What is Codex
Codex is a Python package that provides a simple, yet powerful API designed to make working with the [Grakn](https://grakn.ai/) knowledge graph intuitive.
## Why develop Codex
Building, querying, and interrupting results from the Grakn knowledge graph requires expertise in learning the specific query language graql and learning how to use the verbose API.
Codex abstracts away the mundane and tedious aspects of using knowledge graphs, so you can focus on organizing your data and getting answers.
## Installing Codex
### Dependencies
The following are dependencies need to properly run Codex
* docker (https://docs.docker.com/get-docker/)
* docker-compose (https://docs.docker.com/compose/install/)
* python3.6+ (https://www.python.org/downloads/)
### Installing Codex
Codex is compatible with Python 3.6 or later. The simplest way to install Codex and its dependencies is by...
```bash
git clone https://github.com/banjtheman/grakn-codex.git
cd grakn-codex
pip install grakn-codex
```
In addition to the python package, Codex leverages Grakn and the Redis docker images to store data.
### Quick Start
Here's how you can quickly use Codex
**Setup Database**
```bash
mkdir -p data
mkdir -p redis_data
docker-compose up -d
```
**Load and query**
```python
import logging
import pandas as pd
from codex import CodexKg
#Load csv data
df = pd.read_csv("sample_data/tech_companies.csv")
#Make new codex object
codexkg = CodexKg()
#Create new keyspace
codexkg.create_db("tech_example")
#Load data into Grakn
codexkg.create_entity(df, "Company", entity_key="name")
# Find Company that has a name equal to Google
ans = codexkg.find(
concept="Company",
concept_attrs=["name"],
concept_conds=["equals"],
concept_values=["Google"],
)
#Display data as a DataFrame
logging.info(ans)
# {'Company': name budget
# 0 Google 999.99}
```
For complete documentation on Codex, tutorials and teaching resources, frequently asked questions, and more, please visit our [Wiki](https://github.com/banjtheman/grakn-codex/wiki).
## Codex Workbase
Powered by the Codex API and [Streamlit](https://www.streamlit.io/) you can use a web based GUI to interact with your data.
To start simply run
```bash
streamlit run codex_st.py
```
### You can upload data
### You can do queries
### You can make rules
 
## Contributing to Codex
Codex is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project. Large or small, any contribution makes a big difference; and if you've never contributed to an open source project before, we hope you will start with Codex!
If you are interested in contributing, here are some of the many ways to contribute:
* Submit a bug report or feature request on GitHub Issues.
* Assist us with user testing.
* Add to the documentation or help with our website,
* Write unit or integration tests for our project.
* Answer questions on our issues, mailing list, Stack Overflow, and elsewhere.
* Translate our documentation into another language.
* Write a blog post, tweet, or share our project with others.
* Teach someone how to use Codex.
As you can see, there are lots of ways to get involved and we would be very happy for you to join us! The only thing we ask is that you abide by the principles of openness, respect, and consideration of others as described in the Python Software Foundation Code of Conduct.