Skip to main content

A Python package for executing graph generation from textual inputs.

Project description

PyPI version License: MIT Downloads LinkedIn

eknowledge

eknowledge is a Python package designed to facilitate the generation of knowledge graphs from textual inputs. It leverages language models to parse text and extract relationships between entities, organizing these relationships into a structured graph format. This tool is ideal for developers, researchers, and anyone interested in structured knowledge extraction from unstructured text.

Installation

Install eknowledge using pip:

pip install eknowledge langchain_llm7

Usage

Here's a simple example to get you started with eknowledge. This example demonstrates how to generate a knowledge graph from a given text input using the package.

Example

from eknowledge import execute_graph_generation
from langchain_llm7 import ChatLLM7

# Initialize the language model
MODEL = "deepseek-r1"
llm = ChatLLM7(model=MODEL)

# Define your input text
input_text = "The quick brown fox jumps over the lazy dog."

# Generate the graph
graph = execute_graph_generation(
    text=input_text,
    llm=llm,
    chunk_size=100,
    verbose=True
)

# Output the graph
print(graph)
# > Splitting text into 1 chunks of size 100 words.
# > Processing chunk 1/1...
# > Nodes successfully processed in chunk 1/1.
# > [{'from': 'quick brown fox', 'relationship': 'interacts_with', 'to': 'lazy dog'}]

This script will output a knowledge graph based on the relationships identified in the text.

Contributing

Contributions are welcome! Please open issues or submit pull requests for any bugs, features, or improvements you would like to see.

License

eknowledge is MIT licensed, as found in the LICENSE file.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

eknowledge-2025.4.171239.tar.gz (9.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

eknowledge-2025.4.171239-py3-none-any.whl (10.7 kB view details)

Uploaded Python 3

File details

Details for the file eknowledge-2025.4.171239.tar.gz.

File metadata

  • Download URL: eknowledge-2025.4.171239.tar.gz
  • Upload date:
  • Size: 9.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.11

File hashes

Hashes for eknowledge-2025.4.171239.tar.gz
Algorithm Hash digest
SHA256 896de09374636264dbfd69730c37e7f8a6c0de5652e7c46af394cbdee3669073
MD5 98516e758170e55fad8ef17dcf5c8596
BLAKE2b-256 a4d1a6a90f22ecbc0edb421e673ca3639dabab379598a289725cac879b2b2a8d

See more details on using hashes here.

File details

Details for the file eknowledge-2025.4.171239-py3-none-any.whl.

File metadata

File hashes

Hashes for eknowledge-2025.4.171239-py3-none-any.whl
Algorithm Hash digest
SHA256 4ec1ab5dcbda629a84aee94161b53dae8876aa460c545fc15c2329cfa246a258
MD5 d2ea34970341ae6de8db5bb03f528c96
BLAKE2b-256 332a8113b575085ada8bc8ed1a9b4fe4f14146ac381353e6dd08075116ede200

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page