Pavlo Paliychuk a6fd0ddb75
feat: Initial version of temporal invalidation + tests (#8)
* feat: Initial version of temporal invalidation + tests

* fix: dont run int tests on CI

* fix: dont run int tests on CI

* fix: dont run int tests on CI

* fix: time of day issue

* fix: running non int tests in ci

* fix: running non int tests in ci

* fix: running non int tests in ci

* fix: running non int tests in ci

* fix: running non int tests in ci

* fix: running non int tests in ci

* fix: running non int tests in ci

* revert: Tests structural changes

* chore: Remove idea file

* chore: Get rid of NodesWithEdges class and define a triplet type instead
2024-08-20 16:29:19 -04:00
2024-08-13 14:35:43 -04:00

Graphiti (LLM generated readme)

Graphiti is a Python library for building and managing knowledge graphs using Neo4j and OpenAI's language models. It provides a flexible framework for processing episodes of information, extracting semantic nodes and edges, and maintaining a dynamic graph structure.

Features

  • Asynchronous interaction with Neo4j database
  • Integration with OpenAI's GPT models for natural language processing
  • Automatic extraction of semantic nodes and edges from episodic data
  • Temporal tracking of relationships and facts
  • Flexible schema management

Installation

(Add installation instructions here)

Quick Start

from graphiti import Graphiti

# Initialize Graphiti
graphiti = Graphiti("bolt://localhost:7687", "neo4j", "password")

# Process an episode
await graphiti.process_episode(
    name="Example Episode",
    episode_body="Alice met Bob at the coffee shop.",
    source_description="User input",
    reference_time=datetime.now()
)

# Retrieve recent episodes
recent_episodes = await graphiti.retrieve_episodes(last_n=5)

# Close the connection
graphiti.close()

Documentation

(Add link to full documentation when available)

Contributing

(Add contribution guidelines)

License

(Add license information)

Description
Build Real-Time Knowledge Graphs for AI Agents
Readme Apache-2.0 22 MiB
Languages
Python 99.4%
Dockerfile 0.4%
Makefile 0.2%