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Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning.

Reason this release was yanked:

Lower python version

Project description

cognee

Make data processing for LLMs easy

Cognee logo

Open-source framework for creating knowledge graphs and data models for LLMs.

cognee forks cognee stars cognee pull-requests cognee releases>
  </a>
</p>
<h2>🚀 It's alive</h2>
<p>
<p>Try it yourself on Whatsapp with one of our <a href=partners by typing /save {content you want to save} followed by /query {knowledge you saved previously}

📦 Installation

With pip:

pip install cognee

With poetry:

poetry add cognee

💻 Usage

  • Add a new piece of information to storage
import cognee
cognee.add(data_path, file_name)
  • Use LLMs and cognee to create graphs
cognee.cognify(file_name)
  • Render the graph after adding your Graphistry credentials to .env
graph_url = await render_graph(graph, graph_type = "networkx")
print(graph_url) 
  • Query the graph for a piece of information
query_params = {
    SearchType.SIMILARITY: {'query': 'your search query here'}
}
cognee.search(graph, query_params) 

Demo

Architecture

How Cognee Enhances Your Contextual Memory

Our framework for the OpenAI, Graph (Neo4j) and Vector (Weaviate) databases introduces three key enhancements:

  • Query Classifiers: Navigate information graph using Pydantic OpenAI classifiers.
  • Document Topology: Structure and store documents in public and private domains.
  • Personalized Context: Provide a context object to the LLM for a better response.

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