cognee

Make data processing for LLMs easy

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

🚀 It's alive

Try it yourself on Whatsapp with one of our partners by typing `/save {content you want to save}` followed by `/query {knowledge you saved previously}` For more info here are the docs

📦 Installation

With pip:

pip install "cognee[weaviate]"

With poetry:

poetry add "cognee[weaviate]"

💻 Usage

Setup

import os

os.environ["WEAVIATE_URL"] = "YOUR_WEAVIATE_URL"
os.environ["WEAVIATE_API_KEY"] = "YOUR_WEAVIATE_API_KEY"

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

Run

import cognee

text = """Natural language processing (NLP) is an interdisciplinary
       subfield of computer science and information retrieval"""

cognee.add(text) # Add a new piece of information

cognee.cognify() # Use LLMs and cognee to create knowledge

search_results = cognee.search("SIMILARITY", "computer science") # Query cognee for the knowledge

for result_text in search_results[0]:
    print(result_text)

Add alternative data types:

cognee.add("file://{absolute_path_to_file}", dataset_name)

Or

cognee.add("data://{absolute_path_to_directory}", dataset_name)

# This is useful if you have a directory with files organized in subdirectories.
# You can target which directory to add by providing dataset_name.
# Example:
#            root
#           /    \
#      reports  bills
#     /       \
#   2024     2023
#
# cognee.add("data://{absolute_path_to_root}", "reports.2024")
# This will add just directory 2024 under reports.

Read more here.

Demo

Check out our demo notebook here

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.