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