https://github.com/topoteretes/cognee-starter
Cognee starter repo with examples
https://github.com/topoteretes/cognee-starter
ai graphrag llm semantic-memory
Last synced: 1 day ago
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Cognee starter repo with examples
- Host: GitHub
- URL: https://github.com/topoteretes/cognee-starter
- Owner: topoteretes
- Created: 2025-02-08T00:32:50.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-05-10T14:34:52.000Z (5 months ago)
- Last Synced: 2025-06-12T19:05:50.699Z (4 months ago)
- Topics: ai, graphrag, llm, semantic-memory
- Language: Python
- Homepage: https://www.cognee.ai/
- Size: 244 KB
- Stars: 37
- Watchers: 3
- Forks: 13
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Cognee Starter Kit
Welcome to the cognee Starter Repo! This repository is designed to help you get started quickly by providing a structured dataset and pre-built data pipelines using cognee to build powerful knowledge graphs.
You can use this repo to ingest, process, and visualize data in minutes.
By following this guide, you will:
- Load structured company and employee data
- Utilize pre-built pipelines for data processing
- Perform graph-based search and query operations
- Visualize entity relationships effortlessly on a graph
# How to Use This Repo ðŸ›
## Install uv if you don't have it on your system
```
pip install uv
```
## Install dependencies
```
uv sync
```
## Setup LLM
Add environment variables to `.env` file.
In case you choose to use OpenAI provider, add just the model and api_key.
```
LLM_PROVIDER=""
LLM_MODEL=""
LLM_ENDPOINT=""
LLM_API_KEY=""
LLM_API_VERSION=""
EMBEDDING_PROVIDER=""
EMBEDDING_MODEL=""
EMBEDDING_ENDPOINT=""
EMBEDDING_API_KEY=""
EMBEDDING_API_VERSION=""
```
Activate the Python environment:
```
source .venv/bin/activate
```
## Run the Default Pipeline
This script runs the cognify pipeline with default settings. It ingests text data, builds a knowledge graph, and allows you to run search queries.
```
python src/pipelines/default.py
```
## Run the Low-Level Pipeline
This script implements its own pipeline with custom ingestion task. It processes the given JSON data about companies and employees, making it searchable via a graph.
```
python src/pipelines/low_level.py
```
## Run the Custom Model Pipeline
Custom model uses custom pydantic model for graph extraction. This script categorizes programming languages as an example and visualizes relationships.
```
python src/pipelines/custom-model.py
```
## Graph preview
cognee provides a visualize_graph function that will render the graph for you.
```
graph_file_path = str(
pathlib.Path(
os.path.join(pathlib.Path(__file__).parent, ".artifacts/graph_visualization.html")
).resolve()
)
await visualize_graph(graph_file_path)
```
If you want to use tools like Graphistry for graph visualization:
- create an account and API key from https://www.graphistry.com
- add the following environment variables to `.env` file:
```
GRAPHISTRY_USERNAME=""
GRAPHISTRY_PASSWORD=""
```
Note: `GRAPHISTRY_PASSWORD` is API key.
## Trace
Cognee supports Langfuse tracing to help you monitor and debug your workflows.
To enable tracing:
1. Create an account and obtain your API keys at [Langfuse](https://langfuse.com/).
*(Alternatively, you can self-host Langfuse by following the instructions at [Langfuse Self-Hosting](https://langfuse.com/self-hosting))*
2. Add the following environment variables to your `.env` file:
```env
LANGFUSE_PUBLIC_KEY=""
LANGFUSE_SECRET_KEY=""
LANGFUSE_HOST=""
```
Make sure to replace the empty strings with your actual Langfuse credentials.
# What will you build with cognee?
- Expand the dataset by adding more structured/unstructured data
- Customize the data model to fit your use case
- Use the search API to build an intelligent assistant
- Visualize knowledge graphs for better insights