819e411149
<!-- .github/pull_request_template.md --> ## Description Small clarifications in README.md. ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Updated documentation to feature a single, centrally positioned demo link for clearer navigation. - Clarified setup instructions to indicate that default configurations are applied when custom environment variables are not provided. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
177 lines
6.8 KiB
Markdown
177 lines
6.8 KiB
Markdown
<div align="center">
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<a href="https://github.com/topoteretes/cognee">
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<img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/dev/assets/cognee-logo-transparent.png" alt="Cognee Logo" height="60">
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</a>
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<br />
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cognee - memory layer for AI apps and Agents
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<p align="center">
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<a href="https://www.youtube.com/watch?v=1bezuvLwJmw&t=2s">Demo</a>
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.
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<a href="https://cognee.ai">Learn more</a>
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·
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<a href="https://discord.gg/NQPKmU5CCg">Join Discord</a>
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</p>
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[](https://GitHub.com/topoteretes/cognee/network/)
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[](https://GitHub.com/topoteretes/cognee/stargazers/)
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[](https://GitHub.com/topoteretes/cognee/commit/)
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[](https://github.com/topoteretes/cognee/tags/)
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[](https://pepy.tech/project/cognee)
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[](https://github.com/topoteretes/cognee/blob/main/LICENSE)
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[](https://github.com/topoteretes/cognee/graphs/contributors)
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AI Agent responses you can rely on.
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Build dynamic Agent memory using scalable, modular ECL (Extract, Cognify, Load) pipelines.
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More on [use-cases](https://docs.cognee.ai/use_cases).
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<div style="text-align: center">
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<img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/dev/assets/cognee_benefits.png" alt="Why cognee?" width="100%" />
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</div>
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</div>
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## Features
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- Interconnect and retrieve your past conversations, documents, images and audio transcriptions
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- Reduce hallucinations, developer effort, and cost.
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- Load data to graph and vector databases using only Pydantic
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- Manipulate your data while ingesting from 30+ data sources
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## Get Started
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Get started quickly with a Google Colab <a href="https://colab.research.google.com/drive/1g-Qnx6l_ecHZi0IOw23rg0qC4TYvEvWZ?usp=sharing">notebook</a> or <a href="https://github.com/topoteretes/cognee-starter">starter repo</a>
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## Contributing
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Your contributions are at the core of making this a true open source project. Any contributions you make are **greatly appreciated**. See [`CONTRIBUTING.md`](CONTRIBUTING.md) for more information.
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## 📦 Installation
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You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager.
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### With pip
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```bash
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pip install cognee
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```
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## 💻 Basic Usage
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### Setup
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```
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import os
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os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
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```
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You can also set the variables by creating .env file, using our <a href="https://github.com/topoteretes/cognee/blob/main/.env.template">template.</a>
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To use different LLM providers, for more info check out our <a href="https://docs.cognee.ai">documentation</a>
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### Simple example
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Add LLM_API_KEY to .env using the command bellow.
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```
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echo "LLM_API_KEY=YOUR_OPENAI_API_KEY" > .env
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```
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You can see available env variables in the repository `.env.template` file. If you don't specify it otherwise, like in this example, SQLite (relational database), LanceDB (vector database) and NetworkX (graph store) will be used as default components.
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This script will run the default pipeline:
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```python
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import cognee
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import asyncio
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from cognee.modules.search.types import SearchType
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async def main():
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# Create a clean slate for cognee -- reset data and system state
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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# cognee knowledge graph will be created based on this text
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text = """
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Natural language processing (NLP) is an interdisciplinary
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subfield of computer science and information retrieval.
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"""
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print("Adding text to cognee:")
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print(text.strip())
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# Add the text, and make it available for cognify
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await cognee.add(text)
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# Use LLMs and cognee to create knowledge graph
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await cognee.cognify()
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print("Cognify process complete.\n")
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query_text = "Tell me about NLP"
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print(f"Searching cognee for insights with query: '{query_text}'")
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# Query cognee for insights on the added text
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search_results = await cognee.search(
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query_text=query_text, query_type=SearchType.INSIGHTS
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)
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print("Search results:")
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# Display results
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for result_text in search_results:
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print(result_text)
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# Example output:
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# ({'id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.'}, {'relationship_name': 'is_a_subfield_of', 'source_node_id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'target_node_id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 15, 473137, tzinfo=datetime.timezone.utc)}, {'id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'computer science', 'description': 'The study of computation and information processing.'})
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# (...)
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#
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# It represents nodes and relationships in the knowledge graph:
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# - The first element is the source node (e.g., 'natural language processing').
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# - The second element is the relationship between nodes (e.g., 'is_a_subfield_of').
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# - The third element is the target node (e.g., 'computer science').
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if __name__ == '__main__':
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asyncio.run(main())
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```
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For more advanced usage, have a look at our <a href="https://docs.cognee.ai"> documentation</a>.
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## Understand our architecture
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<div style="text-align: center">
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<img src="assets/cognee_diagram.png" alt="cognee concept diagram" width="100%" />
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</div>
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## Demos
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What is AI memory:
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[Learn about cognee](https://github.com/user-attachments/assets/8b2a0050-5ec4-424c-b417-8269971503f0)
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## Code of Conduct
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We are committed to making open source an enjoyable and respectful experience for our community. See <a href="https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md"><code>CODE_OF_CONDUCT</code></a> for more information.
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## 💫 Contributors
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<a href="https://github.com/topoteretes/cognee/graphs/contributors">
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<img alt="contributors" src="https://contrib.rocks/image?repo=topoteretes/cognee"/>
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</a>
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## Star History
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[](https://star-history.com/#topoteretes/cognee&Date)
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