134 lines
3.5 KiB
Markdown
134 lines
3.5 KiB
Markdown
# cognee
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Deterministic LLMs Outputs for AI Engineers using graphs, LLMs and vector retrieval
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<p>
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<a href="https://cognee.ai" target="_blank">
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<img src="https://raw.githubusercontent.com/topoteretes/cognee/main/assets/cognee-logo.png" width="160px" alt="Cognee logo" />
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</a>
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</p>
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<p>
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<i>Open-source framework for creating self-improving deterministic outputs for LLMs.</i>
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</p>
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<p>
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<a href="https://github.com/topoteretes/cognee/fork">
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<img src="https://img.shields.io/github/forks/topoteretes/cognee?style=for-the-badge" alt="cognee forks"/>
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</a>
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<a href="https://github.com/topoteretes/cognee/stargazers">
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<img src="https://img.shields.io/github/stars/topoteretes/cognee?style=for-the-badge" alt="cognee stars"/>
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</a>
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<a href="https://github.com/topoteretes/cognee/pulls">
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<img src="https://img.shields.io/github/issues-pr/topoteretes/cognee?style=for-the-badge" alt="cognee pull-requests"/>
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</a>
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<a href="https://github.com/topoteretes/cognee/releases">
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<img src="https://img.shields.io/github/release/topoteretes/cognee?&label=Latest&style=for-the-badge" alt="cognee releases" />
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</a>
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</p>
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Try it in a Google collab <a href="https://colab.research.google.com/drive/11k0GtbrKRVGTxhcgad4Wl8YvCnWJVWPl?usp=sharing">notebook</a> or more details, have a look at our <a href="https://topoteretes.github.io/cognee">documentation</a>
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## 📦 Installation
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With pip:
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```bash
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pip install "cognee[weaviate]"
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```
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With poetry:
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```bash
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poetry add "cognee[weaviate]"
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```
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## 💻 Usage
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### Setup
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```
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import os
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os.environ["WEAVIATE_URL"] = "YOUR_WEAVIATE_URL"
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os.environ["WEAVIATE_API_KEY"] = "YOUR_WEAVIATE_API_KEY"
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os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
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```
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You can also use Ollama or Anyscale as your LLM provider. For more info on local models check our [docs](https://topoteretes.github.io/cognee)
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### Run
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```
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import cognee
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text = """Natural language processing (NLP) is an interdisciplinary
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subfield of computer science and information retrieval"""
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cognee.add(text) # Add a new piece of information
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cognee.cognify() # Use LLMs and cognee to create knowledge
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search_results = cognee.search("SIMILARITY", "computer science") # Query cognee for the knowledge
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for result_text in search_results[0]:
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print(result_text)
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```
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Add alternative data types:
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```
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cognee.add("file://{absolute_path_to_file}", dataset_name)
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```
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Or
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```
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cognee.add("data://{absolute_path_to_directory}", dataset_name)
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# This is useful if you have a directory with files organized in subdirectories.
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# You can target which directory to add by providing dataset_name.
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# Example:
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# root
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# / \
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# reports bills
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# / \
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# 2024 2023
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#
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# cognee.add("data://{absolute_path_to_root}", "reports.2024")
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# This will add just directory 2024 under reports.
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```
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Read more [here](docs/index.md#run).
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## Demo
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Check out our demo notebook [here](https://github.com/topoteretes/cognee/blob/main/notebooks/cognee%20-%20Get%20Started.ipynb)
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[<img src="https://i3.ytimg.com/vi/-ARUfIzhzC4/maxresdefault.jpg" width="100%">](https://www.youtube.com/watch?v=BDFt4xVPmro "Learn about cognee: 55")
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## How it works
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## 🚀 It's alive
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<p>
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Try it yourself on Whatsapp with one of our <a href="https://keepi.ai" target="_blank">partners</a> by typing `/save {content you want to save}` followed by `/query {knowledge you saved previously}`
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For more info here are the <a href="https://topoteretes.github.io/cognee">docs</a>
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</p>
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