bb679c2dd7
* Update cognify and the networkx client to prepare for running in Neo4j * Fix for openai model * Add the fix to the infra so that the models can be passed to the library. Enable llm_provider to be passed. * Auto graph generation now works with neo4j * Added fixes for both neo4j and networkx * Explicitly name semantic node connections * Added updated docs, readme, chunkers and updates to cognify * Make docs build trigger only when changes on it happen * Update docs, test git actions * Separate cognify logic into tasks * Introduce dspy knowledge graph extraction --------- Co-authored-by: Boris Arzentar <borisarzentar@gmail.com>
59 lines
2.4 KiB
Markdown
59 lines
2.4 KiB
Markdown
# cognee
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#### Deterministic LLMs Outputs for AI Engineers
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_Open-source framework for loading and structuring LLM context to create accurate and explainable AI solutions using knowledge graphs and vector stores_
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---
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[](https://twitter.com/tricalt)
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[](https://pypi.python.org/pypi/cognee)
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[](https://github.com/topoteretes/cognee)
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### Let's learn about cogneeHub!
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cogneeHub is a free and open-sourced learning platform for those interested in creating deterministic LLM outputs.
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We help people with using graphs, LLMs and adding vector retrieval to their ML stack.
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- **Get started** — [Get started with cognee quickly and try it out for yourself.](quickstart.md)
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- **Conceptual Overview** — Learn about the [core concepts](conceptual_overview.md) of cognee and how it fits into your projects.
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- **Data Engineering and LLMOps** — Learn about some [data engineering and llmops](data_engineering_llm_ops.md) core concepts that will help you build better AI apps.
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- **RAGs** — We provide easy-to-follow [learning materials](rags.md) to help you learn about RAGs.
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- **Research** — A list of resources to help you learn more about [cognee and LLM memory research](research.md)
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- **Blog** — A blog where you can read about the [latest news and updates](blog/index.md) about cognee.
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- **Support** — [Book time](https://www.cognee.ai/#bookTime) with our team.
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[//]: # (- **Case Studies** — Read about [case studies](case_studies.md) that show how cognee can be used in real-world applications.)
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### Vision
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### Architecture
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### Why use cognee?
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The question of using cognee is fundamentally a question of why to have deterministic outputs for your llm workflows.
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1. **Cost-effective** — cognee extends the capabilities of your LLMs without the need for expensive data processing tools.
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2. **Self-contained** — cognee runs as a library and is simple to use
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3. **Interpretable** — Navigate graphs instead of embeddings to understand your data.
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4. **User Guided** — cognee lets you control your input and provide your own Pydantic data models
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## License
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This project is licensed under the terms of the Apache License 2.0.
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