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authors: | ||
tricalt: | ||
name: Vasilije Markovic | ||
description: Creator | ||
avatar: https://avatars.githubusercontent.com/u/8619304?v=4 | ||
url: https://twitter.com/intent/follow?screen_name=tricalt |
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# Welcome to the cognee Blog | ||
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The goal of the blog is to discuss broader topics around the cognee project, including the motivation behind the project, the technical details, and the future of the project. | ||
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## Towards production pipelines step by step | ||
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1. [From demo to production 1](posts/from-demo-to-production-1.md) | ||
2. [From demo to production 2](posts/from-demo-to-production-2.md) | ||
3. [From demo to production 3](posts/from-demo-to-production-3.md) | ||
4. [From demo to production 4](posts/from-demo-to-production-4.md) |
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--- | ||
draft: False | ||
date: 2023-10-05 | ||
tags: | ||
- pydantic | ||
- langchain | ||
- llm | ||
- openai | ||
- functions | ||
- pdfs | ||
authors: | ||
- tricalt | ||
--- | ||
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# First post | ||
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## Conclusion | ||
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If you enjoy the content or want to try out `cognee` please check out the [github](https://github.com/topoteretes/cognee) and give us a star! |
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## Conclusion | ||
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If you enjoy the content or want to try out `cognee` please check out the [github](https://github.com/topoteretes/cognee) and give us a star! |
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## Conclusion | ||
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If you enjoy the content or want to try out `cognee` please check out the [github](https://github.com/topoteretes/cognee) and give us a star! |
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## Conclusion | ||
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If you enjoy the content or want to try out `cognee` please check out the [github](https://github.com/topoteretes/cognee) and give us a star! |
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### HELLO WORLD | ||
# cognee, Make data processing for LLMs easy | ||
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_Open-source framework for creating knowledge graphs and data models for LLMs._ | ||
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--- | ||
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[![Twitter Follow](https://img.shields.io/twitter/follow/tricalt?style=social)](https://twitter.com/tricalt) | ||
[![Downloads](https://img.shields.io/pypi/dm/cognee.svg)](https://pypi.python.org/pypi/instructor) | ||
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cognee makes it easy to reliably enrich data for Large Language Models (LLMs) like GPT-3.5, GPT-4, GPT-4-Vision, including in the future the open source models like Mistral/Mixtral from Together, Anyscale, Ollama, and llama-cpp-python. | ||
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By leveraging various tools like graph databases, function calling, tool calling and Pydantic; cognee stands out for its aim to emulate human memory for LLM apps and frameworks. | ||
We leverage Neo4j to do the heavy lifting and dlt to load the data, and we've built a simple, easy-to-use API on top of it by helping you manage your context | ||
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## Getting Started | ||
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``` | ||
pip install -U cognee | ||
``` | ||
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You can also check out our [cookbook](./examples/index.md) to learn more about how to use cognee. | ||
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## Why use cognee? | ||
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The question of using cognee is fundamentally a question of why to structure data inputs and outputs for your llm workflows. | ||
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1. **Cost effective** — With our upcoming opensource release, cognee will extend 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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## hello hello |
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