Merge pull request #47 from topoteretes/update_blog

added main docs and blog page
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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
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.
## Towards production pipelines step by step
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
---
# First post
## Conclusion
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
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
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
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
_Open-source framework for creating knowledge graphs and data models for LLMs._
---
[![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)
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.
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
## Getting Started
```
pip install -U cognee
```
You can also check out our [cookbook](./examples/index.md) to learn more about how to use cognee.
## Why use cognee?
The question of using cognee is fundamentally a question of why to structure data inputs and outputs for your llm workflows.
1. **Cost effective** — With our upcoming opensource release, cognee will extend the capabilities of your LLMs without the need for expensive data processing tools.
2. **Self contained** — cognee runs as a library and is simple to use
3. **Interpretable** — Navigate graphs instead of embeddings to understand your data.
4. **User Guided** cognee lets you control your input and provide your own Pydantic data models
## hello hello

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{% extends "base.html" %} {% block announce %} For updates follow
<strong>@tricalt</strong> on
<a href="https://twitter.com/jxnlco">
<a href="https://twitter.com/tricalt">
<span class="twemoji twitter">
{% include ".icons/fontawesome/brands/twitter.svg" %}
</span>