Build a Company Brain in Python with Just 2 Function Calls | Self-evolving Knowledge Base Cognee SDK
-Your company's knowledge is siloed. Architecture decisions buried in Confluence, deployment rules in Notion, and the real reason for that one weird workaround sitting in an 8-month-old Slack thread. New developers spend their first two weeks interrupting senior engineers instead of writing code.
In this video, I'll show you how to fix that by building a knowledge graph with cognee just a few lines of code.
We'll ingest a set of company documents — architecture overviews, runbooks, postmortems, onboarding guides — and then query them in natural language to instantly surface answers that used to take hours to track down.
Cognee handles the hard parts for you: chunking documents, generating embeddings, extracting relationships with an LLM, storing everything in a graph database, and indexing it in a vector store. No infrastructure project required.
Just cognee.remember() and cognee.recall().
🔗 What we cover:
00:00 The problem with siloed company knowledge
00:43 What is a knowledge graph and why Cognee?
01:31 Installing Python 3.12 and uv
02:04 Setting up the project and dependencies
02:27 Configuring your OpenAI API key
03:32 Writing ingest.py to build the knowledge graph
04:47 Writing query.py to search your documents
05:35 Querying the graph in natural language
🛠️ Tools used:
Cognee (open source, provider agnostic — works with OpenAI, Azure, AWS Bedrock, vLLM, Ollama)
Python 3.12
uv for virtual environments
OpenAI API (total cost for this demo: a few cents)
If you're tired of the same questions getting asked in your team Slack every week, give it a try: https://github.com/topoteretes/cognee
Learn more about Cognee: https://www.cognee.ai/
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