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decodingai-magazine / llm-twin-course

🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴

Why this job: topics: course, best practices

What it does

  • Consume messages in real-time from a queue through a Bytewax streaming pipeline.
  • Every message will be cleaned, chunked, embedded and loaded into a Qdrant vector DB.
  • In the bonus series, we refactor the cleaning, chunking, and embedding logic using Superlinked, a specialized vector compute engine. We will also load and index the vectors to a Redis vector DB.

Get started

No single install command. Read the steps in the README (opens in new tab)

Maintenance
Inactive
Last commit 6 months ago
Stars
4,411
+3 this week
Licence
MIT
Allows business use
Downloads for
None listed
No release files
Latest version
None yet
No releases
Built with
Python
96% of the code

Is it looked after?

Contributors
Not stated
Last 90 days
Issues
77% closed
5 still open
Pull requests
26 merged
3 waiting
Releases
None
Never published
  • No contributing guide
  • No security policy
  • No code of conduct

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More: languages, topics and project record

Languages

  • Python 96%
  • Makefile 2%
  • Dockerfile 1%
  • Shell 1%

Topics

  • aws
  • bytewax
  • comet-ml
  • course
  • docker
  • generative-ai
  • infrastructure-as-code
  • large-language-models
  • llmops
  • machine-learning-engineering
  • ml-system-design
  • mlops

Project record

  • JobLists and guides
  • StatusListed
  • Commits334
  • Forks732
  • Watching76
  • Stars in 28 days+23
  • CreatedMar 8, 2024
  • First seen by usNov 7, 2026
  • CheckedNov 8, 2026

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