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
