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Project template
Use case
Code snippet
Continuously updating a vector store
Use cases:
LLMs
A three-step pipeline that you can use to ingest embeddings into a vector database as new content as it is published. When new content arrives, an event is emitted to Kafka with the text of the content as a payload. A consumer process listens for new content and passes it to the embedding model to turn the text into vectors. The resulting vectors are passed to a downstream Kafka topic where any vector database can consume and ingest the vectors at its own pace.
Using this template
This project could be easily adapted for use cases such as:
- Retrieval Augmented Generation (RAG)
- Product searches for ecommerce
- Recommendation systems
Interested in this use case?
If you'd like us to focus on building this template next, register your interest and let us know. You can also head over to the Quix Community Slack if you've got any questions.
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