Behind Every Question-Answer AI Is a Data Pipeline Built for Scale - Here's How to Build Your Own | HackerNoon
Briefly

from Hackernoon5 years ago
This article presents a comprehensive guide on building a data pipeline that continuously indexes document embeddings into Redis, utilizing various Google Cloud services and LangChain.
Hackernoonhttps://hackernoon.com/behind-every-question-answer-ai-is-a-data-pipeline-built-for-scale-heres-how-to-build-your-own
The use of a GCP Storage Bucket ensures that all document types are centralised, while Airflow automates the ingestion process from different sources effectively every day.
Hackernoonhttps://hackernoon.com/behind-every-question-answer-ai-is-a-data-pipeline-built-for-scale-heres-how-to-build-your-own
LangChain's RecordManager is pivotal in managing document embeddings, allowing for both incremental and full indexing, handling updates and deletions seamlessly to maintain data integrity.
Hackernoonhttps://hackernoon.com/behind-every-question-answer-ai-is-a-data-pipeline-built-for-scale-heres-how-to-build-your-own
This solution ultimately supports a Retrieval-Augmented Generation (RAG) system, enhancing the capability to execute question-answering tasks by indexing dynamically sourced document data.
Hackernoonhttps://hackernoon.com/behind-every-question-answer-ai-is-a-data-pipeline-built-for-scale-heres-how-to-build-your-own
Read at Hackernoon
#data-pipeline #redis #google-cloud #langchain #document-embeddings
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