Remove tags retrieval
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Building DoorDash’s Product Knowledge Graph with Large Language Models

DoorDash Engineering

Historically, this SKU enrichment of extracting and tagging attributes has been a purely manual process led by contract operators. For instance, given the item name “Dove Silk Glow Body Wash 500 ml,” a token classifier would tag each entity in the item name as shown in Table 1. This approach further boosted our coverage.

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How to Build a Chatbot Using Retrieval Augmented Generation (RAG)

Rockset

Due to these aforementioned limitations, the Retrieval-Augmented Generation (RAG) approach was introduced to overcome the innate challenges of traditional LLMs. RAG (Retrieval-Augmented Generation) is an approach designed to enhance the responses and capabilities of traditional LLMs (Large Language Models). What is RAG?

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How to Create an Amazon Price Tracker Service Using Python?

Workfall

In this blog, we will cover: About Python About Amazon About BeautifulSoup Library Hands-On Conclusion About Python Python is a high-level, interpreted, and versatile programming language known for its simplicity and readability. .” This will help us examine what data we retrieved through our HTTP GET request.

Python 93
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Improving Efficiency Of Goku Time Series Database at Pinterest (Part?—?1)

Pinterest Engineering

In the first blog, we will share a short summary on the GokuS and GokuL architecture, data format for Goku Long Term, and how we improved the bootstrap time for our storage and serving components. short, RocksDB is a key value store that uses a log structure DB engine for storage and retrieval. The sorting is for efficient retrieval.

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How to Get the User’s Location Using Mapbox?

Workfall

In this blog, we aim to address these challenges and provide a clear, concise, and beginner-friendly guide on how to get the user’s location using HTML and JavaScript, empowering developers to seamlessly integrate geolocation functionality into their web projects. In this blog, we will cover: What is Geolocation API?

Coding 69
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How DoorDash Migrated from StatsD to Prometheus

DoorDash Engineering

Challenges Faced With StatsD StatsD was a great asset for our early observability needs, but we began encountering constraints such as losing metrics during surge events, difficulties with naming/standardized tags, and a lack of reporting tools. We’ll briefly introduce StatD’s history before diving into those specific issues.

AWS 82
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Complying with Quebec’s Data Privacy Laws Is Easier with the Data Cloud

Snowflake

This blog post specifically addresses the highlighted sections in P-39.1 – Act respecting the protection of personal information in the private sector. This is made easier if PII data was appropriately classified and tagged as part of the privacy impact assessment, and so this is a best practice for organizations to follow.

Cloud 78