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abir tarchoun

abirT
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reacted to singhsidhukuldeep's post with 👍 about 1 month ago
Fascinating deep dive into Swiggy's Hermes - their in-house Text-to-SQL solution that's revolutionizing data accessibility! Hermes enables natural language querying within Slack, generating and executing SQL queries with an impressive <2 minute turnaround time. The system architecture is particularly intriguing: Technical Implementation: - Built on GPT-4 with a Knowledge Base + RAG approach for Swiggy-specific context - AWS Lambda middleware handles communication between Slack UI and the Gen AI model - Databricks jobs orchestrate query generation and execution Under the Hood: The pipeline employs a sophisticated multi-stage approach: 1. Metrics retrieval using embedding-based vector lookup 2. Table/column identification through metadata descriptions 3. Few-shot SQL retrieval with vector-based search 4. Structured prompt creation with data snapshots 5. Query validation with automated error correction Architecture Highlights: - Compartmentalized by business units (charters) for better context management - Snowflake integration with seamless authentication - Automated metadata onboarding with QA validation - Real-time feedback collection via Slack What's particularly impressive is how they've solved the data context challenge through charter-specific implementations, significantly improving query accuracy for well-defined metadata sets. Kudos to the Swiggy team for democratizing data access across their organization. This is a brilliant example of practical AI implementation solving real business challenges.
updated a Space 8 months ago
abirT/Llama-2-7B-finetuning-ophtalmology
updated a Space 8 months ago
abirT/Llama-2-7b-ophtalmology
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