Text-2-SQL

Context/Background:

  • Companies in e-commerce have large amounts of data stored in various databases.
  • Many potential users of this data (data analysts, business stakeholders) may not have expertise in SQL.
  • Directly writing complex SQL queries can be time-consuming and error-prone.

Problems/Challenges:

  • How to enable non-technical users to easily access and analyze data?
  • How to bridge the gap between natural language questions and structured SQL queries?
  • How to ensure the accuracy and efficiency of generated SQL queries?

Forces/Considerations/Trade-offs:

  • Accuracy vs. Speed: Large Language Models (LLMs) can generate SQL, but their accuracy can vary. It’s crucial to balance generating correct queries with a fast user experience.
  • Data Freshness: SQL query logs used for training might become outdated. It’s essential to consider how to keep the system up-to-date with changes in data schemas and usage patterns.
  • Complexity of Questions: Natural language can be ambiguous. The system needs to handle a wide range of question complexity, from simple aggregations to complex joins and filters.

Solution:

Pinterest’s solution is a Text-to-SQL system that leverages LLMs and vector embeddings.

Solution (Detailed, Intense Steps):

  1. Offline Vector Index Creation:
  • SQL Query Logs: Historical SQL queries are collected.
  • Summarization: These logs, along with table metadata, are summarized using an LLM.
  • Embedding Model: An embedding model (e.g., SentenceBERT) converts these summaries into numerical vectors.
  • Vector Store: Embeddings are stored in a vector database (e.g., FAISS), enabling efficient similarity search.
  1. Online Text2SQL:
  • User Input: The user enters a natural language question and optionally specifies relevant tables.
  • Embedding: The user’s question is converted into a vector using the same embedding model.
  • Similarity Search: The vector store is queried to find the most similar historical queries or table summaries.
  • Table Selection: The system selects the top-N most relevant tables based on the search results.
  • Text2SQL Prompt: The LLM uses the selected tables, the question, and optionally the similar historical queries to generate a SQL prompt.
  • SQL Generation: The LLM generates the final SQL query.

Resulting Consequences:

  • Improved Accessibility: Non-technical users can now easily extract insights from data.
  • Increased Productivity: Data analysts can quickly generate SQL queries, reducing manual effort.
  • Potential for Errors: The generated SQL might not always be perfect and may require manual verification or adjustment.

Related Patterns:

  • Retrieval Augmented Generation (RAG): The core pattern used here, combining information retrieval (similarity search) with LLM generation.
  • Semantic Search: The use of embeddings for finding relevant information in a large corpus.
  • Prompt Engineering: Carefully crafting prompts to guide the LLM to generate accurate SQL queries.
  • Active Learning: Potentially, the system could learn from user feedback on generated SQL to improve over time.
  • Federated Analytics: The system could be extended to query across multiple data sources.