feat(wren-ai-service): Embed the SQL pairs in MDL

[paopa]

Changes

1. Pipeline Simplification

   • Removed SQL intention generation using LLM
   • Simplified document storage to use direct question-SQL pairs
   • Updated pipeline configuration to remove unnecessary LLM dependency

2. Configuration Changes

   • Added SQL_PAIRS_PATH environment variable
   • Added pairs.json configuration file support
   • Updated deployment configurations to include new file mounts

3. Sample Management

   • Added structured JSON format for storing SQL query examples
   • Updated sample format to use question-SQL pairs
   • Improved sample integration in SQL generation prompts

4. Testing

   • Updated existing tests to reflect new pipeline structure
   • Added test data for pairs.json
   • Temporarily skipped outdated tests pending updates

Related Changes

• Updated docker compose configurations
• Modified kustomization files for deployment
• Updated configuration examples
• Added new utility tool for MDL string conversion

Summary by CodeRabbit

• New Features

  • Added SQL pairs indexing functionality.
  • Introduced new configuration options for SQL pairs.
  • Enhanced SQL generation with sample query support.

• Configuration Changes

  • Updated pipeline configurations to reflect new SQL pairs indexing.
  • Added new environment variables for SQL pairs path.
  • Introduced pairs.json configuration file.

• Pipeline Modifications

  • Renamed SQL pairs preparation to SQL pairs indexing.
  • Updated SQL pairs handling in various service components.
  • Improved SQL generation prompt templates.

• Testing

  • Added test data for SQL pairs.
  • Updated test cases to reflect new pipeline structure.
  • Skipped outdated tests related to SQL pairs preparation.

[coderabbitai[bot]]

Walkthrough

This pull request introduces significant changes to the Wren AI service, focusing on SQL pairs indexing and processing. The modifications span multiple files, introducing a new SqlPairs pipeline to replace the previous SqlPairsPreparation approach. Key changes include renaming pipeline stages, updating configuration files, adding new utility functions, and restructuring how SQL pairs are handled throughout the service. The changes aim to improve the flexibility and clarity of SQL pair management, with a more streamlined approach to indexing and processing.

Changes

File Path	Change Summary
deployment/kustomizations/base/cm.yaml	Renamed pipeline step from sql_pairs_preparation to sql_pairs_indexing
docker/config.example.yaml	Updated pipeline configuration, removed llm from sql_pairs_indexing
wren-ai-service/src/config.py	Added new configuration field sql_pairs_path
wren-ai-service/src/globals.py	Updated service container with new SQL pairs handling
wren-ai-service/src/pipelines/indexing/	Introduced new SqlPairs pipeline, removed SqlPairsPreparation
wren-ai-service/tests/	Updated test files to match new SQL pairs indexing approach
wren-ai-service/tools/config/	Updated configuration files with new pipeline naming

Sequence Diagram

sequenceDiagram
    participant User
    participant SqlPairs
    participant DocumentConverter
    participant Embedder
    participant DocumentStore
    participant Cleaner

    User->>SqlPairs: Run with MDL string
    SqlPairs->>DocumentConverter: Convert SQL pairs to documents
    DocumentConverter-->>SqlPairs: Return documents
    SqlPairs->>Embedder: Embed documents
    Embedder-->>SqlPairs: Return embedded documents
    SqlPairs->>Cleaner: Clean SQL pairs
    Cleaner-->>SqlPairs: Return cleaned pairs
    SqlPairs->>DocumentStore: Write documents

Poem

🐰 SQL Pairs Dancing Free

In pipelines of code, a rabbit's glee From preparation to indexing we leap Transforming queries with magical sweep No LLM needed, just pure SQL spree!

🔍✨

Finishing Touches

• [ ] 📝 Generate Docstrings (Beta)

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