Seeking Help designing a RAG application for Code Conversion Application using Azure ML

Question

Hello,

May someone please share your thoughts how can I designed a RAG based solution for converting Scala Spark code to Pyspark.

How to design such application.

Thanks

Answer 1

Here is one way to approach this:

1. Define the RAG Architecture A RAG-based solution combines a retriever that fetches relevant information (such as code examples, documentation, and patterns) with a generator (LLM) to generate accurate conversions.

Components of the Solution

1. Document Store (Retriever)
   • Store mappings of Scala Spark → PySpark transformations.
   • Use embeddings for efficient retrieval.
   • Include:
     • Common API mappings (e.g., df.withColumn("col", expr("...")) → df.withColumn("col", expr("...")))
     • Code snippets from official documentation
     • StackOverflow solutions
     • Best practices
2. Retriever
   • Use vector search (FAISS, Elasticsearch, Weaviate) to fetch similar code snippets from the document store.
   • Implement keyword-based search as a fallback.
3. LLM Generator
   • Fine-tune or use an LLM (GPT, Codex, StarCoder) with RAG to:
     • Convert Scala Spark → PySpark.
     • Explain the transformation.
     • Optimize code for readability and performance.

2. Build the Document Store

• Collect Scala Spark to PySpark mappings:
  • Official Apache Spark documentation
  • Open-source code repositories (e.g., GitHub, Databricks notebooks)
  • Manually curated examples
• Store in:
  • Vector Database (e.g., FAISS, Pinecone, ChromaDB) for semantic search.
  • Traditional DB (e.g., PostgreSQL) for structured lookup.

3. Implement the Retriever Use two-stage retrieval:

1. Keyword Search (BM25 in Elasticsearch)
2. Embedding Similarity Search (FAISS, Pinecone)

4. Implement the LLM Generator Use OpenAI GPT, LLaMA, or Code LLMs (e.g., Codex, StarCoder) to generate the conversion.

5. Post-Processing and Validation

• Implement static analysis (e.g., ast in Python) to check syntax validity.
• Test generated PySpark code in a Databricks Notebook.

6. Optional Enhancements

• Fine-tune an LLM with a dataset of Scala-to-PySpark transformations.
• Provide an interactive UI (Streamlit, Flask, or VS Code extension) for inputting Scala Spark and retrieving PySpark.

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hth

Marcin