You're right to question whether full Retrieval-Augmented Generation (RAG) is the right tool for this. This use case is more naturally framed as structured information retrieval rather than unstructured text generation. The core tasks — ranking, filtering, and aggregating ingredient-category scores — are statistical or database operations. Here’s a better architectural breakdown:
🔧 Data Model Recommendation
Use a structured tabular store (like SQLite, DuckDB, or PostgreSQL), backed by a lightweight semantic retriever if you want natural language queries. Each record:
json
{ "ingredient": "garlic", "category": "pasta", "score": 4.5, "tags": ["strong", "savory"], "notes": "commonly used", "last_updated": "2025-07-22" }
You don't need 30,000 JSON documents — store all rows in a table or a dataframe-like structure.
🧠 Access Strategy
Use a hybrid approach:
- Primary retrieval/filtering via SQL or pandas/Polars queries.
- Pass relevant rows (filtered/top-N) to the LLM as context for interpretation, summarization, or fuzzy reasoning.
Example query types and how to solve them:
| Query Type | Execution Strategy |
| --- | --- |
| Top 5 ingredients for grilling | SQL:
SELECT ingredient FROM ratings WHERE category='grilling' ORDER BY score DESC LIMIT 5;| | Ingredients strong in both pasta and salad | SQL JOIN on ingredient with condition on both categories | | What's the best among the ones I already have? | WHERE ingredient IN (user_ingredients) ORDER BY score DESC |
🧰 Optional Semantic Layer
If you want to support vague or fuzzy input (e.g., "stuff that goes well in summer salads"), then:
- Use embedding search (e.g., OpenAI, SBERT) only to retrieve top matching rows or ingredient-category tuples.
- Then merge with statistical filters (e.g., only scores > 3.5). This avoids hallucination and bloated context windows while retaining LLM power for interpretation.
🧱 Tools You Could Use
- DuckDB: Blazing-fast, in-memory SQL engine — perfect for 30k records.
- LangChain / LlamaIndex (only as glue logic if embedding search is still needed).
- FAISS / Annoy / Qdrant: If you embed the ingredient+category+tags tuple as the retrieval unit.