search-expert 0.1.1

Parse natural language search queries into structured fields using fine-tuned Qwen3.5-0.8B LoRA adapters.

 search-expert

Natural language → structured search queries, instantly.
Fine-tuned Qwen3.5-0.8B LoRA adapters for search query parsing across 10 domains.

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What it does

"Non-stop business class from JFK to Tokyo under $3,000"

{
  "domain":      "flights",
  "origin":      "JFK",
  "destination": "Tokyo",
  "cabin_class": "business",
  "stops":       "lte:0",
  "price":       "lt:3000"
}

Search Expert uses a custom fine-tuned Small Language Model to understand natural language queries and extract only the fields explicitly mentioned — never hallucinating values that aren't there. It works across 10 search verticals out of the box.

Install

pip install search-expert

Usage

Basic

from search_expert import SearchExpert, ModelFormat, ParseResult

expert = SearchExpert()  # loads the JSON adapter by default

result = expert.parse("noise cancelling headphones any colour but red or green, under $200")
print(result.fields)

{
    'domain':  'ecommerce',
    'product': 'headphones',
    'feature': 'noise cancelling',
    'color':   ['ne:red', 'ne:green'],
    'price':   'lt:200'
}

Operator reference

All numeric and exclusion constraints use a consistent prefix so downstream filters need zero NLP — just parse the string.

Query phrase	Output value
"under $200", "below $200"	lt:200
"up to $200", "max $200"	lte:200
"over $150k", "above $150k"	gt:150000
"at least $150k", "$150k+"	gte:150000
"around $200", "~$200"	approx:200
"$100–$200", "between $100 and $200"	between:100:200
"any colour but red or green"	["ne:red", "ne:green"]

Applying a filter in one line:

result = expert.parse("apartments under $2,500/month in Austin")
salary = result.get_numeric_constraint("price")
# {'operator': 'lt', 'value': 2500.0, 'value_hi': None}

filtered = [l for l in listings if l["price"] < salary["value"]]

Supported domains

Domain	Example query
real_estate	"2BR apartment in Austin under $1,500/month"
ecommerce	"Sony noise cancelling headphones under $300"
jobs	"Remote senior ML engineer paying over $150k"
flights	"Non-stop business class JFK to Tokyo under $3,000"
hotels	"5-star hotel in Paris with breakfast under $400/night"
cars	"Electric SUV with 300+ mile range under $50k"
restaurants	"Vegan Italian in NYC with outdoor seating under $40"
movies	"Thriller on Netflix with 8+ IMDB rating"
healthcare	"Female therapist in Chicago accepting Aetna"
courses	"Python ML course for beginners under $30"
events	"Taylor Swift concert in London in July"

The model

Each adapter is a LoRA fine-tune of Qwen3.5-0.8B trained on ~1 million (query, structured output) pairs spanning all 10 domains above.

Adapter	HuggingFace	Format
JSON (default)	sarthakrastogi/search-expert-json-0.8b	JSON
YAML	sarthakrastogi/search-expert-yaml-0.8b	YAML

Format leaderboard (held-out test set, 300 samples per format):

Rank	Format	Key F1	Value Acc	Parse Rate
🥇	JSON	0.913	0.874	98.2%
🥈	YAML	0.901	0.861	97.6%
🥉	TOML	0.887	0.843	96.1%
4.	XML	0.871	0.829	94.8%
5.	CSV key=value	0.856	0.812	93.3%

Both public adapters return the same Python dict — the format only affects the model's internal generation language.

Repo structure

search-expert/
├── search_expert/        # Library source
│   ├── __init__.py
│   ├── expert.py         # SearchExpert class (main API)
│   ├── config.py         # Model IDs, prompts, format enum
│   ├── loader.py         # HF model loading (unsloth / peft / plain)
│   ├── parser.py         # Raw output → dict parsers
│   ├── result.py         # ParseResult dataclass
│   └── exceptions.py     # Custom exceptions
├── training/             # Fine-tuning pipeline
│   ├── finetune.py       # Training script
│   └── evaluate.py       # Format comparison leaderboard
├── tests/
│   └── test_search_expert.py
├── examples/
│   └── search_expert_colab.ipynb
├── pyproject.toml
└── README.md

Development

git clone https://github.com/sarthakrastogi/search-expert
cd search-expert
pip install -e ".[dev]"

pytest tests/ -v                                          # unit tests (no GPU needed)
SEARCH_EXPERT_RUN_MODEL_TESTS=1 pytest tests/ -v          # includes model inference tests

License

MIT © Sarthak Rastogi

Contributing

Contributions are very welcome! Please open an issue or submit a pull request with any improvements.

Contact

For questions, feedback, or just to say hi, you can reach me at:

Email

LinkedIn