smelt-ai 0.1.4

LLM-powered structured data transformation

Smelt AI

LLM-powered structured data transformation. Feed in rows of data, get back strictly typed Pydantic models — batched, concurrent, and validated.

from smelt import Model, Job
from pydantic import BaseModel

class Classification(BaseModel):
    sector: str
    sub_sector: str
    is_public: bool

model = Model(provider="openai", name="gpt-4.1-mini")
job = Job(
    prompt="Classify each company by industry sector and whether it's publicly traded.",
    output_model=Classification,
)

result = job.run(model, data=[
    {"name": "Apple", "desc": "Consumer electronics and software"},
    {"name": "Stripe", "desc": "Payment processing platform"},
    {"name": "Mayo Clinic", "desc": "Nonprofit medical center"},
])

for row in result.data:
    print(row)  # Classification(sector='Technology', sub_sector='Consumer Electronics', is_public=True)

Install

pip install smelt-ai[openai]      # OpenAI models
pip install smelt-ai[anthropic]   # Anthropic models
pip install smelt-ai[google]      # Google Gemini models

Requires Python 3.10+.

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How It Works

list[dict] → Tag with row_id → Split into batches → Concurrent LLM calls → Validate → Reorder → SmeltResult[T]

1. Each input row gets a row_id for tracking
2. Rows are split into batches of configurable size
3. Batches run concurrently through the LLM with structured output
4. Each response is validated (schema, row IDs, count)
5. Results are reordered to match original input order
6. Everything is returned as a typed SmeltResult with metrics

---

API

Model

Wraps a LangChain chat model provider. Any LangChain-supported provider works.

model = Model(
    provider="openai",          # LangChain provider name
    name="gpt-4.1-mini",       # Model identifier
    api_key="sk-...",           # Optional — falls back to env var (e.g. OPENAI_API_KEY)
    params={"temperature": 0},  # Forwarded to the chat model constructor
)

Job

Defines what transformation to run and how to batch it.

job = Job(
    prompt="Your transformation instructions here",
    output_model=MyPydanticModel,  # Schema for each output row
    batch_size=10,                 # Rows per LLM request (default: 10)
    concurrency=3,                 # Max concurrent requests (default: 3)
    max_retries=3,                 # Retries per failed batch (default: 3)
    shuffle=False,                 # Shuffle rows before batching (default: False)
    stop_on_exhaustion=True,       # Raise on failure vs collect errors (default: True)
)

Run:

result = job.run(model, data=rows)          # Sync
result = await job.arun(model, data=rows)   # Async

Test with a single row first:

result = job.test(model, data=rows)         # Sync — runs only the first row
result = await job.atest(model, data=rows)  # Async

SmeltResult[T]

result.data       # list[T] — transformed rows in original order
result.errors     # list[BatchError] — failed batches
result.metrics    # SmeltMetrics — tokens, timing, retries
result.success    # bool — True if no errors

---

Error Handling

All exceptions inherit from SmeltError.

Exception	When
SmeltConfigError	Invalid config (bad provider, empty prompt, etc.)
SmeltValidationError	LLM output fails schema validation
SmeltAPIError	Non-retriable API error (401, 403)
SmeltExhaustionError	Batch exhausted all retries (stop_on_exhaustion=True)

from smelt.errors import SmeltExhaustionError

try:
    result = job.run(model, data=rows)
except SmeltExhaustionError as e:
    print(f"Partial: {len(e.partial_result.data)} rows succeeded")

Or collect errors without raising:

job = Job(prompt="...", output_model=MyModel, stop_on_exhaustion=False)
result = job.run(model, data=rows)

if not result.success:
    for err in result.errors:
        print(f"Batch {err.batch_index} failed: {err.message}")

---

Supported Providers

Provider	provider value	Example models
OpenAI	"openai"	gpt-5.2, gpt-4.1-mini, gpt-4.1, gpt-4o, o4-mini
Anthropic	"anthropic"	claude-sonnet-4-6, claude-opus-4-6, claude-haiku-4-5-20251001
Google Gemini	"google_genai"	gemini-3-flash-preview, gemini-3-pro-preview, gemini-2.5-flash

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Links

• Documentation
• PyPI
• Issue Tracker

License

MIT