predikit 0.3.0

Turn any trained sklearn/XGBoost model into an LLM-callable tool with auto-generated schemas and typed I/O.

predikit

Turn any trained scikit-learn or XGBoost model into an LLM-callable tool — auto-generated JSON schemas, typed I/O, zero boilerplate.

tool = ModelTool(model=clf, name="classify_iris", ...)
tool.to_openai()              # OpenAI function schema, ready to pass to the API
tool.invoke({"sqft": 2200})   # → {"price_usd": 370730}

Install

pip install predikit

# With XGBoost support
pip install predikit[xgboost]

# With LangChain support
pip install predikit[langchain]

# With MLflow Model Registry support
pip install predikit[mlflow]

# With Snowflake Model Registry support
pip install predikit[snowflake]

30-second example

from pydantic import BaseModel, Field
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from predikit import ModelTool

# Train
X, y = load_iris(return_X_y=True)
clf = LogisticRegression(max_iter=200).fit(X, y)

# Define what the LLM will pass in
class IrisInput(BaseModel):
    sepal_length: float = Field(description="Sepal length in cm")
    sepal_width:  float = Field(description="Sepal width in cm")
    petal_length: float = Field(description="Petal length in cm")
    petal_width:  float = Field(description="Petal width in cm")

# Wrap the model
tool = ModelTool(
    model=clf,
    name="classify_iris",
    description="Classify an iris flower: 0=setosa, 1=versicolor, 2=virginica.",
    input_schema=IrisInput,
    output_name="species",
    output_description="Predicted species index",
)

# Get an OpenAI-ready schema
import json
print(json.dumps(tool.to_openai(), indent=2))

# Call it directly
tool.invoke({
    "sepal_length": 5.1, "sepal_width": 3.5,
    "petal_length": 1.4, "petal_width": 0.2,
})
# → {"species": 0}

Core API

ModelTool

ModelTool(
    model,               # fitted sklearn-compatible estimator
    name: str,           # tool name the LLM sees
    description: str,    # tool description the LLM sees
    input_schema,        # Pydantic BaseModel describing inputs
    output_name: str,    # key for the prediction in the returned dict
    output_description: str,
)

Method	Returns	What it does
.invoke(input_dict)	dict	Validates → predicts → returns {output_name: value}
.to_openai()	dict	OpenAI function-calling schema
.to_langchain()	StructuredTool	LangChain tool
.to_callable()	Callable	Plain Python function

ToolRegistry

Group multiple tools for bulk export:

registry = ToolRegistry([price_tool, risk_tool])
registry.to_openai()     # → list[dict], pass directly to OpenAI
registry.to_langchain()  # → list[StructuredTool]
registry.get("name")     # → ModelTool

Field naming rule

Your Pydantic schema field names must exactly match the column names the model was trained on.

predikit maps inputs to features by name, not position. If you trained on a DataFrame with columns ["sqft", "bedrooms"], your schema fields must be sqft and bedrooms — not sq_ft, not Sqft.

# ✓ Columns match: sqft, bedrooms, bathrooms
class GoodInput(BaseModel):
    sqft:      float
    bedrooms:  float
    bathrooms: float

# ✗ Name mismatch — raises ValueError at runtime
class BadInput(BaseModel):
    square_footage: float  # model expects "sqft"
    beds:           float  # model expects "bedrooms"
    baths:          float  # model expects "bathrooms"

When there's a mismatch, predikit tells you exactly which names are wrong:

ValueError: Input schema is missing model features: ['sqft', 'bedrooms'].
Schema has: ['square_footage', 'beds', 'bathrooms'], model expects: ['sqft', 'bedrooms', 'bathrooms']

Tip: If you trained with a numpy array (no DataFrame), predikit has no feature names to check — it uses your schema's field definition order instead.

Cookbook

XGBoost regression

from xgboost import XGBRegressor
from predikit import ModelTool

reg = XGBRegressor().fit(X_train, y_train)

class HouseInput(BaseModel):
    sqft:       float
    bedrooms:   float
    year_built: float

tool = ModelTool(
    model=reg,
    name="price_estimate",
    description="Predict home price in USD.",
    input_schema=HouseInput,
    output_name="price_usd",
    output_description="Predicted sale price in USD",
)

Multiple tools in one registry

registry = ToolRegistry([price_tool, risk_tool, demand_tool])

# OpenAI
response = client.chat.completions.create(
    model="gpt-4o",
    tools=registry.to_openai(),
    ...
)

# LangChain
agent = initialize_agent(tools=registry.to_langchain(), ...)

Bool inputs from an LLM

LLMs sometimes return "yes", "true", or "1" for boolean fields. predikit coerces these automatically before Pydantic validation:

class Input(BaseModel):
    has_pool: bool

tool.invoke({"has_pool": "yes"})   # → coerced to True
tool.invoke({"has_pool": "false"}) # → coerced to False
tool.invoke({"has_pool": "maybe"}) # → raises ValueError with clear message

Supported strings: true/false, yes/no, 1/0, on/off.

Confidence-aware routing

Route uncertain predictions to a fallback tool, or raise an error the agent can catch:

from predikit import ModelTool, LowConfidenceError

tool = ModelTool(
    model=clf,
    name="churn_risk",
    description="Predict member churn risk.",
    input_schema=MemberInput,
    output_name="churn_probability",
    output_description="Probability of churn (0–1)",
    confidence_threshold=0.80,       # classifiers with predict_proba only
    on_low_confidence="warn",        # "warn" | "raise" | "fallback"
    fallback_tool=rule_based_tool,   # used when mode="fallback"
)

result = tool.invoke(inputs)
if result.get("_low_confidence"):
    print(f"Uncertain ({result['_confidence']:.2f}) — consider routing to a human")

mode	behaviour
"warn"	returns prediction + _confidence + _low_confidence: True
"raise"	raises LowConfidenceError
"fallback"	invokes fallback_tool and returns its result

Only applies to classifiers that implement predict_proba. Regressors are unaffected.

Multi-model ensemble

Call multiple models and reconcile their outputs in one step:

from predikit import ModelEnsemble, ToolRegistry

ensemble = ModelEnsemble(
    tools=[price_tool_a, price_tool_b],
    name="averaged_price",
    description="Ensemble price: mean of two XGBoost models.",
    strategy="mean",              # "collect" | "mean" | "vote"
)

result  = ensemble.invoke(inputs)  # → {"price_usd": 370112}
schema  = ensemble.to_openai()     # works exactly like ModelTool

strategy	behaviour
"collect"	merges all outputs into one dict (tools can have different output_name)
"mean"	averages numeric outputs (all tools must share output_name)
"vote"	majority class vote (all tools must share output_name)

Register ensembles alongside individual tools:

registry = ToolRegistry(tools=[price_tool], ensembles=[ensemble])
registry.to_openai()  # includes both tools and ensembles

MLflow Model Registry loader

Load a registered MLflow model directly — no manual .load_model() call:

from predikit.loaders import from_mlflow

tool = from_mlflow(
    model_uri="models:/churn-classifier/Production",
    name="churn_risk",
    description="Predict member churn probability.",
    input_schema=MemberInput,
    output_name="churn_probability",
    output_description="Churn probability 0–1",
)

tool.invoke({"tenure_months": 24, "trips_last_year": 2, "avg_spend": 500})
# → {"churn_probability": 0.73}

The loader auto-detects classes_ and feature_names_in_ from the underlying sklearn model, so confidence routing and ensemble work unchanged. Requires pip install predikit[mlflow].

Snowflake Model Registry loader

Load a model registered in the Snowflake Model Registry via the Snowpark ML Python library:

from predikit.loaders import from_snowflake

tool = from_snowflake(
    session=snowpark_session,
    model_name="VACATION_CHURN",
    model_version="V3",
    name="churn_risk",
    description="Vacation ownership churn classifier.",
    input_schema=MemberInput,
    output_name="churn_probability",
    output_description="Churn probability 0–1",
    output_method="predict",   # method to call on the Snowflake model object
)

Pass output_method="predict_proba" or any other method your Snowflake model exposes. The returned ModelTool is identical to one built directly — all exporters, confidence routing, and ensemble strategies work as-is. Requires pip install predikit[snowflake].

Orlando real estate demo

See examples/03_orlando_real_estate.py for a full end-to-end walkthrough: synthetic dataset → XGBoost training → ModelTool → registry → OpenAI schema → prediction.

Roadmap

Planned for later releases:

• HuggingFace / PyTorch / TensorFlow support
• Async invocation
• Weighted ensemble strategies
• CLI (predikit inspect model.pkl)

License

MIT © Tejas Tumakuru Ashok