Add Example

Add an Example File

Creates a standalone runnable example in examples/.

Before Starting

Ask the user:

• Topic / use case (e.g. "medical record extraction", "product review analysis")
• Extraction method — extract_with_model, stepwise_extract_with_model, extract_and_jsonify, extract_from_data, or render_output
• Provider/model — e.g. openai/gpt-4o, moonshot/kimi-k2-0905-preview, ollama/llama3.1:8b

Two Example Styles

Provider example (e.g. moonshot_example.py, grok_example.py)

Shows extract_and_jsonify with a specific provider. Two sections:

1. Default instruction with the provider's primary model
2. Custom instruction with the same or a different model

Use-case example (e.g. medical_record_example.py, resume_cv_example.py)

Shows extraction for a specific domain using Pydantic models.

Conventions

• File: examples/{descriptive_name}_example.py
• Standalone — no test framework imports
• Always print extracted result and usage metadata (prompt_tokens, completion_tokens, total_tokens, cost, model_name)
• Realistic sample text, not lorem ipsum
• Under 100 lines when possible

Provider Example Template

"""
Example: Using extract_and_jsonify with {Provider}.

This script demonstrates:
1. Initializing the {Provider} driver manually (ignoring AI_PROVIDER).
2. Extracting structured information from text using a JSON schema.
3. Overriding the {Provider} model per call with `model_name`.
4. Running both a default extraction and a custom-instruction extraction.

Setup:
    export {PROVIDER}_API_KEY="your-key-here"
    # or add to .env file
"""

import json

from prompture import extract_and_jsonify

# 1. Define the raw text
text = "Maria is 32 years old and works as a software developer in New York. She loves hiking and photography."

# 2. Define the JSON schema
json_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"},
        "profession": {"type": "string"},
        "city": {"type": "string"},
        "hobbies": {"type": "array", "items": {"type": "string"}},
    },
}

# === FIRST EXAMPLE: Default instruction with {Model} ===
print("Extracting information into JSON with default instruction...")

result = extract_and_jsonify(
    text=text,
    json_schema=json_schema,
    model_name="{provider}/{model}",  # explicitly select model
)

json_output = result["json_string"]
json_object = result["json_object"]
usage = result["usage"]

print("\nRaw JSON output from model:")
print(json_output)

print("\nSuccessfully parsed JSON:")
print(json.dumps(json_object, indent=2))

print("\n=== TOKEN USAGE STATISTICS ===")
print(f"Prompt tokens: {usage['prompt_tokens']}")
print(f"Completion tokens: {usage['completion_tokens']}")
print(f"Total tokens: {usage['total_tokens']}")
print(f"Cost: ${usage['cost']:.6f}")
print(f"Model used: {usage['model_name']}")


# === SECOND EXAMPLE: Custom instruction with {Alt Model} ===
print("\n\n=== SECOND EXAMPLE - CUSTOM INSTRUCTION & DIFFERENT MODEL ===")
print("Extracting information with custom instruction...")

custom_result = extract_and_jsonify(
    text=text,
    json_schema=json_schema,
    instruction_template="Parse the biographical details from this text:",
    model_name="{provider}/{alt_model}",  # override model here
)

custom_json_output = custom_result["json_string"]
custom_json_object = custom_result["json_object"]
custom_usage = custom_result["usage"]

print("\nRaw JSON output with custom instruction:")
print(custom_json_output)

print("\nSuccessfully parsed JSON (custom instruction):")
print(json.dumps(custom_json_object, indent=2))

print("\n=== TOKEN USAGE STATISTICS (Custom Template) ===")
print(f"Prompt tokens: {custom_usage['prompt_tokens']}")
print(f"Completion tokens: {custom_usage['completion_tokens']}")
print(f"Total tokens: {custom_usage['total_tokens']}")
print(f"Cost: ${custom_usage['cost']:.6f}")
print(f"Model used: {custom_usage['model_name']}")

Use-Case Example Template

"""
Example: {Title}

This example demonstrates:
1. {Feature 1}
2. {Feature 2}

Requirements:
    pip install prompture
    # Set up provider credentials in .env
"""

import json

from pydantic import BaseModel, Field

from prompture import extract_with_model

# 1. Define the output model
class MyModel(BaseModel):
    field1: str = Field(description="...")
    field2: int = Field(description="...")

# 2. Input text
text = """
Realistic sample text here.
"""

# 3. Extract
MODEL = "openai/gpt-4o-mini"

result = extract_with_model(
    model_cls=MyModel,
    text=text,
    model_name=MODEL,
)

# 4. Results
print("Extracted model:")
print(result["model"])
print()
print("Usage metadata:")
print(json.dumps(result["usage"], indent=2))

Rules

• Import only from prompture public API
• Include docstring header listing features and setup requirements
• If provider-specific, mention the env var in the docstring
• Check available models for the provider: python -c "from prompture.model_rates import get_all_provider_models; print(get_all_provider_models('{models_dev_name}')[:10])"

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