tools/langextract_tool.py

import os
import langextract as lx
import textwrap
from dotenv import load_dotenv

# Step 1: Load environment variables from a .env file
load_dotenv()
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
os.environ["LANGEXTRACT_API_KEY"] = GEMINI_API_KEY

# 1. Define the prompt and extraction rules
prompt = textwrap.dedent(
    """\
    Extract characters, emotions, and relationships in order of appearance.
    Use exact text for extractions. Do not paraphrase or overlap entities.
    Provide meaningful attributes for each entity to add context."""
)

# 2. Provide a high-quality example to guide the model
examples = [
    lx.data.ExampleData(
        text="ROMEO. But soft! What light through yonder window breaks? It is the east, and Juliet is the sun.",
        extractions=[
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="ROMEO",
                attributes={"emotional_state": "wonder"},
            ),
            lx.data.Extraction(
                extraction_class="emotion",
                extraction_text="But soft!",
                attributes={"feeling": "gentle awe"},
            ),
            lx.data.Extraction(
                extraction_class="relationship",
                extraction_text="Juliet is the sun",
                attributes={"type": "metaphor"},
            ),
        ],
    )
]

# The input text to be processed
input_text = "Lady Juliet gazed longingly at the stars, her heart aching for Romeo"

# Run the extraction
result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.5-flash",
)

# Save the results to a JSONL file
lx.io.save_annotated_documents(
    [result], output_name="extraction_results.jsonl", output_dir="."
)

# Generate the visualization from the file
html_content = lx.visualize("extraction_results.jsonl")
with open("visualization.html", "w", encoding="utf-8") as f:
    f.write(html_content)