LangExtract is a production-grade information extraction backbone: a Python library that turns natural-language instructions plus few-shot examples into structured extraction tasks, chunks and routes arbitrary text through different LLM backends, aggregates results into consistent JSON, and gives every field precise source grounding with an interactive HTML highlight view for audit, traceability, and human review. Parallelism, chunking, and multi-pass extraction make it robust on long documents, while a pluggable provider system unifies access to Gemini, OpenAI, and local Ollama models so teams can quickly ship traceable extraction pipelines for compliance review, clinical text, and customer-support ticket analytics.
| ✕Traditional Pain Points | ✓Innovative Solutions |
|---|---|
| Traditional extraction pipelines rarely offer field-level traceability, making it hard to map structured outputs back to exact source spans and expensive to audit or QA at scale. | Centers on Precise Source Grounding, recording exact character spans for each extraction and exposing them through highlightable visualization to create an auditable evidence chain. |
| On long documents and batch workloads, naive LLM calls suffer from needle-in-a-haystack behavior with unstable recall, unpredictable cost profiles, and ad‑hoc concurrency control. | Bakes in long‑document aware processing via chunking, parallel workers, and multi‑pass extraction so teams can tune the trade‑off between latency, cost, and recall using clear knobs. |
| Heterogeneous models and prompts tend to drift JSON schemas, causing missing or inconsistent fields and forcing brittle post‑processing logic with heavy regex and if/else maintenance. | Ships with a pluggable provider and schema‑aware extraction mode, enabling stronger structural guarantees on supported models while still customizing OpenAI and Ollama backends. |
1python -m venv langextract_env && source langextract_env/bin/activate && pip install langextract1export LANGEXTRACT_API_KEY=your-gemini-key # or install Ollama locally and run: ollama pull gemma2:2b && ollama serve1python - << 'EOF'2import langextract as lx3import textwrap4prompt = textwrap.dedent('''Extract characters, emotions, and relationships in order of appearance. Use exact text for extractions. Do not paraphrase or overlap entities.''')5examples = []6result = lx.extract(7 text_or_documents='Lady Juliet gazed longingly at the stars, her heart aching for Romeo',8 prompt_description=prompt,9 examples=examples,10 model_id='gemini-2.5-flash',11)12lx.io.save_annotated_documents(result, output_name='extraction_results.jsonl', output_dir='.')13html = lx.visualize('extraction_results.jsonl')14with open('visualization.html', 'w', encoding='utf-8') as f:15 f.write(getattr(html, 'data', html))16EOF