# AI use cases for LangExtract (google/langextract)

Repo: [google/langextract](https://github.com/google/langextract) | Use cases: 3 | Page: https://dedupedai.com/use-cases/google-langextract/

LangExtract is a Google Python library that uses LLMs to extract structured information from unstructured text with precise source grounding and an interactive visualization.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Extract structured entities from text with exact sources: https://dedupedai.com/use-cases/google-langextract/extract-entities-with-source-grounding/
- Review extractions visually in context: https://dedupedai.com/use-cases/google-langextract/review-extractions-in-visualization/
- Run extraction with OpenAI or a custom provider: https://dedupedai.com/use-cases/google-langextract/use-langextract-with-other-model-providers/

## LangExtract tutorial: extract entities from text with exact sources

Repo: [google/langextract](https://github.com/google/langextract) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Define a prompt and one good example, then extract entities tied to their exact text spans.

**Who it is for:** An analyst turning hundreds of free-text notes into a structured table.

**The problem:** LLM extraction without grounding is hard to verify.

**The outcome:** Extractions that each point to the exact source text.

### Steps

1. Install the LangExtract package
2. Write a prompt describing what to extract
3. Provide an ExampleData with Extraction entries
4. Run extraction on your text and review the grounded results

### Prompt to paste into your agent

```text
Using google/langextract, extract <entity types> from <document> with one example, and list each extraction with its source span. Verify three spans against the original text.
```

### Caveat

Extraction quality depends heavily on the example you provide.

Risk, credentials: Cloud model extraction uses your API key and bills per call.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/google-langextract/extract-entities-with-source-grounding/

## How to review extractions visually in context with LangExtract

Repo: [google/langextract](https://github.com/google/langextract) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README

Use the interactive visualization to check each extraction highlighted in the original text.

**Who it is for:** A researcher validating extractions before using them in analysis.

**The problem:** Reviewing JSON output against long text is slow.

**The outcome:** An interactive view of extractions highlighted in the source.

### Steps

1. Run an extraction
2. Save the results
3. Generate the interactive visualization
4. Open it and spot-check highlights

### Prompt to paste into your agent

```text
Generate a langextract visualization for <results> and report any extractions whose highlight does not match the attribute.
```

### Caveat

Long documents produce large visualizations.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/google-langextract/review-extractions-in-visualization/

## How to run extraction with OpenAI or a custom provider with LangExtract

Repo: [google/langextract](https://github.com/google/langextract) | Difficulty: Advanced | Time: 45 minutes | Basis: From the README

Configure OpenAI models or add a custom model provider instead of the default.

**Who it is for:** A team standardized on a different model vendor.

**The problem:** Libraries tied to one vendor do not fit every stack.

**The outcome:** Extraction running on the provider you choose.

### Steps

1. Set the API key for your provider
2. Follow the Using OpenAI Models or custom provider section
3. Run the same extraction
4. Compare results across providers

### Prompt to paste into your agent

```text
Run the same langextract job with two providers and compare extraction counts and grounding accuracy.
```

### Caveat

Different models can produce different extraction counts on the same prompt.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/google-langextract/use-langextract-with-other-model-providers/