> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/juanceresa/sift-kg/llms.txt
> Use this file to discover all available pages before exploring further.

# Transformers Research Example

> Knowledge graph from 12 foundational transformer architecture papers

Complete sift-kg pipeline output from 12 foundational academic papers on transformer architectures and large language models.

## Overview

This example demonstrates knowledge graph extraction from academic research papers, producing a graph of **425 entities** and **1,122 relations** across systems, concepts, researchers, methods, phenomena, and findings.

<CardGroup cols={2}>
  <Card title="View Interactive Graph" icon="diagram-project">
    Open `examples/transformers/output/graph.html` in your browser
  </Card>

  <Card title="Source Papers" icon="file-pdf">
    12 PDFs including "Attention Is All You Need", BERT, GPT-2, GPT-3, ViT, DALL-E
  </Card>
</CardGroup>

## Quick Start

```bash theme={null}
# View the interactive graph (no installation needed)
open examples/transformers/output/graph.html     # macOS
xdg-open examples/transformers/output/graph.html # Linux

# Or use sift's built-in viewer
sift view -o examples/transformers/output
```

## Pipeline Output

The `output/` directory contains the complete pipeline results:

```
transformers/
├── docs/                          # 12 source papers (PDF)
├── sift.yaml                      # Domain configuration (academic)
└── output/
    ├── extractions/               # Per-document entity+relation JSON from LLM
    ├── graph_data.json            # Knowledge graph (425 entities, 1122 relations)
    ├── communities.json           # Detected graph communities
    ├── entity_descriptions.json   # AI-generated entity descriptions
    ├── narrative.md               # Prose narrative with entity profiles
    └── graph.html                 # Interactive pyvis graph viewer
```

## Pipeline Statistics

| Metric              | Value                                                                           |
| ------------------- | ------------------------------------------------------------------------------- |
| **Input Documents** | 12 PDFs                                                                         |
| **Source Papers**   | Attention Is All You Need, BERT, GPT-2, GPT-3, ViT, DALL-E, and more            |
| **Total Entities**  | 425                                                                             |
| **Total Relations** | 1,122                                                                           |
| **Entity Types**    | 118 systems, 73 concepts, 71 researchers, 70 methods, 34 phenomena, 25 findings |
| **Domain**          | `academic` (bundled with sift-kg)                                               |
| **Model Used**      | claude-haiku-4-5-20251001                                                       |
| **Total Cost**      | \~\$0.72                                                                        |

## Domain Configuration

This example uses the built-in `academic` domain (defined in `sift.yaml`):

```yaml theme={null}
domain: academic
```

The academic domain includes entity types optimized for research papers:

* **System** — Models, frameworks, architectures (e.g., "GPT-3", "BERT")
* **Concept** — Theoretical ideas and principles
* **Researcher** — Authors and cited researchers
* **Method** — Techniques and approaches
* **Phenomenon** — Observed patterns and behaviors
* **Finding** — Research results and conclusions

## Key Insights from the Graph

The generated narrative (`narrative.md`) provides:

* **Overview** — Synthesis of how transformer architectures evolved
* **Entity descriptions** — AI-generated profiles for key systems, researchers, and concepts
* **Community detection** — Clustered subgraphs (e.g., vision transformers, language models, attention mechanisms)

## Re-running the Example

### Option 1: Build from Existing Extractions (Free)

Use the pre-extracted entities — no LLM API calls:

```bash theme={null}
pip install sift-kg
sift build -o examples/transformers/output
sift view -o examples/transformers/output
```

### Option 2: Full Pipeline from Scratch

Re-extract entities from the source PDFs:

```bash theme={null}
# Extract entities from papers
sift extract examples/transformers/docs \
  --model openai/gpt-4o-mini \
  -o my-output \
  --domain academic

# Build the knowledge graph
sift build -o my-output

# Resolve duplicate entities
sift resolve -o my-output --model openai/gpt-4o-mini

# Review and apply merges
sift review -o my-output
sift apply-merges -o my-output

# Generate narrative
sift narrate -o my-output --model openai/gpt-4o-mini

# View the result
sift view -o my-output
```

### Option 3: Use Your Own Papers

```bash theme={null}
mkdir my-papers
cp your-pdfs/*.pdf my-papers/

sift extract my-papers --model openai/gpt-4o-mini -o my-output --domain academic
sift build -o my-output
sift resolve -o my-output --model openai/gpt-4o-mini
sift review -o my-output
sift apply-merges -o my-output
sift narrate -o my-output --model openai/gpt-4o-mini
sift view -o my-output
```

<Tip>
  Use `--domain academic` when extracting from research papers to get entity types optimized for scholarly content.
</Tip>

## Cost Breakdown

The \~\$0.72 total cost includes:

* **Extraction** — LLM calls to extract entities and relations from each PDF
* **Resolution** — LLM calls to identify duplicate entities
* **Narration** — LLM calls to generate entity descriptions and overview

Costs will vary based on:

* Document length and complexity
* Model chosen (Haiku vs GPT-4o-mini vs others)
* Number of resolution passes needed

## Use Cases

This example pattern works well for:

* **Literature reviews** — Map research landscape across papers
* **Citation analysis** — Track how researchers and concepts connect
* **Technology evolution** — See how systems and methods build on each other
* **Knowledge synthesis** — Generate summaries across multiple papers

## Next Steps

<CardGroup cols={2}>
  <Card title="Explore Other Examples" icon="folder-open" href="/examples/overview">
    See FTX and Epstein examples
  </Card>

  <Card title="Domain Configuration" icon="sliders" href="/concepts/domains">
    Learn how to customize entity types
  </Card>
</CardGroup>