128 lines
3.2 KiB
Markdown
128 lines
3.2 KiB
Markdown
# Quick Start Guide
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## 1. Get Your OpenRouter API Key
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1. Visit [OpenRouter](https://openrouter.ai/)
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2. Sign up for a free account
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3. Go to [Keys](https://openrouter.ai/keys) section
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4. Create a new API key
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5. Copy your API key
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## 2. Configure the Tool
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```bash
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# Copy the environment template
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cp .env.example .env
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# Edit .env and add your API key
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echo "OPENROUTER_API_KEY=your_key_here" >> .env
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```
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Or set it directly in your shell:
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```bash
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export OPENROUTER_API_KEY=your_key_here
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```
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## 3. Run Your First Analysis
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```bash
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# Basic usage
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uv run pdf-to-kcf your_document.pdf
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# This will create a file: your_document_insights.json
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```
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## Example Output
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The tool will generate a JSON file with structured insights like:
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```json
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{
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"insights": [
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{
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"type": "fact",
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"insight": "The company revenue increased by 25% in Q4",
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"content": "According to the financial report, Q4 revenue reached $2.5M, up 25% from Q3...",
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"attributes": [
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{"attribute": "source", "value": "Financial Report"},
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{"attribute": "quarter", "value": "Q4"},
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{"attribute": "confidence", "value": "high"}
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]
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},
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{
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"type": "opinion",
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"insight": "The author recommends investing in AI infrastructure",
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"content": "We strongly believe that investing in AI infrastructure is critical...",
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"attributes": [
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{"attribute": "sentiment", "value": "positive"},
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{"attribute": "section", "value": "recommendations"}
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]
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}
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]
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}
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```
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## Advanced Usage
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### Try Different Models
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```bash
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# Use GPT-4
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uv run pdf-to-kcf document.pdf -m openai/gpt-4o
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# Use Gemini
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uv run pdf-to-kcf document.pdf -m google/gemini-pro-1.5
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# Use Llama 3.1
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uv run pdf-to-kcf document.pdf -m meta-llama/llama-3.1-70b-instruct
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```
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### Custom Output Location
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```bash
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uv run pdf-to-kcf document.pdf -o /path/to/output.json
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```
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### Start from Specific Page
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```bash
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# Start analysis from page 5 (0-indexed, so this is the 6th page)
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uv run pdf-to-kcf document.pdf -s 5
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```
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## How It Works
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1. The tool loads your PDF and extracts text
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2. An AI agent analyzes the content starting from page 0 (or your specified page)
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3. The agent autonomously decides if it needs to read more pages
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4. It extracts structured insights classified as facts, opinions, or comments
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5. Each insight includes the original content and relevant metadata
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6. Results are saved to a JSON file
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## Pricing
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OpenRouter charges based on the model you use:
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- Claude 3.5 Sonnet (default): ~$3 per million input tokens
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- GPT-4o: ~$2.50 per million input tokens
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- Llama 3.1 70B: ~$0.35 per million input tokens
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Most PDFs will cost just a few cents to analyze.
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## Troubleshooting
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### "No API key found"
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Make sure you've set `OPENROUTER_API_KEY` in your `.env` file or environment.
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### "Model not found"
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Check the model name format: `<provider>/<model-name>` (e.g., `anthropic/claude-3.5-sonnet`)
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See available models at https://openrouter.ai/models
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### "PDF not found"
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Use the full path to your PDF file, or navigate to the directory containing it first.
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## Next Steps
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- Read the full [README.md](README.md) for more details
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- Check [CLAUDE.md](CLAUDE.md) for architecture details
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- See [OpenRouter models](https://openrouter.ai/models) for all available models
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