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New Structured Response Routes: Text and PDF Analysis

We're excited to announce two new routes in our API: Text and PDF analysis with structured responses. These additions complement our existing Vision route, providing a comprehensive suite of AI-powered analysis tools.

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Text Analysis Route

The new /v1/text endpoint allows you to send text content and receive structured, schema-validated responses. This is perfect for:

• Sentiment analysis
• Topic extraction
• Text summarization
• Key phrase identification
• Custom text analysis based on your schema

Example request:

POST /v1/text/<your-project-id>
Content-Type: application/json
x-ai-key: <your-api-key>

{
  "text": "Your text content here"
}

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PDF Analysis Route

The /v1/pdf endpoint enables structured analysis of PDF documents. You can send PDFs either as a URL or base64-encoded data. This is ideal for:

• Document summarization
• Information extraction
• Table and figure analysis
• Content structuring
• Custom PDF analysis based on your schema

Example request with URL:

POST /v1/pdf/<your-project-id>
Content-Type: application/json
x-ai-key: <your-api-key>

{
  "pdf": "https://example.com/document.pdf"
}

Example request with base64:

POST /v1/pdf/<your-project-id>
Content-Type: application/json
x-ai-key: <your-api-key>

{
  "pdf": "data:application/pdf;base64,..."
}

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Key Features

Both routes include:

• Schema Validation: Define your response structure in the project settings
• Flexible Input: Support for various input formats
• Security: Full DeviceCheck integration
• Metrics Tracking: Monitor usage, latency, and performance
• Error Handling: Comprehensive error reporting
• Location Awareness: Optional geographic context for responses

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Implementation Example

Here's a Swift example showing how to use both routes:

actor ContentAnalyzer {
    let apiKey = "<your-api-key>"
    let textEndpoint = "https://api.proxed.ai/v1/text/<your-project-id>"
    let pdfEndpoint = "https://api.proxed.ai/v1/pdf/<your-project-id>"

    // Analyze text
    func analyzeText(_ text: String) async throws {
        let token = await SimpleDeviceCheck.retrieveToken()

        var request = URLRequest(url: URL(string: textEndpoint)!)
        request.httpMethod = "POST"
        request.setValue("application/json", forHTTPHeaderField: "Content-Type")
        request.setValue(apiKey, forHTTPHeaderField: "x-ai-key")
        if let token = token {
            request.setValue(token, forHTTPHeaderField: "x-device-token")
        }
        request.httpBody = try JSONEncoder().encode(["text": text])

        let (data, _) = try await URLSession.shared.data(for: request)
        let analysis = try JSONDecoder().decode(Analysis.self, from: data)
        print("Analysis:", analysis)
    }

    // Analyze PDF from URL
    func analyzePDF(url: URL) async throws {
        let token = await SimpleDeviceCheck.retrieveToken()

        var request = URLRequest(url: URL(string: pdfEndpoint)!)
        request.httpMethod = "POST"
        request.setValue("application/json", forHTTPHeaderField: "Content-Type")
        request.setValue(apiKey, forHTTPHeaderField: "x-ai-key")
        if let token = token {
            request.setValue(token, forHTTPHeaderField: "x-device-token")
        }
        request.httpBody = try JSONEncoder().encode(["pdf": url.absoluteString])

        let (data, _) = try await URLSession.shared.data(for: request)
        let analysis = try JSONDecoder().decode(Analysis.self, from: data)
        print("Analysis:", analysis)
    }
}

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Getting Started

1. Create a new project in the dashboard
2. Define your response schema
3. Generate an API key
4. Implement DeviceCheck security
5. Start making requests!

Check out our documentation for detailed implementation guides and best practices.

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What's Next?

We're continuously working on improving our API. Coming soon:

• More input formats
• Enhanced analysis capabilities
• Additional language support
• Advanced schema validation options

Stay tuned for more updates!