LLMs

let ollamaProvider = try OpenAIProvider(
    model: .gpt4o,  // Use any model enum
    apiKey: "ollama", // Ollama doesn't require real API key
    apiBase: "http://localhost:11434/v1",
    allowInsecureAPIBase: true  // Allow HTTP for local development
)

await manager.registerProvider(ollamaProvider)

let lmStudioProvider = try OpenAIProvider(
    model: .gpt4oMini,
    apiKey: "lm-studio",
    apiBase: "http://localhost:1234/v1",
    allowInsecureAPIBase: true
)

public actor CustomModelProvider: LLMProvider, LLMProviderIdentifiable {
    public let providerName: String = "CustomModel"
    public let providerID: LLMProviderID = .custom
    public let modelName: String
    public let maxTokens: Int = 4096
    public let supportsStreaming: Bool = true
    public let supportsToolCalling: Bool = false

    private let endpoint: String
    private let apiKey: String

    public init(modelName: String, endpoint: String, apiKey: String) {
        self.modelName = modelName
        self.endpoint = endpoint
        self.apiKey = apiKey
    }

    public func generateCompletion(
        messages: [ChatMessage],
        temperature: Double?,
        maxTokens: Int?,
        tools: [ToolSchema]?
    ) async throws -> LLMResponse {
        // Implement your custom API integration
        // Convert ChatMessages to your API format
        // Make HTTP request to your endpoint
        // Parse response and return LLMResponse
    }
}

let productSchema = JSONSchema(
    type: .object,
    properties: [
        "name": JSONSchema(type: .string, description: "Product name"),
        "price": JSONSchema(type: .number, description: "Price in USD"),
        "category": JSONSchema(type: .string, description: "Product category"),
        "features": JSONSchema(
            type: .array,
            items: .init(value: JSONSchema(type: .string)),
            description: "List of key features"
        )
    ],
    required: ["name", "price", "category"]
)

let response = try await manager.generateStructuredCompletion(
    request: LLMRequest(messages: [.user("Create a product for wireless headphones")]),
    schema: productSchema,
    preferNative: true
)

let webSearchTool = ToolSchema(
    function: FunctionSchema(
        name: "web_search",
        description: "Search the web for current information",
        parameters: JSONSchema(
            type: .object,
            properties: [
                "query": JSONSchema(
                    type: .string,
                    description: "Search query"
                ),
                "num_results": JSONSchema(
                    type: .integer,
                    description: "Number of results (1-10)"
                )
            ],
            required: ["query"]
        )
    )
)

let requestWithTools = LLMRequest(
    messages: [
        .system("You can search the web for current information."),
        .user("What are the latest developments in AI this week?")
    ],
    tools: [webSearchTool]
)

func processBatch(_ inputs: [String]) async throws -> [String] {
    return try await withThrowingTaskGroup(of: (Int, String).self) { group in
        for (index, input) in inputs.enumerated() {
            group.addTask {
                let request = LLMRequest(messages: [.user(input)])
                let response = try await manager.generateCompletion(request: request)
                return (index, response.content)
            }
        }

        var results = Array(repeating: "", count: inputs.count)
        for try await (index, result) in group {
            results[index] = result
        }
        return results
    }
}

actor ConversationManager {
    private var messages: [ChatMessage] = []
    private let manager: LLMManager
    private let maxContextTokens: Int

    init(manager: LLMManager, maxContextTokens: Int = 8000) {
        self.manager = manager
        self.maxContextTokens = maxContextTokens
    }

    func addUserMessage(_ content: String) async throws -> String {
        messages.append(.user(content))

        // Prune context if needed
        await pruneContextIfNeeded()

        let response = try await manager.generateCompletion(
            request: LLMRequest(messages: messages)
        )

        messages.append(.assistant(response.content))
        return response.content
    }

    private func pruneContextIfNeeded() async {
        let tokenCount = try? await manager.countTokens(
            request: LLMRequest(messages: messages)
        )

        if let count = tokenCount, count > maxContextTokens {
            // Keep system message and recent messages
            let systemMessages = messages.filter { $0.role == .system }
            let recentMessages = Array(messages.suffix(10))
            messages = systemMessages + recentMessages
        }
    }
}

// Verify environment variable
print(ProcessInfo.processInfo.environment["OPENAI_API_KEY"] ?? "Not set")

// Pass API key explicitly
let provider = try OpenAIProvider(
    model: .gpt4o,
    apiKey: "your-actual-api-key-here"
)

// Implement backoff strategy
do {
    let response = try await manager.generateCompletion(request: request)
} catch let error as OrbitAIError {
    if case .llmRateLimitExceeded(let message) = error {
        // Parse Retry-After header
        if let retryAfter = parseRetryAfter(message) {
            try await Task.sleep(for: .seconds(retryAfter))
        }
    }
}

let metrics = manager.getProviderMetrics()
for (provider, metric) in metrics {
    print("\(provider): \(metric.successRate)% success rate")
}

// Increase timeout for complex requests
let response = try await manager.generateCompletion(
    request: request,
    timeout: 60  // Increase from default 30s
)

// Use streaming for long responses
let stream = try await manager.generateStreamingCompletion(request: request)

// Check provider metrics
let metrics = await manager.getProviderMetrics(id: .openAI)
print("Average latency: \(metrics?.averageLatency ?? 0)s")
print("P95 latency: \(metrics?.p95Latency ?? 0)s")
print("Success rate: \((metrics?.successRate ?? 0) * 100)%")

// Check cache performance
print("Cache size: \(manager.cacheSize())")