class MoodMnkyClient {
  constructor(config) {
    this.ollama = new OllamaService(config.ollamaKey);
    this.flowise = new FlowiseService(config.flowiseKey);
    this.langchain = new LangchainService(config.langchainKey);
    this.rateLimit = new RateLimitTracker();
    this.cache = new APICache();
  }

  // Common functionality
  async handleRequest(service, endpoint, options) {
    if (!this.rateLimit.canMakeRequest(service)) {
      throw new RateLimitError();
    }

    const cacheKey = this.getCacheKey(service, endpoint, options);
    const cached = this.cache.get(cacheKey);
    if (cached) return cached;

    const response = await this[service].request(endpoint, options);
    this.cache.set(cacheKey, response);
    this.rateLimit.updateLimits(service, response.headers);

    return response;
  }
}

interface APIError extends Error {
  status: number;
  code: string;
  details: any;
}

class APIErrorHandler {
  static handle(error: APIError) {
    switch (error.status) {
      case 400:
        return this.handleValidationError(error);
      case 401:
        return this.handleAuthenticationError(error);
      case 429:
        return this.handleRateLimitError(error);
      case 500:
        return this.handleServerError(error);
      default:
        return this.handleUnknownError(error);
    }
  }

  static async handleRateLimitError(error: APIError) {
    const retryAfter = parseInt(error.details.retry_after) || 60;
    await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
    return true; // Retry request
  }
}

class BatchProcessor:
    def __init__(self, client, batch_size=100, interval=1.0):
        self.client = client
        self.batch_size = batch_size
        self.interval = interval
        self.queue = []
        self.processing = False

    async def add(self, item):
        self.queue.append(item)
        if len(self.queue) >= self.batch_size and not self.processing:
            await self.process_batch()

    async def process_batch(self):
        self.processing = True
        while self.queue:
            batch = self.queue[:self.batch_size]
            try:
                await self.client.process_batch(batch)
                self.queue = self.queue[self.batch_size:]
                await asyncio.sleep(self.interval)
            except Exception as e:
                logger.error(f"Batch processing error: {e}")
                break
        self.processing = False

from functools import lru_cache
from datetime import datetime, timedelta

class CacheManager:
    def __init__(self):
        self.cache = {}
        self.ttls = {}

    def set(self, key, value, ttl=3600):
        self.cache[key] = value
        self.ttls[key] = datetime.now() + timedelta(seconds=ttl)

    def get(self, key):
        if key not in self.cache:
            return None
        
        if datetime.now() > self.ttls[key]:
            del self.cache[key]
            del self.ttls[key]
            return None
        
        return self.cache[key]

    @lru_cache(maxsize=1000)
    def cached_request(self, endpoint, params):
        # Implementation
        pass

import aiohttp
from contextlib import asynccontextmanager

class ConnectionPool:
    def __init__(self, max_size=100):
        self.session = None
        self.max_size = max_size

    async def __aenter__(self):
        if not self.session:
            self.session = aiohttp.ClientSession(
                connector=aiohttp.TCPConnector(limit=self.max_size)
            )
        return self.session

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        if self.session:
            await self.session.close()
            self.session = None

@asynccontextmanager
async def get_connection():
    async with pool as session:
        yield session

class MemoryManager:
    def __init__(self, max_size_mb=100):
        self.max_size = max_size_mb * 1024 * 1024
        self.current_size = 0
        self.items = {}

    def add(self, key, value):
        size = sys.getsizeof(value)
        if self.current_size + size > self.max_size:
            self._cleanup()
        
        self.items[key] = {
            'value': value,
            'size': size,
            'last_access': time.time()
        }
        self.current_size += size

    def _cleanup(self):
        items = sorted(
            self.items.items(),
            key=lambda x: x[1]['last_access']
        )
        
        while self.current_size > self.max_size * 0.8:
            if not items:
                break
            key, item = items.pop(0)
            self.current_size -= item['size']
            del self.items[key]

class OllamaOptimizer:
    def __init__(self, client):
        self.client = client
        self.context_window = 4096

    def optimize_prompt(self, prompt, system=""):
        # Implement prompt optimization
        tokens = self.count_tokens(prompt)
        if tokens > self.context_window:
            return self.truncate_prompt(prompt)
        return prompt

    def batch_generate(self, prompts, **kwargs):
        optimized = [
            self.optimize_prompt(p) for p in prompts
        ]
        return self.client.generate_batch(optimized, **kwargs)

class StreamManager {
  constructor(client) {
    this.client = client;
    this.activeStreams = new Map();
  }

  async startStream(chatflowId, options) {
    const stream = await this.client.createStream(chatflowId);
    this.activeStreams.set(chatflowId, stream);

    stream.on('data', this.handleData.bind(this));
    stream.on('error', this.handleError.bind(this));
    stream.on('end', () => this.closeStream(chatflowId));

    return stream;
  }

  handleData(data) {
    // Implementation
  }

  handleError(error) {
    // Implementation
  }

  closeStream(chatflowId) {
    const stream = this.activeStreams.get(chatflowId);
    if (stream) {
      stream.close();
      this.activeStreams.delete(chatflowId);
    }
  }
}

class DocumentProcessor:
    def __init__(self, client):
        self.client = client
        self.batch_size = 5
        self.supported_types = {'.pdf', '.txt', '.docx'}

    async def process_documents(self, files):
        tasks = []
        for i in range(0, len(files), self.batch_size):
            batch = files[i:i + self.batch_size]
            tasks.append(self.process_batch(batch))
        
        return await asyncio.gather(*tasks)

    async def process_batch(self, files):
        results = []
        for file in files:
            if self.is_supported(file):
                result = await self.client.process_document(file)
                results.append(result)
        return results

    def is_supported(self, file):
        return Path(file).suffix.lower() in self.supported_types

import structlog

logger = structlog.get_logger()

class APILogger:
    def __init__(self):
        self.logger = structlog.wrap_logger(
            logger,
            processors=[
                structlog.processors.TimeStamper(fmt="iso"),
                structlog.processors.JSONRenderer()
            ]
        )

    def log_request(self, service, endpoint, duration, status):
        self.logger.info(
            "api_request",
            service=service,
            endpoint=endpoint,
            duration_ms=duration,
            status=status
        )

    def log_error(self, service, error):
        self.logger.error(
            "api_error",
            service=service,
            error_type=type(error).__name__,
            error_message=str(error)
        )

class PerformanceMonitor:
    def __init__(self):
        self.metrics = defaultdict(list)

    def record_metric(self, service, endpoint, duration):
        self.metrics[f"{service}.{endpoint}"].append({
            'timestamp': time.time(),
            'duration': duration
        })

    def get_statistics(self, service, endpoint):
        data = self.metrics[f"{service}.{endpoint}"]
        if not data:
            return None

        durations = [d['duration'] for d in data]
        return {
            'count': len(durations),
            'avg': statistics.mean(durations),
            'p95': statistics.quantiles(durations, n=20)[18],
            'max': max(durations)
        }