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```python
# AI Dev Tool: LLM Cache-to-Cache Semantic Communication API
# Description: A production-ready API integration wrapper for direct semantic communication between LLMs
# Category: API Integration Wrapper
# Tags: LLM, Cache-to-Cache, Semantic Communication, API Integration

import os
import json
from typing import Dict, List
from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Define the LLM model and tokenizer
model_name = "t5-base"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define the API app
app = FastAPI()

# Define the request and response models
class LLMRequest(BaseModel):
    """LLM request model"""
    text: str
    cache_id: str

class LLMResponse(BaseModel):
    """LLM response model"""
    response: str
    cache_id: str

# Define the cache storage
cache_storage: Dict[str, str] = {}

# Define the API endpoint for LLM cache-to-cache semantic communication
@app.post("/

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