# The money-burning approach
async def process_query(user_message, conversation_id):
    system_prompt = build_company_context()  # 2000 tokens every time
    history = get_conversation(conversation_id)  # 500 tokens

    messages = [
        {"role": "system", "content": system_prompt},
        *history,
        {"role": "user", "content": user_message}
    ]

    response = await openai.ChatCompletion.acreate(
        model="gpt-4o",
        messages=messages  # Burning 2500+ tokens every time
    )
    return response

import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

# These queries look different but are 89% semantically similar:
query1 = "How do I return this item?"
query2 = "What's the process for sending this back?"

encoder = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = encoder.encode([query1, query2])
similarity = cosine_similarity([embeddings[0]], [embeddings[1]])[0][0]
print(f"Similarity: {similarity:.2f}")  # Output: 0.89

from dataclasses import dataclass
from typing import List, Optional
import time

@dataclass
class CacheEntry:
    query_embedding: np.ndarray
    original_query: str
    response: str
    timestamp: float
    usage_count: int = 0

class SemanticCache:
    def __init__(self, similarity_threshold=0.85):
        self.encoder = SentenceTransformer('all-MiniLM-L6-v2')
        self.similarity_threshold = similarity_threshold
        self.cache: List[CacheEntry] = []

    def find_similar_query(self, user_message: str) -> Optional[CacheEntry]:
        if not self.cache:
            return None

        # Convert query to semantic embedding
        query_embedding = self.encoder.encode([user_message])[0]

        # Compare with all cached embeddings
        cached_embeddings = np.array([entry.query_embedding for entry in self.cache])
        similarities = cosine_similarity([query_embedding], cached_embeddings)[0]

        # Find most similar above threshold
        max_idx = np.argmax(similarities)
        if similarities[max_idx] >= self.similarity_threshold:
            self.cache[max_idx].usage_count += 1
            return self.cache[max_idx]

        return None

    def add_to_cache(self, query: str, response: str):
        query_embedding = self.encoder.encode([query])[0]
        self.cache.append(CacheEntry(
            query_embedding=query_embedding,
            original_query=query,
            response=response,
            timestamp=time.time()
        ))

# Smart context optimization
class ContextOptimizer:
    def __init__(self):
        self.context_templates = {
            "refund_returns": """You are a customer service assistant specializing in refunds.

REFUND POLICY:
- 30-day return window from purchase date
- Items must be unused with original packaging
- Processing takes 3-5 business days""",

            "shipping_delivery": """You are a customer service assistant for shipping inquiries.

SHIPPING INFO:
- Standard shipping: 5-7 business days ($5.99)
- Express shipping: 2-3 business days ($12.99)
- Free shipping on orders over $50"""
        }

    def get_optimized_context(self, query: str) -> str:
        query_embedding = self.encoder.encode([query])[0]

        # Check semantic similarity to context types
        refund_ref = self.encoder.encode(["I want to return this item"])[0]
        shipping_ref = self.encoder.encode(["When will my order arrive"])[0]

        refund_similarity = cosine_similarity([query_embedding], [refund_ref])[0][0]
        shipping_similarity = cosine_similarity([query_embedding], [shipping_ref])[0][0]

        if refund_similarity > 0.7:
            return self.context_templates["refund_returns"]  # 200 tokens vs 2000
        elif shipping_similarity > 0.7:
            return self.context_templates["shipping_delivery"]

        return build_full_company_context()  # Fallback for complex queries

semantic_cache = SemanticCache(similarity_threshold=0.85)
context_optimizer = ContextOptimizer()

async def process_query_with_semantic_caching(user_message, conversation_id):
    # Step 1: Check for semantically similar cached queries
    cached_entry = semantic_cache.find_similar_query(user_message)

    if cached_entry:
        print(f"Cache hit! Similar to: '{cached_entry.original_query}'")
        return cached_entry.response  # Zero GPU cost!

    # Step 2: Use optimized context based on query semantics
    system_context = context_optimizer.get_optimized_context(user_message)

    # Step 3: Generate response with minimal context
    messages = [
        {"role": "system", "content": system_context},  # 200 tokens vs 2000
        {"role": "user", "content": user_message}
    ]

    response = await openai.ChatCompletion.acreate(
        model="gpt-4o",
        messages=messages,
        temperature=0.7,
        max_tokens=150
    )

    # Step 4: Cache for future similar queries
    semantic_cache.add_to_cache(user_message, response.choices[0].message.content)

    return response

# These queries are 89% semantically similar:
"How do I return this item?"
"What's the process for sending this back?"

# These are 92% similar:
"When will my package arrive?"
"What's the delivery timeframe?"

# These are 85% similar:
"I want a refund"
"Can I get my money back?"

import lmcache_vllm.vllm as vllm
from lmcache_vllm.vllm import LLM

# LMCache handles KV cache sharing automatically
llm = LLM(
    model="microsoft/DialoGPT-medium",
    gpu_memory_utilization=0.8
)

async def process_with_lmcache(user_message, conversation_id):
    # LMCache automatically reuses neural network states
    # for any repeated text segments across all instances
    outputs = llm.generate([full_prompt], sampling_params)
    return outputs[0].outputs[0].text