llm-cache-pro 0.1.2

A drop-in, model-agnostic cache for Large Language Model API calls

LLM Cache

A drop-in, model-agnostic cache for Large Language Model API calls. Cache your OpenAI, Anthropic, and other LLM API responses to save costs and improve performance.

Author: Sherin Joseph Roy
Email: [e-mail]
GitHub: @Sherin-SEF-AI

Features

• 🔐 Deterministic Hashing: SHA256-based request signature hashing
• 💾 Multiple Backends: SQLite (default) and Redis support
• 📊 Cost Tracking: Monitor API costs and savings
• ⚡ Streaming Support: Cache and replay streamed responses
• 🔧 Provider Agnostic: Works with OpenAI, Anthropic, Cohere, and more
• 🛡️ Encryption: Optional AES-256 encryption for sensitive data
• 🗜️ Compression: Zstandard compression to reduce storage
• 🌐 HTTP Proxy: Transparent proxy mode for existing applications
• 📈 Metrics: Prometheus-compatible metrics endpoint
• ⚙️ TTL Support: Configurable time-to-live for cache entries

Quick Start

Installation

pip install llm-cache

Basic Usage

Decorator Pattern

from llm_cache import cached_call

@cached_call(provider="openai", model="gpt-4")
def ask_llm(prompt: str):
    # Your existing OpenAI call here
    return openai_client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )

# First call hits the API
response1 = ask_llm("What is Python?")
# Second call returns cached response
response2 = ask_llm("What is Python?")  # Instant!

Context Manager

from llm_cache import wrap_openai
import openai

client = openai.OpenAI()

# Wrap your client with caching
with wrap_openai(client, ttl_days=7):
    # All calls are automatically cached
    response1 = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Hello"}]
    )
    
    # Same request returns cached response
    response2 = client.chat.completions.create(
        model="gpt-4", 
        messages=[{"role": "user", "content": "Hello"}]
    )

Low-level API

from llm_cache import LLMCache

cache = LLMCache()

def fetch_from_openai(prompt):
    # Your actual API call
    return openai_client.chat.completions.create(...)

# Get or set from cache
response = cache.get_or_set(
    key="unique_request_hash",
    fetch_func=lambda: fetch_from_openai("What is AI?"),
    provider="openai",
    model="gpt-4",
    endpoint="/v1/chat/completions",
    request_data={"messages": [{"role": "user", "content": "What is AI?"}]}
)

HTTP Proxy Mode

Start a proxy server that intercepts and caches LLM API calls:

llm-cache serve --host 127.0.0.1 --port 8100

Then point your applications to the proxy instead of the original API:

import openai

# Use proxy instead of direct API
client = openai.OpenAI(
    base_url="http://127.0.0.1:8100",
    api_key="your-api-key"
)

# All calls are automatically cached
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)

CLI Commands

View Statistics

# Basic stats
llm-cache stats

# Detailed stats with provider breakdown
llm-cache stats --verbose

List Cache Entries

# List recent entries
llm-cache list

# Filter by provider
llm-cache list --provider openai

# Filter by model
llm-cache list --model gpt-4

# Limit results
llm-cache list --limit 10

Inspect Entries

# Show entry details
llm-cache show <cache_key>

# Export entry to file
llm-cache show <cache_key> --output entry.json

Purge Cache

# Delete specific entry
llm-cache purge --key <cache_key>

# Delete expired entries
llm-cache purge --expired

# Delete entries older than 30 days
llm-cache purge --older 30

# Delete all entries for a model
llm-cache purge --model gpt-3.5-turbo

# Delete all entries (with confirmation)
llm-cache purge --all

Export Data

# Export to JSONL format
llm-cache export cache_dump.jsonl

# Export to JSON format
llm-cache export cache_dump.json --format json

# Export only OpenAI entries
llm-cache export openai_entries.jsonl --provider openai

Health Check

# Check system health
llm-cache doctor

Configuration

Environment Variables

# Cache settings
export LLMCACHE_TTL=30                    # Default TTL in days
export LLMCACHE_COMPRESSION=true          # Enable compression
export LLMCACHE_ENCRYPTION=false          # Enable encryption
export LLMCACHE_ENCRYPTION_KEY="secret"   # Encryption key

# Storage
export LLMCACHE_BACKEND=sqlite            # Backend (sqlite, redis)
export LLMCACHE_DATABASE_URL="..."        # Database URL

# Proxy settings
export LLMCACHE_PROXY_HOST=127.0.0.1
export LLMCACHE_PROXY_PORT=8100

# Logging
export LLMCACHE_LOG_LEVEL=INFO
export LLMCACHE_LOG_FILE=/path/to/logs

Configuration File

Create ~/.config/llm-cache/config.toml:

# Cache settings
backend = "sqlite"
default_ttl_days = 30
enable_compression = true
enable_encryption = false

# Proxy settings
proxy_host = "127.0.0.1"
proxy_port = 8100

# Pricing table (cost per 1K tokens)
[pricing_table]
openai.gpt-4 = { input = 0.03, output = 0.06 }
openai.gpt-3.5-turbo = { input = 0.0015, output = 0.002 }
anthropic.claude-3 = { input = 0.015, output = 0.075 }

Advanced Usage

Streaming Support

@cached_call(provider="openai", model="gpt-4")
def streaming_call(messages, stream=True):
    return openai_client.chat.completions.create(
        model="gpt-4",
        messages=messages,
        stream=stream
    )

# First call collects the stream
response = streaming_call([{"role": "user", "content": "Hello"}], stream=True)

# Subsequent calls replay the cached stream
for chunk in response:
    print(chunk)

Custom TTL

@cached_call(provider="openai", model="gpt-4", ttl_days=7)
def short_lived_cache(prompt):
    return openai_client.chat.completions.create(...)

Encryption

import os
os.environ["LLMCACHE_ENCRYPTION_KEY"] = "your-secret-key"

cache = LLMCache(enable_encryption=True)
# All cached data will be encrypted

Redis Backend

cache = LLMCache(
    backend="redis",
    database_url="redis://localhost:6379/0"
)

Metrics

When running in proxy mode, access metrics at /metrics:

curl http://localhost:8100/metrics

Example output:

# HELP llm_cache_entries_total Total number of cache entries
# TYPE llm_cache_entries_total counter
llm_cache_entries_total 42

# HELP llm_cache_hits_total Total number of cache hits
# TYPE llm_cache_hits_total counter
llm_cache_hits_total 156

# HELP llm_cache_cost_saved_usd Total cost saved in USD
# TYPE llm_cache_cost_saved_usd counter
llm_cache_cost_saved_usd 12.34

Examples

OpenAI Integration

import openai
from llm_cache import wrap_openai

client = openai.OpenAI()

with wrap_openai(client):
    # All calls are cached
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Explain quantum computing"}],
        temperature=0.7
    )

Anthropic Integration

import anthropic
from llm_cache import cached_call

@cached_call(provider="anthropic", model="claude-3-sonnet")
def ask_claude(prompt):
    client = anthropic.Anthropic()
    return client.messages.create(
        model="claude-3-sonnet",
        max_tokens=1000,
        messages=[{"role": "user", "content": prompt}]
    )

HTTP Client Integration

import httpx
from llm_cache import LLMCache

cache = LLMCache()

def cached_api_call(prompt):
    def fetch():
        with httpx.Client() as client:
            response = client.post(
                "https://api.openai.com/v1/chat/completions",
                headers={"Authorization": f"Bearer {api_key}"},
                json={
                    "model": "gpt-4",
                    "messages": [{"role": "user", "content": prompt}]
                }
            )
            return response.json()
    
    return cache.get_or_set(
        key=f"prompt_{hash(prompt)}",
        fetch_func=fetch,
        provider="openai",
        model="gpt-4",
        endpoint="/v1/chat/completions",
        request_data={"messages": [{"role": "user", "content": prompt}]}
    )

Performance

• Cache Hit Rate: Typically 60-80% for repeated queries
• Cost Savings: 40-60% reduction in API costs
• Latency: Cache hits return in <1ms
• Storage: ~1KB per cached response (compressed)

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Run pytest
6. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions