promptcache-ai 0.1.0

Semantic similarity cache for LLM responses (Redis backend, TTL, cost tracking).

PromptCache

Reduce your LLM API costs by 30--70% with semantic caching.

PromptCache reuses LLM responses for semantically similar prompts, not just exact string matches.

If two users ask:

• "Explain Redis in simple terms"

• "Can you explain Redis simply?"

You shouldn't pay twice.

PromptCache makes sure you don't.

---

---

The Problem

If you're using OpenAI or any LLM API in production, you're likely paying repeatedly for:

• The same question phrased differently

• Similar support requests across users

• Slight variations in prompts

• Background job retries

• RAG pipelines returning near-identical queries

Traditional caching only works for exact matches.

LLMs need semantic caching.

---

What PromptCache Does

1. Embeds your prompt into a vector

2. Searches Redis for similar past prompts

3. If similarity ≥ threshold → returns cached response

4. Otherwise → calls the LLM and stores the result

User Prompt
     ↓
Embed → Redis Vector Search
     ↓
Hit? → Return cached answer
Miss? → Call LLM → Store result

---

10-Second Example

from promptcache import SemanticCache
from promptcache.backends.redis_vector import RedisVectorBackend
from promptcache.embedders.openai import OpenAIEmbedder
from promptcache.types import CacheMeta

embedder = OpenAIEmbedder(model="text-embedding-3-small")

backend = RedisVectorBackend(
    url="redis://localhost:6379/0",
    dim=embedder.dim,
)

cache = SemanticCache(
    backend=backend,
    embedder=embedder,
    namespace="support-bot",
    threshold=0.92,
)

meta = CacheMeta(
    model="gpt-4.1-mini",
    system_prompt="You are a helpful support assistant.",
)

result = cache.get_or_set(
    prompt="How do I reset my password?",
    llm_call=my_llm_call,
    extract_text=lambda r: r.output_text,
    meta=meta,
)

print(result.cache_hit)  # True or False`

That's it.

---

Example Impact

In a SaaS support assistant:

• 62% cache hit rate

• 48% reduction in token usage

• 44% reduction in API spend

Your mileage depends on workload --- but high-volume, repetitive systems benefit the most.

---

Production-Ready Design

PromptCache isolates cache entries by:

• namespace

• model

• system_prompt

• tools_schema

• embedder

This prevents cross-context contamination.

Additional features:

• ✅ Redis HNSW vector search (cosine similarity)

• ✅ TTL support

• ✅ Hit-rate statistics

• ✅ Optional cost tracking

• ✅ In-memory backend (for testing)

• ✅ Framework-agnostic (no LangChain dependency)

---

Installation

pip install promptcache

Optional OpenAI embedder:

pip install promptcache[openai]

---

Redis Setup

PromptCache requires Redis Stack (RediSearch with vector support).

Run locally:

docker run -d\
  --name redis-stack\
  -p 6379:6379\
  redis/redis-stack:latest`

Verify:

redis-cli MODULE LIST

You should see:

search

---

Stats

Measure impact:

print(cache.stats())

Example:

{
    "hits": 1240,
    "misses": 860,
    "total": 2100,
    "hit_rate_percent": 59.05
}

---

When It Helps Most

• Customer support bots

• Internal copilots

• FAQ systems

• Knowledge assistants

• Deterministic / low-temperature tasks

• High-volume similar prompts

---

When It May Not Help

• Highly personalized prompts

• Creative high-temperature tasks

• Frequently changing context

---

Testing

Run unit tests:

pytest

Run Redis integration tests:

export REDIS_URL="redis://localhost:6379/0"
pytest