Redis LangCache

Semantic caching solution for LLM applications that reduces API calls and costs by recognizing semantically similar queries. Achieves up to 73% cost reduction in conversational workloads with sub-millisecond cache retrieval through vector similarity search.

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About this tool

Overview

Redis LangCache is a semantic caching solution that optimizes LLM applications by recognizing when incoming queries are semantically similar to previously answered ones, enabling response reuse and significant cost savings.

Key Innovation

Semantic vs Traditional Caching

Traditional Caching:

Exact string matching
Miss on minor variations
"What's the weather?" ≠ "What is the weather?"

Semantic Caching:

Meaning-based matching
Handles paraphrasing
"What's the weather?" ≈ "Tell me about the weather"
Uses vector embeddings for similarity

How It Works

Architecture

Query Processing:
- User query arrives
- Generate embedding for query
- Store embedding dimension: typically 384-1536
Cache Lookup:
- Vector search in Redis
- Find semantically similar queries
- Similarity threshold: typically 0.85-0.95
Cache Hit/Miss:
- Hit: Return cached response (milliseconds)
- Miss: Call LLM, cache response (seconds)
Response Storage:
- Store query embedding
- Store associated response
- Set TTL (Time To Live) if desired

Performance

In conversational workloads with optimized configurations:

73% cost reduction achieved
Sub-millisecond retrieval from cache
68.8% reduction in typical production workloads
Responses return in milliseconds vs seconds

Implementation

Basic Setup

from redis import Redis
from langchain.cache import RedisSemanticCache
from langchain.embeddings import OpenAIEmbeddings

# Initialize Redis connection
redis_client = Redis(
    host='localhost',
    port=6379,
    decode_responses=True
)

# Create semantic cache
cache = RedisSemanticCache(
    redis_url="redis://localhost:6379",
    embedding=OpenAIEmbeddings(),
    score_threshold=0.85
)

# Use with LangChain
from langchain.llms import OpenAI
from langchain import LLMChain

llm = OpenAI(cache=cache)

Configuration Parameters

score_threshold:

Range: 0.0 - 1.0
Higher: More exact matches required
Lower: More cache hits, less accuracy
Typical: 0.85 - 0.95

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Information

Websiteredis.io

PublishedMar 16, 2026

Tags

3 Items

#Caching #Rag #Optimization

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A RAG optimization technique that compresses retrieved documents by extracting only the most relevant portions relative to the query. Reduces token usage and improves LLM response quality by removing irrelevant context.

Embedding Cache

Caching mechanism for storing and reusing previously computed embeddings to reduce API costs and latency. Essential optimization for production RAG systems processing repeated or similar content.

Semantic Caching

AI caching pattern that stores vector embeddings of LLM queries and responses, serving cached results when new queries are semantically similar. Cuts LLM costs by 50%+ with millisecond response times versus seconds for fresh calls.

DSPy

Programming framework for RAG and AI applications with cutting-edge optimization capabilities, featuring the lowest framework overhead and automatic improvement based on example data.

MeMemo

A JavaScript library that brings vector search and RAG (Retrieval-Augmented Generation) to browser environments, enabling efficient searching through millions of vectors using HNSW algorithm with IndexedDB and Web Workers.

SimSIMD

Open‑source library providing fast SIMD‑accelerated implementations of similarity and distance computations (e.g., vector inner products and distances), serving as an efficient alternative to scipy.spatial.distance and numpy.inner for vector search and vector database workloads.

Overview

Key Innovation

Semantic vs Traditional Caching

Traditional Caching:

Exact string matching
Miss on minor variations
"What's the weather?" ≠ "What is the weather?"

Semantic Caching:

Meaning-based matching
Handles paraphrasing
"What's the weather?" ≈ "Tell me about the weather"
Uses vector embeddings for similarity

How It Works

Architecture

Query Processing:
- User query arrives
- Generate embedding for query
- Store embedding dimension: typically 384-1536
Cache Lookup:
- Vector search in Redis
- Find semantically similar queries
- Similarity threshold: typically 0.85-0.95
Cache Hit/Miss:
- Hit: Return cached response (milliseconds)
- Miss: Call LLM, cache response (seconds)
Response Storage:
- Store query embedding
- Store associated response
- Set TTL (Time To Live) if desired

Performance

In conversational workloads with optimized configurations:

73% cost reduction achieved
Sub-millisecond retrieval from cache
68.8% reduction in typical production workloads
Responses return in milliseconds vs seconds

Implementation

Basic Setup

from redis import Redis
from langchain.cache import RedisSemanticCache
from langchain.embeddings import OpenAIEmbeddings

# Initialize Redis connection
redis_client = Redis(
    host='localhost',
    port=6379,
    decode_responses=True
)

# Create semantic cache
cache = RedisSemanticCache(
    redis_url="redis://localhost:6379",
    embedding=OpenAIEmbeddings(),
    score_threshold=0.85
)

# Use with LangChain
from langchain.llms import OpenAI
from langchain import LLMChain

llm = OpenAI(cache=cache)

Configuration Parameters

score_threshold:

Range: 0.0 - 1.0
Higher: More exact matches required
Lower: More cache hits, less accuracy
Typical: 0.85 - 0.95

Redis LangCache

About this tool

Overview

Key Innovation

Semantic vs Traditional Caching

How It Works

Architecture

Performance

Implementation

Basic Setup

Configuration Parameters

Information

Categories

Tags

Similar Products

Redis LangCache

About this tool

Overview

Key Innovation

Semantic vs Traditional Caching

How It Works

Architecture

Performance

Implementation

Basic Setup

Configuration Parameters

Information

Categories

Tags

Similar Products

Benefits

Cost Reduction

Performance

Consistency

Scalability

Use Cases

Conversational AI

RAG Applications

Content Generation

Integration with Redis Stack

Redis Features Used

Best Practices

Threshold Selection

TTL Strategy

Embedding Model Choice

Monitoring

Advanced Features

Namespace Support

Metadata Filtering

Cache Warming

Analytics and Insights

Production Considerations

Deployment

Performance Tuning

Security

Comparison with Alternatives

vs Exact Match Caching

vs GPTCache

vs No Caching

Cost Analysis

Savings Calculation

Redis Costs

Recent Developments (2026)

Production Recommendations

RAG at Scale

Framework Integration

LangChain

LlamaIndex

Custom Applications

Monitoring and Observability

Key Metrics

Tools

Future Enhancements

Pricing