Semantic Caching

Overview

Semantic caching uses vector similarity search to intelligently cache AI responses, serving cached results for semantically similar requests even when the exact wording differs. This dramatically reduces API costs and latency for repeated or similar queries. Key Benefits:

Cost Reduction: Avoid expensive LLM API calls for similar requests
Improved Performance: Sub-millisecond cache retrieval vs multi-second API calls
Intelligent Matching: Semantic similarity beyond exact text matching
Streaming Support: Full streaming response caching with proper chunk ordering

Core Features

Dual-Layer Caching: Exact hash matching + semantic similarity search (customizable threshold)
Vector-Powered Intelligence: Uses embeddings to find semantically similar requests
Dynamic Configuration: Per-request TTL and threshold overrides via headers/context
Model/Provider Isolation: Separate caching per model and provider combination

Vector Store Setup

Semantic caching requires a configured vector store. Bifrost supports the following vector databases:

Weaviate

Production-ready vector database with gRPC support.

Redis / Valkey

High-performance in-memory vector store using RediSearch-compatible APIs.

Qdrant

Rust-based vector search engine with advanced filtering.

Pinecone

Managed vector database service with serverless options.

For detailed setup instructions and configuration options for each vector store, see the Vector Store documentation.

Quick Example (Weaviate):

Go SDK
config.json

import (
    "context"
    "github.com/maximhq/bifrost/framework/vectorstore"
)

// Configure vector store (example: Weaviate)
vectorConfig := &vectorstore.Config{
    Enabled: true,
    Type:    vectorstore.VectorStoreTypeWeaviate,
    Config: vectorstore.WeaviateConfig{
        Scheme: "http",
        Host:   "localhost:8080",
    },
}

// Create vector store
store, err := vectorstore.NewVectorStore(context.Background(), vectorConfig, logger)
if err != nil {
    log.Fatal("Failed to create vector store:", err)
}

{
  "vector_store": {
    "enabled": true,
    "type": "weaviate",
    "config": {
      "host": "localhost:8080",
      "scheme": "http"
    }
  }
}

Semantic Cache Configuration

UI Note: The current Web UI flow configures provider-backed semantic caching. If you want direct-only mode (dimension: 1 with no provider), configure it through config.json.

Go SDK
Web UI
config.json

import (
    "github.com/maximhq/bifrost/plugins/semanticcache"
    "github.com/maximhq/bifrost/core/schemas"
)

// Configure semantic cache plugin
cacheConfig := &semanticcache.Config{
    // Embedding model configuration (Required)
    Provider:       schemas.OpenAI,
    Keys:          []schemas.Key{{Value: "sk-..."}},
    EmbeddingModel: "text-embedding-3-small",
    Dimension:     1536,
    
    // Cache behavior
    TTL:       5 * time.Minute,  // Time to live for cached responses (default: 5 minutes)
    Threshold: 0.8,              // Similarity threshold for cache lookup (default: 0.8)
    CleanUpOnShutdown: true,     // Clean up cache on shutdown (default: false)
    
    // Conversation behavior
    ConversationHistoryThreshold: 5,    // Skip caching if conversation has > N messages (default: 3)
    ExcludeSystemPrompt: bifrost.Ptr(false), // Exclude system messages from cache key (default: false)
    
    // Advanced options
    CacheByModel:    bifrost.Ptr(true),  // Include model in cache key (default: true)
    CacheByProvider: bifrost.Ptr(true),  // Include provider in cache key (default: true)
}

// Create plugin
plugin, err := semanticcache.Init(context.Background(), cacheConfig, logger, store)
if err != nil {
    log.Fatal("Failed to create semantic cache plugin:", err)
}

// Add to Bifrost config
bifrostConfig := schemas.BifrostConfig{
    LLMPlugins: []schemas.LLMPlugin{plugin},
    // ... other config
}

Note: Make sure you have a vector store setup (using config.json) before configuring the semantic cache plugin.

Navigate to Settings
- Open Bifrost UI at http://localhost:8080
- Go to Settings.
Configure Semantic Cache Plugin

Toggle the plugin switch to enable it, and fill in the required fields.

Required Fields:

Provider: The provider to use for caching.
Embedding Model: The embedding model to use for caching.
Dimension: The embedding dimension for the configured embedding model.

Note: Changes will need a restart of the Bifrost server to take effect, because the plugin is loaded on startup only.

{
  "plugins": [
    {
      "enabled": true,
      "name": "semantic_cache",
      "config": {        
        "provider": "openai",
        "embedding_model": "text-embedding-3-small",
        "dimension": 1536,
        
        "cleanup_on_shutdown": true,
        "ttl": "5m",
        "threshold": 0.8,
        
        "conversation_history_threshold": 3,
        "exclude_system_prompt": false,
        
        "cache_by_model": true,
        "cache_by_provider": true
      }
    }
  ]
}

Note: In config.json setups, provider keys are taken from the provider config on initialization, so you do not need to duplicate keys inside the plugin config. Any updates to the provider keys will not be reflected until next restart.

TTL Format Options:

Duration strings: "30s", "5m", "1h", "24h"
Numeric seconds: 300 (5 minutes), 3600 (1 hour)

Direct Hash Mode (Embedding-Free)

Direct hash mode provides exact-match caching without requiring an embedding provider. Each request is hashed deterministically based on its normalized input, parameters, and stream flag. Identical requests produce cache hits; different wording is a cache miss. Exact-match direct entries are stored and retrieved using a deterministic cache ID. This keeps repeated direct cache lookups fast and consistent across retries, streaming responses, and restarts. When to use direct hash mode:

You only need exact-match deduplication (no fuzzy/semantic matching)
You cannot or do not want to call an external embedding API
You want the lowest possible latency with zero embedding overhead
Cost-sensitive environments where embedding API calls add up

Setup

To enable direct-only mode globally, set dimension: 1 and omit the provider and keys fields from the plugin config. The plugin will automatically fall back to direct search only.

Important: If you specify dimension: 1 and also provide a provider, Bifrost treats the config as provider-backed semantic mode, not direct-only mode. To use direct-only mode, omit the provider field entirely.

A vector store is still required as the storage backend, even in direct hash mode. See Recommended Vector Store below for the best choice.

Go SDK
Helm
config.json

import (
    "github.com/maximhq/bifrost/plugins/semanticcache"
)

cacheConfig := &semanticcache.Config{
    // No Provider, Keys, or EmbeddingModel -- direct hash mode only
    Dimension: 1, // Placeholder; entries are stored as metadata-only (no embedding vectors). Change dimension before switching to dual-layer mode to avoid mixed-dimension issues.

    TTL:               5 * time.Minute,
    CleanUpOnShutdown: true,
    CacheByModel:      bifrost.Ptr(true),
    CacheByProvider:   bifrost.Ptr(true),
}

plugin, err := semanticcache.Init(ctx, cacheConfig, logger, store)

bifrost:
  plugins:
    semanticCache:
      enabled: true
      config:
        dimension: 1
        ttl: "5m"
        cleanup_on_shutdown: true
        cache_by_model: true
        cache_by_provider: true

{
  "plugins": [
    {
      "enabled": true,
      "name": "semantic_cache",
      "config": {
        "dimension": 1,
        "ttl": "5m",
        "cleanup_on_shutdown": true,
        "cache_by_model": true,
        "cache_by_provider": true
      }
    }
  ]
}

When initialized this way, all requests automatically use direct hash matching regardless of the x-bf-cache-type header. No embeddings are generated, and no embedding provider credentials are needed.

Recommended Vector Store

Redis/Valkey-compatible stores are recommended for direct hash mode. They do not require vectors for metadata-only entries, and all cache fields are indexed as TAG fields for fast exact-match lookups.

Qdrant and Pinecone are not compatible with direct hash mode when no embedding provider is configured. These stores require a vector for every entry; the plugin’s zero-vector placeholder codepath requires an initialised embedding client, so storage will fail if no provider is set. Weaviate requires a vector per entry as well and is therefore also not recommended for direct-only mode.

Helm
config.json

vectorStore:
  enabled: true
  type: redis
  redis:
    external:
      enabled: true
      host: "redis-or-valkey.example.com"
      port: 6379
      password: "your-redis-password"

{
  "vector_store": {
    "enabled": true,
    "type": "redis",
    "config": {
      "addr": "localhost:6379"
    }
  }
}

For Valkey deployments, keep vector_store.type as "redis" and point config.addr to your Valkey endpoint.

Per-Request Cache Type Override

When the plugin is initialized without an embedding provider (direct-only mode), all requests use direct hash matching automatically. The x-bf-cache-type header has no effect. When the plugin is initialized with an embedding provider (dual-layer mode), you can force direct-only matching on specific requests using the x-bf-cache-type: direct header. See Cache Type Control for details.

Cache Triggering

Cache Key is mandatory: Semantic caching only activates when a cache key is provided. Without a cache key, requests bypass caching entirely.

Go SDK
HTTP API

Must set cache key in request context:

// This request WILL be cached
ctx = context.WithValue(ctx, semanticcache.CacheKey, "session-123")
response, err := client.ChatCompletionRequest(schemas.NewBifrostContext(ctx, schemas.NoDeadline), request)

// This request will NOT be cached (no context value)
response, err := client.ChatCompletionRequest(schemas.NewBifrostContext(context.Background(), schemas.NoDeadline), request)

Must set cache key in request header x-bf-cache-key:

# This request WILL be cached
curl -H "x-bf-cache-key: session-123" ...

# This request will NOT be cached (no header)
curl ...

Per-Request Overrides

Override default TTL and similarity threshold per request:

Go SDK
HTTP API

You can set TTL and threshold in the request context using the semantic cache context keys:

// Go SDK: Custom TTL and threshold
ctx = context.WithValue(ctx, semanticcache.CacheKey, "session-123")
ctx = context.WithValue(ctx, semanticcache.CacheTTLKey, 30*time.Second)
ctx = context.WithValue(ctx, semanticcache.CacheThresholdKey, 0.9)

You can set TTL and threshold in the request headers x-bf-cache-ttl and x-bf-cache-threshold:

# HTTP: Custom TTL and threshold
curl -H "x-bf-cache-key: session-123" \
     -H "x-bf-cache-ttl: 30s" \
     -H "x-bf-cache-threshold: 0.9" ...

Advanced Cache Control

Cache Type Control

Control which caching mechanism to use per request:

Go SDK
HTTP API

// Use only direct hash matching (fastest)
ctx = context.WithValue(ctx, semanticcache.CacheKey, "session-123")
ctx = context.WithValue(ctx, semanticcache.CacheTypeKey, semanticcache.CacheTypeDirect)

// Use only semantic similarity search
ctx = context.WithValue(ctx, semanticcache.CacheKey, "session-123")  
ctx = context.WithValue(ctx, semanticcache.CacheTypeKey, semanticcache.CacheTypeSemantic)

// Default behavior: Direct + semantic fallback (if not specified)
ctx = context.WithValue(ctx, semanticcache.CacheKey, "session-123")

# Direct hash matching only
curl -H "x-bf-cache-key: session-123" \
     -H "x-bf-cache-type: direct" ...

# Semantic similarity search only  
curl -H "x-bf-cache-key: session-123" \
     -H "x-bf-cache-type: semantic" ...

# Default: Both (if header not specified)
curl -H "x-bf-cache-key: session-123" ...

No-Store Control

Disable response caching while still allowing cache reads:

Go SDK
HTTP API

// Read from cache but don't store the response
ctx = context.WithValue(ctx, semanticcache.CacheKey, "session-123")
ctx = context.WithValue(ctx, semanticcache.CacheNoStoreKey, true)

# Read from cache but don't store response
curl -H "x-bf-cache-key: session-123" \
     -H "x-bf-cache-no-store: true" ...

Conversation Configuration

History Threshold Logic

The ConversationHistoryThreshold setting skips caching for conversations with many messages to prevent false positives: Why this matters:

Semantic False Positives: Long conversation histories have high probability of semantic matches with unrelated conversations due to topic overlap
Direct Cache Inefficiency: Long conversations rarely have exact hash matches, making direct caching less effective
Performance: Reduces vector store load by filtering out low-value caching scenarios

{
  "conversation_history_threshold": 3  // Skip caching if > 3 messages in conversation
}

Recommended Values:

1-2: Very conservative (may miss valuable caching opportunities)
3-5: Balanced approach (default: 3)
10+: Cache longer conversations (higher false positive risk)

System Prompt Handling

Control whether system messages are included in cache key generation:

{
  "exclude_system_prompt": false  // Include system messages in cache key (default)
}

When to exclude (true):

System prompts change frequently but content is similar
Multiple system prompt variations for same use case
Focus caching on user content similarity

When to include (false):

System prompts significantly change response behavior
Each system prompt requires distinct cached responses
Strict response consistency requirements

Cache Management

Cache Metadata Location

When responses are served from semantic cache, 3 key variables are automatically added to the response: Location: response.ExtraFields.CacheDebug (as a JSON object) Fields:

CacheHit (boolean): true if the response was served from the cache, false when lookup fails.
HitType (string): "semantic" for similarity match, "direct" for hash match
CacheID (string): Unique cache entry ID for management operations (present only for cache hits)

Semantic Cache Only:

ProviderUsed (string): Provider used for the calculating semantic match embedding. (present for both cache hits and misses)
ModelUsed (string): Model used for the calculating semantic match embedding. (present for both cache hits and misses)
InputTokens (number): Number of tokens extracted from the request for the semantic match embedding calculation. (present for both cache hits and misses)
Threshold (number): Similarity threshold used for the match. (present only for cache hits)
Similarity (number): Similarity score for the match. (present only for cache hits)

Example HTTP Response:

{
  "extra_fields": {
    "cache_debug": {
      "cache_hit": true,
      "hit_type": "direct",
      "cache_id": "550e8500-e29b-41d4-a725-446655440001",
    }
  }
}

{
  "extra_fields": {
    "cache_debug": {
      "cache_hit": true,
      "hit_type": "semantic",
      "cache_id": "550e8500-e29b-41d4-a725-446655440001",
      "threshold": 0.8,
      "similarity": 0.95,
      "provider_used": "openai",
      "model_used": "gpt-4o-mini",
      "input_tokens": 100
    }
  }
}

{
  "extra_fields": {
    "cache_debug": {
      "cache_hit": false,
      "provider_used": "openai",
      "model_used": "gpt-4o-mini",
      "input_tokens": 20
    }
  }
}

These variables allow you to detect cached responses and get the cache entry ID needed for clearing specific entries.

Clear Specific Cache Entry

Use the request ID from cached responses to clear specific entries:

Go SDK
HTTP API

// Clear specific entry by request ID
err := plugin.ClearCacheForRequestID("550e8400-e29b-41d4-a716-446655440000")

// Clear all entries for a cache key  
err := plugin.ClearCacheForKey("support-session-456")

# Clear specific cached entry by request ID
curl -X DELETE http://localhost:8080/api/cache/clear/550e8400-e29b-41d4-a716-446655440000

# Clear all entries for a cache key
curl -X DELETE http://localhost:8080/api/cache/clear-by-key/support-session-456

Cache Lifecycle & Cleanup

The semantic cache automatically handles cleanup to prevent storage bloat: Automatic Cleanup:

TTL Expiration: Entries are automatically removed when TTL expires
Shutdown Cleanup: All cache entries are cleared from the vector store namespace and the namespace itself when Bifrost client shuts down
Namespace Isolation: Each Bifrost instance uses isolated vector store namespaces to prevent conflicts

Manual Cleanup Options:

Clear specific entries by request ID (see examples above)
Clear all entries for a cache key
Restart Bifrost to clear all cache data

The semantic cache namespace and all its cache entries are deleted when Bifrost client shuts down only if cleanup_on_shutdown is set to true. By default (cleanup_on_shutdown: false), cache data persists between restarts. DO NOT use the plugin’s namespace for external purposes.

Dimension Changes: If you update the dimension config, the existing namespace will contain data with mixed dimensions, causing retrieval issues. To avoid this, either use a different vector_store_namespace or set cleanup_on_shutdown: true before restarting.

Vector Store Requirement: Semantic caching requires a configured vector store. Bifrost supports Weaviate, Redis/Valkey-compatible endpoints, Qdrant, and Pinecone. See the Vector Store documentation for setup details.

Overview

Quick Start

Migration Guides

SDK Integrations

Providers & Guides

MCP Gateway

Custom plugins

Open Source Features

Enterprise Features

Semantic Caching

Overview

Core Features

Vector Store Setup

Weaviate

Redis / Valkey

Qdrant

Pinecone

Semantic Cache Configuration

Direct Hash Mode (Embedding-Free)

Setup

Recommended Vector Store

Per-Request Cache Type Override

Cache Triggering

Per-Request Overrides

Advanced Cache Control

Cache Type Control

No-Store Control

Conversation Configuration

History Threshold Logic

System Prompt Handling

Cache Management

Cache Metadata Location

Clear Specific Cache Entry

Cache Lifecycle & Cleanup

Overview

Quick Start

Migration Guides

SDK Integrations

Providers & Guides

MCP Gateway

Custom plugins

Open Source Features

Enterprise Features

​Overview

​Core Features

​Vector Store Setup

Weaviate

Redis / Valkey

Qdrant

Pinecone

​Semantic Cache Configuration

​Direct Hash Mode (Embedding-Free)

​Setup

​Recommended Vector Store

​Per-Request Cache Type Override

​Cache Triggering

​Per-Request Overrides

​Advanced Cache Control

​Cache Type Control

​No-Store Control

​Conversation Configuration

​History Threshold Logic

​System Prompt Handling

​Cache Management

​Cache Metadata Location

​Clear Specific Cache Entry

​Cache Lifecycle & Cleanup

Overview

Core Features

Vector Store Setup

Semantic Cache Configuration

Direct Hash Mode (Embedding-Free)

Setup

Recommended Vector Store

Per-Request Cache Type Override

Cache Triggering

Per-Request Overrides

Advanced Cache Control

Cache Type Control

No-Store Control

Conversation Configuration

History Threshold Logic

System Prompt Handling

Cache Management

Cache Metadata Location

Clear Specific Cache Entry

Cache Lifecycle & Cleanup