doc fixes

Author: akshaydeo

docs/enterprise/guardrails.mdx

@@ -154,6 +154,21 @@ Bifrost integrates with leading guardrail providers to offer comprehensive prote
 - Custom policy templates
 - Integration with existing safety workflows
 
+### Additional Guardrail Providers
+
+Beyond the natively integrated providers above, the following guardrail solutions are available in the ecosystem:
+
+- **Guardrails AI** - Open-source framework with validators for hallucination prevention, content moderation, and PII detection
+- **Lakera (Lakera Guard)** - Enterprise security platform for prompt injection, jailbreak, and data leakage protection
+- **Aporia** - Multi-modal guardrails (audio, vision, text) with 20+ pre-configured policies
+- **Lasso Security** - Real-time GenAI security with contextual data protection and custom policy wizard
+- **PromptArmor** - LLM security and compliance platform
+- **WhyLabs** - LLM security and observability platform with real-time guardrails and monitoring
+- **CalypsoAI** - Model-agnostic AI security platform with customizable scanners
+- **NeuralTrust** - LLM firewall specializing in prompt injection prevention
+- **Vigil** - Open-source prompt-level security tool
+- **Confident AI** - Red-teaming platform with LLM guardrail capabilities (DeepTeam)
+
 ---
 
 ## Guardrail Rules
@@ -389,7 +404,7 @@ request.model.startsWith("gpt-4") && request.messages.exists(m, m.role == "user"
 Rules can be linked to multiple profiles for comprehensive validation:
 
 <Frame>
-  <img src="/media/guardrails/rule-profile-link.png" alt="Rule configuration showing linked profiles" />
+  <img src="/media/guardrails/query-creation.png" alt="Rule configuration showing linked profiles" />
 </Frame>
 
 **Best Practices:**
@@ -405,7 +420,7 @@ Rules can be linked to multiple profiles for comprehensive validation:
 Profiles are reusable configurations for external guardrail providers. Each profile contains provider-specific settings including credentials, endpoints, and detection thresholds.
 
 <Frame>
-  <img src="/media/guardrails/profiles-list.png" alt="Guardrail profiles list showing configured providers" />
+  <img src="/media/guardrails/provider-aws-create.png" alt="Guardrail profiles list showing configured providers" />
 </Frame>
 
 ### Profile Properties

docs/features/semantic-caching.mdx

@@ -27,6 +27,19 @@ Semantic caching uses vector similarity search to intelligently cache AI respons
 
 ## Vector Store Setup
 
+Semantic caching requires a configured vector store. Bifrost supports the following vector databases:
+
+- **[Weaviate](/architecture/framework/vector-store#weaviate)**: Production-ready vector database with gRPC support
+- **[Redis](/architecture/framework/vector-store#redis)**: High-performance in-memory vector store using RediSearch
+- **[Qdrant](/architecture/framework/vector-store#qdrant)**: Rust-based vector search engine with advanced filtering
+- **[Pinecone](/architecture/framework/vector-store#pinecone)**: Managed vector database service with serverless options
+
+<Info>
+For detailed setup instructions and configuration options for each vector store, see the [Vector Store documentation](/architecture/framework/vector-store).
+</Info>
+
+**Quick Example (Weaviate):**
+
 <Tabs group="vector-store-setup">
 
 <Tab title="Go SDK">
@@ -35,16 +48,15 @@ Semantic caching uses vector similarity search to intelligently cache AI respons
 import (
     "context"
     "github.com/maximhq/bifrost/framework/vectorstore"
-    "github.com/maximhq/bifrost/core/schemas"
 )
 
-// Configure vector store
+// Configure vector store (example: Weaviate)
 vectorConfig := &vectorstore.Config{
     Enabled: true,
     Type:    vectorstore.VectorStoreTypeWeaviate,
     Config: vectorstore.WeaviateConfig{
-        Scheme:    "http",
-        Host:      "localhost:8080",
+        Scheme: "http",
+        Host:   "localhost:8080",
     },
 }
 
@@ -66,22 +78,7 @@ if err != nil {
     "type": "weaviate",
     "config": {
       "host": "localhost:8080",
-      "scheme": "http",
-    }
-  }
-}
-```
-
-**For Weaviate Cloud:**
-```json
-{
-  "vector_store": {
-    "enabled": true,
-    "type": "weaviate",
-    "config": {
-      "host": "your-cluster.weaviate.network",
-      "scheme": "https",
-      "api_key": "your-weaviate-api-key"
+      "scheme": "http"
     }
   }
 }
@@ -515,5 +512,5 @@ The semantic cache namespace and all its cache entries are deleted when Bifrost
 ---
 
 <Info>
-**Vector Store Requirement**: Semantic caching requires a configured vector store (currently Weaviate only). Without vector store setup, the plugin will not function.
+**Vector Store Requirement**: Semantic caching requires a configured vector store. Bifrost supports Weaviate, Redis, Qdrant, and Pinecone. See the [Vector Store documentation](/architecture/framework/vector-store) for setup details.
 </Info>