Merge pull request #613 from Portkey-AI/mintlify/semantic-caching-vector-db-note-24954

Add vector database requirement note to semantic caching docs

Author: sambharia

product/ai-gateway/cache-simple-and-semantic.mdx

@@ -4,7 +4,7 @@ title: "Cache (Simple & Semantic)"
 
 <Info>
 **Simple** caching is available for all plans.<br />
-**Semantic** caching is available for [**Production**](https://portkey.ai/pricing) and [**Enterprise**](https://portkey.ai/docs/product/enterprise-offering) users.
+**Semantic** caching requires a vector database and is only available on select Enterprise plans. [Contact us](https://portkey.ai/docs/support/contact-us) to learn more about enabling this feature.
 </Info>
 
 Speed up and save money on your LLM requests by storing past responses in the Portkey cache. There are 2 cache modes:
@@ -42,6 +42,10 @@ Simple cache performs an exact match on the input prompts. If the exact same req
 "cache": { "mode": "semantic" }
 ```
 
+<Note>
+Semantic caching requires a vector database and is only available on select Enterprise plans. [Contact us](https://portkey.ai/docs/support/contact-us) to learn more about enabling this feature.
+</Note>
+
 ### How it Works
 
 Semantic cache considers the contextual similarity between input requests. It uses cosine similarity to ascertain if the similarity between the input and a cached request exceeds a specific threshold. If the similarity threshold is met, Portkey retrieves the response from the cache, saving model execution time. Check out this [blog](https://portkey.ai/blog/reducing-llm-costs-and-latency-semantic-cache/) for more details on how we do this.