fixed link to published blog (elastic#102)

Author: jeffvestal

supporting-blog-content/elasticsearch_llm_cache/README.md

@@ -2,7 +2,7 @@
 A Python library to utilize Elasticsearch as a caching layer for Generative AI applications. By caching the responses from Language Models (LLMs), this library helps in reducing costs associated with LLM services and improving response speed from the user's perspective.
 
 # Blog Post
-This library is covered in depth in the blog post [Elasticsearch as a GenAI Caching Layer](https://www.elastic.co/blog/lasticsearch-as-a-genai-caching-layer).
+This library is covered in depth in the blog post [Elasticsearch as a GenAI Caching Layer](https://www.elastic.co/search-labs/elasticsearch-as-a-genai-caching-layer).
 
 ## Key Benefits
 - Reduce costs for LLM services by reusing previous responses.
@@ -92,4 +92,4 @@ Sample Streamlit App using RAG with Elasticsearch
  - ELASTIC_INDEX_DOCS
 
 ## [test_elasticsearch_llm_cache.py](test_elasticsearch_llm_cache.py)
-Unit tests for ElasticsearchLLMCache class
+Unit tests for ElasticsearchLLMCache class

supporting-blog-content/elasticsearch_llm_cache/elasticsearch_llm_cache.py

@@ -44,7 +44,7 @@
 
 This library is covered in depth in the blog post
 Elasticsearch as a GenAI Caching Layer
-https://www.elastic.co/blog/lasticsearch-as-a-genai-caching-layer
+https://www.elastic.co/search-labs/elasticsearch-as-a-genai-caching-layer
 
 Author: Jeff Vestal
 Version: 1.0.0