Merge pull request #300912 from Albertyang0/2025_05-Monthly-broken-links-fix-ay-940

2025_05 - Fix monthly broken links

Author: prmerger-automator[bot]

articles/redis/overview-vector-similarity.md

@@ -73,9 +73,9 @@ Finally, vector databases execute vector searches by using the chosen vector com
 Vector similarity search can be used in multiple applications. Some common use-cases include:
 
 - **Semantic Q&A**. Create a chatbot that can respond to questions about your own data. For instance, a chatbot that can respond to employee questions on their healthcare coverage. Hundreds of pages of dense healthcare coverage documentation can be split into chunks, converted into embeddings vectors, and searched based on vector similarity. The resulting documents can then be summarized for employees using another large language model (LLM). [Semantic Q&A Example](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/vector-similarity-search-with-azure-cache-for-redis-enterprise/3822059)
-- **Document Retrieval**. Use the deeper semantic understanding of text provided by LLMs to provide a richer document search experience where traditional keyword-based search falls short. [Document Retrieval Example](https://github.com/RedisVentures/redis-arXiv-search)
-- **Product Recommendation**. Find similar products or services to recommend based on past user activities, like search history or previous purchases. [Product Recommendation Example](https://github.com/RedisVentures/LLM-Recommender)
-- **Visual Search**. Search for products that look similar to a picture taken by a user or a picture of another product. [Visual Search Example](https://github.com/RedisVentures/redis-product-search)
+- **Document Retrieval**. Use the deeper semantic understanding of text provided by LLMs to provide a richer document search experience where traditional keyword-based search falls short. [Document Retrieval Example](https://github.com/redis-developer/redis-arXiv-search)
+- **Product Recommendation**. Find similar products or services to recommend based on past user activities, like search history or previous purchases. [Product Recommendation Example](https://github.com/redis-developer/LLM-Recommender)
+- **Visual Search**. Search for products that look similar to a picture taken by a user or a picture of another product. [Visual Search Example](https://github.com/redis-developer/redis-product-search)
 - **Semantic Caching**. Reduce the cost and latency of LLMs by caching LLM completions. LLM queries are compared using vector similarity. If a new query is similar enough to a previously cached query, the cached query is returned. [Semantic Caching example using LangChain](https://python.langchain.com/docs/integrations/llm_caching/#redis-cache)
 - **LLM Conversation Memory**. Persist conversation history with an LLM as embeddings in a vector database. Your application can use vector search to pull relevant history or "memories" into the response from the LLM. [LLM Conversation Memory example](https://github.com/continuum-llms/chatgpt-memory)