Merge pull request #314663 from JustPies/jpbrokenlinks-4-13

Broken Link Remediation

Author: prmerger-automator[bot]

articles/durable-task/scheduler/durable-task-scheduler-billing.md

@@ -122,7 +122,7 @@ Currently, you're limited to 10 schedulers and five task hubs per region per sub
 
 ### Example 1
 
-A development team is testing simple orchestrations, each with three actions (using [the "Hello City" pattern](https://github.com/Azure-Samples/Durable-Task-Scheduler/tree/main/quickstarts/durable-functions/dotnet/HelloCities)), and runs 10,000 orchestrations per month.
+A development team is testing simple orchestrations, each with three actions (using [the "Hello City" pattern](https://github.com/Azure-Samples/Durable-Task-Scheduler/tree/main/samples/durable-functions/dotnet/HelloCities)), and runs 10,000 orchestrations per month.
 
 | Activity | Calculation | Result |
 | - | ----------- | ------ |

articles/redis/overview-vector-similarity.md

@@ -86,10 +86,10 @@ Vector similarity search can be used in multiple applications. Some common use-c
 Azure Cache for Redis can be used effectively as a vector database to store embeddings vectors and to perform vector similarity searches. Support for vector storage and search has been available in many key machine learning frameworks like:
 
 - [Semantic Kernel](https://github.com/microsoft/semantic-kernel)
-- [LangChain](https://python.langchain.com/docs/integrations/vectorstores/redis)
+- [LangChain](https://docs.langchain.com/oss/python/integrations/vectorstores/)
 - [LlamaIndex](https://developers.llamaindex.ai/python/examples/vector_stores/redisindexdemo)
 
-These frameworks feature rich integrations with Redis. For example, the Redis LangChain integration [automatically generates an index schema for metadata](https://python.langchain.com/docs/integrations/vectorstores/redis#inspecting-the-created-index) passed in when using Redis as a vector store. This makes it much easier to filter results based on metadata.
+These frameworks feature rich integrations with Redis. For example, the Redis LangChain integration automatically generates an index schema for metadata passed in when using Redis as a vector store. This makes it much easier to filter results based on metadata.
 
 Redis has a wide range of search capabilities through the [RediSearch module](redis-modules.md#redisearch), which is available in the Enterprise tier of Azure Cache for Redis. These include: