Fix: Correct typos in README.md (#668)

Author: TripleChecker

README.md

@@ -281,7 +281,7 @@ GPTCache offers the following primary benefits:
 - **Decreased expenses**: Most LLM services charge fees based on a combination of number of requests and [token count](https://openai.com/pricing). GPTCache effectively minimizes your expenses by caching query results, which in turn reduces the number of requests and tokens sent to the LLM service. As a result, you can enjoy a more cost-efficient experience when using the service.
 - **Enhanced performance**: LLMs employ generative AI algorithms to generate responses in real-time, a process that can sometimes be time-consuming. However, when a similar query is cached, the response time significantly improves, as the result is fetched directly from the cache, eliminating the need to interact with the LLM service. In most situations, GPTCache can also provide superior query throughput compared to standard LLM services.
 - **Adaptable development and testing environment**: As a developer working on LLM applications, you're aware that connecting to LLM APIs is generally necessary, and comprehensive testing of your application is crucial before moving it to a production environment. GPTCache provides an interface that mirrors LLM APIs and accommodates storage of both LLM-generated and mocked data. This feature enables you to effortlessly develop and test your application, eliminating the need to connect to the LLM service.
-- **Improved scalability and availability**: LLM services frequently enforce [rate limits](https://platform.openai.com/docs/guides/rate-limits), which are constraints that APIs place on the number of times a user or client can access the server within a given timeframe. Hitting a rate limit means that additional requests will be blocked until a certain period has elapsed, leading to a service outage. With GPTCache, you can easily scale to accommodate an increasing volume of of queries, ensuring consistent performance as your application's user base expands.
+- **Improved scalability and availability**: LLM services frequently enforce [rate limits](https://platform.openai.com/docs/guides/rate-limits), which are constraints that APIs place on the number of times a user or client can access the server within a given timeframe. Hitting a rate limit means that additional requests will be blocked until a certain period has elapsed, leading to a service outage. With GPTCache, you can easily scale to accommodate an increasing volume of queries, ensuring consistent performance as your application's user base expands.
 
 ## 🤔 How does it work?
 
@@ -350,7 +350,7 @@ This module is created to extract embeddings from requests for similarity search
   - [ ] Support other storages.
 - **Vector Store**:
 The **Vector Store** module helps find the K most similar requests from the input request's extracted embedding. The results can help assess similarity. GPTCache provides a user-friendly interface that supports various vector stores, including Milvus, Zilliz Cloud, and FAISS. More options will be available in the future.
-  - [x] Support [Milvus](https://milvus.io/), an open-source vector database for production-ready AI/LLM applicaionts. 
+  - [x] Support [Milvus](https://milvus.io/), an open-source vector database for production-ready AI/LLM applications. 
   - [x] Support [Zilliz Cloud](https://cloud.zilliz.com/), a fully-managed cloud vector database based on Milvus.
   - [x] Support [Milvus Lite](https://github.com/milvus-io/milvus-lite), a lightweight version of Milvus that can be embedded into your Python application.
   - [x] Support [FAISS](https://faiss.ai/), a library for efficient similarity search and clustering of dense vectors.