updated doc

README.md

@@ -11,31 +11,35 @@ The goal of LangChain4j is to simplify integrating AI/LLM capabilities into Java
 
 Here's how:
 1. **Unified APIs:**
-   LLM providers (like OpenAI, Google Vertex AI) and Vector Stores (such as Pinecone, Vespa)
-   use proprietary APIs. LangChain4j offers a unified API to avoid the need for learning and implementing specific APIs for each. To experiment with a different LLM or Vector Store, you can easily switch between them without the need to rewrite your code. LangChain4j currently supports over 10 popular LLM providers and more than 15 embedding stores.
-   Think of it as a Hibernate, but for LLMs and Vector Stores.
+   LLM providers (like OpenAI or Google Vertex AI) and embedding (vector) stores (such as Pinecone or Vespa)
+   use proprietary APIs. LangChain4j offers a unified API to avoid the need for learning and implementing specific APIs for each of them.
+   To experiment with a different LLM or embedding store, you can easily switch between them without the need to rewrite your code.
+   LangChain4j currently supports over 10 popular LLM providers and more than 15 embedding stores.
+   Think of it as a Hibernate, but for LLMs and embedding stores.
 2. **Comprehensive Toolbox:**
-   In the past year, the community has created numerous LLM-powered applications,
-   identifying common patterns and techniques. LangChain4j has refined these into practical code.
+   During the past year, the community has been building numerous LLM-powered applications,
+   identifying common patterns, abstractions, and techniques. LangChain4j has refined these into practical code.
    Our toolbox includes tools ranging from low-level prompt templating, memory management, and output parsing
    to high-level patterns like Agents and RAGs.
-   For each pattern, we provide an interface along with multiple ready-to-use implementations based on proven techniques.
+   For each pattern and abstraction, we provide an interface along with multiple ready-to-use implementations based on proven techniques.
    Whether you're building a chatbot or developing a RAG with a complete pipeline from data ingestion to retrieval,
    LangChain4j offers a wide variety of options.
 3. **Numerous Examples:**
    These [examples](https://github.com/langchain4j/langchain4j-examples) showcase how to begin creating various LLM-powered applications,
    providing inspiration and enabling you to start building quickly.
 
 LangChain4j began development in early 2023 amid the ChatGPT hype.
-We noticed a lack of Java counterparts to the numerous Python and JavaScript LLM libraries and frameworks.
+We noticed a lack of Java counterparts to the numerous Python and JavaScript LLM libraries and frameworks,
+and we had to fix that!
 Although "LangChain" is in our name, the project is a fusion of ideas and concepts from LangChain, Haystack,
 LlamaIndex, and the broader community, spiced up with a touch of our own innovation.
 
-We actively monitor community developments, aiming to quickly incorporate new techniques and integrations, ensuring you stay up-to-date.
+We actively monitor community developments, aiming to quickly incorporate new techniques and integrations,
+ensuring you stay up-to-date.
 The library is under active development. While some features from the Python version of LangChain
 are still being worked on, the core functionality is in place, allowing you to start building LLM-powered apps now!
 
-For easier integration, LangChain4j also includes compatibility with
+For easier integration, LangChain4j also includes integration with
 Quarkus ([extension](https://quarkus.io/extensions/io.quarkiverse.langchain4j/quarkus-langchain4j-core))
 and Spring Boot ([starters](https://github.com/langchain4j/langchain4j-spring)).
 
@@ -55,11 +59,10 @@ Tutorials can be found [here](https://langchain4j.github.io/langchain4j/docs/tut
 
 ## Library Structure
 LangChain4j features a modular design, comprising:
-- The `langchain4j-core` module, which defines core building blocks and their interfaces.
-- The `langchain4j` module, containing common functionalities and implementations of high-level abstractions like AI Services.
-- A wide array of `langchain4j-xyz` modules, each providing integration with various LLM providers and Vector Stores into LangChain4j.
-  You can use the `langchain4j-xyz` modules independently. For additional features, simply import the `langchain4j` dependency.
-
+- The `langchain4j-core` module, which defines core abstractions (such as `ChatLanguageModel` and `EmbeddingStore`) and their APIs.
+- The main `langchain4j` module, containing useful tools like `ChatMemory`, `OutputParser` as well as a high-level features like `AiServices`.
+- A wide array of `langchain4j-xyz` modules, each providing integration with various LLM providers and embedding stores into LangChain4j.
+  You can use the `langchain4j-xyz` modules independently. For additional features, simply import the main `langchain4j` dependency.
 
 ## News
 

docs/docs/intro.md

@@ -43,7 +43,7 @@ For easier integration, LangChain4j also includes integration with
 Quarkus ([extension](https://quarkus.io/extensions/io.quarkiverse.langchain4j/quarkus-langchain4j-core))
 and Spring Boot ([starters](https://github.com/langchain4j/langchain4j-spring)).
 
-### LangChain4j Structure
+### Library Structure
 LangChain4j features a modular design, comprising:
 - The `langchain4j-core` module, which defines core abstractions (such as `ChatLanguageModel` and `EmbeddingStore`) and their APIs.
 - The main `langchain4j` module, containing useful tools like `ChatMemory`, `OutputParser` as well as a high-level features like `AiServices`.