Merge branch 'master' into patch-1

docs/docs/introduction.mdx

@@ -29,9 +29,9 @@ import useBaseUrl from '@docusaurus/useBaseUrl';
 Concretely, the framework consists of the following open-source libraries:
 
 - **`langchain-core`**: Base abstractions and LangChain Expression Language.
-- **`langchain-community`**: Third party integrations.
-  - Partner packages (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Some integrations have been further split into their own lightweight packages that only depend on **`langchain-core`**.
+- Integration packages (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
+- **`langchain-community`**: Third-party integrations that are community maintained.
 - **[LangGraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
 - **[LangServe](/docs/langserve)**: Deploy LangChain chains as REST APIs.
 - **[LangSmith](https://docs.smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor LLM applications.

docs/docs/tutorials/index.mdx

@@ -7,25 +7,25 @@ sidebar_class_name: hidden
 New to LangChain or LLM app development in general? Read this material to quickly get up and running.
 
 ## Basics
-- [Build a Simple LLM Application with LCEL](/docs/tutorials/llm_chain)
-- [Build a Chatbot](/docs/tutorials/chatbot)
-- [Build vector stores and retrievers](/docs/tutorials/retrievers)
-- [Build an Agent](/docs/tutorials/agents)
+- [LLM applications](/docs/tutorials/llm_chain): Build and deploy a simple LLM application.
+- [Chatbots](/docs/tutorials/chatbot): Build a chatbot that incorporates memory.
+- [Vector stores](/docs/tutorials/retrievers): Build vector stores and use them to retrieve data.
+- [Agents](/docs/tutorials/agents): Build an agent that interacts with external tools.
 
 ## Working with external knowledge
-- [Build a Retrieval Augmented Generation (RAG) Application](/docs/tutorials/rag)
-- [Build a Conversational RAG Application](/docs/tutorials/qa_chat_history)
-- [Build a Question/Answering system over SQL data](/docs/tutorials/sql_qa)
-- [Build a Query Analysis System](/docs/tutorials/query_analysis)
-- [Build a local RAG application](/docs/tutorials/local_rag)
-- [Build a Question Answering application over a Graph Database](/docs/tutorials/graph)
-- [Build a PDF ingestion and Question/Answering system](/docs/tutorials/pdf_qa/)
+- [Retrieval Augmented Generation (RAG)](/docs/tutorials/rag): Build an application that uses your own documents to inform its responses.
+- [Conversational RAG](/docs/tutorials/qa_chat_history): Build a RAG application that incorporates a memory of its user interactions.
+- [Question-Answering with SQL](/docs/tutorials/sql_qa): Build a question-answering system that executes SQL queries to inform its responses.
+- [Query Analysis](/docs/tutorials/query_analysis): Build a RAG application that analyzes questions to generate filters and other structured queries.
+- [Local RAG](/docs/tutorials/local_rag): Build a RAG application using LLMs running locally on your machine.
+- [Question-Answering with Graph Databases](/docs/tutorials/graph): Build a question-answering system that queries a graph database to inform its responses.
+- [Question-Answering with PDFs](/docs/tutorials/pdf_qa/): Build a question-answering system that ingests PDFs and uses them to inform its responses.
 
 ## Specialized tasks
-- [Build an Extraction Chain](/docs/tutorials/extraction)
-- [Generate synthetic data](/docs/tutorials/data_generation)
-- [Classify text into labels](/docs/tutorials/classification)
-- [Summarize text](/docs/tutorials/summarization)
+- [Extraction](/docs/tutorials/extraction): Extract structured data from text and other unstructured media.
+- [Synthetic data](/docs/tutorials/data_generation): Generate synthetic data using LLMs.
+- [Classification](/docs/tutorials/classification): Classify text into categories or labels.
+- [Summarization](/docs/tutorials/summarization): Generate summaries of (potentially long) texts.
 
 ## LangGraph