Update README and other links

CONTRIBUTING.md

@@ -25,7 +25,7 @@ LangChain supports several different types of integrations with third-party prov
 
 We welcome such contributions, but ask that you read our dedicated [integration contribution guide](https://github.com/langchain-ai/langchainjs/blob/main/.github/contributing/INTEGRATIONS.md) for specific details and patterns to consider before opening a pull request.
 
-You can also check out the [guide on extending LangChain.js](https://js.langchain.com/docs/guides/extending_langchain/) in our docs.
+You can also check out the [guides on extending LangChain.js](https://js.langchain.com/v0.2/docs/how_to/#custom) in our docs.
 
 #### Integration packages
 

README.md

@@ -57,13 +57,13 @@ This library aims to assist in the development of those types of applications. C
 
 **❓Question Answering over specific documents**
 
-- [Documentation](https://js.langchain.com/docs/use_cases/question_answering/)
+- [Documentation](https://js.langchain.com/v0.2/docs/tutorials/rag/)
 - End-to-end Example: [Doc-Chatbot](https://github.com/dissorial/doc-chatbot)
 
 
 **💬 Chatbots**
 
-- [Documentation](https://js.langchain.com/docs/modules/model_io/chat/)
+- [Documentation](https://js.langchain.com/v0.2/docs/tutorials/chatbot)
 - End-to-end Example: [Chat-LangChain](https://github.com/langchain-ai/chat-langchain)
 
 ## 🚀 How does LangChain help?
@@ -93,9 +93,9 @@ Agents involve an LLM making decisions about which Actions to take, taking that
 Please see [here](https://js.langchain.com) for full documentation, which includes:
 
 - [Getting started](https://js.langchain.com/v0.2/docs/introduction): installation, setting up the environment, simple examples
-- Overview of the [interfaces](https://js.langchain.com/docs/expression_language/), [modules](https://js.langchain.com/docs/modules/) and [integrations](https://js.langchain.com/docs/integrations/platforms)
-- [Use case](https://js.langchain.com/docs/use_cases/) walkthroughs and best practice [guides](https://js.langchain.com/docs/guides/)
-- [Reference](https://v02.api.js.langchain.com): full API docs
+- Overview of the [interfaces](https://js.langchain.com/v0.2/docs/how_to/lcel_cheatsheet/), [modules](https://js.langchain.com/v0.2/docs/concepts) and [integrations](https://js.langchain.com/v0.2/docs/integrations/platforms/)
+- [Tutorial](https://js.langchain.com/v0.2/docs/tutorials/) walkthroughs
+- [Reference](https://api.js.langchain.com): full API docs
 
 ## 💁 Contributing
 

cookbook/rag_fusion.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "## Setup\n",
     "\n",
-    "For this example we'll use an in memory store as our vectorstore/retriever, and some fake data. You can swap out the vectorstore for your [preferred LangChain.js option](https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/) later.\n"
+    "For this example we'll use an in memory store as our vectorstore/retriever, and some fake data. You can swap out the vectorstore for your [preferred LangChain.js option](https://js.langchain.com/v0.2/docs/integrations/vectorstores) later.\n"
    ]
   },
   {

docs/core_docs/docs/how_to/qa_citations.ipynb

@@ -17,7 +17,7 @@
         "\n",
         "How can we get a model to cite which parts of the source documents it referenced in its response?\n",
         "\n",
-        "To explore some techniques for extracting citations, let's first create a simple RAG chain. To start we'll just retrieve from the web using the [`TavilySearchAPIRetriever`](https://js.langchain.com/docs/integrations/retrievers/tavily)."
+        "To explore some techniques for extracting citations, let's first create a simple RAG chain. To start we'll just retrieve from the web using the [`TavilySearchAPIRetriever`](https://v02.api.js.langchain.com/classes/langchain_community_retrievers_tavily_search_api.TavilySearchAPIRetriever.html)."
       ]
     },
     {
@@ -734,7 +734,7 @@
       "source": [
         "## Retrieval post-processing\n",
         "\n",
-        "Another approach is to post-process our retrieved documents to compress the content, so that the source content is already minimal enough that we don't need the model to cite specific sources or spans. For example, we could break up each document into a sentence or two, embed those and keep only the most relevant ones. LangChain has some built-in components for this. Here we'll use a [`RecursiveCharacterTextSplitter`](https://js.langchain.com/docs/modules/data_connection/document_transformers/recursive_text_splitter), which creates chunks of a specified size by splitting on separator substrings, and an [`EmbeddingsFilter`](https://js.langchain.com/docs/modules/data_connection/retrievers/contextual_compression#embeddingsfilter), which keeps only the texts with the most relevant embeddings."
+        "Another approach is to post-process our retrieved documents to compress the content, so that the source content is already minimal enough that we don't need the model to cite specific sources or spans. For example, we could break up each document into a sentence or two, embed those and keep only the most relevant ones. LangChain has some built-in components for this. Here we'll use a [`RecursiveCharacterTextSplitter`](https://js.langchain.com/v0.2/docs/how_to/recursive_text_splitter), which creates chunks of a specified size by splitting on separator substrings, and an [`EmbeddingsFilter`](https://js.langchain.com/v0.2/docs/how_to/contextual_compression), which keeps only the texts with the most relevant embeddings."
       ]
     },
     {

langchain-core/README.md

@@ -83,7 +83,7 @@ Streaming (and streaming of intermediate steps) is needed to show the user that
 Async interfaces are nice when moving into production.
 Rather than having to write multiple implementations for all of those, LCEL allows you to write a runnable once and invoke it in many different ways.
 
-For more check out the [LCEL docs](https://js.langchain.com/docs/expression_language/).
+For more check out the [LCEL docs](https://js.langchain.com/v0.2/docs/concepts#langchain-expression-language).
 
 ![LangChain Stack](../docs/core_docs/static/img/langchain_stack_feb_2024.webp)
 

langchain/README.md

@@ -57,7 +57,7 @@ This library aims to assist in the development of those types of applications. C
 
 **❓Question Answering over specific documents**
 
-- [Documentation](https://js.langchain.com/docs/use_cases/question_answering/)
+- [Documentation](https://js.langchain.com/v0.2/docs/tutorials/rag)
 - End-to-end Example: [Doc-Chatbot](https://github.com/dissorial/doc-chatbot)
 
 
@@ -95,7 +95,7 @@ Please see [here](https://js.langchain.com/v0.2/) for full documentation, which
 - [Getting started](https://js.langchain.com/v0.2/docs/introduction): installation, setting up the environment, simple examples
 - [Tutorials](https://js.langchain.com/v0.2/docs/tutorials/): interactive guides and walkthroughs of common use cases/tasks.
 - [Use case](https://js.langchain.com/v0.2/docs/how_to/) walkthroughs and best practices for every component of the LangChain library.
-- [Reference](https://v02.api.js.langchain.com): full API docs
+- [Reference](https://api.js.langchain.com): full API docs
 
 ## 💁 Contributing