redo intro docs page

docs/core_docs/docs/contributing.mdx

@@ -0,0 +1,7 @@
+---
+title: Developer Guide
+---
+
+import Contributing from "../../../CONTRIBUTING.md";
+
+<Contributing />

docs/core_docs/docs/get_started/introduction.mdx

@@ -3,73 +3,99 @@ title: Introduction
 sidebar_position: 0
 ---
 
+# Introduction
+
 **LangChain** is a framework for developing applications powered by language models. It enables applications that:
 
-- **Are context-aware**: connect a language model to other sources of context (prompt instructions, few shot examples, content to ground it's response in)
-- **Reason**: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc)
+- **Are context-aware**: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
+- **Reason**: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)
+
+This framework consists of several parts.
+
+- **LangChain Libraries**: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
+- **[LangChain Templates](https://python.langchain.com/docs/templates)**: A collection of easily deployable reference architectures for a wide variety of tasks. (_Python only_)
+- **[LangServe](https://python.langchain.com/docs/langserve)**: A library for deploying LangChain chains as a REST API. (_Python only_)
+- **[LangSmith](https://smith.langchain.com/)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
+
+![LangChain Diagram](/img/langchain_stack.png)
+
+Together, these products simplify the entire application lifecycle:
+
+- **Develop**: Write your applications in LangChain/LangChain.js. Hit the ground running using Templates for reference.
+- **Productionize**: Use LangSmith to inspect, test and monitor your chains, so that you can constantly improve and deploy with confidence.
+- **Deploy**: Turn any chain into an API with LangServe.
 
-The main value props of LangChain are:
+## LangChain Libraries
 
-1. **Components**: abstractions for working with language models, along with a collection of implementations for each abstraction. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
-2. **Off-the-shelf chains**: a structured assembly of components for accomplishing specific higher-level tasks
+The main value props of the LangChain packages are:
 
-Off-the-shelf chains make it easy to get started. For more complex applications and nuanced use-cases, components make it easy to customize existing chains or build new ones.
+1. **Components**: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
+2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks
+
+Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
 
 ## Get started
 
-[Here’s](/docs/get_started/installation) how to install LangChain, set up your environment, and start building.
+[Here's](/docs/get_started/installation) how to install LangChain, set up your environment, and start building.
 
 We recommend following our [Quickstart](/docs/get_started/quickstart) guide to familiarize yourself with the framework by building your first LangChain application.
 
-_**Note**: These docs are for the LangChain [JS/TS package](https://github.com/langchain-ai/langchainjs). For documentation on [the Python version](https://github.com/langchain-ai/langchain), [head here](https://python.langchain.com/docs)._
+Read up on our [Security](/docs/security) best practices to make sure you're developing safely with LangChain.
 
-## Modules
+:::note
 
-LangChain provides standard, extendable interfaces and external integrations for the following modules, listed from least to most complex:
+These docs focus on the JS/TS LangChain library. [Head here](https://python.langchain.com) for docs on the Python LangChain library.
 
-#### [Model I/O](/docs/modules/model_io/)
+:::
 
-Interface with language models
+## LangChain Expression Language (LCEL)
 
-#### [Retrieval](/docs/modules/data_connection/)
+LCEL is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains.
 
-Interface with application-specific data
+- **[Overview](/docs/expression_language/)**: LCEL and its benefits
+- **[Interface](/docs/expression_language/interface)**: The standard interface for LCEL objects
+- **[How-to](/docs/expression_language/how_to/routing)**: Key features of LCEL
+- **[Cookbook](/docs/expression_language/cookbook)**: Example code for accomplishing common tasks
 
-#### [Chains](/docs/modules/chains/)
+## Modules
 
-Construct sequences of calls
+LangChain provides standard, extendable interfaces and integrations for the following modules:
 
-#### [Agents](/docs/modules/agents/)
+#### [Model I/O](/docs/modules/model_io/)
 
-Let chains choose which tools to use given high-level directives
+Interface with language models
 
-#### [Memory](/docs/modules/memory/)
+#### [Retrieval](/docs/modules/data_connection/)
 
-Persist application state between runs of a chain
+Interface with application-specific data
 
-#### [Callbacks](/docs/modules/callbacks/)
+#### [Agents](/docs/modules/agents/)
 
-Log and stream intermediate steps of any chain
+Let models choose which tools to use given high-level directives
 
 ## Examples, ecosystem, and resources
 
 ### [Use cases](/docs/use_cases/)
 
-Walkthroughs and best-practices for common end-to-end use cases, like:
+Walkthroughs and techniques for common end-to-end use cases, like:
 
-- [Chatbots](/docs/use_cases/chatbots/)
-- [Answering questions using sources](/docs/use_cases/question_answering/)
-- [Analyzing structured data](/docs/use_cases/tabular)
+- [Document question answering](/docs/use_cases/question_answering/)
+- [RAG](/docs/use_cases/rag/code_understanding)
+- [Agents](/docs/use_cases/autonomous_agents/)
 - and much more...
 
-### [Guides](/docs/guides/)
+### [Integrations](/docs/integrations/platforms/)
+
+LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/platforms/).
+
+### [API reference](https://api.js.langchain.com)
 
-Learn best practices for developing with LangChain.
+Head to the reference section for full documentation of all classes and methods in the LangChain and LangChain Experimental packages.
 
-### [Additional resources](/docs/additional_resources/tutorials)
+### [Developer's guide](/docs/contributing)
 
-Our community is full of prolific developers, creative builders, and fantastic teachers. Check out [our tutorials section](/docs/additional_resources/tutorials) for guides on how to get started with various concepts, and [Gallery](https://github.com/kyrolabs/awesome-langchain) for a list of awesome LangChain projects, compiled by the folks at [KyroLabs](https://kyrolabs.com).
+Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.
 
 ### [Community](/docs/community)
 
-Head to the [Community navigator](/docs/community) to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s.
+Head to the [Community navigator](/docs/community) to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM's.

docs/core_docs/docusaurus.config.js

@@ -181,6 +181,10 @@ const config = {
                 to: "/docs/additional_resources/tutorials",
                 label: "Tutorials",
               },
+              {
+                to: "/docs/contributing",
+                label: "Contributing",
+              },
             ],
           },
           {