diff --git a/.github/workflows/firebase-hosting-merge.yml b/.github/workflows/firebase-hosting-merge.yml
new file mode 100644
index 00000000..262b5a52
--- /dev/null
+++ b/.github/workflows/firebase-hosting-merge.yml
@@ -0,0 +1,21 @@
+name: Deploy docs_v2
+
+on:
+  push:
+    branches:
+      - main
+      
+jobs:
+  build_and_deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332
+      - run: npm ci && npm run build
+        working-directory: docs_v2
+      - uses: FirebaseExtended/action-hosting-deploy@0cbcac4740c2bfb00d632f0b863b57713124eb5a
+        with:
+          repoToken: ${{ secrets.GITHUB_TOKEN }}
+          firebaseServiceAccount: ${{ secrets.FIREBASE_SERVICE_ACCOUNT_LANGCHAIN_DART }}
+          channelId: live
+          projectId: langchain-dart
+          entryPoint: docs_v2
diff --git a/.github/workflows/firebase-hosting-pull-request.yml b/.github/workflows/firebase-hosting-pull-request.yml
new file mode 100644
index 00000000..2a2d9416
--- /dev/null
+++ b/.github/workflows/firebase-hosting-pull-request.yml
@@ -0,0 +1,26 @@
+name: Deploy docs_v2 on PR
+
+on:
+  pull_request:
+    paths:
+      - 'docs_v2/**'
+
+permissions:
+  checks: write
+  contents: read
+  pull-requests: write
+
+jobs:
+  build_and_preview:
+    if: ${{ github.event.pull_request.head.repo.full_name == github.repository }}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332
+      - run: npm ci && npm run build
+        working-directory: docs_v2
+      - uses: FirebaseExtended/action-hosting-deploy@0cbcac4740c2bfb00d632f0b863b57713124eb5a
+        with:
+          repoToken: ${{ secrets.GITHUB_TOKEN }}
+          firebaseServiceAccount: ${{ secrets.FIREBASE_SERVICE_ACCOUNT_LANGCHAIN_DART }}
+          projectId: langchain-dart
+          entryPoint: docs_v2
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index b77c2ed8..06a8deb6 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -4,6 +4,9 @@ on:
   # pull_request_target is dangerous! Review external PRs code before approving to run the workflow
   # We need this to be able to access the secrets required by the workflow
   pull_request_target:
+    paths-ignore:
+      - 'docs/**'
+      - 'docs_v2/**'
   workflow_dispatch:
 
 # Cancel currently running workflow when a new one is triggered
diff --git a/docs_v2/.firebaserc b/docs_v2/.firebaserc
new file mode 100644
index 00000000..15e3b72b
--- /dev/null
+++ b/docs_v2/.firebaserc
@@ -0,0 +1,5 @@
+{
+  "projects": {
+    "default": "langchain-dart"
+  }
+}
diff --git a/docs_v2/.gitignore b/docs_v2/.gitignore
new file mode 100644
index 00000000..0f21febf
--- /dev/null
+++ b/docs_v2/.gitignore
@@ -0,0 +1,21 @@
+# Dependencies
+/node_modules
+
+# Production
+/build
+
+# Generated files
+.docusaurus
+.cache-loader
+
+# Misc
+.DS_Store
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+.firebase
diff --git a/docs_v2/README.md b/docs_v2/README.md
new file mode 100644
index 00000000..0c6c2c27
--- /dev/null
+++ b/docs_v2/README.md
@@ -0,0 +1,41 @@
+# Website
+
+This website is built using [Docusaurus](https://docusaurus.io/), a modern static website generator.
+
+### Installation
+
+```
+$ yarn
+```
+
+### Local Development
+
+```
+$ yarn start
+```
+
+This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
+
+### Build
+
+```
+$ yarn build
+```
+
+This command generates static content into the `build` directory and can be served using any static contents hosting service.
+
+### Deployment
+
+Using SSH:
+
+```
+$ USE_SSH=true yarn deploy
+```
+
+Not using SSH:
+
+```
+$ GIT_USER=<Your GitHub username> yarn deploy
+```
+
+If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
diff --git a/docs_v2/babel.config.js b/docs_v2/babel.config.js
new file mode 100644
index 00000000..e00595da
--- /dev/null
+++ b/docs_v2/babel.config.js
@@ -0,0 +1,3 @@
+module.exports = {
+  presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
+};
diff --git a/docs_v2/docs/01-intro.md b/docs_v2/docs/01-intro.md
new file mode 100644
index 00000000..75428706
--- /dev/null
+++ b/docs_v2/docs/01-intro.md
@@ -0,0 +1,171 @@
+---
+sidebar_position: 0
+sidebar_class_name: hidden
+---
+
+# Introduction
+
+Build Dart/Flutter applications powered by Large Language Models. 
+
+## What is LangChain.dart?
+
+LangChain.dart is an unofficial Dart port of the popular [LangChain](https://github.com/hwchase17/langchain) Python framework created by [Harrison Chase](https://www.linkedin.com/in/harrison-chase-961287118). LangChain is a framework for developing applications that are powered by large language models (LLMs).
+
+It comes with a set of components that make working with LLMs easy. 
+The components can be grouped into a few core modules:
+
+![LangChain.dart](https://raw.githubusercontent.com/davidmigloz/langchain_dart/main/docs/img/langchain.dart.png)
+
+- 📃 **Model I/O:** LangChain offers a unified API for interacting with various LLM providers (e.g. OpenAI, Google, Mistral, Ollama, etc.), allowing developers to switch between them with ease. Additionally, it provides tools for managing model inputs (prompt templates and example selectors) and parsing the resulting model outputs (output parsers).
+- 📚 **Retrieval:** assists in loading user data (via document loaders), transforming it (with text splitters), extracting its meaning (using embedding models), storing (in vector stores) and retrieving it (through retrievers) so that it can be used to ground the model's responses (i.e. Retrieval-Augmented Generation or RAG). 
+- 🤖 **Agents:** "bots" that leverage LLMs to make informed decisions about which available tools (such as web search, calculators, database lookup, etc.) to use to accomplish the designated task.
+
+The different components can be composed together using the [LangChain Expression Language (LCEL)](https://langchaindart.dev/#/expression_language/get_started).
+
+## Motivation
+
+Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP), serving as essential components in a wide range of applications, such as question-answering, summarization, translation, and text generation.
+
+The adoption of LLMs is creating a new tech stack in its wake. However, emerging libraries and tools are predominantly being developed for the Python and JavaScript ecosystems. As a result, the number of applications leveraging LLMs in these ecosystems has grown exponentially.
+
+In contrast, the Dart / Flutter ecosystem has not experienced similar growth, which can likely be attributed to the scarcity of Dart and Flutter libraries that streamline the complexities associated with working with LLMs.
+
+LangChain.dart aims to fill this gap by abstracting the intricacies of working with LLMs in Dart and Flutter, enabling developers to harness their combined potential effectively.
+
+## Packages
+
+LangChain.dart has a modular design that allows developers to import only the components they need. The ecosystem consists of several packages:
+
+### [`langchain_core`](https://pub.dev/packages/langchain_core)
+
+Contains only the core abstractions as well as LangChain Expression Language as a way to compose them together. 
+
+> Depend on this package to build frameworks on top of LangChain.dart or to interoperate with it.
+
+### [`langchain`](https://pub.dev/packages/langchain)
+
+Contains higher-level and use-case specific chains, agents, and retrieval algorithms that are at the core of the application's cognitive architecture.
+
+> Depend on this package to build LLM applications with LangChain.dart.  
+> 
+> This package exposes `langchain_core` so you don't need to depend on it explicitly.
+
+### [`langchain_community`](https://pub.dev/packages/langchain_community)
+
+Contains third-party integrations and community-contributed components that are not part of the core LangChain.dart API.
+
+> Depend on this package if you want to use any of the integrations or components it provides.
+
+### Integration-specific packages
+
+Popular third-party integrations (e.g. [`langchain_openai`](https://pub.dev/packages/langchain_openai), [`langchain_google`](https://pub.dev/packages/langchain_google), [`langchain_ollama`](https://pub.dev/packages/langchain_ollama), etc.) are moved to their own packages so that they can be imported independently without depending on the entire `langchain_community` package.
+
+> Depend on an integration-specific package if you want to use the specific integration.
+
+
+## Getting started
+
+To start using LangChain.dart, add `langchain` as a dependency to your `pubspec.yaml` file. Also, include the dependencies for the specific integrations you want to use (e.g.`langchain_community`, `langchain_openai`, `langchain_google`, etc.):
+
+```yaml
+dependencies:
+  langchain: {version}
+  langchain_community: {version}
+  langchain_openai: {version}
+  langchain_google: {version}
+  ...
+```
+
+The most basic building block of LangChain.dart is calling an LLM on some prompt. LangChain.dart provides a unified interface for calling different LLMs. For example, we can use `ChatGoogleGenerativeAI` to call Google's Gemini model:
+
+```dart
+final model = ChatGoogleGenerativeAI(apiKey: googleApiKey);
+final prompt = PromptValue.string('Hello world!');
+final result = await model.invoke(prompt);
+// Hello everyone! I'm new here and excited to be part of this community.
+```
+
+But the power of LangChain.dart comes from chaining together multiple components to implement complex use cases. For example, a RAG (Retrieval-Augmented Generation) pipeline that would accept a user query, retrieve relevant documents from a vector store, format them using prompt templates, invoke the model, and parse the output:
+
+```dart
+// 1. Create a vector store and add documents to it
+final vectorStore = MemoryVectorStore(
+  embeddings: OpenAIEmbeddings(apiKey: openaiApiKey),
+);
+await vectorStore.addDocuments(
+  documents: [
+    Document(pageContent: 'LangChain was created by Harrison'),
+    Document(pageContent: 'David ported LangChain to Dart in LangChain.dart'),
+  ],
+);
+
+// 2. Define the retrieval chain
+final retriever = vectorStore.asRetriever();
+final setupAndRetrieval = Runnable.fromMap<String>({
+  'context': retriever.pipe(
+    Runnable.mapInput((docs) => docs.map((d) => d.pageContent).join('\n')),
+  ),
+  'question': Runnable.passthrough(),
+});
+
+// 3. Construct a RAG prompt template
+final promptTemplate = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system, 'Answer the question based on only the following context:\n{context}'),
+  (ChatMessageType.human, '{question}'),
+]);
+
+// 4. Define the final chain
+final model = ChatOpenAI(apiKey: openaiApiKey);
+const outputParser = StringOutputParser<ChatResult>();
+final chain = setupAndRetrieval
+    .pipe(promptTemplate)
+    .pipe(model)
+    .pipe(outputParser);
+
+// 5. Run the pipeline
+final res = await chain.invoke('Who created LangChain.dart?');
+print(res);
+// David created LangChain.dart
+```
+
+## Documentation
+
+- [LangChain.dart documentation](https://langchaindart.dev)
+- [Sample apps](https://github.com/davidmigloz/langchain_dart/tree/main/examples)
+- [LangChain.dart blog](https://blog.langchaindart.dev)
+- [Project board](https://github.com/users/davidmigloz/projects/2/views/1)
+
+## Community
+
+Stay up-to-date on the latest news and updates on the field, have great discussions, and get help in the official [LangChain.dart Discord server](https://discord.gg/x4qbhqecVR).
+
+[![LangChain.dart Discord server](https://invidget.switchblade.xyz/x4qbhqecVR?theme=light)](https://discord.gg/x4qbhqecVR)
+
+## Contribute
+
+| 📢 **Call for Collaborators** 📢                                        |
+|-------------------------------------------------------------------------|
+| We are looking for collaborators to join the core group of maintainers. |
+
+New contributors welcome! Check out our [Contributors Guide](https://github.com/davidmigloz/langchain_dart/blob/main/CONTRIBUTING.md) for help getting started.
+
+Join us on [Discord](https://discord.gg/x4qbhqecVR) to meet other maintainers. We'll help you get your first contribution in no time!
+
+## Related projects
+
+- [LangChain](https://github.com/langchain-ai/langchain): The original Python LangChain project.
+- [LangChain.js](https://github.com/langchain-ai/langchainjs): A JavaScript port of LangChain.
+- [LangChain.go](https://github.com/tmc/langchaingo): A Go port of LangChain.
+- [LangChain.rb](https://github.com/andreibondarev/langchainrb): A Ruby port of LangChain.
+
+## Sponsors
+
+<p align="center">
+  <a href="https://github.com/sponsors/davidmigloz">
+    <img src='https://raw.githubusercontent.com/davidmigloz/sponsors/main/sponsors.svg'/>
+  </a>
+</p>
+
+## License
+
+LangChain.dart is licensed under the [MIT License](https://github.com/davidmigloz/langchain_dart/blob/main/LICENSE).
diff --git a/docs_v2/docs/02-tutorials/01-llm_chain.md b/docs_v2/docs/02-tutorials/01-llm_chain.md
new file mode 100644
index 00000000..e40bbb77
--- /dev/null
+++ b/docs_v2/docs/02-tutorials/01-llm_chain.md
@@ -0,0 +1,7 @@
+---
+sidebar_position: 0
+sidebar_class_name: hidden
+---
+
+
+# Build a Simple LLM Application with LCEL
\ No newline at end of file
diff --git a/docs_v2/docs/02-tutorials/index.mdx b/docs_v2/docs/02-tutorials/index.mdx
new file mode 100644
index 00000000..82e56f9e
--- /dev/null
+++ b/docs_v2/docs/02-tutorials/index.mdx
@@ -0,0 +1,28 @@
+---
+sidebar_position: 0
+sidebar_class_name: hidden
+---
+# Tutorials
+
+New to LangChain or to 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)
+
+## 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/)
+
+## 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)
\ No newline at end of file
diff --git a/docs_v2/docs/03-how_to/01-installation.md b/docs_v2/docs/03-how_to/01-installation.md
new file mode 100644
index 00000000..44604573
--- /dev/null
+++ b/docs_v2/docs/03-how_to/01-installation.md
@@ -0,0 +1,77 @@
+# Installation
+Langchain as a framework consists of a number of packages. They're split into different packages allowing you to choose exactly what pieces of the framework to install and use. 
+
+## Installing essential Langchain.dart packages
+
+### [`langchain`](https://pub.dev/packages/langchain)
+Contains higher-level and use-case specific chains, agents, and retrieval algorithms that are at the core of the application's cognitive architecture.
+
+> Depend on this package to build LLM applications with Langchain.dart.  
+> 
+> This package exposes `langchain_core` so you don't need to depend on it explicitly.
+
+```bash
+dart pub add langchain
+```
+
+### [`langchain_core`](https://pub.dev/packages/langchain_core)
+This package contains base abstractions of different components and ways to compose them together.
+The interfaces for core components like LLMs, vector stores, retrievers and more are defined here.
+> Depend on this package to build frameworks on top of Langchain.dart or to interoperate with it.
+
+To install this package in your Dart or Flutter project
+```bash
+dart pub add langchain_core
+```
+
+### [`langchain_community`](https://pub.dev/packages/langchain_community)
+Contains third-party integrations and community-contributed components that are not part of the core Langchain.dart API.
+> Depend on this package if you want to use any of the integrations or components it provides like CSV,JSON,Text or HTML loaders and more.  
+
+```bash
+dart pub add langchain langchain_community
+```
+
+## Integration packages
+Certain integrations like OpenAI and Anthropic have their own packages. Any integrations that require their own package will be documented as such in the Integration docs.
+<!-- //TODO(bettdouglas): Add Integration Docs -->
+
+Let's say you're using [OpenAI](https://platform.openai.com/), install the `langchain_openai` package.
+```bash
+dart pub add langchain langchain_community langchain_openai
+```
+
+Let's say you want Google integration to use (GoogleAI, VertexAI, Gemini etc), install the `langchain_google` package. 
+```bash
+dart pub add langchain langchain_community langchain_google
+```
+The following table contains the list of existing Langchain.dart integration packages. 
+
+| Package                                                             | Version                                                                                                                      | Description                                                                                                                                                                                             |
+|---------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [langchain_core](https://pub.dev/packages/langchain_core)           | [![langchain_core](https://img.shields.io/pub/v/langchain_core.svg)](https://pub.dev/packages/langchain_core)                | Core abstractions and LCEL                                                                                                                                                                              |
+| [langchain](https://pub.dev/packages/langchain)                     | [![langchain](https://img.shields.io/pub/v/langchain.svg)](https://pub.dev/packages/langchain)                               | Higher-level and use-case specific chains, agents, and retrieval algorithms                                                                                                                             |
+| [langchain_community](https://pub.dev/packages/langchain_community) | [![langchain_community](https://img.shields.io/pub/v/langchain_community.svg)](https://pub.dev/packages/langchain_community) | Third-party integrations (without specific packages) and community-contributed components and utilities                                                                                                 |
+| [langchain_openai](https://pub.dev/packages/langchain_openai)       | [![langchain_openai](https://img.shields.io/pub/v/langchain_openai.svg)](https://pub.dev/packages/langchain_openai)          | OpenAI integration (GPT-3.5 Turbo, GPT-4, GPT-4o, Embeddings, Tools, Vision, DALL·E 3, etc.) and OpenAI Compatible services (TogetherAI, Anyscale, OpenRouter, One API, Groq, Llamafile, GPT4All, etc.) |
+| [langchain_google](https://pub.dev/packages/langchain_google)       | [![langchain_google](https://img.shields.io/pub/v/langchain_google.svg)](https://pub.dev/packages/langchain_google)          | Google integration (GoogleAI, VertexAI, Gemini, PaLM 2, Embeddings, Vector Search, etc.)                                                                                                                |
+| [langchain_firebase](https://pub.dev/packages/langchain_firebase)   | [![langchain_firebase](https://img.shields.io/pub/v/langchain_firebase.svg)](https://pub.dev/packages/langchain_firebase)    | Firebase integration (VertexAI for Firebase (Gemini 1.5 Pro, Gemini 1.5 Flash, etc.))                                                                                                                   |
+| [langchain_ollama](https://pub.dev/packages/langchain_ollama)       | [![langchain_ollama](https://img.shields.io/pub/v/langchain_ollama.svg)](https://pub.dev/packages/langchain_ollama)          | Ollama integration (Llama 3, Phi-3, WizardLM-2, Mistral 7B, Gemma, CodeGemma, Command R, LLaVA, DBRX, Qwen 1.5, Dolphin, DeepSeek Coder, Vicuna, Orca, etc.)                                            |
+| [langchain_mistralai](https://pub.dev/packages/langchain_mistralai) | [![langchain_mistralai](https://img.shields.io/pub/v/langchain_mistralai.svg)](https://pub.dev/packages/langchain_mistralai) | Mistral AI integration (Mistral-7B, Mixtral 8x7B, Mixtral 8x22B, Mistral Small, Mistral Large, embeddings, etc.).                                                                                       |
+| [langchain_pinecone](https://pub.dev/packages/langchain_pinecone)   | [![langchain_pinecone](https://img.shields.io/pub/v/langchain_pinecone.svg)](https://pub.dev/packages/langchain_pinecone)    | Pinecone vector database integration                                                                                                                                                                    |
+| [langchain_chroma](https://pub.dev/packages/langchain_chroma)       | [![langchain_chroma](https://img.shields.io/pub/v/langchain_chroma.svg)](https://pub.dev/packages/langchain_chroma)          | Chroma vector database integration                                                                                                                                                                      |
+| [langchain_supabase](https://pub.dev/packages/langchain_supabase)   | [![langchain_supabase](https://img.shields.io/pub/v/langchain_supabase.svg)](https://pub.dev/packages/langchain_supabase)    | Supabase Vector database integration                                                                                                                                                                    |
+## Documentation
+
+Detailed documentation for various integrations can be found in the `/docs/05-integration/` directory:
+
+- [Anthropic](/docs/integrations/anthropic)
+- [Anyscale](/docs/integrations/anyscale)
+- [Firebase VertexAI](/docs/integrations/firebase_vertex_ai)
+- [GCP VertexAI](/docs/integrations/gcp_vertex_ai)
+- [GoogleAI](/docs/integrations/googleai)
+- [MistralAI](/docs/integrations/mistralai)
+- [Ollama](/docs/integrations/ollama)
+- [OpenRouter](/docs/integrations/open_router)
+- [OpenAI](/docs/integrations/openai)
+- [PrEM](/docs/integrations/prem)
+- [TogetherAI](/docs/integrations/together_ai)
\ No newline at end of file
diff --git a/docs_v2/docs/03-how_to/02-structured_output.md b/docs_v2/docs/03-how_to/02-structured_output.md
new file mode 100644
index 00000000..95983cd6
--- /dev/null
+++ b/docs_v2/docs/03-how_to/02-structured_output.md
@@ -0,0 +1,14 @@
+---
+sidebar_position: 3
+keywords: [structured output, json, information extraction, with_structured_output]
+---
+# How to return structured data from a model
+
+> This guide assumes familiarity with the following concepts:
+> - [Chat models](/docs/concepts/#chat-models)
+> - [Function/tool calling](/docs/concepts/#functiontool-calling)
+
+
+It is often useful to have a model return output that matches a specific schema. One common use-case is extracting data from text to insert into a database or use with some other downstream system. This guide covers a few strategies for getting structured outputs from a model.
+
+
diff --git a/docs_v2/docs/03-how_to/index.mdx b/docs_v2/docs/03-how_to/index.mdx
new file mode 100644
index 00000000..81ea6bc7
--- /dev/null
+++ b/docs_v2/docs/03-how_to/index.mdx
@@ -0,0 +1,149 @@
+---
+sidebar_position: 0
+sidebar_class_name: hidden
+---
+
+# How-to guides
+
+Here you'll find answers to "How do I...?" types of questions.
+These guides are *goal-oriented* and *concrete*; they're meant to help you complete a specific task.
+For conceptual explanations see the [Conceptual guide](/docs/concepts/).
+For end-to-end walkthroughs see [Tutorials](/docs/tutorials).
+For comprehensive descriptions of every class and function see the [API Reference](https://pub.dev/documentation/langchain/latest/index.html).
+
+
+## Installation
+
+- [How to: install LangChain packages](/docs/how_to/installation/)
+
+## Key features 
+This highlights functionality that is core to using LangChain.
+
+- [How to: return structured data from a model](/docs/how_to/structured_output/)
+- [How to: use a model to call tools](/docs/how_to/tool_calling)
+- [How to: stream runnables](/docs/how_to/streaming)
+- [How to: debug your LLM apps](/docs/how_to/debugging/)
+
+[LangChain Expression Language](/docs/concepts/#langchain-expression-language-lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html) protocol.
+
+[**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives.
+
+- [How to: chain runnables](/docs/how_to/sequence)
+- [How to: stream runnables](/docs/how_to/streaming)
+- [How to: invoke runnables in parallel](/docs/how_to/parallel/)
+- [How to: add default invocation args to runnables](/docs/how_to/binding/)
+- [How to: turn any function into a runnable](/docs/how_to/functions)
+- [How to: pass through inputs from one chain step to the next](/docs/how_to/passthrough)
+- [How to: configure runnable behavior at runtime](/docs/how_to/configure)
+- [How to: add message history (memory) to a chain](/docs/how_to/message_history)
+- [How to: route between sub-chains](/docs/how_to/routing)
+- [How to: create a dynamic (self-constructing) chain](/docs/how_to/dynamic_chain/)
+- [How to: inspect runnables](/docs/how_to/inspect)
+- [How to: add fallbacks to a runnable](/docs/how_to/fallbacks)
+- [How to: migrate chains to LCEL](/docs/how_to/migrate_chains)
+- [How to: pass runtime secrets to a runnable](/docs/how_to/runnable_runtime_secrets)
+
+
+## Components
+
+These are the core building blocks you can use when building applications.
+
+### Prompt templates
+
+[Prompt Templates](/docs/concepts/#prompt-templates) are responsible for formatting user input into a format that can be passed to a language model.
+
+- [How to: use few shot examples](/docs/how_to/few_shot_examples)
+- [How to: use few shot examples in chat models](/docs/how_to/few_shot_examples_chat/)
+- [How to: partially format prompt templates](/docs/how_to/prompts_partial)
+- [How to: compose prompts together](/docs/how_to/prompts_composition)
+
+### Chat models
+
+[Chat Models](/docs/concepts/#chat-models) are newer forms of language models that take messages in and output a message.
+
+- [How to: do function/tool calling](/docs/how_to/tool_calling)
+- [How to: get models to return structured output](/docs/how_to/structured_output)
+- [How to: cache model responses](/docs/how_to/chat_model_caching)
+- [How to: get log probabilities](/docs/how_to/logprobs)
+- [How to: create a custom chat model class](/docs/how_to/custom_chat_model)
+- [How to: stream a response back](/docs/how_to/chat_streaming)
+- [How to: track token usage](/docs/how_to/chat_token_usage_tracking)
+- [How to: track response metadata across providers](/docs/how_to/response_metadata)
+- [How to: let your end users choose their model](/docs/how_to/chat_models_universal_init/)
+- [How to: use chat model to call tools](/docs/how_to/tool_calling)
+- [How to: stream tool calls](/docs/how_to/tool_streaming)
+- [How to: few shot prompt tool behavior](/docs/how_to/tools_few_shot)
+- [How to: bind model-specific formatted tools](/docs/how_to/tools_model_specific)
+- [How to: force a specific tool call](/docs/how_to/tool_choice)
+- [How to: init any model in one line](/docs/how_to/chat_models_universal_init/)
+
+### LLMs
+
+What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language models that take a string in and output a string.
+
+- [How to: cache model responses](/docs/how_to/llm_caching)
+- [How to: create a custom LLM class](/docs/how_to/custom_llm)
+- [How to: stream a response back](/docs/how_to/streaming_llm)
+- [How to: track token usage](/docs/how_to/llm_token_usage_tracking)
+- [How to: work with local LLMs](/docs/how_to/local_llms)
+
+### Document loaders
+
+[Document Loaders](/docs/concepts/#document-loaders) are responsible for loading documents from a variety of sources.
+
+- [How to: load CSV data](/docs/how_to/document_loader_csv)
+- [How to: load data from a directory](/docs/how_to/document_loader_directory)
+- [How to: load HTML data](/docs/how_to/document_loader_html)
+- [How to: load JSON data](/docs/how_to/document_loader_json)
+- [How to: load Markdown data](/docs/how_to/document_loader_markdown)
+- [How to: load Microsoft Office data](/docs/how_to/document_loader_office_file)
+- [How to: load PDF files](/docs/how_to/document_loader_pdf)
+- [How to: write a custom document loader](/docs/how_to/document_loader_custom)
+
+### Text splitters
+
+[Text Splitters](/docs/concepts/#text-splitters) take a document and split into chunks that can be used for retrieval.
+
+- [How to: recursively split text](/docs/how_to/recursive_text_splitter)
+- [How to: split by HTML headers](/docs/how_to/HTML_header_metadata_splitter)
+- [How to: split by HTML sections](/docs/how_to/HTML_section_aware_splitter)
+- [How to: split by character](/docs/how_to/character_text_splitter)
+- [How to: split code](/docs/how_to/code_splitter)
+- [How to: split Markdown by headers](/docs/how_to/markdown_header_metadata_splitter)
+- [How to: recursively split JSON](/docs/how_to/recursive_json_splitter)
+- [How to: split text into semantic chunks](/docs/how_to/semantic-chunker)
+- [How to: split by tokens](/docs/how_to/split_by_token)
+
+### Vector stores
+
+[Vector stores](/docs/concepts/#vector-stores) are databases that can efficiently store and retrieve embeddings.
+
+- [How to: use a vector store to retrieve data](/docs/how_to/vectorstores)
+
+### Retrievers
+
+[Retrievers](/docs/concepts/#retrievers) are responsible for taking a query and returning relevant documents.
+
+- [How to: use a vector store to retrieve data](/docs/how_to/vectorstore_retriever)
+- [How to: generate multiple queries to retrieve data for](/docs/how_to/MultiQueryRetriever)
+- [How to: use contextual compression to compress the data retrieved](/docs/how_to/contextual_compression)
+- [How to: write a custom retriever class](/docs/how_to/custom_retriever)
+- [How to: add similarity scores to retriever results](/docs/how_to/add_scores_retriever)
+- [How to: combine the results from multiple retrievers](/docs/how_to/ensemble_retriever)
+- [How to: reorder retrieved results to mitigate the "lost in the middle" effect](/docs/how_to/long_context_reorder)
+- [How to: generate multiple embeddings per document](/docs/how_to/multi_vector)
+- [How to: retrieve the whole document for a chunk](/docs/how_to/parent_document_retriever)
+- [How to: generate metadata filters](/docs/how_to/self_query)
+- [How to: create a time-weighted retriever](/docs/how_to/time_weighted_vectorstore)
+- [How to: use hybrid vector and keyword retrieval](/docs/how_to/hybrid)
+
+### Agents
+
+:::note
+
+For in depth how-to guides for agents, please check out [LangGraph](https://langchain-ai.github.io/langgraph/) documentation.
+
+:::
+
+- [How to: use legacy LangChain Agents (AgentExecutor)](/docs/how_to/agent_executor)
+- [How to: migrate from legacy LangChain agents to LangGraph](/docs/how_to/migrate_agent)
\ No newline at end of file
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+# Conceptual guide
+
+This section contains introductions to key parts of LangChain.dart
+
+## Architecture
+
+LangChain.dart as a framework consists of a number of packages. 
+
+### [`langchain_core`](https://pub.dev/packages/langchain_core)
+This package contains base abstractions of different components and ways to compose them together.
+The interfaces for core components like LLMs, vector stores, retrievers and more are defined here.
+No third party integrations are defined here.
+
+> Depend on this package to build frameworks on top of .dart.dart or to interoperate with it.
+
+### [`langchain`](https://pub.dev/packages/langchain)
+
+Contains higher-level and use-case specific chains, agents, and retrieval algorithms that are at the core of the application's cognitive architecture.
+
+> Depend on this package to build LLM applications with .dart.dart.  
+> 
+> This package exposes `langchain_core` so you don't need to depend on it explicitly.
+
+### [`langchain_community`](https://pub.dev/packages/langchain_community)
+
+Contains third-party integrations and community-contributed components that are not part of the core LangChain.dart API.
+
+> Depend on this package if you want to use any of the integrations or components it provides.
+
+### Integration-specific packages
+
+Popular third-party integrations (e.g. [`langchain_openai`](https://pub.dev/packages/langchain_openai), [`langchain_google`](https://pub.dev/packages/langchain_google), [`langchain_ollama`](https://pub.dev/packages/langchain_ollama), etc.) are moved to their own packages so that they can be imported independently without depending on the entire `langchain_community` package.
+
+> Depend on an integration-specific package if you want to use the specific integration.
+
+See [Integrations](/docs/integrations) to integrate with a specific package. 
+
+## LangChain Expression Language (LCEL)
+
+LangChain Expression Language, or LCEL, is a declarative way to easily compose chains together. 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 (we’ve seen folks successfully run LCEL chains with 100s of steps in production). To highlight a few of the reasons you might want to use LCEL:
+
+- **First-class streaming support:** When you build your chains with LCEL you get the best possible time-to-first-token (time elapsed until the first chunk of output comes out). For some chains this means eg. we stream tokens straight from an LLM to a streaming output parser, and you get back parsed, incremental chunks of output at the same rate as the LLM provider outputs the raw tokens.
+- **Optimized concurrent execution:** Whenever your LCEL chains have steps that can be executed concurrently (eg if you fetch documents from multiple retrievers) we automatically do it for the smallest possible latency.
+- **Retries and fallbacks:** Configure retries and fallbacks for any part of your LCEL chain. This is a great way to make your chains more reliable at scale.
+- **Access intermediate results:** For more complex chains it’s often very useful to access the results of intermediate steps even before the final output is produced. This can be used to let end-users know something is happening, or even just to debug your chain.
+
+### Runnable interface
+
+To make it as easy as possible to create custom chains, LangChain provides a `Runnable` interface that most components implement, including chat models, LLMs, output parsers, retrievers, prompt templates, and more.
+
+This is a standard interface, which makes it easy to define custom chains as well as invoke them in a standard way. The standard interface includes:
+
+- `invoke`: call the chain on an input and return the output.
+- `stream`: call the chain on an input and stream the output.
+- `batch`: call the chain on a list of inputs and return a list of outputs.
+
+The type of the input and output varies by component:
+
+| Component                   | Input Type             | Output Type            |
+|-----------------------------|------------------------|------------------------|
+| `PromptTemplate`            | `Map<String, dynamic>` | `PromptValue`          |
+| `ChatMessagePromptTemplate` | `Map<String, dynamic>` | `PromptValue`          |
+| `LLM`                       | `PromptValue`          | `LLMResult`            |
+| `ChatModel`                 | `PromptValue`          | `ChatResult`           |
+| `OutputParser`              | Any object             | Parser output type     |
+| `Retriever`                 | `String`               | `List<Document>`       |
+| `DocumentTransformer`       | `List<Document>`       | `List<Document>`       |
+| `Tool`                      | `Map<String, dynamic>` | `String`               |
+| `Chain`                     | `Map<String, dynamic>` | `Map<String, dynamic>` |
+
+## Components
+
+### Chat models
+Language models that use a sequence of messages as inputs and return chat messages as outputs (as opposed to using plain text).
+These are traditionally newer models (older models are generally `LLMs`, see below).
+Chat models support the assignment of distinct roles to conversation messages, helping to distinguish messages from the AI, users, and instructions such as system messages.
+
+Although the underlying models are messages in, message out, the LangChain wrappers also allow these models to take a string as input. This means you can easily use chat models in place of LLMs.
+
+When a string is passed in as input, it is converted to a `HumanMessage` and then passed to the underlying model.
+
+LangChain does not host any Chat Models, rather we rely on third party integrations.
+
+We have some standardized parameters when constructing ChatModels:
+- `model`: the name of the model
+- `temperature`: the sampling temperature
+- `timeout`: request timeout
+- `maxTokens`: max tokens to generate
+- `apiKey`: API key for the model provider
+- `baseUrl`: endpoint to send requests to
+
+Some important things to note:
+- standard params only apply to model providers that expose parameters with the intended functionality. For example, some providers do not expose a configuration for maximum output tokens, so max_tokens can't be supported on these.
+- standard params are currently only enforced on integrations that have their own integration packages (e.g. `langchain-openai`, `langchain-anthropic`, etc.), they're not enforced on models in ``langchain-community``.
+
+ChatModels also accept other parameters that are specific to that integration. To find all the parameters supported by a ChatModel head to the API reference for that model.
+
+### LLMs
+:::caution
+Pure text-in/text-out LLMs tend to be older or lower-level. Many popular models are best used as [chat completion models](/docs/concepts/#chat-models),
+even for non-chat use cases.
+
+You are probably looking for [the section above instead](/docs/concepts/#chat-models).
+:::
+
+Language models that takes a string as input and returns a string.
+These are traditionally older models (newer models generally are [Chat Models](/docs/concepts/#chat-models), see above).
+
+Although the underlying models are string in, string out, the LangChain wrappers also allow these models to take messages as input.
+This gives them the same interface as [Chat Models](/docs/concepts/#chat-models).
+When messages are passed in as input, they will be formatted into a string under the hood before being passed to the underlying model.
+
+LangChain.dart does not host any LLMs, rather we rely on third party integrations. See (/docs/integrations)
+
+
+
+### Messages
+Some language models take a list of messages as input and return a message.
+
+LangChain provides several objects to easily distinguish between different roles:
+#### HumanChatMessage
+This represents a message from the user.
+
+#### AIChatMessage 
+This represents a message from the model.
+
+#### SystemChatMessage
+This represents a system message, which tells the model how to behave. Not every model provider supports this.
+
+#### FunctionChatMessage / ToolChatMessage
+These represent a decision from an language model to call a tool. They're a subclass of a AIChatMessage. FunctionChatMessage is a legacy message type corresponding to OpenAI's legacy function-calling API. 
+
+### Prompt Templates
+
+Most LLM applications do not pass user input directly into an `LLM`. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.
+
+In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it would be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.
+
+`PromptTemplates` help with exactly this! They bundle up all the logic for going from user input into a fully formatted prompt. This can start off very simple - for example, a prompt to produce the above string would just be:
+
+```dart
+final prompt = PromptTemplate.fromTemplate(
+  'What is a good name for a company that makes {product}?',
+);
+final res = prompt.format({'product': 'colorful socks'});
+print(res);
+// 'What is a good name for a company that makes colorful socks?'
+```
+
+However, the advantages of using these over raw string formatting are several. You can "partial" out variables - e.g. you can format only some of the variables at a time. You can compose them together, easily combining different templates into a single prompt. 
+<!-- For explanations of these functionalities, see the [prompts](/modules/model_io/prompts/prompts.md) for more detail. -->
+For specifics on how to use prompt templates, see the [relevant how-to guides here](/docs/how_to/#prompt-templates).
+
+`PromptTemplates` can also be used to produce a list of messages. In this case, the prompt not only contains information about the content, but also each message (its role, its position in the list, etc) Here, what happens most often is a `ChatPromptTemplate` is a list of `ChatMessagePromptTemplates`. Each `ChatMessagePromptTemplate` contains instructions for how to format that `ChatMessage` - its role, and then also its content. Let's take a look at this below:
+
+```dart
+const template = 'You are a helpful assistant that translates {input_language} to {output_language}.';
+const humanTemplate = '{text}';
+
+final chatPrompt = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system, template),
+  (ChatMessageType.human, humanTemplate),
+]);
+
+final res = chatPrompt.formatMessages({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.',
+});
+print(res);
+// [
+//   SystemChatMessage(content='You are a helpful assistant that translates English to French.'),
+//   HumanChatMessage(content='I love programming.')
+// ]
+```
+
+`ChatPromptTemplates` can also be constructed in other ways - For specifics on how to use prompt templates, see the [relevant how-to guides here](/docs/how_to/#prompt-templates).
+
+### Output parsers
+<span data-heading-keywords="output parser"></span>
+
+:::note
+
+The information here refers to parsers that take a text output from a model try to parse it into a more structured representation.
+More and more models are supporting function (or tool) calling, which handles this automatically.
+It is recommended to use function/tool calling rather than output parsing.
+See documentation for that [here](/docs/concepts/#function-tool-calling).
+
+:::
+`OutputParsers` convert the raw output of an LLM into a format that can be used downstream. There are few main type of `OutputParsers`, including:
+
+- Convert text from LLM -> structured information (e.g. JSON).
+- Convert a `ChatMessage` into just a string.
+- Convert the extra information returned from a call besides the message (like OpenAI function invocation) into a string.
+
+For full information on this, see the section on [output parsers](/docs/how_to/#output-parsers).
+
+### Chat history
+Most LLM applications have a conversational interface. An essential component of a conversation is being able to refer to information introduced earlier in the conversation. At bare minimum, a conversational system should be able to access some window of past messages directly.
+
+The concept of ChatHistory refers to a class in LangChain which can be used to wrap an arbitrary chain. This ChatHistory will keep track of inputs and outputs of the underlying chain, and append them as messages to a message database. Future interactions will then load those messages and pass them into the chain as part of the input.
+
+### Documents
+A Document object in LangChain contains information about some data. It has two attributes:
+- pageContent: `String` - The content of this document. 
+- metadata: `Map<String,dynamic>` - Arbitrary metadata associated with this document. Can track the document id, file name, etc.
+
+### Document loaders
+Use document loaders to load data from a source as `Document`'s. For example, there are document loaders
+for loading a simple .txt file, for loading the text contents of any web page,
+or even for loading a transcript of a YouTube video.
+
+Document loaders expose two methods:
+
+- `lazyLoad()`: returns a `Stream` of `Document`'s. This is useful for loading
+  large amounts of data, as it allows you to process each `Document` as it is
+  loaded, rather than waiting for the entire data set to be loaded in memory.
+- `load()`: returns a list of `Document`'s. Under the hood, this method calls
+  `lazyLoad()` and collects the results into a list. Use this method only with
+  small data sets.
+
+The simplest loader reads in a file as text and places it all into one
+`Document`.
+
+```dart
+
+const filePath = 'example.txt';
+const loader = TextLoader(filePath);
+final docs = await loader.load();
+```
+
+### Text splitters
+Once you've loaded documents, you'll often want to transform them to better suit
+your application. The simplest example is you may want to split a long document
+into smaller chunks that can fit into your model's context window. LangChain has
+a number of built-in document transformers that make it easy to split, combine,
+filter, and otherwise manipulate documents.
+
+## Text splitters
+
+When you want to deal with long pieces of text, it is necessary to split up that
+text into chunks. As simple as this sounds, there is a lot of potential
+complexity here. Ideally, you want to keep the semantically related pieces of
+text together. What "semantically related" means could depend on the type of
+text. This tutorial showcases several ways to do that.
+
+At a high level, text splitters work as following:
+
+1. Split the text up into small, semantically meaningful chunks (often
+   sentences).
+2. Start combining these small chunks into a larger chunk until you reach a
+   certain size (as measured by some function).
+3. Once you reach that size, make that chunk its own piece of text and then
+   start creating a new chunk of text with some overlap (to keep context between
+   chunks).
+
+That means there are two different axes along which you can customize your text
+splitter:
+
+1. How the text is split.
+2. How the chunk size is measured.
+
+The most basic text splitter is the `CharacterTextSplitter`. This splits based
+on characters (by default `\n\n`) and measure chunk length by number of
+characters.
+
+The default recommended text splitter is the `RecursiveCharacterTextSplitter`. This text splitter 
+takes a list of characters. It tries to create chunks based on splitting on the first character, 
+but if any chunks are too large it then moves onto the next character, and so forth. By default 
+the characters it tries to split on are `["\n\n", "\n", " ", ""]`.
+
+In addition to controlling which characters you can split on, you can also
+control a few other things:
+
+- `lengthFunction`: how the length of chunks is calculated. Defaults to just
+  counting number of characters, but it's pretty common to pass a token counter
+  here.
+- `chunkSize`: the maximum size of your chunks (as measured by the length
+  function).
+- `chunkOverlap`: the maximum overlap between chunks. It can be nice to have
+  some overlap to maintain some continuity between chunks (eg do a sliding
+  window).
+- `addStartIndex`: whether to include the starting position of each chunk within
+  the original document in the metadata.
+
+```dart
+const filePath = 'state_of_the_union.txt';
+const loader = TextLoader(filePath);
+final documents = await loader.load();
+const textSplitter = RecursiveCharacterTextSplitter(
+  chunkSize: 800,
+  chunkOverlap: 0,
+);
+final docs = textSplitter.splitDocuments(documents);
+```
+
+### Embedding models
+Embedding models create a vector representation of a piece of text. You can think of a vector as an array of numbers that captures the semantic meaning of the text. By representing the text in this way, you can perform mathematical operations that allow you to do things like search for other pieces of text that are most similar in meaning. These natural language search capabilities underpin many of the context retrieval where we provide an LLM with relevant data it needs to effectively respond to a query. 
+
+The Embeddings class is a class designed for interfacing with text embedding
+models. There are lots of embedding model providers (OpenAI, Cohere, Hugging
+Face, etc) - this class is designed to provide a standard interface for all of
+them.
+
+Embeddings create a vector representation of a piece of text. This is useful
+because it means we can think about text in the vector space, and do things like
+semantic search where we look for pieces of text that are most similar in the
+vector space.
+
+The base Embeddings class in LangChain exposes two methods: one for embedding
+documents and one for embedding a query. The former takes as input multiple
+texts, while the latter takes a single text. The reason for having these as two
+separate methods is that some embedding providers have different embedding
+methods for documents (to be searched over) vs queries (the search query
+itself).
+
+For specifics on how to use embedding models, see the [relevant how-to guides here](/docs/how_to/#embedding-models).
+
+### Vector stores
+One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are 'most similar' to the embedded query. A vector store takes care of storing embedded data and performing vector search for you. 
+
+Most vector stores can also store metadata about embedded vectors and support filtering on that metadata before
+similarity search, allowing you more control over returned documents.
+
+Vector stores can be converted to the retriever interface by doing:
+
+For specifics on how to use vector stores, see the [relevant how-to guides here](/docs/how_to/#vector-stores).
+
+### Retrievers
+A retriever is an interface that returns documents given an unstructured query.
+It is more general than a vector store. A retriever does not need to be able to
+store documents, only to return (or retrieve) it. Vector stores can be used as
+the backbone of a retriever, but there are other types of retrievers as well.
+
+Retrievers accept a string query as input and return a list of Document's as output.
+
+The public API of the `BaseRetriever` class in LangChain is as follows:
+
+```dart
+abstract interface class BaseRetriever {
+  Future<List<Document>> getRelevantDocuments(final String query);
+}
+```
+
+For specifics on how to use retrievers, see the [relevant how-to guides here](/docs/how_to/#retrievers).
+
+### Tools
+Tools are utilities designed to be called by a model. Their inputs are designed to be generated by models, and their outputs are designed to be passed back to models. Tools are needed whenever you want a model to control parts of your code or call out to external APIs. 
+A tool consists of:
+
+1. The name of the tool.
+2. A description of what the tool does.
+3. A JSON schema defining the inputs to the tool.
+4. A function (and, optionally, an async variant of the function).
+
+When a tool is bound to a model, the name, description and JSON schema are provided as context to the model.
+Given a list of tools and a set of instructions, a model can request to call one or more tools with specific inputs.
+
+To define a tool in dart, we use the `ToolSpec` class. 
+```dart
+final openaiApiKey = Platform.environment['OPENAI_API_KEY'];
+final model = ChatOpenAI(apiKey: openaiApiKey);
+
+final promptTemplate = ChatPromptTemplate.fromTemplate(
+  'Tell me a joke about {foo}',
+);
+
+const tool = ToolSpec(
+  name: 'joke',
+  description: 'A joke',
+  inputJsonSchema: {
+    'type': 'object',
+    'properties': {
+      'setup': {
+        'type': 'string',
+        'description': 'The setup for the joke',
+      },
+      'punchline': {
+        'type': 'string',
+        'description': 'The punchline for the joke',
+      },
+    },
+    'required': ['setup', 'punchline'],
+  },
+);
+
+final chain = promptTemplate |
+    model.bind(
+      ChatOpenAIOptions(
+        tools: const [tool],
+        toolChoice: ChatToolChoice.forced(name: tool.name),
+      ),
+    );
+
+final res = await chain.invoke({'foo': 'bears'});
+print(res);
+// ChatResult{
+//   id: chatcmpl-9LBPyaZcFMgjmOvkD0JJKAyA4Cihb,
+//   output: AIChatMessage{
+//     content: ,
+//     toolCalls: [
+//       AIChatMessageToolCall{
+//         id: call_JIhyfu6jdIXaDHfYzbBwCKdb,
+//         name: joke,
+//         argumentsRaw: {"setup":"Why don't bears like fast food?","punchline":"Because they can't catch it!"},
+//         arguments: {
+//           setup: Why don't bears like fast food?,
+//           punchline: Because they can't catch it!
+//         },
+//       }
+//     ],
+//   },
+//   finishReason: FinishReason.stop,
+//   metadata: {
+//     model: gpt-4o-mini,
+//     created: 1714835806,
+//     system_fingerprint: fp_3b956da36b
+//   },
+//   usage: LanguageModelUsage{
+//     promptTokens: 77,
+//     responseTokens: 24,
+//     totalTokens: 101
+//   },
+//   streaming: false
+// }
+```
+
+When designing tools to be used by a model, it is important to keep in mind that:
+
+- Chat models that have explicit [tool-calling APIs](/docs/concepts/#functiontool-calling) will be better at tool calling than non-fine-tuned models.
+- Models will perform better if the tools have well-chosen names, descriptions, and JSON schemas. This another form of prompt engineering.
+- Simple, narrowly scoped tools are easier for models to use than complex tools.
+
+#### Related
+
+For specifics on how to use tools, see the [tools how-to guides](/docs/how_to/#tools).
+
+To use a pre-built tool, see the [tool integration docs](/docs/integrations/tools/).
+
+### Agents
+By themselves, language models can't take actions - they just output text. 
+A big use case for LangChain is creating agents. Agents are systems that use an LLM as a reasoning engine to determine which actions to take and what the inputs to those actions should be. 
+The results of those actions can then be fed back into the agent and it determine whether more actions are needed, or whether it is okay to finish.
+
+### Callbacks
+TODO:
+
+
+### Techniques
+
+#### Streaming
+
+#### Function/tool calling
+
+#### Structured Output
+LLMs are capable of generating arbitrary text. This enables the model to respond appropriately to a wide range of inputs, but for some use-cases, it can be useful to constrain the LLM's output to a specific format or structure. This is referred to as structured output.
+
+
+#### Few-shot prompting
+
+#### Retrieval
+
+#### Text splitting
+
+#### Evaluation
+
+#### Tracing
+##### 
\ No newline at end of file
diff --git a/docs_v2/docs/05-integrations/anthropic.md b/docs_v2/docs/05-integrations/anthropic.md
new file mode 100644
index 00000000..b607ddc7
--- /dev/null
+++ b/docs_v2/docs/05-integrations/anthropic.md
@@ -0,0 +1,145 @@
+# ChatAnthropic
+
+Wrapper around [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) (aka Claude API).
+
+## Setup
+
+The Anthropic API uses API keys for authentication. Visit your [API Keys](https://console.anthropic.com/settings/keys) page to retrieve the API key you'll use in your requests.
+
+The following models are available:
+- `claude-3-5-sonnet-20240620`
+- `claude-3-haiku-20240307`
+- `claude-3-opus-20240229`
+- `claude-3-sonnet-20240229`
+- `claude-2.0`
+- `claude-2.1`
+
+Mind that the list may not be up-to-date. See https://docs.anthropic.com/en/docs/about-claude/models for the updated list.
+
+## Usage
+
+```dart
+final apiKey = Platform.environment['ANTHROPIC_API_KEY'];
+
+final chatModel = ChatAnthropic(
+  apiKey: apiKey,
+  defaultOptions: ChatAnthropicOptions(
+    model: 'claude-3-5-sonnet-20240620',
+    temperature: 0,
+  ),
+);
+
+final chatPrompt = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system, 'You are a helpful assistant that translates {input_language} to {output_language}.'),
+  (ChatMessageType.human, 'Text to translate:\n{text}'),
+]);
+
+final chain = chatPrompt | chatModel | StringOutputParser();
+
+final res = await chain.invoke({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.',
+});
+print(res);
+// -> 'J'adore programmer.'
+```
+
+## Multimodal support
+
+```dart
+final apiKey = Platform.environment['ANTHROPIC_API_KEY'];
+
+final chatModel = ChatAnthropic(
+  apiKey: apiKey,
+  defaultOptions: ChatAnthropicOptions(
+    model: 'claude-3-5-sonnet-20240620',
+    temperature: 0,
+  ),
+);
+
+final res = await chatModel.invoke(
+  PromptValue.chat([
+    ChatMessage.human(
+      ChatMessageContent.multiModal([
+        ChatMessageContent.text('What fruit is this?'),
+        ChatMessageContent.image(
+          mimeType: 'image/jpeg',
+          data: base64.encode(
+            await File('./bin/assets/apple.jpeg').readAsBytes(),
+          ),
+        ),
+      ]),
+    ),
+  ]),
+);
+
+print(res.output.content);
+// -> 'The fruit in the image is an apple.'
+```
+
+## Streaming
+
+```dart
+final apiKey = Platform.environment['ANTHROPIC_API_KEY'];
+
+final promptTemplate = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system, 'You are a helpful assistant that replies only with numbers in order without any spaces or commas.'),
+  (ChatMessageType.human, 'List the numbers from 1 to {max_num}'),
+]);
+
+final chatModel = ChatAnthropic(
+  apiKey: apiKey,
+  defaultOptions: ChatAnthropicOptions(
+    model: 'claude-3-5-sonnet-20240620',
+    temperature: 0,
+  ),
+);
+
+final chain = promptTemplate.pipe(chatModel).pipe(const StringOutputParser());
+
+final stream = chain.stream({'max_num': '30'});
+await stream.forEach(print);
+// 123
+// 456789101
+// 112131415161
+// 718192021222
+// 324252627282
+// 930
+```
+
+## Tool calling
+
+`ChatAnthropic` supports tool calling.
+
+Check the [docs](/modules/model_io/models/chat_models/how_to/tools.md) for more information on how to use tools.
+
+Example:
+```dart
+const tool = ToolSpec(
+  name: 'get_current_weather',
+  description: 'Get the current weather in a given location',
+  inputJsonSchema: {
+    'type': 'object',
+    'properties': {
+      'location': {
+        'type': 'string',
+        'description': 'The city and country, e.g. San Francisco, US',
+      },
+    },
+    'required': ['location'],
+  },
+);
+final chatModel = ChatAnthropic(
+  apiKey: apiKey,
+  defaultOptions: ChatAnthropicOptions(
+    model: 'claude-3-5-sonnet-20240620',
+    temperature: 0,
+    tools: [tool],
+  ),
+);
+
+final res = await model.invoke(
+  PromptValue.string('What’s the weather like in Boston and Madrid right now in celsius?'),
+);
+```
diff --git a/docs_v2/docs/05-integrations/anyscale.md b/docs_v2/docs/05-integrations/anyscale.md
new file mode 100644
index 00000000..9a71c30e
--- /dev/null
+++ b/docs_v2/docs/05-integrations/anyscale.md
@@ -0,0 +1,84 @@
+# Anyscale
+
+[Anyscale](https://www.anyscale.com/) offers a unified OpenAI-compatible API for a broad range of [models](https://docs.endpoints.anyscale.com/guides/models/#chat-models) running serverless or on your own dedicated instances.
+
+It also allows to fine-tune models on your own data or train new models from scratch.
+
+You can consume Anyscale API using the `ChatOpenAI` wrapper in the same way you would use the OpenAI API.
+
+The only difference is that you need to change the base URL to `https://api.endpoints.anyscale.com/v1`:
+
+```dart
+final chatModel = ChatOpenAI(
+  apiKey: anyscaleApiKey,
+  baseUrl: 'https://api.endpoints.anyscale.com/v1',
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'meta-llama/Llama-2-70b-chat-hf',
+  ),
+);
+```
+
+## Invoke
+
+```dart
+final anyscaleApiKey = Platform.environment['ANYSCALE_API_KEY'];
+
+final promptTemplate = ChatPromptTemplate.fromTemplates(const [
+  (
+  ChatMessageType.system,
+  'You are a helpful assistant that translates {input_language} to {output_language}.',
+  ),
+  (ChatMessageType.human, '{text}'),
+]);
+
+final chatModel = ChatOpenAI(
+  apiKey: anyscaleApiKey,
+  baseUrl: 'https://api.endpoints.anyscale.com/v1',
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'meta-llama/Llama-2-70b-chat-hf',
+  ),
+);
+
+final chain = promptTemplate | chatModel | StringOutputParser();
+
+final res = await chain.invoke({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.',
+});
+print(res);
+// -> "I love programming" se traduit en français sous la forme "J'aime passionnément la programmation"
+```
+
+## Stream
+
+```dart
+final anyscaleApiKey = Platform.environment['ANYSCALE_API_KEY'];
+
+final promptTemplate = ChatPromptTemplate.fromTemplates(const [
+  (
+  ChatMessageType.system,
+  'You are a helpful assistant that replies only with numbers '
+      'in order without any spaces or commas',
+  ),
+  (ChatMessageType.human, 'List the numbers from 1 to {max_num}'),
+]);
+
+final chatModel = ChatOpenAI(
+  apiKey: anyscaleApiKey,
+  baseUrl: 'https://api.endpoints.anyscale.com/v1',
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'mistralai/Mixtral-8x7B-Instruct-v0.1',
+  ),
+);
+
+final chain = promptTemplate.pipe(chatModel).pipe(StringOutputParser());
+
+final stream = chain.stream({'max_num': '9'});
+await stream.forEach(print);
+// 1
+// 2
+// 3
+// ...
+// 9
+```
diff --git a/docs_v2/docs/05-integrations/firebase_vertex_ai.md b/docs_v2/docs/05-integrations/firebase_vertex_ai.md
new file mode 100644
index 00000000..cd33daa2
--- /dev/null
+++ b/docs_v2/docs/05-integrations/firebase_vertex_ai.md
@@ -0,0 +1,190 @@
+# Vertex AI for Firebase
+
+The [Vertex AI Gemini API](https://firebase.google.com/docs/vertex-ai) gives you access to the latest generative AI models from Google: the Gemini models. If you need to call the Vertex AI Gemini API directly from your mobile or web app you can use the `ChatFirebaseVertexAI` class instead of the [`ChatVertexAI`](/modules/model_io/models/chat_models/integrations/gcp_vertex_ai.md) class which is designed to be used on the server-side. 
+
+`ChatFirebaseVertexAI` is built specifically for use with mobile and web apps, offering security options against unauthorized clients as well as integrations with other Firebase services.
+
+## Key capabilities
+
+- **Multimodal input**: The Gemini models are multimodal, so prompts sent to the Gemini API can include text, images (even PDFs), video, and audio.
+- **Growing suite of capabilities**: You can call the Gemini API directly from your mobile or web app, build an AI chat experience, use function calling, and more. 
+- **Security for production apps**: Use Firebase App Check to protect the Vertex AI Gemini API from abuse by unauthorized clients.
+- **Robust infrastructure**: Take advantage of scalable infrastructure that's built for use with mobile and web apps, like managing structured data with Firebase database offerings (like Cloud Firestore) and dynamically setting run-time configurations with Firebase Remote Config.
+
+## Setup
+
+### 1. Set up a Firebase project
+
+Check the [Firebase documentation](https://firebase.google.com/docs/vertex-ai/get-started?platform=flutter) for the latest information on how to set up the Vertex AI for Firebase in your Firebase project.
+
+In summary, you need to:
+1. Upgrade your billing plan to the Blaze pay-as-you-go pricing plan.
+2. Enable the required APIs (`aiplatform.googleapis.com` and `firebaseml.googleapis.com`).
+3. Integrate the Firebase SDK into your app (if you haven't already).
+4. Recommended: Enable Firebase App Check to protect the Vertex AI Gemini API from abuse by unauthorized clients.
+
+### 2. Add the LangChain.dart Google package
+
+Add the `langchain_google` package to your `pubspec.yaml` file.
+
+```yaml
+dependencies:
+  langchain: {version}
+  langchain_google: {version}
+```
+
+Internally, `langchain_google` uses the [`firebase_vertexai`](https://pub.dev/packages/firebase_vertexai) SDK to interact with the Vertex AI for Firebase API.
+
+### 3. Initialize your Firebase app
+
+```yaml 
+await Firebase.initializeApp();
+```
+
+### 4. Call the Vertex AI Gemini API
+
+```dart
+final chatModel = ChatFirebaseVertexAI();
+final chatPrompt = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system, 'You are a helpful assistant that translates {input_language} to {output_language}.'),
+  (ChatMessageType.human, 'Text to translate:\n{text}'),
+]);
+
+final chain = chatPrompt | chatModel | StringOutputParser();
+
+final res = await chain.invoke({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.',
+});
+print(res);
+// -> 'J'adore programmer.'
+```
+
+> Check out the [sample project](https://github.com/davidmigloz/langchain_dart/tree/main/packages/langchain_firebase/example) to see a complete project using Vertex AI for Firebase.
+
+## Available models
+
+The following models are available:
+- `gemini-1.5-flash`:
+  * text / image / audio -> text model
+  * Max input token: 1048576
+  * Max output tokens: 8192
+- `gemini-1.5-pro`:
+  * text / image / audio -> text model
+  * Max input token: 1048576
+  * Max output tokens: 8192
+- `gemini-1.0-pro-vision`:
+  * text / image -> text model
+  * Max input token: 12288
+  * Max output tokens: 4096
+- `gemini-1.0-pro`
+  * text -> text model
+  * Max input token: 30720
+  * Max output tokens: 2048
+      
+Mind that this list may not be up-to-date. Refer to the [documentation](https://firebase.google.com/docs/vertex-ai/gemini-models) for the updated list.
+
+## Multimodal support
+
+```dart
+final chatModel = ChatFirebaseVertexAI(
+  defaultOptions: ChatFirebaseVertexAIOptions(
+    model: 'gemini-1.5-pro',
+  ),
+);
+final res = await chatModel.invoke(
+  PromptValue.chat([
+    ChatMessage.human(
+      ChatMessageContent.multiModal([
+        ChatMessageContent.text('What fruit is this?'),
+        ChatMessageContent.image(
+          mimeType: 'image/jpeg',
+          data: base64.encode(
+            await File('./bin/assets/apple.jpeg').readAsBytes(),
+          ),
+        ),
+      ]),
+    ),
+  ]),
+);
+print(res.output.content);
+// -> 'That is an apple.'
+```
+
+## Streaming
+
+```dart
+final promptTemplate = ChatPromptTemplate.fromTemplates(const [
+  (ChatMessageType.system, 'You are a helpful assistant that replies only with numbers in order without any spaces or commas.'),
+  (ChatMessageType.human, 'List the numbers from 1 to {max_num}'),
+]);
+
+final chatModel = ChatFirebaseVertexAI(
+  defaultOptions: ChatFirebaseVertexAIOptions(
+    model: 'gemini-1.5-pro',
+  ),
+);
+
+final chain = promptTemplate.pipe(chatModel).pipe(StringOutputParser());
+
+final stream = chain.stream({'max_num': '30'});
+await stream.forEach(print);
+// 1
+// 2345678910111213
+// 1415161718192021
+// 222324252627282930 
+```
+
+## Tool calling
+
+`ChatGoogleGenerativeAI` supports tool calling.
+
+Check the [docs](https://langchaindart.dev/#/modules/model_io/models/chat_models/how_to/tools) for more information on how to use tools.
+
+Example:
+```dart
+const tool = ToolSpec(
+  name: 'get_current_weather',
+  description: 'Get the current weather in a given location',
+  inputJsonSchema: {
+    'type': 'object',
+    'properties': {
+      'location': {
+        'type': 'string',
+        'description': 'The city and state, e.g. San Francisco, CA',
+      },
+    },
+    'required': ['location'],
+  },
+);
+final chatModel = ChatFirebaseVertexAI(
+  defaultOptions: ChatFirebaseVertexAIOptions(
+    model: 'gemini-1.5-pro',
+    temperature: 0,
+    tools: [tool],
+  ),
+);
+final res = await model.invoke(
+  PromptValue.string('What’s the weather like in Boston and Madrid right now in celsius?'),
+);
+```
+
+## Prevent abuse with Firebase App Check
+
+You can use Firebase App Check to protect the Vertex AI Gemini API from abuse by unauthorized clients. Check the [Firebase documentation](https://firebase.google.com/docs/vertex-ai/app-check) for more information.
+
+## Locations
+
+When initializing the Vertex AI service, you can optionally specify a location in which to run the service and access a model. If you don't specify a location, the default is us-central1. See the list of [available locations](https://firebase.google.com/docs/vertex-ai/locations?platform=flutter#available-locations).
+
+```dart
+final chatModel = ChatFirebaseVertexAI(
+  location: 'us-central1',
+);
+```
+
+## Alternatives
+
+- [`ChatVertexAI`](/modules/model_io/models/chat_models/integrations/gcp_vertex_ai.md): Use this class to call the Vertex AI Gemini API from the server-side.
+- [`ChatGoogleGenerativeAI`](/modules/model_io/models/chat_models/integrations/googleai.md): Use this class to call the "Google AI" version of the Gemini API that provides free-of-charge access (within limits and where available). This API is not intended for production use but for experimentation and prototyping. After you're familiar with how a Gemini API works, migrate to the Vertex AI for Firebase, which have many additional features important for mobile and web apps, like protecting the API from abuse using Firebase App Check.
diff --git a/docs_v2/docs/05-integrations/gcp_vertex_ai.md b/docs_v2/docs/05-integrations/gcp_vertex_ai.md
new file mode 100644
index 00000000..5417aab5
--- /dev/null
+++ b/docs_v2/docs/05-integrations/gcp_vertex_ai.md
@@ -0,0 +1,116 @@
+# GCP Chat Vertex AI
+
+Wrapper around [GCP Vertex AI chat models](https://cloud.google.com/vertex-ai/docs/generative-ai/chat/test-chat-prompts) API (aka PaLM API for chat).
+
+## Set up your Google Cloud Platform project
+
+1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager).
+2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).
+3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).
+4. [Configure the Vertex AI location](https://cloud.google.com/vertex-ai/docs/general/locations).
+
+### Authentication
+
+To create an instance of `ChatVertexAI` you need to provide an HTTP client that handles authentication. The easiest way to do this is to use [`AuthClient`](https://pub.dev/documentation/googleapis_auth/latest/googleapis_auth/AuthClient-class.html) from the [googleapis_auth](https://pub.dev/packages/googleapis_auth) package.
+
+To create an instance of `VertexAI` you need to provide an [`AuthClient`](https://pub.dev/documentation/googleapis_auth/latest/googleapis_auth/AuthClient-class.html) instance.
+
+There are several ways to obtain an `AuthClient` depending on your use case. Check out the [googleapis_auth](https://pub.dev/packages/googleapis_auth) package documentation for more details.
+
+Example using a service account JSON:
+
+```dart
+final serviceAccountCredentials = ServiceAccountCredentials.fromJson(
+  json.decode(serviceAccountJson),
+);
+final authClient = await clientViaServiceAccount(
+  serviceAccountCredentials,
+  [ChatVertexAI.cloudPlatformScope],
+);
+final chatVertexAi = ChatVertexAI(
+  httpClient: authClient,
+  project: 'your-project-id',
+);
+```
+
+The service account should have the following [permission](https://cloud.google.com/vertex-ai/docs/general/iam-permissions):
+- `aiplatform.endpoints.predict`
+
+The required [OAuth2 scope](https://developers.google.com/identity/protocols/oauth2/scopes) is:
+- `https://www.googleapis.com/auth/cloud-platform` (you can use the constant `ChatVertexAI.cloudPlatformScope`)
+
+See: https://cloud.google.com/vertex-ai/docs/generative-ai/access-control
+
+### Available models
+
+- `chat-bison`
+  * Max input token: 4096
+  * Max output tokens: 1024
+  * Training data: Up to Feb 2023
+  * Max turns: 2500
+- `chat-bison-32k`
+  * Max input and output tokens combined: 32k
+  * Training data: Up to Aug 2023
+  * Max turns: 2500
+
+The previous list of models may not be exhaustive or up-to-date. Check out the [Vertex AI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/models) for the latest list of available models.
+
+### Model options
+
+You can define default options to use when calling the model (e.g. temperature, stop sequences, etc. ) using the `defaultOptions` parameter.
+
+The default options can be overridden when calling the model using the `options` parameter.
+
+Example:
+```dart
+final chatModel = ChatVertexAI(
+  httpClient: authClient,
+  project: 'your-project-id',
+  defaultOptions: ChatVertexAIOptions(
+    temperature: 0.9,
+  ),
+);
+final result = await chatModel(
+  [ChatMessage.humanText('Hello')],
+  options: ChatVertexAIOptions(
+    temperature: 0.5,
+   ),
+);
+```
+
+### Full example
+
+```dart
+import 'package:langchain/langchain.dart';
+import 'package:langchain_google/langchain_google.dart';
+
+void main() async {
+  final chat = ChatVertexAI(
+    httpClient: await _getAuthHttpClient(),
+    project: _getProjectId(),
+    defaultOptions: const ChatVertexAIOptions(
+      temperature: 0,
+    ),
+  );
+  while (true) {
+    stdout.write('> ');
+    final usrMsg = ChatMessage.humanText(stdin.readLineSync() ?? '');
+    final aiMsg = await chat([usrMsg]);
+    print(aiMsg.content);
+  }
+}
+
+Future<AuthClient> _getAuthHttpClient() async {
+  final serviceAccountCredentials = ServiceAccountCredentials.fromJson(
+    json.decode(Platform.environment['VERTEX_AI_SERVICE_ACCOUNT']!),
+  );
+  return clientViaServiceAccount(
+    serviceAccountCredentials,
+    [VertexAI.cloudPlatformScope],
+  );
+}
+
+String _getProjectId() {
+  return Platform.environment['VERTEX_AI_PROJECT_ID']!;
+}
+```
diff --git a/docs_v2/docs/05-integrations/googleai.md b/docs_v2/docs/05-integrations/googleai.md
new file mode 100644
index 00000000..033c7672
--- /dev/null
+++ b/docs_v2/docs/05-integrations/googleai.md
@@ -0,0 +1,149 @@
+# ChatGoogleGenerativeAI
+
+Wrapper around [Google AI for Developers](https://ai.google.dev/) API (aka Gemini API).
+
+## Setup
+
+To use `ChatGoogleGenerativeAI` you need to have an API key. You can get one [here](https://aistudio.google.com/app/apikey).
+
+The following models are available:
+- `gemini-1.5-flash`:
+  * text / image / audio -> text model
+  * Max input token: 1048576
+  * Max output tokens: 8192
+- `gemini-1.5-pro`: text / image -> text model
+  * text / image / audio -> text model
+  * Max input token: 1048576
+  * Max output tokens: 8192
+- `gemini-pro-vision`:
+  * text / image -> text model
+  * Max input token: 12288
+  * Max output tokens: 4096
+- `gemini-1.0-pro` (or `gemini-pro`):
+  * text -> text model
+  * Max input token: 30720
+  * Max output tokens: 2048
+
+Mind that this list may not be up-to-date. Refer to the [documentation](https://ai.google.dev/models) for the updated list.
+
+## Usage
+
+```dart
+final apiKey = Platform.environment['GOOGLEAI_API_KEY'];
+
+final chatModel = ChatGoogleGenerativeAI(
+  apiKey: apiKey,
+  defaultOptions: ChatGoogleGenerativeAIOptions(
+    model: 'gemini-1.5-pro',
+    temperature: 0,
+  ),
+);
+
+final chatPrompt = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system, 'You are a helpful assistant that translates {input_language} to {output_language}.'),
+  (ChatMessageType.human, 'Text to translate:\n{text}'),
+]);
+
+final chain = chatPrompt | chatModel | StringOutputParser();
+
+final res = await chain.invoke({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.',
+});
+print(res);
+// -> 'J'adore programmer.'
+```
+
+## Multimodal support
+
+```dart
+final apiKey = Platform.environment['GOOGLEAI_API_KEY'];
+
+final chatModel = ChatGoogleGenerativeAI(
+  apiKey: apiKey,
+  defaultOptions: ChatGoogleGenerativeAIOptions(
+    model: 'gemini-1.5-pro',
+    temperature: 0,
+  ),
+);
+final res = await chatModel.invoke(
+  PromptValue.chat([
+    ChatMessage.human(
+      ChatMessageContent.multiModal([
+        ChatMessageContent.text('What fruit is this?'),
+        ChatMessageContent.image(
+          mimeType: 'image/jpeg',
+          data: base64.encode(
+            await File('./bin/assets/apple.jpeg').readAsBytes(),
+          ),
+        ),
+      ]),
+    ),
+  ]),
+);
+print(res.output.content);
+// -> 'That is an apple.'
+```
+
+## Streaming
+
+```dart
+final apiKey = Platform.environment['GOOGLEAI_API_KEY'];
+
+final promptTemplate = ChatPromptTemplate.fromTemplates(const [
+  (ChatMessageType.system, 'You are a helpful assistant that replies only with numbers in order without any spaces or commas.'),
+  (ChatMessageType.human, 'List the numbers from 1 to {max_num}'),
+]);
+
+final chatModel = ChatGoogleGenerativeAI(
+  apiKey: apiKey,
+  defaultOptions: const ChatGoogleGenerativeAIOptions(
+    model: 'gemini-1.5-pro',
+    temperature: 0,
+  ),
+);
+
+final chain = promptTemplate.pipe(chatModel).pipe(StringOutputParser());
+
+final stream = chain.stream({'max_num': '30'});
+await stream.forEach(print);
+// 1
+// 2345678910111213
+// 1415161718192021
+// 222324252627282930 
+```
+
+## Tool calling
+
+`ChatGoogleGenerativeAI` supports tool calling.
+
+Check the [docs](https://langchaindart.dev/#/modules/model_io/models/chat_models/how_to/tools) for more information on how to use tools.
+
+Example:
+```dart
+const tool = ToolSpec(
+  name: 'get_current_weather',
+  description: 'Get the current weather in a given location',
+  inputJsonSchema: {
+    'type': 'object',
+    'properties': {
+      'location': {
+        'type': 'string',
+        'description': 'The city and state, e.g. San Francisco, CA',
+      },
+    },
+    'required': ['location'],
+  },
+);
+final chatModel = ChatGoogleGenerativeAI(
+  defaultOptions: ChatGoogleGenerativeAIOptions(
+    model: 'gemini-1.5-pro',
+    temperature: 0,
+    tools: [tool],
+  ),
+);
+final res = await model.invoke(
+  PromptValue.string('What’s the weather like in Boston and Madrid right now in celsius?'),
+);
+```
diff --git a/docs_v2/docs/05-integrations/index.mdx b/docs_v2/docs/05-integrations/index.mdx
new file mode 100644
index 00000000..35f38dfc
--- /dev/null
+++ b/docs_v2/docs/05-integrations/index.mdx
@@ -0,0 +1,56 @@
+---
+sidebar_position: 0
+index: auto
+---
+# Integrations
+
+> If you'd like to write your own integration, see Extending Langchain.
+
+The following table contains the list of existing Langchain.dart integration packages. To install a specific integration, see [Installing Langchain components](/docs/03-how_to/01-installation.md)
+
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/davidmigloz/langchain_dart/main/docs/img/langchain_packages.png" width="500"></img>
+</p>
+
+| Package                                                             | Version                                                                                                                      | Description                                                                                                                                                                                             |
+|---------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [langchain_core](https://pub.dev/packages/langchain_core)           | [![langchain_core](https://img.shields.io/pub/v/langchain_core.svg)](https://pub.dev/packages/langchain_core)                | Core abstractions and LCEL                                                                                                                                                                              |
+| [langchain](https://pub.dev/packages/langchain)                     | [![langchain](https://img.shields.io/pub/v/langchain.svg)](https://pub.dev/packages/langchain)                               | Higher-level and use-case specific chains, agents, and retrieval algorithms                                                                                                                             |
+| [langchain_community](https://pub.dev/packages/langchain_community) | [![langchain_community](https://img.shields.io/pub/v/langchain_community.svg)](https://pub.dev/packages/langchain_community) | Third-party integrations (without specific packages) and community-contributed components and utilities                                                                                                 |
+| [langchain_openai](https://pub.dev/packages/langchain_openai)       | [![langchain_openai](https://img.shields.io/pub/v/langchain_openai.svg)](https://pub.dev/packages/langchain_openai)          | OpenAI integration (GPT-3.5 Turbo, GPT-4, GPT-4o, Embeddings, Tools, Vision, DALL·E 3, etc.) and OpenAI Compatible services (TogetherAI, Anyscale, OpenRouter, One API, Groq, Llamafile, GPT4All, etc.) |
+| [langchain_google](https://pub.dev/packages/langchain_google)       | [![langchain_google](https://img.shields.io/pub/v/langchain_google.svg)](https://pub.dev/packages/langchain_google)          | Google integration (GoogleAI, VertexAI, Gemini, PaLM 2, Embeddings, Vector Search, etc.)                                                                                                                |
+| [langchain_firebase](https://pub.dev/packages/langchain_firebase)   | [![langchain_firebase](https://img.shields.io/pub/v/langchain_firebase.svg)](https://pub.dev/packages/langchain_firebase)    | Firebase integration (VertexAI for Firebase (Gemini 1.5 Pro, Gemini 1.5 Flash, etc.))                                                                                                                   |
+| [langchain_ollama](https://pub.dev/packages/langchain_ollama)       | [![langchain_ollama](https://img.shields.io/pub/v/langchain_ollama.svg)](https://pub.dev/packages/langchain_ollama)          | Ollama integration (Llama 3, Phi-3, WizardLM-2, Mistral 7B, Gemma, CodeGemma, Command R, LLaVA, DBRX, Qwen 1.5, Dolphin, DeepSeek Coder, Vicuna, Orca, etc.)                                            |
+| [langchain_mistralai](https://pub.dev/packages/langchain_mistralai) | [![langchain_mistralai](https://img.shields.io/pub/v/langchain_mistralai.svg)](https://pub.dev/packages/langchain_mistralai) | Mistral AI integration (Mistral-7B, Mixtral 8x7B, Mixtral 8x22B, Mistral Small, Mistral Large, embeddings, etc.).                                                                                       |
+| [langchain_pinecone](https://pub.dev/packages/langchain_pinecone)   | [![langchain_pinecone](https://img.shields.io/pub/v/langchain_pinecone.svg)](https://pub.dev/packages/langchain_pinecone)    | Pinecone vector database integration                                                                                                                                                                    |
+| [langchain_chroma](https://pub.dev/packages/langchain_chroma)       | [![langchain_chroma](https://img.shields.io/pub/v/langchain_chroma.svg)](https://pub.dev/packages/langchain_chroma)          | Chroma vector database integration                                                                                                                                                                      |
+| [langchain_supabase](https://pub.dev/packages/langchain_supabase)   | [![langchain_supabase](https://img.shields.io/pub/v/langchain_supabase.svg)](https://pub.dev/packages/langchain_supabase)    | Supabase Vector database integration                                                                                                                                                                    |
+
+Functionality provided by each integration package:
+
+| Package                                                             | LLMs | Chat models | Embeddings | Vector stores | Chains | Agents | Tools |
+|---------------------------------------------------------------------|------|-------------|------------|---------------|--------|--------|-------|
+| [langchain_community](https://pub.dev/packages/langchain_community) |      |             |            | ✔             |        |        | ✔     |
+| [langchain_openai](https://pub.dev/packages/langchain_openai)       | ✔    | ✔           | ✔          |               | ✔      | ✔      | ✔     |
+| [langchain_google](https://pub.dev/packages/langchain_google)       | ✔    | ✔           | ✔          | ✔             |        |        |       |
+| [langchain_firebase](https://pub.dev/packages/langchain_firebase)   |      | ✔           |            |               |        |        |       |
+| [langchain_ollama](https://pub.dev/packages/langchain_ollama)       | ✔    | ✔           | ✔          |               |        |        |       |
+| [langchain_mistralai](https://pub.dev/packages/langchain_mistralai) |      | ✔           | ✔          |               |        |        |       |
+| [langchain_pinecone](https://pub.dev/packages/langchain_pinecone)   |      |             |            | ✔             |        |        |       |
+| [langchain_chroma](https://pub.dev/packages/langchain_chroma)       |      |             |            | ✔             |        |        |       |
+| [langchain_supabase](https://pub.dev/packages/langchain_supabase)   |      |             |            | ✔             |        |        |       |
+
+The following packages are maintained (and used internally) by LangChain.dart, although they can also be used independently:
+
+| Package                                                           | Version                                                                                                                   | Description                                                         | 
+|-------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------|
+| [anthropic_sdk_dart](https://pub.dev/packages/anthropic_sdk_dart) | [![anthropic_sdk_dart](https://img.shields.io/pub/v/anthropic_sdk_dart.svg)](https://pub.dev/packages/anthropic_sdk_dart) | [Anthropic](https://docs.anthropic.com/en/api) API client           |
+| [chromadb](https://pub.dev/packages/chromadb)                     | [![chromadb](https://img.shields.io/pub/v/chromadb.svg)](https://pub.dev/packages/chromadb)                               | [Chroma DB](https://trychroma.com/) API client                      |
+| [googleai_dart](https://pub.dev/packages/googleai_dart)           | [![googleai_dart](https://img.shields.io/pub/v/googleai_dart.svg)](https://pub.dev/packages/googleai_dart)                | [Google AI for Developers](https://ai.google.dev/) API client       |
+| [mistralai_dart](https://pub.dev/packages/mistralai_dart)         | [![mistralai_dart](https://img.shields.io/pub/v/mistralai_dart.svg)](https://pub.dev/packages/mistralai_dart)             | [Mistral AI](https://docs.mistral.ai/api) API client                |
+| [ollama_dart](https://pub.dev/packages/ollama_dart)               | [![ollama_dart](https://img.shields.io/pub/v/ollama_dart.svg)](https://pub.dev/packages/ollama_dart)                      | [Ollama](https://ollama.ai/) API client                             |
+| [openai_dart](https://pub.dev/packages/openai_dart)               | [![openai_dart](https://img.shields.io/pub/v/openai_dart.svg)](https://pub.dev/packages/openai_dart)                      | [OpenAI](https://platform.openai.com/docs/api-reference) API client |
+| [tavily_dart](https://pub.dev/packages/tavily_dart)               | [![tavily_dart](https://img.shields.io/pub/v/tavily_dart.svg)](https://pub.dev/packages/tavily_dart)                      | [Tavily](https://tavily.com) API client                             |
+| [vertex_ai](https://pub.dev/packages/vertex_ai)                   | [![vertex_ai](https://img.shields.io/pub/v/vertex_ai.svg)](https://pub.dev/packages/vertex_ai)                            | [GCP Vertex AI](https://cloud.google.com/vertex-ai) API client      |
+
diff --git a/docs_v2/docs/05-integrations/mistralai.md b/docs_v2/docs/05-integrations/mistralai.md
new file mode 100644
index 00000000..14f21fb1
--- /dev/null
+++ b/docs_v2/docs/05-integrations/mistralai.md
@@ -0,0 +1,76 @@
+# ChatMistralAI
+
+Wrapper around [Mistral AI](https://mistral.ai/) Chat Completions API.
+
+Mistral AI brings the strongest open generative models to the developers, along with efficient ways to deploy and customise them for production.
+
+> Note: Mistral AI API is currently in closed beta. You can request access [here](https://console.mistral.ai).
+
+## Setup
+
+To use `ChatMistralAI` you need to have a Mistral AI account and an API key. You can get one [here](https://console.mistral.ai/users/).
+
+The following models are available at the moment:
+- `mistral-tiny`: Mistral 7B Instruct v0.2 (a minor release of Mistral 7B Instruct). It only works in English and obtains 7.6 on MT-Bench.
+- `mistral-small`: Mixtral 8x7B. It masters English/French/Italian/German/Spanish and code and obtains 8.3 on MT-Bench.
+- `mistral-medium`: a prototype model, that is currently among the top serviced models available based on standard benchmarks. It masters English/French/Italian/German/Spanish and code and obtains a score of 8.6 on MT-Bench.
+
+## Usage
+
+```dart
+final chatModel = ChatMistralAI(
+  apiKey: 'apiKey',
+  defaultOptions: ChatMistralAIOptions(
+    model: 'mistral-small',
+    temperature: 0,
+  ),
+);
+
+const template = 'You are a helpful assistant that translates {input_language} to {output_language}.';
+final systemMessagePrompt = SystemChatMessagePromptTemplate.fromTemplate(template);
+const humanTemplate = '{text}';
+final humanMessagePrompt = HumanChatMessagePromptTemplate.fromTemplate(humanTemplate);
+final chatPrompt = ChatPromptTemplate.fromPromptMessages(
+  [systemMessagePrompt, humanMessagePrompt],
+);
+
+final chain = chatPrompt | chatModel | StringOutputParser();
+
+final res = await chain.invoke({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.',
+});
+print(res);
+// -> 'J'aime la programmation.'
+```
+
+## Streaming
+
+```dart
+final promptTemplate = ChatPromptTemplate.fromPromptMessages([
+  SystemChatMessagePromptTemplate.fromTemplate(
+    'You are a helpful assistant that replies only with numbers '
+        'in order without any spaces or commas',
+  ),
+  HumanChatMessagePromptTemplate.fromTemplate(
+    'List the numbers from 1 to {max_num}',
+  ),
+]);
+final chat = ChatMistralAI(
+  apiKey: 'apiKey',
+  defaultOptions: ChatMistralAIOptions(
+    model: 'mistral-medium',
+    temperature: 0,
+  ),
+);
+
+final chain = promptTemplate.pipe(chat).pipe(StringOutputParser());
+
+final stream = chain.stream({'max_num': '9'});
+await stream.forEach(print);
+// 12
+// 345
+// 67
+// 89
+```
diff --git a/docs_v2/docs/05-integrations/ollama.md b/docs_v2/docs/05-integrations/ollama.md
new file mode 100644
index 00000000..e6cc5907
--- /dev/null
+++ b/docs_v2/docs/05-integrations/ollama.md
@@ -0,0 +1,462 @@
+# ChatOllama
+
+Wrapper around [Ollama](https://ollama.ai) Completions API that enables to interact with the LLMs in a chat-like fashion.
+
+Ollama allows you to run open-source large language models, such as Llama 3.1 or Gemma 2, locally.
+
+Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. It optimizes setup and configuration details, including GPU usage.
+
+## Setup
+
+Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:
+
+1. Download and install [Ollama](https://ollama.ai)
+2. Fetch a model via `ollama pull <model family>`
+  * e.g., for Llama 3: `ollama pull llama3.1`
+3. Instantiate the `ChatOllama` class with the downloaded model.
+
+```dart
+final chatModel = ChatOllama(
+  defaultOptions: ChatOllamaOptions(
+    model: 'llama3.1',
+  ),
+);
+```
+
+For a complete list of supported models and model variants, see the [Ollama model library](https://ollama.ai/library).
+
+### Ollama base URL
+
+By default, `ChatOllama` uses 'http://localhost:11434/api' as base URL (default Ollama API URL). But if you are running Ollama on a different host, you can override it using the `baseUrl` parameter.
+
+```dart
+final chatModel = ChatOllama(
+  defaultOptions: ChatOllamaOptions(
+    baseUrl: 'https://your-remote-server-where-ollama-is-running.com',
+    model: 'llama3.1',
+  ),
+);
+```
+
+## Usage
+
+```dart
+final promptTemplate = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system, 'You are a helpful assistant that translates {input_language} to {output_language}.'),
+  (ChatMessageType.human, '{text}'),
+]);
+
+final chatModel = ChatOllama(
+  defaultOptions: ChatOllamaOptions(
+    model: 'llama3.1',
+    temperature: 0,
+  ),
+);
+
+final chain = promptTemplate | chatModel | StringOutputParser();
+
+final res = await chain.invoke({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.',
+});
+print(res);
+// -> 'La traduction est : "J'aime le programming.'
+```
+
+### Streaming
+
+Ollama supports streaming the output as the model generates it. 
+
+```dart
+final promptTemplate = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system, 'You are a helpful assistant that replies only with numbers in order without any spaces or commas'),
+  (ChatMessageType.human, 'List the numbers from 1 to {max_num}'),
+]);
+final chat = ChatOllama(
+  defaultOptions: ChatOllamaOptions(
+    model: 'llama3.1',
+    temperature: 0,
+  ),
+);
+final chain = promptTemplate.pipe(chat).pipe(StringOutputParser());
+
+final stream = chain.stream({'max_num': '9'});
+await stream.forEach(print);
+// 123
+// 456
+// 789
+```
+
+### Multimodal support
+
+Ollama has support for multi-modal LLMs, such as [bakllava](https://ollama.ai/library/bakllava) and [llava](https://ollama.ai/library/llava).
+
+You can provide several base64-encoded `png` or `jpeg` images. Images up to 100MB in size are supported.
+
+```dart
+final chatModel = ChatOllama(
+  defaultOptions: ChatOllamaOptions(
+    model: 'llava',
+    temperature: 0,
+  ),
+);
+final prompt = ChatMessage.human(
+  ChatMessageContent.multiModal([
+    ChatMessageContent.text('What fruit is this?'),
+    ChatMessageContent.image(
+      data: base64.encode(
+        await File('./bin/assets/apple.jpeg').readAsBytes(),
+      ),
+    ),
+  ]),
+);
+final res = await chatModel.invoke(PromptValue.chat([prompt]));
+print(res.output.content);
+// -> 'An Apple'
+```
+
+### Tool calling
+
+`ChatOllama` offers support for native tool calling. This enables a model to answer a given prompt using tool(s) it knows about, making it possible for models to perform more complex tasks or interact with the outside world. It follows the standard [LangChain.dart tools API](/modules/model_io/models/chat_models/how_to/tools.md), so you can use it in the same way as you would with other providers that support tool-calling (e.g. `ChatOpenAI`, `ChatAnthropic`, etc.).
+
+**Notes:**
+- Tool calling requires [Ollama 0.3.0](https://github.com/ollama/ollama/releases/tag/v0.3.0) or newer.
+- Streaming tool calls is not supported at the moment.
+- Not all models support tool calls. Check the Ollama catalogue for models that have the `Tools` tag (e.g. [`llama3.1`](https://ollama.com/library/llama3.1) or [`llama3-groq-tool-use`](https://ollama.com/library/llama3-groq-tool-use)).
+- At the moment, small models like `llama3.1` [cannot reliably maintain a conversation alongside tool calling definitions](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/#llama-3.1-instruct). They can be used for zero-shot tool calling, but for multi-turn conversations it's recommended to use larger models like `llama3.1:70b` or `llama3.1:405b`.
+
+```dart
+const tool = ToolSpec(
+  name: 'get_current_weather',
+  description: 'Get the current weather in a given location',
+  inputJsonSchema: {
+    'type': 'object',
+    'properties': {
+      'location': {
+        'type': 'string',
+        'description': 'The city and country, e.g. San Francisco, US',
+      },
+    },
+    'required': ['location'],
+  },
+);
+
+final chatModel = ChatOllama(
+  defaultOptions: ChatOllamaOptions(
+    model: 'llama3.1',
+    temperature: 0,
+    tools: [tool],
+  ),
+);
+
+final res = await chatModel.invoke(
+  PromptValue.string('What’s the weather like in Boston and Madrid right now in celsius?'),
+);
+print(res.output.toolCalls);
+// [AIChatMessageToolCall{
+//   id: a621064b-03b3-4ca6-8278-f37504901034,
+//   name: get_current_weather,
+//   arguments: {location: Boston, US},
+// }, 
+// AIChatMessageToolCall{
+//   id: f160d9ba-ae7d-4abc-a910-2b6cd503ec53,
+//   name: get_current_weather,
+//   arguments: {location: Madrid, ES},
+// }]
+```
+
+As you can see, `ChatOllama` support calling multiple tools in a single request.
+
+If you want to customize how the model should respond to tool calls, you can use the `toolChoice` parameter:
+
+```dart
+final chatModel = ChatOllama(
+  defaultOptions: ChatOllamaOptions(
+    model: 'llama3.1',
+    temperature: 0,
+    tools: [tool],
+    toolChoice: ChatToolChoice.forced(name: 'get_current_weather'),
+  ),
+);
+```
+
+**Pro-tip:** You can improve tool-calling performance of small models by using few-shot prompting. You can find out how to do this [here](https://langchaindart.dev/#/modules/model_io/models/chat_models/how_to/tools?id=few-shot-prompting) and in this [blog post](https://blog.langchain.dev/few-shot-prompting-to-improve-tool-calling-performance).
+
+### JSON mode
+
+You can force the model to produce JSON output that you can easily parse using `JsonOutputParser`, useful for extracting structured data.
+
+```dart
+final promptTemplate = ChatPromptTemplate.fromTemplates(const [
+  (ChatMessageType.system, 'You are an assistant that respond question using JSON format.'),
+  (ChatMessageType.human, '{question}'),
+]);
+final chat = ChatOllama(
+  defaultOptions: ChatOllamaOptions(
+    model: 'llama3.1',
+    temperature: 0,
+    format: OllamaResponseFormat.json,
+  ),
+);
+
+final chain = Runnable.getMapFromInput<String>('question')
+    .pipe(promptTemplate)
+    .pipe(chat)
+    .pipe(JsonOutputParser());
+
+final res = await chain.invoke(
+  'What is the population of Spain, The Netherlands, and France?',
+);
+print(res);
+// {Spain: 46735727, The Netherlands: 17398435, France: 65273538}
+```
+
+## Examples
+
+### Answering questions with data from an external API
+
+Imagine you have an API that provides flight times between two cities:
+
+```dart
+// Simulates an API call to get flight times
+// In a real application, this would fetch data from a live database or API
+String getFlightTimes(String departure, String arrival) {
+  final flights = {
+    'NYC-LAX': {
+      'departure': '08:00 AM',
+      'arrival': '11:30 AM',
+      'duration': '5h 30m',
+    },
+    'LAX-NYC': {
+      'departure': '02:00 PM',
+      'arrival': '10:30 PM',
+      'duration': '5h 30m',
+    },
+    'LHR-JFK': {
+      'departure': '10:00 AM',
+      'arrival': '01:00 PM',
+      'duration': '8h 00m',
+    },
+    'JFK-LHR': {
+      'departure': '09:00 PM',
+      'arrival': '09:00 AM',
+      'duration': '7h 00m',
+    },
+    'CDG-DXB': {
+      'departure': '11:00 AM',
+      'arrival': '08:00 PM',
+      'duration': '6h 00m',
+    },
+    'DXB-CDG': {
+      'departure': '03:00 AM',
+      'arrival': '07:30 AM',
+      'duration': '7h 30m',
+    },
+  };
+
+  final key = '${departure.toUpperCase()}-${arrival.toUpperCase()}';
+  return jsonEncode(flights[key] ?? {'error': 'Flight not found'});
+}
+```
+
+Using the tool calling capabilities of Ollama, we can provide the model with the ability to call this API whenever it needs to get flight times to answer a question.
+
+```dart
+const getFlightTimesTool = ToolSpec(
+  name: 'get_flight_times',
+  description: 'Get the flight times between two cities',
+  inputJsonSchema: {
+    'type': 'object',
+    'properties': {
+      'departure': {
+        'type': 'string',
+        'description': 'The departure city (airport code)',
+      },
+      'arrival': {
+        'type': 'string',
+        'description': 'The arrival city (airport code)',
+      },
+    },
+    'required': ['departure', 'arrival'],
+  },
+);
+
+final chatModel = ChatOllama(
+  defaultOptions: const ChatOllamaOptions(
+    model: 'llama3.1',
+    temperature: 0,
+    tools: [getFlightTimesTool],
+  ),
+);
+
+final messages = [
+  ChatMessage.humanText(
+    'What is the flight time from New York (NYC) to Los Angeles (LAX)?',
+  ),
+];
+
+// First API call: Send the query and function description to the model
+final response = await chatModel.invoke(PromptValue.chat(messages));
+
+messages.add(response.output);
+
+// Check if the model decided to use the provided function
+if (response.output.toolCalls.isEmpty) {
+  print("The model didn't use the function. Its response was:");
+  print(response.output.content);
+  return;
+}
+
+// Process function calls made by the model
+for (final toolCall in response.output.toolCalls) {
+  final functionResponse = getFlightTimes(
+    toolCall.arguments['departure'],
+    toolCall.arguments['arrival'],
+  );
+  // Add function response to the conversation
+  messages.add(
+    ChatMessage.tool(
+      toolCallId: toolCall.id,
+      content: functionResponse,
+    ),
+  );
+}
+
+// Second API call: Get final response from the model
+final finalResponse = await chatModel.invoke(PromptValue.chat(messages));
+print(finalResponse.output.content);
+// The flight time from New York (NYC) to Los Angeles (LAX) is approximately 5 hours and 30 minutes.
+```
+
+### Extracting structured data with tools
+
+A useful application of tool calling is extracting structured data from unstructured text. In the following example, we use a tool to extract the names, heights, and hair colors of people mentioned in a passage.
+
+```dart
+const tool = ToolSpec(
+  name: 'information_extraction',
+  description: 'Extracts the relevant information from the passage',
+  inputJsonSchema: {
+    'type': 'object',
+    'properties': {
+      'people': {
+        'type': 'array',
+        'items': {
+          'type': 'object',
+          'properties': {
+            'name': {
+              'type': 'string',
+              'description': 'The name of a person',
+            },
+            'height': {
+              'type': 'number',
+              'description': 'The height of the person in cm',
+            },
+            'hair_color': {
+              'type': 'string',
+              'description': 'The hair color of the person',
+              'enum': ['black', 'brown', 'blonde', 'red', 'gray', 'white'],
+            },
+          },
+          'required': ['name', 'height', 'hair_color'],
+        },
+      },
+    },
+    'required': ['people'],
+  },
+);
+
+final model = ChatOllama(
+  defaultOptions: ChatOllamaOptions(
+    options: ChatOllamaOptions(
+      model: 'llama3.1',
+      temperature: 0,
+    ),
+    tools: [tool],
+    toolChoice: ChatToolChoice.forced(name: tool.name),
+  ),
+);
+
+final promptTemplate = ChatPromptTemplate.fromTemplate('''
+Extract and save the relevant entities mentioned in the following passage together with their properties. 
+
+Passage:
+{input}''');
+
+final chain = Runnable.getMapFromInput<String>()
+    .pipe(promptTemplate)
+    .pipe(model)
+    .pipe(ToolsOutputParser());
+
+final res = await chain.invoke(
+  'Alex is 5 feet tall. '
+  'Claudia is 1 foot taller than Alex and jumps higher than him. '
+  'Claudia has orange hair and Alex is blonde.',
+);
+final extractedData = res.first.arguments;
+print(extractedData);
+// {
+//   people: [
+//     {
+//       name: Alex,
+//       height: 152,
+//       hair_color: blonde
+//     },
+//     {
+//       name: Claudia, 
+//       height: 183,
+//       hair_color: orange
+//     }
+//   ]
+// }
+```
+
+### RAG (Retrieval-Augmented Generation) pipeline
+
+We can easily create a fully local RAG pipeline using `OllamaEmbeddings` and `ChatOllama`.
+
+```dart
+// 1. Create a vector store and add documents to it
+final vectorStore = MemoryVectorStore(
+  embeddings: OllamaEmbeddings(model: 'jina/jina-embeddings-v2-small-en'),
+);
+await vectorStore.addDocuments(
+  documents: [
+    Document(pageContent: 'LangChain was created by Harrison'),
+    Document(pageContent: 'David ported LangChain to Dart in LangChain.dart'),
+  ],
+);
+
+// 2. Construct a RAG prompt template
+final promptTemplate = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system, 'Answer the question based on only the following context:\n{context}'),
+  (ChatMessageType.human, '{question}'),
+]);
+
+// 3. Define the model to use and the vector store retriever
+final chatModel = ChatOllama(
+  defaultOptions: ChatOllamaOptions(model: 'llama3.1'),
+);
+final retriever = vectorStore.asRetriever(
+  defaultOptions: VectorStoreRetrieverOptions(
+    searchType: VectorStoreSimilaritySearch(k: 1),
+  ),
+);
+
+// 4. Create a Runnable that combines the retrieved documents into a single string
+final docCombiner = Runnable.mapInput<List<Document>, String>((docs) {
+  return docs.map((final d) => d.pageContent).join('\n');
+});
+
+// 4. Define the RAG pipeline
+final chain = Runnable.fromMap<String>({
+  'context': retriever.pipe(docCombiner),
+  'question': Runnable.passthrough(),
+}).pipe(promptTemplate).pipe(chatModel).pipe(StringOutputParser());
+
+// 5. Run the pipeline
+final res = await chain.invoke('Who created LangChain.dart?');
+print(res);
+// Based on the context provided, David created LangChain.dart.
+```
diff --git a/docs_v2/docs/05-integrations/open_router.md b/docs_v2/docs/05-integrations/open_router.md
new file mode 100644
index 00000000..c2d63555
--- /dev/null
+++ b/docs_v2/docs/05-integrations/open_router.md
@@ -0,0 +1,157 @@
+# OpenRouter
+
+[OpenRouter](https://openrouter.ai/) offers a unified OpenAI-compatible API for a broad range of [models](https://openrouter.ai/models).
+
+You can also let users pay for their own models via their [OAuth PKCE](https://openrouter.ai/docs#oauth) flow.
+
+You can consume OpenRouter API using the `ChatOpenAI` wrapper in the same way you would use the OpenAI API.
+
+The only difference is that you need to change the base URL to `https://openrouter.ai/api/v1`:
+
+```dart
+final chatModel = ChatOpenAI(
+  apiKey: openRouterApiKey,
+  baseUrl: 'https://openrouter.ai/api/v1',
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'mistralai/mistral-small',
+  ),
+);
+```
+
+OpenRouter allows you to specify an optional `HTTP-Referer` header to identify your app and make it discoverable to users on openrouter.ai. You can also include an optional `X-Title` header to set or modify the title of your app.
+
+```dart
+ final chatModel = ChatOpenAI(
+  apiKey: openRouterApiKey,
+  baseUrl: 'https://openrouter.ai/api/v1',
+  headers: {
+    'HTTP-Referer': 'com.myapp',
+    'X-Title': 'OpenRouterTest',
+  },
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'mistralai/mistral-small',
+  ),
+);
+```
+
+## Invoke
+
+```dart
+final openRouterApiKey = Platform.environment['OPEN_ROUTER_API_KEY'];
+
+final promptTemplate = ChatPromptTemplate.fromTemplates(const [
+  (
+  ChatMessageType.system,
+  'You are a helpful assistant that translates {input_language} to {output_language}.',
+  ),
+  (ChatMessageType.human, '{text}'),
+]);
+
+final chatModel = ChatOpenAI(
+  apiKey: openRouterApiKey,
+  baseUrl: 'https://openrouter.ai/api/v1',
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'mistralai/mistral-small',
+  ),
+);
+
+final chain = promptTemplate | chatModel | StringOutputParser();
+
+final res = await chain.invoke({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.',
+});
+print(res);
+// -> 'J'aime la programmation.'
+```
+
+## Stream
+
+```dart
+final openRouterApiKey = Platform.environment['OPEN_ROUTER_API_KEY'];
+
+final promptTemplate = ChatPromptTemplate.fromTemplates(const [
+  (
+  ChatMessageType.system,
+  'You are a helpful assistant that replies only with numbers '
+      'in order without any spaces or commas',
+  ),
+  (ChatMessageType.human, 'List the numbers from 1 to {max_num}'),
+]);
+
+final chatModel = ChatOpenAI(
+  apiKey: openRouterApiKey,
+  baseUrl: 'https://openrouter.ai/api/v1',
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'mistralai/mistral-small',
+  ),
+);
+
+final chain = promptTemplate.pipe(chatModel).pipe(StringOutputParser());
+
+final stream = chain.stream({'max_num': '9'});
+await stream.forEach(print);
+// 123
+// 456789
+```
+
+## Tool calling
+
+OpenRouter supports [tool calling](https://openrouter.ai/docs#tool-calls). 
+
+Check the [docs](/modules/model_io/models/chat_models/how_to/tools.md) for more information on how to use tools.
+
+In the following example we use the `joke` tool to generate jokes. We stream the joke generation using the `ToolsOutputParser' which tries to "auto-complete" the partial json from each chunk into a valid state.
+
+```dart
+final openRouterApiKey = Platform.environment['OPEN_ROUTER_API_KEY'];
+const tool = ToolSpec(
+  name: 'joke',
+  description: 'A joke',
+  inputJsonSchema: {
+    'type': 'object',
+    'properties': {
+      'setup': {
+        'type': 'string',
+        'description': 'The setup for the joke',
+      },
+      'punchline': {
+        'type': 'string',
+        'description': 'The punchline to the joke',
+      },
+    },
+    'required': ['location', 'punchline'],
+  },
+);
+final promptTemplate = ChatPromptTemplate.fromTemplate(
+  'tell me a long joke about {foo}',
+);
+final chat = ChatOpenAI(
+  apiKey: openRouterApiKey,
+  baseUrl: 'https://openrouter.ai/api/v1',
+  defaultOptions: ChatOpenAIOptions(
+    model: 'gpt-4o',
+    tools: [tool],
+    toolChoice: ChatToolChoice.forced(name: 'joke'),
+  ),
+);
+final outputParser = ToolsOutputParser();
+
+final chain = promptTemplate.pipe(chat).pipe(outputParser);
+
+final stream = chain.stream({'foo': 'bears'});
+await for (final chunk in stream) {
+  final args = chunk.first.arguments;
+  print(args);
+}
+// {}
+// {setup: }
+// {setup: Why don't}
+// {setup: Why don't bears}
+// {setup: Why don't bears like fast food}
+// {setup: Why don't bears like fast food?, punchline: }
+// {setup: Why don't bears like fast food?, punchline: Because}
+// {setup: Why don't bears like fast food?, punchline: Because they can't}
+// {setup: Why don't bears like fast food?, punchline: Because they can't catch it!}
+```
diff --git a/docs_v2/docs/05-integrations/openai.md b/docs_v2/docs/05-integrations/openai.md
new file mode 100644
index 00000000..6b3ccbbc
--- /dev/null
+++ b/docs_v2/docs/05-integrations/openai.md
@@ -0,0 +1,372 @@
+# OpenAI
+
+This notebook provides a quick overview for getting started with [OpenAI](https://platform.openai.com/docs/introduction) chat models. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://pub.dev/documentation/langchain_openai/latest/langchain_openai/ChatOpenAI-class.html).
+
+OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [OpenAI docs](https://platform.openai.com/docs/models).
+
+> Note that certain OpenAI models can also be accessed via the [Microsoft Azure platform](https://azure.microsoft.com/en-us/products/ai-services/openai-service). Check out the API reference for more information on how to use the Azure with `ChatOpenAI`.
+
+## Setup
+
+To access OpenAI models you'll need to create an OpenAI account, get an API key, and install the [langchain_openai](https://pub.dev/packages/langchain_openai) integration package.
+
+### Credentials
+
+Head to the [OpenAI Platform](https://platform.openai.com), sign up and get your [API key](https://platform.openai.com/account/api-keys).
+
+### Installation
+
+The LangChain.dart OpenAI integration lives in the [langchain_openai](https://pub.dev/packages/langchain_openai) package:
+
+```yaml
+dart pub add langchain_openai
+```
+
+## Usage
+
+### Instantiation
+
+Now we can instantiate our model object and generate chat completions:
+
+```dart
+final openaiApiKey = Platform.environment['OPENAI_API_KEY'];
+
+final chatModel = ChatOpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: ChatOpenAIOptions(
+    model: 'gpt-4o'
+    temperature: 0,
+    // ...other options
+  ),
+);
+```
+
+If you are using a proxy, you can override the base URL, headers, and other options:
+
+```dart
+final client = ChatOpenAI(
+  baseUrl: 'https://my-proxy.com',
+  headers: {'x-my-proxy-header': 'value'},
+);
+```
+
+### Invocation
+
+Now you can generate completions by calling the `invoke` method:
+
+```dart
+final messages = [
+  ChatMessage.system('You are a helpful assistant that translates English to French.'),
+  ChatMessage.humanText('I love programming.'),
+];
+final prompt = PromptValue.chat(messages);
+final res = await llm.invoke(prompt);
+// -> 'J'adore la programmation.'
+```
+
+### Chaining
+
+We can chain our model with a prompt template or output parser to create a more complex pipeline:
+
+```dart
+final promptTemplate = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system, 'You are a helpful assistant that translates {input_language} to {output_language}.'),
+  (ChatMessageType.human, '{text}'),
+]);
+
+final chain = promptTemplate | chatModel | StringOutputParser();
+
+final res = await chain.invoke({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.',
+});
+print(res);
+// -> 'J'adore la programmation.'
+```
+
+### Streaming
+
+OpenAI models support [streaming](/expression_language/streaming.md) the output of th model as it is generated. 
+
+```dart
+final openaiApiKey = Platform.environment['OPENAI_API_KEY'];
+
+final promptTemplate = ChatPromptTemplate.fromTemplates([
+  (ChatMessageType.system,
+    'You are a helpful assistant that replies only with numbers '
+      'in order without any spaces or commas',
+  ),
+  (ChatMessageType.human, 'List the numbers from 1 to {max_num}'),
+]);
+
+final chat = ChatOpenAI(apiKey: openaiApiKey);
+
+final chain = promptTemplate.pipe(chat).pipe(StringOutputParser());
+
+final stream = chain.stream({'max_num': '9'});
+await stream.forEach(print);
+// 123
+// 456
+// 789
+```
+
+### Multimodal support
+
+OpenAI's models have [vision capabilities](https://platform.openai.com/docs/guides/vision), meaning the models can take in images and answer questions about them.
+
+You can send the image as a base64-encoded string:
+
+```dart
+final prompt = PromptValue.chat([
+  ChatMessage.system('You are a helpful assistant.'),
+  ChatMessage.human(
+    ChatMessageContent.multiModal([
+      ChatMessageContent.text('What fruit is this?'),
+      ChatMessageContent.image(
+        mimeType: 'image/jpeg',
+        data: '/9j/4AAQSkZJRgABAQAAAQABAAD...Rdu1j//2Q==', // base64-encoded image
+      ),
+    ]),
+  ),
+]);
+```
+
+Or you can send the URL where the image is hosted:
+
+```dart
+final prompt = PromptValue.chat([
+  ChatMessage.system('You are a helpful assistant.'),
+  ChatMessage.human(
+    ChatMessageContent.multiModal([
+      ChatMessageContent.text('What fruit is this?'),
+      ChatMessageContent.image(
+        data: 'https://upload.wikimedia.org/wikipedia/commons/9/92/95apple.jpeg',
+      ),
+    ]),
+  ),
+]);
+```
+
+### Tool calling
+
+OpenAI has a [tool calling](/modules/model_io/models/chat_models/how_to/tools.md) (we use "tool calling" and "function calling" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.
+
+
+```dart
+const tool = ToolSpec(
+  name: 'get_current_weather',
+  description: 'Get the current weather in a given location',
+  inputJsonSchema: {
+    'type': 'object',
+    'properties': {
+      'location': {
+        'type': 'string',
+        'description': 'The city and country, e.g. San Francisco, US',
+      },
+    },
+    'required': ['location'],
+  },
+);
+
+final chatModel = ChatOllama(
+  defaultOptions: ChatOllamaOptions(
+    model: 'gpt-4o'
+    temperature: 0,
+    tools: [tool],
+  ),
+);
+
+final res = await chatModel.invoke(
+  PromptValue.string('What’s the weather like in Boston and Madrid right now in celsius?'),
+);
+print(res.output.toolCalls);
+// [AIChatMessageToolCall{
+//   id: a621064b-03b3-4ca6-8278-f37504901034,
+//   name: get_current_weather,
+//   arguments: {location: Boston, US},
+// }, 
+// AIChatMessageToolCall{
+//   id: f160d9ba-ae7d-4abc-a910-2b6cd503ec53,
+//   name: get_current_weather,
+//   arguments: {location: Madrid, ES},
+// }]
+```
+
+Notice that the returned `AIChatMessage` has a `toolCalls` field. This contains in a standardized tool call format that is model-provider agnostic.
+
+You can also stream OpenAI tool calls. `ToolsOutputParser` is a useful tool for this case, as it concatenates the chunks progressively and tries to complete the partial JSON into a valid one:
+
+```dart
+const tool = ToolSpec(
+  name: 'joke',
+  description: 'A joke',
+  inputJsonSchema: {
+    'type': 'object',
+    'properties': {
+      'setup': {
+        'type': 'string',
+        'description': 'The setup for the joke',
+      },
+      'punchline': {
+        'type': 'string',
+        'description': 'The punchline to the joke',
+      },
+    },
+    'required': ['location', 'punchline'],
+  },
+);
+final promptTemplate = ChatPromptTemplate.fromTemplate(
+  'tell me a long joke about {foo}',
+);
+final chat = ChatOpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: ChatOpenAIOptions(
+    tools: [tool],
+    toolChoice: ChatToolChoice.forced(name: 'joke'),
+  ),
+);
+final outputParser = ToolsOutputParser();
+
+final chain = promptTemplate.pipe(chat).pipe(outputParser);
+
+final stream = chain.stream({'foo': 'bears'});
+await for (final chunk in stream) {
+  final args = chunk.first.arguments;
+  print(args);
+}
+// {}
+// {setup: }
+// {setup: Why don't}
+// {setup: Why don't bears}
+// {setup: Why don't bears like fast food}
+// {setup: Why don't bears like fast food?, punchline: }
+// {setup: Why don't bears like fast food?, punchline: Because}
+// {setup: Why don't bears like fast food?, punchline: Because they can't}
+// {setup: Why don't bears like fast food?, punchline: Because they can't catch it!}
+```
+
+### Structured Outputs
+
+[Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs) is a feature that ensures the model will always generate responses that adhere to your supplied JSON Schema, so you don't need to worry about the model omitting a required key, or hallucinating an invalid enum value.
+
+```dart
+final prompt = PromptValue.chat([
+  ChatMessage.system(
+    'Extract the data of any companies mentioned in the '
+    'following statement. Return a JSON list.',
+  ),
+  ChatMessage.humanText(
+    'Google was founded in the USA, while Deepmind was founded in the UK',
+  ),
+]);
+final chatModel = ChatOpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: ChatOpenAIOptions(
+    model: 'gpt-4o',
+    temperature: 0,
+    responseFormat: ChatOpenAIResponseFormat.jsonSchema(
+      ChatOpenAIJsonSchema(
+        name: 'Companies',
+        description: 'A list of companies',
+        strict: true,
+        schema: {
+          'type': 'object',
+          'properties': {
+            'companies': {
+              'type': 'array',
+              'items': {
+                'type': 'object',
+                'properties': {
+                  'name': {'type': 'string'},
+                  'origin': {'type': 'string'},
+                },
+                'additionalProperties': false,
+                'required': ['name', 'origin'],
+              },
+            },
+          },
+          'additionalProperties': false,
+          'required': ['companies'],
+        },
+      ),
+    ),
+  ),
+);
+
+final res = await chatModel.invoke(prompt);
+// {
+//   "companies": [
+//     {
+//       "name": "Google",
+//       "origin": "USA"
+//     },
+//     {
+//       "name": "Deepmind",
+//       "origin": "UK"
+//     }
+//   ]
+// }
+```
+
+When you use `strict: true`, the model outputs will match the supplied schema exactly. Mind that the strict mode only support a [subset of JSON schema](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas) for performance reasons. Under-the-hood, OpenAI uses a technique known as constrained sampling or constrained decoding. For each JSON Schema, they compute a grammar that represents that schema, and pre-process its components to make it easily accessible during model sampling. This is why the first request with a new schema incurs a latency penalty. Typical schemas take under 10 seconds to process on the first request, but more complex schemas may take up to a minute.
+
+### JSON mode
+
+When [JSON mode](https://platform.openai.com/docs/guides/structured-outputs/json-mode) is turned on, the model's output is ensured to be valid JSON. You can use it in combination with a `JsonOutputParser` to parse the response into a JSON map.
+
+> JSON mode is a more basic version of the Structured Outputs feature. While JSON mode ensures that model output is valid JSON, Structured Outputs reliably matches the model's output to the schema you specify. It is recommended that you use Structured Outputs if it is supported for your use case.
+
+```dart
+final prompt = PromptValue.chat([
+  ChatMessage.system(
+    "Extract the 'name' and 'origin' of any companies mentioned in the "
+        'following statement. Return a JSON list.',
+  ),
+  ChatMessage.humanText(
+    'Google was founded in the USA, while Deepmind was founded in the UK',
+  ),
+]);
+final llm = ChatOpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'gpt-4-turbo',
+    temperature: 0,
+    responseFormat: ChatOpenAIResponseFormat.jsonObject,
+  ),
+);
+final chain = llm.pipe(JsonOutputParser());
+final res = await chain.invoke(prompt);
+print(res);
+// {
+//   "companies": [
+//     {
+//       "name": "Google",
+//       "origin": "USA"
+//     },
+//     {
+//       "name": "Deepmind",
+//       "origin": "UK"
+//     }
+//   ]
+// }
+```
+
+### Fine-tuning
+
+You can call [fine-tuned OpenAI models](https://platform.openai.com/docs/guides/fine-tuning) by passing in your corresponding modelName parameter.
+
+This generally takes the form of `ft:{OPENAI_MODEL_NAME}:{ORG_NAME}::{MODEL_ID}`. For example:
+
+```dart
+final chatModel = ChatOpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: ChatOpenAIOptions(
+    model: 'ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR'
+  ),
+);
+```
+
+## API reference
+
+For detailed documentation of all ChatOpenAI features and configurations head to the [API reference](https://pub.dev/documentation/langchain_openai/latest).
diff --git a/docs_v2/docs/05-integrations/prem.md b/docs_v2/docs/05-integrations/prem.md
new file mode 100644
index 00000000..65258f7c
--- /dev/null
+++ b/docs_v2/docs/05-integrations/prem.md
@@ -0,0 +1,24 @@
+# Prem App
+
+You can easily run local models using [Prem app](https://www.premai.io/#PremApp). 
+It creates a local server that exposes a REST API with the same interface as 
+the OpenAI API.
+
+```dart
+const localUrl = 'http://localhost:8000'; // Check Prem app for the actual URL
+final chat = ChatOpenAI(baseUrl: localUrl);
+
+const template = 'You are a helpful assistant that translates {input_language} to {output_language}.';
+final systemMessagePrompt = SystemChatMessagePromptTemplate.fromTemplate(template);
+const humanTemplate = '{text}';
+final humanMessagePrompt = HumanChatMessagePromptTemplate.fromTemplate(humanTemplate);
+
+final chatPrompt = ChatPromptTemplate.fromPromptMessages([systemMessagePrompt, humanMessagePrompt]);
+final formattedPrompt = chatPrompt.formatPrompt({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.'
+}).toChatMessages();
+
+final output = chat.predictMessages(formattedPrompt);
+```
diff --git a/docs_v2/docs/05-integrations/together_ai.md b/docs_v2/docs/05-integrations/together_ai.md
new file mode 100644
index 00000000..10567455
--- /dev/null
+++ b/docs_v2/docs/05-integrations/together_ai.md
@@ -0,0 +1,84 @@
+# Together AI
+
+[Together AI](https://www.together.ai) offers a unified OpenAI-compatible API for a broad range of [models](https://api.together.xyz/playground) running serverless or on your own dedicated instances.
+
+It also allows to fine-tune models on your own data or train new models from scratch.
+
+You can consume Together AI API using the `ChatOpenAI` wrapper in the same way you would use the OpenAI API.
+
+The only difference is that you need to change the base URL to `https://api.together.xyz/v1`:
+
+```dart
+final chatModel = ChatOpenAI(
+  apiKey: togetherAiApiKey,
+  baseUrl: 'https://api.together.xyz/v1',
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'mistralai/Mistral-7B-Instruct-v0.2',
+  ),
+);
+```
+
+## Invoke
+
+```dart
+final togetherAiApiKey = Platform.environment['TOGETHER_AI_API_KEY'];
+
+final promptTemplate = ChatPromptTemplate.fromTemplates(const [
+  (
+  ChatMessageType.system,
+  'You are a helpful assistant that translates {input_language} to {output_language}.',
+  ),
+  (ChatMessageType.human, '{text}'),
+]);
+
+final chatModel = ChatOpenAI(
+  apiKey: togetherAiApiKey,
+  baseUrl: 'https://api.together.xyz/v1',
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'mistralai/Mistral-7B-Instruct-v0.2',
+  ),
+);
+
+final chain = promptTemplate | chatModel | StringOutputParser();
+
+final res = await chain.invoke({
+  'input_language': 'English',
+  'output_language': 'French',
+  'text': 'I love programming.',
+});
+print(res);
+// -> 'J'aime programmer'
+```
+
+## Stream
+
+```dart
+final togetherAiApiKey = Platform.environment['TOGETHER_AI_API_KEY'];
+
+final promptTemplate = ChatPromptTemplate.fromTemplates(const [
+  (
+  ChatMessageType.system,
+  'You are a helpful assistant that replies only with numbers '
+      'in order without any spaces or commas',
+  ),
+  (ChatMessageType.human, 'List the numbers from 1 to {max_num}'),
+]);
+
+final chatModel = ChatOpenAI(
+  apiKey: togetherAiApiKey,
+  baseUrl: 'https://api.together.xyz/v1',
+  defaultOptions: const ChatOpenAIOptions(
+    model: 'mistralai/Mistral-7B-Instruct-v0.2',
+  ),
+);
+
+final chain = promptTemplate.pipe(chatModel).pipe(StringOutputParser());
+
+final stream = chain.stream({'max_num': '9'});
+await stream.forEach(print);
+// 1
+// 2
+// 3
+// ...
+// 9
+```
diff --git a/docs_v2/docs/05-integrations/tools/index.mdx b/docs_v2/docs/05-integrations/tools/index.mdx
new file mode 100644
index 00000000..211b41de
--- /dev/null
+++ b/docs_v2/docs/05-integrations/tools/index.mdx
@@ -0,0 +1,5 @@
+---
+sidebar_position: 0
+index: auto
+---
+# Tools
\ No newline at end of file
diff --git a/docs_v2/docs/05-integrations/tools/tavily_search.md b/docs_v2/docs/05-integrations/tools/tavily_search.md
new file mode 100644
index 00000000..2f3d461d
--- /dev/null
+++ b/docs_v2/docs/05-integrations/tools/tavily_search.md
@@ -0,0 +1,13 @@
+# Tavily Search
+
+[Tavily's Search API](https://tavily.com) is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.
+
+## Setup
+
+The integration lives in the `langchain-community` package. We also need to install the `tavily-python` package itself.
+
+```bash
+dart pub add langchain langchain_community
+```
+
+We also need to set our Tavily API key.
\ No newline at end of file
diff --git a/docs_v2/docusaurus.config.js b/docs_v2/docusaurus.config.js
new file mode 100644
index 00000000..1376cddc
--- /dev/null
+++ b/docs_v2/docusaurus.config.js
@@ -0,0 +1,130 @@
+// @ts-check
+// `@type` JSDoc annotations allow editor autocompletion and type checking
+// (when paired with `@ts-check`).
+// There are various equivalent ways to declare your Docusaurus config.
+// See: https://docusaurus.io/docs/api/docusaurus-config
+
+import { themes as prismThemes } from 'prism-react-renderer';
+
+/** @type {import('@docusaurus/types').Config} */
+const config = {
+  title: 'LangChain.dart',
+  tagline: 'LangChain.dart Docs',
+  favicon: 'img/favicon.ico',
+
+  // Set the production url of your site here
+  url: 'https://beta.langchaindart.dev/',
+  // Set the /<baseUrl>/ pathname under which your site is served
+  // For GitHub pages deployment, it is often '/<projectName>/'
+  baseUrl: '/',
+
+  // GitHub pages deployment config.
+  // If you aren't using GitHub pages, you don't need these.
+  organizationName: 'davidmigloz', // Usually your GitHub org/user name.
+  projectName: 'langchain_dart', // Usually your repo name.
+
+  onBrokenLinks: 'warn',
+  onBrokenMarkdownLinks: 'warn',
+
+  // Even if you don't use internationalization, you can use this field to set
+  // useful metadata like html lang. For example, if your site is Chinese, you
+  // may want to replace "en" with "zh-Hans".
+  i18n: {
+    defaultLocale: 'en',
+    locales: ['en'],
+  },
+
+  presets: [
+    [
+      'classic',
+      /** @type {import('@docusaurus/preset-classic').Options} */
+      ({
+        docs: {
+          sidebarPath: './sidebars.js',
+          // Please change this to your repo.
+          // Remove this to remove the "edit this page" links.
+          editUrl:
+            'https://github.com/davidmigloz/langchain_dart/tree/main/docs_v2/',
+        },
+        theme: {
+          customCss: './src/css/custom.css',
+        },
+      }),
+    ],
+  ],
+
+  themeConfig:
+    /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
+    ({
+      // Replace with your project's social card
+      image: 'img/langchain.dart.png',
+      navbar: {
+        title: 'LangChain.dart',
+        logo: {
+          alt: 'LangChain Logo',
+          src: 'img/favicon.ico',
+        },
+        items: [
+          {
+            to: '/docs/integrations',
+            position: 'left',
+            label: 'Integrations',
+          },
+          {to: 'https://blog.langchaindart.dev/blog', label: 'Blog', position: 'left'},
+          {
+            href: 'https://github.com/davidmigloz/langchain_dart/',
+            label: 'GitHub',
+            position: 'right',
+          },
+        ],
+      },
+      footer: {
+        style: 'dark',
+        links: [
+          {
+            title: 'Docs',
+            items: [
+              {
+                label: 'Tutorial',
+                to: '/docs/intro',
+              },
+            ],
+          },
+          {
+            title: 'Community',
+            items: [
+              {
+                label: 'Discord',
+                href: 'https://discord.gg/x4qbhqecVR',
+              },
+              {
+                label: 'pub.dev',
+                href: 'https://pub.dev/packages/langchain',
+              },
+            ],
+          },
+          {
+            title: 'More',
+            items: [
+              {
+                label: 'Blog',
+                to: '/blog',
+              },
+              {
+                label: 'GitHub',
+                href: 'https://github.com/davidmigloz/langchain_dart',
+              },
+            ],
+          },
+        ],
+        copyright: `Made with 💙 by the <a href="https://github.com/davidmigloz/langchain_dart/graphs/contributors" target="_blank" rel="noopener noreferrer">LangChain.dart Community</a>`,
+      },
+      prism: {
+        theme: prismThemes.vsLight,
+        darkTheme: prismThemes.vsDark,
+        additionalLanguages: ['yaml','dart','bash'],
+      },
+    }),
+};
+
+export default config;
diff --git a/docs_v2/firebase.json b/docs_v2/firebase.json
new file mode 100644
index 00000000..340ed5b7
--- /dev/null
+++ b/docs_v2/firebase.json
@@ -0,0 +1,16 @@
+{
+  "hosting": {
+    "public": "build",
+    "ignore": [
+      "firebase.json",
+      "**/.*",
+      "**/node_modules/**"
+    ],
+    "rewrites": [
+      {
+        "source": "**",
+        "destination": "/index.html"
+      }
+    ]
+  }
+}
diff --git a/docs_v2/package-lock.json b/docs_v2/package-lock.json
new file mode 100644
index 00000000..21e6a165
--- /dev/null
+++ b/docs_v2/package-lock.json
@@ -0,0 +1,14683 @@
+{
+  "name": "langchain-dart",
+  "version": "0.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "langchain-dart",
+      "version": "0.0.0",
+      "dependencies": {
+        "@docusaurus/core": "^3.5.2",
+        "@docusaurus/preset-classic": "^3.5.2",
+        "@mdx-js/react": "^3.0.0",
+        "clsx": "^2.0.0",
+        "prism-react-renderer": "^2.3.0",
+        "react": "^18.0.0",
+        "react-dom": "^18.0.0"
+      },
+      "devDependencies": {
+        "@docusaurus/module-type-aliases": "^3.5.2",
+        "@docusaurus/types": "^3.5.2"
+      },
+      "engines": {
+        "node": ">=18.0"
+      }
+    },
+    "node_modules/@algolia/autocomplete-core": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-core/-/autocomplete-core-1.9.3.tgz",
+      "integrity": "sha512-009HdfugtGCdC4JdXUbVJClA0q0zh24yyePn+KUGk3rP7j8FEe/m5Yo/z65gn6nP/cM39PxpzqKrL7A6fP6PPw==",
+      "dependencies": {
+        "@algolia/autocomplete-plugin-algolia-insights": "1.9.3",
+        "@algolia/autocomplete-shared": "1.9.3"
+      }
+    },
+    "node_modules/@algolia/autocomplete-plugin-algolia-insights": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-plugin-algolia-insights/-/autocomplete-plugin-algolia-insights-1.9.3.tgz",
+      "integrity": "sha512-a/yTUkcO/Vyy+JffmAnTWbr4/90cLzw+CC3bRbhnULr/EM0fGNvM13oQQ14f2moLMcVDyAx/leczLlAOovhSZg==",
+      "dependencies": {
+        "@algolia/autocomplete-shared": "1.9.3"
+      },
+      "peerDependencies": {
+        "search-insights": ">= 1 < 3"
+      }
+    },
+    "node_modules/@algolia/autocomplete-preset-algolia": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-preset-algolia/-/autocomplete-preset-algolia-1.9.3.tgz",
+      "integrity": "sha512-d4qlt6YmrLMYy95n5TB52wtNDr6EgAIPH81dvvvW8UmuWRgxEtY0NJiPwl/h95JtG2vmRM804M0DSwMCNZlzRA==",
+      "dependencies": {
+        "@algolia/autocomplete-shared": "1.9.3"
+      },
+      "peerDependencies": {
+        "@algolia/client-search": ">= 4.9.1 < 6",
+        "algoliasearch": ">= 4.9.1 < 6"
+      }
+    },
+    "node_modules/@algolia/autocomplete-shared": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-shared/-/autocomplete-shared-1.9.3.tgz",
+      "integrity": "sha512-Wnm9E4Ye6Rl6sTTqjoymD+l8DjSTHsHboVRYrKgEt8Q7UHm9nYbqhN/i0fhUYA3OAEH7WA8x3jfpnmJm3rKvaQ==",
+      "peerDependencies": {
+        "@algolia/client-search": ">= 4.9.1 < 6",
+        "algoliasearch": ">= 4.9.1 < 6"
+      }
+    },
+    "node_modules/@algolia/cache-browser-local-storage": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/cache-browser-local-storage/-/cache-browser-local-storage-4.24.0.tgz",
+      "integrity": "sha512-t63W9BnoXVrGy9iYHBgObNXqYXM3tYXCjDSHeNwnsc324r4o5UiVKUiAB4THQ5z9U5hTj6qUvwg/Ez43ZD85ww==",
+      "dependencies": {
+        "@algolia/cache-common": "4.24.0"
+      }
+    },
+    "node_modules/@algolia/cache-common": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/cache-common/-/cache-common-4.24.0.tgz",
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+    },
+    "node_modules/@algolia/cache-in-memory": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/cache-in-memory/-/cache-in-memory-4.24.0.tgz",
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+      "dependencies": {
+        "@algolia/cache-common": "4.24.0"
+      }
+    },
+    "node_modules/@algolia/client-account": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/client-account/-/client-account-4.24.0.tgz",
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+        "@algolia/client-search": "4.24.0",
+        "@algolia/transporter": "4.24.0"
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+    "node_modules/@algolia/client-account/node_modules/@algolia/client-common": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/client-common/-/client-common-4.24.0.tgz",
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+        "@algolia/requester-common": "4.24.0",
+        "@algolia/transporter": "4.24.0"
+      }
+    },
+    "node_modules/@algolia/client-account/node_modules/@algolia/client-search": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/client-search/-/client-search-4.24.0.tgz",
+      "integrity": "sha512-uRW6EpNapmLAD0mW47OXqTP8eiIx5F6qN9/x/7HHO6owL3N1IXqydGwW5nhDFBrV+ldouro2W1VX3XlcUXEFCA==",
+      "dependencies": {
+        "@algolia/client-common": "4.24.0",
+        "@algolia/requester-common": "4.24.0",
+        "@algolia/transporter": "4.24.0"
+      }
+    },
+    "node_modules/@algolia/client-analytics": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/client-analytics/-/client-analytics-4.24.0.tgz",
+      "integrity": "sha512-y8jOZt1OjwWU4N2qr8G4AxXAzaa8DBvyHTWlHzX/7Me1LX8OayfgHexqrsL4vSBcoMmVw2XnVW9MhL+Y2ZDJXg==",
+      "dependencies": {
+        "@algolia/client-common": "4.24.0",
+        "@algolia/client-search": "4.24.0",
+        "@algolia/requester-common": "4.24.0",
+        "@algolia/transporter": "4.24.0"
+      }
+    },
+    "node_modules/@algolia/client-analytics/node_modules/@algolia/client-common": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/client-common/-/client-common-4.24.0.tgz",
+      "integrity": "sha512-bc2ROsNL6w6rqpl5jj/UywlIYC21TwSSoFHKl01lYirGMW+9Eek6r02Tocg4gZ8HAw3iBvu6XQiM3BEbmEMoiA==",
+      "dependencies": {
+        "@algolia/requester-common": "4.24.0",
+        "@algolia/transporter": "4.24.0"
+      }
+    },
+    "node_modules/@algolia/client-analytics/node_modules/@algolia/client-search": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/client-search/-/client-search-4.24.0.tgz",
+      "integrity": "sha512-uRW6EpNapmLAD0mW47OXqTP8eiIx5F6qN9/x/7HHO6owL3N1IXqydGwW5nhDFBrV+ldouro2W1VX3XlcUXEFCA==",
+      "dependencies": {
+        "@algolia/client-common": "4.24.0",
+        "@algolia/requester-common": "4.24.0",
+        "@algolia/transporter": "4.24.0"
+      }
+    },
+    "node_modules/@algolia/client-common": {
+      "version": "5.5.3",
+      "resolved": "https://registry.npmjs.org/@algolia/client-common/-/client-common-5.5.3.tgz",
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+      "peer": true,
+      "engines": {
+        "node": ">= 14.0.0"
+      }
+    },
+    "node_modules/@algolia/client-personalization": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/client-personalization/-/client-personalization-4.24.0.tgz",
+      "integrity": "sha512-l5FRFm/yngztweU0HdUzz1rC4yoWCFo3IF+dVIVTfEPg906eZg5BOd1k0K6rZx5JzyyoP4LdmOikfkfGsKVE9w==",
+      "dependencies": {
+        "@algolia/client-common": "4.24.0",
+        "@algolia/requester-common": "4.24.0",
+        "@algolia/transporter": "4.24.0"
+      }
+    },
+    "node_modules/@algolia/client-personalization/node_modules/@algolia/client-common": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/client-common/-/client-common-4.24.0.tgz",
+      "integrity": "sha512-bc2ROsNL6w6rqpl5jj/UywlIYC21TwSSoFHKl01lYirGMW+9Eek6r02Tocg4gZ8HAw3iBvu6XQiM3BEbmEMoiA==",
+      "dependencies": {
+        "@algolia/requester-common": "4.24.0",
+        "@algolia/transporter": "4.24.0"
+      }
+    },
+    "node_modules/@algolia/client-search": {
+      "version": "5.5.3",
+      "resolved": "https://registry.npmjs.org/@algolia/client-search/-/client-search-5.5.3.tgz",
+      "integrity": "sha512-qrokD+uoNxchbiF9aP8niQd/9SZ6BgYg4WaesFaubHhr9DFvwGm4IePEMha8vQcc3fSsY6uL+gOtKB3J6RF0NQ==",
+      "peer": true,
+      "dependencies": {
+        "@algolia/client-common": "5.5.3",
+        "@algolia/requester-browser-xhr": "5.5.3",
+        "@algolia/requester-fetch": "5.5.3",
+        "@algolia/requester-node-http": "5.5.3"
+      },
+      "engines": {
+        "node": ">= 14.0.0"
+      }
+    },
+    "node_modules/@algolia/events": {
+      "version": "4.0.1",
+      "resolved": "https://registry.npmjs.org/@algolia/events/-/events-4.0.1.tgz",
+      "integrity": "sha512-FQzvOCgoFXAbf5Y6mYozw2aj5KCJoA3m4heImceldzPSMbdyS4atVjJzXKMsfX3wnZTFYwkkt8/z8UesLHlSBQ=="
+    },
+    "node_modules/@algolia/logger-common": {
+      "version": "4.24.0",
+      "resolved": "https://registry.npmjs.org/@algolia/logger-common/-/logger-common-4.24.0.tgz",
+      "integrity": "sha512-LLUNjkahj9KtKYrQhFKCzMx0BY3RnNP4FEtO+sBybCjJ73E8jNdaKJ/Dd8A/VA4imVHP5tADZ8pn5B8Ga/wTMA=="
+    },
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+      "funding": {
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+}
diff --git a/docs_v2/package.json b/docs_v2/package.json
new file mode 100644
index 00000000..64b6fe3b
--- /dev/null
+++ b/docs_v2/package.json
@@ -0,0 +1,44 @@
+{
+  "name": "langchain-dart",
+  "version": "0.0.0",
+  "private": true,
+  "scripts": {
+    "docusaurus": "docusaurus",
+    "start": "docusaurus start",
+    "build": "docusaurus build",
+    "swizzle": "docusaurus swizzle",
+    "deploy": "docusaurus deploy",
+    "clear": "docusaurus clear",
+    "serve": "docusaurus serve",
+    "write-translations": "docusaurus write-translations",
+    "write-heading-ids": "docusaurus write-heading-ids"
+  },
+  "dependencies": {
+    "@docusaurus/core": "^3.5.2",
+    "@docusaurus/preset-classic": "^3.5.2",
+    "@mdx-js/react": "^3.0.0",
+    "clsx": "^2.0.0",
+    "prism-react-renderer": "^2.3.0",
+    "react": "^18.0.0",
+    "react-dom": "^18.0.0"
+  },
+  "devDependencies": {
+    "@docusaurus/module-type-aliases": "^3.5.2",
+    "@docusaurus/types": "^3.5.2"
+  },
+  "browserslist": {
+    "production": [
+      ">0.5%",
+      "not dead",
+      "not op_mini all"
+    ],
+    "development": [
+      "last 3 chrome version",
+      "last 3 firefox version",
+      "last 5 safari version"
+    ]
+  },
+  "engines": {
+    "node": ">=18.0"
+  }
+}
diff --git a/docs_v2/sidebars.js b/docs_v2/sidebars.js
new file mode 100644
index 00000000..72e4f826
--- /dev/null
+++ b/docs_v2/sidebars.js
@@ -0,0 +1,30 @@
+/**
+ * Creating a sidebar enables you to:
+ - create an ordered group of docs
+ - render a sidebar for each doc of that group
+ - provide next/previous navigation
+
+ The sidebars can be generated from the filesystem, or explicitly defined here.
+
+ Create as many sidebars as you want.
+ */
+
+// @ts-check
+
+/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */
+const sidebars = {
+  // By default, Docusaurus generates a sidebar from the docs folder structure
+  tutorialSidebar: [{type: 'autogenerated', dirName: '.'}],
+  integrations: [{type: 'autogenerated', dirName: '.'}]
+
+  // tutorialSidebar: [
+  //   'intro',
+  //   'tutorials/index',
+  //   'how_to/index',
+  //   'concepts',
+  //   'integrations/index',
+  // ],
+
+};
+
+export default sidebars;
\ No newline at end of file
diff --git a/docs_v2/src/components/HomepageFeatures/index.js b/docs_v2/src/components/HomepageFeatures/index.js
new file mode 100644
index 00000000..acc76219
--- /dev/null
+++ b/docs_v2/src/components/HomepageFeatures/index.js
@@ -0,0 +1,64 @@
+import clsx from 'clsx';
+import Heading from '@theme/Heading';
+import styles from './styles.module.css';
+
+const FeatureList = [
+  {
+    title: 'Easy to Use',
+    Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default,
+    description: (
+      <>
+        Docusaurus was designed from the ground up to be easily installed and
+        used to get your website up and running quickly.
+      </>
+    ),
+  },
+  {
+    title: 'Focus on What Matters',
+    Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default,
+    description: (
+      <>
+        Docusaurus lets you focus on your docs, and we&apos;ll do the chores. Go
+        ahead and move your docs into the <code>docs</code> directory.
+      </>
+    ),
+  },
+  {
+    title: 'Powered by React',
+    Svg: require('@site/static/img/undraw_docusaurus_react.svg').default,
+    description: (
+      <>
+        Extend or customize your website layout by reusing React. Docusaurus can
+        be extended while reusing the same header and footer.
+      </>
+    ),
+  },
+];
+
+function Feature({Svg, title, description}) {
+  return (
+    <div className={clsx('col col--4')}>
+      <div className="text--center">
+        <Svg className={styles.featureSvg} role="img" />
+      </div>
+      <div className="text--center padding-horiz--md">
+        <Heading as="h3">{title}</Heading>
+        <p>{description}</p>
+      </div>
+    </div>
+  );
+}
+
+export default function HomepageFeatures() {
+  return (
+    <section className={styles.features}>
+      <div className="container">
+        <div className="row">
+          {FeatureList.map((props, idx) => (
+            <Feature key={idx} {...props} />
+          ))}
+        </div>
+      </div>
+    </section>
+  );
+}
diff --git a/docs_v2/src/components/HomepageFeatures/styles.module.css b/docs_v2/src/components/HomepageFeatures/styles.module.css
new file mode 100644
index 00000000..b248eb2e
--- /dev/null
+++ b/docs_v2/src/components/HomepageFeatures/styles.module.css
@@ -0,0 +1,11 @@
+.features {
+  display: flex;
+  align-items: center;
+  padding: 2rem 0;
+  width: 100%;
+}
+
+.featureSvg {
+  height: 200px;
+  width: 200px;
+}
diff --git a/docs_v2/src/css/custom.css b/docs_v2/src/css/custom.css
new file mode 100644
index 00000000..2bc6a4cf
--- /dev/null
+++ b/docs_v2/src/css/custom.css
@@ -0,0 +1,30 @@
+/**
+ * Any CSS included here will be global. The classic template
+ * bundles Infima by default. Infima is a CSS framework designed to
+ * work well for content-centric websites.
+ */
+
+/* You can override the default Infima variables here. */
+:root {
+  --ifm-color-primary: #2e8555;
+  --ifm-color-primary-dark: #29784c;
+  --ifm-color-primary-darker: #277148;
+  --ifm-color-primary-darkest: #205d3b;
+  --ifm-color-primary-light: #33925d;
+  --ifm-color-primary-lighter: #359962;
+  --ifm-color-primary-lightest: #3cad6e;
+  --ifm-code-font-size: 95%;
+  --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1);
+}
+
+/* For readability concerns, you should choose a lighter palette in dark mode. */
+[data-theme='dark'] {
+  --ifm-color-primary: #25c2a0;
+  --ifm-color-primary-dark: #21af90;
+  --ifm-color-primary-darker: #1fa588;
+  --ifm-color-primary-darkest: #1a8870;
+  --ifm-color-primary-light: #29d5b0;
+  --ifm-color-primary-lighter: #32d8b4;
+  --ifm-color-primary-lightest: #4fddbf;
+  --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3);
+}
diff --git a/docs_v2/src/pages/index.js b/docs_v2/src/pages/index.js
new file mode 100644
index 00000000..176f838c
--- /dev/null
+++ b/docs_v2/src/pages/index.js
@@ -0,0 +1,7 @@
+import { Redirect } from "@docusaurus/router";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import React from "react";
+
+export default function Home() {
+  return <Redirect to={useBaseUrl("/docs/intro/")} />;
+}
diff --git a/docs_v2/src/pages/index.module.css b/docs_v2/src/pages/index.module.css
new file mode 100644
index 00000000..9f71a5da
--- /dev/null
+++ b/docs_v2/src/pages/index.module.css
@@ -0,0 +1,23 @@
+/**
+ * CSS files with the .module.css suffix will be treated as CSS modules
+ * and scoped locally.
+ */
+
+.heroBanner {
+  padding: 4rem 0;
+  text-align: center;
+  position: relative;
+  overflow: hidden;
+}
+
+@media screen and (max-width: 996px) {
+  .heroBanner {
+    padding: 2rem;
+  }
+}
+
+.buttons {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
diff --git a/docs_v2/src/pages/markdown-page.md b/docs_v2/src/pages/markdown-page.md
new file mode 100644
index 00000000..9756c5b6
--- /dev/null
+++ b/docs_v2/src/pages/markdown-page.md
@@ -0,0 +1,7 @@
+---
+title: Markdown page example
+---
+
+# Markdown page example
+
+You don't need React to write simple standalone pages.
diff --git a/docs_v2/static/.nojekyll b/docs_v2/static/.nojekyll
new file mode 100644
index 00000000..e69de29b
diff --git a/docs_v2/static/img/favicon.ico b/docs_v2/static/img/favicon.ico
new file mode 100644
index 00000000..4c296111
Binary files /dev/null and b/docs_v2/static/img/favicon.ico differ
diff --git a/docs_v2/static/img/langchain.dart.png b/docs_v2/static/img/langchain.dart.png
new file mode 100644
index 00000000..f7fdb39f
Binary files /dev/null and b/docs_v2/static/img/langchain.dart.png differ
diff --git a/docs_v2/static/img/logo.svg b/docs_v2/static/img/logo.svg
new file mode 100644
index 00000000..9db6d0d0
--- /dev/null
+++ b/docs_v2/static/img/logo.svg
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