langchain

Description:

langchain

🦜️🔗 LangChain.dart #






Build LLM-powered Dart/Flutter applications.
What is LangChain.dart? #
LangChain.dart is an unofficial Dart port of the popular LangChain Python framework created by Harrison Chase.
LangChain provides a set of ready-to-use components for working with language models and a standard interface for chaining them together to formulate more advanced use cases (e.g. chatbots, Q&A with RAG, agents, summarization, translation, extraction, recsys, etc.).
The components can be grouped into a few core modules:


📃 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).
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 #
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 #
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 #
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, langchain_google, 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.




Package
Version
Description




langchain_anthropic

Anthopic integration (Claude 3.5 Sonnet, Opus, Haiku, Instant, etc.)


langchain_chroma

Chroma vector database integration


langchain_firebase

Firebase integration (VertexAI for Firebase (Gemini 1.5 Pro, Gemini 1.5 Flash, etc.))


langchain_google

Google integration (GoogleAI, VertexAI, Gemini, PaLM 2, Embeddings, Vector Search, etc.)


langchain_mistralai

Mistral AI integration (Mistral-7B, Mixtral 8x7B, Mixtral 8x22B, Mistral Small, Mistral Large, embeddings, etc.).


langchain_ollama

Ollama integration (Llama 3.1, Gemma 2, Phi-3, Mistral nemo, WizardLM-2, CodeGemma, Command R, LLaVA, DBRX, Qwen, Dolphin, DeepSeek Coder, Vicuna, Orca, etc.)


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_pinecone

Pinecone vector database integration


langchain_supabase

Supabase Vector database integration






API clients packages #
The following packages are maintained (and used internally) by LangChain.dart, although they can also be used independently:

Depend on an API client package if you just want to consume the API of a specific provider directly without using LangChain.dart abstractions.




Package
Version
Description




anthropic_sdk_dart

Anthropic API client


chromadb

Chroma DB API client


googleai_dart

Google AI for Developers API client


mistralai_dart

Mistral AI API client


ollama_dart

Ollama API client


openai_dart

OpenAI API client


tavily_dart

Tavily API client


vertex_ai

GCP Vertex AI API client



Integrations #
The following integrations are available in LangChain.dart:
Chat Models #



Chat model
Package
Streaming
Multi-modal
Tool-call
Description




ChatAnthropic
langchain_anthropic
✔
✔
✔
Anthropic Messages API (aka Claude API)


ChatFirebaseVertexAI
langchain_firebase
✔
✔
✔
Vertex AI for Firebase API (aka Gemini API)


ChatGoogleGenerativeAI
langchain_google
✔
✔
✔
Google AI for Developers API (aka Gemini API)


ChatMistralAI
langchain_mistralai
✔


Mistral Chat API


ChatOllama
langchain_ollama
✔
✔
✔
Ollama Chat API


ChatOpenAI
langchain_openai
✔
✔
✔
OpenAI Chat API and OpenAI Chat API compatible services (TogetherAI, Anyscale, OpenRouter, One API, Groq, Llamafile, GPT4All, FastChat, etc.)


ChatVertexAI
langchain_google



GCP Vertex AI Chat API



LLMs #
Note: Prefer using Chat Models over LLMs as many providers have deprecated them.



LLM
Package
Streaming
Description




Ollama
langchain_ollama
✔
Ollama Completions API


OpenAI
langchain_openai
✔
OpenAI Completions API


VertexAI
langchain_google

GCP Vertex AI Text API



Embedding Models #



Embedding model
Package
Description




GoogleGenerativeAIEmbeddings
langchain_google
Google AI Embeddings API


MistralAIEmbeddings
langchain_mistralai
Mistral Embeddings API


OllamaEmbeddings
langchain_ollama
Ollama Embeddings API


OpenAIEmbeddings
langchain_openai
OpenAI Embeddings API


VertexAIEmbeddings
langchain_google
GCP Vertex AI Embeddings API



Vector Stores #



Vector store
Package
Description




Chroma
langchain_chroma
Chroma integration


MemoryVectorStore
langchain
In-memory vector store for prototype and testing


ObjectBoxVectorStore
langchain_community
ObjectBox integration


Pinecone
langchain_pinecone
Pinecone integration


Supabase
langchain_supabase
Supabase Vector integration


VertexAIMatchingEngine
langchain_google
Vertex AI Vector Search (former Matching Engine) integration



Tools #



Tool
Package
Description




CalculatorTool
langchain_community
To calculate math expressions


OpenAIDallETool
langchain_openai
OpenAI's DALL-E Image Generator


TavilyAnswerTool
langchain_community
Returns an answer for a query using the Tavily search engine


TavilySearchResultsTool
langchain_community
Returns a list of results for a query using the Tavily search engine



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.):
dependencies:
langchain: {version}
langchain_community: {version}
langchain_openai: {version}
langchain_google: {version}
...
copied to clipboard
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:
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.
copied to clipboard
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:
// 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
copied to clipboard
Documentation #

LangChain.dart documentation
Code Assist AI (Chatbot for LangChain.dart documentation)
Sample apps
LangChain.dart blog
Project board

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.

Contribute #



📢 Call for Collaborators 📢




We are looking for collaborators to join the core group of maintainers.



New contributors welcome! Check out our Contributors Guide for help getting started.
Join us on Discord to meet other maintainers. We'll help you get your first contribution in no time!
Related projects #

LangChain: The original Python LangChain project.
LangChain.js: A JavaScript port of LangChain.
LangChain.go: A Go port of LangChain.
LangChain.rb: A Ruby port of LangChain.

Sponsors #





License #
LangChain.dart is licensed under the MIT License.