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langchain4j
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Updated Docs (#500) 

- Updated subpage 0 to `Overview` and made it the landing page upon
clicking `Tutorials` from the horizontal/navigation/top bar.
- Set up GitHub action to build and publish the docs to the GitHub pages
on release. [Preview](https://amithkoujalgi.github.io/langchain4j/) of
the docs on GitHub pages. This action/workflow can also be triggered
manually.
- Added content to `/integrations/language-models/ollama`

PS:
If GitHub pages has been enabled in the repository, the repo admin
should be able to run the new GitHub workflow/action in the Actions tab
and after the completion of the workflow, the docs should be available
in the link provided by GitHub (or a specified custom domain).
This commit is contained in:
Amith Koujalgi 2024-01-11 13:34:38 +05:30 committed by GitHub
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42
.github/workflows/docs.yaml vendored Normal file
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@ -0,0 +1,42 @@
name: Build and Publish Docs to GitHub Pages
on:
release:
types:
- created
# Allow running this workflow manually from the Actions tab
workflow_dispatch:
# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
permissions:
contents: read
pages: write
id-token: write
packages: write
jobs:
deploy:
runs-on: ubuntu-latest
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
steps:
- uses: actions/checkout@v4
- name: Use Node.js
uses: actions/setup-node@v3
with:
node-version: '20.x'
- run: cd docs && npm ci
- run: cd docs && npm run build
- name: Setup Pages
uses: actions/configure-pages@v3
- name: Upload artifact
uses: actions/upload-pages-artifact@v2
with:
# Upload entire repository
path: './docs/build/.'
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v2

4
docs/Makefile
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@ -1,7 +1,7 @@
build:
build-docs:
npm ci; \
npm run build
dev:
run-docs:
npm ci; \
npm run start

166
docs/docs/integrations/language-models/ollama.md
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@ -3,3 +3,169 @@ sidebar_position: 18
---
# Ollama
### What is Ollama?
Ollama is an advanced AI tool that allows users to easily set up and run large language models
locally (in CPU and GPU modes). With Ollama, users can leverage powerful language models such as
Llama 2 and even customize and create their own models. Ollama bundles model weights, configuration,
and data into a single package, defined by a Modelfile. It optimizes setup and configuration
details, including GPU usage.
For more details about Ollama, check these out:
- https://ollama.ai/
- https://github.com/jmorganca/ollama
### Talks
Watch this presentation at [Docker Con 23](https://www.dockercon.com/2023/program):
<iframe width="640" height="480" src="https://www.youtube.com/embed/yPuhGtJT55o" title="Introducing Docker’s Generative AI and Machine Learning Stack (DockerCon 2023)" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
Watch this intro by [Code to the Moon](https://www.youtube.com/@codetothemoon):
<iframe width="640" height="480" src="https://www.youtube.com/embed/jib1wjgIaa4" title="this open source project has a bright future" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
### Get started
To get started, add the following dependencies to your project's `pom.xml`:
```xml
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-ollama</artifactId>
<version>${lanchain4j-ollama.version}</version>
</dependency>
<dependency>
<groupId>org.testcontainers</groupId>
<artifactId>testcontainers</artifactId>
<version>1.19.1</version>
</dependency>
```
Try out a simple chat example code:
```java
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.ollama.OllamaChatModel;
import org.testcontainers.containers.GenericContainer;
public class OllamaChatExample {
public static void main(String[] args) {
// The model name to use (e.g., "orca-mini", "mistral", "llama2", "codellama", "phi", or
// "tinyllama")
String modelName = "orca-mini";
// Create and start the Ollama container
GenericContainer<?> ollama =
new GenericContainer<>("langchain4j/ollama-" + modelName + ":latest")
.withExposedPorts(11434);
ollama.start();
// Build the ChatLanguageModel
ChatLanguageModel model =
OllamaChatModel.builder().baseUrl(baseUrl(ollama)).modelName(modelName).build();
// Example usage
String answer = model.generate("Provide 3 short bullet points explaining why Java is awesome");
System.out.println(answer);
// Stop the Ollama container
ollama.stop();
}
private static String baseUrl(GenericContainer<?> ollama) {
return String.format("http://%s:%d", ollama.getHost(), ollama.getFirstMappedPort());
}
}
```
Try out a simple streaming chat example code:
```java
import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.model.StreamingResponseHandler;
import dev.langchain4j.model.chat.StreamingChatLanguageModel;
import dev.langchain4j.model.ollama.OllamaStreamingChatModel;
import dev.langchain4j.model.output.Response;
import org.testcontainers.containers.GenericContainer;
import java.util.concurrent.CompletableFuture;
public class OllamaStreamingChatExample {
static String MODEL_NAME = "orca-mini"; // try "mistral", "llama2", "codellama" or "phi"
static String DOCKER_IMAGE_NAME = "langchain4j/ollama-" + MODEL_NAME + ":latest";
static Integer PORT = 11434;
static GenericContainer<?> ollama = new GenericContainer<>(DOCKER_IMAGE_NAME)
.withExposedPorts(PORT);
public static void main(String[] args) {
StreamingChatLanguageModel model = OllamaStreamingChatModel.builder()
.baseUrl(String.format("http://%s:%d", ollama.getHost(), ollama.getMappedPort(PORT)))
.modelName(MODEL_NAME)
.temperature(0.0)
.build();
String userMessage = "Write a 100-word poem about Java and AI";
CompletableFuture<Response<AiMessage>> futureResponse = new CompletableFuture<>();
model.generate(userMessage, new StreamingResponseHandler<AiMessage>() {
@Override
public void onNext(String token) {
System.out.print(token);
}
@Override
public void onComplete(Response<AiMessage> response) {
futureResponse.complete(response);
}
@Override
public void onError(Throwable error) {
futureResponse.completeExceptionally(error);
}
});
futureResponse.join();
}
}
```
### Parameters
`OllamaChatModel` and `OllamaStreamingChatModel` classes can be instantiated with the following
params with the builder pattern:
| Parameter | Description | Type |
|-----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------|
| `baseUrl` | The base URL of Ollama server. | `String` |
| `modelName` | The name of the model to use from Ollama server. | `String` |
| `temperature` | Controls the randomness of the generated responses. Higher values (e.g., 1.0) result in more diverse output, while lower values (e.g., 0.2) produce more deterministic responses. | `Double` |
| `topK` | Specifies the number of highest probability tokens to consider for each step during generation. | `Integer` |
| `topP` | Controls the diversity of the generated responses by setting a threshold for the cumulative probability of top tokens. | `Double` |
| `repeatPenalty` | Penalizes the model for repeating similar tokens in the generated output. | `Double` |
| `seed` | Sets the random seed for reproducibility of generated responses. | `Integer` |
| `numPredict` | The number of predictions to generate for each input prompt. | `Integer` |
| `stop` | A list of strings that, if generated, will mark the end of the response. | `List<String>` |
| `format` | The desired format for the generated output. | `String` |
| `timeout` | The maximum time allowed for the API call to complete. | `Duration` |
| `maxRetries` | The maximum number of retries in case of API call failure. | `Integer` |
#### Usage Example:
```java
OllamaChatModel ollamaChatModel = OllamaChatModel.builder()
.baseUrl("http://your-ollama-host:your-ollama-port")
.modelName("llama2")
.temperature(0.8)
.build();
```

5
docs/docs/intro.md
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@ -1,8 +1,9 @@
---
sidebar_position: 1
title: Introduction
---
# Intro
# Introduction
Welcome to the **LangChain4j** documentation website!
@ -14,7 +15,7 @@ It is based on the popular Python library LangChain.
### Supercharge your Java application with the power of LLMs
[![](/img/langchain4j-components.png)](/docs/categories/intro)
[![](/img/langchain4j-components.png)](/docs/intro)
LangChain4j allows for easy interaction with AI/LLMs thanks to:

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docs/docs/tutorials/index.mdx
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@ -1,35 +1,70 @@
---
title: Overview
hide_title: true
sidebar_position: 1
---
# 0. Tutorials
This section will walk you through all LangChain4j functionality, in steps of increasing complexity.
We will typically use OpenAI models for demonstration purposes, but we support a lot of other model providers too. The entire list of supported models can be found [here](/docs/category/integrations).
### Need inspiration?
## Need inspiration?
Watch the talk [Java Meets AI: A Hands On Guide to Building LLM Powered Applications with LangChain4j](https://www.youtube.com/watch?v=BD1MSLbs9KE) by [Lize Raes](https://github.com/LizeRaes) at Devoxx Belgium, or consider some of the following use cases:
### Watch the talk by [Lize Raes](https://github.com/LizeRaes) at Devoxx Belgium
- You want to implement a custom AI-powered chatbot that has access to your data and behaves the way you want it:
- Customer support chatbot that can:
- politely answer customer questions
- take /change/cancel orders
- Educational assistant that can:
- Teach various subjects
- Explain unclear parts
- Assess user's understanding/knowledge
- You want to process a lot of unstructured data (files, web pages, etc) and extract structured information from them.
For example:
- extract insights from customer reviews and support chat history
- extract interesting information from the websites of your competitors
- extract insights from CVs of job applicants
- You want to generate information, for example:
<iframe width="640" height="480" src="https://www.youtube.com/embed/BD1MSLbs9KE"
title="Java Meets AI: A Hands On Guide to Building LLM Powered Applications with LangChain4j By Lize Raes"
frameborder="0"
style={{marginTop: '10px', marginBottom: '20px'}}
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen></iframe>
### Talk by [Vaadin](https://vaadin.com/) team about Building a RAG AI system in Spring Boot & LangChain4j
<iframe width="640" height="480" src="https://www.youtube.com/embed/J-3n7xs98Kc"
title="How to build a retrieval-augmented generation (RAG) AI system in Java (Spring Boot + LangChain4j)"
frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen></iframe>
### Fireside Chat: Langchain4j & Quarkus by [Quarkusio](https://quarkus.io/)
<iframe width="640" height="480"
src="https://www.youtube.com/embed/mYw9ySwmK34"
title="Fireside Chat: Langchain4j &amp; Quarkus"
frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen></iframe>
### The Magic of AI Services with LangChain4J by [Tales from the jar side](https://www.youtube.com/@talesfromthejarside)
<iframe width="640" height="480" src="https://www.youtube.com/embed/Bx2OpE1nj34" title="The Magic of AI Services with LangChain4J" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
### Or consider some of the use cases
- You want to:
- Implement a custom AI-powered chatbot that has access to your data and behaves the way you want it.
- Implement a customer support chatbot that can:
- politely answer customer questions
- take /change/cancel orders
- Implement an educational assistant that can:
- Teach various subjects
- Explain unclear parts
- Assess user's understanding/knowledge
- You want to process a lot of unstructured data (files, web pages, etc) and extract structured information from them. For example:
- extract insights from customer reviews and support chat history
- extract interesting information from the websites of your competitors
- extract insights from CVs of job applicants
- Generate information, for example:
- Emails tailored for each of your customers
- Content for your app/website:
- Blog posts
- Stories
- You want to transform information, for example:
- Generate content for your app/website:
- Blog posts
- Stories
- Transform information, for example:
- Summarize
- Proofread and rewrite
- Translate

22
docs/docusaurus.config.js
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@ -12,8 +12,12 @@ const config = {
tagline: 'Supercharge your Java application with the power of LLMs',
favicon: 'img/favicon.ico',
onBrokenLinks: 'warn', // ideally this should have a stricter value set - 'throw'
onBrokenMarkdownLinks: 'warn', // ideally this should have a stricter value set - 'throw'
onDuplicateRoutes: 'warn', // ideally this should have a stricter value set - 'throw'
// Set the production url of your site here
url: 'https://github.com',
url: 'https://langchain4j.github.io/',
// Set the /<baseUrl>/ pathname under which your site is served
// For GitHub pages deployment, it is often '/<projectName>/'
baseUrl: '/langchain4j/',
@ -23,9 +27,6 @@ const config = {
organizationName: 'LangChain4j', // Usually your GitHub org/user name.
projectName: 'LangChain4j', // Usually your repo name.
onBrokenLinks: 'throw',
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".
@ -65,6 +66,11 @@ const config = {
({
// Replace with your project's social card
image: 'img/docusaurus-social-card.jpg',
docs: {
sidebar: {
hideable: true
}
},
navbar: {
title: 'LangChain4j',
logo: {
@ -78,7 +84,7 @@ const config = {
position: 'left',
label: 'Get Started',
},
{to: '/docs/category/tutorials', label: 'Tutorials', position: 'left'},
{to: '/docs/tutorials', label: 'Tutorials', position: 'left'},
{to: '/docs/category/integrations', label: 'Integrations', position: 'left'},
{to: 'https://github.com/langchain4j/langchain4j/javadoc/', label: 'Javadoc', position: 'left'},
{to: '/blog', label: 'Blog', position: 'left'},
@ -97,7 +103,7 @@ const config = {
items: [
{
label: 'Tutorial',
to: '/docs/welcome',
to: '/docs/tutorials',
},
],
},
@ -129,6 +135,10 @@ const config = {
label: 'GitHub',
href: 'https://github.com/langchain4j/langchain4j',
},
{
label: "Examples",
href: 'https://github.com/langchain4j/langchain4j-examples'
}
],
},
],

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docs/package-lock.json generated
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@ -9,6 +9,7 @@
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@ -2546,6 +2807,35 @@
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@ -2597,6 +2887,35 @@
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1
docs/package.json
View File

@ -15,6 +15,7 @@
},
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