https://github.com/HamaWhiteGG/langchain-java
1. What is this?
This is the Java language implementation of LangChain.
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
This library is aimed at assisting in the development of those types of applications.
Looking for the Python version? Check out LangChain.
2. Quickstart Guide
This tutorial gives you a quick walkthrough about building an end-to-end language model application with LangChain.
View the Quickstart Guide on the LangChain official website.
2.1 Maven Repository
Prerequisites for building:
- Java 17 or later
- Unix-like environment (we use Linux, Mac OS X)
- Maven (we recommend version 3.8.6 and require at least 3.5.4)
<dependency>
<groupId>io.github.hamawhitegg</groupId>
<artifactId>langchain-core</artifactId>
<version>0.1.6</version>
</dependency>2.2 Environment Setup
Using LangChain will usually require integrations with one or more model providers, data stores, apis, etc.
For this example, we will be using OpenAI’s APIs.
We will then need to set the environment variable.
代码语言：shell复制export OPENAI_API_KEY=xxx
# If a proxy is needed, set the OPENAI_PROXY environment variable.
export OPENAI_PROXY=http://host:portIf you want to set the API key and proxy dynamically, you can use the openaiApiKey and openaiProxy parameter when initiating OpenAI class.
代码语言：java复制var llm = OpenAI.builder()
.openaiApiKey("xxx")
.openaiProxy("http://host:port")
.build()
.init();The following test code can be used to view the QuickStart.java
2.3 LLMs: Get predictions from a language model
The most basic building block of LangChain is calling an LLM on some input. Let’s walk through a simple example of how to do this. For this purpose, let’s pretend we are building a service that generates a company name based on what the company makes.
代码语言：java复制var llm = OpenAI.builder()
.temperature(0.9f)
.build()
.init();
String text = "What would be a good company name for a company that makes colorful socks?";
System.out.println(llm.call(text));Feetful of Fun2.4 Prompt Templates: Manage prompts for LLMs
Calling an LLM is a great first step, but it’s just the beginning. Normally when you use an LLM in an application, you are not sending user input directly to the LLM. Instead, you are probably taking user input and constructing a prompt, and then sending that to the LLM.
代码语言：java复制var prompt = new PromptTemplate(List.of("product"),
"What is a good name for a company that makes {product}?");
System.out.println(prompt.format(Map.of("product", "colorful socks")));What is a good name for a company that makes colorful socks?2.5 Chains: Combine LLMs and prompts in multi-step workflows
Up until now, we’ve worked with the PromptTemplate and LLM primitives by themselves. But of course, a real application is not just one primitive, but rather a combination of them.
A chain in LangChain is made up of links, which can be either primitives like LLMs or other chains.
2.5.1 LLM Chain
The most core type of chain is an LLMChain, which consists of a PromptTemplate and an LLM.
代码语言：java复制var llm = OpenAI.builder()
.temperature(0.9f)
.build()
.init();
var prompt = new PromptTemplate(List.of("product"),
"What is a good name for a company that makes {product}?");
var chain = new LLMChain(llm, prompt);
System.out.println(chain.run("colorful socks"));nnSocktastic!2.5.2 SQL Chain
This example demonstrates the use of the SQLDatabaseChain for answering questions over a database.
代码语言：java复制var database = SQLDatabase.fromUri("jdbc:mysql://127.0.0.1:3306/demo", "xxx", "xxx");
var llm = OpenAI.builder()
.temperature(0)
.build()
.init();
var chain = SQLDatabaseChain.fromLLM(llm, database);
System.out.println(chain.run("How many students are there?"));There are 6 students.2.6 Agents: Dynamically Call Chains Based on User Input
Agents no longer do: they use an LLM to determine which actions to take and in what order. An action can either be using a tool and observing its output, or returning to the user.
When used correctly agents can be extremely powerful. In this tutorial, we show you how to easily use agents through the simplest, highest level API.
Set the appropriate environment variables.
代码语言：shell复制export SERPAPI_API_KEY=xxxNow we can get started!
代码语言：java复制var llm = OpenAI.builder()
.temperature(0)
.build()
.init();
// load some tools to use.
var tools = loadTools(List.of("serpapi", "llm-math"), llm);
// initialize an agent with the tools, the language model, and the type of agent
var agent = initializeAgent(tools, llm, AgentType.ZERO_SHOT_REACT_DESCRIPTION);
// let's test it out!
String text =
"What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?";
System.out.println(agent.run(text));I need to find the temperature first, then use the calculator to raise it to the .023 power.
Action: Search
Action Input: "High temperature in SF yesterday"
Observation: San Francisco Weather History for the Previous 24 Hours ; 60 °F · 60 °F · 61 °F ...
Thought: I now have the temperature, so I can use the calculator to raise it to the .023 power.
Action: Calculator
Action Input: 60^.023
Observation: Answer: 1.09874643447
Thought: I now know the final answer
Final Answer: 1.09874643447
1.098746434473. Run Test Cases from Source
代码语言：txt复制git clone https://github.com/HamaWhiteGG/langchain-java.git
cd langchain-java
# export JAVA_HOME=JDK17_INSTALL_HOME && mvn clean test
mvn clean test4. Apply Spotless
代码语言：txt复制cd langchain-java
# export JAVA_HOME=JDK17_INSTALL_HOME && mvn spotless:apply
mvn spotless:apply5. Support
Don’t hesitate to ask!
Open an issue if you find a bug in Flink.
6. Fork and Contribute
This is an active open-source project. We are always open to people who want to use the system or contribute to it.
Contact me if you are looking for implementation tasks that fit your skills.
