当前位置：首页 > news >正文

网站建设过程论文长沙网络开发公司

news 2025/11/2 21:30:21

网站建设过程论文,长沙网络开发公司,适合 wordpress 图标,何炅做的网站广告介绍 j-langchain是一个Java版的LangChain开发框架，旨在简化和加速各类大模型应用在Java平台的落地开发。它提供了一组实用的工具和类，使得开发人员能够更轻松地构建类似于LangChain的Java应用程序。依赖 Maven <dependency><groupId>i…

介绍

j-langchain是一个Java版的LangChain开发框架，旨在简化和加速各类大模型应用在Java平台的落地开发。它提供了一组实用的工具和类，使得开发人员能够更轻松地构建类似于LangChain的Java应用程序。

依赖

Maven

<dependency><groupId>io.github.flower-trees</groupId><artifactId>j-langchain</artifactId><version>1.0.1-preview</version>
</dependency>

Gradle

implementation 'io.github.flower-trees:j-langchain:1.0.1-preview'

💡 Notes:

系统基于salt-function-flow流程编排框架开发，具体语法可参考。

智能链构建

顺序调用

LangChain实现

from langchain_ollama import OllamaLLM
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParsermodel = OllamaLLM(model="qwen2.5:0.5b")
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")chain = prompt | model | StrOutputParser()result = chain.invoke({"topic": "bears"})
print(result)

J-LangChain实现

@Component
public class ChainBuildDemo {@AutowiredChainActor chainActor;public void SimpleDemo() {BaseRunnable<StringPromptValue, ?> prompt = PromptTemplate.fromTemplate("tell me a joke about ${topic}");ChatOpenAI chatOpenAI = ChatOpenAI.builder().model("gpt-4").build();FlowInstance chain = chainActor.builder().next(prompt).next(oll).next(new StrOutputParser()).build();ChatGeneration result = chainActor.invoke(chain, Map.of("topic", "bears"));System.out.println(result);}
}

分支路由

J-LangChain实现

public void SwitchDemo() {BaseRunnable<StringPromptValue, ?> prompt = PromptTemplate.fromTemplate("tell me a joke about ${topic}");ChatOllama chatOllama = ChatOllama.builder().model("llama3:8b").build();ChatOpenAI chatOpenAI = ChatOpenAI.builder().model("gpt-4").build();FlowInstance chain = chainActor.builder().next(prompt).next(Info.c("vendor == 'ollama'", chatOllama),Info.c("vendor == 'chatgpt'", chatOpenAI),Info.c(input -> "sorry, I don't know how to do that")).next(new StrOutputParser()).build();Generation result = chainActor.invoke(chain, Map.of("topic", "bears", "vendor", "ollama"));System.out.println(result);
}

组合嵌套

LangChain实现

analysis_prompt = ChatPromptTemplate.from_template("is this a funny joke? {joke}")
composed_chain = {"joke": chain} | analysis_prompt | model | StrOutputParser()result = composed_chain.invoke({"topic": "bears"})
print(result)

J-LangChain实现

public void ComposeDemo() {ChatOllama llm = ChatOllama.builder().model("llama3:8b").build();StrOutputParser parser = new StrOutputParser();BaseRunnable<StringPromptValue, ?> prompt = PromptTemplate.fromTemplate("tell me a joke about ${topic}");FlowInstance chain = chainActor.builder().next(prompt).next(llm).next(parser).build();BaseRunnable<StringPromptValue, ?> analysisPrompt = PromptTemplate.fromTemplate("is this a funny joke? ${joke}");FlowInstance analysisChain = chainActor.builder().next(chain).next(input -> Map.of("joke", ((Generation)input).getText())).next(analysisPrompt).next(llm).next(parser).build();ChatGeneration result = chainActor.invoke(analysisChain, Map.of("topic", "bears"));System.out.println(result);}

并行执行

LangChain实现

from langchain_core.runnables import RunnableParalleljoke_chain = ChatPromptTemplate.from_template("tell me a joke about {topic}") | model
poem_chain = ChatPromptTemplate.from_template("write a 2-line poem about {topic}") | modelparallel_chain = RunnableParallel(joke=joke_chain, poem=poem_chain)result = parallel_chain.invoke({"topic": "bear"})
print(result)

J-LangChain实现

public void ParallelDemo() {ChatOllama llm = ChatOllama.builder().model("llama3:8b").build();BaseRunnable<StringPromptValue, ?> joke = PromptTemplate.fromTemplate("tell me a joke about ${topic}");BaseRunnable<StringPromptValue, ?> poem = PromptTemplate.fromTemplate("write a 2-line poem about ${topic}");FlowInstance jokeChain = chainActor.builder().next(joke).next(llm).build();FlowInstance poemChain = chainActor.builder().next(poem).next(llm).build();FlowInstance chain = chainActor.builder().concurrent((IResult<Map<String, String>>) (iContextBus, isTimeout) -> {AIMessage jokeResult = iContextBus.getResult(jokeChain.getFlowId());AIMessage poemResult = iContextBus.getResult(poemChain.getFlowId());return Map.of("joke", jokeResult.getContent(), "poem", poemResult.getContent());}, jokeChain, poemChain).build();Map<String, String> result = chainActor.invoke(chain, Map.of("topic", "bears"));System.out.println(JsonUtil.toJson(result));}

动态路由

LangChain实现
通过 RunnableLambda 实现动态路由：

from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnableLambdachain = (PromptTemplate.from_template("""Given the user question below, classify it as either being about `LangChain`, `Anthropic`, or `Other`.Do not respond with more than one word.<question>
{question}
</question>Classification:""")| OllamaLLM(model="qwen2.5:0.5b")| StrOutputParser()
)langchain_chain = PromptTemplate.from_template("""You are an expert in langchain. \
Always answer questions starting with "As Harrison Chase told me". \
Respond to the following question:Question: {question}
Answer:"""
) | OllamaLLM(model="qwen2.5:0.5b")
anthropic_chain = PromptTemplate.from_template("""You are an expert in anthropic. \
Always answer questions starting with "As Dario Amodei told me". \
Respond to the following question:Question: {question}
Answer:"""
) | OllamaLLM(model="qwen2.5:0.5b")
general_chain = PromptTemplate.from_template("""Respond to the following question:Question: {question}
Answer:"""
) | OllamaLLM(model="qwen2.5:0.5b")def route(info):if "anthropic" in info["topic"].lower():return anthropic_chainelif "langchain" in info["topic"].lower():return langchain_chainelse:return general_chainfull_chain = {"topic": chain, "question": lambda x: x["question"]} | RunnableLambda(route)result = full_chain.invoke({"question": "how do I use LangChain?"})
print(result)def route(info):if "anthropic" in info["topic"].lower():return anthropic_chainelif "langchain" in info["topic"].lower():return langchain_chainelse:return general_chainfrom langchain_core.runnables import RunnableLambdafull_chain = {"topic": chain, "question": lambda x: x["question"]} | RunnableLambda(route)result = full_chain.invoke({"question": "how do I use LangChain?"})
print(result)

J-LangChain实现

public void RouteDemo() {ChatOllama llm = ChatOllama.builder().model("llama3:8b").build();BaseRunnable<StringPromptValue, Object> prompt = PromptTemplate.fromTemplate("""Given the user question below, classify it as either being about `LangChain`, `Anthropic`, or `Other`.Do not respond with more than one word.<question>${question}</question>Classification:""");FlowInstance chain = chainActor.builder().next(prompt).next(llm).next(new StrOutputParser()).build();FlowInstance langchainChain = chainActor.builder().next(PromptTemplate.fromTemplate("""You are an expert in langchain. \Always answer questions starting with "As Harrison Chase told me". \Respond to the following question:Question: ${question}Answer:""")).next(ChatOllama.builder().model("llama3:8b").build()).build();FlowInstance anthropicChain = chainActor.builder().next(PromptTemplate.fromTemplate("""You are an expert in anthropic. \Always answer questions starting with "As Dario Amodei told me". \Respond to the following question:Question: ${question}Answer:""")).next(ChatOllama.builder().model("llama3:8b").build()).build();FlowInstance generalChain = chainActor.builder().next(PromptTemplate.fromTemplate("""Respond to the following question:Question: ${question}Answer:""")).next(ChatOllama.builder().model("llama3:8b").build()).build();FlowInstance fullChain = chainActor.builder().next(chain).next(input -> Map.of("topic", input, "question", ((Map<?, ?>)ContextBus.get().getFlowParam()).get("question"))).next(Info.c("topic == 'anthropic'", anthropicChain),Info.c("topic == 'langchain'", langchainChain),Info.c(generalChain)).build();AIMessage result = chainActor.invoke(fullChain, Map.of("question", "how do I use Anthropic?"));System.out.println(result.getContent());}

动态构建

LangChain实现

from langchain_core.runnables import chain, RunnablePassthroughllm = OllamaLLM(model="qwen2.5:0.5b")contextualize_instructions = """Convert the latest user question into a standalone question given the chat history. Don't answer the question, return the question and nothing else (no descriptive text)."""
contextualize_prompt = ChatPromptTemplate.from_messages([("system", contextualize_instructions),("placeholder", "{chat_history}"),("human", "{question}"),]
)
contextualize_question = contextualize_prompt | llm | StrOutputParser()@chain
def contextualize_if_needed(input_: dict):if input_.get("chat_history"):return contextualize_questionelse:return RunnablePassthrough() | itemgetter("question")@chain
def fake_retriever(input_: dict):return "egypt's population in 2024 is about 111 million"qa_instructions = ("""Answer the user question given the following context:\n\n{context}."""
)
qa_prompt = ChatPromptTemplate.from_messages([("system", qa_instructions), ("human", "{question}")]
)full_chain = (RunnablePassthrough.assign(question=contextualize_if_needed).assign(context=fake_retriever)| qa_prompt| llm| StrOutputParser()
)result = full_chain.invoke({"question": "what about egypt","chat_history": [("human", "what's the population of indonesia"),("ai", "about 276 million"),],
})
print(result)

J-LangChain实现

public void DynamicDemo() {ChatOllama llm = ChatOllama.builder().model("llama3:8b").build();String contextualizeInstructions = """Convert the latest user question into a standalone question given the chat history. Don't answer the question, return the question and nothing else (no descriptive text).""";BaseRunnable<ChatPromptValue, Object> contextualizePrompt = ChatPromptTemplate.fromMessages(List.of(Pair.of("system", contextualizeInstructions),Pair.of("placeholder", "${chatHistory}"),Pair.of("human", "${question}")));FlowInstance contextualizeQuestion = chainActor.builder().next(contextualizePrompt).next(llm).next(new StrOutputParser()).build();FlowInstance contextualizeIfNeeded = chainActor.builder().next(Info.c("chatHistory != null", contextualizeQuestion),Info.c(input -> Map.of("question", ((Map<String, String>)input).get("question")))).build();String qaInstructions ="""Answer the user question given the following context:\n\n${context}.""";BaseRunnable<ChatPromptValue, Object>  qaPrompt = ChatPromptTemplate.fromMessages(List.of(Pair.of("system", qaInstructions),Pair.of("human", "${question}")));FlowInstance fullChain = chainActor.builder().all((iContextBus, isTimeout) -> Map.of("question", iContextBus.getResult(contextualizeIfNeeded.getFlowId()).toString(),"context", iContextBus.getResult("fakeRetriever")),Info.c(contextualizeIfNeeded),Info.c(input -> "egypt's population in 2024 is about 111 million").cAlias("fakeRetriever")).next(qaPrompt).next(input -> {System.out.println(JsonUtil.toJson(input)); return input;}).next(llm).next(new StrOutputParser()).build();ChatGeneration result = chainActor.invoke(fullChain,Map.of("question", "what about egypt","chatHistory",List.of(Pair.of("human", "what's the population of indonesia"),Pair.of("ai", "about 276 million"))));System.out.println(result);}