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npentrel
88 lines
3.8 KiB
Plaintext
88 lines
3.8 KiB
Plaintext
```java Java
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import com.langchain.smith.client.LangsmithClient;
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import com.langchain.smith.client.okhttp.LangsmithOkHttpClient;
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import com.langchain.smith.tracing.RunTree;
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import com.langchain.smith.tracing.RunType;
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import com.langchain.smith.tracing.TraceConfig;
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import com.openai.client.OpenAIClient;
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import com.openai.client.okhttp.OpenAIOkHttpClient;
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import com.openai.models.ChatModel;
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import com.openai.models.chat.completions.ChatCompletion;
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import com.openai.models.chat.completions.ChatCompletionCreateParams;
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import com.openai.models.chat.completions.ChatCompletionMessageParam;
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import com.openai.models.chat.completions.ChatCompletionSystemMessageParam;
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import com.openai.models.chat.completions.ChatCompletionUserMessageParam;
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import java.time.Instant;
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import java.util.Arrays;
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import java.util.Collections;
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import java.util.List;
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import java.util.concurrent.ExecutorService;
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import java.util.concurrent.Executors;
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import java.util.concurrent.TimeUnit;
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public class RunTreeExample {
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public static void main(String[] args) throws InterruptedException {
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LangsmithClient langsmith = LangsmithOkHttpClient.fromEnv();
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OpenAIClient openai = OpenAIOkHttpClient.fromEnv();
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ExecutorService executor = Executors.newSingleThreadExecutor();
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try {
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String question = "Can you summarize this morning's meetings?";
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String runId = "01990f3e-7f97-74c5-a9b6-8d3f7e8e2f11";
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RunTree pipeline = RunTree.builder()
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.id(runId)
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.name("Chat Pipeline")
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.runType(RunType.CHAIN)
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.inputs(Collections.singletonMap("question", question))
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.client(langsmith)
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.executor(executor)
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.build();
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pipeline.postRun();
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String context = "During this morning's meeting, we solved all world conflict.";
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List<ChatCompletionMessageParam> messages = Arrays.asList(
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ChatCompletionMessageParam.ofSystem(
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ChatCompletionSystemMessageParam.builder()
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.content(
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"You are a helpful assistant. Please respond to the user's " +
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"request only based on the given context.")
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.build()),
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ChatCompletionMessageParam.ofUser(
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ChatCompletionUserMessageParam.builder()
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.content("Question: " + question + "\nContext: " + context)
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.build()));
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RunTree childRun = pipeline.createChild(
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TraceConfig.builder().name("OpenAI Call").runType(RunType.LLM).build());
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childRun.setInputs(Collections.singletonMap("messages", messages));
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childRun.postRun();
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ChatCompletion chatCompletion = openai.chat().completions().create(
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ChatCompletionCreateParams.builder()
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.model(ChatModel.GPT_5_CHAT_LATEST)
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.messages(messages)
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.build());
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String answer = chatCompletion.choices().get(0).message().content().orElse("");
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System.out.println(answer);
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childRun.setOutputs(Collections.singletonMap("response", chatCompletion.toString()));
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childRun.setEndTime(Instant.now().toString());
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childRun.patchRun();
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pipeline.setOutputs(Collections.singletonMap(
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"answer", answer));
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pipeline.setEndTime(Instant.now().toString());
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pipeline.patchRun();
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} finally {
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executor.shutdown();
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if (!executor.awaitTermination(10, TimeUnit.SECONDS)) {
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throw new IllegalStateException(
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"Timed out waiting for LangSmith traces to submit");
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}
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}
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}
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}
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```
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