dev.langchain4j.model.openai.OpenAiChatModel Maven / Gradle / Ivy
package dev.langchain4j.model.openai;
import dev.ai4j.openai4j.OpenAiClient;
import dev.ai4j.openai4j.OpenAiHttpException;
import dev.ai4j.openai4j.chat.ChatCompletionRequest;
import dev.ai4j.openai4j.chat.ChatCompletionResponse;
import dev.ai4j.openai4j.chat.ResponseFormat;
import dev.ai4j.openai4j.chat.ResponseFormatType;
import dev.langchain4j.agent.tool.ToolSpecification;
import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.data.message.ChatMessage;
import dev.langchain4j.model.Tokenizer;
import dev.langchain4j.model.chat.Capability;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.chat.TokenCountEstimator;
import dev.langchain4j.model.chat.listener.ChatModelErrorContext;
import dev.langchain4j.model.chat.listener.ChatModelListener;
import dev.langchain4j.model.chat.listener.ChatModelRequest;
import dev.langchain4j.model.chat.listener.ChatModelRequestContext;
import dev.langchain4j.model.chat.listener.ChatModelResponse;
import dev.langchain4j.model.chat.listener.ChatModelResponseContext;
import dev.langchain4j.model.chat.request.ChatRequest;
import dev.langchain4j.model.chat.response.ChatResponse;
import dev.langchain4j.model.openai.spi.OpenAiChatModelBuilderFactory;
import dev.langchain4j.model.output.Response;
import lombok.Builder;
import lombok.extern.slf4j.Slf4j;
import java.net.Proxy;
import java.time.Duration;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Locale;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.ConcurrentHashMap;
import static dev.ai4j.openai4j.chat.ResponseFormatType.JSON_SCHEMA;
import static dev.langchain4j.internal.RetryUtils.withRetry;
import static dev.langchain4j.internal.Utils.getOrDefault;
import static dev.langchain4j.model.chat.Capability.RESPONSE_FORMAT_JSON_SCHEMA;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.DEFAULT_USER_AGENT;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.OPENAI_DEMO_API_KEY;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.OPENAI_DEMO_URL;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.OPENAI_URL;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.aiMessageFrom;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.createModelListenerRequest;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.createModelListenerResponse;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.finishReasonFrom;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.toOpenAiMessages;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.toOpenAiResponseFormat;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.toTools;
import static dev.langchain4j.model.openai.InternalOpenAiHelper.tokenUsageFrom;
import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO;
import static dev.langchain4j.spi.ServiceHelper.loadFactories;
import static java.time.Duration.ofSeconds;
import static java.util.Collections.emptyList;
import static java.util.Collections.singletonList;
/**
* Represents an OpenAI language model with a chat completion interface, such as gpt-3.5-turbo and gpt-4.
* You can find description of parameters here.
*/
@Slf4j
public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
private final OpenAiClient client;
private final String modelName;
private final Double temperature;
private final Double topP;
private final List stop;
private final Integer maxTokens;
private final Double presencePenalty;
private final Double frequencyPenalty;
private final Map logitBias;
private final ResponseFormat responseFormat;
private final Boolean strictJsonSchema;
private final Integer seed;
private final String user;
private final Boolean strictTools;
private final Boolean parallelToolCalls;
private final Integer maxRetries;
private final Tokenizer tokenizer;
private final List listeners;
@Builder
public OpenAiChatModel(String baseUrl,
String apiKey,
String organizationId,
String modelName,
Double temperature,
Double topP,
List stop,
Integer maxTokens,
Double presencePenalty,
Double frequencyPenalty,
Map logitBias,
String responseFormat,
Boolean strictJsonSchema,
Integer seed,
String user,
Boolean strictTools,
Boolean parallelToolCalls,
Duration timeout,
Integer maxRetries,
Proxy proxy,
Boolean logRequests,
Boolean logResponses,
Tokenizer tokenizer,
Map customHeaders,
List listeners) {
baseUrl = getOrDefault(baseUrl, OPENAI_URL);
if (OPENAI_DEMO_API_KEY.equals(apiKey)) {
baseUrl = OPENAI_DEMO_URL;
}
timeout = getOrDefault(timeout, ofSeconds(60));
this.client = OpenAiClient.builder()
.openAiApiKey(apiKey)
.baseUrl(baseUrl)
.organizationId(organizationId)
.callTimeout(timeout)
.connectTimeout(timeout)
.readTimeout(timeout)
.writeTimeout(timeout)
.proxy(proxy)
.logRequests(logRequests)
.logResponses(logResponses)
.userAgent(DEFAULT_USER_AGENT)
.customHeaders(customHeaders)
.build();
this.modelName = getOrDefault(modelName, GPT_3_5_TURBO);
this.temperature = getOrDefault(temperature, 0.7);
this.topP = topP;
this.stop = stop;
this.maxTokens = maxTokens;
this.presencePenalty = presencePenalty;
this.frequencyPenalty = frequencyPenalty;
this.logitBias = logitBias;
this.responseFormat = responseFormat == null ? null : ResponseFormat.builder()
.type(ResponseFormatType.valueOf(responseFormat.toUpperCase(Locale.ROOT)))
.build();
this.strictJsonSchema = getOrDefault(strictJsonSchema, false);
this.seed = seed;
this.user = user;
this.strictTools = getOrDefault(strictTools, false);
this.parallelToolCalls = parallelToolCalls;
this.maxRetries = getOrDefault(maxRetries, 3);
this.tokenizer = getOrDefault(tokenizer, OpenAiTokenizer::new);
this.listeners = listeners == null ? emptyList() : new ArrayList<>(listeners);
}
public String modelName() {
return modelName;
}
@Override
public Set supportedCapabilities() {
Set capabilities = new HashSet<>();
if (responseFormat != null && responseFormat.type() == JSON_SCHEMA) {
capabilities.add(RESPONSE_FORMAT_JSON_SCHEMA);
}
return capabilities;
}
@Override
public Response generate(List messages) {
return generate(messages, null, null, this.responseFormat);
}
@Override
public Response generate(List messages, List toolSpecifications) {
return generate(messages, toolSpecifications, null, this.responseFormat);
}
@Override
public Response generate(List messages, ToolSpecification toolSpecification) {
return generate(messages, singletonList(toolSpecification), toolSpecification, this.responseFormat);
}
@Override
public ChatResponse chat(ChatRequest request) {
Response response = generate(
request.messages(),
request.toolSpecifications(),
null,
getOrDefault(toOpenAiResponseFormat(request.responseFormat(), strictJsonSchema), this.responseFormat)
);
return ChatResponse.builder()
.aiMessage(response.content())
.tokenUsage(response.tokenUsage())
.finishReason(response.finishReason())
.build();
}
private Response generate(List messages,
List toolSpecifications,
ToolSpecification toolThatMustBeExecuted,
ResponseFormat responseFormat) {
if (responseFormat != null
&& responseFormat.type() == JSON_SCHEMA
&& responseFormat.jsonSchema() == null) {
responseFormat = null;
}
ChatCompletionRequest.Builder requestBuilder = ChatCompletionRequest.builder()
.model(modelName)
.messages(toOpenAiMessages(messages))
.temperature(temperature)
.topP(topP)
.stop(stop)
.maxTokens(maxTokens)
.presencePenalty(presencePenalty)
.frequencyPenalty(frequencyPenalty)
.logitBias(logitBias)
.responseFormat(responseFormat)
.seed(seed)
.user(user)
.parallelToolCalls(parallelToolCalls);
if (toolSpecifications != null && !toolSpecifications.isEmpty()) {
requestBuilder.tools(toTools(toolSpecifications, strictTools));
}
if (toolThatMustBeExecuted != null) {
requestBuilder.toolChoice(toolThatMustBeExecuted.name());
}
ChatCompletionRequest request = requestBuilder.build();
ChatModelRequest modelListenerRequest = createModelListenerRequest(request, messages, toolSpecifications);
Map attributes = new ConcurrentHashMap<>();
ChatModelRequestContext requestContext = new ChatModelRequestContext(modelListenerRequest, attributes);
listeners.forEach(listener -> {
try {
listener.onRequest(requestContext);
} catch (Exception e) {
log.warn("Exception while calling model listener", e);
}
});
try {
ChatCompletionResponse chatCompletionResponse = withRetry(() -> client.chatCompletion(request).execute(), maxRetries);
Response response = Response.from(
aiMessageFrom(chatCompletionResponse),
tokenUsageFrom(chatCompletionResponse.usage()),
finishReasonFrom(chatCompletionResponse.choices().get(0).finishReason())
);
ChatModelResponse modelListenerResponse = createModelListenerResponse(
chatCompletionResponse.id(),
chatCompletionResponse.model(),
response
);
ChatModelResponseContext responseContext = new ChatModelResponseContext(
modelListenerResponse,
modelListenerRequest,
attributes
);
listeners.forEach(listener -> {
try {
listener.onResponse(responseContext);
} catch (Exception e) {
log.warn("Exception while calling model listener", e);
}
});
return response;
} catch (RuntimeException e) {
Throwable error;
if (e.getCause() instanceof OpenAiHttpException) {
error = e.getCause();
} else {
error = e;
}
ChatModelErrorContext errorContext = new ChatModelErrorContext(
error,
modelListenerRequest,
null,
attributes
);
listeners.forEach(listener -> {
try {
listener.onError(errorContext);
} catch (Exception e2) {
log.warn("Exception while calling model listener", e2);
}
});
throw e;
}
}
@Override
public int estimateTokenCount(List messages) {
return tokenizer.estimateTokenCountInMessages(messages);
}
public static OpenAiChatModel withApiKey(String apiKey) {
return builder().apiKey(apiKey).build();
}
public static OpenAiChatModelBuilder builder() {
for (OpenAiChatModelBuilderFactory factory : loadFactories(OpenAiChatModelBuilderFactory.class)) {
return factory.get();
}
return new OpenAiChatModelBuilder();
}
public static class OpenAiChatModelBuilder {
public OpenAiChatModelBuilder() {
// This is public so it can be extended
// By default with Lombok it becomes package private
}
public OpenAiChatModelBuilder modelName(String modelName) {
this.modelName = modelName;
return this;
}
public OpenAiChatModelBuilder modelName(OpenAiChatModelName modelName) {
this.modelName = modelName.toString();
return this;
}
}
}
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