@langchain/openai
Version:
OpenAI integrations for LangChain.js
315 lines (313 loc) • 10.2 kB
JavaScript
const require_rolldown_runtime = require('./_virtual/rolldown_runtime.cjs');
const require_client = require('./utils/client.cjs');
const require_azure = require('./utils/azure.cjs');
const openai = require_rolldown_runtime.__toESM(require("openai"));
const __langchain_core_outputs = require_rolldown_runtime.__toESM(require("@langchain/core/outputs"));
const __langchain_core_utils_env = require_rolldown_runtime.__toESM(require("@langchain/core/utils/env"));
const __langchain_core_language_models_base = require_rolldown_runtime.__toESM(require("@langchain/core/language_models/base"));
const __langchain_core_language_models_llms = require_rolldown_runtime.__toESM(require("@langchain/core/language_models/llms"));
const __langchain_core_utils_chunk_array = require_rolldown_runtime.__toESM(require("@langchain/core/utils/chunk_array"));
//#region src/llms.ts
/**
* Wrapper around OpenAI large language models.
*
* To use you should have the `openai` package installed, with the
* `OPENAI_API_KEY` environment variable set.
*
* To use with Azure, import the `AzureOpenAI` class.
*
* @remarks
* Any parameters that are valid to be passed to {@link
* https://platform.openai.com/docs/api-reference/completions/create |
* `openai.createCompletion`} can be passed through {@link modelKwargs}, even
* if not explicitly available on this class.
* @example
* ```typescript
* const model = new OpenAI({
* modelName: "gpt-4",
* temperature: 0.7,
* maxTokens: 1000,
* maxRetries: 5,
* });
*
* const res = await model.invoke(
* "Question: What would be a good company name for a company that makes colorful socks?\nAnswer:"
* );
* console.log({ res });
* ```
*/
var OpenAI = class extends __langchain_core_language_models_llms.BaseLLM {
static lc_name() {
return "OpenAI";
}
get callKeys() {
return [...super.callKeys, "options"];
}
lc_serializable = true;
get lc_secrets() {
return {
openAIApiKey: "OPENAI_API_KEY",
apiKey: "OPENAI_API_KEY",
organization: "OPENAI_ORGANIZATION"
};
}
get lc_aliases() {
return {
modelName: "model",
openAIApiKey: "openai_api_key",
apiKey: "openai_api_key"
};
}
temperature;
maxTokens;
topP;
frequencyPenalty;
presencePenalty;
n = 1;
bestOf;
logitBias;
model = "gpt-3.5-turbo-instruct";
/** @deprecated Use "model" instead */
modelName;
modelKwargs;
batchSize = 20;
timeout;
stop;
stopSequences;
user;
streaming = false;
openAIApiKey;
apiKey;
organization;
client;
clientConfig;
constructor(fields) {
super(fields ?? {});
this.openAIApiKey = fields?.apiKey ?? fields?.openAIApiKey ?? (0, __langchain_core_utils_env.getEnvironmentVariable)("OPENAI_API_KEY");
this.apiKey = this.openAIApiKey;
this.organization = fields?.configuration?.organization ?? (0, __langchain_core_utils_env.getEnvironmentVariable)("OPENAI_ORGANIZATION");
this.model = fields?.model ?? fields?.modelName ?? this.model;
if ((this.model?.startsWith("gpt-3.5-turbo") || this.model?.startsWith("gpt-4") || this.model?.startsWith("o1")) && !this.model?.includes("-instruct")) throw new Error([
`Your chosen OpenAI model, "${this.model}", is a chat model and not a text-in/text-out LLM.`,
`Passing it into the "OpenAI" class is no longer supported.`,
`Please use the "ChatOpenAI" class instead.`,
"",
`See this page for more information:`,
"|",
`└> https://js.langchain.com/docs/integrations/chat/openai`
].join("\n"));
this.modelName = this.model;
this.modelKwargs = fields?.modelKwargs ?? {};
this.batchSize = fields?.batchSize ?? this.batchSize;
this.timeout = fields?.timeout;
this.temperature = fields?.temperature ?? this.temperature;
this.maxTokens = fields?.maxTokens ?? this.maxTokens;
this.topP = fields?.topP ?? this.topP;
this.frequencyPenalty = fields?.frequencyPenalty ?? this.frequencyPenalty;
this.presencePenalty = fields?.presencePenalty ?? this.presencePenalty;
this.n = fields?.n ?? this.n;
this.bestOf = fields?.bestOf ?? this.bestOf;
this.logitBias = fields?.logitBias;
this.stop = fields?.stopSequences ?? fields?.stop;
this.stopSequences = this.stop;
this.user = fields?.user;
this.streaming = fields?.streaming ?? false;
if (this.streaming && this.bestOf && this.bestOf > 1) throw new Error("Cannot stream results when bestOf > 1");
this.clientConfig = {
apiKey: this.apiKey,
organization: this.organization,
dangerouslyAllowBrowser: true,
...fields?.configuration
};
}
/**
* Get the parameters used to invoke the model
*/
invocationParams(options) {
return {
model: this.model,
temperature: this.temperature,
max_tokens: this.maxTokens,
top_p: this.topP,
frequency_penalty: this.frequencyPenalty,
presence_penalty: this.presencePenalty,
n: this.n,
best_of: this.bestOf,
logit_bias: this.logitBias,
stop: options?.stop ?? this.stopSequences,
user: this.user,
stream: this.streaming,
...this.modelKwargs
};
}
/** @ignore */
_identifyingParams() {
return {
model_name: this.model,
...this.invocationParams(),
...this.clientConfig
};
}
/**
* Get the identifying parameters for the model
*/
identifyingParams() {
return this._identifyingParams();
}
/**
* Call out to OpenAI's endpoint with k unique prompts
*
* @param [prompts] - The prompts to pass into the model.
* @param [options] - Optional list of stop words to use when generating.
* @param [runManager] - Optional callback manager to use when generating.
*
* @returns The full LLM output.
*
* @example
* ```ts
* import { OpenAI } from "langchain/llms/openai";
* const openai = new OpenAI();
* const response = await openai.generate(["Tell me a joke."]);
* ```
*/
async _generate(prompts, options, runManager) {
const subPrompts = (0, __langchain_core_utils_chunk_array.chunkArray)(prompts, this.batchSize);
const choices = [];
const tokenUsage = {};
const params = this.invocationParams(options);
if (params.max_tokens === -1) {
if (prompts.length !== 1) throw new Error("max_tokens set to -1 not supported for multiple inputs");
params.max_tokens = await (0, __langchain_core_language_models_base.calculateMaxTokens)({
prompt: prompts[0],
modelName: this.model
});
}
for (let i = 0; i < subPrompts.length; i += 1) {
const data = params.stream ? await (async () => {
const choices$1 = [];
let response;
const stream = await this.completionWithRetry({
...params,
stream: true,
prompt: subPrompts[i]
}, options);
for await (const message of stream) {
if (!response) response = {
id: message.id,
object: message.object,
created: message.created,
model: message.model
};
for (const part of message.choices) {
if (!choices$1[part.index]) choices$1[part.index] = part;
else {
const choice = choices$1[part.index];
choice.text += part.text;
choice.finish_reason = part.finish_reason;
choice.logprobs = part.logprobs;
}
runManager?.handleLLMNewToken(part.text, {
prompt: Math.floor(part.index / this.n),
completion: part.index % this.n
});
}
}
if (options.signal?.aborted) throw new Error("AbortError");
return {
...response,
choices: choices$1
};
})() : await this.completionWithRetry({
...params,
stream: false,
prompt: subPrompts[i]
}, {
signal: options.signal,
...options.options
});
choices.push(...data.choices);
const { completion_tokens: completionTokens, prompt_tokens: promptTokens, total_tokens: totalTokens } = data.usage ? data.usage : {
completion_tokens: void 0,
prompt_tokens: void 0,
total_tokens: void 0
};
if (completionTokens) tokenUsage.completionTokens = (tokenUsage.completionTokens ?? 0) + completionTokens;
if (promptTokens) tokenUsage.promptTokens = (tokenUsage.promptTokens ?? 0) + promptTokens;
if (totalTokens) tokenUsage.totalTokens = (tokenUsage.totalTokens ?? 0) + totalTokens;
}
const generations = (0, __langchain_core_utils_chunk_array.chunkArray)(choices, this.n).map((promptChoices) => promptChoices.map((choice) => ({
text: choice.text ?? "",
generationInfo: {
finishReason: choice.finish_reason,
logprobs: choice.logprobs
}
})));
return {
generations,
llmOutput: { tokenUsage }
};
}
async *_streamResponseChunks(input, options, runManager) {
const params = {
...this.invocationParams(options),
prompt: input,
stream: true
};
const stream = await this.completionWithRetry(params, options);
for await (const data of stream) {
const choice = data?.choices[0];
if (!choice) continue;
const chunk = new __langchain_core_outputs.GenerationChunk({
text: choice.text,
generationInfo: { finishReason: choice.finish_reason }
});
yield chunk;
runManager?.handleLLMNewToken(chunk.text ?? "");
}
if (options.signal?.aborted) throw new Error("AbortError");
}
async completionWithRetry(request, options) {
const requestOptions = this._getClientOptions(options);
return this.caller.call(async () => {
try {
const res = await this.client.completions.create(request, requestOptions);
return res;
} catch (e) {
const error = require_client.wrapOpenAIClientError(e);
throw error;
}
});
}
/**
* Calls the OpenAI API with retry logic in case of failures.
* @param request The request to send to the OpenAI API.
* @param options Optional configuration for the API call.
* @returns The response from the OpenAI API.
*/
_getClientOptions(options) {
if (!this.client) {
const openAIEndpointConfig = { baseURL: this.clientConfig.baseURL };
const endpoint = require_azure.getEndpoint(openAIEndpointConfig);
const params = {
...this.clientConfig,
baseURL: endpoint,
timeout: this.timeout,
maxRetries: 0
};
if (!params.baseURL) delete params.baseURL;
params.defaultHeaders = require_azure.getHeadersWithUserAgent(params.defaultHeaders);
this.client = new openai.OpenAI(params);
}
const requestOptions = {
...this.clientConfig,
...options
};
return requestOptions;
}
_llmType() {
return "openai";
}
};
//#endregion
exports.OpenAI = OpenAI;
//# sourceMappingURL=llms.cjs.map