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‎components/mistral_ai/actions/create-batch-job/create-batch-job.mjs‎

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import mistralAI from "../../mistral_ai.app.mjs";
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import constants from "../../common/constants.mjs";
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import { parseObj } from "../../common/utils.mjs";
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export default {
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key: "mistral_ai-create-batch-job",
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name: "Create Batch Job",
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description: "Create a new batch job, it will be queued for processing. [See the Documentation](https://docs.mistral.ai/api/#tag/batch/operation/jobs_api_routes_batch_get_batch_jobs)",
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version: "0.0.1",
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type: "action",
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props: {
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mistralAI,
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inputFiles: {
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propDefinition: [
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mistralAI,
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"fileIds",
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],
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},
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modelId: {
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propDefinition: [
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mistralAI,
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"modelId",
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],
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},
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endpoint: {
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type: "string",
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label: "Endpoint",
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description: "The endpoint to use for the batch job",
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options: constants.BATCH_JOB_ENDPOINT_OPTIONS,
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},
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metadata: {
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type: "object",
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label: "Metadata",
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description: "Optional metadata for the batch job in JSON format.",
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optional: true,
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},
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timeoutHours: {
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type: "integer",
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label: "Timeout Hours",
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description: "Optional timeout duration for the batch job in hours.",
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optional: true,
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default: 24,
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},
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},
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async run({ $ }) {
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const response = await this.mistralAI.createBatchJob({
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$,
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data: {
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input_files: this.inputFiles,
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endpoint: this.endpoint,
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model: this.modelId,
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metadata: parseObj(this.metadata),
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timeoutHours: this.timeoutHours,
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},
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});
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if (response?.id) {
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$.export("$summary", `Successfully created batch job with ID: ${response.id}`);
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}
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return response;
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},
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};

‎components/mistral_ai/actions/create-embeddings/create-embeddings.mjs‎

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import mistralAI from "../../mistral_ai.app.mjs";
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import { parseArray } from "../../common/utils.mjs";
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import constants from "../../common/constants.mjs";
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export default {
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key: "mistral_ai-create-embeddings",
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name: "Create Embeddings",
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description: "Create new embedding in Mistral AI. [See the Documentation](https://docs.mistral.ai/api/#tag/embeddings)",
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version: "0.0.1",
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type: "action",
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props: {
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mistralAI,
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input: {
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type: "string",
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label: "Input",
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description: "The input text for which to create an embedding. May be a string or an array of strings.",
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},
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},
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async run({ $ }) {
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const response = await this.mistralAI.createEmbeddings({
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$,
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data: {
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model: constants.EMBEDDINGS_MODEL,
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input: parseArray(this.input),
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},
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});
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if (response?.id) {
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$.export("$summary", `Successfully created embedding with ID: ${response.id}`);
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}
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return response;
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},
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};

‎components/mistral_ai/actions/generate-text/generate-text.mjs‎

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import mistralAI from "../../mistral_ai.app.mjs";
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export default {
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key: "mistral_ai-generate-text",
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name: "Generate Text",
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description: "Retrieve a list of available Mistral AI models that the user is authorized to access. [See the Documentation](https://docs.mistral.ai/api/#tag/fim/operation/fim_completion_v1_fim_completions_post)",
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version: "0.0.1",
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type: "action",
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props: {
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mistralAI,
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message: {
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type: "string",
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label: "Message",
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description: "The prompt message to send",
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},
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modelId: {
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propDefinition: [
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mistralAI,
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"modelId",
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],
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},
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temperature: {
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type: "string",
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label: "Temperature",
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description: "The sampling temperature to use, we recommend between 0.0 and 0.7. Higher values like 0.7 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both. The default value varies depending on the model you are targeting.",
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optional: true,
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},
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topP: {
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type: "string",
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label: "Top P",
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description: "Nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.",
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optional: true,
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},
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maxTokens: {
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type: "integer",
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label: "Max Tokens",
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description: "The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.",
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optional: true,
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},
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randomSeed: {
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type: "integer",
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label: "Random Seed",
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description: "The seed to use for random sampling. If set, different calls will generate deterministic results.",
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optional: true,
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},
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n: {
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type: "integer",
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label: "N",
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description: "Number of completions to return for each request, input tokens are only billed once.",
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optional: true,
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},
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},
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async run({ $ }) {
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const response = await this.mistralAI.sendPrompt({
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$,
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data: {
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model: this.modelId,
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messages: [
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{
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content: this.message,
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role: "user",
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},
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],
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temperature: this.temperature && +this.temperature,
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top_p: this.topP && +this.topP,
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max_tokens: this.maxTokens,
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random_seed: this.randomSeed,
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n: this.n,
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},
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});
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if (response?.id) {
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$.export("$summary", `Successfully retrieved response with ID: ${response.id}`);
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}
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return response;
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},
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};

‎components/mistral_ai/actions/list-models/list-models.mjs‎

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import mistralAI from "../../mistral_ai.app.mjs";
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export default {
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key: "mistral_ai-list-models",
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name: "List Models",
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description: "Retrieve a list of available Mistral AI models that the user is authorized to access. [See the Documentation](https://docs.mistral.ai/api/#tag/models)",
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version: "0.0.1",
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type: "action",
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props: {
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mistralAI,
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},
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async run({ $ }) {
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const { data } = await this.mistralAI.listModels({
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$,
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});
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if (data?.length) {
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$.export("$summary", `Successfully retrieved ${data.length} model${data.length === 1
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? ""
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: "s"}`);
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}
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return data;
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},
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};

‎components/mistral_ai/common/constants.mjs‎

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const DEFAULT_PAGE_SIZE = 100;
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const EMBEDDINGS_MODEL = "mistral-embed";
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const BATCH_JOB_STATUS_OPTIONS = [
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"SUCCESS",
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"FAILED",
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"TIMEOUT_EXCEEDED",
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"CANCELLATION_REQUESTED",
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"CANCELLED",
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];
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const BATCH_JOB_ENDPOINT_OPTIONS = [
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"/v1/chat/completions",
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"/v1/embeddings",
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"/v1/fim/completions",
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"/v1/moderations",
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"/v1/chat/moderations",
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];
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export default {
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DEFAULT_PAGE_SIZE,
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EMBEDDINGS_MODEL,
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BATCH_JOB_STATUS_OPTIONS,
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BATCH_JOB_ENDPOINT_OPTIONS,
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};

‎components/mistral_ai/common/utils.mjs‎

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function parseArray(arr) {
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if (!arr) {
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return undefined;
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}
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if (typeof arr === "string") {
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try {
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return JSON.parse(arr);
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} catch {
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return arr;
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}
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}
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return arr;
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}
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function parseObj(obj) {
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if (!obj) {
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return undefined;
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}
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if (typeof obj === "string") {
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try {
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return JSON.parse(obj);
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} catch {
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return obj;
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}
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}
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return obj;
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}
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export {
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parseArray,
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parseObj,
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};

‎components/mistral_ai/mistral_ai.app.mjs‎

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import { axios } from "@pipedream/platform";
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import constants from "./common/constants.mjs";
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export default {
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type: "app",
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app: "mistral_ai",
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propDefinitions: {},
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propDefinitions: {
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fileIds: {
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type: "string[]",
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label: "File IDs",
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description: "Array of input file UUIDs for batch processing",
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async options({ page }) {
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const { data } = await this.listFiles({
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params: {
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page,
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page_size: constants.DEFAULT_PAGE_SIZE,
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},
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});
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return data?.map(({
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id: value, filename: label,
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}) => ({
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value,
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label,
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})) || [];
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},
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},
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modelId: {
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type: "string",
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label: "Model ID",
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description: "The identifier of the model to use",
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async options() {
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const { data } = await this.listModels();
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return data?.map(({
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id: value, name: label,
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}) => ({
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value,
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label,
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})) || [];
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},
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},
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},
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methods: {
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// this.$auth contains connected account data
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authKeys() {
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console.log(Object.keys(this.$auth));
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_baseUrl() {
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return "https://api.mistral.ai/v1";
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},
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_makeRequest({
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$ = this,
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path,
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...otherOpts
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}) {
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return axios($, {
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...otherOpts,
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url: `${this._baseUrl()}${path}`,
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headers: {
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"Authorization": `Bearer ${this.$auth.api_key}`,
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"Content-Type": "application/json",
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},
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});
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},
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listModels(opts = {}) {
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return this._makeRequest({
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path: "/models",
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...opts,
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});
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},
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listBatchJobs(opts = {}) {
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return this._makeRequest({
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path: "/batch/jobs",
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...opts,
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});
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},
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listFiles(opts = {}) {
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return this._makeRequest({
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path: "/files",
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...opts,
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});
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},
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createEmbeddings(opts = {}) {
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return this._makeRequest({
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method: "POST",
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path: "/embeddings",
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...opts,
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});
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},
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sendPrompt(opts = {}) {
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return this._makeRequest({
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method: "POST",
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path: "/chat/completions",
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...opts,
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});
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},
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createBatchJob(opts = {}) {
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return this._makeRequest({
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method: "POST",
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path: "/batch/jobs",
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...opts,
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});
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},
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async *paginate({
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fn,
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params = {},
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max,
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}) {
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params = {
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...params,
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page: 0,
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page_size: constants.DEFAULT_PAGE_SIZE,
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};
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let total, count = 0;
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do {
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const { data } = await fn({
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params,
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});
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for (const item of data) {
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yield item;
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if (max && ++count >= max) {
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return;
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}
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}
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total = data?.length;
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params.page++;
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} while (total);
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},
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},
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};
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};

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