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[Components] lamini - new components (#16822)
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‎components/lamini/actions/create-fine-tune-job/create-fine-tune-job.mjs

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import app from "../../lamini.app.mjs";
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import constants from "../../common/constants.mjs";
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import utils from "../../common/utils.mjs";
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export default {
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key: "lamini-create-fine-tune-job",
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name: "Create Fine-Tune Job",
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description: "Create a fine-tuning job with a dataset. [See the documentation](https://docs.lamini.ai/api/).",
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version: "0.0.1",
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type: "action",
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props: {
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app,
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modelName: {
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description: "Base model to be fine-tuned.",
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propDefinition: [
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app,
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"modelName",
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() => ({
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includeFineTunedModels: false,
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}),
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],
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},
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datasetId: {
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type: "string",
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label: "Dataset ID",
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description: "Previously uploaded dataset to use for training. Please use the **Upload Dataset** action to upload a dataset.",
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},
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fineTuneArgs: {
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type: "object",
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label: "Finetune Arguments",
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description: "Optional hyperparameters for fine-tuning. Each property is optional:\n- `index_pq_m`: Number of subquantizers for PQ (eg. 8)\n- `index_max_size`: Maximum index size (eg. 65536)\n- `max_steps`: Maximum number of training steps (eg. 60)\n- `batch_size`: Training batch size (eg. 1)\n- `learning_rate`: Learning rate (eg. 0.0003)\n- `index_pq_nbits`: Number of bits per subquantizer (eg. 8)\n- `max_length`: Maximum sequence length (eg. 2048)\n- `index_ivf_nlist`: Number of IVF lists (eg. 2048)\n- `save_steps`: Steps between checkpoints (eg. 60)\n- `args_name`: Name for the argument set (eg. \"demo\")\n- `r_value`: R value for LoRA (eg. 32)\n- `index_hnsw_m`: Number of neighbors in HNSW (eg. 32)\n- `index_method`: Indexing method (eg. \"IndexIVFPQ\")\n- `optim`: Optimizer to use (eg. \"adafactor\")\n- `index_hnsw_efConstruction`: HNSW construction parameter (eg. 16)\n- `index_hnsw_efSearch`: HNSW search parameter (eg. 8)\n- `index_k`: Number of nearest neighbors (eg. 2)\n- `index_ivf_nprobe`: Number of IVF probes (eg. 48)\n- `eval_steps`: Steps between evaluations (eg. 30)\n[See the documentation](https://docs.lamini.ai/tuning/hyperparameters/#finetune_args).",
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optional: true,
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},
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gpuConfig: {
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type: "object",
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label: "GPU Config",
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description: "Optional GPU configuration for fine-tuning. [See the documentation](https://docs.lamini.ai/tuning/hyperparameters/#gpu_config).",
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optional: true,
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},
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isPublic: {
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type: "boolean",
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label: "Is Public",
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description: "Whether this fine-tuning job and dataset should be publicly accessible.",
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optional: true,
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},
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customModelName: {
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type: "string",
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label: "Custom Model Name",
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description: "A human-readable name for the fine-tuned model.",
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optional: true,
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},
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},
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methods: {
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createFineTuneJob(args = {}) {
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return this.app.post({
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versionPath: constants.VERSION_PATH.V1,
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path: "/train",
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...args,
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});
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},
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},
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async run({ $ }) {
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const {
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app,
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createFineTuneJob,
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modelName,
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datasetId,
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fineTuneArgs,
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gpuConfig,
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isPublic,
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customModelName,
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} = this;
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const { upload_base_path: uploadBasePath } =
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await app.getUploadBasePath({
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$,
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});
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await app.getExistingDataset({
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$,
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data: {
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dataset_id: datasetId,
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upload_base_path: uploadBasePath,
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},
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});
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const response = await createFineTuneJob({
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$,
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data: {
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model_name: modelName,
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dataset_id: datasetId,
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upload_file_path: `${uploadBasePath}/${datasetId}.jsonlines`,
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finetune_args: utils.parseJson(fineTuneArgs),
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gpu_config: utils.parseJson(gpuConfig),
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is_public: isPublic,
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custom_model_name: customModelName,
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},
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});
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$.export("$summary", `Successfully created a fine-tune job with ID \`${response.job_id}\`.`);
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return response;
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},
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};

‎components/lamini/actions/evaluate-job/evaluate-job.mjs

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import app from "../../lamini.app.mjs";
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export default {
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key: "lamini-evaluate-job",
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name: "Evaluate Job",
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description: "Evaluate a fine-tuning job by job ID. [See the documentation](https://docs.lamini.ai/api/).",
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version: "0.0.1",
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type: "action",
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props: {
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app,
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jobId: {
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propDefinition: [
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app,
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"jobId",
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],
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description: "The ID of the fine-tuning job to evaluate.",
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},
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},
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methods: {
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evaluateJob({
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jobId, ...args
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} = {}) {
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return this.app.makeRequest({
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path: `/finetune_eval/jobs/${jobId}`,
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...args,
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});
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},
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},
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async run({ $ }) {
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const {
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evaluateJob,
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jobId,
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} = this;
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const response = await evaluateJob({
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$,
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jobId,
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});
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$.export("$summary", `Successfully evaluated job with ID \`${jobId}\`.`);
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return response;
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},
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};

‎components/lamini/actions/generate-completion/generate-completion.mjs

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import app from "../../lamini.app.mjs";
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import utils from "../../common/utils.mjs";
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export default {
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key: "lamini-generate-completion",
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name: "Generate Completion",
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description: "Generate completions using a Lamini model. [See the documentation](https://docs.lamini.ai/api/).",
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version: "0.0.1",
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type: "action",
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props: {
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app,
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modelName: {
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propDefinition: [
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app,
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"modelName",
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],
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description: "The model to use for completion.",
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},
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prompt: {
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type: "string",
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label: "Prompt",
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description: "The prompt to send to the model.",
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},
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outputType: {
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propDefinition: [
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app,
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"outputType",
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],
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},
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maxTokens: {
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propDefinition: [
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app,
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"maxTokens",
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],
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},
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maxNewTokens: {
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propDefinition: [
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app,
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"maxNewTokens",
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],
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},
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},
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methods: {
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generateCompletion(args = {}) {
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return this.app.post({
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path: "/completions",
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...args,
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});
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},
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},
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async run({ $ }) {
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const {
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generateCompletion,
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modelName,
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prompt,
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outputType,
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maxTokens,
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maxNewTokens,
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} = this;
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const response = await generateCompletion({
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$,
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data: {
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model_name: modelName,
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prompt,
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output_type: utils.parseJson(outputType),
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max_tokens: maxTokens,
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max_new_tokens: maxNewTokens,
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},
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});
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$.export("$summary", `Successfully generated completion for prompt with model ${modelName}.`);
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return response;
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},
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};

‎components/lamini/actions/get-batch-completions/get-batch-completions.mjs

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import app from "../../lamini.app.mjs";
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import utils from "../../common/utils.mjs";
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export default {
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key: "lamini-get-batch-completions",
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name: "Get Batch Completions",
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description: "Retrieve the results of a batch completion request from Lamini. [See the documentation](https://docs.lamini.ai/api/).",
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version: "0.0.1",
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type: "action",
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props: {
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app,
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modelName: {
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propDefinition: [
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app,
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"modelName",
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],
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},
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prompt: {
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type: "string[]",
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label: "Prompts",
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description: "The prompts to use for completion.",
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},
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outputType: {
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propDefinition: [
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app,
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"outputType",
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],
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},
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maxTokens: {
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propDefinition: [
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app,
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"maxTokens",
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],
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},
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maxNewTokens: {
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propDefinition: [
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app,
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"maxNewTokens",
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],
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},
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},
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methods: {
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submitBatchCompletions(args = {}) {
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return this.app.post({
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path: "/batch_completions",
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...args,
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});
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},
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getBatchCompletions({
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id, ...args
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} = {}) {
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return this.app.makeRequest({
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path: `/batch_completions/${id}/result`,
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...args,
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});
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},
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},
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async run({ $ }) {
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const {
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submitBatchCompletions,
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getBatchCompletions,
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modelName,
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prompt,
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outputType,
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maxTokens,
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maxNewTokens,
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} = this;
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const { id } = await submitBatchCompletions({
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$,
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data: {
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model_name: modelName,
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prompt,
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output_type: utils.parseJson(outputType),
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max_tokens: maxTokens,
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max_new_tokens: maxNewTokens,
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},
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});
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const response = await getBatchCompletions({
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$,
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id,
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});
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$.export("$summary", `Successfully submitted batch completion with ID \`${id}\`.`);
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return response;
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},
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};

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