[add] added support for scriptset, scriptget, scriptrun, scriptdel, m…

…odeldel, and modelget

README.md

@@ -86,6 +86,28 @@ var fs = require("fs");
 ```
 
 
+### Supported RedisAI Commands
+
+| Command | Recommended API and JSDoc  |
+| :---          |  ----: |
+AI.TENSORSET | tensorset
+AI.TENSORGET | tensorget
+AI.MODELSET | modelset
+AI.MODELGET | modelget
+AI.MODELDEL | modeldet
+AI.MODELRUN | modelrun
+AI._MODELSCAN | N/A
+AI.SCRIPTSET | scriptset
+AI.SCRIPTGET | scriptget
+AI.SCRIPTDEL | scriptdel
+AI.SCRIPTRUN | scriptrun
+AI._SCRIPTSCAN | N/A  
+AI.DAGRUN | N/A
+AI.DAGRUN_RO | N/A
+AI.INFO | 
+AI.CONFIG * | N/A
+
+
 ### Running tests
 
 A simple test suite is provided, and can be run with:

package.json

@@ -6,7 +6,7 @@
   "types": "lib/index.d.ts",
   "scripts": {
     "build": "tsc",
-    "format": "prettier --write \"src/**/*.ts\" ",
+    "format": "prettier --write \"src/**/*.ts\"  \"tests/**/*.ts\" ",
     "lint": "tslint -p tsconfig.json",
     "prepare": "npm run build",
     "prepublishOnly": "npm test && npm run lint",

src/client.ts

@@ -3,6 +3,8 @@ import { Tensor } from './tensor';
 import { Model } from './model';
 import * as util from 'util';
 import { DTypeMap } from './dtype';
+import { Script } from './script';
+import { BackendMap } from './backend';
 
 export class Client {
   private _sendCommand: any;
@@ -67,6 +69,10 @@ export class Client {
 
   public modelset(keName: string, m: Model): Promise<any> {
     const args = [keName, m.backend, m.device];
+    if (m.tag !== undefined) {
+      args.push('TAG');
+      args.push(m.tag.toString());
+    }
     if (m.inputs.length > 0) {
       args.push('INPUTS');
       m.inputs.forEach((value) => args.push(value));
@@ -87,4 +93,112 @@ export class Client {
     outputs.forEach((value) => args.push(value));
     return this._sendCommand('ai.modelrun', args);
   }
+
+  public modeldel(modelName: string): Promise<any> {
+    const args = [modelName];
+    return this._sendCommand('ai.modeldel', args);
+  }
+
+  public modelget(modelName: string): Promise<any> {
+    const args = [modelName, 'META', 'BLOB'];
+    return this._sendCommand('ai.modelget', args)
+      .then((reply: any[]) => {
+        let backend = null;
+        let device = null;
+        let tag = null;
+        let blob = null;
+        for (let i = 0; i < reply.length; i += 2) {
+          const key = reply[i];
+          const obj = reply[i + 1];
+          switch (key.toString()) {
+            case 'backend':
+              backend = BackendMap[obj.toString()];
+              break;
+            case 'device':
+              // @ts-ignore
+              device = obj.toString();
+              break;
+            case 'tag':
+              tag = obj.toString();
+              break;
+            case 'blob':
+              // blob = obj;
+              blob = Buffer.from(obj);
+              break;
+          }
+        }
+        if (backend == null || device == null || blob == null) {
+          throw Error('modelget reply did not had the full elements to build the tensor');
+        }
+        const model = new Model(backend, device, [], [], blob);
+        if (tag !== null) {
+          model.tag = tag;
+        }
+        return model;
+      })
+      .catch((error: any) => {
+        throw error;
+      });
+  }
+
+  public scriptset(keName: string, s: Script): Promise<any> {
+    const args = [keName, s.device];
+    if (s.tag !== undefined) {
+      args.push('TAG');
+      args.push(s.tag);
+    }
+    args.push('SOURCE');
+    args.push(s.script);
+    return this._sendCommand('ai.scriptset', args);
+  }
+
+  public scriptrun(scriptName: string, functionName: string, inputs: string[], outputs: string[]): Promise<any> {
+    const args = [scriptName, functionName, 'INPUTS'];
+    inputs.forEach((value) => args.push(value));
+    args.push('OUTPUTS');
+    outputs.forEach((value) => args.push(value));
+    return this._sendCommand('ai.scriptrun', args);
+  }
+
+  public scriptdel(scriptName: string): Promise<any> {
+    const args = [scriptName];
+    return this._sendCommand('ai.scriptdel', args);
+  }
+
+  public scriptget(scriptName: string): Promise<any> {
+    const args = [scriptName, 'META', 'SOURCE'];
+    return this._sendCommand('ai.scriptget', args)
+      .then((reply: any[]) => {
+        let device = null;
+        let tag = null;
+        let source = null;
+        for (let i = 0; i < reply.length; i += 2) {
+          const key = reply[i];
+          const obj = reply[i + 1];
+          switch (key.toString()) {
+            case 'device':
+              // @ts-ignore
+              device = obj.toString();
+              break;
+            case 'tag':
+              tag = obj.toString();
+              break;
+            case 'source':
+              source = obj.toString();
+              break;
+          }
+        }
+        if (device == null || source == null) {
+          throw Error('scriptget reply did not had the full elements to build the tensor');
+        }
+        const script = new Script(device, source);
+        if (tag !== null) {
+          script.tag = tag;
+        }
+        return script;
+      })
+      .catch((error: any) => {
+        throw error;
+      });
+  }
 }

src/index.ts

@@ -1,7 +1,8 @@
-import { DTypeMap, Dtype } from './dtype';
-import { Model } from './model';
-import { BackendMap, Backend } from './backend';
+import { Dtype, DTypeMap } from './dtype';
+import { Backend, BackendMap } from './backend';
 import { Tensor } from './tensor';
+import { Model } from './model';
+import { Script } from './script';
 import { Client } from './client';
 
-export { DTypeMap, Dtype, BackendMap, Backend, Model, Tensor, Client };
+export { DTypeMap, Dtype, BackendMap, Backend, Model, Script, Tensor, Client };

src/model.ts

@@ -4,12 +4,36 @@
 import { Backend } from './backend';
 
 export class Model {
+  /**
+   *
+   * @param backend - the backend for the model. can be one of TF, TFLITE, TORCH or ONNX
+   * @param device - the device that will execute the model. can be of CPU or GPU
+   * @param inputs - one or more names of the model's input nodes (applicable only for TensorFlow models)
+   * @param outputs - one or more names of the model's output nodes (applicable only for TensorFlow models)
+   * @param blob - the Protobuf-serialized model
+   */
   constructor(backend: Backend, device: string, inputs: string[], outputs: string[], blob: Buffer | undefined) {
     this._backend = backend;
     this._device = device;
     this._inputs = inputs;
     this._outputs = outputs;
     this._blob = blob;
+    this._tag = undefined;
+  }
+
+  // tag is an optional string for tagging the model such as a version number or any arbitrary identifier
+  private _tag: string | undefined;
+
+  get tag(): string | undefined {
+    return this._tag;
+  }
+
+  /**
+   * sets an optional string for tagging the model such as a version number or any arbitrary identifier
+   * @param value
+   */
+  set tag(value: string | undefined) {
+    this._tag = value;
   }
 
   private _backend: Backend;

src/script.ts

@@ -0,0 +1,51 @@
+/**
+ * Direct mapping to RedisAI Script
+ */
+
+export class Script {
+  /**
+   *
+   * @param device - the device that will execute the model. can be of CPU or GPU
+   * @param script - a string containing TorchScript source code
+   */
+  constructor(device: string, script: string) {
+    this._device = device;
+    this._script = script;
+    this._tag = undefined;
+  }
+
+  // tag is an optional string for tagging the model such as a version number or any arbitrary identifier
+  private _tag: string | undefined;
+
+  get tag(): string | undefined {
+    return this._tag;
+  }
+
+  /**
+   * sets an optional string for tagging the model such as a version number or any arbitrary identifier
+   * @param value
+   */
+  set tag(value: string | undefined) {
+    this._tag = value;
+  }
+
+  private _device: string;
+
+  get device(): string {
+    return this._device;
+  }
+
+  set device(value: string) {
+    this._device = value;
+  }
+
+  private _script: string;
+
+  get script(): string {
+    return this._script;
+  }
+
+  set script(value: string) {
+    this._script = value;
+  }
+}

src/tensor.ts

@@ -4,6 +4,12 @@ import { Dtype } from './dtype';
  * Direct mapping to RedisAI Tensors - represents an n-dimensional array of values
  */
 export class Tensor {
+  /**
+   *  Creates a tensor - represents an n-dimensional array of values
+   * @param dtype the tensor's data type
+   * @param shape one or more dimensions, or the number of elements per axis, for the tensor
+   * @param data numeric data provided by one or more subsequent val arguments
+   */
   constructor(dtype: Dtype, shape: number[], data: number[] | null) {
     this._shape = shape;
     this._dtype = dtype;