Bringing Legacy components for backcompatibility

package.json

@@ -1,6 +1,6 @@
 {
     "name": "sre",
-    "version": "0.1.0",
+    "version": "0.1.1",
     "description": "",
     "author": "Alaa-eddine KADDOURI",
     "license": "MIT",

packages/core/package.json

@@ -1,6 +1,6 @@
 {
     "name": "@smythos/sre",
-    "version": "1.5.20",
+    "version": "1.5.21",
     "description": "Smyth Runtime Environment",
     "author": "Alaa-eddine KADDOURI",
     "license": "MIT",

packages/core/src/Components/MultimodalLLM.class.ts

@@ -0,0 +1,128 @@
+import Joi from 'joi';
+import { Component } from './Component.class';
+import { LLMInference } from '@sre/LLMManager/LLM.inference';
+import { TemplateString } from '@sre/helpers/TemplateString.helper';
+
+import { AccessCandidate } from '@sre/Security/AccessControl/AccessCandidate.class';
+
+export class MultimodalLLM extends Component {
+    protected configSchema = Joi.object({
+        prompt: Joi.string().required().max(8_000_000).label('Prompt'), // 2M tokens is around 8M characters
+        maxTokens: Joi.number().min(1).label('Maximum Tokens'),
+        model: Joi.string().max(200).required(),
+        passthrough: Joi.boolean().optional().label('Passthrough'),
+    });
+
+    constructor() {
+        super();
+    }
+
+    init() {}
+
+    async process(input, config, agent) {
+        await super.process(input, config, agent);
+
+        const logger = this.createComponentLogger(agent, config);
+
+        logger.debug(`=== Multimodal LLM Log ===`);
+
+        try {
+            const passThrough: boolean = config.data.passthrough || false;
+            const model: string = config.data.model || 'gpt-4o-mini';
+            const llmInference: LLMInference = await LLMInference.getInstance(model, AccessCandidate.agent(agent.id));
+
+            if (!llmInference.connector) {
+                return {
+                    _error: `The model '${model}' is not available. Please try a different one.`,
+                    _debug: logger.output,
+                };
+            }
+
+            const modelId = await agent.modelsProvider.getModelId(model);
+            logger.debug(` Model : ${modelId || model}`);
+
+            let prompt: any = TemplateString(config.data.prompt).parse(input).result;
+
+            logger.debug(` Prompt\n`, prompt, '\n');
+
+            const outputs = {};
+            for (let con of config.outputs) {
+                if (con.default) continue;
+                outputs[con.name] = con?.description ? `<${con?.description}>` : '';
+            }
+
+            const excludedKeys = ['_debug', '_error'];
+            const outputKeys = Object.keys(outputs).filter((key) => !excludedKeys.includes(key));
+
+            if (outputKeys.length > 0) {
+                const outputFormat = {};
+                outputKeys.forEach((key) => (outputFormat[key] = '<value>'));
+
+                prompt +=
+                    '\n\nExpected output format = ' +
+                    JSON.stringify(outputFormat) +
+                    '\n\n The output JSON should only use the entries from the output format.';
+
+                logger.debug(`[Component enhanced prompt]\n${prompt}\n\n`);
+            }
+
+            const files = Array.isArray(input.Input) ? input.Input : [input.Input];
+
+            let response: any;
+            if (passThrough) {
+                const contentPromise = new Promise(async (resolve, reject) => {
+                    let _content = '';
+                    const eventEmitter: any = await llmInference.multimodalStreamRequestLegacy(prompt, files, config, agent).catch((error) => {
+                        console.error('Error on multimodalStreamRequest: ', error);
+                        reject(error);
+                    });
+                    eventEmitter.on('content', (content) => {
+                        if (typeof agent.callback === 'function') {
+                            agent.callback({ content });
+                        }
+                        agent.sse.send('llm/passthrough/content', content);
+                        _content += content;
+                    });
+                    eventEmitter.on('thinking', (thinking) => {
+                        if (typeof agent.callback === 'function') {
+                            agent.callback({ thinking });
+                        }
+                        agent.sse.send('llm/passthrough/thinking', thinking);
+                    });
+                    eventEmitter.on('end', () => {
+                        console.log('end');
+                        resolve(_content);
+                    });
+                });
+                response = await contentPromise;
+            } else {
+                response = await llmInference.prompt({ query: prompt, files, params: { ...config, agentId: agent.id } });
+            }
+
+            // in case we have the response but it's empty string, undefined or null
+            if (!response) {
+                return { _error: ' LLM Error = Empty Response!', _debug: logger.output };
+            }
+
+            if (response?.error) {
+                const error = response?.error + ' ' + (response?.details || '');
+                logger.error(` LLM Error=`, error);
+                return { Reply: response?.data, _error: error, _debug: logger.output };
+            }
+
+            logger.debug(' Response \n', response);
+
+            const result = { Reply: response };
+            result['_debug'] = logger.output;
+
+            return result;
+        } catch (error: any) {
+            const _error = `${error?.error || ''} ${error?.details || ''}`.trim() || error?.message || 'Something went wrong!';
+            logger.error(`Error processing File(s)!`, _error);
+            return {
+                _error,
+                _debug: logger.output,
+            };
+        }
+    }
+}

packages/core/src/Components/OpenAPI.class.ts

@@ -0,0 +1,72 @@
+import Joi from 'joi';
+
+import { Agent } from '@sre/AgentManager/Agent.class';
+import { Conversation } from '@sre/helpers/Conversation.helper';
+import { TemplateString } from '@sre/helpers/TemplateString.helper';
+
+import { Component } from './Component.class';
+
+export class OpenAPI extends Component {
+    protected configSchema = Joi.object({
+        model: Joi.string().optional(),
+        openAiModel: Joi.string().optional(), // for backward compatibility
+        specUrl: Joi.string().max(2048).uri().required().description('URL of the OpenAPI specification'),
+        descForModel: Joi.string().max(5000).required().allow('').label('Description for Model'),
+        name: Joi.string().max(500).required().allow(''),
+        desc: Joi.string().max(5000).required().allow('').label('Description'),
+        logoUrl: Joi.string().max(8192).allow(''),
+        id: Joi.string().max(200),
+        version: Joi.string().max(100).allow(''),
+        domain: Joi.string().max(253).allow(''),
+    });
+
+    constructor() {
+        super();
+    }
+
+    init() {}
+
+    async process(input, config, agent: Agent) {
+        await super.process(input, config, agent);
+        const logger = this.createComponentLogger(agent, config);
+
+        logger.debug(`=== Open API Log ===`);
+
+        try {
+            const specUrl = config?.data?.specUrl;
+
+            if (!specUrl) {
+                return { _error: 'Please provide a Open API Specification URL!', _debug: logger.output };
+            }
+
+            const model = config?.data?.model || config?.data?.openAiModel;
+            const descForModel = TemplateString(config?.data?.descForModel).parse(input).result;
+            let prompt = '';
+
+            if (input?.Prompt) {
+                prompt = typeof input?.Prompt === 'string' ? input?.Prompt : JSON.stringify(input?.Prompt);
+            } else if (input?.Query) {
+                prompt = typeof input?.Query === 'string' ? input?.Query : JSON.stringify(input?.Query);
+            }
+
+            if (!prompt) {
+                return { _error: 'Please provide a prompt', _debug: logger.output };
+            }
+
+            // TODO [Forhad]: Need to check and validate input prompt token
+
+            const conv = new Conversation(model, specUrl, { systemPrompt: descForModel, agentId: agent?.id });
+
+            const result = await conv.prompt(prompt);
+
+            logger.debug(`Response:\n`, result, '\n');
+
+            return { Output: result, _debug: logger.output };
+        } catch (error: any) {
+            console.error('Error on running Open API: ', error);
+            return { _error: `Error on running Open API!\n${error?.message || JSON.stringify(error)}`, _debug: logger.output };
+        }
+    }
+}
+
+export default OpenAPI;

packages/core/src/Components/VisionLLM.class.ts

@@ -0,0 +1,104 @@
+import Joi from 'joi';
+
+import { TemplateString } from '@sre/helpers/TemplateString.helper';
+import { Component } from './Component.class';
+import { LLMInference } from '@sre/LLMManager/LLM.inference';
+import { AccessCandidate } from '@sre/Security/AccessControl/AccessCandidate.class';
+
+export class VisionLLM extends Component {
+    protected configSchema = Joi.object({
+        prompt: Joi.string().required().max(8_000_000).label('Prompt'), // 2M tokens is around 8M characters
+        maxTokens: Joi.number().min(1).label('Maximum Tokens'),
+        model: Joi.string().max(200).required(),
+        passthrough: Joi.boolean().optional().label('Passthrough'),
+    });
+
+    constructor() {
+        super();
+    }
+
+    init() {}
+
+    async process(input, config, agent) {
+        await super.process(input, config, agent);
+
+        const logger = this.createComponentLogger(agent, config);
+        try {
+            logger.debug(`=== Vision LLM Log ===`);
+
+            const passThrough: boolean = config.data.passthrough || false;
+            const model: string = config.data?.model;
+
+            const llmInference: LLMInference = await LLMInference.getInstance(model, AccessCandidate.agent(agent.id));
+            // if the llm is undefined, then it means we removed the model from our system
+            if (!llmInference.connector) {
+                return {
+                    _error: `The model '${model}' is not available. Please try a different one.`,
+                    _debug: logger.output,
+                };
+            }
+
+            const modelId = await agent.modelsProvider.getModelId(model);
+            logger.debug(` Model : ${modelId || model}`);
+
+            let prompt: any = TemplateString(config.data.prompt).parse(input).result;
+
+            logger.debug(` Prompt\n`, prompt, '\n');
+
+            const files = Array.isArray(input.Images) ? input.Images : [input.Images];
+
+            let response: any;
+            if (passThrough) {
+                const contentPromise = new Promise(async (resolve, reject) => {
+                    let _content = '';
+                    const eventEmitter: any = await llmInference.multimodalStreamRequestLegacy(prompt, files, config, agent).catch((error) => {
+                        console.error('Error on multimodalStreamRequest: ', error);
+                        reject(error);
+                    });
+                    eventEmitter.on('content', (content) => {
+                        if (typeof agent.callback === 'function') {
+                            agent.callback({ content });
+                        }
+                        agent.sse.send('llm/passthrough/content', content);
+                        _content += content;
+                    });
+                    eventEmitter.on('thinking', (thinking) => {
+                        if (typeof agent.callback === 'function') {
+                            agent.callback({ thinking });
+                        }
+                        agent.sse.send('llm/passthrough/thinking', thinking);
+                    });
+                    eventEmitter.on('end', () => {
+                        console.log('end');
+                        resolve(_content);
+                    });
+                });
+                response = await contentPromise;
+            } else {
+                response = await llmInference.prompt({ query: prompt, files, params: { ...config, agentId: agent.id } });
+            }
+
+            // in case we have the response but it's empty string, undefined or null
+            if (!response) {
+                return { _error: ' LLM Error = Empty Response!', _debug: logger.output };
+            }
+
+            if (response?.error) {
+                const error = response?.error + ' ' + (response?.details || '');
+                logger.error(` LLM Error=`, error);
+
+                return { Reply: response?.data, _error: error, _debug: logger.output };
+            }
+
+            logger.debug(' Response \n', response);
+
+            const result = { Reply: response };
+
+            result['_debug'] = logger.output;
+
+            return result;
+        } catch (error: any) {
+            return { _error: error.message, _debug: logger.output };
+        }
+    }
+}