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samestrinsamestrin
committedJul 12, 2024
feat: examples v2.0.10
·
v2.0.14v2.0.11
1 parent 20d35f1 commit fe2df7d

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‎examples/basic-usage/chat.js

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/**
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* @file examples/basic-usage/chat.js
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* @description This example demonstrates a chat using an OpenAI compatible structure.
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*
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* To run this example, you first need to install the required modules by executing:
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*
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* npm install dotenv
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*/
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const { LLMInterface } = require('../../src/index.js');
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const { prettyHeader,
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prettyText,
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prettyResult,
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GREEN,
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RESET,
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} = require('../../src/utils/utils.js');
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require('dotenv').config({ path: '../../.env' });
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// Setup your key and interface
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const interfaceName = 'groq';
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const apiKey = process.env.GROQ_API_KEY;
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// Example description
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const description = `This example demonstrates a chat using an OpenAI compatible structure.
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To run this example, you first need to install the required modules by executing:
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npm install dotenv`;
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/**
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* Main exampleUsage() function.
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*/
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async function exampleUsage() {
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try {
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console.time('Timer');
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// OpenAI chat.completion structure
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const openaiCompatibleStructure = {
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"model": "gemma-7b-it",
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"messages":
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[
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{ "role": "system", "content": "You are a helpful assistant." },
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{ "role": "user", "content": "Say hello with a polite greeting!" },
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{ "role": "system", "content": "Hello there! It's an absolute pleasure to make your acquaintance. How may I have the honor of assisting you today?" },
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{ "role": "user", "content": "I need help understanding low latency LLMs!" }
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],
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"max_tokens": 100
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}
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// Concatenate messages into a single string
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const concatenatedMessages = openaiCompatibleStructure.messages.map(message => `${message.role}: ${message.content}`).join('\n');
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prettyHeader(
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'Chat Example',
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description,
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false,
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interfaceName,
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);
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prettyText(`\n\n${GREEN}Prompt (OpenAI Compatible Structure):${RESET}\n\n`);
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console.log(openaiCompatibleStructure)
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console.log()
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LLMInterface.setApiKey(interfaceName, apiKey);
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const response = await LLMInterface.sendMessage(interfaceName, openaiCompatibleStructure);
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/*
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or for the OpenAI API fans
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const response = await LLMInterface.chat.completions.create(
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interfaceName
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openaiCompatibleStructure
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);
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*/
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prettyResult(response.results);
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console.timeEnd('Timer');
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console.log();
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} catch (error) {
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console.error('Error processing openaiCompatibleStructure:', error);
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}
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}
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exampleUsage();

‎examples/basic-usage/prompt.js

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/**
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* @file examples/basic-usage/prompt.js
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* @description This example demonstrates submitting a string prompt.
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*
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* To run this example, you first need to install the required modules by executing:
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*
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* npm install dotenv
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*/
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const { LLMInterface } = require('../../src/index.js');
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const { prettyHeader, prettyResult } = require('../../src/utils/utils.js');
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const { simplePrompt, options } = require('../../src/utils/defaults.js');
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require('dotenv').config({ path: '../../.env' });
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// Setup your key and interface
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const interfaceName = 'groq';
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const apiKey = process.env.GROQ_API_KEY;
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// Example description
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const description = `This example demonstrates submitting a string prompt.
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To run this example, you first need to install the required modules by executing:
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npm install dotenv`;
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/**
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* Main exampleUsage() function.
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*/
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async function exampleUsage() {
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try {
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console.time('Timer');
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prettyHeader(
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'Prompt Example',
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description,
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simplePrompt,
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interfaceName,
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);
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LLMInterface.setApiKey(interfaceName, apiKey);
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const response = await LLMInterface.sendMessage(interfaceName, simplePrompt, options);
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prettyResult(response.results);
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console.timeEnd('Timer');
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console.log();
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} catch (error) {
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console.error('Error processing stream:', error);
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}
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}
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exampleUsage();

‎examples/misc/streaming-mode.js ‎examples/basic-usage/streaming-mode.js

+24-13
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,5 @@
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/**
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* @file examples/misc/streaming-mode.js
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* @file examples/basic-usage/streaming-mode.js
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* @description This example demonstrates the new beta streaming functionality with the "groq" interface.
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*
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* To run this example, you first need to install the required modules by executing:
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*/
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const { Readable } = require('stream');
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const { simplePrompt, options } = require('../../src/utils/defaults.js');
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const { simplePrompt } = require('../../src/utils/defaults.js');
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const { LLMInterface } = require('../../src/index.js');
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const { prettyHeader } = require('../../src/utils/utils.js');
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const { prettyHeader,
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prettyText,
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YELLOW,
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GREEN,
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RESET,
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} = require('../../src/utils/utils.js');
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require('dotenv').config({ path: '../../.env' });
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// Setup your key and interface
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* Main exampleUsage() function.
8186
*/
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async function exampleUsage() {
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console.log('Streaming Mode (Groq):');
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console.log();
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LLMInterface.setApiKey(interfaceName, apiKey);
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8888
try {
89-
console.log('Process Stream');
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console.time('Timer');
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prettyHeader(
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'Streaming Mode',
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description,
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simplePrompt,
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interfaceName,
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);
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console.log();
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prettyText(`\n${GREEN}Response:${RESET}\n`);
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console.log();
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LLMInterface.setApiKey(interfaceName, apiKey);
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92101
const stream = await LLMInterface.sendMessage(interfaceName, simplePrompt, {
93102
stream: true,
94-
...options,
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max_tokens: 25
95104
});
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/*
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105114
106115
*/
107116

108-
const result = await processStream(stream.data);
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await processStream(stream.data);
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console.log();
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console.timeEnd('Timer');
109121
console.log();
110-
console.log('Concatenated Content');
111-
console.log(result);
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112123
} catch (error) {
113124
console.error('Error processing stream:', error);
114125
}

‎examples/caching/memory-response-cache.js ‎examples/caching/memory-cache.js

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/**
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* @file examples/caching/memory-response-cache.js
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* @file examples/caching/memory-cache.js
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* @description This example demonstrates the usage of the memory cache for caching API requests.
44
*
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* This example show LLMInterface configured with a memory cache. Subsequent calls to LLMInterface.sendMessage()
@@ -38,7 +38,7 @@ Note: This script will run faster on subsequent executions within the same sessi
3838
*/
3939
async function exampleUsage() {
4040
prettyHeader(
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'Memory Response Cache Example',
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'Memory Cache Example',
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description,
4343
simplePrompt,
4444
interfaceName,

‎examples/embeddings/embeddings-failover-custom.js ‎examples/embeddings/embeddings-custom-failover.js

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11
/**
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* @file examples/embeddings/embeddings-failover-custom.js
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* @file examples/embeddings/embeddings-custom-failover.js
33
* @description This example demonstrates the usage of LLMInterface.embeddings() with a custom failover mechanism.
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* To use a custom failover, ensure your selected service supports embeddings and provide LLMInterface with the associated API key.
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*

‎examples/langchain/rag.js

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@@ -1,13 +1,13 @@
11
/**
22
* @file examples/langchain/rag.js
3-
* @description This example demonstrates Retrieval-Augmented Generation (RAG) with custom models built using LLMInterface, which are compatible with LangChain.
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* @description This example demonstrates Retrieval-Augmented Generation (RAG) with custom models built using LLMInterface, which are compatible with LangChain.js.
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*
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* To run this example, you need to install the required modules by executing:
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* "npm install langchain dotenv".
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*
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* This example showcases how to retrieve relevant documents from a local directory, generate embeddings using a custom model built with LLMInterface, identify the most relevant context for answering a question, and construct a prompt for a language model to generate a response.
99
*
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* The workflow employs cosine similarity to determine document relevance and utilizes LangChain to format and process the final prompt. After completing the RAG process, a final direct query is sent to the provider, and the control answer is displayed for comparison.
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* The workflow employs cosine similarity to determine document relevance and utilizes LangChain.js to format and process the final prompt. After completing the RAG process, a final direct query is sent to the provider, and the control answer is displayed for comparison.
1111
*/
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1313
const fs = require('fs');
@@ -16,7 +16,6 @@ const {
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prettyHeader,
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prettyResult,
1818
prettyText,
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YELLOW,
2019
GREEN,
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RESET,
2221
} = require('../../src/utils/utils.js');
@@ -27,9 +26,9 @@ const HuggingFaceModel = require('./models/huggingfaceModel');
2726
const AI21Model = require('./models/ai21Model');
2827

2928
// Example description
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const description = `This example demonstrates the use of Retrieval-Augmented Generation (RAG) with custom models built using LLMInterface, which are compatible with LangChain. The process involves retrieving relevant documents from a local directory, generating embeddings, identifying the most pertinent context for answering a question, and constructing a prompt for a language model to generate a response.
29+
const description = `This example demonstrates the use of Retrieval-Augmented Generation (RAG) with custom models built using LLMInterface, which are compatible with LangChain.js. The process involves retrieving relevant documents from a local directory, generating embeddings, identifying the most pertinent context for answering a question, and constructing a prompt for a language model to generate a response.
3130
32-
The workflow employs cosine similarity to determine the relevance of documents and utilizes LangChain to format and process the final prompt. After completing the RAG process, a final direct query is sent to the provider, and the control answer is displayed for comparison.`;
31+
The workflow employs cosine similarity to determine the relevance of documents and utilizes LangChain.js to format and process the final prompt. After completing the RAG process, a final direct query is sent to the provider, and the control answer is displayed for comparison.`;
3332

3433
require('dotenv').config({ path: '../../.env' });
3534

@@ -112,7 +111,7 @@ async function exampleUsage(provider) {
112111

113112
console.time('Timer');
114113
prettyText(
115-
`\n${YELLOW}Use Langchain to create the PromptTemplate and invoke LLMChain${RESET}\n`,
114+
`\n${YELLOW}Use Langchain.js to create the PromptTemplate and invoke LLMChain${RESET}\n`,
116115
);
117116

118117
const promptTemplate = new PromptTemplate({

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