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langchain-gigachat

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<div align="center" id="top"> [![GitHub License](https://img.shields.io/github/license/ai-forever/langchain-gigachat?style=flat-square)](https://opensource.org/license/MIT) ![npm](https://img.shields.io/npm/dm/langchain-gigachat) [![GitHub star chart](https://img.shields.io/github/stars/ai-forever/langchainjs?style=flat-square)](https://www.star-history.com/#ai-forever/langchainjs) [English](README.md) | [Русский](README-ru_RU.md) </div> # langchain-gigachat This is a library integration with [GigaChat](https://giga.chat/). ## Installation ```bash npm install --save langchain-gigachat ``` ## Quickstart Follow these simple steps to get up and running quickly. ### Installation To install the package use following command: ```shell npm install --save langchain-gigachat ``` ### Initialization To initialize chat model: ```js import { GigaChat } from "langchain-gigachat" import { Agent } from 'node:https'; const httpsAgent = new Agent({ rejectUnauthorized: false, }); const giga = new GigaChat({ credentials: 'YOUR_AUTHORIZATION_KEY', model: 'GigaChat-Max', httpsAgent }) ``` ### Usage Use the GigaChat object to generate responses: ```typescript import { HumanMessage, SystemMessage } from "@langchain/core/messages"; const messages = [ new SystemMessage("Translate following messages to portugese"), new HumanMessage("Hello, world!"), ]; const resp = await giga.invoke(messages); console.log(resp.content); ``` Use the GigaChat object to create embeddings: ```js import { GigaChatEmbeddings } from "langchain-gigachat"; import { Agent } from 'node:https'; const httpsAgent = new Agent({ rejectUnauthorized: false, }); async function main() { const embeddings = new GigaChatEmbeddings({ credentials: 'YOUR_AUTHORIZATION_KEY', httpsAgent }); console.log(await embeddings.embedDocuments(["Словасловаслова"])); } main(); ``` Now you can use the GigaChat object with LangChainJS's standard primitives to create LLM-applications.