CRAG Implementation using TS
The article discusses the implementation of CRAG using TypeScript. It outlines the various components and functions involved in the process, including vector retrieval and document grading. The implementation leverages the LangChain library and OpenAI's GPT-4 model for enhanced performance.
- ▪The CRAG implementation utilizes TypeScript and the LangChain library.
- ▪Key functions include retrieving documents, grading their relevance, and generating answers based on user queries.
- ▪The system employs OpenAI's GPT-4 model to improve the accuracy of document retrieval and response generation.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3327574) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Tej Hagargi Posted on May 16 CRAG Implementation using TS #rag #graphrag #crag #genai import { StateGraph, Annotation, MessagesAnnotation, END, } from "@langchain/langgraph"; import { ChatOpenAI } from "@langchain/openai"; import { HumanMessage, SystemMessage } from "@langchain/core/messages"; import { PineconeStore } from "@langchain/pinecone"; import { embeddings } from "./embeddings"; import { promptTemplate } from "./ragPrompt"; const model = new ChatOpenAI({ modelName: "gpt-4",…
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