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a/src/components/Sidebar/chat/ChatInput.tsx +++ b/src/components/Sidebar/chat/ChatInput.tsx @@ -50,7 +50,6 @@ export function SidebarInput({ }) context = (await pageContent) as string } - console.log('context', context) submitMessage(text, isWebpageContextOn ? context : undefined) setText('') } diff --git a/src/hooks/useChatCompletion.ts b/src/hooks/useChatCompletion.ts index fe174143..de852e61 100644 --- a/src/hooks/useChatCompletion.ts +++ b/src/hooks/useChatCompletion.ts @@ -1,13 +1,12 @@ -import { AvailableModels, Mode } from '../config/settings' +import endent from 'endent' import { ChatOpenAI } from 'langchain/chat_models/openai' -import { useCurrentChat, ChatRole } from './useCurrentChat' -import { useMemo } from 'react' +import { OpenAIEmbeddings } from 'langchain/embeddings/openai' import { AIMessage, HumanMessage, SystemMessage } from 'langchain/schema' -import { useState } from 'react' import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter' -import { HNSWLib } from 'langchain/vectorstores/hnswlib' -import { OpenAIEmbeddings } from 'langchain/embeddings/openai' -import { loadQAMapReduceChain } from 'langchain/chains' +import { MemoryVectorStore } from 'langchain/vectorstores/memory' +import { useMemo, useState } from 'react' +import { AvailableModels, Mode } from '../config/settings' +import { ChatRole, useCurrentChat } from './useCurrentChat' interface UseChatCompletionProps { model: AvailableModels @@ -67,11 +66,6 @@ export const useChatCompletion = ({ const submitQuery = async (query: string, context?: string) => { await addNewMessage(ChatRole.USER, query) - const messages = [ - new SystemMessage(systemPrompt), - ...previousMessages, - new HumanMessage(query), - ] const options = { signal: controller.signal, callbacks: [{ handleLLMNewToken: updateAssistantMessage }], @@ -79,34 +73,47 @@ export const useChatCompletion = ({ setGenerating(true) - // if there is no web page context, run with a simple llm call - if (!context) { - llm.call(messages, options).then(() => { - commitToStoredMessages() - setGenerating(false) + /** + * If context is provided, we need to use the LLM to get the relevant documents + * and then run the LLM on those documents. We use in memory vector store to + * get the relevant documents + */ + let matchedContext + if (context) { + const textSplitter = new RecursiveCharacterTextSplitter({ + chunkSize: 1000, }) - return + const docs = await textSplitter.createDocuments([context]) + const vectorStore = await MemoryVectorStore.fromDocuments( + docs, + new OpenAIEmbeddings({ + openAIApiKey: apiKey, + }), + ) + const retriever = vectorStore.asRetriever() + const relevantDocs = await retriever.getRelevantDocuments(query) + console.log(relevantDocs) + matchedContext = relevantDocs.map((doc) => doc.pageContent).join('\n') } - // if there is a web page context, run with a map reduce chain - const textSplitter = new RecursiveCharacterTextSplitter({ - chunkSize: 1000, - }) - const docs = await textSplitter.createDocuments([context]) - const vectorStore = await HNSWLib.fromDocuments( - docs, - new OpenAIEmbeddings(), - ) - const retriever = vectorStore.asRetriever() - const relevantDocs = await retriever.getRelevantDocuments(query) - const mapReduceChain = loadQAMapReduceChain(llm) - await mapReduceChain.invoke( - { - messages: messages, - input_documents: relevantDocs, - }, - options, - ) + const expandedQuery = matchedContext + ? endent` + ### Context + ${matchedContext} + ### Question: + ${query} + ` + : query + + const messages = [ + new SystemMessage(systemPrompt), + ...previousMessages, + new HumanMessage(expandedQuery), + ] + + await llm.call(messages, options) + commitToStoredMessages() + setGenerating(false) } const cancelRequest = () => {