Merge branch 'feat/single-search'
This commit is contained in:
ItzCrazyKns
commit
fdb3d09d12
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@ -1,280 +0,0 @@
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import { BaseMessage } from '@langchain/core/messages';
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import {
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PromptTemplate,
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ChatPromptTemplate,
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MessagesPlaceholder,
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} from '@langchain/core/prompts';
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import {
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RunnableSequence,
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RunnableMap,
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RunnableLambda,
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||||||
} from '@langchain/core/runnables';
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import { StringOutputParser } from '@langchain/core/output_parsers';
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import { Document } from '@langchain/core/documents';
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import { searchSearxng } from '../lib/searxng';
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import type { StreamEvent } from '@langchain/core/tracers/log_stream';
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import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
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import type { Embeddings } from '@langchain/core/embeddings';
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import formatChatHistoryAsString from '../utils/formatHistory';
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import eventEmitter from 'events';
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import computeSimilarity from '../utils/computeSimilarity';
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import logger from '../utils/logger';
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import { IterableReadableStream } from '@langchain/core/utils/stream';
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const basicAcademicSearchRetrieverPrompt = `
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You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
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If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
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Example:
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1. Follow up question: How does stable diffusion work?
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Rephrased: Stable diffusion working
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||||||
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2. Follow up question: What is linear algebra?
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Rephrased: Linear algebra
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3. Follow up question: What is the third law of thermodynamics?
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Rephrased: Third law of thermodynamics
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Conversation:
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{chat_history}
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Follow up question: {query}
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Rephrased question:
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`;
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const basicAcademicSearchResponsePrompt = `
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You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
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||||||
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||||||
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
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You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
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You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
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||||||
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
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|
||||||
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
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||||||
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
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||||||
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
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||||||
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||||||
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
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||||||
talk about the context in your response.
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<context>
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{context}
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</context>
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If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
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Anything between the \`context\` is retrieved from a search engine and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
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`;
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const strParser = new StringOutputParser();
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||||||
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||||||
const handleStream = async (
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||||||
stream: IterableReadableStream<StreamEvent>,
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||||||
emitter: eventEmitter,
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||||||
) => {
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for await (const event of stream) {
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if (
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event.event === 'on_chain_end' &&
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||||||
event.name === 'FinalSourceRetriever'
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||||||
) {
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||||||
emitter.emit(
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||||||
'data',
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JSON.stringify({ type: 'sources', data: event.data.output }),
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||||||
);
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}
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||||||
if (
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event.event === 'on_chain_stream' &&
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||||||
event.name === 'FinalResponseGenerator'
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||||||
) {
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emitter.emit(
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'data',
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JSON.stringify({ type: 'response', data: event.data.chunk }),
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);
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}
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if (
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event.event === 'on_chain_end' &&
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||||||
event.name === 'FinalResponseGenerator'
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||||||
) {
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||||||
emitter.emit('end');
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||||||
}
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||||||
}
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|
||||||
};
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type BasicChainInput = {
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chat_history: BaseMessage[];
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query: string;
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};
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const createBasicAcademicSearchRetrieverChain = (llm: BaseChatModel) => {
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return RunnableSequence.from([
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PromptTemplate.fromTemplate(basicAcademicSearchRetrieverPrompt),
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llm,
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strParser,
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RunnableLambda.from(async (input: string) => {
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if (input === 'not_needed') {
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return { query: '', docs: [] };
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}
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const res = await searchSearxng(input, {
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language: 'en',
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engines: ['arxiv', 'google scholar', 'pubmed'],
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});
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const documents = res.results.map(
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(result) =>
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new Document({
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pageContent: result.content,
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metadata: {
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title: result.title,
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url: result.url,
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...(result.img_src && { img_src: result.img_src }),
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},
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}),
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);
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return { query: input, docs: documents };
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}),
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]);
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};
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const createBasicAcademicSearchAnsweringChain = (
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llm: BaseChatModel,
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embeddings: Embeddings,
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optimizationMode: 'speed' | 'balanced' | 'quality',
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) => {
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const basicAcademicSearchRetrieverChain =
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createBasicAcademicSearchRetrieverChain(llm);
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const processDocs = async (docs: Document[]) => {
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return docs
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.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
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.join('\n');
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};
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const rerankDocs = async ({
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query,
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docs,
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}: {
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query: string;
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docs: Document[];
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}) => {
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if (docs.length === 0) {
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return docs;
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}
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const docsWithContent = docs.filter(
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(doc) => doc.pageContent && doc.pageContent.length > 0,
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);
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if (optimizationMode === 'speed') {
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return docsWithContent.slice(0, 15);
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} else if (optimizationMode === 'balanced') {
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const [docEmbeddings, queryEmbedding] = await Promise.all([
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embeddings.embedDocuments(
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docsWithContent.map((doc) => doc.pageContent),
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),
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embeddings.embedQuery(query),
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]);
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const similarity = docEmbeddings.map((docEmbedding, i) => {
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const sim = computeSimilarity(queryEmbedding, docEmbedding);
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return {
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index: i,
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similarity: sim,
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};
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});
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const sortedDocs = similarity
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.sort((a, b) => b.similarity - a.similarity)
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.slice(0, 15)
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.map((sim) => docsWithContent[sim.index]);
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return sortedDocs;
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}
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};
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return RunnableSequence.from([
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RunnableMap.from({
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query: (input: BasicChainInput) => input.query,
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chat_history: (input: BasicChainInput) => input.chat_history,
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context: RunnableSequence.from([
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(input) => ({
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query: input.query,
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chat_history: formatChatHistoryAsString(input.chat_history),
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||||||
}),
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||||||
basicAcademicSearchRetrieverChain
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.pipe(rerankDocs)
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||||||
.withConfig({
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runName: 'FinalSourceRetriever',
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||||||
})
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.pipe(processDocs),
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]),
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||||||
}),
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ChatPromptTemplate.fromMessages([
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||||||
['system', basicAcademicSearchResponsePrompt],
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new MessagesPlaceholder('chat_history'),
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['user', '{query}'],
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]),
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llm,
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||||||
strParser,
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||||||
]).withConfig({
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|
||||||
runName: 'FinalResponseGenerator',
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||||||
});
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|
||||||
};
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||||||
|
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||||||
const basicAcademicSearch = (
|
|
||||||
query: string,
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|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
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|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
) => {
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||||||
const emitter = new eventEmitter();
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|
||||||
|
|
||||||
try {
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|
||||||
const basicAcademicSearchAnsweringChain =
|
|
||||||
createBasicAcademicSearchAnsweringChain(
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||||||
llm,
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|
||||||
embeddings,
|
|
||||||
optimizationMode,
|
|
||||||
);
|
|
||||||
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|
||||||
const stream = basicAcademicSearchAnsweringChain.streamEvents(
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|
||||||
{
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|
||||||
chat_history: history,
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|
||||||
query: query,
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|
||||||
},
|
|
||||||
{
|
|
||||||
version: 'v1',
|
|
||||||
},
|
|
||||||
);
|
|
||||||
|
|
||||||
handleStream(stream, emitter);
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|
||||||
} catch (err) {
|
|
||||||
emitter.emit(
|
|
||||||
'error',
|
|
||||||
JSON.stringify({ data: 'An error has occurred please try again later' }),
|
|
||||||
);
|
|
||||||
logger.error(`Error in academic search: ${err}`);
|
|
||||||
}
|
|
||||||
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
const handleAcademicSearch = (
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|
||||||
message: string,
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|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
) => {
|
|
||||||
const emitter = basicAcademicSearch(
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|
||||||
message,
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|
||||||
history,
|
|
||||||
llm,
|
|
||||||
embeddings,
|
|
||||||
optimizationMode,
|
|
||||||
);
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
export default handleAcademicSearch;
|
|
||||||
|
|
@ -1,276 +0,0 @@
|
||||||
import { BaseMessage } from '@langchain/core/messages';
|
|
||||||
import {
|
|
||||||
PromptTemplate,
|
|
||||||
ChatPromptTemplate,
|
|
||||||
MessagesPlaceholder,
|
|
||||||
} from '@langchain/core/prompts';
|
|
||||||
import {
|
|
||||||
RunnableSequence,
|
|
||||||
RunnableMap,
|
|
||||||
RunnableLambda,
|
|
||||||
} from '@langchain/core/runnables';
|
|
||||||
import { StringOutputParser } from '@langchain/core/output_parsers';
|
|
||||||
import { Document } from '@langchain/core/documents';
|
|
||||||
import { searchSearxng } from '../lib/searxng';
|
|
||||||
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
|
|
||||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
|
||||||
import type { Embeddings } from '@langchain/core/embeddings';
|
|
||||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
|
||||||
import eventEmitter from 'events';
|
|
||||||
import computeSimilarity from '../utils/computeSimilarity';
|
|
||||||
import logger from '../utils/logger';
|
|
||||||
import { IterableReadableStream } from '@langchain/core/utils/stream';
|
|
||||||
|
|
||||||
const basicRedditSearchRetrieverPrompt = `
|
|
||||||
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
|
|
||||||
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
1. Follow up question: Which company is most likely to create an AGI
|
|
||||||
Rephrased: Which company is most likely to create an AGI
|
|
||||||
|
|
||||||
2. Follow up question: Is Earth flat?
|
|
||||||
Rephrased: Is Earth flat?
|
|
||||||
|
|
||||||
3. Follow up question: Is there life on Mars?
|
|
||||||
Rephrased: Is there life on Mars?
|
|
||||||
|
|
||||||
Conversation:
|
|
||||||
{chat_history}
|
|
||||||
|
|
||||||
Follow up question: {query}
|
|
||||||
Rephrased question:
|
|
||||||
`;
|
|
||||||
|
|
||||||
const basicRedditSearchResponsePrompt = `
|
|
||||||
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Reddit', this means you will be searching for information, opinions and discussions on the web using Reddit.
|
|
||||||
|
|
||||||
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
|
|
||||||
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
|
|
||||||
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
|
|
||||||
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
|
|
||||||
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
|
|
||||||
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
|
||||||
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
|
||||||
|
|
||||||
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Reddit and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
|
|
||||||
talk about the context in your response.
|
|
||||||
|
|
||||||
<context>
|
|
||||||
{context}
|
|
||||||
</context>
|
|
||||||
|
|
||||||
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
|
|
||||||
Anything between the \`context\` is retrieved from Reddit and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
|
|
||||||
`;
|
|
||||||
|
|
||||||
const strParser = new StringOutputParser();
|
|
||||||
|
|
||||||
const handleStream = async (
|
|
||||||
stream: IterableReadableStream<StreamEvent>,
|
|
||||||
emitter: eventEmitter,
|
|
||||||
) => {
|
|
||||||
for await (const event of stream) {
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_end' &&
|
|
||||||
event.name === 'FinalSourceRetriever'
|
|
||||||
) {
|
|
||||||
emitter.emit(
|
|
||||||
'data',
|
|
||||||
JSON.stringify({ type: 'sources', data: event.data.output }),
|
|
||||||
);
|
|
||||||
}
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_stream' &&
|
|
||||||
event.name === 'FinalResponseGenerator'
|
|
||||||
) {
|
|
||||||
emitter.emit(
|
|
||||||
'data',
|
|
||||||
JSON.stringify({ type: 'response', data: event.data.chunk }),
|
|
||||||
);
|
|
||||||
}
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_end' &&
|
|
||||||
event.name === 'FinalResponseGenerator'
|
|
||||||
) {
|
|
||||||
emitter.emit('end');
|
|
||||||
}
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
type BasicChainInput = {
|
|
||||||
chat_history: BaseMessage[];
|
|
||||||
query: string;
|
|
||||||
};
|
|
||||||
|
|
||||||
const createBasicRedditSearchRetrieverChain = (llm: BaseChatModel) => {
|
|
||||||
return RunnableSequence.from([
|
|
||||||
PromptTemplate.fromTemplate(basicRedditSearchRetrieverPrompt),
|
|
||||||
llm,
|
|
||||||
strParser,
|
|
||||||
RunnableLambda.from(async (input: string) => {
|
|
||||||
if (input === 'not_needed') {
|
|
||||||
return { query: '', docs: [] };
|
|
||||||
}
|
|
||||||
|
|
||||||
const res = await searchSearxng(input, {
|
|
||||||
language: 'en',
|
|
||||||
engines: ['reddit'],
|
|
||||||
});
|
|
||||||
|
|
||||||
const documents = res.results.map(
|
|
||||||
(result) =>
|
|
||||||
new Document({
|
|
||||||
pageContent: result.content ? result.content : result.title,
|
|
||||||
metadata: {
|
|
||||||
title: result.title,
|
|
||||||
url: result.url,
|
|
||||||
...(result.img_src && { img_src: result.img_src }),
|
|
||||||
},
|
|
||||||
}),
|
|
||||||
);
|
|
||||||
|
|
||||||
return { query: input, docs: documents };
|
|
||||||
}),
|
|
||||||
]);
|
|
||||||
};
|
|
||||||
|
|
||||||
const createBasicRedditSearchAnsweringChain = (
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
) => {
|
|
||||||
const basicRedditSearchRetrieverChain =
|
|
||||||
createBasicRedditSearchRetrieverChain(llm);
|
|
||||||
|
|
||||||
const processDocs = async (docs: Document[]) => {
|
|
||||||
return docs
|
|
||||||
.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
|
|
||||||
.join('\n');
|
|
||||||
};
|
|
||||||
|
|
||||||
const rerankDocs = async ({
|
|
||||||
query,
|
|
||||||
docs,
|
|
||||||
}: {
|
|
||||||
query: string;
|
|
||||||
docs: Document[];
|
|
||||||
}) => {
|
|
||||||
if (docs.length === 0) {
|
|
||||||
return docs;
|
|
||||||
}
|
|
||||||
|
|
||||||
const docsWithContent = docs.filter(
|
|
||||||
(doc) => doc.pageContent && doc.pageContent.length > 0,
|
|
||||||
);
|
|
||||||
|
|
||||||
if (optimizationMode === 'speed') {
|
|
||||||
return docsWithContent.slice(0, 15);
|
|
||||||
} else if (optimizationMode === 'balanced') {
|
|
||||||
const [docEmbeddings, queryEmbedding] = await Promise.all([
|
|
||||||
embeddings.embedDocuments(
|
|
||||||
docsWithContent.map((doc) => doc.pageContent),
|
|
||||||
),
|
|
||||||
embeddings.embedQuery(query),
|
|
||||||
]);
|
|
||||||
|
|
||||||
const similarity = docEmbeddings.map((docEmbedding, i) => {
|
|
||||||
const sim = computeSimilarity(queryEmbedding, docEmbedding);
|
|
||||||
|
|
||||||
return {
|
|
||||||
index: i,
|
|
||||||
similarity: sim,
|
|
||||||
};
|
|
||||||
});
|
|
||||||
|
|
||||||
const sortedDocs = similarity
|
|
||||||
.filter((sim) => sim.similarity > 0.3)
|
|
||||||
.sort((a, b) => b.similarity - a.similarity)
|
|
||||||
.slice(0, 15)
|
|
||||||
.map((sim) => docsWithContent[sim.index]);
|
|
||||||
|
|
||||||
return sortedDocs;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
return RunnableSequence.from([
|
|
||||||
RunnableMap.from({
|
|
||||||
query: (input: BasicChainInput) => input.query,
|
|
||||||
chat_history: (input: BasicChainInput) => input.chat_history,
|
|
||||||
context: RunnableSequence.from([
|
|
||||||
(input) => ({
|
|
||||||
query: input.query,
|
|
||||||
chat_history: formatChatHistoryAsString(input.chat_history),
|
|
||||||
}),
|
|
||||||
basicRedditSearchRetrieverChain
|
|
||||||
.pipe(rerankDocs)
|
|
||||||
.withConfig({
|
|
||||||
runName: 'FinalSourceRetriever',
|
|
||||||
})
|
|
||||||
.pipe(processDocs),
|
|
||||||
]),
|
|
||||||
}),
|
|
||||||
ChatPromptTemplate.fromMessages([
|
|
||||||
['system', basicRedditSearchResponsePrompt],
|
|
||||||
new MessagesPlaceholder('chat_history'),
|
|
||||||
['user', '{query}'],
|
|
||||||
]),
|
|
||||||
llm,
|
|
||||||
strParser,
|
|
||||||
]).withConfig({
|
|
||||||
runName: 'FinalResponseGenerator',
|
|
||||||
});
|
|
||||||
};
|
|
||||||
|
|
||||||
const basicRedditSearch = (
|
|
||||||
query: string,
|
|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
) => {
|
|
||||||
const emitter = new eventEmitter();
|
|
||||||
|
|
||||||
try {
|
|
||||||
const basicRedditSearchAnsweringChain =
|
|
||||||
createBasicRedditSearchAnsweringChain(llm, embeddings, optimizationMode);
|
|
||||||
const stream = basicRedditSearchAnsweringChain.streamEvents(
|
|
||||||
{
|
|
||||||
chat_history: history,
|
|
||||||
query: query,
|
|
||||||
},
|
|
||||||
{
|
|
||||||
version: 'v1',
|
|
||||||
},
|
|
||||||
);
|
|
||||||
|
|
||||||
handleStream(stream, emitter);
|
|
||||||
} catch (err) {
|
|
||||||
emitter.emit(
|
|
||||||
'error',
|
|
||||||
JSON.stringify({ data: 'An error has occurred please try again later' }),
|
|
||||||
);
|
|
||||||
logger.error(`Error in RedditSearch: ${err}`);
|
|
||||||
}
|
|
||||||
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
const handleRedditSearch = (
|
|
||||||
message: string,
|
|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
) => {
|
|
||||||
const emitter = basicRedditSearch(
|
|
||||||
message,
|
|
||||||
history,
|
|
||||||
llm,
|
|
||||||
embeddings,
|
|
||||||
optimizationMode,
|
|
||||||
);
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
export default handleRedditSearch;
|
|
||||||
|
|
@ -1,541 +0,0 @@
|
||||||
import { BaseMessage } from '@langchain/core/messages';
|
|
||||||
import {
|
|
||||||
PromptTemplate,
|
|
||||||
ChatPromptTemplate,
|
|
||||||
MessagesPlaceholder,
|
|
||||||
} from '@langchain/core/prompts';
|
|
||||||
import {
|
|
||||||
RunnableSequence,
|
|
||||||
RunnableMap,
|
|
||||||
RunnableLambda,
|
|
||||||
} from '@langchain/core/runnables';
|
|
||||||
import { StringOutputParser } from '@langchain/core/output_parsers';
|
|
||||||
import { Document } from '@langchain/core/documents';
|
|
||||||
import { searchSearxng } from '../lib/searxng';
|
|
||||||
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
|
|
||||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
|
||||||
import type { Embeddings } from '@langchain/core/embeddings';
|
|
||||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
|
||||||
import eventEmitter from 'events';
|
|
||||||
import computeSimilarity from '../utils/computeSimilarity';
|
|
||||||
import logger from '../utils/logger';
|
|
||||||
import LineListOutputParser from '../lib/outputParsers/listLineOutputParser';
|
|
||||||
import LineOutputParser from '../lib/outputParsers/lineOutputParser';
|
|
||||||
import { IterableReadableStream } from '@langchain/core/utils/stream';
|
|
||||||
import { ChatOpenAI } from '@langchain/openai';
|
|
||||||
import path from 'path';
|
|
||||||
import fs from 'fs';
|
|
||||||
import { getDocumentsFromLinks } from '../utils/documents';
|
|
||||||
|
|
||||||
const basicSearchRetrieverPrompt = `
|
|
||||||
You are an AI question rephraser. You will be given a conversation and a follow-up question, you will have to rephrase the follow up question so it is a standalone question and can be used by another LLM to search the web for information to answer it.
|
|
||||||
If it is a smple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
|
|
||||||
If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
|
|
||||||
You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
|
|
||||||
|
|
||||||
There are several examples attached for your reference inside the below \`examples\` XML block
|
|
||||||
|
|
||||||
<examples>
|
|
||||||
1. Follow up question: What is the capital of France
|
|
||||||
Rephrased question:\`
|
|
||||||
<question>
|
|
||||||
Capital of france
|
|
||||||
</question>
|
|
||||||
\`
|
|
||||||
|
|
||||||
2. Hi, how are you?
|
|
||||||
Rephrased question\`
|
|
||||||
<question>
|
|
||||||
not_needed
|
|
||||||
</question>
|
|
||||||
\`
|
|
||||||
|
|
||||||
3. Follow up question: What is Docker?
|
|
||||||
Rephrased question: \`
|
|
||||||
<question>
|
|
||||||
What is Docker
|
|
||||||
</question>
|
|
||||||
\`
|
|
||||||
|
|
||||||
4. Follow up question: Can you tell me what is X from https://example.com
|
|
||||||
Rephrased question: \`
|
|
||||||
<question>
|
|
||||||
Can you tell me what is X?
|
|
||||||
</question>
|
|
||||||
|
|
||||||
<links>
|
|
||||||
https://example.com
|
|
||||||
</links>
|
|
||||||
\`
|
|
||||||
|
|
||||||
5. Follow up question: Summarize the content from https://example.com
|
|
||||||
Rephrased question: \`
|
|
||||||
<question>
|
|
||||||
summarize
|
|
||||||
</question>
|
|
||||||
|
|
||||||
<links>
|
|
||||||
https://example.com
|
|
||||||
</links>
|
|
||||||
\`
|
|
||||||
</examples>
|
|
||||||
|
|
||||||
Anything below is the part of the actual conversation and you need to use conversation and the follow-up question to rephrase the follow-up question as a standalone question based on the guidelines shared above.
|
|
||||||
|
|
||||||
<conversation>
|
|
||||||
{chat_history}
|
|
||||||
</conversation>
|
|
||||||
|
|
||||||
Follow up question: {query}
|
|
||||||
Rephrased question:
|
|
||||||
`;
|
|
||||||
|
|
||||||
const basicWebSearchResponsePrompt = `
|
|
||||||
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are also an expert at summarizing web pages or documents and searching for content in them.
|
|
||||||
|
|
||||||
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
|
|
||||||
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
|
|
||||||
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
|
|
||||||
If the query contains some links and the user asks to answer from those links you will be provided the entire content of the page inside the \`context\` XML block. You can then use this content to answer the user's query.
|
|
||||||
If the user asks to summarize content from some links, you will be provided the entire content of the page inside the \`context\` XML block. You can then use this content to summarize the text. The content provided inside the \`context\` block will be already summarized by another model so you just need to use that content to answer the user's query.
|
|
||||||
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
|
|
||||||
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
|
|
||||||
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
|
||||||
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
|
||||||
|
|
||||||
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
|
|
||||||
talk about the context in your response.
|
|
||||||
|
|
||||||
<context>
|
|
||||||
{context}
|
|
||||||
</context>
|
|
||||||
|
|
||||||
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'. You do not need to do this for summarization tasks.
|
|
||||||
Anything between the \`context\` is retrieved from a search engine and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
|
|
||||||
`;
|
|
||||||
|
|
||||||
const strParser = new StringOutputParser();
|
|
||||||
|
|
||||||
const handleStream = async (
|
|
||||||
stream: IterableReadableStream<StreamEvent>,
|
|
||||||
emitter: eventEmitter,
|
|
||||||
) => {
|
|
||||||
for await (const event of stream) {
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_end' &&
|
|
||||||
event.name === 'FinalSourceRetriever'
|
|
||||||
) {
|
|
||||||
emitter.emit(
|
|
||||||
'data',
|
|
||||||
JSON.stringify({ type: 'sources', data: event.data.output }),
|
|
||||||
);
|
|
||||||
}
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_stream' &&
|
|
||||||
event.name === 'FinalResponseGenerator'
|
|
||||||
) {
|
|
||||||
emitter.emit(
|
|
||||||
'data',
|
|
||||||
JSON.stringify({ type: 'response', data: event.data.chunk }),
|
|
||||||
);
|
|
||||||
}
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_end' &&
|
|
||||||
event.name === 'FinalResponseGenerator'
|
|
||||||
) {
|
|
||||||
emitter.emit('end');
|
|
||||||
}
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
type BasicChainInput = {
|
|
||||||
chat_history: BaseMessage[];
|
|
||||||
query: string;
|
|
||||||
};
|
|
||||||
|
|
||||||
const createBasicWebSearchRetrieverChain = (llm: BaseChatModel) => {
|
|
||||||
(llm as unknown as ChatOpenAI).temperature = 0;
|
|
||||||
|
|
||||||
return RunnableSequence.from([
|
|
||||||
PromptTemplate.fromTemplate(basicSearchRetrieverPrompt),
|
|
||||||
llm,
|
|
||||||
strParser,
|
|
||||||
RunnableLambda.from(async (input: string) => {
|
|
||||||
const linksOutputParser = new LineListOutputParser({
|
|
||||||
key: 'links',
|
|
||||||
});
|
|
||||||
|
|
||||||
const questionOutputParser = new LineOutputParser({
|
|
||||||
key: 'question',
|
|
||||||
});
|
|
||||||
|
|
||||||
const links = await linksOutputParser.parse(input);
|
|
||||||
let question = await questionOutputParser.parse(input);
|
|
||||||
|
|
||||||
if (question === 'not_needed') {
|
|
||||||
return { query: '', docs: [] };
|
|
||||||
}
|
|
||||||
|
|
||||||
if (links.length > 0) {
|
|
||||||
if (question.length === 0) {
|
|
||||||
question = 'summarize';
|
|
||||||
}
|
|
||||||
|
|
||||||
let docs = [];
|
|
||||||
|
|
||||||
const linkDocs = await getDocumentsFromLinks({ links });
|
|
||||||
|
|
||||||
const docGroups: Document[] = [];
|
|
||||||
|
|
||||||
linkDocs.map((doc) => {
|
|
||||||
const URLDocExists = docGroups.find(
|
|
||||||
(d) =>
|
|
||||||
d.metadata.url === doc.metadata.url && d.metadata.totalDocs < 10,
|
|
||||||
);
|
|
||||||
|
|
||||||
if (!URLDocExists) {
|
|
||||||
docGroups.push({
|
|
||||||
...doc,
|
|
||||||
metadata: {
|
|
||||||
...doc.metadata,
|
|
||||||
totalDocs: 1,
|
|
||||||
},
|
|
||||||
});
|
|
||||||
}
|
|
||||||
|
|
||||||
const docIndex = docGroups.findIndex(
|
|
||||||
(d) =>
|
|
||||||
d.metadata.url === doc.metadata.url && d.metadata.totalDocs < 10,
|
|
||||||
);
|
|
||||||
|
|
||||||
if (docIndex !== -1) {
|
|
||||||
docGroups[docIndex].pageContent =
|
|
||||||
docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
|
|
||||||
docGroups[docIndex].metadata.totalDocs += 1;
|
|
||||||
}
|
|
||||||
});
|
|
||||||
|
|
||||||
await Promise.all(
|
|
||||||
docGroups.map(async (doc) => {
|
|
||||||
const res = await llm.invoke(`
|
|
||||||
You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the
|
|
||||||
text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
|
|
||||||
If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
|
|
||||||
|
|
||||||
- **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
|
|
||||||
- **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
|
|
||||||
- **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
|
|
||||||
|
|
||||||
The text will be shared inside the \`text\` XML tag, and the query inside the \`query\` XML tag.
|
|
||||||
|
|
||||||
<example>
|
|
||||||
1. \`<text>
|
|
||||||
Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers.
|
|
||||||
It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications
|
|
||||||
by using containers.
|
|
||||||
</text>
|
|
||||||
|
|
||||||
<query>
|
|
||||||
What is Docker and how does it work?
|
|
||||||
</query>
|
|
||||||
|
|
||||||
Response:
|
|
||||||
Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application
|
|
||||||
deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in
|
|
||||||
any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
|
|
||||||
\`
|
|
||||||
2. \`<text>
|
|
||||||
The theory of relativity, or simply relativity, encompasses two interrelated theories of Albert Einstein: special relativity and general
|
|
||||||
relativity. However, the word "relativity" is sometimes used in reference to Galilean invariance. The term "theory of relativity" was based
|
|
||||||
on the expression "relative theory" used by Max Planck in 1906. The theory of relativity usually encompasses two interrelated theories by
|
|
||||||
Albert Einstein: special relativity and general relativity. Special relativity applies to all physical phenomena in the absence of gravity.
|
|
||||||
General relativity explains the law of gravitation and its relation to other forces of nature. It applies to the cosmological and astrophysical
|
|
||||||
realm, including astronomy.
|
|
||||||
</text>
|
|
||||||
|
|
||||||
<query>
|
|
||||||
summarize
|
|
||||||
</query>
|
|
||||||
|
|
||||||
Response:
|
|
||||||
The theory of relativity, developed by Albert Einstein, encompasses two main theories: special relativity and general relativity. Special
|
|
||||||
relativity applies to all physical phenomena in the absence of gravity, while general relativity explains the law of gravitation and its
|
|
||||||
relation to other forces of nature. The theory of relativity is based on the concept of "relative theory," as introduced by Max Planck in
|
|
||||||
1906. It is a fundamental theory in physics that has revolutionized our understanding of the universe.
|
|
||||||
\`
|
|
||||||
</example>
|
|
||||||
|
|
||||||
Everything below is the actual data you will be working with. Good luck!
|
|
||||||
|
|
||||||
<query>
|
|
||||||
${question}
|
|
||||||
</query>
|
|
||||||
|
|
||||||
<text>
|
|
||||||
${doc.pageContent}
|
|
||||||
</text>
|
|
||||||
|
|
||||||
Make sure to answer the query in the summary.
|
|
||||||
`);
|
|
||||||
|
|
||||||
const document = new Document({
|
|
||||||
pageContent: res.content as string,
|
|
||||||
metadata: {
|
|
||||||
title: doc.metadata.title,
|
|
||||||
url: doc.metadata.url,
|
|
||||||
},
|
|
||||||
});
|
|
||||||
|
|
||||||
docs.push(document);
|
|
||||||
}),
|
|
||||||
);
|
|
||||||
|
|
||||||
return { query: question, docs: docs };
|
|
||||||
} else {
|
|
||||||
const res = await searchSearxng(question, {
|
|
||||||
language: 'en',
|
|
||||||
});
|
|
||||||
|
|
||||||
const documents = res.results.map(
|
|
||||||
(result) =>
|
|
||||||
new Document({
|
|
||||||
pageContent: result.content,
|
|
||||||
metadata: {
|
|
||||||
title: result.title,
|
|
||||||
url: result.url,
|
|
||||||
...(result.img_src && { img_src: result.img_src }),
|
|
||||||
},
|
|
||||||
}),
|
|
||||||
);
|
|
||||||
|
|
||||||
return { query: question, docs: documents };
|
|
||||||
}
|
|
||||||
}),
|
|
||||||
]);
|
|
||||||
};
|
|
||||||
|
|
||||||
const createBasicWebSearchAnsweringChain = (
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
fileIds: string[],
|
|
||||||
) => {
|
|
||||||
const basicWebSearchRetrieverChain = createBasicWebSearchRetrieverChain(llm);
|
|
||||||
|
|
||||||
const processDocs = async (docs: Document[]) => {
|
|
||||||
return docs
|
|
||||||
.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
|
|
||||||
.join('\n');
|
|
||||||
};
|
|
||||||
|
|
||||||
const rerankDocs = async ({
|
|
||||||
query,
|
|
||||||
docs,
|
|
||||||
}: {
|
|
||||||
query: string;
|
|
||||||
docs: Document[];
|
|
||||||
}) => {
|
|
||||||
if (docs.length === 0) {
|
|
||||||
return docs;
|
|
||||||
}
|
|
||||||
|
|
||||||
const filesData = fileIds
|
|
||||||
.map((file) => {
|
|
||||||
const filePath = path.join(process.cwd(), 'uploads', file);
|
|
||||||
|
|
||||||
const contentPath = filePath + '-extracted.json';
|
|
||||||
const embeddingsPath = filePath + '-embeddings.json';
|
|
||||||
|
|
||||||
const content = JSON.parse(fs.readFileSync(contentPath, 'utf8'));
|
|
||||||
const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
|
|
||||||
|
|
||||||
const fileSimilaritySearchObject = content.contents.map(
|
|
||||||
(c: string, i) => {
|
|
||||||
return {
|
|
||||||
fileName: content.title,
|
|
||||||
content: c,
|
|
||||||
embeddings: embeddings.embeddings[i],
|
|
||||||
};
|
|
||||||
},
|
|
||||||
);
|
|
||||||
|
|
||||||
return fileSimilaritySearchObject;
|
|
||||||
})
|
|
||||||
.flat();
|
|
||||||
|
|
||||||
if (query.toLocaleLowerCase() === 'summarize') {
|
|
||||||
return docs.slice(0, 15);
|
|
||||||
}
|
|
||||||
|
|
||||||
const docsWithContent = docs.filter(
|
|
||||||
(doc) => doc.pageContent && doc.pageContent.length > 0,
|
|
||||||
);
|
|
||||||
|
|
||||||
if (optimizationMode === 'speed') {
|
|
||||||
if (filesData.length > 0) {
|
|
||||||
const [queryEmbedding] = await Promise.all([
|
|
||||||
embeddings.embedQuery(query),
|
|
||||||
]);
|
|
||||||
|
|
||||||
const fileDocs = filesData.map((fileData) => {
|
|
||||||
return new Document({
|
|
||||||
pageContent: fileData.content,
|
|
||||||
metadata: {
|
|
||||||
title: fileData.fileName,
|
|
||||||
url: `File`,
|
|
||||||
},
|
|
||||||
});
|
|
||||||
});
|
|
||||||
|
|
||||||
const similarity = filesData.map((fileData, i) => {
|
|
||||||
const sim = computeSimilarity(queryEmbedding, fileData.embeddings);
|
|
||||||
|
|
||||||
return {
|
|
||||||
index: i,
|
|
||||||
similarity: sim,
|
|
||||||
};
|
|
||||||
});
|
|
||||||
|
|
||||||
const sortedDocs = similarity
|
|
||||||
.filter((sim) => sim.similarity > 0.3)
|
|
||||||
.sort((a, b) => b.similarity - a.similarity)
|
|
||||||
.slice(0, 8)
|
|
||||||
.map((sim) => fileDocs[sim.index]);
|
|
||||||
|
|
||||||
return [
|
|
||||||
...sortedDocs,
|
|
||||||
...docsWithContent.slice(0, 15 - sortedDocs.length),
|
|
||||||
];
|
|
||||||
} else {
|
|
||||||
return docsWithContent.slice(0, 15);
|
|
||||||
}
|
|
||||||
} else if (optimizationMode === 'balanced') {
|
|
||||||
const [docEmbeddings, queryEmbedding] = await Promise.all([
|
|
||||||
embeddings.embedDocuments(
|
|
||||||
docsWithContent.map((doc) => doc.pageContent),
|
|
||||||
),
|
|
||||||
embeddings.embedQuery(query),
|
|
||||||
]);
|
|
||||||
|
|
||||||
docsWithContent.push(
|
|
||||||
...filesData.map((fileData) => {
|
|
||||||
return new Document({
|
|
||||||
pageContent: fileData.content,
|
|
||||||
metadata: {
|
|
||||||
title: fileData.fileName,
|
|
||||||
url: `File`,
|
|
||||||
},
|
|
||||||
});
|
|
||||||
}),
|
|
||||||
);
|
|
||||||
|
|
||||||
docEmbeddings.push(...filesData.map((fileData) => fileData.embeddings));
|
|
||||||
|
|
||||||
const similarity = docEmbeddings.map((docEmbedding, i) => {
|
|
||||||
const sim = computeSimilarity(queryEmbedding, docEmbedding);
|
|
||||||
|
|
||||||
return {
|
|
||||||
index: i,
|
|
||||||
similarity: sim,
|
|
||||||
};
|
|
||||||
});
|
|
||||||
|
|
||||||
const sortedDocs = similarity
|
|
||||||
.filter((sim) => sim.similarity > 0.3)
|
|
||||||
.sort((a, b) => b.similarity - a.similarity)
|
|
||||||
.slice(0, 15)
|
|
||||||
.map((sim) => docsWithContent[sim.index]);
|
|
||||||
|
|
||||||
return sortedDocs;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
return RunnableSequence.from([
|
|
||||||
RunnableMap.from({
|
|
||||||
query: (input: BasicChainInput) => input.query,
|
|
||||||
chat_history: (input: BasicChainInput) => input.chat_history,
|
|
||||||
context: RunnableSequence.from([
|
|
||||||
(input) => ({
|
|
||||||
query: input.query,
|
|
||||||
chat_history: formatChatHistoryAsString(input.chat_history),
|
|
||||||
}),
|
|
||||||
basicWebSearchRetrieverChain
|
|
||||||
.pipe(rerankDocs)
|
|
||||||
.withConfig({
|
|
||||||
runName: 'FinalSourceRetriever',
|
|
||||||
})
|
|
||||||
.pipe(processDocs),
|
|
||||||
]),
|
|
||||||
}),
|
|
||||||
ChatPromptTemplate.fromMessages([
|
|
||||||
['system', basicWebSearchResponsePrompt],
|
|
||||||
new MessagesPlaceholder('chat_history'),
|
|
||||||
['user', '{query}'],
|
|
||||||
]),
|
|
||||||
llm,
|
|
||||||
strParser,
|
|
||||||
]).withConfig({
|
|
||||||
runName: 'FinalResponseGenerator',
|
|
||||||
});
|
|
||||||
};
|
|
||||||
|
|
||||||
const basicWebSearch = (
|
|
||||||
query: string,
|
|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
fileIds: string[] = [],
|
|
||||||
) => {
|
|
||||||
const emitter = new eventEmitter();
|
|
||||||
|
|
||||||
try {
|
|
||||||
const basicWebSearchAnsweringChain = createBasicWebSearchAnsweringChain(
|
|
||||||
llm,
|
|
||||||
embeddings,
|
|
||||||
optimizationMode,
|
|
||||||
fileIds,
|
|
||||||
);
|
|
||||||
|
|
||||||
const stream = basicWebSearchAnsweringChain.streamEvents(
|
|
||||||
{
|
|
||||||
chat_history: history,
|
|
||||||
query: query,
|
|
||||||
},
|
|
||||||
{
|
|
||||||
version: 'v1',
|
|
||||||
},
|
|
||||||
);
|
|
||||||
|
|
||||||
handleStream(stream, emitter);
|
|
||||||
} catch (err) {
|
|
||||||
emitter.emit(
|
|
||||||
'error',
|
|
||||||
JSON.stringify({ data: 'An error has occurred please try again later' }),
|
|
||||||
);
|
|
||||||
logger.error(`Error in websearch: ${err}`);
|
|
||||||
}
|
|
||||||
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
const handleWebSearch = (
|
|
||||||
message: string,
|
|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
fileIds: string[],
|
|
||||||
) => {
|
|
||||||
const emitter = basicWebSearch(
|
|
||||||
message,
|
|
||||||
history,
|
|
||||||
llm,
|
|
||||||
embeddings,
|
|
||||||
optimizationMode,
|
|
||||||
fileIds,
|
|
||||||
);
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
export default handleWebSearch;
|
|
||||||
|
|
@ -1,220 +0,0 @@
|
||||||
import { BaseMessage } from '@langchain/core/messages';
|
|
||||||
import {
|
|
||||||
PromptTemplate,
|
|
||||||
ChatPromptTemplate,
|
|
||||||
MessagesPlaceholder,
|
|
||||||
} from '@langchain/core/prompts';
|
|
||||||
import {
|
|
||||||
RunnableSequence,
|
|
||||||
RunnableMap,
|
|
||||||
RunnableLambda,
|
|
||||||
} from '@langchain/core/runnables';
|
|
||||||
import { StringOutputParser } from '@langchain/core/output_parsers';
|
|
||||||
import { Document } from '@langchain/core/documents';
|
|
||||||
import { searchSearxng } from '../lib/searxng';
|
|
||||||
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
|
|
||||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
|
||||||
import type { Embeddings } from '@langchain/core/embeddings';
|
|
||||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
|
||||||
import eventEmitter from 'events';
|
|
||||||
import logger from '../utils/logger';
|
|
||||||
import { IterableReadableStream } from '@langchain/core/utils/stream';
|
|
||||||
|
|
||||||
const basicWolframAlphaSearchRetrieverPrompt = `
|
|
||||||
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
|
|
||||||
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
1. Follow up question: What is the atomic radius of S?
|
|
||||||
Rephrased: Atomic radius of S
|
|
||||||
|
|
||||||
2. Follow up question: What is linear algebra?
|
|
||||||
Rephrased: Linear algebra
|
|
||||||
|
|
||||||
3. Follow up question: What is the third law of thermodynamics?
|
|
||||||
Rephrased: Third law of thermodynamics
|
|
||||||
|
|
||||||
Conversation:
|
|
||||||
{chat_history}
|
|
||||||
|
|
||||||
Follow up question: {query}
|
|
||||||
Rephrased question:
|
|
||||||
`;
|
|
||||||
|
|
||||||
const basicWolframAlphaSearchResponsePrompt = `
|
|
||||||
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Wolfram Alpha', this means you will be searching for information on the web using Wolfram Alpha. It is a computational knowledge engine that can answer factual queries and perform computations.
|
|
||||||
|
|
||||||
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
|
|
||||||
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
|
|
||||||
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
|
|
||||||
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
|
|
||||||
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
|
|
||||||
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
|
||||||
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
|
||||||
|
|
||||||
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Wolfram Alpha and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
|
|
||||||
talk about the context in your response.
|
|
||||||
|
|
||||||
<context>
|
|
||||||
{context}
|
|
||||||
</context>
|
|
||||||
|
|
||||||
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
|
|
||||||
Anything between the \`context\` is retrieved from Wolfram Alpha and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
|
|
||||||
`;
|
|
||||||
|
|
||||||
const strParser = new StringOutputParser();
|
|
||||||
|
|
||||||
const handleStream = async (
|
|
||||||
stream: IterableReadableStream<StreamEvent>,
|
|
||||||
emitter: eventEmitter,
|
|
||||||
) => {
|
|
||||||
for await (const event of stream) {
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_end' &&
|
|
||||||
event.name === 'FinalSourceRetriever'
|
|
||||||
) {
|
|
||||||
emitter.emit(
|
|
||||||
'data',
|
|
||||||
JSON.stringify({ type: 'sources', data: event.data.output }),
|
|
||||||
);
|
|
||||||
}
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_stream' &&
|
|
||||||
event.name === 'FinalResponseGenerator'
|
|
||||||
) {
|
|
||||||
emitter.emit(
|
|
||||||
'data',
|
|
||||||
JSON.stringify({ type: 'response', data: event.data.chunk }),
|
|
||||||
);
|
|
||||||
}
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_end' &&
|
|
||||||
event.name === 'FinalResponseGenerator'
|
|
||||||
) {
|
|
||||||
emitter.emit('end');
|
|
||||||
}
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
type BasicChainInput = {
|
|
||||||
chat_history: BaseMessage[];
|
|
||||||
query: string;
|
|
||||||
};
|
|
||||||
|
|
||||||
const createBasicWolframAlphaSearchRetrieverChain = (llm: BaseChatModel) => {
|
|
||||||
return RunnableSequence.from([
|
|
||||||
PromptTemplate.fromTemplate(basicWolframAlphaSearchRetrieverPrompt),
|
|
||||||
llm,
|
|
||||||
strParser,
|
|
||||||
RunnableLambda.from(async (input: string) => {
|
|
||||||
if (input === 'not_needed') {
|
|
||||||
return { query: '', docs: [] };
|
|
||||||
}
|
|
||||||
|
|
||||||
const res = await searchSearxng(input, {
|
|
||||||
language: 'en',
|
|
||||||
engines: ['wolframalpha'],
|
|
||||||
});
|
|
||||||
|
|
||||||
const documents = res.results.map(
|
|
||||||
(result) =>
|
|
||||||
new Document({
|
|
||||||
pageContent: result.content,
|
|
||||||
metadata: {
|
|
||||||
title: result.title,
|
|
||||||
url: result.url,
|
|
||||||
...(result.img_src && { img_src: result.img_src }),
|
|
||||||
},
|
|
||||||
}),
|
|
||||||
);
|
|
||||||
|
|
||||||
return { query: input, docs: documents };
|
|
||||||
}),
|
|
||||||
]);
|
|
||||||
};
|
|
||||||
|
|
||||||
const createBasicWolframAlphaSearchAnsweringChain = (llm: BaseChatModel) => {
|
|
||||||
const basicWolframAlphaSearchRetrieverChain =
|
|
||||||
createBasicWolframAlphaSearchRetrieverChain(llm);
|
|
||||||
|
|
||||||
const processDocs = (docs: Document[]) => {
|
|
||||||
return docs
|
|
||||||
.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
|
|
||||||
.join('\n');
|
|
||||||
};
|
|
||||||
|
|
||||||
return RunnableSequence.from([
|
|
||||||
RunnableMap.from({
|
|
||||||
query: (input: BasicChainInput) => input.query,
|
|
||||||
chat_history: (input: BasicChainInput) => input.chat_history,
|
|
||||||
context: RunnableSequence.from([
|
|
||||||
(input) => ({
|
|
||||||
query: input.query,
|
|
||||||
chat_history: formatChatHistoryAsString(input.chat_history),
|
|
||||||
}),
|
|
||||||
basicWolframAlphaSearchRetrieverChain
|
|
||||||
.pipe(({ query, docs }) => {
|
|
||||||
return docs;
|
|
||||||
})
|
|
||||||
.withConfig({
|
|
||||||
runName: 'FinalSourceRetriever',
|
|
||||||
})
|
|
||||||
.pipe(processDocs),
|
|
||||||
]),
|
|
||||||
}),
|
|
||||||
ChatPromptTemplate.fromMessages([
|
|
||||||
['system', basicWolframAlphaSearchResponsePrompt],
|
|
||||||
new MessagesPlaceholder('chat_history'),
|
|
||||||
['user', '{query}'],
|
|
||||||
]),
|
|
||||||
llm,
|
|
||||||
strParser,
|
|
||||||
]).withConfig({
|
|
||||||
runName: 'FinalResponseGenerator',
|
|
||||||
});
|
|
||||||
};
|
|
||||||
|
|
||||||
const basicWolframAlphaSearch = (
|
|
||||||
query: string,
|
|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
|
|
||||||
) => {
|
|
||||||
const emitter = new eventEmitter();
|
|
||||||
|
|
||||||
try {
|
|
||||||
const basicWolframAlphaSearchAnsweringChain =
|
|
||||||
createBasicWolframAlphaSearchAnsweringChain(llm);
|
|
||||||
const stream = basicWolframAlphaSearchAnsweringChain.streamEvents(
|
|
||||||
{
|
|
||||||
chat_history: history,
|
|
||||||
query: query,
|
|
||||||
},
|
|
||||||
{
|
|
||||||
version: 'v1',
|
|
||||||
},
|
|
||||||
);
|
|
||||||
|
|
||||||
handleStream(stream, emitter);
|
|
||||||
} catch (err) {
|
|
||||||
emitter.emit(
|
|
||||||
'error',
|
|
||||||
JSON.stringify({ data: 'An error has occurred please try again later' }),
|
|
||||||
);
|
|
||||||
logger.error(`Error in WolframAlphaSearch: ${err}`);
|
|
||||||
}
|
|
||||||
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
const handleWolframAlphaSearch = (
|
|
||||||
message: string,
|
|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
) => {
|
|
||||||
const emitter = basicWolframAlphaSearch(message, history, llm);
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
export default handleWolframAlphaSearch;
|
|
||||||
|
|
@ -1,91 +0,0 @@
|
||||||
import { BaseMessage } from '@langchain/core/messages';
|
|
||||||
import {
|
|
||||||
ChatPromptTemplate,
|
|
||||||
MessagesPlaceholder,
|
|
||||||
} from '@langchain/core/prompts';
|
|
||||||
import { RunnableSequence } from '@langchain/core/runnables';
|
|
||||||
import { StringOutputParser } from '@langchain/core/output_parsers';
|
|
||||||
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
|
|
||||||
import eventEmitter from 'events';
|
|
||||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
|
||||||
import type { Embeddings } from '@langchain/core/embeddings';
|
|
||||||
import logger from '../utils/logger';
|
|
||||||
import { IterableReadableStream } from '@langchain/core/utils/stream';
|
|
||||||
|
|
||||||
const writingAssistantPrompt = `
|
|
||||||
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query.
|
|
||||||
Since you are a writing assistant, you would not perform web searches. If you think you lack information to answer the query, you can ask the user for more information or suggest them to switch to a different focus mode.
|
|
||||||
`;
|
|
||||||
|
|
||||||
const strParser = new StringOutputParser();
|
|
||||||
|
|
||||||
const handleStream = async (
|
|
||||||
stream: IterableReadableStream<StreamEvent>,
|
|
||||||
emitter: eventEmitter,
|
|
||||||
) => {
|
|
||||||
for await (const event of stream) {
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_stream' &&
|
|
||||||
event.name === 'FinalResponseGenerator'
|
|
||||||
) {
|
|
||||||
emitter.emit(
|
|
||||||
'data',
|
|
||||||
JSON.stringify({ type: 'response', data: event.data.chunk }),
|
|
||||||
);
|
|
||||||
}
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_end' &&
|
|
||||||
event.name === 'FinalResponseGenerator'
|
|
||||||
) {
|
|
||||||
emitter.emit('end');
|
|
||||||
}
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
const createWritingAssistantChain = (llm: BaseChatModel) => {
|
|
||||||
return RunnableSequence.from([
|
|
||||||
ChatPromptTemplate.fromMessages([
|
|
||||||
['system', writingAssistantPrompt],
|
|
||||||
new MessagesPlaceholder('chat_history'),
|
|
||||||
['user', '{query}'],
|
|
||||||
]),
|
|
||||||
llm,
|
|
||||||
strParser,
|
|
||||||
]).withConfig({
|
|
||||||
runName: 'FinalResponseGenerator',
|
|
||||||
});
|
|
||||||
};
|
|
||||||
|
|
||||||
const handleWritingAssistant = (
|
|
||||||
query: string,
|
|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
) => {
|
|
||||||
const emitter = new eventEmitter();
|
|
||||||
|
|
||||||
try {
|
|
||||||
const writingAssistantChain = createWritingAssistantChain(llm);
|
|
||||||
const stream = writingAssistantChain.streamEvents(
|
|
||||||
{
|
|
||||||
chat_history: history,
|
|
||||||
query: query,
|
|
||||||
},
|
|
||||||
{
|
|
||||||
version: 'v1',
|
|
||||||
},
|
|
||||||
);
|
|
||||||
|
|
||||||
handleStream(stream, emitter);
|
|
||||||
} catch (err) {
|
|
||||||
emitter.emit(
|
|
||||||
'error',
|
|
||||||
JSON.stringify({ data: 'An error has occurred please try again later' }),
|
|
||||||
);
|
|
||||||
logger.error(`Error in writing assistant: ${err}`);
|
|
||||||
}
|
|
||||||
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
export default handleWritingAssistant;
|
|
||||||
|
|
@ -1,277 +0,0 @@
|
||||||
import { BaseMessage } from '@langchain/core/messages';
|
|
||||||
import {
|
|
||||||
PromptTemplate,
|
|
||||||
ChatPromptTemplate,
|
|
||||||
MessagesPlaceholder,
|
|
||||||
} from '@langchain/core/prompts';
|
|
||||||
import {
|
|
||||||
RunnableSequence,
|
|
||||||
RunnableMap,
|
|
||||||
RunnableLambda,
|
|
||||||
} from '@langchain/core/runnables';
|
|
||||||
import { StringOutputParser } from '@langchain/core/output_parsers';
|
|
||||||
import { Document } from '@langchain/core/documents';
|
|
||||||
import { searchSearxng } from '../lib/searxng';
|
|
||||||
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
|
|
||||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
|
||||||
import type { Embeddings } from '@langchain/core/embeddings';
|
|
||||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
|
||||||
import eventEmitter from 'events';
|
|
||||||
import computeSimilarity from '../utils/computeSimilarity';
|
|
||||||
import logger from '../utils/logger';
|
|
||||||
import { IterableReadableStream } from '@langchain/core/utils/stream';
|
|
||||||
|
|
||||||
const basicYoutubeSearchRetrieverPrompt = `
|
|
||||||
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
|
|
||||||
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
1. Follow up question: How does an A.C work?
|
|
||||||
Rephrased: A.C working
|
|
||||||
|
|
||||||
2. Follow up question: Linear algebra explanation video
|
|
||||||
Rephrased: What is linear algebra?
|
|
||||||
|
|
||||||
3. Follow up question: What is theory of relativity?
|
|
||||||
Rephrased: What is theory of relativity?
|
|
||||||
|
|
||||||
Conversation:
|
|
||||||
{chat_history}
|
|
||||||
|
|
||||||
Follow up question: {query}
|
|
||||||
Rephrased question:
|
|
||||||
`;
|
|
||||||
|
|
||||||
const basicYoutubeSearchResponsePrompt = `
|
|
||||||
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Youtube', this means you will be searching for videos on the web using Youtube and providing information based on the video's transcript.
|
|
||||||
|
|
||||||
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
|
|
||||||
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
|
|
||||||
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
|
|
||||||
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
|
|
||||||
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
|
|
||||||
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
|
||||||
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
|
||||||
|
|
||||||
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Youtube and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
|
|
||||||
talk about the context in your response.
|
|
||||||
|
|
||||||
<context>
|
|
||||||
{context}
|
|
||||||
</context>
|
|
||||||
|
|
||||||
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
|
|
||||||
Anything between the \`context\` is retrieved from Youtube and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
|
|
||||||
`;
|
|
||||||
|
|
||||||
const strParser = new StringOutputParser();
|
|
||||||
|
|
||||||
const handleStream = async (
|
|
||||||
stream: IterableReadableStream<StreamEvent>,
|
|
||||||
emitter: eventEmitter,
|
|
||||||
) => {
|
|
||||||
for await (const event of stream) {
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_end' &&
|
|
||||||
event.name === 'FinalSourceRetriever'
|
|
||||||
) {
|
|
||||||
emitter.emit(
|
|
||||||
'data',
|
|
||||||
JSON.stringify({ type: 'sources', data: event.data.output }),
|
|
||||||
);
|
|
||||||
}
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_stream' &&
|
|
||||||
event.name === 'FinalResponseGenerator'
|
|
||||||
) {
|
|
||||||
emitter.emit(
|
|
||||||
'data',
|
|
||||||
JSON.stringify({ type: 'response', data: event.data.chunk }),
|
|
||||||
);
|
|
||||||
}
|
|
||||||
if (
|
|
||||||
event.event === 'on_chain_end' &&
|
|
||||||
event.name === 'FinalResponseGenerator'
|
|
||||||
) {
|
|
||||||
emitter.emit('end');
|
|
||||||
}
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
type BasicChainInput = {
|
|
||||||
chat_history: BaseMessage[];
|
|
||||||
query: string;
|
|
||||||
};
|
|
||||||
|
|
||||||
const createBasicYoutubeSearchRetrieverChain = (llm: BaseChatModel) => {
|
|
||||||
return RunnableSequence.from([
|
|
||||||
PromptTemplate.fromTemplate(basicYoutubeSearchRetrieverPrompt),
|
|
||||||
llm,
|
|
||||||
strParser,
|
|
||||||
RunnableLambda.from(async (input: string) => {
|
|
||||||
if (input === 'not_needed') {
|
|
||||||
return { query: '', docs: [] };
|
|
||||||
}
|
|
||||||
|
|
||||||
const res = await searchSearxng(input, {
|
|
||||||
language: 'en',
|
|
||||||
engines: ['youtube'],
|
|
||||||
});
|
|
||||||
|
|
||||||
const documents = res.results.map(
|
|
||||||
(result) =>
|
|
||||||
new Document({
|
|
||||||
pageContent: result.content ? result.content : result.title,
|
|
||||||
metadata: {
|
|
||||||
title: result.title,
|
|
||||||
url: result.url,
|
|
||||||
...(result.img_src && { img_src: result.img_src }),
|
|
||||||
},
|
|
||||||
}),
|
|
||||||
);
|
|
||||||
|
|
||||||
return { query: input, docs: documents };
|
|
||||||
}),
|
|
||||||
]);
|
|
||||||
};
|
|
||||||
|
|
||||||
const createBasicYoutubeSearchAnsweringChain = (
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
) => {
|
|
||||||
const basicYoutubeSearchRetrieverChain =
|
|
||||||
createBasicYoutubeSearchRetrieverChain(llm);
|
|
||||||
|
|
||||||
const processDocs = async (docs: Document[]) => {
|
|
||||||
return docs
|
|
||||||
.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
|
|
||||||
.join('\n');
|
|
||||||
};
|
|
||||||
|
|
||||||
const rerankDocs = async ({
|
|
||||||
query,
|
|
||||||
docs,
|
|
||||||
}: {
|
|
||||||
query: string;
|
|
||||||
docs: Document[];
|
|
||||||
}) => {
|
|
||||||
if (docs.length === 0) {
|
|
||||||
return docs;
|
|
||||||
}
|
|
||||||
|
|
||||||
const docsWithContent = docs.filter(
|
|
||||||
(doc) => doc.pageContent && doc.pageContent.length > 0,
|
|
||||||
);
|
|
||||||
|
|
||||||
if (optimizationMode === 'speed') {
|
|
||||||
return docsWithContent.slice(0, 15);
|
|
||||||
} else {
|
|
||||||
const [docEmbeddings, queryEmbedding] = await Promise.all([
|
|
||||||
embeddings.embedDocuments(
|
|
||||||
docsWithContent.map((doc) => doc.pageContent),
|
|
||||||
),
|
|
||||||
embeddings.embedQuery(query),
|
|
||||||
]);
|
|
||||||
|
|
||||||
const similarity = docEmbeddings.map((docEmbedding, i) => {
|
|
||||||
const sim = computeSimilarity(queryEmbedding, docEmbedding);
|
|
||||||
|
|
||||||
return {
|
|
||||||
index: i,
|
|
||||||
similarity: sim,
|
|
||||||
};
|
|
||||||
});
|
|
||||||
|
|
||||||
const sortedDocs = similarity
|
|
||||||
.filter((sim) => sim.similarity > 0.3)
|
|
||||||
.sort((a, b) => b.similarity - a.similarity)
|
|
||||||
.slice(0, 15)
|
|
||||||
.map((sim) => docsWithContent[sim.index]);
|
|
||||||
|
|
||||||
return sortedDocs;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
return RunnableSequence.from([
|
|
||||||
RunnableMap.from({
|
|
||||||
query: (input: BasicChainInput) => input.query,
|
|
||||||
chat_history: (input: BasicChainInput) => input.chat_history,
|
|
||||||
context: RunnableSequence.from([
|
|
||||||
(input) => ({
|
|
||||||
query: input.query,
|
|
||||||
chat_history: formatChatHistoryAsString(input.chat_history),
|
|
||||||
}),
|
|
||||||
basicYoutubeSearchRetrieverChain
|
|
||||||
.pipe(rerankDocs)
|
|
||||||
.withConfig({
|
|
||||||
runName: 'FinalSourceRetriever',
|
|
||||||
})
|
|
||||||
.pipe(processDocs),
|
|
||||||
]),
|
|
||||||
}),
|
|
||||||
ChatPromptTemplate.fromMessages([
|
|
||||||
['system', basicYoutubeSearchResponsePrompt],
|
|
||||||
new MessagesPlaceholder('chat_history'),
|
|
||||||
['user', '{query}'],
|
|
||||||
]),
|
|
||||||
llm,
|
|
||||||
strParser,
|
|
||||||
]).withConfig({
|
|
||||||
runName: 'FinalResponseGenerator',
|
|
||||||
});
|
|
||||||
};
|
|
||||||
|
|
||||||
const basicYoutubeSearch = (
|
|
||||||
query: string,
|
|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
) => {
|
|
||||||
const emitter = new eventEmitter();
|
|
||||||
|
|
||||||
try {
|
|
||||||
const basicYoutubeSearchAnsweringChain =
|
|
||||||
createBasicYoutubeSearchAnsweringChain(llm, embeddings, optimizationMode);
|
|
||||||
|
|
||||||
const stream = basicYoutubeSearchAnsweringChain.streamEvents(
|
|
||||||
{
|
|
||||||
chat_history: history,
|
|
||||||
query: query,
|
|
||||||
},
|
|
||||||
{
|
|
||||||
version: 'v1',
|
|
||||||
},
|
|
||||||
);
|
|
||||||
|
|
||||||
handleStream(stream, emitter);
|
|
||||||
} catch (err) {
|
|
||||||
emitter.emit(
|
|
||||||
'error',
|
|
||||||
JSON.stringify({ data: 'An error has occurred please try again later' }),
|
|
||||||
);
|
|
||||||
logger.error(`Error in youtube search: ${err}`);
|
|
||||||
}
|
|
||||||
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
const handleYoutubeSearch = (
|
|
||||||
message: string,
|
|
||||||
history: BaseMessage[],
|
|
||||||
llm: BaseChatModel,
|
|
||||||
embeddings: Embeddings,
|
|
||||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
|
||||||
) => {
|
|
||||||
const emitter = basicYoutubeSearch(
|
|
||||||
message,
|
|
||||||
history,
|
|
||||||
llm,
|
|
||||||
embeddings,
|
|
||||||
optimizationMode,
|
|
||||||
);
|
|
||||||
return emitter;
|
|
||||||
};
|
|
||||||
|
|
||||||
export default handleYoutubeSearch;
|
|
||||||
|
|
@ -0,0 +1,42 @@
|
||||||
|
export const academicSearchRetrieverPrompt = `
|
||||||
|
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
|
||||||
|
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
1. Follow up question: How does stable diffusion work?
|
||||||
|
Rephrased: Stable diffusion working
|
||||||
|
|
||||||
|
2. Follow up question: What is linear algebra?
|
||||||
|
Rephrased: Linear algebra
|
||||||
|
|
||||||
|
3. Follow up question: What is the third law of thermodynamics?
|
||||||
|
Rephrased: Third law of thermodynamics
|
||||||
|
|
||||||
|
Conversation:
|
||||||
|
{chat_history}
|
||||||
|
|
||||||
|
Follow up question: {query}
|
||||||
|
Rephrased question:
|
||||||
|
`;
|
||||||
|
|
||||||
|
export const academicSearchResponsePrompt = `
|
||||||
|
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
|
||||||
|
|
||||||
|
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
|
||||||
|
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
|
||||||
|
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
|
||||||
|
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
|
||||||
|
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
|
||||||
|
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
||||||
|
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
||||||
|
|
||||||
|
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
|
||||||
|
talk about the context in your response.
|
||||||
|
|
||||||
|
<context>
|
||||||
|
{context}
|
||||||
|
</context>
|
||||||
|
|
||||||
|
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
|
||||||
|
Anything between the \`context\` is retrieved from a search engine and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
|
||||||
|
`;
|
||||||
|
|
@ -0,0 +1,32 @@
|
||||||
|
import {
|
||||||
|
academicSearchResponsePrompt,
|
||||||
|
academicSearchRetrieverPrompt,
|
||||||
|
} from './academicSearch';
|
||||||
|
import {
|
||||||
|
redditSearchResponsePrompt,
|
||||||
|
redditSearchRetrieverPrompt,
|
||||||
|
} from './redditSearch';
|
||||||
|
import { webSearchResponsePrompt, webSearchRetrieverPrompt } from './webSearch';
|
||||||
|
import {
|
||||||
|
wolframAlphaSearchResponsePrompt,
|
||||||
|
wolframAlphaSearchRetrieverPrompt,
|
||||||
|
} from './wolframAlpha';
|
||||||
|
import { writingAssistantPrompt } from './writingAssistant';
|
||||||
|
import {
|
||||||
|
youtubeSearchResponsePrompt,
|
||||||
|
youtubeSearchRetrieverPrompt,
|
||||||
|
} from './youtubeSearch';
|
||||||
|
|
||||||
|
export default {
|
||||||
|
webSearchResponsePrompt,
|
||||||
|
webSearchRetrieverPrompt,
|
||||||
|
academicSearchResponsePrompt,
|
||||||
|
academicSearchRetrieverPrompt,
|
||||||
|
redditSearchResponsePrompt,
|
||||||
|
redditSearchRetrieverPrompt,
|
||||||
|
wolframAlphaSearchResponsePrompt,
|
||||||
|
wolframAlphaSearchRetrieverPrompt,
|
||||||
|
writingAssistantPrompt,
|
||||||
|
youtubeSearchResponsePrompt,
|
||||||
|
youtubeSearchRetrieverPrompt,
|
||||||
|
};
|
||||||
|
|
@ -0,0 +1,42 @@
|
||||||
|
export const redditSearchRetrieverPrompt = `
|
||||||
|
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
|
||||||
|
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
1. Follow up question: Which company is most likely to create an AGI
|
||||||
|
Rephrased: Which company is most likely to create an AGI
|
||||||
|
|
||||||
|
2. Follow up question: Is Earth flat?
|
||||||
|
Rephrased: Is Earth flat?
|
||||||
|
|
||||||
|
3. Follow up question: Is there life on Mars?
|
||||||
|
Rephrased: Is there life on Mars?
|
||||||
|
|
||||||
|
Conversation:
|
||||||
|
{chat_history}
|
||||||
|
|
||||||
|
Follow up question: {query}
|
||||||
|
Rephrased question:
|
||||||
|
`;
|
||||||
|
|
||||||
|
export const redditSearchResponsePrompt = `
|
||||||
|
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Reddit', this means you will be searching for information, opinions and discussions on the web using Reddit.
|
||||||
|
|
||||||
|
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
|
||||||
|
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
|
||||||
|
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
|
||||||
|
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
|
||||||
|
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
|
||||||
|
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
||||||
|
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
||||||
|
|
||||||
|
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Reddit and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
|
||||||
|
talk about the context in your response.
|
||||||
|
|
||||||
|
<context>
|
||||||
|
{context}
|
||||||
|
</context>
|
||||||
|
|
||||||
|
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
|
||||||
|
Anything between the \`context\` is retrieved from Reddit and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
|
||||||
|
`;
|
||||||
|
|
@ -0,0 +1,86 @@
|
||||||
|
export const webSearchRetrieverPrompt = `
|
||||||
|
You are an AI question rephraser. You will be given a conversation and a follow-up question, you will have to rephrase the follow up question so it is a standalone question and can be used by another LLM to search the web for information to answer it.
|
||||||
|
If it is a smple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
|
||||||
|
If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
|
||||||
|
You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
|
||||||
|
|
||||||
|
There are several examples attached for your reference inside the below \`examples\` XML block
|
||||||
|
|
||||||
|
<examples>
|
||||||
|
1. Follow up question: What is the capital of France
|
||||||
|
Rephrased question:\`
|
||||||
|
<question>
|
||||||
|
Capital of france
|
||||||
|
</question>
|
||||||
|
\`
|
||||||
|
|
||||||
|
2. Hi, how are you?
|
||||||
|
Rephrased question\`
|
||||||
|
<question>
|
||||||
|
not_needed
|
||||||
|
</question>
|
||||||
|
\`
|
||||||
|
|
||||||
|
3. Follow up question: What is Docker?
|
||||||
|
Rephrased question: \`
|
||||||
|
<question>
|
||||||
|
What is Docker
|
||||||
|
</question>
|
||||||
|
\`
|
||||||
|
|
||||||
|
4. Follow up question: Can you tell me what is X from https://example.com
|
||||||
|
Rephrased question: \`
|
||||||
|
<question>
|
||||||
|
Can you tell me what is X?
|
||||||
|
</question>
|
||||||
|
|
||||||
|
<links>
|
||||||
|
https://example.com
|
||||||
|
</links>
|
||||||
|
\`
|
||||||
|
|
||||||
|
5. Follow up question: Summarize the content from https://example.com
|
||||||
|
Rephrased question: \`
|
||||||
|
<question>
|
||||||
|
summarize
|
||||||
|
</question>
|
||||||
|
|
||||||
|
<links>
|
||||||
|
https://example.com
|
||||||
|
</links>
|
||||||
|
\`
|
||||||
|
</examples>
|
||||||
|
|
||||||
|
Anything below is the part of the actual conversation and you need to use conversation and the follow-up question to rephrase the follow-up question as a standalone question based on the guidelines shared above.
|
||||||
|
|
||||||
|
<conversation>
|
||||||
|
{chat_history}
|
||||||
|
</conversation>
|
||||||
|
|
||||||
|
Follow up question: {query}
|
||||||
|
Rephrased question:
|
||||||
|
`;
|
||||||
|
|
||||||
|
export const webSearchResponsePrompt = `
|
||||||
|
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are also an expert at summarizing web pages or documents and searching for content in them.
|
||||||
|
|
||||||
|
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
|
||||||
|
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
|
||||||
|
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
|
||||||
|
If the query contains some links and the user asks to answer from those links you will be provided the entire content of the page inside the \`context\` XML block. You can then use this content to answer the user's query.
|
||||||
|
If the user asks to summarize content from some links, you will be provided the entire content of the page inside the \`context\` XML block. You can then use this content to summarize the text. The content provided inside the \`context\` block will be already summarized by another model so you just need to use that content to answer the user's query.
|
||||||
|
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
|
||||||
|
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
|
||||||
|
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
||||||
|
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
||||||
|
|
||||||
|
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
|
||||||
|
talk about the context in your response.
|
||||||
|
|
||||||
|
<context>
|
||||||
|
{context}
|
||||||
|
</context>
|
||||||
|
|
||||||
|
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'. You do not need to do this for summarization tasks.
|
||||||
|
Anything between the \`context\` is retrieved from a search engine and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
|
||||||
|
`;
|
||||||
|
|
@ -0,0 +1,42 @@
|
||||||
|
export const wolframAlphaSearchRetrieverPrompt = `
|
||||||
|
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
|
||||||
|
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
1. Follow up question: What is the atomic radius of S?
|
||||||
|
Rephrased: Atomic radius of S
|
||||||
|
|
||||||
|
2. Follow up question: What is linear algebra?
|
||||||
|
Rephrased: Linear algebra
|
||||||
|
|
||||||
|
3. Follow up question: What is the third law of thermodynamics?
|
||||||
|
Rephrased: Third law of thermodynamics
|
||||||
|
|
||||||
|
Conversation:
|
||||||
|
{chat_history}
|
||||||
|
|
||||||
|
Follow up question: {query}
|
||||||
|
Rephrased question:
|
||||||
|
`;
|
||||||
|
|
||||||
|
export const wolframAlphaSearchResponsePrompt = `
|
||||||
|
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Wolfram Alpha', this means you will be searching for information on the web using Wolfram Alpha. It is a computational knowledge engine that can answer factual queries and perform computations.
|
||||||
|
|
||||||
|
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
|
||||||
|
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
|
||||||
|
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
|
||||||
|
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
|
||||||
|
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
|
||||||
|
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
||||||
|
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
||||||
|
|
||||||
|
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Wolfram Alpha and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
|
||||||
|
talk about the context in your response.
|
||||||
|
|
||||||
|
<context>
|
||||||
|
{context}
|
||||||
|
</context>
|
||||||
|
|
||||||
|
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
|
||||||
|
Anything between the \`context\` is retrieved from Wolfram Alpha and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
|
||||||
|
`;
|
||||||
|
|
@ -0,0 +1,13 @@
|
||||||
|
export const writingAssistantPrompt = `
|
||||||
|
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query.
|
||||||
|
Since you are a writing assistant, you would not perform web searches. If you think you lack information to answer the query, you can ask the user for more information or suggest them to switch to a different focus mode.
|
||||||
|
You will be shared a context that can contain information from files user has uploaded to get answers from. You will have to generate answers upon that.
|
||||||
|
|
||||||
|
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
|
||||||
|
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
||||||
|
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
||||||
|
|
||||||
|
<context>
|
||||||
|
{context}
|
||||||
|
</context>
|
||||||
|
`;
|
||||||
|
|
@ -0,0 +1,42 @@
|
||||||
|
export const youtubeSearchRetrieverPrompt = `
|
||||||
|
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
|
||||||
|
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
1. Follow up question: How does an A.C work?
|
||||||
|
Rephrased: A.C working
|
||||||
|
|
||||||
|
2. Follow up question: Linear algebra explanation video
|
||||||
|
Rephrased: What is linear algebra?
|
||||||
|
|
||||||
|
3. Follow up question: What is theory of relativity?
|
||||||
|
Rephrased: What is theory of relativity?
|
||||||
|
|
||||||
|
Conversation:
|
||||||
|
{chat_history}
|
||||||
|
|
||||||
|
Follow up question: {query}
|
||||||
|
Rephrased question:
|
||||||
|
`;
|
||||||
|
|
||||||
|
export const youtubeSearchResponsePrompt = `
|
||||||
|
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Youtube', this means you will be searching for videos on the web using Youtube and providing information based on the video's transcript.
|
||||||
|
|
||||||
|
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
|
||||||
|
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
|
||||||
|
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
|
||||||
|
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
|
||||||
|
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
|
||||||
|
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
||||||
|
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
||||||
|
|
||||||
|
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Youtube and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
|
||||||
|
talk about the context in your response.
|
||||||
|
|
||||||
|
<context>
|
||||||
|
{context}
|
||||||
|
</context>
|
||||||
|
|
||||||
|
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
|
||||||
|
Anything between the \`context\` is retrieved from Youtube and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
|
||||||
|
`;
|
||||||
|
|
@ -1,5 +1,5 @@
|
||||||
import express from 'express';
|
import express from 'express';
|
||||||
import handleImageSearch from '../agents/imageSearchAgent';
|
import handleImageSearch from '../chains/imageSearchAgent';
|
||||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||||
import { getAvailableChatModelProviders } from '../lib/providers';
|
import { getAvailableChatModelProviders } from '../lib/providers';
|
||||||
import { HumanMessage, AIMessage } from '@langchain/core/messages';
|
import { HumanMessage, AIMessage } from '@langchain/core/messages';
|
||||||
|
|
|
||||||
|
|
@ -1,7 +1,7 @@
|
||||||
import express from 'express';
|
import express from 'express';
|
||||||
import logger from '../utils/logger';
|
import logger from '../utils/logger';
|
||||||
import { BaseChatModel } from 'langchain/chat_models/base';
|
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||||
import { Embeddings } from 'langchain/embeddings/base';
|
import type { Embeddings } from '@langchain/core/embeddings';
|
||||||
import { ChatOpenAI } from '@langchain/openai';
|
import { ChatOpenAI } from '@langchain/openai';
|
||||||
import {
|
import {
|
||||||
getAvailableChatModelProviders,
|
getAvailableChatModelProviders,
|
||||||
|
|
@ -9,6 +9,7 @@ import {
|
||||||
} from '../lib/providers';
|
} from '../lib/providers';
|
||||||
import { searchHandlers } from '../websocket/messageHandler';
|
import { searchHandlers } from '../websocket/messageHandler';
|
||||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
||||||
|
import { MetaSearchAgentType } from '../search/metaSearchAgent';
|
||||||
|
|
||||||
const router = express.Router();
|
const router = express.Router();
|
||||||
|
|
||||||
|
|
@ -115,18 +116,19 @@ router.post('/', async (req, res) => {
|
||||||
return res.status(400).json({ message: 'Invalid model selected' });
|
return res.status(400).json({ message: 'Invalid model selected' });
|
||||||
}
|
}
|
||||||
|
|
||||||
const searchHandler = searchHandlers[body.focusMode];
|
const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
|
||||||
|
|
||||||
if (!searchHandler) {
|
if (!searchHandler) {
|
||||||
return res.status(400).json({ message: 'Invalid focus mode' });
|
return res.status(400).json({ message: 'Invalid focus mode' });
|
||||||
}
|
}
|
||||||
|
|
||||||
const emitter = searchHandler(
|
const emitter = await searchHandler.searchAndAnswer(
|
||||||
body.query,
|
body.query,
|
||||||
history,
|
history,
|
||||||
llm,
|
llm,
|
||||||
embeddings,
|
embeddings,
|
||||||
body.optimizationMode,
|
body.optimizationMode,
|
||||||
|
[],
|
||||||
);
|
);
|
||||||
|
|
||||||
let message = '';
|
let message = '';
|
||||||
|
|
|
||||||
|
|
@ -1,5 +1,5 @@
|
||||||
import express from 'express';
|
import express from 'express';
|
||||||
import generateSuggestions from '../agents/suggestionGeneratorAgent';
|
import generateSuggestions from '../chains/suggestionGeneratorAgent';
|
||||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||||
import { getAvailableChatModelProviders } from '../lib/providers';
|
import { getAvailableChatModelProviders } from '../lib/providers';
|
||||||
import { HumanMessage, AIMessage } from '@langchain/core/messages';
|
import { HumanMessage, AIMessage } from '@langchain/core/messages';
|
||||||
|
|
|
||||||
|
|
@ -3,7 +3,7 @@ import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||||
import { getAvailableChatModelProviders } from '../lib/providers';
|
import { getAvailableChatModelProviders } from '../lib/providers';
|
||||||
import { HumanMessage, AIMessage } from '@langchain/core/messages';
|
import { HumanMessage, AIMessage } from '@langchain/core/messages';
|
||||||
import logger from '../utils/logger';
|
import logger from '../utils/logger';
|
||||||
import handleVideoSearch from '../agents/videoSearchAgent';
|
import handleVideoSearch from '../chains/videoSearchAgent';
|
||||||
import { ChatOpenAI } from '@langchain/openai';
|
import { ChatOpenAI } from '@langchain/openai';
|
||||||
|
|
||||||
const router = express.Router();
|
const router = express.Router();
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,486 @@
|
||||||
|
import { ChatOpenAI } from '@langchain/openai';
|
||||||
|
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||||
|
import type { Embeddings } from '@langchain/core/embeddings';
|
||||||
|
import {
|
||||||
|
ChatPromptTemplate,
|
||||||
|
MessagesPlaceholder,
|
||||||
|
PromptTemplate,
|
||||||
|
} from '@langchain/core/prompts';
|
||||||
|
import {
|
||||||
|
RunnableLambda,
|
||||||
|
RunnableMap,
|
||||||
|
RunnableSequence,
|
||||||
|
} from '@langchain/core/runnables';
|
||||||
|
import { BaseMessage } from '@langchain/core/messages';
|
||||||
|
import { StringOutputParser } from '@langchain/core/output_parsers';
|
||||||
|
import LineListOutputParser from '../lib/outputParsers/listLineOutputParser';
|
||||||
|
import LineOutputParser from '../lib/outputParsers/lineOutputParser';
|
||||||
|
import { getDocumentsFromLinks } from '../utils/documents';
|
||||||
|
import { Document } from 'langchain/document';
|
||||||
|
import { searchSearxng } from '../lib/searxng';
|
||||||
|
import path from 'path';
|
||||||
|
import fs from 'fs';
|
||||||
|
import computeSimilarity from '../utils/computeSimilarity';
|
||||||
|
import formatChatHistoryAsString from '../utils/formatHistory';
|
||||||
|
import eventEmitter from 'events';
|
||||||
|
import { StreamEvent } from '@langchain/core/tracers/log_stream';
|
||||||
|
import { IterableReadableStream } from '@langchain/core/utils/stream';
|
||||||
|
|
||||||
|
export interface MetaSearchAgentType {
|
||||||
|
searchAndAnswer: (
|
||||||
|
message: string,
|
||||||
|
history: BaseMessage[],
|
||||||
|
llm: BaseChatModel,
|
||||||
|
embeddings: Embeddings,
|
||||||
|
optimizationMode: 'speed' | 'balanced' | 'quality',
|
||||||
|
fileIds: string[],
|
||||||
|
) => Promise<eventEmitter>;
|
||||||
|
}
|
||||||
|
|
||||||
|
interface Config {
|
||||||
|
searchWeb: boolean;
|
||||||
|
rerank: boolean;
|
||||||
|
summarizer: boolean;
|
||||||
|
rerankThreshold: number;
|
||||||
|
queryGeneratorPrompt: string;
|
||||||
|
responsePrompt: string;
|
||||||
|
activeEngines: string[];
|
||||||
|
}
|
||||||
|
|
||||||
|
type BasicChainInput = {
|
||||||
|
chat_history: BaseMessage[];
|
||||||
|
query: string;
|
||||||
|
};
|
||||||
|
|
||||||
|
class MetaSearchAgent implements MetaSearchAgentType {
|
||||||
|
private config: Config;
|
||||||
|
private strParser = new StringOutputParser();
|
||||||
|
|
||||||
|
constructor(config: Config) {
|
||||||
|
this.config = config;
|
||||||
|
}
|
||||||
|
|
||||||
|
private async createSearchRetrieverChain(llm: BaseChatModel) {
|
||||||
|
(llm as unknown as ChatOpenAI).temperature = 0;
|
||||||
|
|
||||||
|
return RunnableSequence.from([
|
||||||
|
PromptTemplate.fromTemplate(this.config.queryGeneratorPrompt),
|
||||||
|
llm,
|
||||||
|
this.strParser,
|
||||||
|
RunnableLambda.from(async (input: string) => {
|
||||||
|
const linksOutputParser = new LineListOutputParser({
|
||||||
|
key: 'links',
|
||||||
|
});
|
||||||
|
|
||||||
|
const questionOutputParser = new LineOutputParser({
|
||||||
|
key: 'question',
|
||||||
|
});
|
||||||
|
|
||||||
|
const links = await linksOutputParser.parse(input);
|
||||||
|
let question = this.config.summarizer
|
||||||
|
? await questionOutputParser.parse(input)
|
||||||
|
: input;
|
||||||
|
|
||||||
|
if (question === 'not_needed') {
|
||||||
|
return { query: '', docs: [] };
|
||||||
|
}
|
||||||
|
|
||||||
|
if (links.length > 0) {
|
||||||
|
if (question.length === 0) {
|
||||||
|
question = 'summarize';
|
||||||
|
}
|
||||||
|
|
||||||
|
let docs = [];
|
||||||
|
|
||||||
|
const linkDocs = await getDocumentsFromLinks({ links });
|
||||||
|
|
||||||
|
const docGroups: Document[] = [];
|
||||||
|
|
||||||
|
linkDocs.map((doc) => {
|
||||||
|
const URLDocExists = docGroups.find(
|
||||||
|
(d) =>
|
||||||
|
d.metadata.url === doc.metadata.url &&
|
||||||
|
d.metadata.totalDocs < 10,
|
||||||
|
);
|
||||||
|
|
||||||
|
if (!URLDocExists) {
|
||||||
|
docGroups.push({
|
||||||
|
...doc,
|
||||||
|
metadata: {
|
||||||
|
...doc.metadata,
|
||||||
|
totalDocs: 1,
|
||||||
|
},
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
const docIndex = docGroups.findIndex(
|
||||||
|
(d) =>
|
||||||
|
d.metadata.url === doc.metadata.url &&
|
||||||
|
d.metadata.totalDocs < 10,
|
||||||
|
);
|
||||||
|
|
||||||
|
if (docIndex !== -1) {
|
||||||
|
docGroups[docIndex].pageContent =
|
||||||
|
docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
|
||||||
|
docGroups[docIndex].metadata.totalDocs += 1;
|
||||||
|
}
|
||||||
|
});
|
||||||
|
|
||||||
|
await Promise.all(
|
||||||
|
docGroups.map(async (doc) => {
|
||||||
|
const res = await llm.invoke(`
|
||||||
|
You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the
|
||||||
|
text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
|
||||||
|
If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
|
||||||
|
|
||||||
|
- **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
|
||||||
|
- **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
|
||||||
|
- **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
|
||||||
|
|
||||||
|
The text will be shared inside the \`text\` XML tag, and the query inside the \`query\` XML tag.
|
||||||
|
|
||||||
|
<example>
|
||||||
|
1. \`<text>
|
||||||
|
Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers.
|
||||||
|
It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications
|
||||||
|
by using containers.
|
||||||
|
</text>
|
||||||
|
|
||||||
|
<query>
|
||||||
|
What is Docker and how does it work?
|
||||||
|
</query>
|
||||||
|
|
||||||
|
Response:
|
||||||
|
Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application
|
||||||
|
deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in
|
||||||
|
any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
|
||||||
|
\`
|
||||||
|
2. \`<text>
|
||||||
|
The theory of relativity, or simply relativity, encompasses two interrelated theories of Albert Einstein: special relativity and general
|
||||||
|
relativity. However, the word "relativity" is sometimes used in reference to Galilean invariance. The term "theory of relativity" was based
|
||||||
|
on the expression "relative theory" used by Max Planck in 1906. The theory of relativity usually encompasses two interrelated theories by
|
||||||
|
Albert Einstein: special relativity and general relativity. Special relativity applies to all physical phenomena in the absence of gravity.
|
||||||
|
General relativity explains the law of gravitation and its relation to other forces of nature. It applies to the cosmological and astrophysical
|
||||||
|
realm, including astronomy.
|
||||||
|
</text>
|
||||||
|
|
||||||
|
<query>
|
||||||
|
summarize
|
||||||
|
</query>
|
||||||
|
|
||||||
|
Response:
|
||||||
|
The theory of relativity, developed by Albert Einstein, encompasses two main theories: special relativity and general relativity. Special
|
||||||
|
relativity applies to all physical phenomena in the absence of gravity, while general relativity explains the law of gravitation and its
|
||||||
|
relation to other forces of nature. The theory of relativity is based on the concept of "relative theory," as introduced by Max Planck in
|
||||||
|
1906. It is a fundamental theory in physics that has revolutionized our understanding of the universe.
|
||||||
|
\`
|
||||||
|
</example>
|
||||||
|
|
||||||
|
Everything below is the actual data you will be working with. Good luck!
|
||||||
|
|
||||||
|
<query>
|
||||||
|
${question}
|
||||||
|
</query>
|
||||||
|
|
||||||
|
<text>
|
||||||
|
${doc.pageContent}
|
||||||
|
</text>
|
||||||
|
|
||||||
|
Make sure to answer the query in the summary.
|
||||||
|
`);
|
||||||
|
|
||||||
|
const document = new Document({
|
||||||
|
pageContent: res.content as string,
|
||||||
|
metadata: {
|
||||||
|
title: doc.metadata.title,
|
||||||
|
url: doc.metadata.url,
|
||||||
|
},
|
||||||
|
});
|
||||||
|
|
||||||
|
docs.push(document);
|
||||||
|
}),
|
||||||
|
);
|
||||||
|
|
||||||
|
return { query: question, docs: docs };
|
||||||
|
} else {
|
||||||
|
const res = await searchSearxng(question, {
|
||||||
|
language: 'en',
|
||||||
|
engines: this.config.activeEngines,
|
||||||
|
});
|
||||||
|
|
||||||
|
const documents = res.results.map(
|
||||||
|
(result) =>
|
||||||
|
new Document({
|
||||||
|
pageContent: result.content,
|
||||||
|
metadata: {
|
||||||
|
title: result.title,
|
||||||
|
url: result.url,
|
||||||
|
...(result.img_src && { img_src: result.img_src }),
|
||||||
|
},
|
||||||
|
}),
|
||||||
|
);
|
||||||
|
|
||||||
|
return { query: question, docs: documents };
|
||||||
|
}
|
||||||
|
}),
|
||||||
|
]);
|
||||||
|
}
|
||||||
|
|
||||||
|
private async createAnsweringChain(
|
||||||
|
llm: BaseChatModel,
|
||||||
|
fileIds: string[],
|
||||||
|
embeddings: Embeddings,
|
||||||
|
optimizationMode: 'speed' | 'balanced' | 'quality',
|
||||||
|
) {
|
||||||
|
return RunnableSequence.from([
|
||||||
|
RunnableMap.from({
|
||||||
|
query: (input: BasicChainInput) => input.query,
|
||||||
|
chat_history: (input: BasicChainInput) => input.chat_history,
|
||||||
|
context: RunnableLambda.from(async (input: BasicChainInput) => {
|
||||||
|
const processedHistory = formatChatHistoryAsString(
|
||||||
|
input.chat_history,
|
||||||
|
);
|
||||||
|
|
||||||
|
let docs: Document[] | null = null;
|
||||||
|
let query = input.query;
|
||||||
|
|
||||||
|
if (this.config.searchWeb) {
|
||||||
|
const searchRetrieverChain =
|
||||||
|
await this.createSearchRetrieverChain(llm);
|
||||||
|
|
||||||
|
const searchRetrieverResult = await searchRetrieverChain.invoke({
|
||||||
|
chat_history: processedHistory,
|
||||||
|
query,
|
||||||
|
});
|
||||||
|
|
||||||
|
query = searchRetrieverResult.query;
|
||||||
|
docs = searchRetrieverResult.docs;
|
||||||
|
}
|
||||||
|
|
||||||
|
const sortedDocs = await this.rerankDocs(
|
||||||
|
query,
|
||||||
|
docs ?? [],
|
||||||
|
fileIds,
|
||||||
|
embeddings,
|
||||||
|
optimizationMode,
|
||||||
|
);
|
||||||
|
|
||||||
|
return sortedDocs;
|
||||||
|
})
|
||||||
|
.withConfig({
|
||||||
|
runName: 'FinalSourceRetriever',
|
||||||
|
})
|
||||||
|
.pipe(this.processDocs),
|
||||||
|
}),
|
||||||
|
ChatPromptTemplate.fromMessages([
|
||||||
|
['system', this.config.responsePrompt],
|
||||||
|
new MessagesPlaceholder('chat_history'),
|
||||||
|
['user', '{query}'],
|
||||||
|
]),
|
||||||
|
llm,
|
||||||
|
this.strParser,
|
||||||
|
]).withConfig({
|
||||||
|
runName: 'FinalResponseGenerator',
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
private async rerankDocs(
|
||||||
|
query: string,
|
||||||
|
docs: Document[],
|
||||||
|
fileIds: string[],
|
||||||
|
embeddings: Embeddings,
|
||||||
|
optimizationMode: 'speed' | 'balanced' | 'quality',
|
||||||
|
) {
|
||||||
|
if (docs.length === 0 && fileIds.length === 0) {
|
||||||
|
return docs;
|
||||||
|
}
|
||||||
|
|
||||||
|
const filesData = fileIds
|
||||||
|
.map((file) => {
|
||||||
|
const filePath = path.join(process.cwd(), 'uploads', file);
|
||||||
|
|
||||||
|
const contentPath = filePath + '-extracted.json';
|
||||||
|
const embeddingsPath = filePath + '-embeddings.json';
|
||||||
|
|
||||||
|
const content = JSON.parse(fs.readFileSync(contentPath, 'utf8'));
|
||||||
|
const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
|
||||||
|
|
||||||
|
const fileSimilaritySearchObject = content.contents.map(
|
||||||
|
(c: string, i) => {
|
||||||
|
return {
|
||||||
|
fileName: content.title,
|
||||||
|
content: c,
|
||||||
|
embeddings: embeddings.embeddings[i],
|
||||||
|
};
|
||||||
|
},
|
||||||
|
);
|
||||||
|
|
||||||
|
return fileSimilaritySearchObject;
|
||||||
|
})
|
||||||
|
.flat();
|
||||||
|
|
||||||
|
if (query.toLocaleLowerCase() === 'summarize') {
|
||||||
|
return docs.slice(0, 15);
|
||||||
|
}
|
||||||
|
|
||||||
|
const docsWithContent = docs.filter(
|
||||||
|
(doc) => doc.pageContent && doc.pageContent.length > 0,
|
||||||
|
);
|
||||||
|
|
||||||
|
if (optimizationMode === 'speed' || this.config.rerank === false) {
|
||||||
|
if (filesData.length > 0) {
|
||||||
|
const [queryEmbedding] = await Promise.all([
|
||||||
|
embeddings.embedQuery(query),
|
||||||
|
]);
|
||||||
|
|
||||||
|
const fileDocs = filesData.map((fileData) => {
|
||||||
|
return new Document({
|
||||||
|
pageContent: fileData.content,
|
||||||
|
metadata: {
|
||||||
|
title: fileData.fileName,
|
||||||
|
url: `File`,
|
||||||
|
},
|
||||||
|
});
|
||||||
|
});
|
||||||
|
|
||||||
|
const similarity = filesData.map((fileData, i) => {
|
||||||
|
const sim = computeSimilarity(queryEmbedding, fileData.embeddings);
|
||||||
|
|
||||||
|
return {
|
||||||
|
index: i,
|
||||||
|
similarity: sim,
|
||||||
|
};
|
||||||
|
});
|
||||||
|
|
||||||
|
let sortedDocs = similarity
|
||||||
|
.filter(
|
||||||
|
(sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3),
|
||||||
|
)
|
||||||
|
.sort((a, b) => b.similarity - a.similarity)
|
||||||
|
.slice(0, 15)
|
||||||
|
.map((sim) => fileDocs[sim.index]);
|
||||||
|
|
||||||
|
sortedDocs =
|
||||||
|
docsWithContent.length > 0 ? sortedDocs.slice(0, 8) : sortedDocs;
|
||||||
|
|
||||||
|
return [
|
||||||
|
...sortedDocs,
|
||||||
|
...docsWithContent.slice(0, 15 - sortedDocs.length),
|
||||||
|
];
|
||||||
|
} else {
|
||||||
|
return docsWithContent.slice(0, 15);
|
||||||
|
}
|
||||||
|
} else if (optimizationMode === 'balanced') {
|
||||||
|
const [docEmbeddings, queryEmbedding] = await Promise.all([
|
||||||
|
embeddings.embedDocuments(
|
||||||
|
docsWithContent.map((doc) => doc.pageContent),
|
||||||
|
),
|
||||||
|
embeddings.embedQuery(query),
|
||||||
|
]);
|
||||||
|
|
||||||
|
docsWithContent.push(
|
||||||
|
...filesData.map((fileData) => {
|
||||||
|
return new Document({
|
||||||
|
pageContent: fileData.content,
|
||||||
|
metadata: {
|
||||||
|
title: fileData.fileName,
|
||||||
|
url: `File`,
|
||||||
|
},
|
||||||
|
});
|
||||||
|
}),
|
||||||
|
);
|
||||||
|
|
||||||
|
docEmbeddings.push(...filesData.map((fileData) => fileData.embeddings));
|
||||||
|
|
||||||
|
const similarity = docEmbeddings.map((docEmbedding, i) => {
|
||||||
|
const sim = computeSimilarity(queryEmbedding, docEmbedding);
|
||||||
|
|
||||||
|
return {
|
||||||
|
index: i,
|
||||||
|
similarity: sim,
|
||||||
|
};
|
||||||
|
});
|
||||||
|
|
||||||
|
const sortedDocs = similarity
|
||||||
|
.filter((sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3))
|
||||||
|
.sort((a, b) => b.similarity - a.similarity)
|
||||||
|
.slice(0, 15)
|
||||||
|
.map((sim) => docsWithContent[sim.index]);
|
||||||
|
|
||||||
|
return sortedDocs;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
private processDocs(docs: Document[]) {
|
||||||
|
return docs
|
||||||
|
.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
|
||||||
|
.join('\n');
|
||||||
|
}
|
||||||
|
|
||||||
|
private async handleStream(
|
||||||
|
stream: IterableReadableStream<StreamEvent>,
|
||||||
|
emitter: eventEmitter,
|
||||||
|
) {
|
||||||
|
for await (const event of stream) {
|
||||||
|
if (
|
||||||
|
event.event === 'on_chain_end' &&
|
||||||
|
event.name === 'FinalSourceRetriever'
|
||||||
|
) {
|
||||||
|
``;
|
||||||
|
emitter.emit(
|
||||||
|
'data',
|
||||||
|
JSON.stringify({ type: 'sources', data: event.data.output }),
|
||||||
|
);
|
||||||
|
}
|
||||||
|
if (
|
||||||
|
event.event === 'on_chain_stream' &&
|
||||||
|
event.name === 'FinalResponseGenerator'
|
||||||
|
) {
|
||||||
|
emitter.emit(
|
||||||
|
'data',
|
||||||
|
JSON.stringify({ type: 'response', data: event.data.chunk }),
|
||||||
|
);
|
||||||
|
}
|
||||||
|
if (
|
||||||
|
event.event === 'on_chain_end' &&
|
||||||
|
event.name === 'FinalResponseGenerator'
|
||||||
|
) {
|
||||||
|
emitter.emit('end');
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
async searchAndAnswer(
|
||||||
|
message: string,
|
||||||
|
history: BaseMessage[],
|
||||||
|
llm: BaseChatModel,
|
||||||
|
embeddings: Embeddings,
|
||||||
|
optimizationMode: 'speed' | 'balanced' | 'quality',
|
||||||
|
fileIds: string[],
|
||||||
|
) {
|
||||||
|
const emitter = new eventEmitter();
|
||||||
|
|
||||||
|
const answeringChain = await this.createAnsweringChain(
|
||||||
|
llm,
|
||||||
|
fileIds,
|
||||||
|
embeddings,
|
||||||
|
optimizationMode,
|
||||||
|
);
|
||||||
|
|
||||||
|
const stream = answeringChain.streamEvents(
|
||||||
|
{
|
||||||
|
chat_history: history,
|
||||||
|
query: message,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
version: 'v1',
|
||||||
|
},
|
||||||
|
);
|
||||||
|
|
||||||
|
this.handleStream(stream, emitter);
|
||||||
|
|
||||||
|
return emitter;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
export default MetaSearchAgent;
|
||||||
|
|
@ -1,11 +1,5 @@
|
||||||
import { EventEmitter, WebSocket } from 'ws';
|
import { EventEmitter, WebSocket } from 'ws';
|
||||||
import { BaseMessage, AIMessage, HumanMessage } from '@langchain/core/messages';
|
import { BaseMessage, AIMessage, HumanMessage } from '@langchain/core/messages';
|
||||||
import handleWebSearch from '../agents/webSearchAgent';
|
|
||||||
import handleAcademicSearch from '../agents/academicSearchAgent';
|
|
||||||
import handleWritingAssistant from '../agents/writingAssistant';
|
|
||||||
import handleWolframAlphaSearch from '../agents/wolframAlphaSearchAgent';
|
|
||||||
import handleYoutubeSearch from '../agents/youtubeSearchAgent';
|
|
||||||
import handleRedditSearch from '../agents/redditSearchAgent';
|
|
||||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||||
import type { Embeddings } from '@langchain/core/embeddings';
|
import type { Embeddings } from '@langchain/core/embeddings';
|
||||||
import logger from '../utils/logger';
|
import logger from '../utils/logger';
|
||||||
|
|
@ -14,6 +8,10 @@ import { chats, messages as messagesSchema } from '../db/schema';
|
||||||
import { eq, asc, gt } from 'drizzle-orm';
|
import { eq, asc, gt } from 'drizzle-orm';
|
||||||
import crypto from 'crypto';
|
import crypto from 'crypto';
|
||||||
import { getFileDetails } from '../utils/files';
|
import { getFileDetails } from '../utils/files';
|
||||||
|
import MetaSearchAgent, {
|
||||||
|
MetaSearchAgentType,
|
||||||
|
} from '../search/metaSearchAgent';
|
||||||
|
import prompts from '../prompts';
|
||||||
|
|
||||||
type Message = {
|
type Message = {
|
||||||
messageId: string;
|
messageId: string;
|
||||||
|
|
@ -23,7 +21,7 @@ type Message = {
|
||||||
|
|
||||||
type WSMessage = {
|
type WSMessage = {
|
||||||
message: Message;
|
message: Message;
|
||||||
optimizationMode: string;
|
optimizationMode: 'speed' | 'balanced' | 'quality';
|
||||||
type: string;
|
type: string;
|
||||||
focusMode: string;
|
focusMode: string;
|
||||||
history: Array<[string, string]>;
|
history: Array<[string, string]>;
|
||||||
|
|
@ -31,12 +29,60 @@ type WSMessage = {
|
||||||
};
|
};
|
||||||
|
|
||||||
export const searchHandlers = {
|
export const searchHandlers = {
|
||||||
webSearch: handleWebSearch,
|
webSearch: new MetaSearchAgent({
|
||||||
academicSearch: handleAcademicSearch,
|
activeEngines: [],
|
||||||
writingAssistant: handleWritingAssistant,
|
queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
|
||||||
wolframAlphaSearch: handleWolframAlphaSearch,
|
responsePrompt: prompts.webSearchResponsePrompt,
|
||||||
youtubeSearch: handleYoutubeSearch,
|
rerank: true,
|
||||||
redditSearch: handleRedditSearch,
|
rerankThreshold: 0.3,
|
||||||
|
searchWeb: true,
|
||||||
|
summarizer: true,
|
||||||
|
}),
|
||||||
|
academicSearch: new MetaSearchAgent({
|
||||||
|
activeEngines: ['arxiv', 'google scholar', 'pubmed'],
|
||||||
|
queryGeneratorPrompt: prompts.academicSearchRetrieverPrompt,
|
||||||
|
responsePrompt: prompts.academicSearchResponsePrompt,
|
||||||
|
rerank: true,
|
||||||
|
rerankThreshold: 0,
|
||||||
|
searchWeb: true,
|
||||||
|
summarizer: false,
|
||||||
|
}),
|
||||||
|
writingAssistant: new MetaSearchAgent({
|
||||||
|
activeEngines: [],
|
||||||
|
queryGeneratorPrompt: '',
|
||||||
|
responsePrompt: prompts.writingAssistantPrompt,
|
||||||
|
rerank: true,
|
||||||
|
rerankThreshold: 0,
|
||||||
|
searchWeb: false,
|
||||||
|
summarizer: false,
|
||||||
|
}),
|
||||||
|
wolframAlphaSearch: new MetaSearchAgent({
|
||||||
|
activeEngines: ['wolframalpha'],
|
||||||
|
queryGeneratorPrompt: prompts.wolframAlphaSearchRetrieverPrompt,
|
||||||
|
responsePrompt: prompts.wolframAlphaSearchResponsePrompt,
|
||||||
|
rerank: false,
|
||||||
|
rerankThreshold: 0,
|
||||||
|
searchWeb: true,
|
||||||
|
summarizer: false,
|
||||||
|
}),
|
||||||
|
youtubeSearch: new MetaSearchAgent({
|
||||||
|
activeEngines: ['youtube'],
|
||||||
|
queryGeneratorPrompt: prompts.youtubeSearchRetrieverPrompt,
|
||||||
|
responsePrompt: prompts.youtubeSearchResponsePrompt,
|
||||||
|
rerank: true,
|
||||||
|
rerankThreshold: 0.3,
|
||||||
|
searchWeb: true,
|
||||||
|
summarizer: false,
|
||||||
|
}),
|
||||||
|
redditSearch: new MetaSearchAgent({
|
||||||
|
activeEngines: ['reddit'],
|
||||||
|
queryGeneratorPrompt: prompts.redditSearchRetrieverPrompt,
|
||||||
|
responsePrompt: prompts.redditSearchResponsePrompt,
|
||||||
|
rerank: true,
|
||||||
|
rerankThreshold: 0.3,
|
||||||
|
searchWeb: true,
|
||||||
|
summarizer: false,
|
||||||
|
}),
|
||||||
};
|
};
|
||||||
|
|
||||||
const handleEmitterEvents = (
|
const handleEmitterEvents = (
|
||||||
|
|
@ -139,59 +185,64 @@ export const handleMessage = async (
|
||||||
});
|
});
|
||||||
|
|
||||||
if (parsedWSMessage.type === 'message') {
|
if (parsedWSMessage.type === 'message') {
|
||||||
const handler = searchHandlers[parsedWSMessage.focusMode];
|
const handler: MetaSearchAgentType =
|
||||||
|
searchHandlers[parsedWSMessage.focusMode];
|
||||||
|
|
||||||
if (handler) {
|
if (handler) {
|
||||||
const emitter = handler(
|
try {
|
||||||
parsedMessage.content,
|
const emitter = await handler.searchAndAnswer(
|
||||||
history,
|
parsedMessage.content,
|
||||||
llm,
|
history,
|
||||||
embeddings,
|
llm,
|
||||||
parsedWSMessage.optimizationMode,
|
embeddings,
|
||||||
parsedWSMessage.files,
|
parsedWSMessage.optimizationMode,
|
||||||
);
|
parsedWSMessage.files,
|
||||||
|
);
|
||||||
|
|
||||||
handleEmitterEvents(emitter, ws, aiMessageId, parsedMessage.chatId);
|
handleEmitterEvents(emitter, ws, aiMessageId, parsedMessage.chatId);
|
||||||
|
|
||||||
const chat = await db.query.chats.findFirst({
|
const chat = await db.query.chats.findFirst({
|
||||||
where: eq(chats.id, parsedMessage.chatId),
|
where: eq(chats.id, parsedMessage.chatId),
|
||||||
});
|
});
|
||||||
|
|
||||||
if (!chat) {
|
if (!chat) {
|
||||||
await db
|
await db
|
||||||
.insert(chats)
|
.insert(chats)
|
||||||
.values({
|
.values({
|
||||||
id: parsedMessage.chatId,
|
id: parsedMessage.chatId,
|
||||||
title: parsedMessage.content,
|
title: parsedMessage.content,
|
||||||
createdAt: new Date().toString(),
|
createdAt: new Date().toString(),
|
||||||
focusMode: parsedWSMessage.focusMode,
|
focusMode: parsedWSMessage.focusMode,
|
||||||
files: parsedWSMessage.files.map(getFileDetails),
|
files: parsedWSMessage.files.map(getFileDetails),
|
||||||
})
|
})
|
||||||
.execute();
|
.execute();
|
||||||
}
|
}
|
||||||
|
|
||||||
const messageExists = await db.query.messages.findFirst({
|
const messageExists = await db.query.messages.findFirst({
|
||||||
where: eq(messagesSchema.messageId, humanMessageId),
|
where: eq(messagesSchema.messageId, humanMessageId),
|
||||||
});
|
});
|
||||||
|
|
||||||
if (!messageExists) {
|
if (!messageExists) {
|
||||||
await db
|
await db
|
||||||
.insert(messagesSchema)
|
.insert(messagesSchema)
|
||||||
.values({
|
.values({
|
||||||
content: parsedMessage.content,
|
content: parsedMessage.content,
|
||||||
chatId: parsedMessage.chatId,
|
chatId: parsedMessage.chatId,
|
||||||
messageId: humanMessageId,
|
messageId: humanMessageId,
|
||||||
role: 'user',
|
role: 'user',
|
||||||
metadata: JSON.stringify({
|
metadata: JSON.stringify({
|
||||||
createdAt: new Date(),
|
createdAt: new Date(),
|
||||||
}),
|
}),
|
||||||
})
|
})
|
||||||
.execute();
|
.execute();
|
||||||
} else {
|
} else {
|
||||||
await db
|
await db
|
||||||
.delete(messagesSchema)
|
.delete(messagesSchema)
|
||||||
.where(gt(messagesSchema.id, messageExists.id))
|
.where(gt(messagesSchema.id, messageExists.id))
|
||||||
.execute();
|
.execute();
|
||||||
|
}
|
||||||
|
} catch (err) {
|
||||||
|
console.log(err);
|
||||||
}
|
}
|
||||||
} else {
|
} else {
|
||||||
ws.send(
|
ws.send(
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue