feat(config): Use toml instead of env
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ItzCrazyKns
parent
dd1ce4e324
commit
c6a5790d33
26 changed files with 799 additions and 596 deletions
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@ -9,24 +9,16 @@ import {
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RunnableMap,
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RunnableLambda,
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} from '@langchain/core/runnables';
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import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
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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 '../core/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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const llm = new ChatOpenAI({
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modelName: process.env.MODEL_NAME,
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temperature: 0.7,
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});
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const embeddings = new OpenAIEmbeddings({
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modelName: 'text-embedding-3-large',
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});
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const basicRedditSearchRetrieverPrompt = `
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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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@ -104,118 +96,135 @@ const handleStream = async (
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}
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};
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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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const docEmbeddings = await embeddings.embedDocuments(
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docsWithContent.map((doc) => doc.pageContent),
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);
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const queryEmbedding = await embeddings.embedQuery(query);
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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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.filter((sim) => sim.similarity > 0.3)
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.map((sim) => docsWithContent[sim.index]);
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return sortedDocs;
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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 basicRedditSearchRetrieverChain = RunnableSequence.from([
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PromptTemplate.fromTemplate(basicRedditSearchRetrieverPrompt),
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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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const createBasicRedditSearchRetrieverChain = (llm: BaseChatModel) => {
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return RunnableSequence.from([
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PromptTemplate.fromTemplate(basicRedditSearchRetrieverPrompt),
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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: ['reddit'],
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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 ? result.content : result.title,
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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 createBasicRedditSearchAnsweringChain = (
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llm: BaseChatModel,
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embeddings: Embeddings,
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) => {
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const basicRedditSearchRetrieverChain =
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createBasicRedditSearchRetrieverChain(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 res = await searchSearxng(input, {
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language: 'en',
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engines: ['reddit'],
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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 ? result.content : result.title,
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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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const docsWithContent = docs.filter(
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(doc) => doc.pageContent && doc.pageContent.length > 0,
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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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const docEmbeddings = await embeddings.embedDocuments(
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docsWithContent.map((doc) => doc.pageContent),
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);
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const basicRedditSearchAnsweringChain = 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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basicRedditSearchRetrieverChain
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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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const queryEmbedding = await embeddings.embedQuery(query);
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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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.filter((sim) => sim.similarity > 0.3)
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.map((sim) => docsWithContent[sim.index]);
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return sortedDocs;
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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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basicRedditSearchRetrieverChain
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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', basicRedditSearchResponsePrompt],
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new MessagesPlaceholder('chat_history'),
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['user', '{query}'],
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]),
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}),
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ChatPromptTemplate.fromMessages([
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['system', basicRedditSearchResponsePrompt],
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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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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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const basicRedditSearch = (query: string, history: BaseMessage[]) => {
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const basicRedditSearch = (
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query: string,
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history: BaseMessage[],
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llm: BaseChatModel,
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embeddings: Embeddings,
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) => {
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const emitter = new eventEmitter();
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try {
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const basicRedditSearchAnsweringChain =
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createBasicRedditSearchAnsweringChain(llm, embeddings);
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const stream = basicRedditSearchAnsweringChain.streamEvents(
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{
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chat_history: history,
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@ -238,8 +247,13 @@ const basicRedditSearch = (query: string, history: BaseMessage[]) => {
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return emitter;
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};
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const handleRedditSearch = (message: string, history: BaseMessage[]) => {
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const emitter = basicRedditSearch(message, history);
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const handleRedditSearch = (
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message: string,
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history: BaseMessage[],
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llm: BaseChatModel,
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embeddings: Embeddings,
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) => {
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const emitter = basicRedditSearch(message, history, llm, embeddings);
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return emitter;
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};
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