prettier
This commit is contained in:
Jin Yucong
parent
5b1aaee605
commit
3b737a078a
63 changed files with 1132 additions and 1853 deletions
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@ -1,24 +1,16 @@
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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 { BaseMessage } from "@langchain/core/messages";
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import { PromptTemplate, ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";
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import { RunnableSequence, RunnableMap, RunnableLambda } 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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const basicSearchRetrieverPrompt = `
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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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@ -65,34 +57,16 @@ const basicWebSearchResponsePrompt = `
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const strParser = new StringOutputParser();
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const handleStream = async (
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stream: AsyncGenerator<StreamEvent, unknown, unknown>,
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emitter: eventEmitter,
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) => {
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const handleStream = async (stream: AsyncGenerator<StreamEvent, unknown, unknown>, emitter: eventEmitter) => {
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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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if (event.event === "on_chain_end" && event.name === "FinalSourceRetriever") {
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emitter.emit("data", JSON.stringify({ type: "sources", data: event.data.output }));
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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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if (event.event === "on_chain_stream" && event.name === "FinalResponseGenerator") {
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emitter.emit("data", JSON.stringify({ type: "response", data: event.data.chunk }));
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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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if (event.event === "on_chain_end" && event.name === "FinalResponseGenerator") {
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emitter.emit("end");
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}
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}
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};
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@ -108,16 +82,16 @@ const createBasicWebSearchRetrieverChain = (llm: BaseChatModel) => {
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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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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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language: "en",
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});
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const documents = res.results.map(
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(result) =>
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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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@ -133,35 +107,22 @@ const createBasicWebSearchRetrieverChain = (llm: BaseChatModel) => {
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]);
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};
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const createBasicWebSearchAnsweringChain = (
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llm: BaseChatModel,
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embeddings: Embeddings,
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) => {
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const createBasicWebSearchAnsweringChain = (llm: BaseChatModel, embeddings: Embeddings) => {
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const basicWebSearchRetrieverChain = createBasicWebSearchRetrieverChain(llm);
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const processDocs = async (docs: Document[]) => {
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return docs
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.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
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.join('\n');
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return docs.map((_, index) => `${index + 1}. ${docs[index].pageContent}`).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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const rerankDocs = async ({ query, docs }: { query: string; docs: Document[] }) => {
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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 docsWithContent = docs.filter(doc => doc.pageContent && doc.pageContent.length > 0);
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const [docEmbeddings, queryEmbedding] = await Promise.all([
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embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
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embeddings.embedDocuments(docsWithContent.map(doc => doc.pageContent)),
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embeddings.embedQuery(query),
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]);
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@ -176,9 +137,9 @@ const createBasicWebSearchAnsweringChain = (
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const sortedDocs = similarity
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.sort((a, b) => b.similarity - a.similarity)
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.filter((sim) => sim.similarity > 0.5)
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.filter(sim => sim.similarity > 0.5)
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.slice(0, 15)
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.map((sim) => docsWithContent[sim.index]);
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.map(sim => docsWithContent[sim.index]);
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return sortedDocs;
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};
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@ -188,43 +149,35 @@ const createBasicWebSearchAnsweringChain = (
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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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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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basicWebSearchRetrieverChain
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.pipe(rerankDocs)
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.withConfig({
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runName: 'FinalSourceRetriever',
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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', basicWebSearchResponsePrompt],
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new MessagesPlaceholder('chat_history'),
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['user', '{query}'],
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["system", basicWebSearchResponsePrompt],
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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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runName: "FinalResponseGenerator",
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});
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};
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const basicWebSearch = (
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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 basicWebSearch = (query: string, history: BaseMessage[], llm: BaseChatModel, embeddings: Embeddings) => {
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const emitter = new eventEmitter();
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try {
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const basicWebSearchAnsweringChain = createBasicWebSearchAnsweringChain(
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llm,
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embeddings,
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);
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const basicWebSearchAnsweringChain = createBasicWebSearchAnsweringChain(llm, embeddings);
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const stream = basicWebSearchAnsweringChain.streamEvents(
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{
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@ -232,28 +185,20 @@ const basicWebSearch = (
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query: query,
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},
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{
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version: 'v1',
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version: "v1",
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},
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);
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handleStream(stream, emitter);
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} catch (err) {
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emitter.emit(
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'error',
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JSON.stringify({ data: 'An error has occurred please try again later' }),
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);
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emitter.emit("error", JSON.stringify({ data: "An error has occurred please try again later" }));
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logger.error(`Error in websearch: ${err}`);
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}
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return emitter;
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};
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const handleWebSearch = (
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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 handleWebSearch = (message: string, history: BaseMessage[], llm: BaseChatModel, embeddings: Embeddings) => {
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const emitter = basicWebSearch(message, history, llm, embeddings);
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return emitter;
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};
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