sk/Perplexica
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

feat(app): Introduce quality mode. Improve functionality of balanced mode using readability to get page content and pull relevant excerpts

Browse source 
feat(UI): Show progress during inferrence
feat(security): Don't show API keys in the UI any more
feat(models): Support Claude 4 Anthropic models
This commit is contained in:
Willie Zutz 2025-05-23 18:03:35 -06:00
parent 288120dc1d
commit c47a630372
17 changed files with 2142 additions and 818 deletions
Download patch file Download diff file Expand all files Collapse all files

20
src/app/api/chat/route.ts
View file

@ -18,10 +18,7 @@ import { ChatOpenAI } from '@langchain/openai';
import crypto from 'crypto';
import { and, eq, gt } from 'drizzle-orm';
import { EventEmitter } from 'stream';
import {
registerCancelToken,
cleanupCancelToken,
} from '@/lib/cancel-tokens';
import { registerCancelToken, cleanupCancelToken } from '@/lib/cancel-tokens';
export const runtime = 'nodejs';
export const dynamic = 'force-dynamic';
@ -115,6 +112,21 @@ const handleEmitterEvents = async (
modelName: '',
};
stream.on('progress', (data) => {
const parsedData = JSON.parse(data);
if (parsedData.type === 'progress') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'progress',
data: parsedData.data,
messageId: aiMessageId,
}) + '\n',
),
);
}
});
stream.on('stats', (data) => {
const parsedData = JSON.parse(data);
if (parsedData.type === 'modelStats') {

34
src/app/api/config/route.ts
View file

@ -53,7 +53,7 @@ export const GET = async (req: Request) => {
// Helper function to obfuscate API keys
const protectApiKey = (key: string | null | undefined) => {
return key ? "protected" : key;
return key ? 'protected' : key;
};
// Obfuscate all API keys in the response
@ -85,39 +85,57 @@ export const POST = async (req: Request) => {
try {
const config = await req.json();
const getUpdatedProtectedValue = (newValue: string, currentConfig: string) => {
const getUpdatedProtectedValue = (
newValue: string,
currentConfig: string,
) => {
if (newValue === 'protected') {
return currentConfig;
}
return newValue;
}
};
const updatedConfig = {
MODELS: {
OPENAI: {
API_KEY: getUpdatedProtectedValue(config.openaiApiKey, getOpenaiApiKey()),
API_KEY: getUpdatedProtectedValue(
config.openaiApiKey,
getOpenaiApiKey(),
),
},
GROQ: {
API_KEY: getUpdatedProtectedValue(config.groqApiKey, getGroqApiKey()),
},
ANTHROPIC: {
API_KEY: getUpdatedProtectedValue(config.anthropicApiKey, getAnthropicApiKey()),
API_KEY: getUpdatedProtectedValue(
config.anthropicApiKey,
getAnthropicApiKey(),
),
},
GEMINI: {
API_KEY: getUpdatedProtectedValue(config.geminiApiKey, getGeminiApiKey()),
API_KEY: getUpdatedProtectedValue(
config.geminiApiKey,
getGeminiApiKey(),
),
},
OLLAMA: {
API_URL: config.ollamaApiUrl,
},
DEEPSEEK: {
API_KEY: getUpdatedProtectedValue(config.deepseekApiKey, getDeepseekApiKey()),
API_KEY: getUpdatedProtectedValue(
config.deepseekApiKey,
getDeepseekApiKey(),
),
},
LM_STUDIO: {
API_URL: config.lmStudioApiUrl,
},
CUSTOM_OPENAI: {
API_URL: config.customOpenaiApiUrl,
API_KEY: getUpdatedProtectedValue(config.customOpenaiApiKey, getCustomOpenaiApiKey()),
API_KEY: getUpdatedProtectedValue(
config.customOpenaiApiKey,
getCustomOpenaiApiKey(),
),
MODEL_NAME: config.customOpenaiModelName,
},
},

18
src/app/settings/page.tsx
View file

@ -1,6 +1,11 @@
'use client';
import { Settings as SettingsIcon, ArrowLeft, Loader2, Info } from 'lucide-react';
import {
Settings as SettingsIcon,
ArrowLeft,
Loader2,
Info,
} from 'lucide-react';
import { useEffect, useState } from 'react';
import { cn } from '@/lib/utils';
import { Switch } from '@headlessui/react';
@ -128,7 +133,10 @@ const SettingsSection = ({
<h2 className="text-black/90 dark:text-white/90 font-medium">{title}</h2>
{tooltip && (
<div className="relative group">
<Info size={16} className="text-black/70 dark:text-white/70 cursor-help" />
<Info
size={16}
className="text-black/70 dark:text-white/70 cursor-help"
/>
<div className="absolute left-1/2 -translate-x-1/2 bottom-full mb-2 px-3 py-2 bg-black/90 dark:bg-white/90 text-white dark:text-black text-xs rounded-lg opacity-0 group-hover:opacity-100 whitespace-nowrap transition-opacity">
{tooltip}
</div>
@ -238,7 +246,7 @@ const Page = () => {
fetchConfig();
}, []);
const saveConfig = async (key: string, value: any) => {
const saveConfig = async (key: string, value: any) => {
setSavingStates((prev) => ({ ...prev, [key]: true }));
try {
@ -798,8 +806,8 @@ const Page = () => {
)}
</SettingsSection>
<SettingsSection
title="API Keys"
<SettingsSection
title="API Keys"
tooltip="API Key values can be viewed in the config.toml file"
>
<div className="flex flex-col space-y-4">

8
src/components/Chat.tsx
View file

@ -21,6 +21,7 @@ const Chat = ({
focusMode,
setFocusMode,
handleEditMessage,
analysisProgress,
}: {
messages: Message[];
sendMessage: (
@ -43,6 +44,11 @@ const Chat = ({
focusMode: string;
setFocusMode: (mode: string) => void;
handleEditMessage: (messageId: string, content: string) => void;
analysisProgress: {
message: string;
current: number;
total: number;
} | null;
}) => {
const [isAtBottom, setIsAtBottom] = useState(true);
const [manuallyScrolledUp, setManuallyScrolledUp] = useState(false);
@ -220,7 +226,7 @@ const Chat = ({
</Fragment>
);
})}
{loading && <MessageBoxLoading />}
{loading && <MessageBoxLoading progress={analysisProgress} />}
<div className="fixed bottom-24 lg:bottom-10 z-40" style={inputStyle}>
{/* Scroll to bottom button - appears above the MessageInput when user has scrolled up */}
{manuallyScrolledUp && !isAtBottom && (

19
src/components/ChatWindow.tsx
View file

@ -29,6 +29,11 @@ export type Message = {
modelStats?: ModelStats;
searchQuery?: string;
searchUrl?: string;
progress?: {
message: string;
current: number;
total: number;
};
};
export interface File {
@ -270,6 +275,11 @@ const ChatWindow = ({ id }: { id?: string }) => {
const [loading, setLoading] = useState(false);
const [scrollTrigger, setScrollTrigger] = useState(0);
const [analysisProgress, setAnalysisProgress] = useState<{
message: string;
current: number;
total: number;
} | null>(null);
const [chatHistory, setChatHistory] = useState<[string, string][]>([]);
const [messages, setMessages] = useState<Message[]>([]);
@ -405,6 +415,11 @@ const ChatWindow = ({ id }: { id?: string }) => {
return;
}
if (data.type === 'progress') {
setAnalysisProgress(data.data);
return;
}
if (data.type === 'sources') {
sources = data.data;
if (!added) {
@ -460,6 +475,9 @@ const ChatWindow = ({ id }: { id?: string }) => {
}
if (data.type === 'messageEnd') {
// Clear analysis progress
setAnalysisProgress(null);
setChatHistory((prevHistory) => [
...prevHistory,
['human', message],
@ -656,6 +674,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
focusMode={focusMode}
setFocusMode={setFocusMode}
handleEditMessage={handleEditMessage}
analysisProgress={analysisProgress}
/>
</>
) : (

38
src/components/MessageBoxLoading.tsx
View file

@ -1,9 +1,37 @@
const MessageBoxLoading = () => {
interface MessageBoxLoadingProps {
progress: {
message: string;
current: number;
total: number;
} | null;
}
const MessageBoxLoading = ({ progress }: MessageBoxLoadingProps) => {
return (
<div className="flex flex-col space-y-2 w-full lg:w-9/12 bg-light-primary dark:bg-dark-primary animate-pulse rounded-lg py-3">
<div className="h-2 rounded-full w-full bg-light-secondary dark:bg-dark-secondary" />
<div className="h-2 rounded-full w-9/12 bg-light-secondary dark:bg-dark-secondary" />
<div className="h-2 rounded-full w-10/12 bg-light-secondary dark:bg-dark-secondary" />
<div className="flex flex-col space-y-4 w-full lg:w-9/12">
{progress && progress.current !== progress.total ? (
<div className="bg-light-primary dark:bg-dark-primary rounded-lg p-4">
<div className="flex flex-col space-y-3">
<p className="text-sm text-black/70 dark:text-white/70">
{progress.message}
</p>
<div className="w-full bg-light-secondary dark:bg-dark-secondary rounded-full h-2 overflow-hidden">
<div
className="h-full bg-[#24A0ED] transition-all duration-300 ease-in-out"
style={{
width: `${(progress.current / progress.total) * 100}%`,
}}
/>
</div>
</div>
</div>
) : (
<div className="bg-light-primary dark:bg-dark-primary animate-pulse rounded-lg py-3">
<div className="h-2 rounded-full w-full bg-light-secondary dark:bg-dark-secondary" />
<div className="h-2 mt-2 rounded-full w-9/12 bg-light-secondary dark:bg-dark-secondary" />
<div className="h-2 mt-2 rounded-full w-10/12 bg-light-secondary dark:bg-dark-secondary" />
</div>
)}
</div>
);
};

4
src/components/MessageInputActions/Optimization.tsx
View file

@ -22,7 +22,7 @@ const OptimizationModes = [
},
{
key: 'quality',
title: 'Quality (Soon)',
title: 'Quality',
description: 'Get the most thorough and accurate answer',
icon: (
<Star
@ -80,13 +80,11 @@ const Optimization = ({
<PopoverButton
onClick={() => handleOptimizationChange(mode.key)}
key={i}
disabled={mode.key === 'quality'}
className={cn(
'p-2 rounded-lg flex flex-col items-start justify-start text-start space-y-1 duration-200 cursor-pointer transition',
optimizationMode === mode.key
? 'bg-light-secondary dark:bg-dark-secondary'
: 'hover:bg-light-secondary dark:hover:bg-dark-secondary',
mode.key === 'quality' && 'opacity-50 cursor-not-allowed',
)}
>
<div className="flex flex-row items-center space-x-1 text-black dark:text-white">

3
src/lib/prompts/webSearch.ts
View file

@ -3,7 +3,8 @@ export const webSearchRetrieverPrompt = `
- You are an AI question rephraser
- You will be given a conversation and a user question
- Rephrase the question so it is appropriate for web search
- Only add additional information or change the meaning of the question if it is necessary for clarity or relevance to the conversation
- Only add additional information or change the meaning of the question if it is necessary for clarity or relevance to the conversation such as adding a date or time for current events, or using historical content to augment the question with relevant context
- Do not make up any new information like links or URLs
- Condense the question to its essence and remove any unnecessary details
- Ensure the question is grammatically correct and free of spelling errors
- If it is a simple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. instead of a question then you need to return \`not_needed\` as the response in the <answer> XML block

12
src/lib/providers/anthropic.ts
View file

@ -9,6 +9,14 @@ export const PROVIDER_INFO = {
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
const anthropicChatModels: Record<string, string>[] = [
{
displayName: 'Claude 4 Opus',
key: 'claude-opus-4-20250514',
},
{
displayName: 'Claude 4 Sonnet',
key: 'claude-sonnet-4-20250514',
},
{
displayName: 'Claude 3.7 Sonnet',
key: 'claude-3-7-sonnet-20250219',
@ -29,10 +37,6 @@ const anthropicChatModels: Record<string, string>[] = [
displayName: 'Claude 3 Opus',
key: 'claude-3-opus-20240229',
},
{
displayName: 'Claude 3 Sonnet',
key: 'claude-3-sonnet-20240229',
},
{
displayName: 'Claude 3 Haiku',
key: 'claude-3-haiku-20240307',

2
src/lib/search/index.ts
View file

@ -64,6 +64,6 @@ export const searchHandlers: Record<string, MetaSearchAgent> = {
rerankThreshold: 0.3,
searchWeb: true,
summarizer: false,
additionalSearchCriteria: '\'site:reddit.com\'',
additionalSearchCriteria: "'site:reddit.com'",
}),
};

303
src/lib/search/metaSearchAgent.ts
View file

@ -22,8 +22,9 @@ import LineOutputParser from '../outputParsers/lineOutputParser';
import LineListOutputParser from '../outputParsers/listLineOutputParser';
import { searchSearxng } from '../searxng';
import computeSimilarity from '../utils/computeSimilarity';
import { getDocumentsFromLinks } from '../utils/documents';
import { getDocumentsFromLinks, getWebContent } from '../utils/documents';
import formatChatHistoryAsString from '../utils/formatHistory';
import { getModelName } from '../utils/modelUtils';
export interface MetaSearchAgentType {
searchAndAnswer: (
@ -64,9 +65,35 @@ class MetaSearchAgent implements MetaSearchAgentType {
this.config = config;
}
private async createSearchRetrieverChain(llm: BaseChatModel) {
/**
* Emit a progress event with the given percentage and message
*/
private emitProgress(
emitter: eventEmitter,
percentage: number,
message: string,
) {
emitter.emit(
'progress',
JSON.stringify({
type: 'progress',
data: {
message,
current: percentage,
total: 100,
},
}),
);
}
private async createSearchRetrieverChain(
llm: BaseChatModel,
emitter: eventEmitter,
) {
(llm as unknown as ChatOpenAI).temperature = 0;
this.emitProgress(emitter, 10, `Building search query`);
return RunnableSequence.from([
PromptTemplate.fromTemplate(this.config.queryGeneratorPrompt),
llm,
@ -131,6 +158,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
}
});
this.emitProgress(emitter, 20, `Summarizing content`);
await Promise.all(
docGroups.map(async (doc) => {
const res = await llm.invoke(`
@ -208,6 +237,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
return { query: question, docs: docs };
} else {
this.emitProgress(emitter, 20, `Searching the web`);
if (this.config.additionalSearchCriteria) {
question = `${question} ${this.config.additionalSearchCriteria}`;
}
@ -249,6 +279,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
optimizationMode: 'speed' | 'balanced' | 'quality',
systemInstructions: string,
signal: AbortSignal,
emitter: eventEmitter,
) {
return RunnableSequence.from([
RunnableMap.from({
@ -276,7 +307,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
if (this.config.searchWeb) {
const searchRetrieverChain =
await this.createSearchRetrieverChain(llm);
await this.createSearchRetrieverChain(llm, emitter);
var date = new Date().toISOString();
const searchRetrieverResult = await searchRetrieverChain.invoke(
@ -303,8 +334,14 @@ class MetaSearchAgent implements MetaSearchAgentType {
fileIds,
embeddings,
optimizationMode,
llm,
emitter,
signal,
);
console.log('Ranked docs:', sortedDocs);
this.emitProgress(emitter, 100, `Done`);
return sortedDocs;
},
)
@ -331,11 +368,18 @@ class MetaSearchAgent implements MetaSearchAgentType {
fileIds: string[],
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) {
llm: BaseChatModel,
emitter: eventEmitter,
signal: AbortSignal,
): Promise<Document[]> {
if (docs.length === 0 && fileIds.length === 0) {
return docs;
}
if (query.toLocaleLowerCase() === 'summarize') {
return docs.slice(0, 15);
}
const filesData = fileIds
.map((file) => {
const filePath = path.join(process.cwd(), 'uploads', file);
@ -360,107 +404,216 @@ class MetaSearchAgent implements MetaSearchAgentType {
})
.flat();
if (query.toLocaleLowerCase() === 'summarize') {
return docs.slice(0, 15);
}
const docsWithContent = docs.filter(
let 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 queryEmbedding = await embeddings.embedQuery(query);
const getRankedDocs = async (
queryEmbedding: number[],
includeFiles: boolean,
includeNonFileDocs: boolean,
maxDocs: number,
) => {
let docsToRank = includeNonFileDocs ? docsWithContent : [];
if (includeFiles) {
// Add file documents to the ranking
const fileDocs = filesData.map((fileData) => {
return new Document({
pageContent: fileData.content,
metadata: {
title: fileData.fileName,
url: `File`,
embeddings: fileData.embeddings,
},
});
});
docsToRank.push(...fileDocs);
}
const similarity = filesData.map((fileData, i) => {
const sim = computeSimilarity(queryEmbedding, fileData.embeddings);
const similarity = await Promise.all(
docsToRank.map(async (doc, i) => {
const sim = computeSimilarity(
queryEmbedding,
doc.metadata?.embeddings
? doc.metadata?.embeddings
: (await embeddings.embedDocuments([doc.pageContent]))[0],
);
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]);
let rankedDocs = similarity
.filter((sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3))
.sort((a, b) => b.similarity - a.similarity)
.map((sim) => docsToRank[sim.index]);
sortedDocs =
docsWithContent.length > 0 ? sortedDocs.slice(0, 8) : sortedDocs;
rankedDocs =
docsToRank.length > 0 ? rankedDocs.slice(0, maxDocs) : rankedDocs;
return rankedDocs;
};
if (optimizationMode === 'speed' || this.config.rerank === false) {
this.emitProgress(emitter, 50, `Ranking sources`);
if (filesData.length > 0) {
const sortedFiles = await getRankedDocs(queryEmbedding, true, false, 8);
return [
...sortedDocs,
...docsWithContent.slice(0, 15 - sortedDocs.length),
...sortedFiles,
...docsWithContent.slice(0, 15 - sortedFiles.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),
]);
this.emitProgress(emitter, 40, `Ranking sources`);
let sortedDocs = await getRankedDocs(queryEmbedding, true, true, 10);
docsWithContent.push(
...filesData.map((fileData) => {
return new Document({
pageContent: fileData.content,
metadata: {
title: fileData.fileName,
url: `File`,
},
this.emitProgress(emitter, 60, `Enriching sources`);
sortedDocs = await Promise.all(
sortedDocs.map(async (doc) => {
const webContent = await getWebContent(doc.metadata.url);
const chunks =
webContent?.pageContent
.match(/.{1,500}/g)
?.map((chunk) => chunk.trim()) || [];
const chunkEmbeddings = await embeddings.embedDocuments(chunks);
const similarities = chunkEmbeddings.map((chunkEmbedding) => {
return computeSimilarity(queryEmbedding, chunkEmbedding);
});
const topChunks = similarities
.map((similarity, index) => ({ similarity, index }))
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 5)
.map((chunk) => chunks[chunk.index]);
const excerpt = topChunks.join('\n\n');
let newDoc = {
...doc,
pageContent: excerpt
? `${excerpt}\n\n${doc.pageContent}`
: doc.pageContent,
};
return newDoc;
}),
);
docEmbeddings.push(...filesData.map((fileData) => fileData.embeddings));
return sortedDocs;
} else if (optimizationMode === 'quality') {
this.emitProgress(emitter, 30, 'Ranking sources...');
const similarity = docEmbeddings.map((docEmbedding, i) => {
const sim = computeSimilarity(queryEmbedding, docEmbedding);
// Get the top ranked web results for detailed analysis based off their preview embeddings
const topWebResults = await getRankedDocs(
queryEmbedding,
false,
true,
30,
);
return {
index: i,
similarity: sim,
};
const summaryParser = new LineOutputParser({
key: 'summary',
});
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]);
// Get full content and generate detailed summaries for top results sequentially
const enhancedDocs: Document[] = [];
const maxEnhancedDocs = 5;
for (let i = 0; i < topWebResults.length; i++) {
if (signal.aborted) {
return [];
}
if (enhancedDocs.length >= maxEnhancedDocs) {
break; // Limit to 5 documents
}
const result = topWebResults[i];
return sortedDocs;
this.emitProgress(
emitter,
enhancedDocs.length * 10 + 40,
`Deep analyzing sources: ${enhancedDocs.length + 1}/${maxEnhancedDocs}`,
);
try {
const url = result.metadata.url;
const webContent = await getWebContent(url, true);
if (webContent) {
// Generate a detailed summary using the LLM
const summary = await llm.invoke(`
You are a web content summarizer, tasked with creating a detailed, accurate summary of content from a webpage
Your summary should:
- Be thorough and comprehensive, capturing all key points
- Format the content using markdown, including headings, lists, and tables
- Include specific details, numbers, and quotes when relevant
- Be concise and to the point, avoiding unnecessary fluff
- Answer the user's query, which is: ${query}
- Output your answer in an XML format, with the summary inside the \`summary\` XML tag
- If the content is not relevant to the query, respond with "not_needed" to start the summary tag, followed by a one line description of why the source is not needed
- E.g. "not_needed: There is relevant information in the source, but it doesn't contain specifics about X"
- Make sure the reason the source is not needed is very specific and detailed
- Include useful links to external resources, if applicable
Here is the content to summarize:
${webContent.metadata.html ? webContent.metadata.html : webContent.pageContent}
`);
const summarizedContent = await summaryParser.parse(
summary.content as string,
);
if (
summarizedContent.toLocaleLowerCase().startsWith('not_needed')
) {
console.log(
`LLM response for URL "${url}" indicates it's not needed:`,
summarizedContent,
);
continue; // Skip this document if not needed
}
//console.log(`LLM response for URL "${url}":`, summarizedContent);
enhancedDocs.push(
new Document({
pageContent: summarizedContent,
metadata: {
...webContent.metadata,
url: url,
},
}),
);
}
} catch (error) {
console.error(`Error processing URL ${result.metadata.url}:`, error);
}
}
// Add relevant file documents
const fileDocs = await getRankedDocs(queryEmbedding, true, false, 5);
return [...enhancedDocs, ...fileDocs];
}
return [];
}
private processDocs(docs: Document[]) {
return docs
const fullDocs = docs
.map(
(_, index) =>
`${index + 1}. ${docs[index].metadata.title} ${docs[index].pageContent}`,
`<${index + 1}>\n
<title>${docs[index].metadata.title}</title>\n
${docs[index].metadata?.url.toLowerCase().includes('file') ? '' : '\n<url>' + docs[index].metadata.url + '</url>\n'}
<content>\n${docs[index].pageContent}\n</content>\n
</${index + 1}>\n`,
)
.join('\n');
// console.log('Processed docs:', fullDocs);
return fullDocs;
}
private async handleStream(
@ -513,38 +666,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
event.event === 'on_chain_end' &&
event.name === 'FinalResponseGenerator'
) {
// Get model name safely with better detection
let modelName = 'Unknown';
try {
// @ts-ignore - Different LLM implementations have different properties
if (llm.modelName) {
// @ts-ignore
modelName = llm.modelName;
// @ts-ignore
} else if (llm._llm && llm._llm.modelName) {
// @ts-ignore
modelName = llm._llm.modelName;
// @ts-ignore
} else if (llm.model && llm.model.modelName) {
// @ts-ignore
modelName = llm.model.modelName;
} else if ('model' in llm) {
// @ts-ignore
const model = llm.model;
if (typeof model === 'string') {
modelName = model;
// @ts-ignore
} else if (model && model.modelName) {
// @ts-ignore
modelName = model.modelName;
}
} else if (llm.constructor && llm.constructor.name) {
// Last resort: use the class name
modelName = llm.constructor.name;
}
} catch (e) {
console.error('Failed to get model name:', e);
}
const modelName = getModelName(llm);
// Send model info before ending
emitter.emit(
@ -581,6 +703,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
optimizationMode,
systemInstructions,
signal,
emitter,
);
const stream = answeringChain.streamEvents(

55
src/lib/utils/documents.ts
View file

@ -3,6 +3,9 @@ import { htmlToText } from 'html-to-text';
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
import { Document } from '@langchain/core/documents';
import pdfParse from 'pdf-parse';
import { JSDOM } from 'jsdom';
import { Readability } from '@mozilla/readability';
import fetch from 'node-fetch';
export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
const splitter = new RecursiveCharacterTextSplitter();
@ -97,3 +100,55 @@ export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
return docs;
};
export const getWebContent = async (
url: string,
getHtml: boolean = false,
): Promise<Document | null> => {
try {
const response = await fetch(url, { timeout: 5000 });
const html = await response.text();
// Create a DOM from the fetched HTML
const dom = new JSDOM(html, { url });
// Get title before we modify the DOM
const originalTitle = dom.window.document.title;
// Use Readability to parse the article content
const reader = new Readability(dom.window.document, { charThreshold: 25 });
const article = reader.parse();
if (!article) {
console.warn(`Failed to parse article content for URL: ${url}`);
return null;
}
// Normalize the text content by removing extra spaces and newlines. Iterate through the lines one by one and throw out the ones that are empty or contain only whitespace.
const normalizedText =
article?.textContent
?.split('\n')
.map((line) => line.trim())
.filter((line) => line.length > 0)
.join('\n') || '';
// Create a Document with the parsed content
return new Document({
pageContent: normalizedText || '',
metadata: {
html: getHtml ? article.content : undefined,
title: article.title || originalTitle,
url: url,
excerpt: article.excerpt || undefined,
byline: article.byline || undefined,
siteName: article.siteName || undefined,
readingTime: article.length
? Math.ceil(article.length / 1000)
: undefined,
},
});
} catch (error) {
console.error(`Error fetching/parsing URL ${url}:`); //, error);
return null;
}
};

52
src/lib/utils/modelUtils.ts Normal file
View file

@ -0,0 +1,52 @@
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
/**
* Extract the model name from an LLM instance
* Handles different LLM implementations that may store the model name in different properties
* @param llm The LLM instance
* @returns The model name or 'Unknown' if not found
*/
export function getModelName(llm: BaseChatModel): string {
try {
// @ts-ignore - Different LLM implementations have different properties
if (llm.modelName) {
// @ts-ignore
return llm.modelName;
}
// @ts-ignore
if (llm._llm && llm._llm.modelName) {
// @ts-ignore
return llm._llm.modelName;
}
// @ts-ignore
if (llm.model && llm.model.modelName) {
// @ts-ignore
return llm.model.modelName;
}
if ('model' in llm) {
// @ts-ignore
const model = llm.model;
if (typeof model === 'string') {
return model;
}
// @ts-ignore
if (model && model.modelName) {
// @ts-ignore
return model.modelName;
}
}
if (llm.constructor && llm.constructor.name) {
// Last resort: use the class name
return llm.constructor.name;
}
return 'Unknown';
} catch (e) {
console.error('Failed to get model name:', e);
return 'Unknown';
}
}