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3
backend.dockerfile
View file

@ -1,6 +1,7 @@
FROM node:slim
ARG SEARXNG_API_URL
ENV SEARXNG_API_URL=${SEARXNG_API_URL}
WORKDIR /home/perplexica
@ -11,8 +12,6 @@ COPY drizzle.config.ts /home/perplexica/
COPY package.json /home/perplexica/
COPY yarn.lock /home/perplexica/
RUN sed -i "s|SEARXNG = \".*\"|SEARXNG = \"${SEARXNG_API_URL}\"|g" /home/perplexica/config.toml
RUN mkdir /home/perplexica/data
RUN yarn install

1
docker-compose.yaml
View file

@ -21,6 +21,7 @@ services:
- 3001:3001
volumes:
- backend-dbstore:/home/perplexica/data
- ./config.toml:/home/perplexica/config.toml
extra_hosts:
- 'host.docker.internal:host-gateway'
networks:

1
docs/installation/UPDATING.md
View file

@ -17,6 +17,7 @@ To update Perplexica to the latest version, follow these steps:
```bash
docker compose up -d --build
```
4. Once the command completes running go to http://localhost:3000 and verify the latest changes.
## For non Docker users

6
package.json
View file

@ -1,6 +1,6 @@
{
"name": "perplexica-backend",
"version": "1.8.0",
"version": "1.9.0-rc2",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {
@ -15,6 +15,8 @@
"@types/better-sqlite3": "^7.6.10",
"@types/cors": "^2.8.17",
"@types/express": "^4.17.21",
"@types/html-to-text": "^9.0.4",
"@types/pdf-parse": "^1.1.4",
"@types/readable-stream": "^4.0.11",
"drizzle-kit": "^0.22.7",
"nodemon": "^3.1.0",
@ -37,7 +39,9 @@
"dotenv": "^16.4.5",
"drizzle-orm": "^0.31.2",
"express": "^4.19.2",
"html-to-text": "^9.0.5",
"langchain": "^0.1.30",
"pdf-parse": "^1.1.1",
"winston": "^3.13.0",
"ws": "^8.17.1",
"zod": "^3.22.4"

2345
searxng/settings.yml
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File diff suppressed because it is too large Load diff

3
src/agents/academicSearchAgent.ts
View file

@ -19,6 +19,7 @@ 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 basicAcademicSearchRetrieverPrompt = `
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.
@ -66,7 +67,7 @@ const basicAcademicSearchResponsePrompt = `
const strParser = new StringOutputParser();
const handleStream = async (
stream: AsyncGenerator<StreamEvent, any, unknown>,
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {

3
src/agents/redditSearchAgent.ts
View file

@ -19,6 +19,7 @@ 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.
@ -66,7 +67,7 @@ const basicRedditSearchResponsePrompt = `
const strParser = new StringOutputParser();
const handleStream = async (
stream: AsyncGenerator<StreamEvent, any, unknown>,
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {

197
src/agents/webSearchAgent.ts
View file

@ -19,34 +19,83 @@ 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 { getDocumentsFromLinks } from '../lib/linkDocument';
import LineOutputParser from '../lib/outputParsers/lineOutputParser';
import { IterableReadableStream } from '@langchain/core/utils/stream';
import { ChatOpenAI } from '@langchain/openai';
const basicSearchRetrieverPrompt = `
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.
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.
Example:
1. Follow up question: What is the capital of France?
Rephrased: Capital of france
There are several examples attached for your reference inside the below \`examples\` XML block
2. Follow up question: What is the population of New York City?
Rephrased: Population of New York City
<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: What is Docker
Rephrased question: \`
<question>
What is Docker
</question>
\`
Conversation:
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 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].
@ -59,14 +108,14 @@ const basicWebSearchResponsePrompt = `
{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?'.
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: AsyncGenerator<StreamEvent, any, unknown>,
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {
@ -103,32 +152,120 @@ type BasicChainInput = {
};
const createBasicWebSearchRetrieverChain = (llm: BaseChatModel) => {
(llm as unknown as ChatOpenAI).temperature = 0;
return RunnableSequence.from([
PromptTemplate.fromTemplate(basicSearchRetrieverPrompt),
llm,
strParser,
RunnableLambda.from(async (input: string) => {
if (input === 'not_needed') {
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: [] };
}
const res = await searchSearxng(input, {
language: 'en',
});
if (links.length > 0) {
if (question.length === 0) {
question = 'summarize';
}
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 }),
},
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 text summarizer. You need to summarize the text provided inside the \`text\` XML block.
You need to summarize the text into 1 or 2 sentences capturing the main idea of the text.
You need to make sure that you don't miss any point while summarizing the text.
You will also be given a \`query\` XML block which will contain the query of the user. Try to answer the query in the summary from the text provided.
If the query says Summarize then you just need to summarize the text without answering the query.
Only return the summarized text without any other messages, text or XML block.
<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: input, docs: documents };
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 };
}
}),
]);
};
@ -156,6 +293,10 @@ const createBasicWebSearchAnsweringChain = (
return docs;
}
if (query.toLocaleLowerCase() === 'summarize') {
return docs;
}
const docsWithContent = docs.filter(
(doc) => doc.pageContent && doc.pageContent.length > 0,
);
@ -175,7 +316,7 @@ const createBasicWebSearchAnsweringChain = (
});
const sortedDocs = similarity
.filter((sim) => sim.similarity > 0.5)
.filter((sim) => sim.similarity > 0.3)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => docsWithContent[sim.index]);

3
src/agents/wolframAlphaSearchAgent.ts
View file

@ -18,6 +18,7 @@ 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.
@ -65,7 +66,7 @@ const basicWolframAlphaSearchResponsePrompt = `
const strParser = new StringOutputParser();
const handleStream = async (
stream: AsyncGenerator<StreamEvent, any, unknown>,
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {

3
src/agents/writingAssistant.ts
View file

@ -10,6 +10,7 @@ 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.
@ -19,7 +20,7 @@ Since you are a writing assistant, you would not perform web searches. If you th
const strParser = new StringOutputParser();
const handleStream = async (
stream: AsyncGenerator<StreamEvent, any, unknown>,
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {

3
src/agents/youtubeSearchAgent.ts
View file

@ -19,6 +19,7 @@ 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.
@ -66,7 +67,7 @@ const basicYoutubeSearchResponsePrompt = `
const strParser = new StringOutputParser();
const handleStream = async (
stream: AsyncGenerator<StreamEvent, any, unknown>,
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {

8
src/app.ts
View file

@ -28,3 +28,11 @@ server.listen(port, () => {
});
startWebSocketServer(server);
process.on('uncaughtException', (err, origin) => {
logger.error(`Uncaught Exception at ${origin}: ${err}`);
});
process.on('unhandledRejection', (reason, promise) => {
logger.error(`Unhandled Rejection at: ${promise}, reason: ${reason}`);
});

4
src/config.ts
View file

@ -41,10 +41,14 @@ export const getGroqApiKey = () => loadConfig().API_KEYS.GROQ;
export const getAnthropicApiKey = () => loadConfig().API_KEYS.ANTHROPIC;
export const getGeminiApiKey = () => loadConfig().API_KEYS.Gemini;
export const getSearxngApiEndpoint = () => loadConfig().API_ENDPOINTS.SEARXNG;
export const getSearxngApiEndpoint = () =>
process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
export const getOllamaApiEndpoint = () => loadConfig().API_ENDPOINTS.OLLAMA;
export const updateConfig = (config: RecursivePartial<Config>) => {

99
src/lib/linkDocument.ts Normal file
View file

@ -0,0 +1,99 @@
import axios from 'axios';
import { htmlToText } from 'html-to-text';
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
import { Document } from '@langchain/core/documents';
import pdfParse from 'pdf-parse';
import logger from '../utils/logger';
export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
const splitter = new RecursiveCharacterTextSplitter();
let docs: Document[] = [];
await Promise.all(
links.map(async (link) => {
link =
link.startsWith('http://') || link.startsWith('https://')
? link
: `https://${link}`;
try {
const res = await axios.get(link, {
responseType: 'arraybuffer',
});
const isPdf = res.headers['content-type'] === 'application/pdf';
if (isPdf) {
const pdfText = await pdfParse(res.data);
const parsedText = pdfText.text
.replace(/(\r\n|\n|\r)/gm, ' ')
.replace(/\s+/g, ' ')
.trim();
const splittedText = await splitter.splitText(parsedText);
const title = 'PDF Document';
const linkDocs = splittedText.map((text) => {
return new Document({
pageContent: text,
metadata: {
title: title,
url: link,
},
});
});
docs.push(...linkDocs);
return;
}
const parsedText = htmlToText(res.data.toString('utf8'), {
selectors: [
{
selector: 'a',
options: {
ignoreHref: true,
},
},
],
})
.replace(/(\r\n|\n|\r)/gm, ' ')
.replace(/\s+/g, ' ')
.trim();
const splittedText = await splitter.splitText(parsedText);
const title = res.data
.toString('utf8')
.match(/<title>(.*?)<\/title>/)?.[1];
const linkDocs = splittedText.map((text) => {
return new Document({
pageContent: text,
metadata: {
title: title || link,
url: link,
},
});
});
docs.push(...linkDocs);
} catch (err) {
logger.error(
`Error at generating documents from links: ${err.message}`,
);
docs.push(
new Document({
pageContent: `Failed to retrieve content from the link: ${err.message}`,
metadata: {
title: 'Failed to retrieve content',
url: link,
},
}),
);
}
}),
);
return docs;
};

46
src/lib/outputParsers/lineOutputParser.ts Normal file
View file

@ -0,0 +1,46 @@
import { BaseOutputParser } from '@langchain/core/output_parsers';
interface LineOutputParserArgs {
key?: string;
}
class LineOutputParser extends BaseOutputParser<string> {
private key = 'questions';
constructor(args?: LineOutputParserArgs) {
super();
this.key = args.key ?? this.key;
}
static lc_name() {
return 'LineOutputParser';
}
lc_namespace = ['langchain', 'output_parsers', 'line_output_parser'];
async parse(text: string): Promise<string> {
const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
const startKeyIndex = text.indexOf(`<${this.key}>`);
const endKeyIndex = text.indexOf(`</${this.key}>`);
if (startKeyIndex === -1 || endKeyIndex === -1) {
return '';
}
const questionsStartIndex =
startKeyIndex === -1 ? 0 : startKeyIndex + `<${this.key}>`.length;
const questionsEndIndex = endKeyIndex === -1 ? text.length : endKeyIndex;
const line = text
.slice(questionsStartIndex, questionsEndIndex)
.trim()
.replace(regex, '');
return line;
}
getFormatInstructions(): string {
throw new Error('Not implemented.');
}
}
export default LineOutputParser;

5
src/lib/outputParsers/listLineOutputParser.ts
View file

@ -22,6 +22,11 @@ class LineListOutputParser extends BaseOutputParser<string[]> {
const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
const startKeyIndex = text.indexOf(`<${this.key}>`);
const endKeyIndex = text.indexOf(`</${this.key}>`);
if (startKeyIndex === -1 || endKeyIndex === -1) {
return [];
}
const questionsStartIndex =
startKeyIndex === -1 ? 0 : startKeyIndex + `<${this.key}>`.length;
const questionsEndIndex = endKeyIndex === -1 ? text.length : endKeyIndex;

2
ui/app/layout.tsx
View file

@ -34,7 +34,7 @@ export default function RootLayout({
unstyled: true,
classNames: {
toast:
'bg-light-primary dark:bg-dark-primary text-white rounded-lg p-4 flex flex-row items-center space-x-2',
'bg-light-primary dark:bg-dark-secondary dark:text-white/70 text-black-70 rounded-lg p-4 flex flex-row items-center space-x-2',
},
}}
/>

101
ui/components/ChatWindow.tsx
View file

@ -38,43 +38,56 @@ const useSocket = (
'embeddingModelProvider',
);
const providers = await fetch(
`${process.env.NEXT_PUBLIC_API_URL}/models`,
{
headers: {
'Content-Type': 'application/json',
},
},
).then(async (res) => await res.json());
if (
!chatModel ||
!chatModelProvider ||
!embeddingModel ||
!embeddingModelProvider
) {
const providers = await fetch(
`${process.env.NEXT_PUBLIC_API_URL}/models`,
{
headers: {
'Content-Type': 'application/json',
},
},
).then(async (res) => await res.json());
if (!chatModel || !chatModelProvider) {
const chatModelProviders = providers.chatModelProviders;
const chatModelProviders = providers.chatModelProviders;
const embeddingModelProviders = providers.embeddingModelProviders;
chatModelProvider = Object.keys(chatModelProviders)[0];
if (
!chatModelProviders ||
Object.keys(chatModelProviders).length === 0
)
return toast.error('No chat models available');
if (chatModelProvider === 'custom_openai') {
toast.error(
'Seems like you are using the custom OpenAI provider, please open the settings and configure the API key and base URL',
);
setError(true);
return;
} else {
chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
if (
!chatModelProviders ||
Object.keys(chatModelProviders).length === 0
)
return toast.error('No chat models available');
}
}
if (
!embeddingModelProviders ||
Object.keys(embeddingModelProviders).length === 0
)
return toast.error('No embedding models available');
if (!embeddingModel || !embeddingModelProvider) {
const embeddingModelProviders = providers.embeddingModelProviders;
chatModelProvider = Object.keys(chatModelProviders)[0];
chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
if (
!embeddingModelProviders ||
Object.keys(embeddingModelProviders).length === 0
)
return toast.error('No embedding models available');
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
embeddingModel = Object.keys(
embeddingModelProviders[embeddingModelProvider],
)[0];
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
embeddingModel = Object.keys(
embeddingModelProviders[embeddingModelProvider],
)[0];
}
localStorage.setItem('chatModel', chatModel!);
localStorage.setItem('chatModelProvider', chatModelProvider);
@ -84,15 +97,6 @@ const useSocket = (
embeddingModelProvider,
);
} else {
const providers = await fetch(
`${process.env.NEXT_PUBLIC_API_URL}/models`,
{
headers: {
'Content-Type': 'application/json',
},
},
).then(async (res) => await res.json());
const chatModelProviders = providers.chatModelProviders;
const embeddingModelProviders = providers.embeddingModelProviders;
@ -106,6 +110,7 @@ const useSocket = (
if (
chatModelProvider &&
chatModelProvider != 'custom_openai' &&
!chatModelProviders[chatModelProvider][chatModel]
) {
chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
@ -160,8 +165,6 @@ const useSocket = (
const timeoutId = setTimeout(() => {
if (ws.readyState !== 1) {
ws.close();
setError(true);
toast.error(
'Failed to connect to the server. Please try again later.',
);
@ -171,7 +174,6 @@ const useSocket = (
ws.onopen = () => {
console.log('[DEBUG] open');
clearTimeout(timeoutId);
setError(false);
setIsWSReady(true);
};
@ -187,18 +189,18 @@ const useSocket = (
console.log('[DEBUG] closed');
};
ws.addEventListener('message', (e) => {
const data = JSON.parse(e.data);
if (data.type === 'error') {
toast.error(data.data);
}
});
setWs(ws);
};
connectWs();
}
return () => {
if (ws?.readyState === 1) {
ws?.close();
console.log('[DEBUG] closed');
}
};
}, [ws, url, setIsWSReady, setError]);
return ws;
@ -304,6 +306,15 @@ const ChatWindow = ({ id }: { id?: string }) => {
// eslint-disable-next-line react-hooks/exhaustive-deps
}, []);
useEffect(() => {
return () => {
if (ws?.readyState === 1) {
ws.close();
console.log('[DEBUG] closed');
}
};
}, []);
const messagesRef = useRef<Message[]>([]);
useEffect(() => {

21
ui/components/EmptyChatMessageInput.tsx
View file

@ -18,14 +18,21 @@ const EmptyChatMessageInput = ({
const inputRef = useRef<HTMLTextAreaElement | null>(null);
const handleKeyDown = (e: KeyboardEvent) => {
if (e.key === '/') {
e.preventDefault();
inputRef.current?.focus();
}
};
useEffect(() => {
const handleKeyDown = (e: KeyboardEvent) => {
const activeElement = document.activeElement;
const isInputFocused =
activeElement?.tagName === 'INPUT' ||
activeElement?.tagName === 'TEXTAREA' ||
activeElement?.hasAttribute('contenteditable');
if (e.key === '/' && !isInputFocused) {
e.preventDefault();
inputRef.current?.focus();
}
};
document.addEventListener('keydown', handleKeyDown);
return () => {

21
ui/components/MessageInput.tsx
View file

@ -27,14 +27,21 @@ const MessageInput = ({
const inputRef = useRef<HTMLTextAreaElement | null>(null);
const handleKeyDown = (e: KeyboardEvent) => {
if (e.key === '/') {
e.preventDefault();
inputRef.current?.focus();
}
};
useEffect(() => {
const handleKeyDown = (e: KeyboardEvent) => {
const activeElement = document.activeElement;
const isInputFocused =
activeElement?.tagName === 'INPUT' ||
activeElement?.tagName === 'TEXTAREA' ||
activeElement?.hasAttribute('contenteditable');
if (e.key === '/' && !isInputFocused) {
e.preventDefault();
inputRef.current?.focus();
}
};
document.addEventListener('keydown', handleKeyDown);
return () => {

10
ui/components/SettingsDialog.tsx
View file

@ -226,9 +226,13 @@ const SettingsDialog = ({
value={selectedChatModelProvider ?? undefined}
onChange={(e) => {
setSelectedChatModelProvider(e.target.value);
setSelectedChatModel(
config.chatModelProviders[e.target.value][0],
);
if (e.target.value === 'custom_openai') {
setSelectedChatModel('');
} else {
setSelectedChatModel(
config.chatModelProviders[e.target.value][0],
);
}
}}
options={Object.keys(config.chatModelProviders).map(
(provider) => ({

2
ui/package.json
View file

@ -1,6 +1,6 @@
{
"name": "perplexica-frontend",
"version": "1.8.0",
"version": "1.9.0-rc2",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {

124
yarn.lock
View file

@ -509,6 +509,14 @@
resolved "https://registry.yarnpkg.com/@protobufjs/utf8/-/utf8-1.1.0.tgz#a777360b5b39a1a2e5106f8e858f2fd2d060c570"
integrity sha512-Vvn3zZrhQZkkBE8LSuW3em98c0FwgO4nxzv6OdSxPKJIEKY2bGbHn+mhGIPerzI4twdxaP8/0+06HBpwf345Lw==
"@selderee/plugin-htmlparser2@^0.11.0":
version "0.11.0"
resolved "https://registry.yarnpkg.com/@selderee/plugin-htmlparser2/-/plugin-htmlparser2-0.11.0.tgz#d5b5e29a7ba6d3958a1972c7be16f4b2c188c517"
integrity sha512-P33hHGdldxGabLFjPPpaTxVolMrzrcegejx+0GxjrIb9Zv48D8yAIA/QTDR2dFl7Uz7urX8aX6+5bCZslr+gWQ==
dependencies:
domhandler "^5.0.3"
selderee "^0.11.0"
"@tsconfig/node10@^1.0.7":
version "1.0.11"
resolved "https://registry.yarnpkg.com/@tsconfig/node10/-/node10-1.0.11.tgz#6ee46400685f130e278128c7b38b7e031ff5b2f2"
@ -578,6 +586,11 @@
"@types/qs" "*"
"@types/serve-static" "*"
"@types/html-to-text@^9.0.4":
version "9.0.4"
resolved "https://registry.yarnpkg.com/@types/html-to-text/-/html-to-text-9.0.4.tgz#4a83dd8ae8bfa91457d0b1ffc26f4d0537eff58c"
integrity sha512-pUY3cKH/Nm2yYrEmDlPR1mR7yszjGx4DrwPjQ702C4/D5CwHuZTgZdIdwPkRbcuhs7BAh2L5rg3CL5cbRiGTCQ==
"@types/http-errors@*":
version "2.0.4"
resolved "https://registry.yarnpkg.com/@types/http-errors/-/http-errors-2.0.4.tgz#7eb47726c391b7345a6ec35ad7f4de469cf5ba4f"
@ -622,6 +635,11 @@
dependencies:
undici-types "~5.26.4"
"@types/pdf-parse@^1.1.4":
version "1.1.4"
resolved "https://registry.yarnpkg.com/@types/pdf-parse/-/pdf-parse-1.1.4.tgz#21a539efd2f16009d08aeed3350133948b5d7ed1"
integrity sha512-+gbBHbNCVGGYw1S9lAIIvrHW47UYOhMIFUsJcMkMrzy1Jf0vulBN3XQIjPgnoOXveMuHnF3b57fXROnY/Or7eg==
"@types/qs@*":
version "6.9.14"
resolved "https://registry.yarnpkg.com/@types/qs/-/qs-6.9.14.tgz#169e142bfe493895287bee382af6039795e9b75b"
@ -1092,6 +1110,13 @@ debug@2.6.9:
dependencies:
ms "2.0.0"
debug@^3.1.0:
version "3.2.7"
resolved "https://registry.yarnpkg.com/debug/-/debug-3.2.7.tgz#72580b7e9145fb39b6676f9c5e5fb100b934179a"
integrity sha512-CFjzYYAi4ThfiQvizrFQevTTXHtnCqWfe7x1AhgEscTz6ZbLbfoLRLPugTQyBth6f8ZERVUSyWHFD/7Wu4t1XQ==
dependencies:
ms "^2.1.1"
debug@^4:
version "4.3.4"
resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.4.tgz#1319f6579357f2338d3337d2cdd4914bb5dcc865"
@ -1123,6 +1148,11 @@ deep-extend@^0.6.0:
resolved "https://registry.yarnpkg.com/deep-extend/-/deep-extend-0.6.0.tgz#c4fa7c95404a17a9c3e8ca7e1537312b736330ac"
integrity sha512-LOHxIOaPYdHlJRtCQfDIVZtfw/ufM8+rVj649RIHzcm/vGwQRXFt6OPqIFWsm2XEMrNIEtWR64sY1LEKD2vAOA==
deepmerge@^4.3.1:
version "4.3.1"
resolved "https://registry.yarnpkg.com/deepmerge/-/deepmerge-4.3.1.tgz#44b5f2147cd3b00d4b56137685966f26fd25dd4a"
integrity sha512-3sUqbMEc77XqpdNO7FRyRog+eW3ph+GYCbj+rK+uYyRMuwsVy0rMiVtPn+QJlKFvWP/1PYpapqYn0Me2knFn+A==
define-data-property@^1.1.4:
version "1.1.4"
resolved "https://registry.yarnpkg.com/define-data-property/-/define-data-property-1.1.4.tgz#894dc141bb7d3060ae4366f6a0107e68fbe48c5e"
@ -1165,6 +1195,36 @@ digest-fetch@^1.3.0:
base-64 "^0.1.0"
md5 "^2.3.0"
dom-serializer@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/dom-serializer/-/dom-serializer-2.0.0.tgz#e41b802e1eedf9f6cae183ce5e622d789d7d8e53"
integrity sha512-wIkAryiqt/nV5EQKqQpo3SToSOV9J0DnbJqwK7Wv/Trc92zIAYZ4FlMu+JPFW1DfGFt81ZTCGgDEabffXeLyJg==
dependencies:
domelementtype "^2.3.0"
domhandler "^5.0.2"
entities "^4.2.0"
domelementtype@^2.3.0:
version "2.3.0"
resolved "https://registry.yarnpkg.com/domelementtype/-/domelementtype-2.3.0.tgz#5c45e8e869952626331d7aab326d01daf65d589d"
integrity sha512-OLETBj6w0OsagBwdXnPdN0cnMfF9opN69co+7ZrbfPGrdpPVNBUj02spi6B1N7wChLQiPn4CSH/zJvXw56gmHw==
domhandler@^5.0.2, domhandler@^5.0.3:
version "5.0.3"
resolved "https://registry.yarnpkg.com/domhandler/-/domhandler-5.0.3.tgz#cc385f7f751f1d1fc650c21374804254538c7d31"
integrity sha512-cgwlv/1iFQiFnU96XXgROh8xTeetsnJiDsTc7TYCLFd9+/WNkIqPTxiM/8pSd8VIrhXGTf1Ny1q1hquVqDJB5w==
dependencies:
domelementtype "^2.3.0"
domutils@^3.0.1:
version "3.1.0"
resolved "https://registry.yarnpkg.com/domutils/-/domutils-3.1.0.tgz#c47f551278d3dc4b0b1ab8cbb42d751a6f0d824e"
integrity sha512-H78uMmQtI2AhgDJjWeQmHwJJ2bLPD3GMmO7Zja/ZZh84wkm+4ut+IUnUdRa8uCGX88DiVx1j6FRe1XfxEgjEZA==
dependencies:
dom-serializer "^2.0.0"
domelementtype "^2.3.0"
domhandler "^5.0.3"
dotenv@^16.4.5:
version "16.4.5"
resolved "https://registry.yarnpkg.com/dotenv/-/dotenv-16.4.5.tgz#cdd3b3b604cb327e286b4762e13502f717cb099f"
@ -1206,6 +1266,11 @@ end-of-stream@^1.1.0, end-of-stream@^1.4.1:
dependencies:
once "^1.4.0"
entities@^4.2.0, entities@^4.4.0:
version "4.5.0"
resolved "https://registry.yarnpkg.com/entities/-/entities-4.5.0.tgz#5d268ea5e7113ec74c4d033b79ea5a35a488fb48"
integrity sha512-V0hjH4dGPh9Ao5p0MoRY6BVqtwCjhz6vI5LT8AJ55H+4g9/4vbHx1I54fS0XuclLhDHArPQCiMjDxjaL8fPxhw==
es-define-property@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/es-define-property/-/es-define-property-1.0.0.tgz#c7faefbdff8b2696cf5f46921edfb77cc4ba3845"
@ -1529,6 +1594,27 @@ hasown@^2.0.0:
dependencies:
function-bind "^1.1.2"
html-to-text@^9.0.5:
version "9.0.5"
resolved "https://registry.yarnpkg.com/html-to-text/-/html-to-text-9.0.5.tgz#6149a0f618ae7a0db8085dca9bbf96d32bb8368d"
integrity sha512-qY60FjREgVZL03vJU6IfMV4GDjGBIoOyvuFdpBDIX9yTlDw0TjxVBQp+P8NvpdIXNJvfWBTNul7fsAQJq2FNpg==
dependencies:
"@selderee/plugin-htmlparser2" "^0.11.0"
deepmerge "^4.3.1"
dom-serializer "^2.0.0"
htmlparser2 "^8.0.2"
selderee "^0.11.0"
htmlparser2@^8.0.2:
version "8.0.2"
resolved "https://registry.yarnpkg.com/htmlparser2/-/htmlparser2-8.0.2.tgz#f002151705b383e62433b5cf466f5b716edaec21"
integrity sha512-GYdjWKDkbRLkZ5geuHs5NY1puJ+PXwP7+fHPRz06Eirsb9ugf6d8kkXav6ADhcODhFFPMIXyxkxSuMf3D6NCFA==
dependencies:
domelementtype "^2.3.0"
domhandler "^5.0.3"
domutils "^3.0.1"
entities "^4.4.0"
http-errors@2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/http-errors/-/http-errors-2.0.0.tgz#b7774a1486ef73cf7667ac9ae0858c012c57b9d3"
@ -1727,6 +1813,11 @@ langsmith@~0.1.30:
p-retry "4"
uuid "^9.0.0"
leac@^0.6.0:
version "0.6.0"
resolved "https://registry.yarnpkg.com/leac/-/leac-0.6.0.tgz#dcf136e382e666bd2475f44a1096061b70dc0912"
integrity sha512-y+SqErxb8h7nE/fiEX07jsbuhrpO9lL8eca7/Y1nuWV2moNlXhyd59iDGcRf6moVyDMbmTNzL40SUyrFU/yDpg==
lodash.set@^4.3.2:
version "4.3.2"
resolved "https://registry.yarnpkg.com/lodash.set/-/lodash.set-4.3.2.tgz#d8757b1da807dde24816b0d6a84bea1a76230b23"
@ -1907,6 +1998,11 @@ node-domexception@1.0.0:
resolved "https://registry.yarnpkg.com/node-domexception/-/node-domexception-1.0.0.tgz#6888db46a1f71c0b76b3f7555016b63fe64766e5"
integrity sha512-/jKZoMpw0F8GRwl4/eLROPA3cfcXtLApP0QzLmUT/HuPCZWyB7IY9ZrMeKw2O/nFIqPQB3PVM9aYm0F312AXDQ==
node-ensure@^0.0.0:
version "0.0.0"
resolved "https://registry.yarnpkg.com/node-ensure/-/node-ensure-0.0.0.tgz#ecae764150de99861ec5c810fd5d096b183932a7"
integrity sha512-DRI60hzo2oKN1ma0ckc6nQWlHU69RH6xN0sjQTjMpChPfTYvKZdcQFfdYK2RWbJcKyUizSIy/l8OTGxMAM1QDw==
node-fetch@^2.6.7:
version "2.7.0"
resolved "https://registry.yarnpkg.com/node-fetch/-/node-fetch-2.7.0.tgz#d0f0fa6e3e2dc1d27efcd8ad99d550bda94d187d"
@ -2071,6 +2167,14 @@ p-timeout@^3.2.0:
dependencies:
p-finally "^1.0.0"
parseley@^0.12.0:
version "0.12.1"
resolved "https://registry.yarnpkg.com/parseley/-/parseley-0.12.1.tgz#4afd561d50215ebe259e3e7a853e62f600683aef"
integrity sha512-e6qHKe3a9HWr0oMRVDTRhKce+bRO8VGQR3NyVwcjwrbhMmFCX9KszEV35+rn4AdilFAq9VPxP/Fe1wC9Qjd2lw==
dependencies:
leac "^0.6.0"
peberminta "^0.9.0"
parseurl@~1.3.3:
version "1.3.3"
resolved "https://registry.yarnpkg.com/parseurl/-/parseurl-1.3.3.tgz#9da19e7bee8d12dff0513ed5b76957793bc2e8d4"
@ -2081,6 +2185,19 @@ path-to-regexp@0.1.7:
resolved "https://registry.yarnpkg.com/path-to-regexp/-/path-to-regexp-0.1.7.tgz#df604178005f522f15eb4490e7247a1bfaa67f8c"
integrity sha512-5DFkuoqlv1uYQKxy8omFBeJPQcdoE07Kv2sferDCrAq1ohOU+MSDswDIbnx3YAM60qIOnYa53wBhXW0EbMonrQ==
pdf-parse@^1.1.1:
version "1.1.1"
resolved "https://registry.yarnpkg.com/pdf-parse/-/pdf-parse-1.1.1.tgz#745e07408679548b3995ff896fd38e96e19d14a7"
integrity sha512-v6ZJ/efsBpGrGGknjtq9J/oC8tZWq0KWL5vQrk2GlzLEQPUDB1ex+13Rmidl1neNN358Jn9EHZw5y07FFtaC7A==
dependencies:
debug "^3.1.0"
node-ensure "^0.0.0"
peberminta@^0.9.0:
version "0.9.0"
resolved "https://registry.yarnpkg.com/peberminta/-/peberminta-0.9.0.tgz#8ec9bc0eb84b7d368126e71ce9033501dca2a352"
integrity sha512-XIxfHpEuSJbITd1H3EeQwpcZbTLHc+VVr8ANI9t5sit565tsI4/xK3KWTUFE2e6QiangUkh3B0jihzmGnNrRsQ==
picomatch@^2.0.4, picomatch@^2.2.1:
version "2.3.1"
resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.3.1.tgz#3ba3833733646d9d3e4995946c1365a67fb07a42"
@ -2242,6 +2359,13 @@ safe-stable-stringify@^2.3.1:
resolved "https://registry.yarnpkg.com/safer-buffer/-/safer-buffer-2.1.2.tgz#44fa161b0187b9549dd84bb91802f9bd8385cd6a"
integrity sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg==
selderee@^0.11.0:
version "0.11.0"
resolved "https://registry.yarnpkg.com/selderee/-/selderee-0.11.0.tgz#6af0c7983e073ad3e35787ffe20cefd9daf0ec8a"
integrity sha512-5TF+l7p4+OsnP8BCCvSyZiSPc4x4//p5uPwK8TCnVPJYRmU2aYKMpOXvw8zM5a5JvuuCGN1jmsMwuU2W02ukfA==
dependencies:
parseley "^0.12.0"
semver@^7.3.5, semver@^7.5.3, semver@^7.5.4:
version "7.6.0"
resolved "https://registry.yarnpkg.com/semver/-/semver-7.6.0.tgz#1a46a4db4bffcccd97b743b5005c8325f23d4e2d"