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Merge remote-tracking branch 'origin/master' into ollama-auth

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projectmoon 2024-12-19 20:01:53 +01:00
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.prettierignore
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@ -35,4 +35,7 @@ coverage
*.swp
# Ignore all files with the .DS_Store extension (macOS specific)
.DS_Store
.DS_Store
# Ignore all files in uploads directory
uploads

4
CONTRIBUTING.md
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@ -8,6 +8,7 @@ Perplexica's design consists of two main domains:
- **Frontend (`ui` directory)**: This is a Next.js application holding all user interface components. It's a self-contained environment that manages everything the user interacts with.
- **Backend (root and `src` directory)**: The backend logic is situated in the `src` folder, but the root directory holds the main `package.json` for backend dependency management.
- All of the focus modes are created using the Meta Search Agent class present in `src/search/metaSearchAgent.ts`. The main logic behind Perplexica lies there.
## Setting Up Your Environment
@ -18,7 +19,8 @@ Before diving into coding, setting up your local environment is key. Here's what
1. In the root directory, locate the `sample.config.toml` file.
2. Rename it to `config.toml` and fill in the necessary configuration fields specific to the backend.
3. Run `npm install` to install dependencies.
4. Use `npm run dev` to start the backend in development mode.
4. Run `npm run db:push` to set up the local sqlite.
5. Use `npm run dev` to start the backend in development mode.
### Frontend

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README.md
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@ -1,6 +1,9 @@
# 🚀 Perplexica - An AI-powered search engine 🔎 <!-- omit in toc -->
![preview](.assets/perplexica-screenshot.png)
[![Discord](https://dcbadge.vercel.app/api/server/26aArMy8tT?style=flat&compact=true)](https://discord.gg/26aArMy8tT)
![preview](.assets/perplexica-screenshot.png?)
## Table of Contents <!-- omit in toc -->

2
app.dockerfile
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@ -1,4 +1,4 @@
FROM node:alpine
FROM node:20.18.0-alpine
ARG NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
ARG NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api

1
backend.dockerfile
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@ -9,6 +9,7 @@ COPY package.json /home/perplexica/
COPY yarn.lock /home/perplexica/
RUN mkdir /home/perplexica/data
RUN mkdir /home/perplexica/uploads
RUN yarn install --frozen-lockfile --network-timeout 600000
RUN yarn build

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docker-compose.yaml
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@ -22,6 +22,7 @@ services:
- 3001:3001
volumes:
- backend-dbstore:/home/perplexica/data
- uploads:/home/perplexica/uploads
- ./config.toml:/home/perplexica/config.toml
extra_hosts:
- 'host.docker.internal:host-gateway'
@ -50,3 +51,4 @@ networks:
volumes:
backend-dbstore:
uploads:

34
docs/API/SEARCH.md
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@ -79,24 +79,24 @@ The response from the API includes both the final message and the sources used t
```json
{
"message": "Perplexica is an innovative, open-source AI-powered search engine designed to enhance the way users search for information online. Here are some key features and characteristics of Perplexica:\n\n- **AI-Powered Technology**: It utilizes advanced machine learning algorithms to not only retrieve information but also to understand the context and intent behind user queries, providing more relevant results [1][5].\n\n- **Open-Source**: Being open-source, Perplexica offers flexibility and transparency, allowing users to explore its functionalities without the constraints of proprietary software [3][10].",
"sources": [
{
"pageContent": "Perplexica is an innovative, open-source AI-powered search engine designed to enhance the way users search for information online.",
"metadata": {
"title": "What is Perplexica, and how does it function as an AI-powered search ...",
"url": "https://askai.glarity.app/search/What-is-Perplexica--and-how-does-it-function-as-an-AI-powered-search-engine"
}
},
{
"pageContent": "Perplexica is an open-source AI-powered search tool that dives deep into the internet to find precise answers.",
"metadata": {
"title": "Sahar Mor's Post",
"url": "https://www.linkedin.com/posts/sahar-mor_a-new-open-source-project-called-perplexica-activity-7204489745668694016-ncja"
}
}
"message": "Perplexica is an innovative, open-source AI-powered search engine designed to enhance the way users search for information online. Here are some key features and characteristics of Perplexica:\n\n- **AI-Powered Technology**: It utilizes advanced machine learning algorithms to not only retrieve information but also to understand the context and intent behind user queries, providing more relevant results [1][5].\n\n- **Open-Source**: Being open-source, Perplexica offers flexibility and transparency, allowing users to explore its functionalities without the constraints of proprietary software [3][10].",
"sources": [
{
"pageContent": "Perplexica is an innovative, open-source AI-powered search engine designed to enhance the way users search for information online.",
"metadata": {
"title": "What is Perplexica, and how does it function as an AI-powered search ...",
"url": "https://askai.glarity.app/search/What-is-Perplexica--and-how-does-it-function-as-an-AI-powered-search-engine"
}
},
{
"pageContent": "Perplexica is an open-source AI-powered search tool that dives deep into the internet to find precise answers.",
"metadata": {
"title": "Sahar Mor's Post",
"url": "https://www.linkedin.com/posts/sahar-mor_a-new-open-source-project-called-perplexica-activity-7204489745668694016-ncja"
}
}
....
]
]
}
```

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docs/architecture/README.md
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@ -1,4 +1,4 @@
## Perplexica's Architecture
# Perplexica's Architecture
Perplexica's architecture consists of the following key components:

6
docs/architecture/WORKING.md
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@ -1,4 +1,4 @@
## How does Perplexica work?
# How does Perplexica work?
Curious about how Perplexica works? Don't worry, we'll cover it here. Before we begin, make sure you've read about the architecture of Perplexica to ensure you understand what it's made up of. Haven't read it? You can read it [here](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/architecture/README.md).
@ -10,10 +10,10 @@ We'll understand how Perplexica works by taking an example of a scenario where a
4. After the information is retrieved, it is based on keyword-based search. We then convert the information into embeddings and the query as well, then we perform a similarity search to find the most relevant sources to answer the query.
5. After all this is done, the sources are passed to the response generator. This chain takes all the chat history, the query, and the sources. It generates a response that is streamed to the UI.
### How are the answers cited?
## How are the answers cited?
The LLMs are prompted to do so. We've prompted them so well that they cite the answers themselves, and using some UI magic, we display it to the user.
### Image and Video Search
## Image and Video Search
Image and video searches are conducted in a similar manner. A query is always generated first, then we search the web for images and videos that match the query. These results are then returned to the user.

90
docs/installation/NETWORKING.md
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@ -10,27 +10,27 @@ This guide will show you how to make Perplexica available over a network. Follow
3. Stop and remove the existing Perplexica containers and images:
```
docker compose down --rmi all
```
```bash
docker compose down --rmi all
```
4. Open the `docker-compose.yaml` file in a text editor like Notepad++
5. Replace `127.0.0.1` with the IP address of the server Perplexica is running on in these two lines:
```
args:
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
```
```bash
args:
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
```
6. Save and close the `docker-compose.yaml` file
7. Rebuild and restart the Perplexica container:
```
docker compose up -d --build
```
```bash
docker compose up -d --build
```
## macOS
@ -38,37 +38,37 @@ docker compose up -d --build
2. Navigate to the directory with the `docker-compose.yaml` file:
```
cd /path/to/docker-compose.yaml
```
```bash
cd /path/to/docker-compose.yaml
```
3. Stop and remove existing containers and images:
```
docker compose down --rmi all
```
```bash
docker compose down --rmi all
```
4. Open `docker-compose.yaml` in a text editor like Sublime Text:
```
nano docker-compose.yaml
```
```bash
nano docker-compose.yaml
```
5. Replace `127.0.0.1` with the server IP in these lines:
```
args:
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
```
```bash
args:
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
```
6. Save and exit the editor
7. Rebuild and restart Perplexica:
```
docker compose up -d --build
```
```bash
docker compose up -d --build
```
## Linux
@ -76,34 +76,34 @@ docker compose up -d --build
2. Navigate to the `docker-compose.yaml` directory:
```
cd /path/to/docker-compose.yaml
```
```bash
cd /path/to/docker-compose.yaml
```
3. Stop and remove containers and images:
```
docker compose down --rmi all
```
```bash
docker compose down --rmi all
```
4. Edit `docker-compose.yaml`:
```
nano docker-compose.yaml
```
```bash
nano docker-compose.yaml
```
5. Replace `127.0.0.1` with the server IP:
```
args:
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
```
```bash
args:
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
```
6. Save and exit the editor
7. Rebuild and restart Perplexica:
```
docker compose up -d --build
```
```bash
docker compose up -d --build
```

24
docs/installation/UPDATING.md
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@ -6,23 +6,23 @@ To update Perplexica to the latest version, follow these steps:
1. Clone the latest version of Perplexica from GitHub:
```bash
git clone https://github.com/ItzCrazyKns/Perplexica.git
```
```bash
git clone https://github.com/ItzCrazyKns/Perplexica.git
```
2. Navigate to the Project Directory.
3. Pull latest images from registry.
```bash
docker compose pull
```
```bash
docker compose pull
```
4. Update and Recreate containers.
```bash
docker compose up -d
```
```bash
docker compose up -d
```
5. Once the command completes running go to http://localhost:3000 and verify the latest changes.
@ -30,9 +30,9 @@ docker compose up -d
1. Clone the latest version of Perplexica from GitHub:
```bash
git clone https://github.com/ItzCrazyKns/Perplexica.git
```
```bash
git clone https://github.com/ItzCrazyKns/Perplexica.git
```
2. Navigate to the Project Directory
3. Execute `npm i` in both the `ui` folder and the root directory.

8
package.json
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@ -1,12 +1,12 @@
{
"name": "perplexica-backend",
"version": "1.9.2",
"version": "1.10.0-rc2",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {
"start": "npm run db:push && node dist/app.js",
"build": "tsc",
"dev": "nodemon src/app.ts",
"dev": "nodemon --ignore uploads/ src/app.ts ",
"db:push": "drizzle-kit push sqlite",
"format": "prettier . --check",
"format:write": "prettier . --write"
@ -16,6 +16,7 @@
"@types/cors": "^2.8.17",
"@types/express": "^4.17.21",
"@types/html-to-text": "^9.0.4",
"@types/multer": "^1.4.12",
"@types/pdf-parse": "^1.1.4",
"@types/readable-stream": "^4.0.11",
"@types/ws": "^8.5.12",
@ -30,6 +31,7 @@
"@langchain/anthropic": "^0.2.3",
"@langchain/community": "^0.2.16",
"@langchain/openai": "^0.0.25",
"@langchain/google-genai": "^0.0.23",
"@xenova/transformers": "^2.17.1",
"axios": "^1.6.8",
"better-sqlite3": "^11.0.0",
@ -41,6 +43,8 @@
"express": "^4.19.2",
"html-to-text": "^9.0.5",
"langchain": "^0.1.30",
"mammoth": "^1.8.0",
"multer": "^1.4.5-lts.1",
"pdf-parse": "^1.1.1",
"winston": "^3.13.0",
"ws": "^8.17.1",

2
sample.config.toml
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@ -1,11 +1,13 @@
[GENERAL]
PORT = 3001 # Port to run the server on
SIMILARITY_MEASURE = "cosine" # "cosine" or "dot"
KEEP_ALIVE = "5m" # How long to keep Ollama models loaded into memory. (Instead of using -1 use "-1m")
[API_KEYS]
OPENAI = "" # OpenAI API key - sk-1234567890abcdef1234567890abcdef
GROQ = "" # Groq API key - gsk_1234567890abcdef1234567890abcdef
ANTHROPIC = "" # Anthropic API key - sk-ant-1234567890abcdef1234567890abcdef
GEMINI = "" # Gemini API key - sk-1234567890abcdef1234567890abcdef
[API_ENDPOINTS]
SEARXNG = "http://localhost:32768" # SearxNG API URL

280
src/agents/academicSearchAgent.ts
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@ -1,280 +0,0 @@
import { BaseMessage } from '@langchain/core/messages';
import {
PromptTemplate,
ChatPromptTemplate,
MessagesPlaceholder,
} from '@langchain/core/prompts';
import {
RunnableSequence,
RunnableMap,
RunnableLambda,
} from '@langchain/core/runnables';
import { StringOutputParser } from '@langchain/core/output_parsers';
import { Document } from '@langchain/core/documents';
import { searchSearxng } from '../lib/searxng';
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import formatChatHistoryAsString from '../utils/formatHistory';
import eventEmitter from 'events';
import computeSimilarity from '../utils/computeSimilarity';
import logger from '../utils/logger';
import { IterableReadableStream } from '@langchain/core/utils/stream';
const 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.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: How does stable diffusion work?
Rephrased: Stable diffusion working
2. Follow up question: What is linear algebra?
Rephrased: Linear algebra
3. Follow up question: What is the third law of thermodynamics?
Rephrased: Third law of thermodynamics
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
const basicAcademicSearchResponsePrompt = `
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
talk about the context in your response.
<context>
{context}
</context>
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
Anything between the \`context\` is retrieved from a search engine and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
`;
const strParser = new StringOutputParser();
const handleStream = async (
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {
if (
event.event === 'on_chain_end' &&
event.name === 'FinalSourceRetriever'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'sources', data: event.data.output }),
);
}
if (
event.event === 'on_chain_stream' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'response', data: event.data.chunk }),
);
}
if (
event.event === 'on_chain_end' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit('end');
}
}
};
type BasicChainInput = {
chat_history: BaseMessage[];
query: string;
};
const createBasicAcademicSearchRetrieverChain = (llm: BaseChatModel) => {
return RunnableSequence.from([
PromptTemplate.fromTemplate(basicAcademicSearchRetrieverPrompt),
llm,
strParser,
RunnableLambda.from(async (input: string) => {
if (input === 'not_needed') {
return { query: '', docs: [] };
}
const res = await searchSearxng(input, {
language: 'en',
engines: ['arxiv', 'google scholar', 'pubmed'],
});
const documents = res.results.map(
(result) =>
new Document({
pageContent: result.content,
metadata: {
title: result.title,
url: result.url,
...(result.img_src && { img_src: result.img_src }),
},
}),
);
return { query: input, docs: documents };
}),
]);
};
const createBasicAcademicSearchAnsweringChain = (
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const basicAcademicSearchRetrieverChain =
createBasicAcademicSearchRetrieverChain(llm);
const processDocs = async (docs: Document[]) => {
return docs
.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
.join('\n');
};
const rerankDocs = async ({
query,
docs,
}: {
query: string;
docs: Document[];
}) => {
if (docs.length === 0) {
return docs;
}
const docsWithContent = docs.filter(
(doc) => doc.pageContent && doc.pageContent.length > 0,
);
if (optimizationMode === 'speed') {
return docsWithContent.slice(0, 15);
} else if (optimizationMode === 'balanced') {
const [docEmbeddings, queryEmbedding] = await Promise.all([
embeddings.embedDocuments(
docsWithContent.map((doc) => doc.pageContent),
),
embeddings.embedQuery(query),
]);
const similarity = docEmbeddings.map((docEmbedding, i) => {
const sim = computeSimilarity(queryEmbedding, docEmbedding);
return {
index: i,
similarity: sim,
};
});
const sortedDocs = similarity
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => docsWithContent[sim.index]);
return sortedDocs;
}
};
return RunnableSequence.from([
RunnableMap.from({
query: (input: BasicChainInput) => input.query,
chat_history: (input: BasicChainInput) => input.chat_history,
context: RunnableSequence.from([
(input) => ({
query: input.query,
chat_history: formatChatHistoryAsString(input.chat_history),
}),
basicAcademicSearchRetrieverChain
.pipe(rerankDocs)
.withConfig({
runName: 'FinalSourceRetriever',
})
.pipe(processDocs),
]),
}),
ChatPromptTemplate.fromMessages([
['system', basicAcademicSearchResponsePrompt],
new MessagesPlaceholder('chat_history'),
['user', '{query}'],
]),
llm,
strParser,
]).withConfig({
runName: 'FinalResponseGenerator',
});
};
const basicAcademicSearch = (
query: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const emitter = new eventEmitter();
try {
const basicAcademicSearchAnsweringChain =
createBasicAcademicSearchAnsweringChain(
llm,
embeddings,
optimizationMode,
);
const stream = basicAcademicSearchAnsweringChain.streamEvents(
{
chat_history: history,
query: query,
},
{
version: 'v1',
},
);
handleStream(stream, emitter);
} catch (err) {
emitter.emit(
'error',
JSON.stringify({ data: 'An error has occurred please try again later' }),
);
logger.error(`Error in academic search: ${err}`);
}
return emitter;
};
const handleAcademicSearch = (
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const emitter = basicAcademicSearch(
message,
history,
llm,
embeddings,
optimizationMode,
);
return emitter;
};
export default handleAcademicSearch;

276
src/agents/redditSearchAgent.ts
View File

@ -1,276 +0,0 @@
import { BaseMessage } from '@langchain/core/messages';
import {
PromptTemplate,
ChatPromptTemplate,
MessagesPlaceholder,
} from '@langchain/core/prompts';
import {
RunnableSequence,
RunnableMap,
RunnableLambda,
} from '@langchain/core/runnables';
import { StringOutputParser } from '@langchain/core/output_parsers';
import { Document } from '@langchain/core/documents';
import { searchSearxng } from '../lib/searxng';
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import formatChatHistoryAsString from '../utils/formatHistory';
import eventEmitter from 'events';
import computeSimilarity from '../utils/computeSimilarity';
import logger from '../utils/logger';
import { IterableReadableStream } from '@langchain/core/utils/stream';
const basicRedditSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: Which company is most likely to create an AGI
Rephrased: Which company is most likely to create an AGI
2. Follow up question: Is Earth flat?
Rephrased: Is Earth flat?
3. Follow up question: Is there life on Mars?
Rephrased: Is there life on Mars?
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
const basicRedditSearchResponsePrompt = `
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Reddit', this means you will be searching for information, opinions and discussions on the web using Reddit.
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Reddit and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
talk about the context in your response.
<context>
{context}
</context>
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
Anything between the \`context\` is retrieved from Reddit and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
`;
const strParser = new StringOutputParser();
const handleStream = async (
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {
if (
event.event === 'on_chain_end' &&
event.name === 'FinalSourceRetriever'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'sources', data: event.data.output }),
);
}
if (
event.event === 'on_chain_stream' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'response', data: event.data.chunk }),
);
}
if (
event.event === 'on_chain_end' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit('end');
}
}
};
type BasicChainInput = {
chat_history: BaseMessage[];
query: string;
};
const createBasicRedditSearchRetrieverChain = (llm: BaseChatModel) => {
return RunnableSequence.from([
PromptTemplate.fromTemplate(basicRedditSearchRetrieverPrompt),
llm,
strParser,
RunnableLambda.from(async (input: string) => {
if (input === 'not_needed') {
return { query: '', docs: [] };
}
const res = await searchSearxng(input, {
language: 'en',
engines: ['reddit'],
});
const documents = res.results.map(
(result) =>
new Document({
pageContent: result.content ? result.content : result.title,
metadata: {
title: result.title,
url: result.url,
...(result.img_src && { img_src: result.img_src }),
},
}),
);
return { query: input, docs: documents };
}),
]);
};
const createBasicRedditSearchAnsweringChain = (
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const basicRedditSearchRetrieverChain =
createBasicRedditSearchRetrieverChain(llm);
const processDocs = async (docs: Document[]) => {
return docs
.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
.join('\n');
};
const rerankDocs = async ({
query,
docs,
}: {
query: string;
docs: Document[];
}) => {
if (docs.length === 0) {
return docs;
}
const docsWithContent = docs.filter(
(doc) => doc.pageContent && doc.pageContent.length > 0,
);
if (optimizationMode === 'speed') {
return docsWithContent.slice(0, 15);
} else if (optimizationMode === 'balanced') {
const [docEmbeddings, queryEmbedding] = await Promise.all([
embeddings.embedDocuments(
docsWithContent.map((doc) => doc.pageContent),
),
embeddings.embedQuery(query),
]);
const similarity = docEmbeddings.map((docEmbedding, i) => {
const sim = computeSimilarity(queryEmbedding, docEmbedding);
return {
index: i,
similarity: sim,
};
});
const sortedDocs = similarity
.filter((sim) => sim.similarity > 0.3)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => docsWithContent[sim.index]);
return sortedDocs;
}
};
return RunnableSequence.from([
RunnableMap.from({
query: (input: BasicChainInput) => input.query,
chat_history: (input: BasicChainInput) => input.chat_history,
context: RunnableSequence.from([
(input) => ({
query: input.query,
chat_history: formatChatHistoryAsString(input.chat_history),
}),
basicRedditSearchRetrieverChain
.pipe(rerankDocs)
.withConfig({
runName: 'FinalSourceRetriever',
})
.pipe(processDocs),
]),
}),
ChatPromptTemplate.fromMessages([
['system', basicRedditSearchResponsePrompt],
new MessagesPlaceholder('chat_history'),
['user', '{query}'],
]),
llm,
strParser,
]).withConfig({
runName: 'FinalResponseGenerator',
});
};
const basicRedditSearch = (
query: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const emitter = new eventEmitter();
try {
const basicRedditSearchAnsweringChain =
createBasicRedditSearchAnsweringChain(llm, embeddings, optimizationMode);
const stream = basicRedditSearchAnsweringChain.streamEvents(
{
chat_history: history,
query: query,
},
{
version: 'v1',
},
);
handleStream(stream, emitter);
} catch (err) {
emitter.emit(
'error',
JSON.stringify({ data: 'An error has occurred please try again later' }),
);
logger.error(`Error in RedditSearch: ${err}`);
}
return emitter;
};
const handleRedditSearch = (
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const emitter = basicRedditSearch(
message,
history,
llm,
embeddings,
optimizationMode,
);
return emitter;
};
export default handleRedditSearch;

460
src/agents/webSearchAgent.ts
View File

@ -1,460 +0,0 @@
import { BaseMessage } from '@langchain/core/messages';
import {
PromptTemplate,
ChatPromptTemplate,
MessagesPlaceholder,
} from '@langchain/core/prompts';
import {
RunnableSequence,
RunnableMap,
RunnableLambda,
} from '@langchain/core/runnables';
import { StringOutputParser } from '@langchain/core/output_parsers';
import { Document } from '@langchain/core/documents';
import { searchSearxng } from '../lib/searxng';
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import formatChatHistoryAsString from '../utils/formatHistory';
import eventEmitter from 'events';
import computeSimilarity from '../utils/computeSimilarity';
import logger from '../utils/logger';
import LineListOutputParser from '../lib/outputParsers/listLineOutputParser';
import { 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 are an AI question rephraser. You will be given a conversation and a follow-up question, you will have to rephrase the follow up question so it is a standalone question and can be used by another LLM to search the web for information to answer it.
If it is a smple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
There are several examples attached for your reference inside the below \`examples\` XML block
<examples>
1. Follow up question: What is the capital of France
Rephrased question:\`
<question>
Capital of france
</question>
\`
2. Hi, how are you?
Rephrased question\`
<question>
not_needed
</question>
\`
3. Follow up question: What is Docker?
Rephrased question: \`
<question>
What is Docker
</question>
\`
4. Follow up question: Can you tell me what is X from https://example.com
Rephrased question: \`
<question>
Can you tell me what is X?
</question>
<links>
https://example.com
</links>
\`
5. Follow up question: Summarize the content from https://example.com
Rephrased question: \`
<question>
summarize
</question>
<links>
https://example.com
</links>
\`
</examples>
Anything below is the part of the actual conversation and you need to use conversation and the follow-up question to rephrase the follow-up question as a standalone question based on the guidelines shared above.
<conversation>
{chat_history}
</conversation>
Follow up question: {query}
Rephrased question:
`;
const basicWebSearchResponsePrompt = `
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are also an expert at summarizing web pages or documents and searching for content in them.
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
If the query contains some links and the user asks to answer from those links you will be provided the entire content of the page inside the \`context\` XML block. You can then use this content to answer the user's query.
If the user asks to summarize content from some links, you will be provided the entire content of the page inside the \`context\` XML block. You can then use this content to summarize the text. The content provided inside the \`context\` block will be already summarized by another model so you just need to use that content to answer the user's query.
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
talk about the context in your response.
<context>
{context}
</context>
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'. You do not need to do this for summarization tasks.
Anything between the \`context\` is retrieved from a search engine and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
`;
const strParser = new StringOutputParser();
const handleStream = async (
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {
if (
event.event === 'on_chain_end' &&
event.name === 'FinalSourceRetriever'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'sources', data: event.data.output }),
);
}
if (
event.event === 'on_chain_stream' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'response', data: event.data.chunk }),
);
}
if (
event.event === 'on_chain_end' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit('end');
}
}
};
type BasicChainInput = {
chat_history: BaseMessage[];
query: string;
};
const createBasicWebSearchRetrieverChain = (llm: BaseChatModel) => {
(llm as unknown as ChatOpenAI).temperature = 0;
return RunnableSequence.from([
PromptTemplate.fromTemplate(basicSearchRetrieverPrompt),
llm,
strParser,
RunnableLambda.from(async (input: string) => {
const linksOutputParser = new LineListOutputParser({
key: 'links',
});
const questionOutputParser = new LineOutputParser({
key: 'question',
});
const links = await linksOutputParser.parse(input);
let question = await questionOutputParser.parse(input);
if (question === 'not_needed') {
return { query: '', docs: [] };
}
if (links.length > 0) {
if (question.length === 0) {
question = 'summarize';
}
let docs = [];
const linkDocs = await getDocumentsFromLinks({ links });
const docGroups: Document[] = [];
linkDocs.map((doc) => {
const URLDocExists = docGroups.find(
(d) =>
d.metadata.url === doc.metadata.url && d.metadata.totalDocs < 10,
);
if (!URLDocExists) {
docGroups.push({
...doc,
metadata: {
...doc.metadata,
totalDocs: 1,
},
});
}
const docIndex = docGroups.findIndex(
(d) =>
d.metadata.url === doc.metadata.url && d.metadata.totalDocs < 10,
);
if (docIndex !== -1) {
docGroups[docIndex].pageContent =
docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
docGroups[docIndex].metadata.totalDocs += 1;
}
});
await Promise.all(
docGroups.map(async (doc) => {
const res = await llm.invoke(`
You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the
text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
- **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
- **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
- **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
The text will be shared inside the \`text\` XML tag, and the query inside the \`query\` XML tag.
<example>
1. \`<text>
Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers.
It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications
by using containers.
</text>
<query>
What is Docker and how does it work?
</query>
Response:
Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application
deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in
any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
\`
2. \`<text>
The theory of relativity, or simply relativity, encompasses two interrelated theories of Albert Einstein: special relativity and general
relativity. However, the word "relativity" is sometimes used in reference to Galilean invariance. The term "theory of relativity" was based
on the expression "relative theory" used by Max Planck in 1906. The theory of relativity usually encompasses two interrelated theories by
Albert Einstein: special relativity and general relativity. Special relativity applies to all physical phenomena in the absence of gravity.
General relativity explains the law of gravitation and its relation to other forces of nature. It applies to the cosmological and astrophysical
realm, including astronomy.
</text>
<query>
summarize
</query>
Response:
The theory of relativity, developed by Albert Einstein, encompasses two main theories: special relativity and general relativity. Special
relativity applies to all physical phenomena in the absence of gravity, while general relativity explains the law of gravitation and its
relation to other forces of nature. The theory of relativity is based on the concept of "relative theory," as introduced by Max Planck in
1906. It is a fundamental theory in physics that has revolutionized our understanding of the universe.
\`
</example>
Everything below is the actual data you will be working with. Good luck!
<query>
${question}
</query>
<text>
${doc.pageContent}
</text>
Make sure to answer the query in the summary.
`);
const document = new Document({
pageContent: res.content as string,
metadata: {
title: doc.metadata.title,
url: doc.metadata.url,
},
});
docs.push(document);
}),
);
return { query: question, docs: docs };
} else {
const res = await searchSearxng(question, {
language: 'en',
});
const documents = res.results.map(
(result) =>
new Document({
pageContent: result.content,
metadata: {
title: result.title,
url: result.url,
...(result.img_src && { img_src: result.img_src }),
},
}),
);
return { query: question, docs: documents };
}
}),
]);
};
const createBasicWebSearchAnsweringChain = (
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const basicWebSearchRetrieverChain = createBasicWebSearchRetrieverChain(llm);
const processDocs = async (docs: Document[]) => {
return docs
.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
.join('\n');
};
const rerankDocs = async ({
query,
docs,
}: {
query: string;
docs: Document[];
}) => {
if (docs.length === 0) {
return docs;
}
if (query.toLocaleLowerCase() === 'summarize') {
return docs;
}
const docsWithContent = docs.filter(
(doc) => doc.pageContent && doc.pageContent.length > 0,
);
if (optimizationMode === 'speed') {
return docsWithContent.slice(0, 15);
} else if (optimizationMode === 'balanced') {
const [docEmbeddings, queryEmbedding] = await Promise.all([
embeddings.embedDocuments(
docsWithContent.map((doc) => doc.pageContent),
),
embeddings.embedQuery(query),
]);
const similarity = docEmbeddings.map((docEmbedding, i) => {
const sim = computeSimilarity(queryEmbedding, docEmbedding);
return {
index: i,
similarity: sim,
};
});
const sortedDocs = similarity
.filter((sim) => sim.similarity > 0.3)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => docsWithContent[sim.index]);
return sortedDocs;
}
};
return RunnableSequence.from([
RunnableMap.from({
query: (input: BasicChainInput) => input.query,
chat_history: (input: BasicChainInput) => input.chat_history,
context: RunnableSequence.from([
(input) => ({
query: input.query,
chat_history: formatChatHistoryAsString(input.chat_history),
}),
basicWebSearchRetrieverChain
.pipe(rerankDocs)
.withConfig({
runName: 'FinalSourceRetriever',
})
.pipe(processDocs),
]),
}),
ChatPromptTemplate.fromMessages([
['system', basicWebSearchResponsePrompt],
new MessagesPlaceholder('chat_history'),
['user', '{query}'],
]),
llm,
strParser,
]).withConfig({
runName: 'FinalResponseGenerator',
});
};
const basicWebSearch = (
query: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const emitter = new eventEmitter();
try {
const basicWebSearchAnsweringChain = createBasicWebSearchAnsweringChain(
llm,
embeddings,
optimizationMode,
);
const stream = basicWebSearchAnsweringChain.streamEvents(
{
chat_history: history,
query: query,
},
{
version: 'v1',
},
);
handleStream(stream, emitter);
} catch (err) {
emitter.emit(
'error',
JSON.stringify({ data: 'An error has occurred please try again later' }),
);
logger.error(`Error in websearch: ${err}`);
}
return emitter;
};
const handleWebSearch = (
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const emitter = basicWebSearch(
message,
history,
llm,
embeddings,
optimizationMode,
);
return emitter;
};
export default handleWebSearch;

220
src/agents/wolframAlphaSearchAgent.ts
View File

@ -1,220 +0,0 @@
import { BaseMessage } from '@langchain/core/messages';
import {
PromptTemplate,
ChatPromptTemplate,
MessagesPlaceholder,
} from '@langchain/core/prompts';
import {
RunnableSequence,
RunnableMap,
RunnableLambda,
} from '@langchain/core/runnables';
import { StringOutputParser } from '@langchain/core/output_parsers';
import { Document } from '@langchain/core/documents';
import { searchSearxng } from '../lib/searxng';
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import formatChatHistoryAsString from '../utils/formatHistory';
import eventEmitter from 'events';
import logger from '../utils/logger';
import { IterableReadableStream } from '@langchain/core/utils/stream';
const basicWolframAlphaSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: What is the atomic radius of S?
Rephrased: Atomic radius of S
2. Follow up question: What is linear algebra?
Rephrased: Linear algebra
3. Follow up question: What is the third law of thermodynamics?
Rephrased: Third law of thermodynamics
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
const basicWolframAlphaSearchResponsePrompt = `
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Wolfram Alpha', this means you will be searching for information on the web using Wolfram Alpha. It is a computational knowledge engine that can answer factual queries and perform computations.
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Wolfram Alpha and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
talk about the context in your response.
<context>
{context}
</context>
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
Anything between the \`context\` is retrieved from Wolfram Alpha and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
`;
const strParser = new StringOutputParser();
const handleStream = async (
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {
if (
event.event === 'on_chain_end' &&
event.name === 'FinalSourceRetriever'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'sources', data: event.data.output }),
);
}
if (
event.event === 'on_chain_stream' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'response', data: event.data.chunk }),
);
}
if (
event.event === 'on_chain_end' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit('end');
}
}
};
type BasicChainInput = {
chat_history: BaseMessage[];
query: string;
};
const createBasicWolframAlphaSearchRetrieverChain = (llm: BaseChatModel) => {
return RunnableSequence.from([
PromptTemplate.fromTemplate(basicWolframAlphaSearchRetrieverPrompt),
llm,
strParser,
RunnableLambda.from(async (input: string) => {
if (input === 'not_needed') {
return { query: '', docs: [] };
}
const res = await searchSearxng(input, {
language: 'en',
engines: ['wolframalpha'],
});
const documents = res.results.map(
(result) =>
new Document({
pageContent: result.content,
metadata: {
title: result.title,
url: result.url,
...(result.img_src && { img_src: result.img_src }),
},
}),
);
return { query: input, docs: documents };
}),
]);
};
const createBasicWolframAlphaSearchAnsweringChain = (llm: BaseChatModel) => {
const basicWolframAlphaSearchRetrieverChain =
createBasicWolframAlphaSearchRetrieverChain(llm);
const processDocs = (docs: Document[]) => {
return docs
.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
.join('\n');
};
return RunnableSequence.from([
RunnableMap.from({
query: (input: BasicChainInput) => input.query,
chat_history: (input: BasicChainInput) => input.chat_history,
context: RunnableSequence.from([
(input) => ({
query: input.query,
chat_history: formatChatHistoryAsString(input.chat_history),
}),
basicWolframAlphaSearchRetrieverChain
.pipe(({ query, docs }) => {
return docs;
})
.withConfig({
runName: 'FinalSourceRetriever',
})
.pipe(processDocs),
]),
}),
ChatPromptTemplate.fromMessages([
['system', basicWolframAlphaSearchResponsePrompt],
new MessagesPlaceholder('chat_history'),
['user', '{query}'],
]),
llm,
strParser,
]).withConfig({
runName: 'FinalResponseGenerator',
});
};
const basicWolframAlphaSearch = (
query: string,
history: BaseMessage[],
llm: BaseChatModel,
) => {
const emitter = new eventEmitter();
try {
const basicWolframAlphaSearchAnsweringChain =
createBasicWolframAlphaSearchAnsweringChain(llm);
const stream = basicWolframAlphaSearchAnsweringChain.streamEvents(
{
chat_history: history,
query: query,
},
{
version: 'v1',
},
);
handleStream(stream, emitter);
} catch (err) {
emitter.emit(
'error',
JSON.stringify({ data: 'An error has occurred please try again later' }),
);
logger.error(`Error in WolframAlphaSearch: ${err}`);
}
return emitter;
};
const handleWolframAlphaSearch = (
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
) => {
const emitter = basicWolframAlphaSearch(message, history, llm);
return emitter;
};
export default handleWolframAlphaSearch;

91
src/agents/writingAssistant.ts
View File

@ -1,91 +0,0 @@
import { BaseMessage } from '@langchain/core/messages';
import {
ChatPromptTemplate,
MessagesPlaceholder,
} from '@langchain/core/prompts';
import { RunnableSequence } from '@langchain/core/runnables';
import { StringOutputParser } from '@langchain/core/output_parsers';
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
import eventEmitter from 'events';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import logger from '../utils/logger';
import { IterableReadableStream } from '@langchain/core/utils/stream';
const writingAssistantPrompt = `
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query.
Since you are a writing assistant, you would not perform web searches. If you think you lack information to answer the query, you can ask the user for more information or suggest them to switch to a different focus mode.
`;
const strParser = new StringOutputParser();
const handleStream = async (
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {
if (
event.event === 'on_chain_stream' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'response', data: event.data.chunk }),
);
}
if (
event.event === 'on_chain_end' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit('end');
}
}
};
const createWritingAssistantChain = (llm: BaseChatModel) => {
return RunnableSequence.from([
ChatPromptTemplate.fromMessages([
['system', writingAssistantPrompt],
new MessagesPlaceholder('chat_history'),
['user', '{query}'],
]),
llm,
strParser,
]).withConfig({
runName: 'FinalResponseGenerator',
});
};
const handleWritingAssistant = (
query: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
) => {
const emitter = new eventEmitter();
try {
const writingAssistantChain = createWritingAssistantChain(llm);
const stream = writingAssistantChain.streamEvents(
{
chat_history: history,
query: query,
},
{
version: 'v1',
},
);
handleStream(stream, emitter);
} catch (err) {
emitter.emit(
'error',
JSON.stringify({ data: 'An error has occurred please try again later' }),
);
logger.error(`Error in writing assistant: ${err}`);
}
return emitter;
};
export default handleWritingAssistant;

277
src/agents/youtubeSearchAgent.ts
View File

@ -1,277 +0,0 @@
import { BaseMessage } from '@langchain/core/messages';
import {
PromptTemplate,
ChatPromptTemplate,
MessagesPlaceholder,
} from '@langchain/core/prompts';
import {
RunnableSequence,
RunnableMap,
RunnableLambda,
} from '@langchain/core/runnables';
import { StringOutputParser } from '@langchain/core/output_parsers';
import { Document } from '@langchain/core/documents';
import { searchSearxng } from '../lib/searxng';
import type { StreamEvent } from '@langchain/core/tracers/log_stream';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import formatChatHistoryAsString from '../utils/formatHistory';
import eventEmitter from 'events';
import computeSimilarity from '../utils/computeSimilarity';
import logger from '../utils/logger';
import { IterableReadableStream } from '@langchain/core/utils/stream';
const basicYoutubeSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: How does an A.C work?
Rephrased: A.C working
2. Follow up question: Linear algebra explanation video
Rephrased: What is linear algebra?
3. Follow up question: What is theory of relativity?
Rephrased: What is theory of relativity?
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
const basicYoutubeSearchResponsePrompt = `
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Youtube', this means you will be searching for videos on the web using Youtube and providing information based on the video's transcript.
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Youtube and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
talk about the context in your response.
<context>
{context}
</context>
If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
Anything between the \`context\` is retrieved from Youtube and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
`;
const strParser = new StringOutputParser();
const handleStream = async (
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) => {
for await (const event of stream) {
if (
event.event === 'on_chain_end' &&
event.name === 'FinalSourceRetriever'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'sources', data: event.data.output }),
);
}
if (
event.event === 'on_chain_stream' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'response', data: event.data.chunk }),
);
}
if (
event.event === 'on_chain_end' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit('end');
}
}
};
type BasicChainInput = {
chat_history: BaseMessage[];
query: string;
};
const createBasicYoutubeSearchRetrieverChain = (llm: BaseChatModel) => {
return RunnableSequence.from([
PromptTemplate.fromTemplate(basicYoutubeSearchRetrieverPrompt),
llm,
strParser,
RunnableLambda.from(async (input: string) => {
if (input === 'not_needed') {
return { query: '', docs: [] };
}
const res = await searchSearxng(input, {
language: 'en',
engines: ['youtube'],
});
const documents = res.results.map(
(result) =>
new Document({
pageContent: result.content ? result.content : result.title,
metadata: {
title: result.title,
url: result.url,
...(result.img_src && { img_src: result.img_src }),
},
}),
);
return { query: input, docs: documents };
}),
]);
};
const createBasicYoutubeSearchAnsweringChain = (
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const basicYoutubeSearchRetrieverChain =
createBasicYoutubeSearchRetrieverChain(llm);
const processDocs = async (docs: Document[]) => {
return docs
.map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
.join('\n');
};
const rerankDocs = async ({
query,
docs,
}: {
query: string;
docs: Document[];
}) => {
if (docs.length === 0) {
return docs;
}
const docsWithContent = docs.filter(
(doc) => doc.pageContent && doc.pageContent.length > 0,
);
if (optimizationMode === 'speed') {
return docsWithContent.slice(0, 15);
} else {
const [docEmbeddings, queryEmbedding] = await Promise.all([
embeddings.embedDocuments(
docsWithContent.map((doc) => doc.pageContent),
),
embeddings.embedQuery(query),
]);
const similarity = docEmbeddings.map((docEmbedding, i) => {
const sim = computeSimilarity(queryEmbedding, docEmbedding);
return {
index: i,
similarity: sim,
};
});
const sortedDocs = similarity
.filter((sim) => sim.similarity > 0.3)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => docsWithContent[sim.index]);
return sortedDocs;
}
};
return RunnableSequence.from([
RunnableMap.from({
query: (input: BasicChainInput) => input.query,
chat_history: (input: BasicChainInput) => input.chat_history,
context: RunnableSequence.from([
(input) => ({
query: input.query,
chat_history: formatChatHistoryAsString(input.chat_history),
}),
basicYoutubeSearchRetrieverChain
.pipe(rerankDocs)
.withConfig({
runName: 'FinalSourceRetriever',
})
.pipe(processDocs),
]),
}),
ChatPromptTemplate.fromMessages([
['system', basicYoutubeSearchResponsePrompt],
new MessagesPlaceholder('chat_history'),
['user', '{query}'],
]),
llm,
strParser,
]).withConfig({
runName: 'FinalResponseGenerator',
});
};
const basicYoutubeSearch = (
query: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const emitter = new eventEmitter();
try {
const basicYoutubeSearchAnsweringChain =
createBasicYoutubeSearchAnsweringChain(llm, embeddings, optimizationMode);
const stream = basicYoutubeSearchAnsweringChain.streamEvents(
{
chat_history: history,
query: query,
},
{
version: 'v1',
},
);
handleStream(stream, emitter);
} catch (err) {
emitter.emit(
'error',
JSON.stringify({ data: 'An error has occurred please try again later' }),
);
logger.error(`Error in youtube search: ${err}`);
}
return emitter;
};
const handleYoutubeSearch = (
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) => {
const emitter = basicYoutubeSearch(
message,
history,
llm,
embeddings,
optimizationMode,
);
return emitter;
};
export default handleYoutubeSearch;

0
src/agents/imageSearchAgent.ts → src/chains/imageSearchAgent.ts
View File

0
src/agents/suggestionGeneratorAgent.ts → src/chains/suggestionGeneratorAgent.ts
View File

0
src/agents/videoSearchAgent.ts → src/chains/videoSearchAgent.ts
View File

6
src/config.ts
View File

@ -8,11 +8,13 @@ interface Config {
GENERAL: {
PORT: number;
SIMILARITY_MEASURE: string;
KEEP_ALIVE: string;
};
API_KEYS: {
OPENAI: string;
GROQ: string;
ANTHROPIC: string;
GEMINI: string;
};
API_ENDPOINTS: {
SEARXNG: string;
@ -35,12 +37,16 @@ export const getPort = () => loadConfig().GENERAL.PORT;
export const getSimilarityMeasure = () =>
loadConfig().GENERAL.SIMILARITY_MEASURE;
export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
export const getOpenaiApiKey = () => loadConfig().API_KEYS.OPENAI;
export const getGroqApiKey = () => loadConfig().API_KEYS.GROQ;
export const getAnthropicApiKey = () => loadConfig().API_KEYS.ANTHROPIC;
export const getGeminiApiKey = () => loadConfig().API_KEYS.GEMINI;
export const getSearxngApiEndpoint = () =>
process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;

9
src/db/schema.ts
View File

@ -1,3 +1,4 @@
import { sql } from 'drizzle-orm';
import { text, integer, sqliteTable } from 'drizzle-orm/sqlite-core';
export const messages = sqliteTable('messages', {
@ -11,11 +12,19 @@ export const messages = sqliteTable('messages', {
}),
});
interface File {
name: string;
fileId: string;
}
export const chats = sqliteTable('chats', {
id: text('id').primaryKey(),
title: text('title').notNull(),
createdAt: text('createdAt').notNull(),
focusMode: text('focusMode').notNull(),
files: text('files', { mode: 'json' })
.$type<File[]>()
.default(sql`'[]'`),
});
export const settings = sqliteTable('settings', {

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

@ -9,12 +9,20 @@ export const loadAnthropicChatModels = async () => {
try {
const chatModels = {
'claude-3-5-sonnet-20240620': {
'claude-3-5-sonnet-20241022': {
displayName: 'Claude 3.5 Sonnet',
model: new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-5-sonnet-20240620',
model: 'claude-3-5-sonnet-20241022',
}),
},
'claude-3-5-haiku-20241022': {
displayName: 'Claude 3.5 Haiku',
model: new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-5-haiku-20241022',
}),
},
'claude-3-opus-20240229': {

69
src/lib/providers/gemini.ts Normal file
View File

@ -0,0 +1,69 @@
import {
ChatGoogleGenerativeAI,
GoogleGenerativeAIEmbeddings,
} from '@langchain/google-genai';
import { getGeminiApiKey } from '../../config';
import logger from '../../utils/logger';
export const loadGeminiChatModels = async () => {
const geminiApiKey = getGeminiApiKey();
if (!geminiApiKey) return {};
try {
const chatModels = {
'gemini-1.5-flash': {
displayName: 'Gemini 1.5 Flash',
model: new ChatGoogleGenerativeAI({
modelName: 'gemini-1.5-flash',
temperature: 0.7,
apiKey: geminiApiKey,
}),
},
'gemini-1.5-flash-8b': {
displayName: 'Gemini 1.5 Flash 8B',
model: new ChatGoogleGenerativeAI({
modelName: 'gemini-1.5-flash-8b',
temperature: 0.7,
apiKey: geminiApiKey,
}),
},
'gemini-1.5-pro': {
displayName: 'Gemini 1.5 Pro',
model: new ChatGoogleGenerativeAI({
modelName: 'gemini-1.5-pro',
temperature: 0.7,
apiKey: geminiApiKey,
}),
},
};
return chatModels;
} catch (err) {
logger.error(`Error loading Gemini models: ${err}`);
return {};
}
};
export const loadGeminiEmbeddingsModels = async () => {
const geminiApiKey = getGeminiApiKey();
if (!geminiApiKey) return {};
try {
const embeddingModels = {
'text-embedding-004': {
displayName: 'Text Embedding',
model: new GoogleGenerativeAIEmbeddings({
apiKey: geminiApiKey,
modelName: 'text-embedding-004',
}),
},
};
return embeddingModels;
} catch (err) {
logger.error(`Error loading Gemini embeddings model: ${err}`);
return {};
}
};

3
src/lib/providers/index.ts
View File

@ -3,18 +3,21 @@ import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
import { loadAnthropicChatModels } from './anthropic';
import { loadTransformersEmbeddingsModels } from './transformers';
import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
const chatModelProviders = {
openai: loadOpenAIChatModels,
groq: loadGroqChatModels,
ollama: loadOllamaChatModels,
anthropic: loadAnthropicChatModels,
gemini: loadGeminiChatModels,
};
const embeddingModelProviders = {
openai: loadOpenAIEmbeddingsModels,
local: loadTransformersEmbeddingsModels,
ollama: loadOllamaEmbeddingsModels,
gemini: loadGeminiEmbeddingsModels,
};
export const getAvailableChatModelProviders = async () => {

124
src/lib/providers/ollama.ts
View File

@ -1,83 +1,89 @@
import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
import { getOllamaApiEndpoint, getOllamaAuthHeader } from '../../config';
import {
getKeepAlive,
getOllamaApiEndpoint,
getOllamaAuthHeader,
} from '../../config';
import logger from '../../utils/logger';
import { ChatOllama } from '@langchain/community/chat_models/ollama';
function getOllamaHeaders() {
const ollamaAuthHeader = getOllamaAuthHeader();
if (typeof ollamaAuthHeader !== undefined) {
return {
'Content-Type': 'application/json',
'Authorization': ollamaAuthHeader
};
} else {
return { 'Content-Type': 'application/json' };
}
const ollamaAuthHeader = getOllamaAuthHeader();
if (typeof ollamaAuthHeader !== undefined) {
return {
'Content-Type': 'application/json',
Authorization: ollamaAuthHeader,
};
} else {
return { 'Content-Type': 'application/json' };
}
}
export const loadOllamaChatModels = async () => {
const ollamaEndpoint = getOllamaApiEndpoint();
const ollamaHeaders = getOllamaHeaders();
const ollamaEndpoint = getOllamaApiEndpoint();
const ollamaHeaders = getOllamaHeaders();
const keepAlive = getKeepAlive();
if (!ollamaEndpoint) return {};
if (!ollamaEndpoint) return {};
try {
const response = await fetch(`${ollamaEndpoint}/api/tags`, {
headers: ollamaHeaders
});
try {
const response = await fetch(`${ollamaEndpoint}/api/tags`, {
headers: ollamaHeaders,
});
const { models: ollamaModels } = (await response.json()) as any;
const { models: ollamaModels } = (await response.json()) as any;
const chatModels = ollamaModels.reduce((acc, model) => {
acc[model.model] = {
displayName: model.name,
model: new ChatOllama({
baseUrl: ollamaEndpoint,
headers: ollamaHeaders,
model: model.model,
temperature: 0.7,
}),
};
const chatModels = ollamaModels.reduce((acc, model) => {
acc[model.model] = {
displayName: model.name,
model: new ChatOllama({
headers: ollamaHeaders,
baseUrl: ollamaEndpoint,
model: model.model,
temperature: 0.7,
keepAlive: keepAlive,
}),
};
return acc;
}, {});
return acc;
}, {});
return chatModels;
} catch (err) {
logger.error(`Error loading Ollama models: ${err}`);
return {};
}
return chatModels;
} catch (err) {
logger.error(`Error loading Ollama models: ${err}`);
return {};
}
};
export const loadOllamaEmbeddingsModels = async () => {
const ollamaEndpoint = getOllamaApiEndpoint();
const ollamaHeaders = getOllamaHeaders();
const ollamaEndpoint = getOllamaApiEndpoint();
const ollamaHeaders = getOllamaHeaders();
if (!ollamaEndpoint) return {};
if (!ollamaEndpoint) return {};
try {
const response = await fetch(`${ollamaEndpoint}/api/tags`, {
headers: ollamaHeaders
});
try {
const response = await fetch(`${ollamaEndpoint}/api/tags`, {
headers: ollamaHeaders,
});
const { models: ollamaModels } = (await response.json()) as any;
const { models: ollamaModels } = (await response.json()) as any;
const embeddingsModels = ollamaModels.reduce((acc, model) => {
acc[model.model] = {
displayName: model.name,
model: new OllamaEmbeddings({
baseUrl: ollamaEndpoint,
headers: ollamaHeaders,
model: model.model,
}),
};
const embeddingsModels = ollamaModels.reduce((acc, model) => {
acc[model.model] = {
displayName: model.name,
model: new OllamaEmbeddings({
baseUrl: ollamaEndpoint,
headers: ollamaHeaders,
model: model.model,
}),
};
return acc;
}, {});
return acc;
}, {});
return embeddingsModels;
} catch (err) {
logger.error(`Error loading Ollama embeddings model: ${err}`);
return {};
}
return embeddingsModels;
} catch (err) {
logger.error(`Error loading Ollama embeddings model: ${err}`);
return {};
}
};

65
src/prompts/academicSearch.ts Normal file
View File

@ -0,0 +1,65 @@
export const academicSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: How does stable diffusion work?
Rephrased: Stable diffusion working
2. Follow up question: What is linear algebra?
Rephrased: Linear algebra
3. Follow up question: What is the third law of thermodynamics?
Rephrased: Third law of thermodynamics
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
export const academicSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
- You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

32
src/prompts/index.ts Normal file
View File

@ -0,0 +1,32 @@
import {
academicSearchResponsePrompt,
academicSearchRetrieverPrompt,
} from './academicSearch';
import {
redditSearchResponsePrompt,
redditSearchRetrieverPrompt,
} from './redditSearch';
import { webSearchResponsePrompt, webSearchRetrieverPrompt } from './webSearch';
import {
wolframAlphaSearchResponsePrompt,
wolframAlphaSearchRetrieverPrompt,
} from './wolframAlpha';
import { writingAssistantPrompt } from './writingAssistant';
import {
youtubeSearchResponsePrompt,
youtubeSearchRetrieverPrompt,
} from './youtubeSearch';
export default {
webSearchResponsePrompt,
webSearchRetrieverPrompt,
academicSearchResponsePrompt,
academicSearchRetrieverPrompt,
redditSearchResponsePrompt,
redditSearchRetrieverPrompt,
wolframAlphaSearchResponsePrompt,
wolframAlphaSearchRetrieverPrompt,
writingAssistantPrompt,
youtubeSearchResponsePrompt,
youtubeSearchRetrieverPrompt,
};

65
src/prompts/redditSearch.ts Normal file
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export const redditSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: Which company is most likely to create an AGI
Rephrased: Which company is most likely to create an AGI
2. Follow up question: Is Earth flat?
Rephrased: Is Earth flat?
3. Follow up question: Is there life on Mars?
Rephrased: Is there life on Mars?
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
export const redditSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
- You are set on focus mode 'Reddit', this means you will be searching for information, opinions and discussions on the web using Reddit.
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

106
src/prompts/webSearch.ts Normal file
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export const webSearchRetrieverPrompt = `
You are an AI question rephraser. You will be given a conversation and a follow-up question, you will have to rephrase the follow up question so it is a standalone question and can be used by another LLM to search the web for information to answer it.
If it is a smple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
There are several examples attached for your reference inside the below \`examples\` XML block
<examples>
1. Follow up question: What is the capital of France
Rephrased question:\`
<question>
Capital of france
</question>
\`
2. Hi, how are you?
Rephrased question\`
<question>
not_needed
</question>
\`
3. Follow up question: What is Docker?
Rephrased question: \`
<question>
What is Docker
</question>
\`
4. Follow up question: Can you tell me what is X from https://example.com
Rephrased question: \`
<question>
Can you tell me what is X?
</question>
<links>
https://example.com
</links>
\`
5. Follow up question: Summarize the content from https://example.com
Rephrased question: \`
<question>
summarize
</question>
<links>
https://example.com
</links>
\`
</examples>
Anything below is the part of the actual conversation and you need to use conversation and the follow-up question to rephrase the follow-up question as a standalone question based on the guidelines shared above.
<conversation>
{chat_history}
</conversation>
Follow up question: {query}
Rephrased question:
`;
export const webSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

65
src/prompts/wolframAlpha.ts Normal file
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export const wolframAlphaSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: What is the atomic radius of S?
Rephrased: Atomic radius of S
2. Follow up question: What is linear algebra?
Rephrased: Linear algebra
3. Follow up question: What is the third law of thermodynamics?
Rephrased: Third law of thermodynamics
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
export const wolframAlphaSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
- You are set on focus mode 'Wolfram Alpha', this means you will be searching for information on the web using Wolfram Alpha. It is a computational knowledge engine that can answer factual queries and perform computations.
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

13
src/prompts/writingAssistant.ts Normal file
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@ -0,0 +1,13 @@
export const writingAssistantPrompt = `
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query.
Since you are a writing assistant, you would not perform web searches. If you think you lack information to answer the query, you can ask the user for more information or suggest them to switch to a different focus mode.
You will be shared a context that can contain information from files user has uploaded to get answers from. You will have to generate answers upon that.
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
<context>
{context}
</context>
`;

65
src/prompts/youtubeSearch.ts Normal file
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@ -0,0 +1,65 @@
export const youtubeSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: How does an A.C work?
Rephrased: A.C working
2. Follow up question: Linear algebra explanation video
Rephrased: What is linear algebra?
3. Follow up question: What is theory of relativity?
Rephrased: What is theory of relativity?
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
export const youtubeSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
- You are set on focus mode 'Youtube', this means you will be searching for videos on the web using Youtube and providing information based on the video's transcrip
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

3
src/routes/config.ts
View File

@ -7,6 +7,7 @@ import {
getGroqApiKey,
getOllamaApiEndpoint,
getAnthropicApiKey,
getGeminiApiKey,
getOpenaiApiKey,
updateConfig,
} from '../config';
@ -52,6 +53,7 @@ router.get('/', async (_, res) => {
config['ollamaApiUrl'] = getOllamaApiEndpoint();
config['anthropicApiKey'] = getAnthropicApiKey();
config['groqApiKey'] = getGroqApiKey();
config['geminiApiKey'] = getGeminiApiKey();
res.status(200).json(config);
} catch (err: any) {
@ -68,6 +70,7 @@ router.post('/', async (req, res) => {
OPENAI: config.openaiApiKey,
GROQ: config.groqApiKey,
ANTHROPIC: config.anthropicApiKey,
GEMINI: config.geminiApiKey,
},
API_ENDPOINTS: {
OLLAMA: config.ollamaApiUrl,

2
src/routes/images.ts
View File

@ -1,5 +1,5 @@
import express from 'express';
import handleImageSearch from '../agents/imageSearchAgent';
import handleImageSearch from '../chains/imageSearchAgent';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { getAvailableChatModelProviders } from '../lib/providers';
import { HumanMessage, AIMessage } from '@langchain/core/messages';

2
src/routes/index.ts
View File

@ -8,6 +8,7 @@ import chatsRouter from './chats';
import settingsRouter from './settings';
import searchRouter from './search';
import discoverRouter from './discover';
import uploadsRouter from './uploads';
const router = express.Router();
@ -20,5 +21,6 @@ router.use('/chats', chatsRouter);
router.use('/settings', settingsRouter);
router.use('/search', searchRouter);
router.use('/discover', discoverRouter);
router.use('/uploads', uploadsRouter);
export default router;

10
src/routes/search.ts
View File

@ -1,7 +1,7 @@
import express from 'express';
import logger from '../utils/logger';
import { BaseChatModel } from 'langchain/chat_models/base';
import { Embeddings } from 'langchain/embeddings/base';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import { ChatOpenAI } from '@langchain/openai';
import {
getAvailableChatModelProviders,
@ -9,6 +9,7 @@ import {
} from '../lib/providers';
import { searchHandlers } from '../websocket/messageHandler';
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
import { MetaSearchAgentType } from '../search/metaSearchAgent';
const router = express.Router();
@ -115,18 +116,19 @@ router.post('/', async (req, res) => {
return res.status(400).json({ message: 'Invalid model selected' });
}
const searchHandler = searchHandlers[body.focusMode];
const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
if (!searchHandler) {
return res.status(400).json({ message: 'Invalid focus mode' });
}
const emitter = searchHandler(
const emitter = await searchHandler.searchAndAnswer(
body.query,
history,
llm,
embeddings,
body.optimizationMode,
[],
);
let message = '';

2
src/routes/suggestions.ts
View File

@ -1,5 +1,5 @@
import express from 'express';
import generateSuggestions from '../agents/suggestionGeneratorAgent';
import generateSuggestions from '../chains/suggestionGeneratorAgent';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { getAvailableChatModelProviders } from '../lib/providers';
import { HumanMessage, AIMessage } from '@langchain/core/messages';

151
src/routes/uploads.ts Normal file
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@ -0,0 +1,151 @@
import express from 'express';
import logger from '../utils/logger';
import multer from 'multer';
import path from 'path';
import crypto from 'crypto';
import fs from 'fs';
import { Embeddings } from '@langchain/core/embeddings';
import { getAvailableEmbeddingModelProviders } from '../lib/providers';
import { PDFLoader } from '@langchain/community/document_loaders/fs/pdf';
import { DocxLoader } from '@langchain/community/document_loaders/fs/docx';
import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';
import { Document } from 'langchain/document';
const router = express.Router();
const splitter = new RecursiveCharacterTextSplitter({
chunkSize: 500,
chunkOverlap: 100,
});
const storage = multer.diskStorage({
destination: (req, file, cb) => {
cb(null, path.join(process.cwd(), './uploads'));
},
filename: (req, file, cb) => {
const splitedFileName = file.originalname.split('.');
const fileExtension = splitedFileName[splitedFileName.length - 1];
if (!['pdf', 'docx', 'txt'].includes(fileExtension)) {
return cb(new Error('File type is not supported'), '');
}
cb(null, `${crypto.randomBytes(16).toString('hex')}.${fileExtension}`);
},
});
const upload = multer({ storage });
router.post(
'/',
upload.fields([
{ name: 'files' },
{ name: 'embedding_model', maxCount: 1 },
{ name: 'embedding_model_provider', maxCount: 1 },
]),
async (req, res) => {
try {
const { embedding_model, embedding_model_provider } = req.body;
if (!embedding_model || !embedding_model_provider) {
res
.status(400)
.json({ message: 'Missing embedding model or provider' });
return;
}
const embeddingModels = await getAvailableEmbeddingModelProviders();
const provider =
embedding_model_provider ?? Object.keys(embeddingModels)[0];
const embeddingModel: Embeddings =
embedding_model ?? Object.keys(embeddingModels[provider])[0];
let embeddingsModel: Embeddings | undefined;
if (
embeddingModels[provider] &&
embeddingModels[provider][embeddingModel]
) {
embeddingsModel = embeddingModels[provider][embeddingModel].model as
| Embeddings
| undefined;
}
if (!embeddingsModel) {
res.status(400).json({ message: 'Invalid LLM model selected' });
return;
}
const files = req.files['files'] as Express.Multer.File[];
if (!files || files.length === 0) {
res.status(400).json({ message: 'No files uploaded' });
return;
}
await Promise.all(
files.map(async (file) => {
let docs: Document[] = [];
if (file.mimetype === 'application/pdf') {
const loader = new PDFLoader(file.path);
docs = await loader.load();
} else if (
file.mimetype ===
'application/vnd.openxmlformats-officedocument.wordprocessingml.document'
) {
const loader = new DocxLoader(file.path);
docs = await loader.load();
} else if (file.mimetype === 'text/plain') {
const text = fs.readFileSync(file.path, 'utf-8');
docs = [
new Document({
pageContent: text,
metadata: {
title: file.originalname,
},
}),
];
}
const splitted = await splitter.splitDocuments(docs);
const json = JSON.stringify({
title: file.originalname,
contents: splitted.map((doc) => doc.pageContent),
});
const pathToSave = file.path.replace(/\.\w+$/, '-extracted.json');
fs.writeFileSync(pathToSave, json);
const embeddings = await embeddingsModel.embedDocuments(
splitted.map((doc) => doc.pageContent),
);
const embeddingsJSON = JSON.stringify({
title: file.originalname,
embeddings: embeddings,
});
const pathToSaveEmbeddings = file.path.replace(
/\.\w+$/,
'-embeddings.json',
);
fs.writeFileSync(pathToSaveEmbeddings, embeddingsJSON);
}),
);
res.status(200).json({
files: files.map((file) => {
return {
fileName: file.originalname,
fileExtension: file.filename.split('.').pop(),
fileId: file.filename.replace(/\.\w+$/, ''),
};
}),
});
} catch (err: any) {
logger.error(`Error in uploading file results: ${err.message}`);
res.status(500).json({ message: 'An error has occurred.' });
}
},
);
export default router;

2
src/routes/videos.ts
View File

@ -3,7 +3,7 @@ import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { getAvailableChatModelProviders } from '../lib/providers';
import { HumanMessage, AIMessage } from '@langchain/core/messages';
import logger from '../utils/logger';
import handleVideoSearch from '../agents/videoSearchAgent';
import handleVideoSearch from '../chains/videoSearchAgent';
import { ChatOpenAI } from '@langchain/openai';
const router = express.Router();

494
src/search/metaSearchAgent.ts Normal file
View File

@ -0,0 +1,494 @@
import { ChatOpenAI } from '@langchain/openai';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import {
ChatPromptTemplate,
MessagesPlaceholder,
PromptTemplate,
} from '@langchain/core/prompts';
import {
RunnableLambda,
RunnableMap,
RunnableSequence,
} from '@langchain/core/runnables';
import { BaseMessage } from '@langchain/core/messages';
import { StringOutputParser } from '@langchain/core/output_parsers';
import LineListOutputParser from '../lib/outputParsers/listLineOutputParser';
import LineOutputParser from '../lib/outputParsers/lineOutputParser';
import { getDocumentsFromLinks } from '../utils/documents';
import { Document } from 'langchain/document';
import { searchSearxng } from '../lib/searxng';
import path from 'path';
import fs from 'fs';
import computeSimilarity from '../utils/computeSimilarity';
import formatChatHistoryAsString from '../utils/formatHistory';
import eventEmitter from 'events';
import { StreamEvent } from '@langchain/core/tracers/log_stream';
import { IterableReadableStream } from '@langchain/core/utils/stream';
export interface MetaSearchAgentType {
searchAndAnswer: (
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
fileIds: string[],
) => Promise<eventEmitter>;
}
interface Config {
searchWeb: boolean;
rerank: boolean;
summarizer: boolean;
rerankThreshold: number;
queryGeneratorPrompt: string;
responsePrompt: string;
activeEngines: string[];
}
type BasicChainInput = {
chat_history: BaseMessage[];
query: string;
};
class MetaSearchAgent implements MetaSearchAgentType {
private config: Config;
private strParser = new StringOutputParser();
constructor(config: Config) {
this.config = config;
}
private async createSearchRetrieverChain(llm: BaseChatModel) {
(llm as unknown as ChatOpenAI).temperature = 0;
return RunnableSequence.from([
PromptTemplate.fromTemplate(this.config.queryGeneratorPrompt),
llm,
this.strParser,
RunnableLambda.from(async (input: string) => {
const linksOutputParser = new LineListOutputParser({
key: 'links',
});
const questionOutputParser = new LineOutputParser({
key: 'question',
});
const links = await linksOutputParser.parse(input);
let question = this.config.summarizer
? await questionOutputParser.parse(input)
: input;
if (question === 'not_needed') {
return { query: '', docs: [] };
}
if (links.length > 0) {
if (question.length === 0) {
question = 'summarize';
}
let docs = [];
const linkDocs = await getDocumentsFromLinks({ links });
const docGroups: Document[] = [];
linkDocs.map((doc) => {
const URLDocExists = docGroups.find(
(d) =>
d.metadata.url === doc.metadata.url &&
d.metadata.totalDocs < 10,
);
if (!URLDocExists) {
docGroups.push({
...doc,
metadata: {
...doc.metadata,
totalDocs: 1,
},
});
}
const docIndex = docGroups.findIndex(
(d) =>
d.metadata.url === doc.metadata.url &&
d.metadata.totalDocs < 10,
);
if (docIndex !== -1) {
docGroups[docIndex].pageContent =
docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
docGroups[docIndex].metadata.totalDocs += 1;
}
});
await Promise.all(
docGroups.map(async (doc) => {
const res = await llm.invoke(`
You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the
text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
- **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
- **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
- **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
The text will be shared inside the \`text\` XML tag, and the query inside the \`query\` XML tag.
<example>
1. \`<text>
Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers.
It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications
by using containers.
</text>
<query>
What is Docker and how does it work?
</query>
Response:
Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application
deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in
any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
\`
2. \`<text>
The theory of relativity, or simply relativity, encompasses two interrelated theories of Albert Einstein: special relativity and general
relativity. However, the word "relativity" is sometimes used in reference to Galilean invariance. The term "theory of relativity" was based
on the expression "relative theory" used by Max Planck in 1906. The theory of relativity usually encompasses two interrelated theories by
Albert Einstein: special relativity and general relativity. Special relativity applies to all physical phenomena in the absence of gravity.
General relativity explains the law of gravitation and its relation to other forces of nature. It applies to the cosmological and astrophysical
realm, including astronomy.
</text>
<query>
summarize
</query>
Response:
The theory of relativity, developed by Albert Einstein, encompasses two main theories: special relativity and general relativity. Special
relativity applies to all physical phenomena in the absence of gravity, while general relativity explains the law of gravitation and its
relation to other forces of nature. The theory of relativity is based on the concept of "relative theory," as introduced by Max Planck in
1906. It is a fundamental theory in physics that has revolutionized our understanding of the universe.
\`
</example>
Everything below is the actual data you will be working with. Good luck!
<query>
${question}
</query>
<text>
${doc.pageContent}
</text>
Make sure to answer the query in the summary.
`);
const document = new Document({
pageContent: res.content as string,
metadata: {
title: doc.metadata.title,
url: doc.metadata.url,
},
});
docs.push(document);
}),
);
return { query: question, docs: docs };
} else {
const res = await searchSearxng(question, {
language: 'en',
engines: this.config.activeEngines,
});
const documents = res.results.map(
(result) =>
new Document({
pageContent:
result.content ||
this.config.activeEngines.includes('youtube')
? result.title
: '' /* Todo: Implement transcript grabbing using Youtubei (source: https://www.npmjs.com/package/youtubei) */,
metadata: {
title: result.title,
url: result.url,
...(result.img_src && { img_src: result.img_src }),
},
}),
);
return { query: question, docs: documents };
}
}),
]);
}
private async createAnsweringChain(
llm: BaseChatModel,
fileIds: string[],
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) {
return RunnableSequence.from([
RunnableMap.from({
query: (input: BasicChainInput) => input.query,
chat_history: (input: BasicChainInput) => input.chat_history,
date: () => new Date().toISOString(),
context: RunnableLambda.from(async (input: BasicChainInput) => {
const processedHistory = formatChatHistoryAsString(
input.chat_history,
);
let docs: Document[] | null = null;
let query = input.query;
if (this.config.searchWeb) {
const searchRetrieverChain =
await this.createSearchRetrieverChain(llm);
const searchRetrieverResult = await searchRetrieverChain.invoke({
chat_history: processedHistory,
query,
});
query = searchRetrieverResult.query;
docs = searchRetrieverResult.docs;
}
const sortedDocs = await this.rerankDocs(
query,
docs ?? [],
fileIds,
embeddings,
optimizationMode,
);
return sortedDocs;
})
.withConfig({
runName: 'FinalSourceRetriever',
})
.pipe(this.processDocs),
}),
ChatPromptTemplate.fromMessages([
['system', this.config.responsePrompt],
new MessagesPlaceholder('chat_history'),
['user', '{query}'],
]),
llm,
this.strParser,
]).withConfig({
runName: 'FinalResponseGenerator',
});
}
private async rerankDocs(
query: string,
docs: Document[],
fileIds: string[],
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) {
if (docs.length === 0 && fileIds.length === 0) {
return docs;
}
const filesData = fileIds
.map((file) => {
const filePath = path.join(process.cwd(), 'uploads', file);
const contentPath = filePath + '-extracted.json';
const embeddingsPath = filePath + '-embeddings.json';
const content = JSON.parse(fs.readFileSync(contentPath, 'utf8'));
const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
const fileSimilaritySearchObject = content.contents.map(
(c: string, i) => {
return {
fileName: content.title,
content: c,
embeddings: embeddings.embeddings[i],
};
},
);
return fileSimilaritySearchObject;
})
.flat();
if (query.toLocaleLowerCase() === 'summarize') {
return docs.slice(0, 15);
}
const docsWithContent = docs.filter(
(doc) => doc.pageContent && doc.pageContent.length > 0,
);
if (optimizationMode === 'speed' || this.config.rerank === false) {
if (filesData.length > 0) {
const [queryEmbedding] = await Promise.all([
embeddings.embedQuery(query),
]);
const fileDocs = filesData.map((fileData) => {
return new Document({
pageContent: fileData.content,
metadata: {
title: fileData.fileName,
url: `File`,
},
});
});
const similarity = filesData.map((fileData, i) => {
const sim = computeSimilarity(queryEmbedding, fileData.embeddings);
return {
index: i,
similarity: sim,
};
});
let sortedDocs = similarity
.filter(
(sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3),
)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => fileDocs[sim.index]);
sortedDocs =
docsWithContent.length > 0 ? sortedDocs.slice(0, 8) : sortedDocs;
return [
...sortedDocs,
...docsWithContent.slice(0, 15 - sortedDocs.length),
];
} else {
return docsWithContent.slice(0, 15);
}
} else if (optimizationMode === 'balanced') {
const [docEmbeddings, queryEmbedding] = await Promise.all([
embeddings.embedDocuments(
docsWithContent.map((doc) => doc.pageContent),
),
embeddings.embedQuery(query),
]);
docsWithContent.push(
...filesData.map((fileData) => {
return new Document({
pageContent: fileData.content,
metadata: {
title: fileData.fileName,
url: `File`,
},
});
}),
);
docEmbeddings.push(...filesData.map((fileData) => fileData.embeddings));
const similarity = docEmbeddings.map((docEmbedding, i) => {
const sim = computeSimilarity(queryEmbedding, docEmbedding);
return {
index: i,
similarity: sim,
};
});
const sortedDocs = similarity
.filter((sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3))
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => docsWithContent[sim.index]);
return sortedDocs;
}
}
private processDocs(docs: Document[]) {
return docs
.map(
(_, index) =>
`${index + 1}. ${docs[index].metadata.title} ${docs[index].pageContent}`,
)
.join('\n');
}
private async handleStream(
stream: IterableReadableStream<StreamEvent>,
emitter: eventEmitter,
) {
for await (const event of stream) {
if (
event.event === 'on_chain_end' &&
event.name === 'FinalSourceRetriever'
) {
``;
emitter.emit(
'data',
JSON.stringify({ type: 'sources', data: event.data.output }),
);
}
if (
event.event === 'on_chain_stream' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'response', data: event.data.chunk }),
);
}
if (
event.event === 'on_chain_end' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit('end');
}
}
}
async searchAndAnswer(
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
fileIds: string[],
) {
const emitter = new eventEmitter();
const answeringChain = await this.createAnsweringChain(
llm,
fileIds,
embeddings,
optimizationMode,
);
const stream = answeringChain.streamEvents(
{
chat_history: history,
query: message,
},
{
version: 'v1',
},
);
this.handleStream(stream, emitter);
return emitter;
}
}
export default MetaSearchAgent;

2
src/lib/linkDocument.ts → src/utils/documents.ts
View File

@ -3,7 +3,7 @@ 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';
import logger from './logger';
export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
const splitter = new RecursiveCharacterTextSplitter();

17
src/utils/files.ts Normal file
View File

@ -0,0 +1,17 @@
import path from 'path';
import fs from 'fs';
export const getFileDetails = (fileId: string) => {
const fileLoc = path.join(
process.cwd(),
'./uploads',
fileId + '-extracted.json',
);
const parsedFile = JSON.parse(fs.readFileSync(fileLoc, 'utf8'));
return {
name: parsedFile.title,
fileId: fileId,
};
};

174
src/websocket/messageHandler.ts
View File

@ -1,11 +1,5 @@
import { EventEmitter, WebSocket } from 'ws';
import { BaseMessage, AIMessage, HumanMessage } from '@langchain/core/messages';
import handleWebSearch from '../agents/webSearchAgent';
import handleAcademicSearch from '../agents/academicSearchAgent';
import handleWritingAssistant from '../agents/writingAssistant';
import handleWolframAlphaSearch from '../agents/wolframAlphaSearchAgent';
import handleYoutubeSearch from '../agents/youtubeSearchAgent';
import handleRedditSearch from '../agents/redditSearchAgent';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import logger from '../utils/logger';
@ -13,6 +7,11 @@ import db from '../db';
import { chats, messages as messagesSchema } from '../db/schema';
import { eq, asc, gt } from 'drizzle-orm';
import crypto from 'crypto';
import { getFileDetails } from '../utils/files';
import MetaSearchAgent, {
MetaSearchAgentType,
} from '../search/metaSearchAgent';
import prompts from '../prompts';
type Message = {
messageId: string;
@ -22,19 +21,68 @@ type Message = {
type WSMessage = {
message: Message;
optimizationMode: string;
optimizationMode: 'speed' | 'balanced' | 'quality';
type: string;
focusMode: string;
history: Array<[string, string]>;
files: Array<string>;
};
export const searchHandlers = {
webSearch: handleWebSearch,
academicSearch: handleAcademicSearch,
writingAssistant: handleWritingAssistant,
wolframAlphaSearch: handleWolframAlphaSearch,
youtubeSearch: handleYoutubeSearch,
redditSearch: handleRedditSearch,
webSearch: new MetaSearchAgent({
activeEngines: [],
queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
responsePrompt: prompts.webSearchResponsePrompt,
rerank: true,
rerankThreshold: 0.3,
searchWeb: true,
summarizer: true,
}),
academicSearch: new MetaSearchAgent({
activeEngines: ['arxiv', 'google scholar', 'pubmed'],
queryGeneratorPrompt: prompts.academicSearchRetrieverPrompt,
responsePrompt: prompts.academicSearchResponsePrompt,
rerank: true,
rerankThreshold: 0,
searchWeb: true,
summarizer: false,
}),
writingAssistant: new MetaSearchAgent({
activeEngines: [],
queryGeneratorPrompt: '',
responsePrompt: prompts.writingAssistantPrompt,
rerank: true,
rerankThreshold: 0,
searchWeb: false,
summarizer: false,
}),
wolframAlphaSearch: new MetaSearchAgent({
activeEngines: ['wolframalpha'],
queryGeneratorPrompt: prompts.wolframAlphaSearchRetrieverPrompt,
responsePrompt: prompts.wolframAlphaSearchResponsePrompt,
rerank: false,
rerankThreshold: 0,
searchWeb: true,
summarizer: false,
}),
youtubeSearch: new MetaSearchAgent({
activeEngines: ['youtube'],
queryGeneratorPrompt: prompts.youtubeSearchRetrieverPrompt,
responsePrompt: prompts.youtubeSearchResponsePrompt,
rerank: true,
rerankThreshold: 0.3,
searchWeb: true,
summarizer: false,
}),
redditSearch: new MetaSearchAgent({
activeEngines: ['reddit'],
queryGeneratorPrompt: prompts.redditSearchRetrieverPrompt,
responsePrompt: prompts.redditSearchResponsePrompt,
rerank: true,
rerankThreshold: 0.3,
searchWeb: true,
summarizer: false,
}),
};
const handleEmitterEvents = (
@ -106,6 +154,11 @@ export const handleMessage = async (
const parsedWSMessage = JSON.parse(message) as WSMessage;
const parsedMessage = parsedWSMessage.message;
if (parsedWSMessage.files.length > 0) {
/* TODO: Implement uploads in other classes/single meta class system*/
parsedWSMessage.focusMode = 'webSearch';
}
const humanMessageId =
parsedMessage.messageId ?? crypto.randomBytes(7).toString('hex');
const aiMessageId = crypto.randomBytes(7).toString('hex');
@ -132,57 +185,64 @@ export const handleMessage = async (
});
if (parsedWSMessage.type === 'message') {
const handler = searchHandlers[parsedWSMessage.focusMode];
const handler: MetaSearchAgentType =
searchHandlers[parsedWSMessage.focusMode];
if (handler) {
const emitter = handler(
parsedMessage.content,
history,
llm,
embeddings,
parsedWSMessage.optimizationMode,
);
try {
const emitter = await handler.searchAndAnswer(
parsedMessage.content,
history,
llm,
embeddings,
parsedWSMessage.optimizationMode,
parsedWSMessage.files,
);
handleEmitterEvents(emitter, ws, aiMessageId, parsedMessage.chatId);
handleEmitterEvents(emitter, ws, aiMessageId, parsedMessage.chatId);
const chat = await db.query.chats.findFirst({
where: eq(chats.id, parsedMessage.chatId),
});
const chat = await db.query.chats.findFirst({
where: eq(chats.id, parsedMessage.chatId),
});
if (!chat) {
await db
.insert(chats)
.values({
id: parsedMessage.chatId,
title: parsedMessage.content,
createdAt: new Date().toString(),
focusMode: parsedWSMessage.focusMode,
})
.execute();
}
if (!chat) {
await db
.insert(chats)
.values({
id: parsedMessage.chatId,
title: parsedMessage.content,
createdAt: new Date().toString(),
focusMode: parsedWSMessage.focusMode,
files: parsedWSMessage.files.map(getFileDetails),
})
.execute();
}
const messageExists = await db.query.messages.findFirst({
where: eq(messagesSchema.messageId, humanMessageId),
});
const messageExists = await db.query.messages.findFirst({
where: eq(messagesSchema.messageId, humanMessageId),
});
if (!messageExists) {
await db
.insert(messagesSchema)
.values({
content: parsedMessage.content,
chatId: parsedMessage.chatId,
messageId: humanMessageId,
role: 'user',
metadata: JSON.stringify({
createdAt: new Date(),
}),
})
.execute();
} else {
await db
.delete(messagesSchema)
.where(gt(messagesSchema.id, messageExists.id))
.execute();
if (!messageExists) {
await db
.insert(messagesSchema)
.values({
content: parsedMessage.content,
chatId: parsedMessage.chatId,
messageId: humanMessageId,
role: 'user',
metadata: JSON.stringify({
createdAt: new Date(),
}),
})
.execute();
} else {
await db
.delete(messagesSchema)
.where(gt(messagesSchema.id, messageExists.id))
.execute();
}
} catch (err) {
console.log(err);
}
} else {
ws.send(

1
ui/app/discover/page.tsx
View File

@ -83,6 +83,7 @@ const Page = () => {
href={`/?q=Summary: ${item.url}`}
key={i}
className="max-w-sm rounded-lg overflow-hidden bg-light-secondary dark:bg-dark-secondary hover:-translate-y-[1px] transition duration-200"
target="_blank"
>
<img
className="object-cover w-full aspect-video"

19
ui/components/Chat.tsx
View File

@ -2,7 +2,7 @@
import { Fragment, useEffect, useRef, useState } from 'react';
import MessageInput from './MessageInput';
import { Message } from './ChatWindow';
import { File, Message } from './ChatWindow';
import MessageBox from './MessageBox';
import MessageBoxLoading from './MessageBoxLoading';
@ -12,12 +12,20 @@ const Chat = ({
sendMessage,
messageAppeared,
rewrite,
fileIds,
setFileIds,
files,
setFiles,
}: {
messages: Message[];
sendMessage: (message: string) => void;
loading: boolean;
messageAppeared: boolean;
rewrite: (messageId: string) => void;
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
files: File[];
setFiles: (files: File[]) => void;
}) => {
const [dividerWidth, setDividerWidth] = useState(0);
const dividerRef = useRef<HTMLDivElement | null>(null);
@ -78,7 +86,14 @@ const Chat = ({
className="bottom-24 lg:bottom-10 fixed z-40"
style={{ width: dividerWidth }}
>
<MessageInput loading={loading} sendMessage={sendMessage} />
<MessageInput
loading={loading}
sendMessage={sendMessage}
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
</div>
)}
</div>

65
ui/components/ChatWindow.tsx
View File

@ -21,7 +21,6 @@ export type Message = {
sources?: Document[];
};
const fetchSettings = async () => {
try {
const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/settings`);
@ -30,7 +29,10 @@ const fetchSettings = async () => {
if (settings) {
localStorage.setItem('chatModelProvider', settings.chatModelProvider);
localStorage.setItem('chatModel', settings.chatModel);
localStorage.setItem('embeddingModelProvider', settings.embeddingModelProvider);
localStorage.setItem(
'embeddingModelProvider',
settings.embeddingModelProvider,
);
localStorage.setItem('embeddingModel', settings.embeddingModel);
localStorage.setItem('openAIApiKey', settings.openAIApiKey);
localStorage.setItem('openAIBaseURL', settings.openAIBaseURL);
@ -40,11 +42,17 @@ const fetchSettings = async () => {
}
};
export interface File {
fileName: string;
fileExtension: string;
fileId: string;
}
const useSocket = (
url: string,
setIsWSReady: (ready: boolean) => void,
setError: (error: boolean) => void,
hasError: boolean
hasError: boolean,
) => {
const [ws, setWs] = useState<WebSocket | null>(null);
const reconnectTimeout = useRef(0);
@ -200,13 +208,14 @@ const useSocket = (
if (data.type === 'signal' && data.data === 'open') {
const interval = setInterval(() => {
if (ws.readyState === 1) {
reconnectTimeout.current = 0;setIsWSReady(true);
reconnectTimeout.current = 0;
setIsWSReady(true);
clearInterval(interval);
}
}, 5);
clearTimeout(timeoutId);
reconnectTimeout.current = 0;
reconnectAttempts.current = 0;
reconnectTimeout.current = 0;
reconnectAttempts.current = 0;
console.log('[DEBUG] opened');
}
if (data.type === 'error') {
@ -232,13 +241,16 @@ const useSocket = (
};
if (reconnectAttempts.current < 3) {
console.log(`[DEBUG] Attempting to reconnect (${reconnectAttempts.current + 1}/3)`);
setTimeout(connectWs, reconnectTimeout.current);
reconnectTimeout.current = reconnectTimeout.current > 0 ? reconnectTimeout.current * 2 : 1000;
reconnectAttempts.current += 1;
console.log(
`[DEBUG] Attempting to reconnect (${reconnectAttempts.current + 1}/3)`,
);
setTimeout(connectWs, reconnectTimeout.current);
reconnectTimeout.current =
reconnectTimeout.current > 0 ? reconnectTimeout.current * 2 : 1000;
reconnectAttempts.current += 1;
} else {
console.log('[DEBUG] WebSocket reconnect failure after 3 retries');
setError(true);
console.log('[DEBUG] WebSocket reconnect failure after 3 retries');
setError(true);
}
}
}, [ws, url, setIsWSReady, setError]);
@ -253,6 +265,8 @@ const loadMessages = async (
setChatHistory: (history: [string, string][]) => void,
setFocusMode: (mode: string) => void,
setNotFound: (notFound: boolean) => void,
setFiles: (files: File[]) => void,
setFileIds: (fileIds: string[]) => void,
) => {
const res = await fetch(
`${process.env.NEXT_PUBLIC_API_URL}/chats/${chatId}`,
@ -289,6 +303,17 @@ const loadMessages = async (
document.title = messages[0].content;
const files = data.chat.files.map((file: any) => {
return {
fileName: file.name,
fileExtension: file.name.split('.').pop(),
fileId: file.fileId,
};
});
setFiles(files);
setFileIds(files.map((file: File) => file.fileId));
setChatHistory(history);
setFocusMode(data.chat.focusMode);
setIsMessagesLoaded(true);
@ -309,7 +334,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
process.env.NEXT_PUBLIC_WS_URL!,
setIsWSReady,
setHasError,
hasError
hasError,
);
const [loading, setLoading] = useState(false);
@ -318,6 +343,9 @@ const ChatWindow = ({ id }: { id?: string }) => {
const [chatHistory, setChatHistory] = useState<[string, string][]>([]);
const [messages, setMessages] = useState<Message[]>([]);
const [files, setFiles] = useState<File[]>([]);
const [fileIds, setFileIds] = useState<string[]>([]);
const [focusMode, setFocusMode] = useState('webSearch');
const [optimizationMode, setOptimizationMode] = useState('speed');
@ -339,6 +367,8 @@ const ChatWindow = ({ id }: { id?: string }) => {
setChatHistory,
setFocusMode,
setNotFound,
setFiles,
setFileIds,
);
} else if (!chatId) {
setNewChatCreated(true);
@ -394,6 +424,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
chatId: chatId!,
content: message,
},
files: fileIds,
focusMode: focusMode,
optimizationMode: optimizationMode,
history: [...chatHistory, ['human', message]],
@ -551,6 +582,10 @@ const ChatWindow = ({ id }: { id?: string }) => {
sendMessage={sendMessage}
messageAppeared={messageAppeared}
rewrite={rewrite}
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
</>
) : (
@ -560,6 +595,10 @@ const ChatWindow = ({ id }: { id?: string }) => {
setFocusMode={setFocusMode}
optimizationMode={optimizationMode}
setOptimizationMode={setOptimizationMode}
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
)}
</div>

13
ui/components/EmptyChat.tsx
View File

@ -2,6 +2,7 @@ import { Settings } from 'lucide-react';
import EmptyChatMessageInput from './EmptyChatMessageInput';
import SettingsDialog from './SettingsDialog';
import { useState } from 'react';
import { File } from './ChatWindow';
const EmptyChat = ({
sendMessage,
@ -9,12 +10,20 @@ const EmptyChat = ({
setFocusMode,
optimizationMode,
setOptimizationMode,
fileIds,
setFileIds,
files,
setFiles,
}: {
sendMessage: (message: string) => void;
focusMode: string;
setFocusMode: (mode: string) => void;
optimizationMode: string;
setOptimizationMode: (mode: string) => void;
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
files: File[];
setFiles: (files: File[]) => void;
}) => {
const [isSettingsOpen, setIsSettingsOpen] = useState(false);
@ -37,6 +46,10 @@ const EmptyChat = ({
setFocusMode={setFocusMode}
optimizationMode={optimizationMode}
setOptimizationMode={setOptimizationMode}
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
</div>
</div>

21
ui/components/EmptyChatMessageInput.tsx
View File

@ -4,6 +4,8 @@ import TextareaAutosize from 'react-textarea-autosize';
import CopilotToggle from './MessageInputActions/Copilot';
import Focus from './MessageInputActions/Focus';
import Optimization from './MessageInputActions/Optimization';
import Attach from './MessageInputActions/Attach';
import { File } from './ChatWindow';
const EmptyChatMessageInput = ({
sendMessage,
@ -11,12 +13,20 @@ const EmptyChatMessageInput = ({
setFocusMode,
optimizationMode,
setOptimizationMode,
fileIds,
setFileIds,
files,
setFiles,
}: {
sendMessage: (message: string) => void;
focusMode: string;
setFocusMode: (mode: string) => void;
optimizationMode: string;
setOptimizationMode: (mode: string) => void;
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
files: File[];
setFiles: (files: File[]) => void;
}) => {
const [copilotEnabled, setCopilotEnabled] = useState(false);
const [message, setMessage] = useState('');
@ -40,6 +50,8 @@ const EmptyChatMessageInput = ({
document.addEventListener('keydown', handleKeyDown);
inputRef.current?.focus();
return () => {
document.removeEventListener('keydown', handleKeyDown);
};
@ -71,8 +83,15 @@ const EmptyChatMessageInput = ({
placeholder="Ask anything..."
/>
<div className="flex flex-row items-center justify-between mt-4">
<div className="flex flex-row items-center space-x-4">
<div className="flex flex-row items-center space-x-2 lg:space-x-4">
<Focus focusMode={focusMode} setFocusMode={setFocusMode} />
<Attach
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
showText
/>
</div>
<div className="flex flex-row items-center space-x-1 sm:space-x-4">
<Optimization

4
ui/components/MessageBox.tsx
View File

@ -107,8 +107,8 @@ const MessageBox = ({
</div>
<Markdown
className={cn(
'prose dark:prose-invert prose-p:leading-relaxed prose-pre:p-0',
'max-w-none break-words text-black dark:text-white text-sm md:text-base font-medium',
'prose prose-h1:mb-3 prose-h2:mb-2 prose-h2:mt-6 prose-h2:font-[800] prose-h3:mt-4 prose-h3:mb-1.5 prose-h3:font-[600] dark:prose-invert prose-p:leading-relaxed prose-pre:p-0 font-[400]',
'max-w-none break-words text-black dark:text-white',
)}
>
{parsedMessage}

26
ui/components/MessageInput.tsx
View File

@ -4,13 +4,23 @@ import { useEffect, useRef, useState } from 'react';
import TextareaAutosize from 'react-textarea-autosize';
import Attach from './MessageInputActions/Attach';
import CopilotToggle from './MessageInputActions/Copilot';
import { File } from './ChatWindow';
import AttachSmall from './MessageInputActions/AttachSmall';
const MessageInput = ({
sendMessage,
loading,
fileIds,
setFileIds,
files,
setFiles,
}: {
sendMessage: (message: string) => void;
loading: boolean;
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
files: File[];
setFiles: (files: File[]) => void;
}) => {
const [copilotEnabled, setCopilotEnabled] = useState(false);
const [message, setMessage] = useState('');
@ -69,7 +79,14 @@ const MessageInput = ({
mode === 'multi' ? 'flex-col rounded-lg' : 'flex-row rounded-full',
)}
>
{mode === 'single' && <Attach />}
{mode === 'single' && (
<AttachSmall
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
)}
<TextareaAutosize
ref={inputRef}
value={message}
@ -96,7 +113,12 @@ const MessageInput = ({
)}
{mode === 'multi' && (
<div className="flex flex-row items-center justify-between w-full pt-2">
<Attach />
<AttachSmall
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
<div className="flex flex-row items-center space-x-4">
<CopilotToggle
copilotEnabled={copilotEnabled}

181
ui/components/MessageInputActions/Attach.tsx
View File

@ -1,12 +1,183 @@
import { CopyPlus } from 'lucide-react';
import { cn } from '@/lib/utils';
import {
Popover,
PopoverButton,
PopoverPanel,
Transition,
} from '@headlessui/react';
import { CopyPlus, File, LoaderCircle, Plus, Trash } from 'lucide-react';
import { Fragment, useRef, useState } from 'react';
import { File as FileType } from '../ChatWindow';
const Attach = () => {
return (
const Attach = ({
fileIds,
setFileIds,
showText,
files,
setFiles,
}: {
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
showText?: boolean;
files: FileType[];
setFiles: (files: FileType[]) => void;
}) => {
const [loading, setLoading] = useState(false);
const fileInputRef = useRef<any>();
const handleChange = async (e: React.ChangeEvent<HTMLInputElement>) => {
setLoading(true);
const data = new FormData();
for (let i = 0; i < e.target.files!.length; i++) {
data.append('files', e.target.files![i]);
}
const embeddingModelProvider = localStorage.getItem(
'embeddingModelProvider',
);
const embeddingModel = localStorage.getItem('embeddingModel');
data.append('embedding_model_provider', embeddingModelProvider!);
data.append('embedding_model', embeddingModel!);
const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/uploads`, {
method: 'POST',
body: data,
});
const resData = await res.json();
setFiles([...files, ...resData.files]);
setFileIds([...fileIds, ...resData.files.map((file: any) => file.fileId)]);
setLoading(false);
};
return loading ? (
<div className="flex flex-row items-center justify-between space-x-1">
<LoaderCircle size={18} className="text-sky-400 animate-spin" />
<p className="text-sky-400 inline whitespace-nowrap text-xs font-medium">
Uploading..
</p>
</div>
) : files.length > 0 ? (
<Popover className="relative w-full max-w-[15rem] md:max-w-md lg:max-w-lg">
<PopoverButton
type="button"
className={cn(
'flex flex-row items-center justify-between space-x-1 p-2 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary active:scale-95 transition duration-200 hover:text-black dark:hover:text-white',
files.length > 0 ? '-ml-2 lg:-ml-3' : '',
)}
>
{files.length > 1 && (
<>
<File size={19} className="text-sky-400" />
<p className="text-sky-400 inline whitespace-nowrap text-xs font-medium">
{files.length} files
</p>
</>
)}
{files.length === 1 && (
<>
<File size={18} className="text-sky-400" />
<p className="text-sky-400 text-xs font-medium">
{files[0].fileName.length > 10
? files[0].fileName.replace(/\.\w+$/, '').substring(0, 3) +
'...' +
files[0].fileExtension
: files[0].fileName}
</p>
</>
)}
</PopoverButton>
<Transition
as={Fragment}
enter="transition ease-out duration-150"
enterFrom="opacity-0 translate-y-1"
enterTo="opacity-100 translate-y-0"
leave="transition ease-in duration-150"
leaveFrom="opacity-100 translate-y-0"
leaveTo="opacity-0 translate-y-1"
>
<PopoverPanel className="absolute z-10 w-64 md:w-[350px] right-0">
<div className="bg-light-primary dark:bg-dark-primary border rounded-md border-light-200 dark:border-dark-200 w-full max-h-[200px] md:max-h-none overflow-y-auto flex flex-col">
<div className="flex flex-row items-center justify-between px-3 py-2">
<h4 className="text-black dark:text-white font-medium text-sm">
Attached files
</h4>
<div className="flex flex-row items-center space-x-4">
<button
type="button"
onClick={() => fileInputRef.current.click()}
className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<Plus size={18} />
<p className="text-xs">Add</p>
</button>
<button
onClick={() => {
setFiles([]);
setFileIds([]);
}}
className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
>
<Trash size={14} />
<p className="text-xs">Clear</p>
</button>
</div>
</div>
<div className="h-[0.5px] mx-2 bg-white/10" />
<div className="flex flex-col items-center">
{files.map((file, i) => (
<div
key={i}
className="flex flex-row items-center justify-start w-full space-x-3 p-3"
>
<div className="bg-dark-100 flex items-center justify-center w-10 h-10 rounded-md">
<File size={16} className="text-white/70" />
</div>
<p className="text-white/70 text-sm">
{file.fileName.length > 25
? file.fileName.replace(/\.\w+$/, '').substring(0, 25) +
'...' +
file.fileExtension
: file.fileName}
</p>
</div>
))}
</div>
</div>
</PopoverPanel>
</Transition>
</Popover>
) : (
<button
type="button"
className="p-2 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white"
onClick={() => fileInputRef.current.click()}
className={cn(
'flex flex-row items-center space-x-1 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white',
showText ? '' : 'p-2',
)}
>
<CopyPlus />
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<CopyPlus size={showText ? 18 : undefined} />
{showText && <p className="text-xs font-medium pl-[1px]">Attach</p>}
</button>
);
};

153
ui/components/MessageInputActions/AttachSmall.tsx Normal file
View File

@ -0,0 +1,153 @@
import { cn } from '@/lib/utils';
import {
Popover,
PopoverButton,
PopoverPanel,
Transition,
} from '@headlessui/react';
import { CopyPlus, File, LoaderCircle, Plus, Trash } from 'lucide-react';
import { Fragment, useRef, useState } from 'react';
import { File as FileType } from '../ChatWindow';
const AttachSmall = ({
fileIds,
setFileIds,
files,
setFiles,
}: {
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
files: FileType[];
setFiles: (files: FileType[]) => void;
}) => {
const [loading, setLoading] = useState(false);
const fileInputRef = useRef<any>();
const handleChange = async (e: React.ChangeEvent<HTMLInputElement>) => {
setLoading(true);
const data = new FormData();
for (let i = 0; i < e.target.files!.length; i++) {
data.append('files', e.target.files![i]);
}
const embeddingModelProvider = localStorage.getItem(
'embeddingModelProvider',
);
const embeddingModel = localStorage.getItem('embeddingModel');
data.append('embedding_model_provider', embeddingModelProvider!);
data.append('embedding_model', embeddingModel!);
const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/uploads`, {
method: 'POST',
body: data,
});
const resData = await res.json();
setFiles([...files, ...resData.files]);
setFileIds([...fileIds, ...resData.files.map((file: any) => file.fileId)]);
setLoading(false);
};
return loading ? (
<div className="flex flex-row items-center justify-between space-x-1 p-1">
<LoaderCircle size={20} className="text-sky-400 animate-spin" />
</div>
) : files.length > 0 ? (
<Popover className="max-w-[15rem] md:max-w-md lg:max-w-lg">
<PopoverButton
type="button"
className="flex flex-row items-center justify-between space-x-1 p-1 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
>
<File size={20} className="text-sky-400" />
</PopoverButton>
<Transition
as={Fragment}
enter="transition ease-out duration-150"
enterFrom="opacity-0 translate-y-1"
enterTo="opacity-100 translate-y-0"
leave="transition ease-in duration-150"
leaveFrom="opacity-100 translate-y-0"
leaveTo="opacity-0 translate-y-1"
>
<PopoverPanel className="absolute z-10 w-64 md:w-[350px] bottom-14 -ml-3">
<div className="bg-light-primary dark:bg-dark-primary border rounded-md border-light-200 dark:border-dark-200 w-full max-h-[200px] md:max-h-none overflow-y-auto flex flex-col">
<div className="flex flex-row items-center justify-between px-3 py-2">
<h4 className="text-black dark:text-white font-medium text-sm">
Attached files
</h4>
<div className="flex flex-row items-center space-x-4">
<button
type="button"
onClick={() => fileInputRef.current.click()}
className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<Plus size={18} />
<p className="text-xs">Add</p>
</button>
<button
onClick={() => {
setFiles([]);
setFileIds([]);
}}
className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
>
<Trash size={14} />
<p className="text-xs">Clear</p>
</button>
</div>
</div>
<div className="h-[0.5px] mx-2 bg-white/10" />
<div className="flex flex-col items-center">
{files.map((file, i) => (
<div
key={i}
className="flex flex-row items-center justify-start w-full space-x-3 p-3"
>
<div className="bg-dark-100 flex items-center justify-center w-10 h-10 rounded-md">
<File size={16} className="text-white/70" />
</div>
<p className="text-white/70 text-sm">
{file.fileName.length > 25
? file.fileName.replace(/\.\w+$/, '').substring(0, 25) +
'...' +
file.fileExtension
: file.fileName}
</p>
</div>
))}
</div>
</div>
</PopoverPanel>
</Transition>
</Popover>
) : (
<button
type="button"
onClick={() => fileInputRef.current.click()}
className="flex flex-row items-center space-x-1 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white p-1"
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<CopyPlus size={20} />
</button>
);
};
export default AttachSmall;

11
ui/components/MessageInputActions/Focus.tsx
View File

@ -75,7 +75,7 @@ const Focus = ({
setFocusMode: (mode: string) => void;
}) => {
return (
<Popover className="relative w-full max-w-[15rem] md:max-w-md lg:max-w-lg">
<Popover className="relative w-full max-w-[15rem] md:max-w-md lg:max-w-lg mt-[6.5px]">
<PopoverButton
type="button"
className=" text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
@ -83,13 +83,16 @@ const Focus = ({
{focusMode !== 'webSearch' ? (
<div className="flex flex-row items-center space-x-1">
{focusModes.find((mode) => mode.key === focusMode)?.icon}
<p className="text-xs font-medium">
<p className="text-xs font-medium hidden lg:block">
{focusModes.find((mode) => mode.key === focusMode)?.title}
</p>
<ChevronDown size={20} />
<ChevronDown size={20} className="-translate-x-1" />
</div>
) : (
<ScanEye />
<div className="flex flex-row items-center space-x-1">
<ScanEye size={20} />
<p className="text-xs font-medium hidden lg:block">Focus</p>
</div>
)}
</PopoverButton>
<Transition

66
ui/components/MessageSources.tsx
View File

@ -7,6 +7,7 @@ import {
TransitionChild,
} from '@headlessui/react';
import { Document } from '@langchain/core/documents';
import { File } from 'lucide-react';
import { Fragment, useState } from 'react';
const MessageSources = ({ sources }: { sources: Document[] }) => {
@ -36,13 +37,19 @@ const MessageSources = ({ sources }: { sources: Document[] }) => {
</p>
<div className="flex flex-row items-center justify-between">
<div className="flex flex-row items-center space-x-1">
<img
src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
width={16}
height={16}
alt="favicon"
className="rounded-lg h-4 w-4"
/>
{source.metadata.url === 'File' ? (
<div className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full">
<File size={12} className="text-white/70" />
</div>
) : (
<img
src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
width={16}
height={16}
alt="favicon"
className="rounded-lg h-4 w-4"
/>
)}
<p className="text-xs text-black/50 dark:text-white/50 overflow-hidden whitespace-nowrap text-ellipsis">
{source.metadata.url.replace(/.+\/\/|www.|\..+/g, '')}
</p>
@ -60,16 +67,21 @@ const MessageSources = ({ sources }: { sources: Document[] }) => {
className="bg-light-100 hover:bg-light-200 dark:bg-dark-100 dark:hover:bg-dark-200 transition duration-200 rounded-lg p-3 flex flex-col space-y-2 font-medium"
>
<div className="flex flex-row items-center space-x-1">
{sources.slice(3, 6).map((source, i) => (
<img
src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
width={16}
height={16}
alt="favicon"
className="rounded-lg h-4 w-4"
key={i}
/>
))}
{sources.slice(3, 6).map((source, i) => {
return source.metadata.url === 'File' ? (
<div className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full">
<File size={12} className="text-white/70" />
</div>
) : (
<img
src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
width={16}
height={16}
alt="favicon"
className="rounded-lg h-4 w-4"
/>
);
})}
</div>
<p className="text-xs text-black/50 dark:text-white/50">
View {sources.length - 3} more
@ -106,13 +118,19 @@ const MessageSources = ({ sources }: { sources: Document[] }) => {
</p>
<div className="flex flex-row items-center justify-between">
<div className="flex flex-row items-center space-x-1">
<img
src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
width={16}
height={16}
alt="favicon"
className="rounded-lg h-4 w-4"
/>
{source.metadata.url === 'File' ? (
<div className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full">
<File size={12} className="text-white/70" />
</div>
) : (
<img
src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
width={16}
height={16}
alt="favicon"
className="rounded-lg h-4 w-4"
/>
)}
<p className="text-xs text-black/50 dark:text-white/50 overflow-hidden whitespace-nowrap text-ellipsis">
{source.metadata.url.replace(
/.+\/\/|www.|\..+/g,

17
ui/components/SettingsDialog.tsx
View File

@ -63,6 +63,7 @@ interface SettingsType {
openaiApiKey: string;
groqApiKey: string;
anthropicApiKey: string;
geminiApiKey: string;
ollamaApiUrl: string;
}
@ -480,6 +481,22 @@ const SettingsDialog = ({
}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Gemini API Key
</p>
<Input
type="text"
placeholder="Gemini API key"
defaultValue={config.geminiApiKey}
onChange={(e) =>
setConfig({
...config,
geminiApiKey: e.target.value,
})
}
/>
</div>
</div>
)}
{isLoading && (

6
ui/package.json
View File

@ -1,6 +1,6 @@
{
"name": "perplexica-frontend",
"version": "1.9.2",
"version": "1.10.0-rc2",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {
@ -11,14 +11,14 @@
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},
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76
ui/yarn.lock
View File

@ -74,9 +74,9 @@
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@ -89,9 +89,9 @@
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@ -317,20 +317,20 @@
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@ -380,11 +380,11 @@
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dependencies:
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tslib "^2.8.0"
"@tailwindcss/typography@^0.5.12":
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@ -397,16 +397,16 @@
postcss-selector-parser "6.0.10"
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@ -2210,10 +2210,10 @@ lucide-react@^0.363.0:
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md5@^2.3.0:
version "2.3.0"
@ -3192,10 +3192,10 @@ tsconfig-paths@^3.15.0:
minimist "^1.2.6"
strip-bom "^3.0.0"
tslib@^2.4.0:
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resolved "https://registry.yarnpkg.com/tslib/-/tslib-2.6.2.tgz#703ac29425e7b37cd6fd456e92404d46d1f3e4ae"
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type-check@^0.4.0, type-check@~0.4.0:
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2
uploads/.gitignore vendored Normal file
View File

@ -0,0 +1,2 @@
*
!.gitignore

295
yarn.lock
View File

@ -293,6 +293,11 @@
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@ -380,6 +385,23 @@
zod "^3.22.4"
zod-to-json-schema "^3.22.3"
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camelcase "6"
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langsmith "^0.1.56-rc.1"
mustache "^4.2.0"
p-queue "^6.6.2"
p-retry "4"
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zod "^3.22.4"
zod-to-json-schema "^3.22.3"
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zod "^3.22.4"
zod-to-json-schema "^3.22.3"
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dependencies:
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@ -576,6 +607,26 @@
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resolved "https://registry.yarnpkg.com/@types/express-serve-static-core/-/express-serve-static-core-5.0.1.tgz#3c9997ae9d00bc236e45c6374e84f2596458d9db"
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dependencies:
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dependencies:
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@ -606,6 +657,13 @@
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@ -708,6 +771,11 @@
optionalDependencies:
onnxruntime-node "1.14.0"
"@xmldom/xmldom@^0.8.6":
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resolved "https://registry.yarnpkg.com/@xmldom/xmldom/-/xmldom-0.8.10.tgz#a1337ca426aa61cef9fe15b5b28e340a72f6fa99"
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resolved "https://registry.yarnpkg.com/abbrev/-/abbrev-1.1.1.tgz#f8f2c887ad10bf67f634f005b6987fed3179aac8"
@ -758,6 +826,11 @@ anymatch@~3.1.2:
normalize-path "^3.0.0"
picomatch "^2.0.4"
append-field@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/append-field/-/append-field-1.0.0.tgz#1e3440e915f0b1203d23748e78edd7b9b5b43e56"
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resolved "https://registry.yarnpkg.com/arg/-/arg-4.1.3.tgz#269fc7ad5b8e42cb63c896d5666017261c144089"
@ -768,6 +841,13 @@ argparse@^2.0.1:
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argparse@~1.0.3:
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dependencies:
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array-flatten@1.1.1:
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resolved "https://registry.yarnpkg.com/array-flatten/-/array-flatten-1.1.1.tgz#9a5f699051b1e7073328f2a008968b64ea2955d2"
@ -879,6 +959,11 @@ bl@^4.0.3:
inherits "^2.0.4"
readable-stream "^3.4.0"
bluebird@~3.4.0:
version "3.4.7"
resolved "https://registry.yarnpkg.com/bluebird/-/bluebird-3.4.7.tgz#f72d760be09b7f76d08ed8fae98b289a8d05fab3"
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body-parser@1.20.2:
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@ -925,6 +1010,13 @@ buffer@^5.5.0:
base64-js "^1.3.1"
ieee754 "^1.1.13"
busboy@^1.0.0:
version "1.6.0"
resolved "https://registry.yarnpkg.com/busboy/-/busboy-1.6.0.tgz#966ea36a9502e43cdb9146962523b92f531f6893"
integrity sha512-8SFQbg/0hQ9xy3UNTB0YEnsNBbWfhf7RtnzpL7TkBiTBRfrQ9Fxcnz7VJsleJpyp6rVLvXiuORqjlHi5q+PYuA==
dependencies:
streamsearch "^1.1.0"
bytes@3.1.2:
version "3.1.2"
resolved "https://registry.yarnpkg.com/bytes/-/bytes-3.1.2.tgz#8b0beeb98605adf1b128fa4386403c009e0221a5"
@ -1070,6 +1162,16 @@ concat-map@0.0.1:
resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==
concat-stream@^1.5.2:
version "1.6.2"
resolved "https://registry.yarnpkg.com/concat-stream/-/concat-stream-1.6.2.tgz#904bdf194cd3122fc675c77fc4ac3d4ff0fd1a34"
integrity sha512-27HBghJxjiZtIk3Ycvn/4kbJk/1uZuJFfuPEns6LaEvpvG1f0hTea8lilrouyo9mVc2GWdcEZ8OLoGmSADlrCw==
dependencies:
buffer-from "^1.0.0"
inherits "^2.0.3"
readable-stream "^2.2.2"
typedarray "^0.0.6"
content-disposition@0.5.4:
version "0.5.4"
resolved "https://registry.yarnpkg.com/content-disposition/-/content-disposition-0.5.4.tgz#8b82b4efac82512a02bb0b1dcec9d2c5e8eb5bfe"
@ -1092,6 +1194,11 @@ cookie@0.6.0:
resolved "https://registry.yarnpkg.com/cookie/-/cookie-0.6.0.tgz#2798b04b071b0ecbff0dbb62a505a8efa4e19051"
integrity sha512-U71cyTamuh1CRNCfpGY6to28lxvNwPG4Guz/EVjgf3Jmzv0vlDp1atT9eS5dDjMYHucpHbWns6Lwf3BKz6svdw==
core-util-is@~1.0.0:
version "1.0.3"
resolved "https://registry.yarnpkg.com/core-util-is/-/core-util-is-1.0.3.tgz#a6042d3634c2b27e9328f837b965fac83808db85"
integrity sha512-ZQBvi1DcpJ4GDqanjucZ2Hj3wEO5pZDS89BWbkcrvdxksJorwUDDZamX9ldFkp9aw2lmBDLgkObEA4DWNJ9FYQ==
cors@^2.8.5:
version "2.8.5"
resolved "https://registry.yarnpkg.com/cors/-/cors-2.8.5.tgz#eac11da51592dd86b9f06f6e7ac293b3df875d29"
@ -1202,6 +1309,11 @@ digest-fetch@^1.3.0:
base-64 "^0.1.0"
md5 "^2.3.0"
dingbat-to-unicode@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/dingbat-to-unicode/-/dingbat-to-unicode-1.0.1.tgz#5091dd673241453e6b5865e26e5a4452cdef5c83"
integrity sha512-98l0sW87ZT58pU4i61wa2OHwxbiYSbuxsCBozaVnYX2iCnr3bLM3fIes1/ej7h1YdOKuKt/MLs706TVnALA65w==
dom-serializer@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/dom-serializer/-/dom-serializer-2.0.0.tgz#e41b802e1eedf9f6cae183ce5e622d789d7d8e53"
@ -1251,6 +1363,13 @@ drizzle-orm@^0.31.2:
resolved "https://registry.yarnpkg.com/drizzle-orm/-/drizzle-orm-0.31.2.tgz#221a257dd487bab49ddb88a17bd82388600cf655"
integrity sha512-QnenevbnnAzmbNzQwbhklvIYrDE8YER8K7kSrAWQSV1YvFCdSQPzj+jzqRdTSsV2cDqSpQ0NXGyL1G9I43LDLg==
duck@^0.1.12:
version "0.1.12"
resolved "https://registry.yarnpkg.com/duck/-/duck-0.1.12.tgz#de7adf758421230b6d7aee799ce42670586b9efa"
integrity sha512-wkctla1O6VfP89gQ+J/yDesM0S7B7XLXjKGzXxMDVFg7uEn706niAtyYovKbyq1oT9YwDcly721/iUWoc8MVRg==
dependencies:
underscore "^1.13.1"
ee-first@1.1.1:
version "1.1.1"
resolved "https://registry.yarnpkg.com/ee-first/-/ee-first-1.1.1.tgz#590c61156b0ae2f4f0255732a158b266bc56b21d"
@ -1657,7 +1776,12 @@ ignore-by-default@^1.0.1:
resolved "https://registry.yarnpkg.com/ignore-by-default/-/ignore-by-default-1.0.1.tgz#48ca6d72f6c6a3af00a9ad4ae6876be3889e2b09"
integrity sha512-Ius2VYcGNk7T90CppJqcIkS5ooHUZyIQK+ClZfMfMNFEF9VSE73Fq+906u/CWu92x4gzZMWOwfFYckPObzdEbA==
inherits@2.0.4, inherits@^2.0.3, inherits@^2.0.4:
immediate@~3.0.5:
version "3.0.6"
resolved "https://registry.yarnpkg.com/immediate/-/immediate-3.0.6.tgz#9db1dbd0faf8de6fbe0f5dd5e56bb606280de69b"
integrity sha512-XXOFtyqDjNDAQxVfYxuF7g9Il/IbWmmlQg2MYKOH8ExIT1qg6xc4zyS3HaEEATgs1btfzxq15ciUiY7gjSXRGQ==
inherits@2.0.4, inherits@^2.0.3, inherits@^2.0.4, inherits@~2.0.3:
version "2.0.4"
resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c"
integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==
@ -1716,6 +1840,11 @@ is-stream@^2.0.0:
resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-2.0.1.tgz#fac1e3d53b97ad5a9d0ae9cef2389f5810a5c077"
integrity sha512-hFoiJiTl63nn+kstHGBtewWSKnQLpyb155KHheA1l39uvtO9nWIop1p3udqPcUd/xbF1VLMO4n7OI6p7RbngDg==
isarray@~1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/isarray/-/isarray-1.0.0.tgz#bb935d48582cba168c06834957a54a3e07124f11"
integrity sha512-VLghIWNM6ELQzo7zwmcg0NmTVyWKYjvIeM83yjp0wRDTmUnrM678fQbcKBo6n2CJEF0szoG//ytg+TKla89ALQ==
js-tiktoken@^1.0.12:
version "1.0.12"
resolved "https://registry.yarnpkg.com/js-tiktoken/-/js-tiktoken-1.0.12.tgz#af0f5cf58e5e7318240d050c8413234019424211"
@ -1742,6 +1871,16 @@ jsonpointer@^5.0.1:
resolved "https://registry.yarnpkg.com/jsonpointer/-/jsonpointer-5.0.1.tgz#2110e0af0900fd37467b5907ecd13a7884a1b559"
integrity sha512-p/nXbhSEcu3pZRdkW1OfJhpsVtW1gd4Wa1fnQc9YLiTfAjn0312eMKimbdIQzuZl9aa9xUGaRlP9T/CJE/ditQ==
jszip@^3.7.1:
version "3.10.1"
resolved "https://registry.yarnpkg.com/jszip/-/jszip-3.10.1.tgz#34aee70eb18ea1faec2f589208a157d1feb091c2"
integrity sha512-xXDvecyTpGLrqFrvkrUSoxxfJI5AH7U8zxxtVclpsUtMCq4JQ290LY8AW5c7Ggnr/Y/oK+bQMbqK2qmtk3pN4g==
dependencies:
lie "~3.3.0"
pako "~1.0.2"
readable-stream "~2.3.6"
setimmediate "^1.0.5"
kuler@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/kuler/-/kuler-2.0.0.tgz#e2c570a3800388fb44407e851531c1d670b061b3"
@ -1797,6 +1936,18 @@ langchainhub@~0.0.8:
resolved "https://registry.yarnpkg.com/langchainhub/-/langchainhub-0.0.8.tgz#fd4b96dc795e22e36c1a20bad31b61b0c33d3110"
integrity sha512-Woyb8YDHgqqTOZvWIbm2CaFDGfZ4NTSyXV687AG4vXEfoNo7cGQp7nhl7wL3ehenKWmNEmcxCLgOZzW8jE6lOQ==
langsmith@^0.1.56-rc.1:
version "0.1.68"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.1.68.tgz#848332e822fe5e6734a07f1c36b6530cc1798afb"
integrity sha512-otmiysWtVAqzMx3CJ4PrtUBhWRG5Co8Z4o7hSZENPjlit9/j3/vm3TSvbaxpDYakZxtMjhkcJTqrdYFipISEiQ==
dependencies:
"@types/uuid" "^10.0.0"
commander "^10.0.1"
p-queue "^6.6.2"
p-retry "4"
semver "^7.6.3"
uuid "^10.0.0"
langsmith@~0.1.1, langsmith@~0.1.7:
version "0.1.14"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.1.14.tgz#2b889dbcfb49547614df276a4a5a063092a1585d"
@ -1825,6 +1976,13 @@ leac@^0.6.0:
resolved "https://registry.yarnpkg.com/leac/-/leac-0.6.0.tgz#dcf136e382e666bd2475f44a1096061b70dc0912"
integrity sha512-y+SqErxb8h7nE/fiEX07jsbuhrpO9lL8eca7/Y1nuWV2moNlXhyd59iDGcRf6moVyDMbmTNzL40SUyrFU/yDpg==
lie@~3.3.0:
version "3.3.0"
resolved "https://registry.yarnpkg.com/lie/-/lie-3.3.0.tgz#dcf82dee545f46074daf200c7c1c5a08e0f40f6a"
integrity sha512-UaiMJzeWRlEujzAuw5LokY1L5ecNQYZKfmyZ9L7wDHb/p5etKaxXhohBcrw0EYby+G/NA52vRSN4N39dxHAIwQ==
dependencies:
immediate "~3.0.5"
lodash.set@^4.3.2:
version "4.3.2"
resolved "https://registry.yarnpkg.com/lodash.set/-/lodash.set-4.3.2.tgz#d8757b1da807dde24816b0d6a84bea1a76230b23"
@ -1847,6 +2005,15 @@ long@^4.0.0:
resolved "https://registry.yarnpkg.com/long/-/long-4.0.0.tgz#9a7b71cfb7d361a194ea555241c92f7468d5bf28"
integrity sha512-XsP+KhQif4bjX1kbuSiySJFNAehNxgLb6hPRGJ9QsUr8ajHkuXGdrHmFUTUUXhDwVX2R5bY4JNZEwbUiMhV+MA==
lop@^0.4.1:
version "0.4.2"
resolved "https://registry.yarnpkg.com/lop/-/lop-0.4.2.tgz#c9c2f958a39b9da1c2f36ca9ad66891a9fe84640"
integrity sha512-RefILVDQ4DKoRZsJ4Pj22TxE3omDO47yFpkIBoDKzkqPRISs5U1cnAdg/5583YPkWPaLIYHOKRMQSvjFsO26cw==
dependencies:
duck "^0.1.12"
option "~0.2.1"
underscore "^1.13.1"
lru-cache@^6.0.0:
version "6.0.0"
resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-6.0.0.tgz#6d6fe6570ebd96aaf90fcad1dafa3b2566db3a94"
@ -1859,6 +2026,22 @@ make-error@^1.1.1:
resolved "https://registry.yarnpkg.com/make-error/-/make-error-1.3.6.tgz#2eb2e37ea9b67c4891f684a1394799af484cf7a2"
integrity sha512-s8UhlNe7vPKomQhC1qFelMokr/Sc3AgNbso3n74mVPA5LTZwkB9NlXf4XPamLxJE8h0gh73rM94xvwRT2CVInw==
mammoth@^1.8.0:
version "1.8.0"
resolved "https://registry.yarnpkg.com/mammoth/-/mammoth-1.8.0.tgz#d8f1b0d3a0355fda129270346e9dc853f223028f"
integrity sha512-pJNfxSk9IEGVpau+tsZFz22ofjUsl2mnA5eT8PjPs2n0BP+rhVte4Nez6FdgEuxv3IGI3afiV46ImKqTGDVlbA==
dependencies:
"@xmldom/xmldom" "^0.8.6"
argparse "~1.0.3"
base64-js "^1.5.1"
bluebird "~3.4.0"
dingbat-to-unicode "^1.0.1"
jszip "^3.7.1"
lop "^0.4.1"
path-is-absolute "^1.0.0"
underscore "^1.13.1"
xmlbuilder "^10.0.0"
md5@^2.3.0:
version "2.3.0"
resolved "https://registry.yarnpkg.com/md5/-/md5-2.3.0.tgz#c3da9a6aae3a30b46b7b0c349b87b110dc3bda4f"
@ -1912,7 +2095,7 @@ minimatch@^3.1.2:
dependencies:
brace-expansion "^1.1.7"
minimist@^1.2.0, minimist@^1.2.3:
minimist@^1.2.0, minimist@^1.2.3, minimist@^1.2.6:
version "1.2.8"
resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.8.tgz#c1a464e7693302e082a075cee0c057741ac4772c"
integrity sha512-2yyAR8qBkN3YuheJanUpWC5U3bb5osDywNB8RzDVlDwDHbocAJveqqj1u8+SVD7jkWT4yvsHCpWqqWqAxb0zCA==
@ -1922,6 +2105,13 @@ mkdirp-classic@^0.5.2, mkdirp-classic@^0.5.3:
resolved "https://registry.yarnpkg.com/mkdirp-classic/-/mkdirp-classic-0.5.3.tgz#fa10c9115cc6d8865be221ba47ee9bed78601113"
integrity sha512-gKLcREMhtuZRwRAfqP3RFW+TK4JqApVBtOIftVgjuABpAtpxhPGaDcfvbhNvD0B8iD1oUr/txX35NjcaY6Ns/A==
mkdirp@^0.5.4:
version "0.5.6"
resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.6.tgz#7def03d2432dcae4ba1d611445c48396062255f6"
integrity sha512-FP+p8RB8OWpF3YZBCrP5gtADmtXApB5AMLn+vdyA+PyxCjrCs00mjyUozssO33cwDeT3wNGdLxJ5M//YqtHAJw==
dependencies:
minimist "^1.2.6"
ml-array-mean@^1.1.6:
version "1.1.6"
resolved "https://registry.yarnpkg.com/ml-array-mean/-/ml-array-mean-1.1.6.tgz#d951a700dc8e3a17b3e0a583c2c64abd0c619c56"
@ -1973,6 +2163,19 @@ ms@2.1.3, ms@^2.0.0, ms@^2.1.1:
resolved "https://registry.yarnpkg.com/ms/-/ms-2.1.3.tgz#574c8138ce1d2b5861f0b44579dbadd60c6615b2"
integrity sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==
multer@^1.4.5-lts.1:
version "1.4.5-lts.1"
resolved "https://registry.yarnpkg.com/multer/-/multer-1.4.5-lts.1.tgz#803e24ad1984f58edffbc79f56e305aec5cfd1ac"
integrity sha512-ywPWvcDMeH+z9gQq5qYHCCy+ethsk4goepZ45GLD63fOu0YcNecQxi64nDs3qluZB+murG3/D4dJ7+dGctcCQQ==
dependencies:
append-field "^1.0.0"
busboy "^1.0.0"
concat-stream "^1.5.2"
mkdirp "^0.5.4"
object-assign "^4.1.1"
type-is "^1.6.4"
xtend "^4.0.0"
mustache@^4.2.0:
version "4.2.0"
resolved "https://registry.yarnpkg.com/mustache/-/mustache-4.2.0.tgz#e5892324d60a12ec9c2a73359edca52972bf6f64"
@ -2050,7 +2253,7 @@ num-sort@^2.0.0:
resolved "https://registry.yarnpkg.com/num-sort/-/num-sort-2.1.0.tgz#1cbb37aed071329fdf41151258bc011898577a9b"
integrity sha512-1MQz1Ed8z2yckoBeSfkQHHO9K1yDRxxtotKSJ9yvcTUUxSvfvzEq5GwBrjjHEpMlq/k5gvXdmJ1SbYxWtpNoVg==
object-assign@^4:
object-assign@^4, object-assign@^4.1.1:
version "4.1.1"
resolved "https://registry.yarnpkg.com/object-assign/-/object-assign-4.1.1.tgz#2109adc7965887cfc05cbbd442cac8bfbb360863"
integrity sha512-rJgTQnkUnH1sFw8yT6VSU3zD3sWmu6sZhIseY8VX+GRu3P6F7Fu+JNDoXfklElbLJSnc3FUQHVe4cU5hj+BcUg==
@ -2146,6 +2349,11 @@ openapi-types@^12.1.3:
resolved "https://registry.yarnpkg.com/openapi-types/-/openapi-types-12.1.3.tgz#471995eb26c4b97b7bd356aacf7b91b73e777dd3"
integrity sha512-N4YtSYJqghVu4iek2ZUvcN/0aqH1kRDuNqzcycDxhOUpg7GdvLa2F3DgS6yBNhInhv2r/6I0Flkn7CqL8+nIcw==
option@~0.2.1:
version "0.2.4"
resolved "https://registry.yarnpkg.com/option/-/option-0.2.4.tgz#fd475cdf98dcabb3cb397a3ba5284feb45edbfe4"
integrity sha512-pkEqbDyl8ou5cpq+VsnQbe/WlEy5qS7xPzMS1U55OCG9KPvwFD46zDbxQIj3egJSFc3D+XhYOPUzz49zQAVy7A==
p-finally@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/p-finally/-/p-finally-1.0.0.tgz#3fbcfb15b899a44123b34b6dcc18b724336a2cae"
@ -2174,6 +2382,11 @@ p-timeout@^3.2.0:
dependencies:
p-finally "^1.0.0"
pako@~1.0.2:
version "1.0.11"
resolved "https://registry.yarnpkg.com/pako/-/pako-1.0.11.tgz#6c9599d340d54dfd3946380252a35705a6b992bf"
integrity sha512-4hLB8Py4zZce5s4yd9XzopqwVv/yGNhV1Bl8NTmCq1763HeK2+EwVTv+leGeL13Dnh2wfbqowVPXCIO0z4taYw==
parseley@^0.12.0:
version "0.12.1"
resolved "https://registry.yarnpkg.com/parseley/-/parseley-0.12.1.tgz#4afd561d50215ebe259e3e7a853e62f600683aef"
@ -2187,6 +2400,11 @@ parseurl@~1.3.3:
resolved "https://registry.yarnpkg.com/parseurl/-/parseurl-1.3.3.tgz#9da19e7bee8d12dff0513ed5b76957793bc2e8d4"
integrity sha512-CiyeOxFT/JZyN5m0z9PfXw4SCBJ6Sygz1Dpl0wqjlhDEGGBP1GnsUVEL0p63hoG1fcj3fHynXi9NYO4nWOL+qQ==
path-is-absolute@^1.0.0:
version "1.0.1"
resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
integrity sha512-AVbw3UJ2e9bq64vSaS9Am0fje1Pa8pbGqTTsmXfaIiMpnr5DlDhfJOuLj9Sf95ZPVDAUerDfEk88MPmPe7UCQg==
path-to-regexp@0.1.7:
version "0.1.7"
resolved "https://registry.yarnpkg.com/path-to-regexp/-/path-to-regexp-0.1.7.tgz#df604178005f522f15eb4490e7247a1bfaa67f8c"
@ -2238,6 +2456,11 @@ prettier@^3.2.5:
resolved "https://registry.yarnpkg.com/prettier/-/prettier-3.2.5.tgz#e52bc3090586e824964a8813b09aba6233b28368"
integrity sha512-3/GWa9aOC0YeD7LUfvOG2NiDyhOWRvt1k+rcKhOuYnMY24iiCphgneUfJDyFXd6rZCAnuLBv6UeAULtrhT/F4A==
process-nextick-args@~2.0.0:
version "2.0.1"
resolved "https://registry.yarnpkg.com/process-nextick-args/-/process-nextick-args-2.0.1.tgz#7820d9b16120cc55ca9ae7792680ae7dba6d7fe2"
integrity sha512-3ouUOpQhtgrbOa17J7+uxOTpITYWaGP7/AhoR3+A+/1e9skrzelGi/dXzEYyvbxubEF6Wn2ypscTKiKJFFn1ag==
protobufjs@^6.8.8:
version "6.11.4"
resolved "https://registry.yarnpkg.com/protobufjs/-/protobufjs-6.11.4.tgz#29a412c38bf70d89e537b6d02d904a6f448173aa"
@ -2320,6 +2543,19 @@ rc@^1.2.7:
minimist "^1.2.0"
strip-json-comments "~2.0.1"
readable-stream@^2.2.2, readable-stream@~2.3.6:
version "2.3.8"
resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-2.3.8.tgz#91125e8042bba1b9887f49345f6277027ce8be9b"
integrity sha512-8p0AUk4XODgIewSi0l8Epjs+EVnWiK7NoDIEGU0HhE7+ZyY8D1IMY7odu5lRrFXGg71L15KG8QrPmum45RTtdA==
dependencies:
core-util-is "~1.0.0"
inherits "~2.0.3"
isarray "~1.0.0"
process-nextick-args "~2.0.0"
safe-buffer "~5.1.1"
string_decoder "~1.1.1"
util-deprecate "~1.0.1"
readable-stream@^3.1.1, readable-stream@^3.4.0, readable-stream@^3.6.0:
version "3.6.2"
resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-3.6.2.tgz#56a9b36ea965c00c5a93ef31eb111a0f11056967"
@ -2351,7 +2587,7 @@ safe-buffer@5.2.1, safe-buffer@^5.0.1, safe-buffer@~5.2.0:
resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.2.1.tgz#1eaf9fa9bdb1fdd4ec75f58f9cdb4e6b7827eec6"
integrity sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==
safe-buffer@~5.1.1:
safe-buffer@~5.1.0, safe-buffer@~5.1.1:
version "5.1.2"
resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.1.2.tgz#991ec69d296e0313747d59bdfd2b745c35f8828d"
integrity sha512-Gd2UZBJDkXlY7GbJxfsE8/nvKkUEU1G38c1siN6QP6a9PT9MmHB8GnpscSmMJSoF8LOIrt8ud/wPtojys4G6+g==
@ -2380,6 +2616,11 @@ semver@^7.3.5, semver@^7.5.3, semver@^7.5.4:
dependencies:
lru-cache "^6.0.0"
semver@^7.6.3:
version "7.6.3"
resolved "https://registry.yarnpkg.com/semver/-/semver-7.6.3.tgz#980f7b5550bc175fb4dc09403085627f9eb33143"
integrity sha512-oVekP1cKtI+CTDvHWYFUcMtsK/00wmAEfyqKfNdARm8u1wNVhSgaX7A8d4UuIlUI5e84iEwOhs7ZPYRmzU9U6A==
send@0.18.0:
version "0.18.0"
resolved "https://registry.yarnpkg.com/send/-/send-0.18.0.tgz#670167cc654b05f5aa4a767f9113bb371bc706be"
@ -2421,6 +2662,11 @@ set-function-length@^1.2.1:
gopd "^1.0.1"
has-property-descriptors "^1.0.2"
setimmediate@^1.0.5:
version "1.0.5"
resolved "https://registry.yarnpkg.com/setimmediate/-/setimmediate-1.0.5.tgz#290cbb232e306942d7d7ea9b83732ab7856f8285"
integrity sha512-MATJdZp8sLqDl/68LfQmbP8zKPLQNV6BIZoIgrscFDQ+RsvK/BxeDQOgyxKKoh0y/8h3BqVFnCqQ/gd+reiIXA==
setprototypeof@1.2.0:
version "1.2.0"
resolved "https://registry.yarnpkg.com/setprototypeof/-/setprototypeof-1.2.0.tgz#66c9a24a73f9fc28cbe66b09fed3d33dcaf1b424"
@ -2491,6 +2737,11 @@ source-map@^0.6.0:
resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
sprintf-js@~1.0.2:
version "1.0.3"
resolved "https://registry.yarnpkg.com/sprintf-js/-/sprintf-js-1.0.3.tgz#04e6926f662895354f3dd015203633b857297e2c"
integrity sha512-D9cPgkvLlV3t3IzL0D0YLvGA9Ahk4PcvVwUbN0dSGr1aP0Nrt4AEnTUbuGvquEC0mA64Gqt1fzirlRs5ibXx8g==
stack-trace@0.0.x:
version "0.0.10"
resolved "https://registry.yarnpkg.com/stack-trace/-/stack-trace-0.0.10.tgz#547c70b347e8d32b4e108ea1a2a159e5fdde19c0"
@ -2501,6 +2752,11 @@ statuses@2.0.1:
resolved "https://registry.yarnpkg.com/statuses/-/statuses-2.0.1.tgz#55cb000ccf1d48728bd23c685a063998cf1a1b63"
integrity sha512-RwNA9Z/7PrK06rYLIzFMlaF+l73iwpzsqRIFgbMLbTcLD6cOao82TaWefPXQvB2fOC4AjuYSEndS7N/mTCbkdQ==
streamsearch@^1.1.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/streamsearch/-/streamsearch-1.1.0.tgz#404dd1e2247ca94af554e841a8ef0eaa238da764"
integrity sha512-Mcc5wHehp9aXz1ax6bZUyY5afg9u2rv5cqQI3mRrYkGC8rW2hM02jWuwjtL++LS5qinSyhj2QfLyNsuc+VsExg==
streamx@^2.15.0, streamx@^2.16.1:
version "2.16.1"
resolved "https://registry.yarnpkg.com/streamx/-/streamx-2.16.1.tgz#2b311bd34832f08aa6bb4d6a80297c9caef89614"
@ -2518,6 +2774,13 @@ string_decoder@^1.1.1:
dependencies:
safe-buffer "~5.2.0"
string_decoder@~1.1.1:
version "1.1.1"
resolved "https://registry.yarnpkg.com/string_decoder/-/string_decoder-1.1.1.tgz#9cf1611ba62685d7030ae9e4ba34149c3af03fc8"
integrity sha512-n/ShnvDi6FHbbVfviro+WojiFzv+s8MPMHBczVePfUpDJLwoLT0ht1l4YwBCbi8pJAveEEdnkHyPyTP/mzRfwg==
dependencies:
safe-buffer "~5.1.0"
strip-json-comments@~2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-2.0.1.tgz#3c531942e908c2697c0ec344858c286c7ca0a60a"
@ -2636,7 +2899,7 @@ tunnel-agent@^0.6.0:
dependencies:
safe-buffer "^5.0.1"
type-is@~1.6.18:
type-is@^1.6.4, type-is@~1.6.18:
version "1.6.18"
resolved "https://registry.yarnpkg.com/type-is/-/type-is-1.6.18.tgz#4e552cd05df09467dcbc4ef739de89f2cf37c131"
integrity sha512-TkRKr9sUTxEH8MdfuCSP7VizJyzRNMjj2J2do2Jr3Kym598JVdEksuzPQCnlFPW4ky9Q+iA+ma9BGm06XQBy8g==
@ -2644,6 +2907,11 @@ type-is@~1.6.18:
media-typer "0.3.0"
mime-types "~2.1.24"
typedarray@^0.0.6:
version "0.0.6"
resolved "https://registry.yarnpkg.com/typedarray/-/typedarray-0.0.6.tgz#867ac74e3864187b1d3d47d996a78ec5c8830777"
integrity sha512-/aCDEGatGvZ2BIk+HmLf4ifCJFwvKFNb9/JeZPMulfgFracn9QFcAf5GO8B/mweUjSoblS5In0cWhqpfs/5PQA==
typescript@^5.4.3:
version "5.4.3"
resolved "https://registry.yarnpkg.com/typescript/-/typescript-5.4.3.tgz#5c6fedd4c87bee01cd7a528a30145521f8e0feff"
@ -2654,6 +2922,11 @@ undefsafe@^2.0.5:
resolved "https://registry.yarnpkg.com/undefsafe/-/undefsafe-2.0.5.tgz#38733b9327bdcd226db889fb723a6efd162e6e2c"
integrity sha512-WxONCrssBM8TSPRqN5EmsjVrsv4A8X12J4ArBiiayv3DyyG3ZlIg6yysuuSYdZsVz3TKcTg2fd//Ujd4CHV1iA==
underscore@^1.13.1:
version "1.13.7"
resolved "https://registry.yarnpkg.com/underscore/-/underscore-1.13.7.tgz#970e33963af9a7dda228f17ebe8399e5fbe63a10"
integrity sha512-GMXzWtsc57XAtguZgaQViUOzs0KTkk8ojr3/xAxXLITqf/3EMwxC0inyETfDFjH/Krbhuep0HNbbjI9i/q3F3g==
undici-types@~5.26.4:
version "5.26.5"
resolved "https://registry.yarnpkg.com/undici-types/-/undici-types-5.26.5.tgz#bcd539893d00b56e964fd2657a4866b221a65617"
@ -2664,7 +2937,7 @@ unpipe@1.0.0, unpipe@~1.0.0:
resolved "https://registry.yarnpkg.com/unpipe/-/unpipe-1.0.0.tgz#b2bf4ee8514aae6165b4817829d21b2ef49904ec"
integrity sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ==
util-deprecate@^1.0.1:
util-deprecate@^1.0.1, util-deprecate@~1.0.1:
version "1.0.2"
resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
integrity sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==
@ -2763,6 +3036,16 @@ ws@^8.17.1:
resolved "https://registry.yarnpkg.com/ws/-/ws-8.17.1.tgz#9293da530bb548febc95371d90f9c878727d919b"
integrity sha512-6XQFvXTkbfUOZOKKILFG1PDK2NDQs4azKQl26T0YS5CxqWLgXajbPZ+h4gZekJyRqFU8pvnbAbbs/3TgRPy+GQ==
xmlbuilder@^10.0.0:
version "10.1.1"
resolved "https://registry.yarnpkg.com/xmlbuilder/-/xmlbuilder-10.1.1.tgz#8cae6688cc9b38d850b7c8d3c0a4161dcaf475b0"
integrity sha512-OyzrcFLL/nb6fMGHbiRDuPup9ljBycsdCypwuyg5AAHvyWzGfChJpCXMG88AGTIMFhGZ9RccFN1e6lhg3hkwKg==
xtend@^4.0.0:
version "4.0.2"
resolved "https://registry.yarnpkg.com/xtend/-/xtend-4.0.2.tgz#bb72779f5fa465186b1f438f674fa347fdb5db54"
integrity sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==
yallist@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"