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

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projectmoon 2024-07-10 13:22:08 +02:00
parent 52c5478342 27e6f5b9e1
commit f9dbab46b1
23 changed files with 623 additions and 231 deletions
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1
.github/FUNDING.yml vendored Normal file
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@ -0,0 +1 @@
patreon: itzcrazykns

25
README.md
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@ -85,11 +85,12 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
### Non-Docker Installation
1. Clone the repository and rename the `sample.config.toml` file to `config.toml` in the root directory. Ensure you complete all required fields in this file.
2. Rename the `.env.example` file to `.env` in the `ui` folder and fill in all necessary fields.
3. After populating the configuration and environment files, run `npm i` in both the `ui` folder and the root directory.
4. Install the dependencies and then execute `npm run build` in both the `ui` folder and the root directory.
5. Finally, start both the frontend and the backend by running `npm run start` in both the `ui` folder and the root directory.
1. Install SearXNG and allow `JSON` format in the SearXNG settings.
2. Clone the repository and rename the `sample.config.toml` file to `config.toml` in the root directory. Ensure you complete all required fields in this file.
3. Rename the `.env.example` file to `.env` in the `ui` folder and fill in all necessary fields.
4. After populating the configuration and environment files, run `npm i` in both the `ui` folder and the root directory.
5. Install the dependencies and then execute `npm run build` in both the `ui` folder and the root directory.
6. Finally, start both the frontend and the backend by running `npm run start` in both the `ui` folder and the root directory.
**Note**: Using Docker is recommended as it simplifies the setup process, especially for managing environment variables and dependencies.
@ -110,11 +111,7 @@ If you're encountering an Ollama connection error, it is likely due to the backe
3. **Linux Users - Expose Ollama to Network:**
- Serve Ollama over your network with the command:
```bash
OLLAMA_HOST=0.0.0.0 ollama serve
```
- Inside `/etc/systemd/system/ollama.service`, you need to add `Environment="OLLAMA_HOST=0.0.0.0"`. Then restart Ollama by `systemctl restart ollama`. For more information see [Ollama docs](https://github.com/ollama/ollama/blob/main/docs/faq.md#setting-environment-variables-on-linux)
- Ensure that the port (default is 11434) is not blocked by your firewall.
@ -146,11 +143,11 @@ If you find Perplexica useful, consider giving us a star on GitHub. This helps m
### Donations
We also accept donations to help sustain our project. If you would like to contribute, you can use the following button to make a donation in cryptocurrency. Thank you for your support!
We also accept donations to help sustain our project. If you would like to contribute, you can use the following options to donate. Thank you for your support!
<a href="https://nowpayments.io/donation?api_key=RFFKJH1-GRR4DQG-HFV1DZP-00G6MMK&source=lk_donation&medium=referral" target="_blank">
<img src="https://nowpayments.io/images/embeds/donation-button-white.svg" alt="Crypto donation button by NOWPayments">
</a>
| Cards | Ethereum |
| ----------------------------------- | ----------------------------------------------------- |
| https://www.patreon.com/itzcrazykns | Address: `0xB025a84b2F269570Eb8D4b05DEdaA41D8525B6DD` |
## Contribution

2
backend.dockerfile
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@ -1,4 +1,4 @@
FROM nikolaik/python-nodejs:python3.12-nodejs20-bullseye
FROM node:slim
ARG SEARXNG_API_URL

3
package.json
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@ -1,6 +1,6 @@
{
"name": "perplexica-backend",
"version": "1.7.0",
"version": "1.7.1",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {
@ -24,6 +24,7 @@
},
"dependencies": {
"@iarna/toml": "^2.2.5",
"@langchain/community": "^0.2.16",
"@langchain/openai": "^0.0.25",
"@xenova/transformers": "^2.17.1",
"axios": "^1.6.8",

4
src/agents/academicSearchAgent.ts
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@ -44,7 +44,7 @@ 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 containg a brief description of the content of that page).
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.
@ -52,7 +52,7 @@ const basicAcademicSearchResponsePrompt = `
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.
Aything 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
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>

4
src/agents/redditSearchAgent.ts
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@ -44,7 +44,7 @@ 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 containg a brief description of the content of that page).
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.
@ -52,7 +52,7 @@ const basicRedditSearchResponsePrompt = `
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.
Aything 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
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>

2
src/agents/suggestionGeneratorAgent.ts
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@ -47,7 +47,7 @@ const generateSuggestions = (
input: SuggestionGeneratorInput,
llm: BaseChatModel,
) => {
(llm as ChatOpenAI).temperature = 0;
(llm as unknown as ChatOpenAI).temperature = 0;
const suggestionGeneratorChain = createSuggestionGeneratorChain(llm);
return suggestionGeneratorChain.invoke(input);
};

4
src/agents/webSearchAgent.ts
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@ -44,7 +44,7 @@ Rephrased question:
const basicWebSearchResponsePrompt = `
You are Perplexica, an AI model who is expert at searching the web and answering user's queries.
Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containg a brief description of the content of that page).
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.
@ -52,7 +52,7 @@ const basicWebSearchResponsePrompt = `
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.
Aything 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
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>

4
src/agents/wolframAlphaSearchAgent.ts
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@ -43,7 +43,7 @@ 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 containg a brief description of the content of that page).
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.
@ -51,7 +51,7 @@ const basicWolframAlphaSearchResponsePrompt = `
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.
Aything 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
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>

4
src/agents/youtubeSearchAgent.ts
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@ -44,7 +44,7 @@ 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 containg a brief description of the content of that page).
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.
@ -52,7 +52,7 @@ const basicYoutubeSearchResponsePrompt = `
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.
Aything 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
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>

198
src/lib/providers.ts
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@ -1,198 +0,0 @@
import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
import { ChatOllama } from '@langchain/community/chat_models/ollama';
import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
import { HuggingFaceTransformersEmbeddings } from './huggingfaceTransformer';
import {
getGroqApiKey,
getOllamaApiEndpoint,
getOllamaAuthHeader,
getOpenaiApiKey,
} from '../config';
import logger from '../utils/logger';
function getOllamaHeaders() {
const ollamaAuthHeader = getOllamaAuthHeader();
let headers;
if (typeof ollamaAuthHeader !== undefined) {
return {
'Content-Type': 'application/json',
'Authorization': ollamaAuthHeader
};
} else {
return { 'Content-Type': 'application/json' };
}
}
export const getAvailableChatModelProviders = async () => {
const openAIApiKey = getOpenaiApiKey();
const groqApiKey = getGroqApiKey();
const ollamaEndpoint = getOllamaApiEndpoint();
const ollamaAuthHeader = getOllamaAuthHeader();
const models = {};
if (openAIApiKey) {
try {
models['openai'] = {
'GPT-3.5 turbo': new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-3.5-turbo',
temperature: 0.7,
}),
'GPT-4': new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4',
temperature: 0.7,
}),
'GPT-4 turbo': new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4-turbo',
temperature: 0.7,
}),
'GPT-4 omni': new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4o',
temperature: 0.7,
}),
};
} catch (err) {
logger.error(`Error loading OpenAI models: ${err}`);
}
}
if (groqApiKey) {
try {
models['groq'] = {
'LLaMA3 8b': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama3-8b-8192',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
'LLaMA3 70b': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama3-70b-8192',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
'Mixtral 8x7b': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'mixtral-8x7b-32768',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
'Gemma 7b': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'gemma-7b-it',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
};
} catch (err) {
logger.error(`Error loading Groq models: ${err}`);
}
}
if (ollamaEndpoint) {
try {
const headers = getOllamaHeaders();
const response = await fetch(`${ollamaEndpoint}/api/tags`, { headers });
const { models: ollamaModels } = (await response.json()) as any;
models['ollama'] = ollamaModels.reduce((acc, model) => {
acc[model.model] = new ChatOllama({
baseUrl: ollamaEndpoint,
model: model.model,
headers,
temperature: 0.7,
});
return acc;
}, {});
} catch (err) {
logger.error(`Error loading Ollama models: ${err}`);
}
}
models['custom_openai'] = {};
return models;
};
export const getAvailableEmbeddingModelProviders = async () => {
const openAIApiKey = getOpenaiApiKey();
const ollamaEndpoint = getOllamaApiEndpoint();
const ollamaAuthHeader = getOllamaAuthHeader();
const models = {};
if (openAIApiKey) {
try {
models['openai'] = {
'Text embedding 3 small': new OpenAIEmbeddings({
openAIApiKey,
modelName: 'text-embedding-3-small',
}),
'Text embedding 3 large': new OpenAIEmbeddings({
openAIApiKey,
modelName: 'text-embedding-3-large',
}),
};
} catch (err) {
logger.error(`Error loading OpenAI embeddings: ${err}`);
}
}
if (ollamaEndpoint) {
const headers = getOllamaHeaders();
try {
const response = await fetch(`${ollamaEndpoint}/api/tags`, { headers });
const { models: ollamaModels } = (await response.json()) as any;
models['ollama'] = ollamaModels.reduce((acc, model) => {
acc[model.model] = new OllamaEmbeddings({
baseUrl: ollamaEndpoint,
headers,
model: model.model,
});
return acc;
}, {});
} catch (err) {
logger.error(`Error loading Ollama embeddings: ${err}`);
}
}
try {
models['local'] = {
'BGE Small': new HuggingFaceTransformersEmbeddings({
modelName: 'Xenova/bge-small-en-v1.5',
}),
'GTE Small': new HuggingFaceTransformersEmbeddings({
modelName: 'Xenova/gte-small',
}),
'Bert Multilingual': new HuggingFaceTransformersEmbeddings({
modelName: 'Xenova/bert-base-multilingual-uncased',
}),
};
} catch (err) {
logger.error(`Error loading local embeddings: ${err}`);
}
return models;
};

59
src/lib/providers/groq.ts Normal file
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@ -0,0 +1,59 @@
import { ChatOpenAI } from '@langchain/openai';
import { getGroqApiKey } from '../../config';
import logger from '../../utils/logger';
export const loadGroqChatModels = async () => {
const groqApiKey = getGroqApiKey();
if (!groqApiKey) return {};
try {
const chatModels = {
'LLaMA3 8b': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama3-8b-8192',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
'LLaMA3 70b': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama3-70b-8192',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
'Mixtral 8x7b': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'mixtral-8x7b-32768',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
'Gemma 7b': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'gemma-7b-it',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
};
return chatModels;
} catch (err) {
logger.error(`Error loading Groq models: ${err}`);
return {};
}
};

44
src/lib/providers/index.ts Normal file
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@ -0,0 +1,44 @@
import { loadGroqChatModels } from './groq';
import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
import { loadTransformersEmbeddingsModels } from './transformers';
const chatModelProviders = {
openai: loadOpenAIChatModels,
groq: loadGroqChatModels,
ollama: loadOllamaChatModels,
};
const embeddingModelProviders = {
openai: loadOpenAIEmbeddingsModels,
local: loadTransformersEmbeddingsModels,
ollama: loadOllamaEmbeddingsModels,
};
export const getAvailableChatModelProviders = async () => {
const models = {};
for (const provider in chatModelProviders) {
const providerModels = await chatModelProviders[provider]();
if (Object.keys(providerModels).length > 0) {
models[provider] = providerModels
}
}
models['custom_openai'] = {}
return models;
};
export const getAvailableEmbeddingModelProviders = async () => {
const models = {};
for (const provider in embeddingModelProviders) {
const providerModels = await embeddingModelProviders[provider]();
if (Object.keys(providerModels).length > 0) {
models[provider] = providerModels
}
}
return models;
};

75
src/lib/providers/ollama.ts Normal file
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@ -0,0 +1,75 @@
import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
import { 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' };
}
}
export const loadOllamaChatModels = async () => {
const ollamaEndpoint = getOllamaApiEndpoint();
const ollamaHeaders = getOllamaHeaders();
if (!ollamaEndpoint) return {};
try {
const response = await fetch(`${ollamaEndpoint}/api/tags`, {
headers: ollamaHeaders
});
const { models: ollamaModels } = (await response.json()) as any;
const chatModels = ollamaModels.reduce((acc, model) => {
acc[model.model] = new ChatOllama({
baseUrl: ollamaEndpoint,
headers: ollamaHeaders,
model: model.model,
temperature: 0.7,
});
return acc;
}, {});
return chatModels;
} catch (err) {
logger.error(`Error loading Ollama models: ${err}`);
return {};
}
};
export const loadOllamaEmbeddingsModels = async () => {
const ollamaEndpoint = getOllamaApiEndpoint();
const ollamaHeaders = getOllamaHeaders();
if (!ollamaEndpoint) return {};
try {
const response = await fetch(`${ollamaEndpoint}/api/tags`, {
headers: ollamaHeaders
});
const { models: ollamaModels } = (await response.json()) as any;
const embeddingsModels = ollamaModels.reduce((acc, model) => {
acc[model.model] = new OllamaEmbeddings({
baseUrl: ollamaEndpoint,
headers: ollamaHeaders,
model: model.model,
});
return acc;
}, {});
return embeddingsModels;
} catch (err) {
logger.error(`Error loading Ollama embeddings model: ${err}`);
return {};
}
};

63
src/lib/providers/openai.ts Normal file
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@ -0,0 +1,63 @@
import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
import { getOpenaiApiKey } from '../../config';
import logger from '../../utils/logger';
export const loadOpenAIChatModels = async () => {
const openAIApiKey = getOpenaiApiKey();
if (!openAIApiKey) return {};
try {
const chatModels = {
'GPT-3.5 turbo': new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-3.5-turbo',
temperature: 0.7,
}),
'GPT-4': new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4',
temperature: 0.7,
}),
'GPT-4 turbo': new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4-turbo',
temperature: 0.7,
}),
'GPT-4 omni': new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4o',
temperature: 0.7,
}),
};
return chatModels;
} catch (err) {
logger.error(`Error loading OpenAI models: ${err}`);
return {};
}
};
export const loadOpenAIEmbeddingsModels = async () => {
const openAIApiKey = getOpenaiApiKey();
if (!openAIApiKey) return {};
try {
const embeddingModels = {
'Text embedding 3 small': new OpenAIEmbeddings({
openAIApiKey,
modelName: 'text-embedding-3-small',
}),
'Text embedding 3 large': new OpenAIEmbeddings({
openAIApiKey,
modelName: 'text-embedding-3-large',
}),
};
return embeddingModels;
} catch (err) {
logger.error(`Error loading OpenAI embeddings model: ${err}`);
return {};
}
};

23
src/lib/providers/transformers.ts Normal file
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@ -0,0 +1,23 @@
import logger from '../../utils/logger';
import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
export const loadTransformersEmbeddingsModels = async () => {
try {
const embeddingModels = {
'BGE Small': new HuggingFaceTransformersEmbeddings({
modelName: 'Xenova/bge-small-en-v1.5',
}),
'GTE Small': new HuggingFaceTransformersEmbeddings({
modelName: 'Xenova/gte-small',
}),
'Bert Multilingual': new HuggingFaceTransformersEmbeddings({
modelName: 'Xenova/bert-base-multilingual-uncased',
}),
};
return embeddingModels;
} catch (err) {
logger.error(`Error loading Transformers embeddings model: ${err}`);
return {};
}
};

23
src/routes/chats.ts
View File

@ -40,4 +40,27 @@ router.get('/:id', async (req, res) => {
}
});
router.delete(`/:id`, async (req, res) => {
try {
const chatExists = await db.query.chats.findFirst({
where: eq(chats.id, req.params.id),
});
if (!chatExists) {
return res.status(404).json({ message: 'Chat not found' });
}
await db.delete(chats).where(eq(chats.id, req.params.id)).execute();
await db
.delete(messages)
.where(eq(messages.chatId, req.params.id))
.execute();
return res.status(200).json({ message: 'Chat deleted successfully' });
} catch (err) {
res.status(500).json({ message: 'An error has occurred.' });
logger.error(`Error in deleting chat: ${err.message}`);
}
});
export default router;

4
src/websocket/connectionManager.ts
View File

@ -45,7 +45,7 @@ export const handleConnection = async (
chatModelProviders[chatModelProvider][chatModel] &&
chatModelProvider != 'custom_openai'
) {
llm = chatModelProviders[chatModelProvider][chatModel] as
llm = chatModelProviders[chatModelProvider][chatModel] as unknown as
| BaseChatModel
| undefined;
} else if (chatModelProvider == 'custom_openai') {
@ -56,7 +56,7 @@ export const handleConnection = async (
configuration: {
baseURL: searchParams.get('openAIBaseURL'),
},
});
}) as unknown as BaseChatModel;
}
if (

10
ui/app/library/page.tsx
View File

@ -1,11 +1,12 @@
'use client';
import DeleteChat from '@/components/DeleteChat';
import { formatTimeDifference } from '@/lib/utils';
import { BookOpenText, ClockIcon, ScanEye } from 'lucide-react';
import { BookOpenText, ClockIcon, Delete, ScanEye } from 'lucide-react';
import Link from 'next/link';
import { useEffect, useState } from 'react';
interface Chat {
export interface Chat {
id: string;
title: string;
createdAt: string;
@ -92,6 +93,11 @@ const Page = () => {
{formatTimeDifference(new Date(), chat.createdAt)} Ago
</p>
</div>
<DeleteChat
chatId={chat.id}
chats={chats}
setChats={setChats}
/>
</div>
</div>
))}

49
ui/components/ChatWindow.tsx
View File

@ -83,6 +83,55 @@ const useSocket = (
'embeddingModelProvider',
embeddingModelProvider,
);
} else {
const providers = await fetch(
`${process.env.NEXT_PUBLIC_API_URL}/models`,
{
headers: {
'Content-Type': 'application/json',
},
},
).then(async (res) => await res.json());
const chatModelProviders = providers.chatModelProviders;
const embeddingModelProviders = providers.embeddingModelProviders;
if (
Object.keys(chatModelProviders).length > 0 &&
!chatModelProviders[chatModelProvider]
) {
chatModelProvider = Object.keys(chatModelProviders)[0];
localStorage.setItem('chatModelProvider', chatModelProvider);
}
if (
chatModelProvider &&
!chatModelProviders[chatModelProvider][chatModel]
) {
chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
localStorage.setItem('chatModel', chatModel);
}
if (
Object.keys(embeddingModelProviders).length > 0 &&
!embeddingModelProviders[embeddingModelProvider]
) {
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
localStorage.setItem(
'embeddingModelProvider',
embeddingModelProvider,
);
}
if (
embeddingModelProvider &&
!embeddingModelProviders[embeddingModelProvider][embeddingModel]
) {
embeddingModel = Object.keys(
embeddingModelProviders[embeddingModelProvider],
)[0];
localStorage.setItem('embeddingModel', embeddingModel);
}
}
const wsURL = new URL(url);

114
ui/components/DeleteChat.tsx Normal file
View File

@ -0,0 +1,114 @@
import { Delete, Trash } from 'lucide-react';
import { Dialog, Transition } from '@headlessui/react';
import { Fragment, useState } from 'react';
import { toast } from 'sonner';
import { Chat } from '@/app/library/page';
const DeleteChat = ({
chatId,
chats,
setChats,
}: {
chatId: string;
chats: Chat[];
setChats: (chats: Chat[]) => void;
}) => {
const [confirmationDialogOpen, setConfirmationDialogOpen] = useState(false);
const [loading, setLoading] = useState(false);
const handleDelete = async () => {
setLoading(true);
try {
const res = await fetch(
`${process.env.NEXT_PUBLIC_API_URL}/chats/${chatId}`,
{
method: 'DELETE',
headers: {
'Content-Type': 'application/json',
},
},
);
if (res.status != 200) {
throw new Error('Failed to delete chat');
}
const newChats = chats.filter((chat) => chat.id !== chatId);
setChats(newChats);
} catch (err: any) {
toast.error(err.message);
} finally {
setConfirmationDialogOpen(false);
setLoading(false);
}
};
return (
<>
<button
onClick={() => {
setConfirmationDialogOpen(true);
}}
className="bg-transparent text-red-400 hover:scale-105 transition duration-200"
>
<Trash size={17} />
</button>
<Transition appear show={confirmationDialogOpen} as={Fragment}>
<Dialog
as="div"
className="relative z-50"
onClose={() => {
if (!loading) {
setConfirmationDialogOpen(false);
}
}}
>
<Dialog.Backdrop className="fixed inset-0 bg-black/30" />
<div className="fixed inset-0 overflow-y-auto">
<div className="flex min-h-full items-center justify-center p-4 text-center">
<Transition.Child
as={Fragment}
enter="ease-out duration-200"
enterFrom="opacity-0 scale-95"
enterTo="opacity-100 scale-100"
leave="ease-in duration-100"
leaveFrom="opacity-100 scale-200"
leaveTo="opacity-0 scale-95"
>
<Dialog.Panel className="w-full max-w-md transform rounded-2xl bg-light-secondary dark:bg-dark-secondary border border-light-200 dark:border-dark-200 p-6 text-left align-middle shadow-xl transition-all">
<Dialog.Title className="text-lg font-medium leading-6 dark:text-white">
Delete Confirmation
</Dialog.Title>
<Dialog.Description className="text-sm dark:text-white/70 text-black/70">
Are you sure you want to delete this chat?
</Dialog.Description>
<div className="flex flex-row items-end justify-end space-x-4 mt-6">
<button
onClick={() => {
if (!loading) {
setConfirmationDialogOpen(false);
}
}}
className="text-black/50 dark:text-white/50 text-sm hover:text-black/70 hover:dark:text-white/70 transition duration-200"
>
Cancel
</button>
<button
onClick={handleDelete}
className="text-red-400 text-sm hover:text-red-500 transition duration200"
>
Delete
</button>
</div>
</Dialog.Panel>
</Transition.Child>
</div>
</div>
</Dialog>
</Transition>
</>
);
};
export default DeleteChat;

2
ui/package.json
View File

@ -1,6 +1,6 @@
{
"name": "perplexica-frontend",
"version": "1.7.0",
"version": "1.7.1",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {

135
yarn.lock
View File

@ -307,6 +307,23 @@
"@jridgewell/resolve-uri" "^3.0.3"
"@jridgewell/sourcemap-codec" "^1.4.10"
"@langchain/community@^0.2.16":
version "0.2.16"
resolved "https://registry.yarnpkg.com/@langchain/community/-/community-0.2.16.tgz#5888baf7fc7ea272c5f91aaa0e71bc444167262d"
integrity sha512-dFDcMabKACvuRd0w6EIRLWf1ubPGZEeEwFt9v1jiEr4HCFxH0OF+iM1QUCcVRbB2fK5lqmKeTD1XAeZV8+AyXA==
dependencies:
"@langchain/core" "~0.2.11"
"@langchain/openai" "~0.1.0"
binary-extensions "^2.2.0"
expr-eval "^2.0.2"
flat "^5.0.2"
js-yaml "^4.1.0"
langchain "0.2.3"
langsmith "~0.1.30"
uuid "^9.0.0"
zod "^3.22.3"
zod-to-json-schema "^3.22.5"
"@langchain/community@~0.0.41":
version "0.0.43"
resolved "https://registry.yarnpkg.com/@langchain/community/-/community-0.0.43.tgz#017e2f9b3209b3999482f10df5aec2520731a63c"
@ -320,6 +337,24 @@
uuid "^9.0.0"
zod "^3.22.3"
"@langchain/core@>0.1.56 <0.3.0", "@langchain/core@>0.2.0 <0.3.0", "@langchain/core@>=0.2.5 <0.3.0", "@langchain/core@~0.2.0", "@langchain/core@~0.2.11":
version "0.2.11"
resolved "https://registry.yarnpkg.com/@langchain/core/-/core-0.2.11.tgz#5f47467e20e56b250831baef20083657c6facb4c"
integrity sha512-d4SNL7WI0c3oHrV4WxCRH1/TNqdePXEzYjYwIb4aEH6lW1aM0utGhLbNthX+aYkOL4Ynx2FoG4h91ECIipiKWQ==
dependencies:
ansi-styles "^5.0.0"
camelcase "6"
decamelize "1.2.0"
js-tiktoken "^1.0.12"
langsmith "~0.1.30"
ml-distance "^4.0.0"
mustache "^4.2.0"
p-queue "^6.6.2"
p-retry "4"
uuid "^9.0.0"
zod "^3.22.4"
zod-to-json-schema "^3.22.3"
"@langchain/core@~0.1.44", "@langchain/core@~0.1.45":
version "0.1.52"
resolved "https://registry.yarnpkg.com/@langchain/core/-/core-0.1.52.tgz#7619310b83ffa841628efe2e1eda873ca714d068"
@ -348,6 +383,36 @@
zod "^3.22.4"
zod-to-json-schema "^3.22.3"
"@langchain/openai@~0.0.28":
version "0.0.34"
resolved "https://registry.yarnpkg.com/@langchain/openai/-/openai-0.0.34.tgz#36c9bca0721ab9f7e5d40927e7c0429cacbd5b56"
integrity sha512-M+CW4oXle5fdoz2T2SwdOef8pl3/1XmUx1vjn2mXUVM/128aO0l23FMF0SNBsAbRV6P+p/TuzjodchJbi0Ht/A==
dependencies:
"@langchain/core" ">0.1.56 <0.3.0"
js-tiktoken "^1.0.12"
openai "^4.41.1"
zod "^3.22.4"
zod-to-json-schema "^3.22.3"
"@langchain/openai@~0.1.0":
version "0.1.3"
resolved "https://registry.yarnpkg.com/@langchain/openai/-/openai-0.1.3.tgz#6eb0994e970d85ffa9aaeafb94449024ccf6ca63"
integrity sha512-riv/JC9x2A8b7GcHu8sx+mlZJ8KAwSSi231IPTlcciYnKozmrQ5H0vrtiD31fxiDbaRsk7tyCpkSBIOQEo7CyQ==
dependencies:
"@langchain/core" ">=0.2.5 <0.3.0"
js-tiktoken "^1.0.12"
openai "^4.49.1"
zod "^3.22.4"
zod-to-json-schema "^3.22.3"
"@langchain/textsplitters@~0.0.0":
version "0.0.3"
resolved "https://registry.yarnpkg.com/@langchain/textsplitters/-/textsplitters-0.0.3.tgz#1a3cc93dd2ab330edb225400ded190a22fea14e3"
integrity sha512-cXWgKE3sdWLSqAa8ykbCcUsUF1Kyr5J3HOWYGuobhPEycXW4WI++d5DhzdpL238mzoEXTi90VqfSCra37l5YqA==
dependencies:
"@langchain/core" ">0.2.0 <0.3.0"
js-tiktoken "^1.0.12"
"@protobufjs/aspromise@^1.1.1", "@protobufjs/aspromise@^1.1.2":
version "1.1.2"
resolved "https://registry.yarnpkg.com/@protobufjs/aspromise/-/aspromise-1.1.2.tgz#9b8b0cc663d669a7d8f6f5d0893a14d348f30fbf"
@ -1508,6 +1573,13 @@ is-stream@^2.0.0:
resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-2.0.1.tgz#fac1e3d53b97ad5a9d0ae9cef2389f5810a5c077"
integrity sha512-hFoiJiTl63nn+kstHGBtewWSKnQLpyb155KHheA1l39uvtO9nWIop1p3udqPcUd/xbF1VLMO4n7OI6p7RbngDg==
js-tiktoken@^1.0.12:
version "1.0.12"
resolved "https://registry.yarnpkg.com/js-tiktoken/-/js-tiktoken-1.0.12.tgz#af0f5cf58e5e7318240d050c8413234019424211"
integrity sha512-L7wURW1fH9Qaext0VzaUDpFGVQgjkdE3Dgsy9/+yXyGEpBKnylTd0mU0bfbNkKDlXRb6TEsZkwuflu1B8uQbJQ==
dependencies:
base64-js "^1.5.1"
js-tiktoken@^1.0.7, js-tiktoken@^1.0.8:
version "1.0.10"
resolved "https://registry.yarnpkg.com/js-tiktoken/-/js-tiktoken-1.0.10.tgz#2b343ec169399dcee8f9ef9807dbd4fafd3b30dc"
@ -1532,6 +1604,28 @@ kuler@^2.0.0:
resolved "https://registry.yarnpkg.com/kuler/-/kuler-2.0.0.tgz#e2c570a3800388fb44407e851531c1d670b061b3"
integrity sha512-Xq9nH7KlWZmXAtodXDDRE7vs6DU1gTU8zYDHDiWLSip45Egwq3plLHzPn27NgvzL2r1LMPC1vdqh98sQxtqj4A==
langchain@0.2.3:
version "0.2.3"
resolved "https://registry.yarnpkg.com/langchain/-/langchain-0.2.3.tgz#c14bb05cf871b21bd63b84b3ab89580b1d62539f"
integrity sha512-T9xR7zd+Nj0oXy6WoYKmZLy0DlQiDLFPGYWdOXDxy+AvqlujoPdVQgDSpdqiOHvAjezrByAoKxoHCz5XMwTP/Q==
dependencies:
"@langchain/core" "~0.2.0"
"@langchain/openai" "~0.0.28"
"@langchain/textsplitters" "~0.0.0"
binary-extensions "^2.2.0"
js-tiktoken "^1.0.12"
js-yaml "^4.1.0"
jsonpointer "^5.0.1"
langchainhub "~0.0.8"
langsmith "~0.1.7"
ml-distance "^4.0.0"
openapi-types "^12.1.3"
p-retry "4"
uuid "^9.0.0"
yaml "^2.2.1"
zod "^3.22.4"
zod-to-json-schema "^3.22.3"
langchain@^0.1.30:
version "0.1.30"
resolved "https://registry.yarnpkg.com/langchain/-/langchain-0.1.30.tgz#e1adb3f1849fcd5c596c668300afd5dc8cb37a97"
@ -1571,6 +1665,23 @@ langsmith@~0.1.1, langsmith@~0.1.7:
p-retry "4"
uuid "^9.0.0"
langsmith@~0.1.30:
version "0.1.34"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.1.34.tgz#801310495fef258ed9c22bb5575120e2c06d51cf"
integrity sha512-aMv2k8kEaovhTuZnK6/6DMCoM7Jurvm1AzdESn+yN+HramRxp3sK32jFRz3ogkXP6GjAjOIofcnNkzhHXSUXGA==
dependencies:
"@types/uuid" "^9.0.1"
commander "^10.0.1"
lodash.set "^4.3.2"
p-queue "^6.6.2"
p-retry "4"
uuid "^9.0.0"
lodash.set@^4.3.2:
version "4.3.2"
resolved "https://registry.yarnpkg.com/lodash.set/-/lodash.set-4.3.2.tgz#d8757b1da807dde24816b0d6a84bea1a76230b23"
integrity sha512-4hNPN5jlm/N/HLMCO43v8BXKq9Z7QdAGc/VGrRD61w8gN9g/6jF9A4L1pbUgBLCffi0w9VsXfTOij5x8iTyFvg==
logform@^2.3.2, logform@^2.4.0:
version "2.6.0"
resolved "https://registry.yarnpkg.com/logform/-/logform-2.6.0.tgz#8c82a983f05d6eaeb2d75e3decae7a768b2bf9b5"
@ -1714,6 +1825,11 @@ 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==
mustache@^4.2.0:
version "4.2.0"
resolved "https://registry.yarnpkg.com/mustache/-/mustache-4.2.0.tgz#e5892324d60a12ec9c2a73359edca52972bf6f64"
integrity sha512-71ippSywq5Yb7/tVYyGbkBggbU8H3u5Rz56fH60jGFgr8uHwxs+aSKeqmluIVzM0m0kB7xQjKS6qPfd0b2ZoqQ==
napi-build-utils@^1.0.1:
version "1.0.2"
resolved "https://registry.yarnpkg.com/napi-build-utils/-/napi-build-utils-1.0.2.tgz#b1fddc0b2c46e380a0b7a76f984dd47c41a13806"
@ -1858,6 +1974,20 @@ openai@^4.26.0:
node-fetch "^2.6.7"
web-streams-polyfill "^3.2.1"
openai@^4.41.1, openai@^4.49.1:
version "4.52.2"
resolved "https://registry.yarnpkg.com/openai/-/openai-4.52.2.tgz#5d67271f3df84c0b54676b08990eaa9402151759"
integrity sha512-mMc0XgFuVSkcm0lRIi8zaw++otC82ZlfkCur1qguXYWPETr/+ZwL9A/vvp3YahX+shpaT6j03dwsmUyLAfmEfg==
dependencies:
"@types/node" "^18.11.18"
"@types/node-fetch" "^2.6.4"
abort-controller "^3.0.0"
agentkeepalive "^4.2.1"
form-data-encoder "1.7.2"
formdata-node "^4.3.2"
node-fetch "^2.6.7"
web-streams-polyfill "^3.2.1"
openapi-types@^12.1.3:
version "12.1.3"
resolved "https://registry.yarnpkg.com/openapi-types/-/openapi-types-12.1.3.tgz#471995eb26c4b97b7bd356aacf7b91b73e777dd3"
@ -2462,6 +2592,11 @@ zod-to-json-schema@^3.22.3:
resolved "https://registry.yarnpkg.com/zod-to-json-schema/-/zod-to-json-schema-3.22.5.tgz#3646e81cfc318dbad2a22519e5ce661615418673"
integrity sha512-+akaPo6a0zpVCCseDed504KBJUQpEW5QZw7RMneNmKw+fGaML1Z9tUNLnHHAC8x6dzVRO1eB2oEMyZRnuBZg7Q==
zod-to-json-schema@^3.22.5:
version "3.23.1"
resolved "https://registry.yarnpkg.com/zod-to-json-schema/-/zod-to-json-schema-3.23.1.tgz#5225925b8ed5fa20096bd99be076c4b29b53d309"
integrity sha512-oT9INvydob1XV0v1d2IadrR74rLtDInLvDFfAa1CG0Pmg/vxATk7I2gSelfj271mbzeM4Da0uuDQE/Nkj3DWNw==
zod@^3.22.3, zod@^3.22.4:
version "3.22.4"
resolved "https://registry.yarnpkg.com/zod/-/zod-3.22.4.tgz#f31c3a9386f61b1f228af56faa9255e845cf3fff"