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

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yeet 2024-09-25 13:55:17 +02:00
parent f16d4f52b1 a0aad69f62
commit b86cf3c315
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9
README.md
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@ -12,6 +12,7 @@
- [Non-Docker Installation](#non-docker-installation)
- [Ollama Connection Errors](#ollama-connection-errors)
- [Using as a Search Engine](#using-as-a-search-engine)
- [Using Perplexica's API](#using-perplexicas-api)
- [One-Click Deployment](#one-click-deployment)
- [Upcoming Features](#upcoming-features)
- [Support Us](#support-us)
@ -45,6 +46,7 @@ Want to know more about its architecture and how it works? You can read it [here
- **Wolfram Alpha Search Mode:** Answers queries that need calculations or data analysis using Wolfram Alpha.
- **Reddit Search Mode:** Searches Reddit for discussions and opinions related to the query.
- **Current Information:** Some search tools might give you outdated info because they use data from crawling bots and convert them into embeddings and store them in a index. Unlike them, Perplexica uses SearxNG, a metasearch engine to get the results and rerank and get the most relevant source out of it, ensuring you always get the latest information without the overhead of daily data updates.
- **API**: Integrate Perplexica into your existing applications and make use of its capibilities.
It has many more features like image and video search. Some of the planned features are mentioned in [upcoming features](#upcoming-features).
@ -125,6 +127,12 @@ If you wish to use Perplexica as an alternative to traditional search engines li
3. Add a new site search with the following URL: `http://localhost:3000/?q=%s`. Replace `localhost` with your IP address or domain name, and `3000` with the port number if Perplexica is not hosted locally.
4. Click the add button. Now, you can use Perplexica directly from your browser's search bar.
## Using Perplexica's API
Perplexica also provides an API for developers looking to integrate its powerful search engine into their own applications. You can run searches, use multiple models and get answers to your queries.
For more details, check out the full documentation [here](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/API/SEARCH.md).
## One-Click Deployment
[![Deploy to RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploylobe.svg)](https://repocloud.io/details/?app_id=267)
@ -135,6 +143,7 @@ If you wish to use Perplexica as an alternative to traditional search engines li
- [x] Adding support for local LLMs
- [x] History Saving features
- [x] Introducing various Focus Modes
- [x] Adding API support
- [ ] Finalizing Copilot Mode
- [ ] Adding Discover

3
backend.dockerfile
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@ -1,6 +1,7 @@
FROM node:slim
ARG SEARXNG_API_URL
ENV SEARXNG_API_URL=${SEARXNG_API_URL}
WORKDIR /home/perplexica
@ -11,8 +12,6 @@ COPY drizzle.config.ts /home/perplexica/
COPY package.json /home/perplexica/
COPY yarn.lock /home/perplexica/
RUN sed -i "s|SEARXNG = \".*\"|SEARXNG = \"${SEARXNG_API_URL}\"|g" /home/perplexica/config.toml
RUN mkdir /home/perplexica/data
RUN yarn install

1
docker-compose.yaml
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@ -21,6 +21,7 @@ services:
- 3001:3001
volumes:
- backend-dbstore:/home/perplexica/data
- ./config.toml:/home/perplexica/config.toml
extra_hosts:
- 'host.docker.internal:host-gateway'
networks:

105
docs/API/SEARCH.md Normal file
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@ -0,0 +1,105 @@
# Perplexica Search API Documentation
## Overview
Perplexica’s Search API makes it easy to use our AI-powered search engine. You can run different types of searches, pick the models you want to use, and get the most recent info. Follow the following headings to learn more about Perplexica's search API.
## Endpoint
### **POST** `/api/search`
### Request
The API accepts a JSON object in the request body, where you define the focus mode, chat models, embedding models, and your query.
#### Request Body Structure
```json
{
"chatModel": {
"provider": "openai",
"model": "gpt-4o-mini"
},
"embeddingModel": {
"provider": "openai",
"model": "text-embedding-3-large"
},
"focusMode": "webSearch",
"query": "What is Perplexica",
"history": []
}
```
### Request Parameters
- **`chatModel`** (object, optional): Defines the chat model to be used for the query.
- `provider`: Specifies the provider for the chat model (e.g., `openai`, `ollama`).
- `model`: The specific model from the chosen provider (e.g., `gpt-4o-mini`).
- Optional fields for custom OpenAI configuration:
- `customOpenAIBaseURL`: If you’re using a custom OpenAI instance, provide the base URL.
- `customOpenAIKey`: The API key for a custom OpenAI instance.
- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching.
- `provider`: The provider for the embedding model (e.g., `openai`).
- `model`: The specific embedding model (e.g., `text-embedding-3-large`).
- **`focusMode`** (string, required): Specifies which focus mode to use. Available modes:
- `webSearch`, `academicSearch`, `writingAssistant`, `wolframAlphaSearch`, `youtubeSearch`, `redditSearch`.
- **`query`** (string, required): The search query or question.
- **`history`** (array, optional): An array of message pairs representing the conversation history. Each pair consists of a role (either 'human' or 'assistant') and the message content. This allows the system to use the context of the conversation to refine results. Example:
```json
[
["human", "What is Perplexica?"],
["assistant", "Perplexica is an AI-powered search engine..."]
]
```
### Response
The response from the API includes both the final message and the sources used to generate that message.
#### Example Response
```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"
}
}
....
]
}
```
### Fields in the Response
- **`message`** (string): The search result, generated based on the query and focus mode.
- **`sources`** (array): A list of sources that were used to generate the search result. Each source includes:
- `pageContent`: A snippet of the relevant content from the source.
- `metadata`: Metadata about the source, including:
- `title`: The title of the webpage.
- `url`: The URL of the webpage.
### Error Handling
If an error occurs during the search process, the API will return an appropriate error message with an HTTP status code.
- **400**: If the request is malformed or missing required fields (e.g., no focus mode or query).
- **500**: If an internal server error occurs during the search.

2
package.json
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@ -1,6 +1,6 @@
{
"name": "perplexica-backend",
"version": "1.9.0-rc2",
"version": "1.9.0-rc3",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {

3
src/config.ts
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@ -41,7 +41,8 @@ export const getGroqApiKey = () => loadConfig().API_KEYS.GROQ;
export const getAnthropicApiKey = () => loadConfig().API_KEYS.ANTHROPIC;
export const getSearxngApiEndpoint = () => loadConfig().API_ENDPOINTS.SEARXNG;
export const getSearxngApiEndpoint = () =>
process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
export const getOllamaApiEndpoint = () => loadConfig().API_ENDPOINTS.OLLAMA;

52
src/lib/providers/anthropic.ts
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@ -9,26 +9,38 @@ export const loadAnthropicChatModels = async () => {
try {
const chatModels = {
'Claude 3.5 Sonnet': new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-5-sonnet-20240620',
}),
'Claude 3 Opus': new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-opus-20240229',
}),
'Claude 3 Sonnet': new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-sonnet-20240229',
}),
'Claude 3 Haiku': new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-haiku-20240307',
}),
'claude-3-5-sonnet-20240620': {
displayName: 'Claude 3.5 Sonnet',
model: new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-5-sonnet-20240620',
}),
},
'claude-3-opus-20240229': {
displayName: 'Claude 3 Opus',
model: new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-opus-20240229',
}),
},
'claude-3-sonnet-20240229': {
displayName: 'Claude 3 Sonnet',
model: new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-sonnet-20240229',
}),
},
'claude-3-haiku-20240307': {
displayName: 'Claude 3 Haiku',
model: new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-haiku-20240307',
}),
},
};
return chatModels;

161
src/lib/providers/groq.ts
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@ -9,76 +9,97 @@ export const loadGroqChatModels = async () => {
try {
const chatModels = {
'Llama 3.1 70B': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama-3.1-70b-versatile',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
'Llama 3.1 8B': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama-3.1-8b-instant',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
'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',
},
),
'Gemma2 9b': new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'gemma2-9b-it',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
'llama-3.1-70b-versatile': {
displayName: 'Llama 3.1 70B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama-3.1-70b-versatile',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'llama-3.1-8b-instant': {
displayName: 'Llama 3.1 8B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama-3.1-8b-instant',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'llama3-8b-8192': {
displayName: 'LLaMA3 8B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama3-8b-8192',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'llama3-70b-8192': {
displayName: 'LLaMA3 70B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama3-70b-8192',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'mixtral-8x7b-32768': {
displayName: 'Mixtral 8x7B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'mixtral-8x7b-32768',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'gemma-7b-it': {
displayName: 'Gemma 7B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'gemma-7b-it',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'gemma2-9b-it': {
displayName: 'Gemma2 9B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'gemma2-9b-it',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
};
return chatModels;

106
src/lib/providers/ollama.ts
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@ -4,72 +4,80 @@ import logger from '../../utils/logger';
import { ChatOllama } from '@langchain/community/chat_models/ollama';
function getOllamaHeaders() {
const ollamaAuthHeader = getOllamaAuthHeader();
const ollamaAuthHeader = getOllamaAuthHeader();
if (typeof ollamaAuthHeader !== undefined) {
return {
'Content-Type': 'application/json',
'Authorization': ollamaAuthHeader
};
return {
'Content-Type': 'application/json',
'Authorization': ollamaAuthHeader
};
} else {
return { 'Content-Type': 'application/json' };
return { 'Content-Type': 'application/json' };
}
}
export const loadOllamaChatModels = 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 chatModels = ollamaModels.reduce((acc, model) => {
acc[model.model] = new ChatOllama({
baseUrl: ollamaEndpoint,
headers: ollamaHeaders,
model: model.model,
temperature: 0.7,
});
return acc;
}, {});
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,
}),
};
return chatModels;
} catch (err) {
logger.error(`Error loading Ollama models: ${err}`);
return {};
}
return acc;
}, {});
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] = new OllamaEmbeddings({
baseUrl: ollamaEndpoint,
headers: ollamaHeaders,
model: model.model,
});
return acc;
}, {});
const embeddingsModels = ollamaModels.reduce((acc, model) => {
acc[model.model] = {
displayName: model.name,
model: new OllamaEmbeddings({
baseUrl: ollamaEndpoint,
headers: ollamaHeaders,
model: model.model,
}),
};
return embeddingsModels;
} catch (err) {
logger.error(`Error loading Ollama embeddings model: ${err}`);
return {};
}
return acc;
}, {});
return embeddingsModels;
} catch (err) {
logger.error(`Error loading Ollama embeddings model: ${err}`);
return {};
}
};

87
src/lib/providers/openai.ts
View File

@ -9,31 +9,46 @@ export const loadOpenAIChatModels = async () => {
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,
}),
'GPT-4 omni mini': new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4o-mini',
temperature: 0.7,
}),
'gpt-3.5-turbo': {
displayName: 'GPT-3.5 Turbo',
model: new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-3.5-turbo',
temperature: 0.7,
}),
},
'gpt-4': {
displayName: 'GPT-4',
model: new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4',
temperature: 0.7,
}),
},
'gpt-4-turbo': {
displayName: 'GPT-4 turbo',
model: new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4-turbo',
temperature: 0.7,
}),
},
'gpt-4o': {
displayName: 'GPT-4 omni',
model: new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4o',
temperature: 0.7,
}),
},
'gpt-4o-mini': {
displayName: 'GPT-4 omni mini',
model: new ChatOpenAI({
openAIApiKey,
modelName: 'gpt-4o-mini',
temperature: 0.7,
}),
},
};
return chatModels;
@ -50,14 +65,20 @@ export const loadOpenAIEmbeddingsModels = async () => {
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',
}),
'text-embedding-3-small': {
displayName: 'Text Embedding 3 Small',
model: new OpenAIEmbeddings({
openAIApiKey,
modelName: 'text-embedding-3-small',
}),
},
'text-embedding-3-large': {
displayName: 'Text Embedding 3 Large',
model: new OpenAIEmbeddings({
openAIApiKey,
modelName: 'text-embedding-3-large',
}),
},
};
return embeddingModels;

27
src/lib/providers/transformers.ts
View File

@ -4,15 +4,24 @@ 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',
}),
'xenova-bge-small-en-v1.5': {
displayName: 'BGE Small',
model: new HuggingFaceTransformersEmbeddings({
modelName: 'Xenova/bge-small-en-v1.5',
}),
},
'xenova-gte-small': {
displayName: 'GTE Small',
model: new HuggingFaceTransformersEmbeddings({
modelName: 'Xenova/gte-small',
}),
},
'xenova-bert-base-multilingual-uncased': {
displayName: 'Bert Multilingual',
model: new HuggingFaceTransformersEmbeddings({
modelName: 'Xenova/bert-base-multilingual-uncased',
}),
},
};
return embeddingModels;

64
src/routes/config.ts
View File

@ -10,38 +10,54 @@ import {
getOpenaiApiKey,
updateConfig,
} from '../config';
import logger from '../utils/logger';
const router = express.Router();
router.get('/', async (_, res) => {
const config = {};
try {
const config = {};
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
getAvailableChatModelProviders(),
getAvailableEmbeddingModelProviders(),
]);
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
getAvailableChatModelProviders(),
getAvailableEmbeddingModelProviders(),
]);
config['chatModelProviders'] = {};
config['embeddingModelProviders'] = {};
config['chatModelProviders'] = {};
config['embeddingModelProviders'] = {};
for (const provider in chatModelProviders) {
config['chatModelProviders'][provider] = Object.keys(
chatModelProviders[provider],
);
for (const provider in chatModelProviders) {
config['chatModelProviders'][provider] = Object.keys(
chatModelProviders[provider],
).map((model) => {
return {
name: model,
displayName: chatModelProviders[provider][model].displayName,
};
});
}
for (const provider in embeddingModelProviders) {
config['embeddingModelProviders'][provider] = Object.keys(
embeddingModelProviders[provider],
).map((model) => {
return {
name: model,
displayName: embeddingModelProviders[provider][model].displayName,
};
});
}
config['openaiApiKey'] = getOpenaiApiKey();
config['ollamaApiUrl'] = getOllamaApiEndpoint();
config['anthropicApiKey'] = getAnthropicApiKey();
config['groqApiKey'] = getGroqApiKey();
res.status(200).json(config);
} catch (err: any) {
res.status(500).json({ message: 'An error has occurred.' });
logger.error(`Error getting config: ${err.message}`);
}
for (const provider in embeddingModelProviders) {
config['embeddingModelProviders'][provider] = Object.keys(
embeddingModelProviders[provider],
);
}
config['openaiApiKey'] = getOpenaiApiKey();
config['ollamaApiUrl'] = getOllamaApiEndpoint();
config['anthropicApiKey'] = getAnthropicApiKey();
config['groqApiKey'] = getGroqApiKey();
res.status(200).json(config);
});
router.post('/', async (req, res) => {

2
src/routes/images.ts
View File

@ -26,7 +26,7 @@ router.post('/', async (req, res) => {
let llm: BaseChatModel | undefined;
if (chatModels[provider] && chatModels[provider][chatModel]) {
llm = chatModels[provider][chatModel] as BaseChatModel | undefined;
llm = chatModels[provider][chatModel].model as BaseChatModel | undefined;
}
if (!llm) {

2
src/routes/index.ts
View File

@ -6,6 +6,7 @@ import modelsRouter from './models';
import suggestionsRouter from './suggestions';
import chatsRouter from './chats';
import settingsRouter from './settings';
import searchRouter from './search';
const router = express.Router();
@ -16,5 +17,6 @@ router.use('/models', modelsRouter);
router.use('/suggestions', suggestionsRouter);
router.use('/chats', chatsRouter);
router.use('/settings', settingsRouter);
router.use('/search', searchRouter);
export default router;

150
src/routes/search.ts Normal file
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@ -0,0 +1,150 @@
import express from 'express';
import logger from '../utils/logger';
import { BaseChatModel } from 'langchain/chat_models/base';
import { Embeddings } from 'langchain/embeddings/base';
import { ChatOpenAI } from '@langchain/openai';
import {
getAvailableChatModelProviders,
getAvailableEmbeddingModelProviders,
} from '../lib/providers';
import { searchHandlers } from '../websocket/messageHandler';
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
const router = express.Router();
interface chatModel {
provider: string;
model: string;
customOpenAIBaseURL?: string;
customOpenAIKey?: string;
}
interface embeddingModel {
provider: string;
model: string;
}
interface ChatRequestBody {
focusMode: string;
chatModel?: chatModel;
embeddingModel?: embeddingModel;
query: string;
history: Array<[string, string]>;
}
router.post('/', async (req, res) => {
try {
const body: ChatRequestBody = req.body;
if (!body.focusMode || !body.query) {
return res.status(400).json({ message: 'Missing focus mode or query' });
}
body.history = body.history || [];
const history: BaseMessage[] = body.history.map((msg) => {
if (msg[0] === 'human') {
return new HumanMessage({
content: msg[1],
});
} else {
return new AIMessage({
content: msg[1],
});
}
});
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
getAvailableChatModelProviders(),
getAvailableEmbeddingModelProviders(),
]);
const chatModelProvider =
body.chatModel?.provider || Object.keys(chatModelProviders)[0];
const chatModel =
body.chatModel?.model ||
Object.keys(chatModelProviders[chatModelProvider])[0];
const embeddingModelProvider =
body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0];
const embeddingModel =
body.embeddingModel?.model ||
Object.keys(embeddingModelProviders[embeddingModelProvider])[0];
let llm: BaseChatModel | undefined;
let embeddings: Embeddings | undefined;
if (body.chatModel?.provider === 'custom_openai') {
if (
!body.chatModel?.customOpenAIBaseURL ||
!body.chatModel?.customOpenAIKey
) {
return res
.status(400)
.json({ message: 'Missing custom OpenAI base URL or key' });
}
llm = new ChatOpenAI({
modelName: body.chatModel.model,
openAIApiKey: body.chatModel.customOpenAIKey,
temperature: 0.7,
configuration: {
baseURL: body.chatModel.customOpenAIBaseURL,
},
}) as unknown as BaseChatModel;
} else if (
chatModelProviders[chatModelProvider] &&
chatModelProviders[chatModelProvider][chatModel]
) {
llm = chatModelProviders[chatModelProvider][chatModel]
.model as unknown as BaseChatModel | undefined;
}
if (
embeddingModelProviders[embeddingModelProvider] &&
embeddingModelProviders[embeddingModelProvider][embeddingModel]
) {
embeddings = embeddingModelProviders[embeddingModelProvider][
embeddingModel
].model as Embeddings | undefined;
}
if (!llm || !embeddings) {
return res.status(400).json({ message: 'Invalid model selected' });
}
const searchHandler = searchHandlers[body.focusMode];
if (!searchHandler) {
return res.status(400).json({ message: 'Invalid focus mode' });
}
const emitter = searchHandler(body.query, history, llm, embeddings);
let message = '';
let sources = [];
emitter.on('data', (data) => {
const parsedData = JSON.parse(data);
if (parsedData.type === 'response') {
message += parsedData.data;
} else if (parsedData.type === 'sources') {
sources = parsedData.data;
}
});
emitter.on('end', () => {
res.status(200).json({ message, sources });
});
emitter.on('error', (data) => {
const parsedData = JSON.parse(data);
res.status(500).json({ message: parsedData.data });
});
} catch (err: any) {
logger.error(`Error in getting search results: ${err.message}`);
res.status(500).json({ message: 'An error has occurred.' });
}
});
export default router;

2
src/routes/suggestions.ts
View File

@ -26,7 +26,7 @@ router.post('/', async (req, res) => {
let llm: BaseChatModel | undefined;
if (chatModels[provider] && chatModels[provider][chatModel]) {
llm = chatModels[provider][chatModel] as BaseChatModel | undefined;
llm = chatModels[provider][chatModel].model as BaseChatModel | undefined;
}
if (!llm) {

2
src/routes/videos.ts
View File

@ -26,7 +26,7 @@ router.post('/', async (req, res) => {
let llm: BaseChatModel | undefined;
if (chatModels[provider] && chatModels[provider][chatModel]) {
llm = chatModels[provider][chatModel] as BaseChatModel | undefined;
llm = chatModels[provider][chatModel].model as BaseChatModel | undefined;
}
if (!llm) {

7
src/websocket/connectionManager.ts
View File

@ -45,9 +45,8 @@ export const handleConnection = async (
chatModelProviders[chatModelProvider][chatModel] &&
chatModelProvider != 'custom_openai'
) {
llm = chatModelProviders[chatModelProvider][chatModel] as unknown as
| BaseChatModel
| undefined;
llm = chatModelProviders[chatModelProvider][chatModel]
.model as unknown as BaseChatModel | undefined;
} else if (chatModelProvider == 'custom_openai') {
llm = new ChatOpenAI({
modelName: chatModel,
@ -65,7 +64,7 @@ export const handleConnection = async (
) {
embeddings = embeddingModelProviders[embeddingModelProvider][
embeddingModel
] as Embeddings | undefined;
].model as Embeddings | undefined;
}
if (!llm || !embeddings) {

2
src/websocket/messageHandler.ts
View File

@ -28,7 +28,7 @@ type WSMessage = {
history: Array<[string, string]>;
};
const searchHandlers = {
export const searchHandlers = {
webSearch: handleWebSearch,
academicSearch: handleAcademicSearch,
writingAssistant: handleWritingAssistant,

21
ui/components/EmptyChatMessageInput.tsx
View File

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

21
ui/components/MessageInput.tsx
View File

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

2
ui/components/SearchImages.tsx
View File

@ -51,7 +51,7 @@ const SearchImages = ({
const data = await res.json();
const images = data.images;
const images = data.images ?? [];
setImages(images);
setSlides(
images.map((image: Image) => {

2
ui/components/SearchVideos.tsx
View File

@ -64,7 +64,7 @@ const Searchvideos = ({
const data = await res.json();
const videos = data.videos;
const videos = data.videos ?? [];
setVideos(videos);
setSlides(
videos.map((video: Video) => {

28
ui/components/SettingsDialog.tsx
View File

@ -49,10 +49,10 @@ export const Select = ({ className, options, ...restProps }: SelectProps) => {
interface SettingsType {
chatModelProviders: {
[key: string]: string[];
[key: string]: [Record<string, any>];
};
embeddingModelProviders: {
[key: string]: string[];
[key: string]: [Record<string, any>];
};
openaiApiKey: string;
groqApiKey: string;
@ -68,6 +68,10 @@ const SettingsDialog = ({
setIsOpen: (isOpen: boolean) => void;
}) => {
const [config, setConfig] = useState<SettingsType | null>(null);
const [chatModels, setChatModels] = useState<Record<string, any>>({});
const [embeddingModels, setEmbeddingModels] = useState<Record<string, any>>(
{},
);
const [selectedChatModelProvider, setSelectedChatModelProvider] = useState<
string | null
>(null);
@ -118,7 +122,7 @@ const SettingsDialog = ({
const chatModel =
localStorage.getItem('chatModel') ||
(data.chatModelProviders &&
data.chatModelProviders[chatModelProvider]?.[0]) ||
data.chatModelProviders[chatModelProvider]?.[0].name) ||
'';
const embeddingModelProvider =
localStorage.getItem('embeddingModelProvider') ||
@ -127,7 +131,7 @@ const SettingsDialog = ({
const embeddingModel =
localStorage.getItem('embeddingModel') ||
(data.embeddingModelProviders &&
data.embeddingModelProviders[embeddingModelProvider]?.[0]) ||
data.embeddingModelProviders[embeddingModelProvider]?.[0].name) ||
'';
setSelectedChatModelProvider(chatModelProvider);
@ -136,6 +140,8 @@ const SettingsDialog = ({
setSelectedEmbeddingModel(embeddingModel);
setCustomOpenAIApiKey(localStorage.getItem('openAIApiKey') || '');
setCustomOpenAIBaseURL(localStorage.getItem('openAIBaseURL') || '');
setChatModels(data.chatModelProviders || {});
setEmbeddingModels(data.embeddingModelProviders || {});
setIsLoading(false);
};
@ -233,7 +239,8 @@ const SettingsDialog = ({
setSelectedChatModel('');
} else {
setSelectedChatModel(
config.chatModelProviders[e.target.value][0],
config.chatModelProviders[e.target.value][0]
.name,
);
}
}}
@ -268,8 +275,8 @@ const SettingsDialog = ({
return chatModelProvider
? chatModelProvider.length > 0
? chatModelProvider.map((model) => ({
value: model,
label: model,
value: model.name,
label: model.displayName,
}))
: [
{
@ -345,7 +352,8 @@ const SettingsDialog = ({
onChange={(e) => {
setSelectedEmbeddingModelProvider(e.target.value);
setSelectedEmbeddingModel(
config.embeddingModelProviders[e.target.value][0],
config.embeddingModelProviders[e.target.value][0]
.name,
);
}}
options={Object.keys(
@ -378,8 +386,8 @@ const SettingsDialog = ({
return embeddingModelProvider
? embeddingModelProvider.length > 0
? embeddingModelProvider.map((model) => ({
label: model,
value: model,
label: model.displayName,
value: model.name,
}))
: [
{

2
ui/package.json
View File

@ -1,6 +1,6 @@
{
"name": "perplexica-frontend",
"version": "1.9.0-rc2",
"version": "1.9.0-rc3",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {