Merge branch 'pr/703'

feat(models): Update Gemini chat models

.github/workflows/docker-build.yaml

@@ -8,18 +8,12 @@ on:
     types: [published]
 
 jobs:
-  build-and-push:
+  build-amd64:
     runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        service: [backend, app]
     steps:
       - name: Checkout code
         uses: actions/checkout@v3
 
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
         with:
@@ -36,38 +30,104 @@ jobs:
         id: version
         run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
 
-      - name: Build and push Docker image for ${{ matrix.service }}
+      - name: Build and push AMD64 Docker image
         if: github.ref == 'refs/heads/master' && github.event_name == 'push'
         run: |
-          docker buildx create --use
-          if [[ "${{ matrix.service }}" == "backend" ]]; then \
-            DOCKERFILE=backend.dockerfile; \
-            IMAGE_NAME=perplexica-backend; \
-          else \
-            DOCKERFILE=app.dockerfile; \
-            IMAGE_NAME=perplexica-frontend; \
-          fi
-          docker buildx build --platform linux/amd64,linux/arm64 \
-            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:main \
+          DOCKERFILE=app.dockerfile
+          IMAGE_NAME=perplexica
+          docker buildx build --platform linux/amd64 \
+            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:amd64 \
             --cache-to=type=inline \
+            --provenance false \
             -f $DOCKERFILE \
-            -t itzcrazykns1337/${IMAGE_NAME}:main \
+            -t itzcrazykns1337/${IMAGE_NAME}:amd64 \
             --push .
 
-      - name: Build and push release Docker image for ${{ matrix.service }}
+      - name: Build and push AMD64 release Docker image
         if: github.event_name == 'release'
         run: |
-          docker buildx create --use
-          if [[ "${{ matrix.service }}" == "backend" ]]; then \
-            DOCKERFILE=backend.dockerfile; \
-            IMAGE_NAME=perplexica-backend; \
-          else \
-            DOCKERFILE=app.dockerfile; \
-            IMAGE_NAME=perplexica-frontend; \
-          fi
-          docker buildx build --platform linux/amd64,linux/arm64 \
-            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
+          DOCKERFILE=app.dockerfile
+          IMAGE_NAME=perplexica
+          docker buildx build --platform linux/amd64 \
+            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
             --cache-to=type=inline \
+            --provenance false \
             -f $DOCKERFILE \
-            -t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
+            -t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
             --push .
+
+  build-arm64:
+    runs-on: ubuntu-24.04-arm
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+        with:
+          install: true
+
+      - name: Log in to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Extract version from release tag
+        if: github.event_name == 'release'
+        id: version
+        run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
+
+      - name: Build and push ARM64 Docker image
+        if: github.ref == 'refs/heads/master' && github.event_name == 'push'
+        run: |
+          DOCKERFILE=app.dockerfile
+          IMAGE_NAME=perplexica
+          docker buildx build --platform linux/arm64 \
+            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:arm64 \
+            --cache-to=type=inline \
+            --provenance false \
+            -f $DOCKERFILE \
+            -t itzcrazykns1337/${IMAGE_NAME}:arm64 \
+            --push .
+
+      - name: Build and push ARM64 release Docker image
+        if: github.event_name == 'release'
+        run: |
+          DOCKERFILE=app.dockerfile
+          IMAGE_NAME=perplexica
+          docker buildx build --platform linux/arm64 \
+            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64 \
+            --cache-to=type=inline \
+            --provenance false \
+            -f $DOCKERFILE \
+            -t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64 \
+            --push .
+
+  manifest:
+    needs: [build-amd64, build-arm64]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Log in to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Create and push multi-arch manifest for main
+        if: github.ref == 'refs/heads/master' && github.event_name == 'push'
+        run: |
+          IMAGE_NAME=perplexica
+          docker manifest create itzcrazykns1337/${IMAGE_NAME}:main \
+            --amend itzcrazykns1337/${IMAGE_NAME}:amd64 \
+            --amend itzcrazykns1337/${IMAGE_NAME}:arm64
+          docker manifest push itzcrazykns1337/${IMAGE_NAME}:main
+
+      - name: Create and push multi-arch manifest for releases
+        if: github.event_name == 'release'
+        run: |
+          IMAGE_NAME=perplexica
+          docker manifest create itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
+            --amend itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
+            --amend itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64
+          docker manifest push itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}

.gitignore

@@ -4,9 +4,9 @@ npm-debug.log
 yarn-error.log
 
 # Build output
-/.next/
-/out/
-/dist/
+.next/
+out/
+dist/
 
 # IDE/Editor specific
 .vscode/

.prettierrc.js

@@ -6,7 +6,6 @@ const config = {
   endOfLine: 'auto',
   singleQuote: true,
   tabWidth: 2,
-  semi: true,
 };
 
 module.exports = config;

CONTRIBUTING.md

@@ -1,31 +1,43 @@
 # How to Contribute to Perplexica
 
-Hey there, thanks for deciding to contribute to Perplexica. Anything you help with will support the development of Perplexica and will make it better. Let's walk you through the key aspects to ensure your contributions are effective and in harmony with the project's setup.
+Thanks for your interest in contributing to Perplexica! Your help makes this project better. This guide explains how to contribute effectively.
+
+Perplexica is a modern AI chat application with advanced search capabilities.
 
 ## Project Structure
 
-Perplexica's design consists of two main domains:
+Perplexica's codebase is organized as follows:
 
-- **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.
+- **UI Components and Pages**:
+  - **Components (`src/components`)**: Reusable UI components.
+  - **Pages and Routes (`src/app`)**: Next.js app directory structure with page components.
+    - Main app routes include: home (`/`), chat (`/c`), discover (`/discover`), library (`/library`), and settings (`/settings`).
+  - **API Routes (`src/app/api`)**: API endpoints implemented with Next.js API routes.
+    - `/api/chat`: Handles chat interactions.
+    - `/api/search`: Provides direct access to Perplexica's search capabilities.
+    - Other endpoints for models, files, and suggestions.
+- **Backend Logic (`src/lib`)**: Contains all the backend functionality including search, database, and API logic.
+  - The search functionality is present inside `src/lib/search` directory.
+  - All of the focus modes are implemented using the Meta Search Agent class in `src/lib/search/metaSearchAgent.ts`.
+  - Database functionality is in `src/lib/db`.
+  - Chat model and embedding model providers are managed in `src/lib/providers`.
+  - Prompt templates and LLM chain definitions are in `src/lib/prompts` and `src/lib/chains` respectively.
+
+## API Documentation
+
+Perplexica exposes several API endpoints for programmatic access, including:
+
+- **Search API**: Access Perplexica's advanced search capabilities directly via the `/api/search` endpoint. For detailed documentation, see `docs/api/search.md`.
 
 ## Setting Up Your Environment
 
 Before diving into coding, setting up your local environment is key. Here's what you need to do:
 
-### Backend
-
 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. Run `npm run db:push` to set up the local sqlite.
-5. Use `npm run dev` to start the backend in development mode.
-
-### Frontend
-
-1. Navigate to the `ui` folder and repeat the process of renaming `.env.example` to `.env`, making sure to provide the frontend-specific variables.
-2. Execute `npm install` within the `ui` directory to get the frontend dependencies ready.
-3. Launch the frontend development server with `npm run dev`.
+2. Rename it to `config.toml` and fill in the necessary configuration fields.
+3. Run `npm install` to install all dependencies.
+4. Run `npm run db:push` to set up the local sqlite database.
+5. Use `npm run dev` to start the application in development mode.
 
 **Please note**: Docker configurations are present for setting up production environments, whereas `npm run dev` is used for development purposes.
 

README.md

@@ -1,5 +1,23 @@
 # 🚀 Perplexica - An AI-powered search engine 🔎 <!-- omit in toc -->
 
+<div align="center" markdown="1">
+   <sup>Special thanks to:</sup>
+   <br>
+   <br>
+   <a href="https://www.warp.dev/perplexica">
+      <img alt="Warp sponsorship" width="400" src="https://github.com/user-attachments/assets/775dd593-9b5f-40f1-bf48-479faff4c27b">
+   </a>
+
+### [Warp, the AI Devtool that lives in your terminal](https://www.warp.dev/perplexica)
+
+[Available for MacOS, Linux, & Windows](https://www.warp.dev/perplexica)
+
+</div>
+
+<hr/>
+
+[![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 -->
@@ -41,7 +59,7 @@ Want to know more about its architecture and how it works? You can read it [here
   - **Normal Mode:** Processes your query and performs a web search.
 - **Focus Modes:** Special modes to better answer specific types of questions. Perplexica currently has 6 focus modes:
   - **All Mode:** Searches the entire web to find the best results.
-  - **Writing Assistant Mode:** Helpful for writing tasks that does not require searching the web.
+  - **Writing Assistant Mode:** Helpful for writing tasks that do not require searching the web.
   - **Academic Search Mode:** Finds articles and papers, ideal for academic research.
   - **YouTube Search Mode:** Finds YouTube videos based on the search query.
   - **Wolfram Alpha Search Mode:** Answers queries that need calculations or data analysis using Wolfram Alpha.
@@ -91,14 +109,13 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
 
 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.
+3. After populating the configuration run `npm i`.
+4. Install the dependencies and then execute `npm run build`.
+5. Finally, start the app by running `npm rum start`
 
 **Note**: Using Docker is recommended as it simplifies the setup process, especially for managing environment variables and dependencies.
 
-See the [installation documentation](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/installation) for more information like exposing it your network, etc.
+See the [installation documentation](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/installation) for more information like updating, etc.
 
 ### Ollama Connection Errors
 
@@ -136,10 +153,11 @@ For more details, check out the full documentation [here](https://github.com/Itz
 
 ## Expose Perplexica to network
 
-You can access Perplexica over your home network by following our networking guide [here](https://github.com/ItzCrazyKns/Perplexica/blob/master/docs/installation/NETWORKING.md).
+Perplexica runs on Next.js and handles all API requests. It works right away on the same network and stays accessible even with port forwarding.
 
 ## One-Click Deployment
 
+[![Deploy to Sealos](https://raw.githubusercontent.com/labring-actions/templates/main/Deploy-on-Sealos.svg)](https://usw.sealos.io/?openapp=system-template%3FtemplateName%3Dperplexica)
 [![Deploy to RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploylobe.svg)](https://repocloud.io/details/?app_id=267)
 
 ## Upcoming Features

app.dockerfile

@@ -1,15 +1,27 @@
-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
-ENV NEXT_PUBLIC_WS_URL=${NEXT_PUBLIC_WS_URL}
-ENV NEXT_PUBLIC_API_URL=${NEXT_PUBLIC_API_URL}
+FROM node:20.18.0-slim AS builder
 
 WORKDIR /home/perplexica
 
-COPY ui /home/perplexica/
+COPY package.json yarn.lock ./
+RUN yarn install --frozen-lockfile --network-timeout 600000
 
-RUN yarn install --frozen-lockfile
+COPY tsconfig.json next.config.mjs next-env.d.ts postcss.config.js drizzle.config.ts tailwind.config.ts ./
+COPY src ./src
+COPY public ./public
+
+RUN mkdir -p /home/perplexica/data
 RUN yarn build
 
-CMD ["yarn", "start"]
+FROM node:20.18.0-slim
+
+WORKDIR /home/perplexica
+
+COPY --from=builder /home/perplexica/public ./public
+COPY --from=builder /home/perplexica/.next/static ./public/_next/static
+
+COPY --from=builder /home/perplexica/.next/standalone ./
+COPY --from=builder /home/perplexica/data ./data
+
+RUN mkdir /home/perplexica/uploads
+
+CMD ["node", "server.js"]

backend.dockerfile

@@ -1,17 +0,0 @@
-FROM node:18-slim
-
-WORKDIR /home/perplexica
-
-COPY src /home/perplexica/src
-COPY tsconfig.json /home/perplexica/
-COPY drizzle.config.ts /home/perplexica/
-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
-
-CMD ["yarn", "start"]

docker-compose.yaml

@@ -9,41 +9,21 @@ services:
       - perplexica-network
     restart: unless-stopped
 
-  perplexica-backend:
-    build:
-      context: .
-      dockerfile: backend.dockerfile
-    image: itzcrazykns1337/perplexica-backend:main
-    environment:
-      - SEARXNG_API_URL=http://searxng:8080
-    depends_on:
-      - searxng
-    ports:
-      - 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'
-    networks:
-      - perplexica-network
-    restart: unless-stopped
-
-  perplexica-frontend:
+  app:
+    image: itzcrazykns1337/perplexica:main
     build:
       context: .
       dockerfile: app.dockerfile
-      args:
-        - NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
-        - NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
-    image: itzcrazykns1337/perplexica-frontend:main
-    depends_on:
-      - perplexica-backend
+    environment:
+      - SEARXNG_API_URL=http://searxng:8080
     ports:
       - 3000:3000
     networks:
       - perplexica-network
+    volumes:
+      - backend-dbstore:/home/perplexica/data
+      - uploads:/home/perplexica/uploads
+      - ./config.toml:/home/perplexica/config.toml
     restart: unless-stopped
 
 networks:

docs/API/SEARCH.md

@@ -6,9 +6,9 @@ Perplexica’s Search API makes it easy to use our AI-powered search engine. You
 
 ## Endpoint
 
-### **POST** `http://localhost:3001/api/search`
+### **POST** `http://localhost:3000/api/search`
 
-**Note**: Replace `3001` with any other port if you've changed the default PORT
+**Note**: Replace `3000` with any other port if you've changed the default PORT
 
 ### Request
 
@@ -20,11 +20,11 @@ The API accepts a JSON object in the request body, where you define the focus mo
 {
   "chatModel": {
     "provider": "openai",
-    "model": "gpt-4o-mini"
+    "name": "gpt-4o-mini"
   },
   "embeddingModel": {
     "provider": "openai",
-    "model": "text-embedding-3-large"
+    "name": "text-embedding-3-large"
   },
   "optimizationMode": "speed",
   "focusMode": "webSearch",
@@ -32,24 +32,25 @@ The API accepts a JSON object in the request body, where you define the focus mo
   "history": [
     ["human", "Hi, how are you?"],
     ["assistant", "I am doing well, how can I help you today?"]
-  ]
+  ],
+  "stream": false
 }
 ```
 
 ### Request Parameters
 
-- **`chatModel`** (object, optional): Defines the chat model to be used for the query. For model details you can send a GET request at `http://localhost:3001/api/models`. Make sure to use the key value (For example "gpt-4o-mini" instead of the display name "GPT 4 omni mini").
+- **`chatModel`** (object, optional): Defines the chat model to be used for the query. For model details you can send a GET request at `http://localhost:3000/api/models`. Make sure to use the key value (For example "gpt-4o-mini" instead of the display name "GPT 4 omni mini").
 
   - `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`).
+  - `name`: 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. For model details you can send a GET request at `http://localhost:3001/api/models`. Make sure to use the key value (For example "text-embedding-3-large" instead of the display name "Text Embedding 3 Large").
+- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching. For model details you can send a GET request at `http://localhost:3000/api/models`. Make sure to use the key value (For example "text-embedding-3-large" instead of the display name "Text Embedding 3 Large").
 
   - `provider`: The provider for the embedding model (e.g., `openai`).
-  - `model`: The specific embedding model (e.g., `text-embedding-3-large`).
+  - `name`: The specific embedding model (e.g., `text-embedding-3-large`).
 
 - **`focusMode`** (string, required): Specifies which focus mode to use. Available modes:
 
@@ -71,35 +72,59 @@ The API accepts a JSON object in the request body, where you define the focus mo
   ]
   ```
 
+- **`stream`** (boolean, optional): When set to `true`, enables streaming responses. Default is `false`.
+
 ### Response
 
 The response from the API includes both the final message and the sources used to generate that message.
 
-#### Example Response
+#### Standard Response (stream: false)
 
 ```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"
+      }
+    }
         ....
-	]
+  ]
 }
 ```
 
+#### Streaming Response (stream: true)
+
+When streaming is enabled, the API returns a stream of newline-delimited JSON objects. Each line contains a complete, valid JSON object. The response has Content-Type: application/json.
+
+Example of streamed response objects:
+
+```
+{"type":"init","data":"Stream connected"}
+{"type":"sources","data":[{"pageContent":"...","metadata":{"title":"...","url":"..."}},...]}
+{"type":"response","data":"Perplexica is an "}
+{"type":"response","data":"innovative, open-source "}
+{"type":"response","data":"AI-powered search engine..."}
+{"type":"done"}
+```
+
+Clients should process each line as a separate JSON object. The different message types include:
+
+- **`init`**: Initial connection message
+- **`sources`**: All sources used for the response
+- **`response`**: Chunks of the generated answer text
+- **`done`**: Indicates the stream is complete
+
 ### Fields in the Response
 
 - **`message`** (string): The search result, generated based on the query and focus mode.

docs/architecture/README.md

@@ -1,4 +1,4 @@
-## Perplexica's Architecture
+# Perplexica's Architecture
 
 Perplexica's architecture consists of the following key components:
 

docs/architecture/WORKING.md

@@ -1,19 +1,19 @@
-## 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).
 
 We'll understand how Perplexica works by taking an example of a scenario where a user asks: "How does an A.C. work?". We'll break down the process into steps to make it easier to understand. The steps are as follows:
 
-1. The message is sent via WS to the backend server where it invokes the chain. The chain will depend on your focus mode. For this example, let's assume we use the "webSearch" focus mode.
+1. The message is sent to the `/api/chat` route where it invokes the chain. The chain will depend on your focus mode. For this example, let's assume we use the "webSearch" focus mode.
 2. The chain is now invoked; first, the message is passed to another chain where it first predicts (using the chat history and the question) whether there is a need for sources and searching the web. If there is, it will generate a query (in accordance with the chat history) for searching the web that we'll take up later. If not, the chain will end there, and then the answer generator chain, also known as the response generator, will be started.
 3. The query returned by the first chain is passed to SearXNG to search the web for information.
 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.

docs/installation/NETWORKING.md

@@ -1,109 +0,0 @@
-# Expose Perplexica to a network
-
-This guide will show you how to make Perplexica available over a network. Follow these steps to allow computers on the same network to interact with Perplexica. Choose the instructions that match the operating system you are using.
-
-## Windows
-
-1. Open PowerShell as Administrator
-
-2. Navigate to the directory containing the `docker-compose.yaml` file
-
-3. Stop and remove the existing Perplexica containers and images:
-
-```
-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
-```
-
-6. Save and close the `docker-compose.yaml` file
-
-7. Rebuild and restart the Perplexica container:
-
-```
-docker compose up -d --build
-```
-
-## macOS
-
-1. Open the Terminal application
-
-2. Navigate to the directory with the `docker-compose.yaml` file:
-
-```
-cd /path/to/docker-compose.yaml
-```
-
-3. Stop and remove existing containers and images:
-
-```
-docker compose down --rmi all
-```
-
-4. Open `docker-compose.yaml` in a text editor like Sublime Text:
-
-```
-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
-```
-
-6. Save and exit the editor
-
-7. Rebuild and restart Perplexica:
-
-```
-docker compose up -d --build
-```
-
-## Linux
-
-1. Open the terminal
-
-2. Navigate to the `docker-compose.yaml` directory:
-
-```
-cd /path/to/docker-compose.yaml
-```
-
-3. Stop and remove containers and images:
-
-```
-docker compose down --rmi all
-```
-
-4. Edit `docker-compose.yaml`:
-
-```
-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
-```
-
-6. Save and exit the editor
-
-7. Rebuild and restart Perplexica:
-
-```
-docker compose up -d --build
-```

docs/installation/UPDATING.md

@@ -6,35 +6,41 @@ To update Perplexica to the latest version, follow these steps:
 
 1. Clone the latest version of Perplexica from GitHub:
 
-```bash
+   ```bash
    git clone https://github.com/ItzCrazyKns/Perplexica.git
-```
+   ```
 
-2. Navigate to the Project Directory.
+2. Navigate to the project directory.
 
-3. Pull latest images from registry.
+3. Check for changes in the configuration files. If the `sample.config.toml` file contains new fields, delete your existing `config.toml` file, rename `sample.config.toml` to `config.toml`, and update the configuration accordingly.
 
-```bash
-docker compose pull
-```
+4. Pull the latest images from the registry.
 
-4. Update and Recreate containers.
+   ```bash
+   docker compose pull
+   ```
 
-```bash
-docker compose up -d
-```
+5. Update and recreate the containers.
 
-5. Once the command completes running go to http://localhost:3000 and verify the latest changes.
+   ```bash
+   docker compose up -d
+   ```
 
-## For non Docker users
+6. Once the command completes, go to http://localhost:3000 and verify the latest changes.
+
+## For non-Docker users
 
 1. Clone the latest version of Perplexica from GitHub:
 
-```bash
+   ```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.
-4. Once packages are updated, 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.
+2. Navigate to the project directory.
+
+3. Check for changes in the configuration files. If the `sample.config.toml` file contains new fields, delete your existing `config.toml` file, rename `sample.config.toml` to `config.toml`, and update the configuration accordingly.
+4. After populating the configuration run `npm i`.
+5. Install the dependencies and then execute `npm run build`.
+6. Finally, start the app by running `npm rum start`
+
+---

drizzle.config.ts

@@ -2,7 +2,7 @@ import { defineConfig } from 'drizzle-kit';
 
 export default defineConfig({
   dialect: 'sqlite',
-  schema: './src/db/schema.ts',
+  schema: './src/lib/db/schema.ts',
   out: './drizzle',
   dbCredentials: {
     url: './data/db.sqlite',

next-env.d.ts

@@ -0,0 +1,5 @@
+/// <reference types="next" />
+/// <reference types="next/image-types/global" />
+
+// NOTE: This file should not be edited
+// see https://nextjs.org/docs/app/api-reference/config/typescript for more information.

next.config.mjs

@@ -1,5 +1,6 @@
 /** @type {import('next').NextConfig} */
 const nextConfig = {
+  output: 'standalone',
   images: {
     remotePatterns: [
       {
@@ -7,6 +8,7 @@ const nextConfig = {
       },
     ],
   },
+  serverExternalPackages: ['pdf-parse'],
 };
 
 export default nextConfig;

package.json

@@ -1,52 +1,65 @@
 {
-  "name": "perplexica-backend",
-  "version": "1.9.3",
+  "name": "perplexica-frontend",
+  "version": "1.10.1",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {
-    "start": "npm run db:push && node dist/app.js",
-    "build": "tsc",
-    "dev": "nodemon --ignore uploads/ src/app.ts ",
-    "db:push": "drizzle-kit push sqlite",
-    "format": "prettier . --check",
-    "format:write": "prettier . --write"
-  },
-  "devDependencies": {
-    "@types/better-sqlite3": "^7.6.10",
-    "@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",
-    "drizzle-kit": "^0.22.7",
-    "nodemon": "^3.1.0",
-    "prettier": "^3.2.5",
-    "ts-node": "^10.9.2",
-    "typescript": "^5.4.3"
+    "dev": "next dev",
+    "build": "npm run db:push && next build",
+    "start": "next start",
+    "lint": "next lint",
+    "format:write": "prettier . --write",
+    "db:push": "drizzle-kit push"
   },
   "dependencies": {
+    "@headlessui/react": "^2.2.0",
     "@iarna/toml": "^2.2.5",
-    "@langchain/anthropic": "^0.2.3",
-    "@langchain/community": "^0.2.16",
+    "@icons-pack/react-simple-icons": "^12.3.0",
+    "@langchain/anthropic": "^0.3.15",
+    "@langchain/community": "^0.3.36",
+    "@langchain/core": "^0.3.42",
+    "@langchain/google-genai": "^0.1.12",
     "@langchain/openai": "^0.0.25",
-    "@xenova/transformers": "^2.17.1",
-    "axios": "^1.6.8",
-    "better-sqlite3": "^11.0.0",
+    "@langchain/textsplitters": "^0.1.0",
+    "@tailwindcss/typography": "^0.5.12",
+    "@xenova/transformers": "^2.17.2",
+    "axios": "^1.8.3",
+    "better-sqlite3": "^11.9.1",
+    "clsx": "^2.1.0",
     "compute-cosine-similarity": "^1.1.0",
     "compute-dot": "^1.1.0",
-    "cors": "^2.8.5",
-    "dotenv": "^16.4.5",
-    "drizzle-orm": "^0.31.2",
-    "express": "^4.19.2",
+    "drizzle-orm": "^0.40.1",
     "html-to-text": "^9.0.5",
     "langchain": "^0.1.30",
-    "mammoth": "^1.8.0",
-    "multer": "^1.4.5-lts.1",
+    "lucide-react": "^0.363.0",
+    "markdown-to-jsx": "^7.7.2",
+    "next": "^15.2.2",
+    "next-themes": "^0.3.0",
     "pdf-parse": "^1.1.1",
-    "winston": "^3.13.0",
-    "ws": "^8.17.1",
+    "react": "^18",
+    "react-dom": "^18",
+    "react-text-to-speech": "^0.14.5",
+    "react-textarea-autosize": "^8.5.3",
+    "sonner": "^1.4.41",
+    "tailwind-merge": "^2.2.2",
+    "winston": "^3.17.0",
+    "yet-another-react-lightbox": "^3.17.2",
     "zod": "^3.22.4"
+  },
+  "devDependencies": {
+    "@types/better-sqlite3": "^7.6.12",
+    "@types/html-to-text": "^9.0.4",
+    "@types/node": "^20",
+    "@types/pdf-parse": "^1.1.4",
+    "@types/react": "^18",
+    "@types/react-dom": "^18",
+    "autoprefixer": "^10.0.1",
+    "drizzle-kit": "^0.30.5",
+    "eslint": "^8",
+    "eslint-config-next": "14.1.4",
+    "postcss": "^8",
+    "prettier": "^3.2.5",
+    "tailwindcss": "^3.3.0",
+    "typescript": "^5"
   }
 }

sample.config.toml

@@ -1,13 +1,26 @@
 [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
+[MODELS.OPENAI]
+API_KEY = ""
+
+[MODELS.GROQ]
+API_KEY = ""
+
+[MODELS.ANTHROPIC]
+API_KEY = ""
+
+[MODELS.GEMINI]
+API_KEY = ""
+
+[MODELS.CUSTOM_OPENAI]
+API_KEY = ""
+API_URL = ""
+MODEL_NAME = ""
+
+[MODELS.OLLAMA]
+API_URL = "" # Ollama API URL - http://host.docker.internal:11434
 
 [API_ENDPOINTS]
-SEARXNG = "http://localhost:32768" # SearxNG API URL
-OLLAMA = "" # Ollama API URL - http://host.docker.internal:11434
+SEARXNG = "" # SearxNG API URL - http://localhost:32768

src/app.ts

@@ -1,38 +0,0 @@
-import { startWebSocketServer } from './websocket';
-import express from 'express';
-import cors from 'cors';
-import http from 'http';
-import routes from './routes';
-import { getPort } from './config';
-import logger from './utils/logger';
-
-const port = getPort();
-
-const app = express();
-const server = http.createServer(app);
-
-const corsOptions = {
-  origin: '*',
-};
-
-app.use(cors(corsOptions));
-app.use(express.json());
-
-app.use('/api', routes);
-app.get('/api', (_, res) => {
-  res.status(200).json({ status: 'ok' });
-});
-
-server.listen(port, () => {
-  logger.info(`Server is running on port ${port}`);
-});
-
-startWebSocketServer(server);
-
-process.on('uncaughtException', (err, origin) => {
-  logger.error(`Uncaught Exception at ${origin}: ${err}`);
-});
-
-process.on('unhandledRejection', (reason, promise) => {
-  logger.error(`Unhandled Rejection at: ${promise}, reason: ${reason}`);
-});

src/app/api/chat/route.ts

@@ -0,0 +1,304 @@
+import prompts from '@/lib/prompts';
+import MetaSearchAgent from '@/lib/search/metaSearchAgent';
+import crypto from 'crypto';
+import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
+import { EventEmitter } from 'stream';
+import {
+  chatModelProviders,
+  embeddingModelProviders,
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '@/lib/providers';
+import db from '@/lib/db';
+import { chats, messages as messagesSchema } from '@/lib/db/schema';
+import { and, eq, gt } from 'drizzle-orm';
+import { getFileDetails } from '@/lib/utils/files';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { ChatOpenAI } from '@langchain/openai';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '@/lib/config';
+import { searchHandlers } from '@/lib/search';
+
+export const runtime = 'nodejs';
+export const dynamic = 'force-dynamic';
+
+type Message = {
+  messageId: string;
+  chatId: string;
+  content: string;
+};
+
+type ChatModel = {
+  provider: string;
+  name: string;
+};
+
+type EmbeddingModel = {
+  provider: string;
+  name: string;
+};
+
+type Body = {
+  message: Message;
+  optimizationMode: 'speed' | 'balanced' | 'quality';
+  focusMode: string;
+  history: Array<[string, string]>;
+  files: Array<string>;
+  chatModel: ChatModel;
+  embeddingModel: EmbeddingModel;
+};
+
+const handleEmitterEvents = async (
+  stream: EventEmitter,
+  writer: WritableStreamDefaultWriter,
+  encoder: TextEncoder,
+  aiMessageId: string,
+  chatId: string,
+) => {
+  let recievedMessage = '';
+  let sources: any[] = [];
+
+  stream.on('data', (data) => {
+    const parsedData = JSON.parse(data);
+    if (parsedData.type === 'response') {
+      writer.write(
+        encoder.encode(
+          JSON.stringify({
+            type: 'message',
+            data: parsedData.data,
+            messageId: aiMessageId,
+          }) + '\n',
+        ),
+      );
+
+      recievedMessage += parsedData.data;
+    } else if (parsedData.type === 'sources') {
+      writer.write(
+        encoder.encode(
+          JSON.stringify({
+            type: 'sources',
+            data: parsedData.data,
+            messageId: aiMessageId,
+          }) + '\n',
+        ),
+      );
+
+      sources = parsedData.data;
+    }
+  });
+  stream.on('end', () => {
+    writer.write(
+      encoder.encode(
+        JSON.stringify({
+          type: 'messageEnd',
+          messageId: aiMessageId,
+        }) + '\n',
+      ),
+    );
+    writer.close();
+
+    db.insert(messagesSchema)
+      .values({
+        content: recievedMessage,
+        chatId: chatId,
+        messageId: aiMessageId,
+        role: 'assistant',
+        metadata: JSON.stringify({
+          createdAt: new Date(),
+          ...(sources && sources.length > 0 && { sources }),
+        }),
+      })
+      .execute();
+  });
+  stream.on('error', (data) => {
+    const parsedData = JSON.parse(data);
+    writer.write(
+      encoder.encode(
+        JSON.stringify({
+          type: 'error',
+          data: parsedData.data,
+        }),
+      ),
+    );
+    writer.close();
+  });
+};
+
+const handleHistorySave = async (
+  message: Message,
+  humanMessageId: string,
+  focusMode: string,
+  files: string[],
+) => {
+  const chat = await db.query.chats.findFirst({
+    where: eq(chats.id, message.chatId),
+  });
+
+  if (!chat) {
+    await db
+      .insert(chats)
+      .values({
+        id: message.chatId,
+        title: message.content,
+        createdAt: new Date().toString(),
+        focusMode: focusMode,
+        files: files.map(getFileDetails),
+      })
+      .execute();
+  }
+
+  const messageExists = await db.query.messages.findFirst({
+    where: eq(messagesSchema.messageId, humanMessageId),
+  });
+
+  if (!messageExists) {
+    await db
+      .insert(messagesSchema)
+      .values({
+        content: message.content,
+        chatId: message.chatId,
+        messageId: humanMessageId,
+        role: 'user',
+        metadata: JSON.stringify({
+          createdAt: new Date(),
+        }),
+      })
+      .execute();
+  } else {
+    await db
+      .delete(messagesSchema)
+      .where(
+        and(
+          gt(messagesSchema.id, messageExists.id),
+          eq(messagesSchema.chatId, message.chatId),
+        ),
+      )
+      .execute();
+  }
+};
+
+export const POST = async (req: Request) => {
+  try {
+    const body = (await req.json()) as Body;
+    const { message } = body;
+
+    if (message.content === '') {
+      return Response.json(
+        {
+          message: 'Please provide a message to process',
+        },
+        { status: 400 },
+      );
+    }
+
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
+
+    const chatModelProvider =
+      chatModelProviders[
+        body.chatModel?.provider || Object.keys(chatModelProviders)[0]
+      ];
+    const chatModel =
+      chatModelProvider[
+        body.chatModel?.name || Object.keys(chatModelProvider)[0]
+      ];
+
+    const embeddingProvider =
+      embeddingModelProviders[
+        body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0]
+      ];
+    const embeddingModel =
+      embeddingProvider[
+        body.embeddingModel?.name || Object.keys(embeddingProvider)[0]
+      ];
+
+    let llm: BaseChatModel | undefined;
+    let embedding = embeddingModel.model;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        openAIApiKey: getCustomOpenaiApiKey(),
+        modelName: getCustomOpenaiModelName(),
+        temperature: 0.7,
+        configuration: {
+          baseURL: getCustomOpenaiApiUrl(),
+        },
+      }) as unknown as BaseChatModel;
+    } else if (chatModelProvider && chatModel) {
+      llm = chatModel.model;
+    }
+
+    if (!llm) {
+      return Response.json({ error: 'Invalid chat model' }, { status: 400 });
+    }
+
+    if (!embedding) {
+      return Response.json(
+        { error: 'Invalid embedding model' },
+        { status: 400 },
+      );
+    }
+
+    const humanMessageId =
+      message.messageId ?? crypto.randomBytes(7).toString('hex');
+    const aiMessageId = crypto.randomBytes(7).toString('hex');
+
+    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 handler = searchHandlers[body.focusMode];
+
+    if (!handler) {
+      return Response.json(
+        {
+          message: 'Invalid focus mode',
+        },
+        { status: 400 },
+      );
+    }
+
+    const stream = await handler.searchAndAnswer(
+      message.content,
+      history,
+      llm,
+      embedding,
+      body.optimizationMode,
+      body.files,
+    );
+
+    const responseStream = new TransformStream();
+    const writer = responseStream.writable.getWriter();
+    const encoder = new TextEncoder();
+
+    handleEmitterEvents(stream, writer, encoder, aiMessageId, message.chatId);
+    handleHistorySave(message, humanMessageId, body.focusMode, body.files);
+
+    return new Response(responseStream.readable, {
+      headers: {
+        'Content-Type': 'text/event-stream',
+        Connection: 'keep-alive',
+        'Cache-Control': 'no-cache, no-transform',
+      },
+    });
+  } catch (err) {
+    console.error('An error occurred while processing chat request:', err);
+    return Response.json(
+      { message: 'An error occurred while processing chat request' },
+      { status: 500 },
+    );
+  }
+};

src/app/api/chats/[id]/route.ts

@@ -0,0 +1,69 @@
+import db from '@/lib/db';
+import { chats, messages } from '@/lib/db/schema';
+import { eq } from 'drizzle-orm';
+
+export const GET = async (
+  req: Request,
+  { params }: { params: Promise<{ id: string }> },
+) => {
+  try {
+    const { id } = await params;
+
+    const chatExists = await db.query.chats.findFirst({
+      where: eq(chats.id, id),
+    });
+
+    if (!chatExists) {
+      return Response.json({ message: 'Chat not found' }, { status: 404 });
+    }
+
+    const chatMessages = await db.query.messages.findMany({
+      where: eq(messages.chatId, id),
+    });
+
+    return Response.json(
+      {
+        chat: chatExists,
+        messages: chatMessages,
+      },
+      { status: 200 },
+    );
+  } catch (err) {
+    console.error('Error in getting chat by id: ', err);
+    return Response.json(
+      { message: 'An error has occurred.' },
+      { status: 500 },
+    );
+  }
+};
+
+export const DELETE = async (
+  req: Request,
+  { params }: { params: Promise<{ id: string }> },
+) => {
+  try {
+    const { id } = await params;
+
+    const chatExists = await db.query.chats.findFirst({
+      where: eq(chats.id, id),
+    });
+
+    if (!chatExists) {
+      return Response.json({ message: 'Chat not found' }, { status: 404 });
+    }
+
+    await db.delete(chats).where(eq(chats.id, id)).execute();
+    await db.delete(messages).where(eq(messages.chatId, id)).execute();
+
+    return Response.json(
+      { message: 'Chat deleted successfully' },
+      { status: 200 },
+    );
+  } catch (err) {
+    console.error('Error in deleting chat by id: ', err);
+    return Response.json(
+      { message: 'An error has occurred.' },
+      { status: 500 },
+    );
+  }
+};

src/app/api/chats/route.ts

@@ -0,0 +1,15 @@
+import db from '@/lib/db';
+
+export const GET = async (req: Request) => {
+  try {
+    let chats = await db.query.chats.findMany();
+    chats = chats.reverse();
+    return Response.json({ chats: chats }, { status: 200 });
+  } catch (err) {
+    console.error('Error in getting chats: ', err);
+    return Response.json(
+      { message: 'An error has occurred.' },
+      { status: 500 },
+    );
+  }
+};

src/app/api/config/route.ts

@@ -0,0 +1,109 @@
+import {
+  getAnthropicApiKey,
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+  getGeminiApiKey,
+  getGroqApiKey,
+  getOllamaApiEndpoint,
+  getOpenaiApiKey,
+  updateConfig,
+} from '@/lib/config';
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '@/lib/providers';
+
+export const GET = async (req: Request) => {
+  try {
+    const config: Record<string, any> = {};
+
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
+
+    config['chatModelProviders'] = {};
+    config['embeddingModelProviders'] = {};
+
+    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();
+    config['geminiApiKey'] = getGeminiApiKey();
+    config['customOpenaiApiUrl'] = getCustomOpenaiApiUrl();
+    config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
+    config['customOpenaiModelName'] = getCustomOpenaiModelName();
+
+    return Response.json({ ...config }, { status: 200 });
+  } catch (err) {
+    console.error('An error occurred while getting config:', err);
+    return Response.json(
+      { message: 'An error occurred while getting config' },
+      { status: 500 },
+    );
+  }
+};
+
+export const POST = async (req: Request) => {
+  try {
+    const config = await req.json();
+
+    const updatedConfig = {
+      MODELS: {
+        OPENAI: {
+          API_KEY: config.openaiApiKey,
+        },
+        GROQ: {
+          API_KEY: config.groqApiKey,
+        },
+        ANTHROPIC: {
+          API_KEY: config.anthropicApiKey,
+        },
+        GEMINI: {
+          API_KEY: config.geminiApiKey,
+        },
+        OLLAMA: {
+          API_URL: config.ollamaApiUrl,
+        },
+        CUSTOM_OPENAI: {
+          API_URL: config.customOpenaiApiUrl,
+          API_KEY: config.customOpenaiApiKey,
+          MODEL_NAME: config.customOpenaiModelName,
+        },
+      },
+    };
+
+    updateConfig(updatedConfig);
+
+    return Response.json({ message: 'Config updated' }, { status: 200 });
+  } catch (err) {
+    console.error('An error occurred while updating config:', err);
+    return Response.json(
+      { message: 'An error occurred while updating config' },
+      { status: 500 },
+    );
+  }
+};

src/app/api/discover/route.ts

@@ -0,0 +1,61 @@
+import { searchSearxng } from '@/lib/searxng';
+
+const articleWebsites = [
+  'yahoo.com',
+  'www.exchangewire.com',
+  'businessinsider.com',
+  /* 'wired.com',
+  'mashable.com',
+  'theverge.com',
+  'gizmodo.com',
+  'cnet.com',
+  'venturebeat.com', */
+];
+
+const topics = ['AI', 'tech']; /* TODO: Add UI to customize this */
+
+export const GET = async (req: Request) => {
+  try {
+    const data = (
+      await Promise.all([
+        ...new Array(articleWebsites.length * topics.length)
+          .fill(0)
+          .map(async (_, i) => {
+            return (
+              await searchSearxng(
+                `site:${articleWebsites[i % articleWebsites.length]} ${
+                  topics[i % topics.length]
+                }`,
+                {
+                  engines: ['bing news'],
+                  pageno: 1,
+                },
+              )
+            ).results;
+          }),
+      ])
+    )
+      .map((result) => result)
+      .flat()
+      .sort(() => Math.random() - 0.5);
+
+    return Response.json(
+      {
+        blogs: data,
+      },
+      {
+        status: 200,
+      },
+    );
+  } catch (err) {
+    console.error(`An error occurred in discover route: ${err}`);
+    return Response.json(
+      {
+        message: 'An error has occurred',
+      },
+      {
+        status: 500,
+      },
+    );
+  }
+};

src/app/api/images/route.ts

@@ -0,0 +1,83 @@
+import handleImageSearch from '@/lib/chains/imageSearchAgent';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '@/lib/config';
+import { getAvailableChatModelProviders } from '@/lib/providers';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
+import { ChatOpenAI } from '@langchain/openai';
+
+interface ChatModel {
+  provider: string;
+  model: string;
+}
+
+interface ImageSearchBody {
+  query: string;
+  chatHistory: any[];
+  chatModel?: ChatModel;
+}
+
+export const POST = async (req: Request) => {
+  try {
+    const body: ImageSearchBody = await req.json();
+
+    const chatHistory = body.chatHistory
+      .map((msg: any) => {
+        if (msg.role === 'user') {
+          return new HumanMessage(msg.content);
+        } else if (msg.role === 'assistant') {
+          return new AIMessage(msg.content);
+        }
+      })
+      .filter((msg) => msg !== undefined) as BaseMessage[];
+
+    const chatModelProviders = await getAvailableChatModelProviders();
+
+    const chatModelProvider =
+      chatModelProviders[
+        body.chatModel?.provider || Object.keys(chatModelProviders)[0]
+      ];
+    const chatModel =
+      chatModelProvider[
+        body.chatModel?.model || Object.keys(chatModelProvider)[0]
+      ];
+
+    let llm: BaseChatModel | undefined;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        openAIApiKey: getCustomOpenaiApiKey(),
+        modelName: getCustomOpenaiModelName(),
+        temperature: 0.7,
+        configuration: {
+          baseURL: getCustomOpenaiApiUrl(),
+        },
+      }) as unknown as BaseChatModel;
+    } else if (chatModelProvider && chatModel) {
+      llm = chatModel.model;
+    }
+
+    if (!llm) {
+      return Response.json({ error: 'Invalid chat model' }, { status: 400 });
+    }
+
+    const images = await handleImageSearch(
+      {
+        chat_history: chatHistory,
+        query: body.query,
+      },
+      llm,
+    );
+
+    return Response.json({ images }, { status: 200 });
+  } catch (err) {
+    console.error(`An error occurred while searching images: ${err}`);
+    return Response.json(
+      { message: 'An error occurred while searching images' },
+      { status: 500 },
+    );
+  }
+};

src/app/api/models/route.ts

@@ -0,0 +1,47 @@
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '@/lib/providers';
+
+export const GET = async (req: Request) => {
+  try {
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
+
+    Object.keys(chatModelProviders).forEach((provider) => {
+      Object.keys(chatModelProviders[provider]).forEach((model) => {
+        delete (chatModelProviders[provider][model] as { model?: unknown })
+          .model;
+      });
+    });
+
+    Object.keys(embeddingModelProviders).forEach((provider) => {
+      Object.keys(embeddingModelProviders[provider]).forEach((model) => {
+        delete (embeddingModelProviders[provider][model] as { model?: unknown })
+          .model;
+      });
+    });
+
+    return Response.json(
+      {
+        chatModelProviders,
+        embeddingModelProviders,
+      },
+      {
+        status: 200,
+      },
+    );
+  } catch (err) {
+    console.error('An error occurred while fetching models', err);
+    return Response.json(
+      {
+        message: 'An error has occurred.',
+      },
+      {
+        status: 500,
+      },
+    );
+  }
+};

src/app/api/search/route.ts

@@ -0,0 +1,268 @@
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
+import { ChatOpenAI } from '@langchain/openai';
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '@/lib/providers';
+import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
+import { MetaSearchAgentType } from '@/lib/search/metaSearchAgent';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '@/lib/config';
+import { searchHandlers } from '@/lib/search';
+
+interface chatModel {
+  provider: string;
+  name: string;
+  customOpenAIKey?: string;
+  customOpenAIBaseURL?: string;
+}
+
+interface embeddingModel {
+  provider: string;
+  name: string;
+}
+
+interface ChatRequestBody {
+  optimizationMode: 'speed' | 'balanced';
+  focusMode: string;
+  chatModel?: chatModel;
+  embeddingModel?: embeddingModel;
+  query: string;
+  history: Array<[string, string]>;
+  stream?: boolean;
+}
+
+export const POST = async (req: Request) => {
+  try {
+    const body: ChatRequestBody = await req.json();
+
+    if (!body.focusMode || !body.query) {
+      return Response.json(
+        { message: 'Missing focus mode or query' },
+        { status: 400 },
+      );
+    }
+
+    body.history = body.history || [];
+    body.optimizationMode = body.optimizationMode || 'balanced';
+    body.stream = body.stream || false;
+
+    const history: BaseMessage[] = body.history.map((msg) => {
+      return msg[0] === 'human'
+        ? new HumanMessage({ content: msg[1] })
+        : 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?.name ||
+      Object.keys(chatModelProviders[chatModelProvider])[0];
+
+    const embeddingModelProvider =
+      body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0];
+    const embeddingModel =
+      body.embeddingModel?.name ||
+      Object.keys(embeddingModelProviders[embeddingModelProvider])[0];
+
+    let llm: BaseChatModel | undefined;
+    let embeddings: Embeddings | undefined;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        modelName: body.chatModel?.name || getCustomOpenaiModelName(),
+        openAIApiKey:
+          body.chatModel?.customOpenAIKey || getCustomOpenaiApiKey(),
+        temperature: 0.7,
+        configuration: {
+          baseURL:
+            body.chatModel?.customOpenAIBaseURL || getCustomOpenaiApiUrl(),
+        },
+      }) 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 Response.json(
+        { message: 'Invalid model selected' },
+        { status: 400 },
+      );
+    }
+
+    const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
+
+    if (!searchHandler) {
+      return Response.json({ message: 'Invalid focus mode' }, { status: 400 });
+    }
+
+    const emitter = await searchHandler.searchAndAnswer(
+      body.query,
+      history,
+      llm,
+      embeddings,
+      body.optimizationMode,
+      [],
+    );
+
+    if (!body.stream) {
+      return new Promise(
+        (
+          resolve: (value: Response) => void,
+          reject: (value: Response) => void,
+        ) => {
+          let message = '';
+          let sources: any[] = [];
+
+          emitter.on('data', (data: string) => {
+            try {
+              const parsedData = JSON.parse(data);
+              if (parsedData.type === 'response') {
+                message += parsedData.data;
+              } else if (parsedData.type === 'sources') {
+                sources = parsedData.data;
+              }
+            } catch (error) {
+              reject(
+                Response.json(
+                  { message: 'Error parsing data' },
+                  { status: 500 },
+                ),
+              );
+            }
+          });
+
+          emitter.on('end', () => {
+            resolve(Response.json({ message, sources }, { status: 200 }));
+          });
+
+          emitter.on('error', (error: any) => {
+            reject(
+              Response.json(
+                { message: 'Search error', error },
+                { status: 500 },
+              ),
+            );
+          });
+        },
+      );
+    }
+
+    const encoder = new TextEncoder();
+
+    const abortController = new AbortController();
+    const { signal } = abortController;
+
+    const stream = new ReadableStream({
+      start(controller) {
+        let sources: any[] = [];
+
+        controller.enqueue(
+          encoder.encode(
+            JSON.stringify({
+              type: 'init',
+              data: 'Stream connected',
+            }) + '\n',
+          ),
+        );
+
+        signal.addEventListener('abort', () => {
+          emitter.removeAllListeners();
+
+          try {
+            controller.close();
+          } catch (error) {}
+        });
+
+        emitter.on('data', (data: string) => {
+          if (signal.aborted) return;
+
+          try {
+            const parsedData = JSON.parse(data);
+
+            if (parsedData.type === 'response') {
+              controller.enqueue(
+                encoder.encode(
+                  JSON.stringify({
+                    type: 'response',
+                    data: parsedData.data,
+                  }) + '\n',
+                ),
+              );
+            } else if (parsedData.type === 'sources') {
+              sources = parsedData.data;
+              controller.enqueue(
+                encoder.encode(
+                  JSON.stringify({
+                    type: 'sources',
+                    data: sources,
+                  }) + '\n',
+                ),
+              );
+            }
+          } catch (error) {
+            controller.error(error);
+          }
+        });
+
+        emitter.on('end', () => {
+          if (signal.aborted) return;
+
+          controller.enqueue(
+            encoder.encode(
+              JSON.stringify({
+                type: 'done',
+              }) + '\n',
+            ),
+          );
+          controller.close();
+        });
+
+        emitter.on('error', (error: any) => {
+          if (signal.aborted) return;
+
+          controller.error(error);
+        });
+      },
+      cancel() {
+        abortController.abort();
+      },
+    });
+
+    return new Response(stream, {
+      headers: {
+        'Content-Type': 'text/event-stream',
+        'Cache-Control': 'no-cache, no-transform',
+        Connection: 'keep-alive',
+      },
+    });
+  } catch (err: any) {
+    console.error(`Error in getting search results: ${err.message}`);
+    return Response.json(
+      { message: 'An error has occurred.' },
+      { status: 500 },
+    );
+  }
+};

src/app/api/suggestions/route.ts

@@ -0,0 +1,81 @@
+import generateSuggestions from '@/lib/chains/suggestionGeneratorAgent';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '@/lib/config';
+import { getAvailableChatModelProviders } from '@/lib/providers';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
+import { ChatOpenAI } from '@langchain/openai';
+
+interface ChatModel {
+  provider: string;
+  model: string;
+}
+
+interface SuggestionsGenerationBody {
+  chatHistory: any[];
+  chatModel?: ChatModel;
+}
+
+export const POST = async (req: Request) => {
+  try {
+    const body: SuggestionsGenerationBody = await req.json();
+
+    const chatHistory = body.chatHistory
+      .map((msg: any) => {
+        if (msg.role === 'user') {
+          return new HumanMessage(msg.content);
+        } else if (msg.role === 'assistant') {
+          return new AIMessage(msg.content);
+        }
+      })
+      .filter((msg) => msg !== undefined) as BaseMessage[];
+
+    const chatModelProviders = await getAvailableChatModelProviders();
+
+    const chatModelProvider =
+      chatModelProviders[
+        body.chatModel?.provider || Object.keys(chatModelProviders)[0]
+      ];
+    const chatModel =
+      chatModelProvider[
+        body.chatModel?.model || Object.keys(chatModelProvider)[0]
+      ];
+
+    let llm: BaseChatModel | undefined;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        openAIApiKey: getCustomOpenaiApiKey(),
+        modelName: getCustomOpenaiModelName(),
+        temperature: 0.7,
+        configuration: {
+          baseURL: getCustomOpenaiApiUrl(),
+        },
+      }) as unknown as BaseChatModel;
+    } else if (chatModelProvider && chatModel) {
+      llm = chatModel.model;
+    }
+
+    if (!llm) {
+      return Response.json({ error: 'Invalid chat model' }, { status: 400 });
+    }
+
+    const suggestions = await generateSuggestions(
+      {
+        chat_history: chatHistory,
+      },
+      llm,
+    );
+
+    return Response.json({ suggestions }, { status: 200 });
+  } catch (err) {
+    console.error(`An error occurred while generating suggestions: ${err}`);
+    return Response.json(
+      { message: 'An error occurred while generating suggestions' },
+      { status: 500 },
+    );
+  }
+};

src/app/api/uploads/route.ts

@@ -0,0 +1,134 @@
+import { NextResponse } from 'next/server';
+import fs from 'fs';
+import path from 'path';
+import crypto from 'crypto';
+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';
+
+interface FileRes {
+  fileName: string;
+  fileExtension: string;
+  fileId: string;
+}
+
+const uploadDir = path.join(process.cwd(), 'uploads');
+
+if (!fs.existsSync(uploadDir)) {
+  fs.mkdirSync(uploadDir, { recursive: true });
+}
+
+const splitter = new RecursiveCharacterTextSplitter({
+  chunkSize: 500,
+  chunkOverlap: 100,
+});
+
+export async function POST(req: Request) {
+  try {
+    const formData = await req.formData();
+
+    const files = formData.getAll('files') as File[];
+    const embedding_model = formData.get('embedding_model');
+    const embedding_model_provider = formData.get('embedding_model_provider');
+
+    if (!embedding_model || !embedding_model_provider) {
+      return NextResponse.json(
+        { message: 'Missing embedding model or provider' },
+        { status: 400 },
+      );
+    }
+
+    const embeddingModels = await getAvailableEmbeddingModelProviders();
+    const provider =
+      embedding_model_provider ?? Object.keys(embeddingModels)[0];
+    const embeddingModel =
+      embedding_model ?? Object.keys(embeddingModels[provider as string])[0];
+
+    let embeddingsModel =
+      embeddingModels[provider as string]?.[embeddingModel as string]?.model;
+    if (!embeddingsModel) {
+      return NextResponse.json(
+        { message: 'Invalid embedding model selected' },
+        { status: 400 },
+      );
+    }
+
+    const processedFiles: FileRes[] = [];
+
+    await Promise.all(
+      files.map(async (file: any) => {
+        const fileExtension = file.name.split('.').pop();
+        if (!['pdf', 'docx', 'txt'].includes(fileExtension!)) {
+          return NextResponse.json(
+            { message: 'File type not supported' },
+            { status: 400 },
+          );
+        }
+
+        const uniqueFileName = `${crypto.randomBytes(16).toString('hex')}.${fileExtension}`;
+        const filePath = path.join(uploadDir, uniqueFileName);
+
+        const buffer = Buffer.from(await file.arrayBuffer());
+        fs.writeFileSync(filePath, new Uint8Array(buffer));
+
+        let docs: any[] = [];
+        if (fileExtension === 'pdf') {
+          const loader = new PDFLoader(filePath);
+          docs = await loader.load();
+        } else if (fileExtension === 'docx') {
+          const loader = new DocxLoader(filePath);
+          docs = await loader.load();
+        } else if (fileExtension === 'txt') {
+          const text = fs.readFileSync(filePath, 'utf-8');
+          docs = [
+            new Document({ pageContent: text, metadata: { title: file.name } }),
+          ];
+        }
+
+        const splitted = await splitter.splitDocuments(docs);
+
+        const extractedDataPath = filePath.replace(/\.\w+$/, '-extracted.json');
+        fs.writeFileSync(
+          extractedDataPath,
+          JSON.stringify({
+            title: file.name,
+            contents: splitted.map((doc) => doc.pageContent),
+          }),
+        );
+
+        const embeddings = await embeddingsModel.embedDocuments(
+          splitted.map((doc) => doc.pageContent),
+        );
+        const embeddingsDataPath = filePath.replace(
+          /\.\w+$/,
+          '-embeddings.json',
+        );
+        fs.writeFileSync(
+          embeddingsDataPath,
+          JSON.stringify({
+            title: file.name,
+            embeddings,
+          }),
+        );
+
+        processedFiles.push({
+          fileName: file.name,
+          fileExtension: fileExtension,
+          fileId: uniqueFileName.replace(/\.\w+$/, ''),
+        });
+      }),
+    );
+
+    return NextResponse.json({
+      files: processedFiles,
+    });
+  } catch (error) {
+    console.error('Error uploading file:', error);
+    return NextResponse.json(
+      { message: 'An error has occurred.' },
+      { status: 500 },
+    );
+  }
+}

src/app/api/videos/route.ts

@@ -0,0 +1,83 @@
+import handleVideoSearch from '@/lib/chains/videoSearchAgent';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '@/lib/config';
+import { getAvailableChatModelProviders } from '@/lib/providers';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
+import { ChatOpenAI } from '@langchain/openai';
+
+interface ChatModel {
+  provider: string;
+  model: string;
+}
+
+interface VideoSearchBody {
+  query: string;
+  chatHistory: any[];
+  chatModel?: ChatModel;
+}
+
+export const POST = async (req: Request) => {
+  try {
+    const body: VideoSearchBody = await req.json();
+
+    const chatHistory = body.chatHistory
+      .map((msg: any) => {
+        if (msg.role === 'user') {
+          return new HumanMessage(msg.content);
+        } else if (msg.role === 'assistant') {
+          return new AIMessage(msg.content);
+        }
+      })
+      .filter((msg) => msg !== undefined) as BaseMessage[];
+
+    const chatModelProviders = await getAvailableChatModelProviders();
+
+    const chatModelProvider =
+      chatModelProviders[
+        body.chatModel?.provider || Object.keys(chatModelProviders)[0]
+      ];
+    const chatModel =
+      chatModelProvider[
+        body.chatModel?.model || Object.keys(chatModelProvider)[0]
+      ];
+
+    let llm: BaseChatModel | undefined;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        openAIApiKey: getCustomOpenaiApiKey(),
+        modelName: getCustomOpenaiModelName(),
+        temperature: 0.7,
+        configuration: {
+          baseURL: getCustomOpenaiApiUrl(),
+        },
+      }) as unknown as BaseChatModel;
+    } else if (chatModelProvider && chatModel) {
+      llm = chatModel.model;
+    }
+
+    if (!llm) {
+      return Response.json({ error: 'Invalid chat model' }, { status: 400 });
+    }
+
+    const videos = await handleVideoSearch(
+      {
+        chat_history: chatHistory,
+        query: body.query,
+      },
+      llm,
+    );
+
+    return Response.json({ videos }, { status: 200 });
+  } catch (err) {
+    console.error(`An error occurred while searching videos: ${err}`);
+    return Response.json(
+      { message: 'An error occurred while searching videos' },
+      { status: 500 },
+    );
+  }
+};

src/app/c/[chatId]/page.tsx

@@ -0,0 +1,9 @@
+import ChatWindow from '@/components/ChatWindow';
+import React from 'react';
+
+const Page = ({ params }: { params: Promise<{ chatId: string }> }) => {
+  const { chatId } = React.use(params);
+  return <ChatWindow id={chatId} />;
+};
+
+export default Page;

src/app/discover/page.tsx

@@ -19,7 +19,7 @@ const Page = () => {
   useEffect(() => {
     const fetchData = async () => {
       try {
-        const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/discover`, {
+        const res = await fetch(`/api/discover`, {
           method: 'GET',
           headers: {
             'Content-Type': 'application/json',

src/app/library/page.tsx

@@ -21,7 +21,7 @@ const Page = () => {
     const fetchChats = async () => {
       setLoading(true);
 
-      const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/chats`, {
+      const res = await fetch(`/api/chats`, {
         method: 'GET',
         headers: {
           'Content-Type': 'application/json',

src/app/settings/page.tsx

@@ -0,0 +1,800 @@
+'use client';
+
+import { Settings as SettingsIcon, ArrowLeft, Loader2 } from 'lucide-react';
+import { useEffect, useState } from 'react';
+import { cn } from '@/lib/utils';
+import { Switch } from '@headlessui/react';
+import ThemeSwitcher from '@/components/theme/Switcher';
+import { ImagesIcon, VideoIcon } from 'lucide-react';
+import Link from 'next/link';
+
+interface SettingsType {
+  chatModelProviders: {
+    [key: string]: [Record<string, any>];
+  };
+  embeddingModelProviders: {
+    [key: string]: [Record<string, any>];
+  };
+  openaiApiKey: string;
+  groqApiKey: string;
+  anthropicApiKey: string;
+  geminiApiKey: string;
+  ollamaApiUrl: string;
+  customOpenaiApiKey: string;
+  customOpenaiApiUrl: string;
+  customOpenaiModelName: string;
+}
+
+interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
+  isSaving?: boolean;
+  onSave?: (value: string) => void;
+}
+
+const Input = ({ className, isSaving, onSave, ...restProps }: InputProps) => {
+  return (
+    <div className="relative">
+      <input
+        {...restProps}
+        className={cn(
+          'bg-light-secondary dark:bg-dark-secondary w-full px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
+          isSaving && 'pr-10',
+          className,
+        )}
+        onBlur={(e) => onSave?.(e.target.value)}
+      />
+      {isSaving && (
+        <div className="absolute right-3 top-1/2 -translate-y-1/2">
+          <Loader2
+            size={16}
+            className="animate-spin text-black/70 dark:text-white/70"
+          />
+        </div>
+      )}
+    </div>
+  );
+};
+
+const Select = ({
+  className,
+  options,
+  ...restProps
+}: React.SelectHTMLAttributes<HTMLSelectElement> & {
+  options: { value: string; label: string; disabled?: boolean }[];
+}) => {
+  return (
+    <select
+      {...restProps}
+      className={cn(
+        'bg-light-secondary dark:bg-dark-secondary px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
+        className,
+      )}
+    >
+      {options.map(({ label, value, disabled }) => (
+        <option key={value} value={value} disabled={disabled}>
+          {label}
+        </option>
+      ))}
+    </select>
+  );
+};
+
+const SettingsSection = ({
+  title,
+  children,
+}: {
+  title: string;
+  children: React.ReactNode;
+}) => (
+  <div className="flex flex-col space-y-4 p-4 bg-light-secondary/50 dark:bg-dark-secondary/50 rounded-xl border border-light-200 dark:border-dark-200">
+    <h2 className="text-black/90 dark:text-white/90 font-medium">{title}</h2>
+    {children}
+  </div>
+);
+
+const Page = () => {
+  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);
+  const [selectedChatModel, setSelectedChatModel] = useState<string | null>(
+    null,
+  );
+  const [selectedEmbeddingModelProvider, setSelectedEmbeddingModelProvider] =
+    useState<string | null>(null);
+  const [selectedEmbeddingModel, setSelectedEmbeddingModel] = useState<
+    string | null
+  >(null);
+  const [isLoading, setIsLoading] = useState(false);
+  const [automaticImageSearch, setAutomaticImageSearch] = useState(false);
+  const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
+  const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
+
+  useEffect(() => {
+    const fetchConfig = async () => {
+      setIsLoading(true);
+      const res = await fetch(`/api/config`, {
+        headers: {
+          'Content-Type': 'application/json',
+        },
+      });
+
+      const data = (await res.json()) as SettingsType;
+      setConfig(data);
+
+      const chatModelProvidersKeys = Object.keys(data.chatModelProviders || {});
+      const embeddingModelProvidersKeys = Object.keys(
+        data.embeddingModelProviders || {},
+      );
+
+      const defaultChatModelProvider =
+        chatModelProvidersKeys.length > 0 ? chatModelProvidersKeys[0] : '';
+      const defaultEmbeddingModelProvider =
+        embeddingModelProvidersKeys.length > 0
+          ? embeddingModelProvidersKeys[0]
+          : '';
+
+      const chatModelProvider =
+        localStorage.getItem('chatModelProvider') ||
+        defaultChatModelProvider ||
+        '';
+      const chatModel =
+        localStorage.getItem('chatModel') ||
+        (data.chatModelProviders &&
+        data.chatModelProviders[chatModelProvider]?.length > 0
+          ? data.chatModelProviders[chatModelProvider][0].name
+          : undefined) ||
+        '';
+      const embeddingModelProvider =
+        localStorage.getItem('embeddingModelProvider') ||
+        defaultEmbeddingModelProvider ||
+        '';
+      const embeddingModel =
+        localStorage.getItem('embeddingModel') ||
+        (data.embeddingModelProviders &&
+          data.embeddingModelProviders[embeddingModelProvider]?.[0].name) ||
+        '';
+
+      setSelectedChatModelProvider(chatModelProvider);
+      setSelectedChatModel(chatModel);
+      setSelectedEmbeddingModelProvider(embeddingModelProvider);
+      setSelectedEmbeddingModel(embeddingModel);
+      setChatModels(data.chatModelProviders || {});
+      setEmbeddingModels(data.embeddingModelProviders || {});
+
+      setAutomaticImageSearch(
+        localStorage.getItem('autoImageSearch') === 'true',
+      );
+      setAutomaticVideoSearch(
+        localStorage.getItem('autoVideoSearch') === 'true',
+      );
+
+      setIsLoading(false);
+    };
+
+    fetchConfig();
+  }, []);
+
+  const saveConfig = async (key: string, value: any) => {
+    setSavingStates((prev) => ({ ...prev, [key]: true }));
+
+    try {
+      const updatedConfig = {
+        ...config,
+        [key]: value,
+      } as SettingsType;
+
+      const response = await fetch(`/api/config`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify(updatedConfig),
+      });
+
+      if (!response.ok) {
+        throw new Error('Failed to update config');
+      }
+
+      setConfig(updatedConfig);
+
+      if (
+        key.toLowerCase().includes('api') ||
+        key.toLowerCase().includes('url')
+      ) {
+        const res = await fetch(`/api/config`, {
+          headers: {
+            'Content-Type': 'application/json',
+          },
+        });
+
+        if (!res.ok) {
+          throw new Error('Failed to fetch updated config');
+        }
+
+        const data = await res.json();
+
+        setChatModels(data.chatModelProviders || {});
+        setEmbeddingModels(data.embeddingModelProviders || {});
+
+        const currentChatProvider = selectedChatModelProvider;
+        const newChatProviders = Object.keys(data.chatModelProviders || {});
+
+        if (!currentChatProvider && newChatProviders.length > 0) {
+          const firstProvider = newChatProviders[0];
+          const firstModel = data.chatModelProviders[firstProvider]?.[0]?.name;
+
+          if (firstModel) {
+            setSelectedChatModelProvider(firstProvider);
+            setSelectedChatModel(firstModel);
+            localStorage.setItem('chatModelProvider', firstProvider);
+            localStorage.setItem('chatModel', firstModel);
+          }
+        } else if (
+          currentChatProvider &&
+          (!data.chatModelProviders ||
+            !data.chatModelProviders[currentChatProvider] ||
+            !Array.isArray(data.chatModelProviders[currentChatProvider]) ||
+            data.chatModelProviders[currentChatProvider].length === 0)
+        ) {
+          const firstValidProvider = Object.entries(
+            data.chatModelProviders || {},
+          ).find(
+            ([_, models]) => Array.isArray(models) && models.length > 0,
+          )?.[0];
+
+          if (firstValidProvider) {
+            setSelectedChatModelProvider(firstValidProvider);
+            setSelectedChatModel(
+              data.chatModelProviders[firstValidProvider][0].name,
+            );
+            localStorage.setItem('chatModelProvider', firstValidProvider);
+            localStorage.setItem(
+              'chatModel',
+              data.chatModelProviders[firstValidProvider][0].name,
+            );
+          } else {
+            setSelectedChatModelProvider(null);
+            setSelectedChatModel(null);
+            localStorage.removeItem('chatModelProvider');
+            localStorage.removeItem('chatModel');
+          }
+        }
+
+        const currentEmbeddingProvider = selectedEmbeddingModelProvider;
+        const newEmbeddingProviders = Object.keys(
+          data.embeddingModelProviders || {},
+        );
+
+        if (!currentEmbeddingProvider && newEmbeddingProviders.length > 0) {
+          const firstProvider = newEmbeddingProviders[0];
+          const firstModel =
+            data.embeddingModelProviders[firstProvider]?.[0]?.name;
+
+          if (firstModel) {
+            setSelectedEmbeddingModelProvider(firstProvider);
+            setSelectedEmbeddingModel(firstModel);
+            localStorage.setItem('embeddingModelProvider', firstProvider);
+            localStorage.setItem('embeddingModel', firstModel);
+          }
+        } else if (
+          currentEmbeddingProvider &&
+          (!data.embeddingModelProviders ||
+            !data.embeddingModelProviders[currentEmbeddingProvider] ||
+            !Array.isArray(
+              data.embeddingModelProviders[currentEmbeddingProvider],
+            ) ||
+            data.embeddingModelProviders[currentEmbeddingProvider].length === 0)
+        ) {
+          const firstValidProvider = Object.entries(
+            data.embeddingModelProviders || {},
+          ).find(
+            ([_, models]) => Array.isArray(models) && models.length > 0,
+          )?.[0];
+
+          if (firstValidProvider) {
+            setSelectedEmbeddingModelProvider(firstValidProvider);
+            setSelectedEmbeddingModel(
+              data.embeddingModelProviders[firstValidProvider][0].name,
+            );
+            localStorage.setItem('embeddingModelProvider', firstValidProvider);
+            localStorage.setItem(
+              'embeddingModel',
+              data.embeddingModelProviders[firstValidProvider][0].name,
+            );
+          } else {
+            setSelectedEmbeddingModelProvider(null);
+            setSelectedEmbeddingModel(null);
+            localStorage.removeItem('embeddingModelProvider');
+            localStorage.removeItem('embeddingModel');
+          }
+        }
+
+        setConfig(data);
+      }
+
+      if (key === 'automaticImageSearch') {
+        localStorage.setItem('autoImageSearch', value.toString());
+      } else if (key === 'automaticVideoSearch') {
+        localStorage.setItem('autoVideoSearch', value.toString());
+      } else if (key === 'chatModelProvider') {
+        localStorage.setItem('chatModelProvider', value);
+      } else if (key === 'chatModel') {
+        localStorage.setItem('chatModel', value);
+      } else if (key === 'embeddingModelProvider') {
+        localStorage.setItem('embeddingModelProvider', value);
+      } else if (key === 'embeddingModel') {
+        localStorage.setItem('embeddingModel', value);
+      }
+    } catch (err) {
+      console.error('Failed to save:', err);
+      setConfig((prev) => ({ ...prev! }));
+    } finally {
+      setTimeout(() => {
+        setSavingStates((prev) => ({ ...prev, [key]: false }));
+      }, 500);
+    }
+  };
+
+  return (
+    <div className="max-w-3xl mx-auto">
+      <div className="flex flex-col pt-4">
+        <div className="flex items-center space-x-2">
+          <Link href="/" className="lg:hidden">
+            <ArrowLeft className="text-black/70 dark:text-white/70" />
+          </Link>
+          <div className="flex flex-row space-x-0.5 items-center">
+            <SettingsIcon size={23} />
+            <h1 className="text-3xl font-medium p-2">Settings</h1>
+          </div>
+        </div>
+        <hr className="border-t border-[#2B2C2C] my-4 w-full" />
+      </div>
+
+      {isLoading ? (
+        <div className="flex flex-row items-center justify-center min-h-[50vh]">
+          <svg
+            aria-hidden="true"
+            className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
+            viewBox="0 0 100 101"
+            fill="none"
+            xmlns="http://www.w3.org/2000/svg"
+          >
+            <path
+              d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
+              fill="currentColor"
+            />
+            <path
+              d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
+              fill="currentFill"
+            />
+          </svg>
+        </div>
+      ) : (
+        config && (
+          <div className="flex flex-col space-y-6 pb-28 lg:pb-8">
+            <SettingsSection title="Appearance">
+              <div className="flex flex-col space-y-1">
+                <p className="text-black/70 dark:text-white/70 text-sm">
+                  Theme
+                </p>
+                <ThemeSwitcher />
+              </div>
+            </SettingsSection>
+
+            <SettingsSection title="Automatic Search">
+              <div className="flex flex-col space-y-4">
+                <div className="flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors">
+                  <div className="flex items-center space-x-3">
+                    <div className="p-2 bg-light-200 dark:bg-dark-200 rounded-lg">
+                      <ImagesIcon
+                        size={18}
+                        className="text-black/70 dark:text-white/70"
+                      />
+                    </div>
+                    <div>
+                      <p className="text-sm text-black/90 dark:text-white/90 font-medium">
+                        Automatic Image Search
+                      </p>
+                      <p className="text-xs text-black/60 dark:text-white/60 mt-0.5">
+                        Automatically search for relevant images in chat
+                        responses
+                      </p>
+                    </div>
+                  </div>
+                  <Switch
+                    checked={automaticImageSearch}
+                    onChange={(checked) => {
+                      setAutomaticImageSearch(checked);
+                      saveConfig('automaticImageSearch', checked);
+                    }}
+                    className={cn(
+                      automaticImageSearch
+                        ? 'bg-[#24A0ED]'
+                        : 'bg-light-200 dark:bg-dark-200',
+                      'relative inline-flex h-6 w-11 items-center rounded-full transition-colors focus:outline-none',
+                    )}
+                  >
+                    <span
+                      className={cn(
+                        automaticImageSearch
+                          ? 'translate-x-6'
+                          : 'translate-x-1',
+                        'inline-block h-4 w-4 transform rounded-full bg-white transition-transform',
+                      )}
+                    />
+                  </Switch>
+                </div>
+
+                <div className="flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors">
+                  <div className="flex items-center space-x-3">
+                    <div className="p-2 bg-light-200 dark:bg-dark-200 rounded-lg">
+                      <VideoIcon
+                        size={18}
+                        className="text-black/70 dark:text-white/70"
+                      />
+                    </div>
+                    <div>
+                      <p className="text-sm text-black/90 dark:text-white/90 font-medium">
+                        Automatic Video Search
+                      </p>
+                      <p className="text-xs text-black/60 dark:text-white/60 mt-0.5">
+                        Automatically search for relevant videos in chat
+                        responses
+                      </p>
+                    </div>
+                  </div>
+                  <Switch
+                    checked={automaticVideoSearch}
+                    onChange={(checked) => {
+                      setAutomaticVideoSearch(checked);
+                      saveConfig('automaticVideoSearch', checked);
+                    }}
+                    className={cn(
+                      automaticVideoSearch
+                        ? 'bg-[#24A0ED]'
+                        : 'bg-light-200 dark:bg-dark-200',
+                      'relative inline-flex h-6 w-11 items-center rounded-full transition-colors focus:outline-none',
+                    )}
+                  >
+                    <span
+                      className={cn(
+                        automaticVideoSearch
+                          ? 'translate-x-6'
+                          : 'translate-x-1',
+                        'inline-block h-4 w-4 transform rounded-full bg-white transition-transform',
+                      )}
+                    />
+                  </Switch>
+                </div>
+              </div>
+            </SettingsSection>
+
+            <SettingsSection title="Model Settings">
+              {config.chatModelProviders && (
+                <div className="flex flex-col space-y-4">
+                  <div className="flex flex-col space-y-1">
+                    <p className="text-black/70 dark:text-white/70 text-sm">
+                      Chat Model Provider
+                    </p>
+                    <Select
+                      value={selectedChatModelProvider ?? undefined}
+                      onChange={(e) => {
+                        const value = e.target.value;
+                        setSelectedChatModelProvider(value);
+                        saveConfig('chatModelProvider', value);
+                        const firstModel =
+                          config.chatModelProviders[value]?.[0]?.name;
+                        if (firstModel) {
+                          setSelectedChatModel(firstModel);
+                          saveConfig('chatModel', firstModel);
+                        }
+                      }}
+                      options={Object.keys(config.chatModelProviders).map(
+                        (provider) => ({
+                          value: provider,
+                          label:
+                            provider.charAt(0).toUpperCase() +
+                            provider.slice(1),
+                        }),
+                      )}
+                    />
+                  </div>
+
+                  {selectedChatModelProvider &&
+                    selectedChatModelProvider != 'custom_openai' && (
+                      <div className="flex flex-col space-y-1">
+                        <p className="text-black/70 dark:text-white/70 text-sm">
+                          Chat Model
+                        </p>
+                        <Select
+                          value={selectedChatModel ?? undefined}
+                          onChange={(e) => {
+                            const value = e.target.value;
+                            setSelectedChatModel(value);
+                            saveConfig('chatModel', value);
+                          }}
+                          options={(() => {
+                            const chatModelProvider =
+                              config.chatModelProviders[
+                                selectedChatModelProvider
+                              ];
+                            return chatModelProvider
+                              ? chatModelProvider.length > 0
+                                ? chatModelProvider.map((model) => ({
+                                    value: model.name,
+                                    label: model.displayName,
+                                  }))
+                                : [
+                                    {
+                                      value: '',
+                                      label: 'No models available',
+                                      disabled: true,
+                                    },
+                                  ]
+                              : [
+                                  {
+                                    value: '',
+                                    label:
+                                      'Invalid provider, please check backend logs',
+                                    disabled: true,
+                                  },
+                                ];
+                          })()}
+                        />
+                      </div>
+                    )}
+                </div>
+              )}
+
+              {selectedChatModelProvider &&
+                selectedChatModelProvider === 'custom_openai' && (
+                  <div className="flex flex-col space-y-4">
+                    <div className="flex flex-col space-y-1">
+                      <p className="text-black/70 dark:text-white/70 text-sm">
+                        Model Name
+                      </p>
+                      <Input
+                        type="text"
+                        placeholder="Model name"
+                        value={config.customOpenaiModelName}
+                        isSaving={savingStates['customOpenaiModelName']}
+                        onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
+                          setConfig((prev) => ({
+                            ...prev!,
+                            customOpenaiModelName: e.target.value,
+                          }));
+                        }}
+                        onSave={(value) =>
+                          saveConfig('customOpenaiModelName', value)
+                        }
+                      />
+                    </div>
+                    <div className="flex flex-col space-y-1">
+                      <p className="text-black/70 dark:text-white/70 text-sm">
+                        Custom OpenAI API Key
+                      </p>
+                      <Input
+                        type="text"
+                        placeholder="Custom OpenAI API Key"
+                        value={config.customOpenaiApiKey}
+                        isSaving={savingStates['customOpenaiApiKey']}
+                        onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
+                          setConfig((prev) => ({
+                            ...prev!,
+                            customOpenaiApiKey: e.target.value,
+                          }));
+                        }}
+                        onSave={(value) =>
+                          saveConfig('customOpenaiApiKey', value)
+                        }
+                      />
+                    </div>
+                    <div className="flex flex-col space-y-1">
+                      <p className="text-black/70 dark:text-white/70 text-sm">
+                        Custom OpenAI Base URL
+                      </p>
+                      <Input
+                        type="text"
+                        placeholder="Custom OpenAI Base URL"
+                        value={config.customOpenaiApiUrl}
+                        isSaving={savingStates['customOpenaiApiUrl']}
+                        onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
+                          setConfig((prev) => ({
+                            ...prev!,
+                            customOpenaiApiUrl: e.target.value,
+                          }));
+                        }}
+                        onSave={(value) =>
+                          saveConfig('customOpenaiApiUrl', value)
+                        }
+                      />
+                    </div>
+                  </div>
+                )}
+
+              {config.embeddingModelProviders && (
+                <div className="flex flex-col space-y-4 mt-4 pt-4 border-t border-light-200 dark:border-dark-200">
+                  <div className="flex flex-col space-y-1">
+                    <p className="text-black/70 dark:text-white/70 text-sm">
+                      Embedding Model Provider
+                    </p>
+                    <Select
+                      value={selectedEmbeddingModelProvider ?? undefined}
+                      onChange={(e) => {
+                        const value = e.target.value;
+                        setSelectedEmbeddingModelProvider(value);
+                        saveConfig('embeddingModelProvider', value);
+                        const firstModel =
+                          config.embeddingModelProviders[value]?.[0]?.name;
+                        if (firstModel) {
+                          setSelectedEmbeddingModel(firstModel);
+                          saveConfig('embeddingModel', firstModel);
+                        }
+                      }}
+                      options={Object.keys(config.embeddingModelProviders).map(
+                        (provider) => ({
+                          value: provider,
+                          label:
+                            provider.charAt(0).toUpperCase() +
+                            provider.slice(1),
+                        }),
+                      )}
+                    />
+                  </div>
+
+                  {selectedEmbeddingModelProvider && (
+                    <div className="flex flex-col space-y-1">
+                      <p className="text-black/70 dark:text-white/70 text-sm">
+                        Embedding Model
+                      </p>
+                      <Select
+                        value={selectedEmbeddingModel ?? undefined}
+                        onChange={(e) => {
+                          const value = e.target.value;
+                          setSelectedEmbeddingModel(value);
+                          saveConfig('embeddingModel', value);
+                        }}
+                        options={(() => {
+                          const embeddingModelProvider =
+                            config.embeddingModelProviders[
+                              selectedEmbeddingModelProvider
+                            ];
+                          return embeddingModelProvider
+                            ? embeddingModelProvider.length > 0
+                              ? embeddingModelProvider.map((model) => ({
+                                  value: model.name,
+                                  label: model.displayName,
+                                }))
+                              : [
+                                  {
+                                    value: '',
+                                    label: 'No models available',
+                                    disabled: true,
+                                  },
+                                ]
+                            : [
+                                {
+                                  value: '',
+                                  label:
+                                    'Invalid provider, please check backend logs',
+                                  disabled: true,
+                                },
+                              ];
+                        })()}
+                      />
+                    </div>
+                  )}
+                </div>
+              )}
+            </SettingsSection>
+
+            <SettingsSection title="API Keys">
+              <div className="flex flex-col space-y-4">
+                <div className="flex flex-col space-y-1">
+                  <p className="text-black/70 dark:text-white/70 text-sm">
+                    OpenAI API Key
+                  </p>
+                  <Input
+                    type="text"
+                    placeholder="OpenAI API Key"
+                    value={config.openaiApiKey}
+                    isSaving={savingStates['openaiApiKey']}
+                    onChange={(e) => {
+                      setConfig((prev) => ({
+                        ...prev!,
+                        openaiApiKey: e.target.value,
+                      }));
+                    }}
+                    onSave={(value) => saveConfig('openaiApiKey', value)}
+                  />
+                </div>
+
+                <div className="flex flex-col space-y-1">
+                  <p className="text-black/70 dark:text-white/70 text-sm">
+                    Ollama API URL
+                  </p>
+                  <Input
+                    type="text"
+                    placeholder="Ollama API URL"
+                    value={config.ollamaApiUrl}
+                    isSaving={savingStates['ollamaApiUrl']}
+                    onChange={(e) => {
+                      setConfig((prev) => ({
+                        ...prev!,
+                        ollamaApiUrl: e.target.value,
+                      }));
+                    }}
+                    onSave={(value) => saveConfig('ollamaApiUrl', value)}
+                  />
+                </div>
+
+                <div className="flex flex-col space-y-1">
+                  <p className="text-black/70 dark:text-white/70 text-sm">
+                    GROQ API Key
+                  </p>
+                  <Input
+                    type="text"
+                    placeholder="GROQ API Key"
+                    value={config.groqApiKey}
+                    isSaving={savingStates['groqApiKey']}
+                    onChange={(e) => {
+                      setConfig((prev) => ({
+                        ...prev!,
+                        groqApiKey: e.target.value,
+                      }));
+                    }}
+                    onSave={(value) => saveConfig('groqApiKey', value)}
+                  />
+                </div>
+
+                <div className="flex flex-col space-y-1">
+                  <p className="text-black/70 dark:text-white/70 text-sm">
+                    Anthropic API Key
+                  </p>
+                  <Input
+                    type="text"
+                    placeholder="Anthropic API key"
+                    value={config.anthropicApiKey}
+                    isSaving={savingStates['anthropicApiKey']}
+                    onChange={(e) => {
+                      setConfig((prev) => ({
+                        ...prev!,
+                        anthropicApiKey: e.target.value,
+                      }));
+                    }}
+                    onSave={(value) => saveConfig('anthropicApiKey', value)}
+                  />
+                </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"
+                    value={config.geminiApiKey}
+                    isSaving={savingStates['geminiApiKey']}
+                    onChange={(e) => {
+                      setConfig((prev) => ({
+                        ...prev!,
+                        geminiApiKey: e.target.value,
+                      }));
+                    }}
+                    onSave={(value) => saveConfig('geminiApiKey', value)}
+                  />
+                </div>
+              </div>
+            </SettingsSection>
+          </div>
+        )
+      )}
+    </div>
+  );
+};
+
+export default Page;

src/components/Chat.tsx

@@ -48,11 +48,17 @@ const Chat = ({
   });
 
   useEffect(() => {
-    messageEnd.current?.scrollIntoView({ behavior: 'smooth' });
+    const scroll = () => {
+      messageEnd.current?.scrollIntoView({ behavior: 'smooth' });
+    };
 
     if (messages.length === 1) {
       document.title = `${messages[0].content.substring(0, 30)} - Perplexica`;
     }
+
+    if (messages[messages.length - 1]?.role == 'user') {
+      scroll();
+    }
   }, [messages]);
 
   return (

src/components/ChatWindow.tsx

@@ -9,7 +9,9 @@ import crypto from 'crypto';
 import { toast } from 'sonner';
 import { useSearchParams } from 'next/navigation';
 import { getSuggestions } from '@/lib/actions';
-import Error from 'next/error';
+import { Settings } from 'lucide-react';
+import Link from 'next/link';
+import NextError from 'next/error';
 
 export type Message = {
   messageId: string;
@@ -27,193 +29,154 @@ export interface File {
   fileId: string;
 }
 
-const useSocket = (
-  url: string,
-  setIsWSReady: (ready: boolean) => void,
-  setError: (error: boolean) => void,
+interface ChatModelProvider {
+  name: string;
+  provider: string;
+}
+
+interface EmbeddingModelProvider {
+  name: string;
+  provider: string;
+}
+
+const checkConfig = async (
+  setChatModelProvider: (provider: ChatModelProvider) => void,
+  setEmbeddingModelProvider: (provider: EmbeddingModelProvider) => void,
+  setIsConfigReady: (ready: boolean) => void,
+  setHasError: (hasError: boolean) => void,
 ) => {
-  const [ws, setWs] = useState<WebSocket | null>(null);
+  try {
+    let chatModel = localStorage.getItem('chatModel');
+    let chatModelProvider = localStorage.getItem('chatModelProvider');
+    let embeddingModel = localStorage.getItem('embeddingModel');
+    let embeddingModelProvider = localStorage.getItem('embeddingModelProvider');
 
-  useEffect(() => {
-    if (!ws) {
-      const connectWs = async () => {
-        let chatModel = localStorage.getItem('chatModel');
-        let chatModelProvider = localStorage.getItem('chatModelProvider');
-        let embeddingModel = localStorage.getItem('embeddingModel');
-        let embeddingModelProvider = localStorage.getItem(
-          'embeddingModelProvider',
+    const autoImageSearch = localStorage.getItem('autoImageSearch');
+    const autoVideoSearch = localStorage.getItem('autoVideoSearch');
+
+    if (!autoImageSearch) {
+      localStorage.setItem('autoImageSearch', 'true');
+    }
+
+    if (!autoVideoSearch) {
+      localStorage.setItem('autoVideoSearch', 'false');
+    }
+
+    const providers = await fetch(`/api/models`, {
+      headers: {
+        'Content-Type': 'application/json',
+      },
+    }).then(async (res) => {
+      if (!res.ok)
+        throw new Error(
+          `Failed to fetch models: ${res.status} ${res.statusText}`,
         );
+      return res.json();
+    });
 
-        const providers = await fetch(
-          `${process.env.NEXT_PUBLIC_API_URL}/models`,
-          {
-            headers: {
-              'Content-Type': 'application/json',
-            },
-          },
-        ).then(async (res) => await res.json());
+    if (
+      !chatModel ||
+      !chatModelProvider ||
+      !embeddingModel ||
+      !embeddingModelProvider
+    ) {
+      if (!chatModel || !chatModelProvider) {
+        const chatModelProviders = providers.chatModelProviders;
+
+        chatModelProvider =
+          chatModelProvider || Object.keys(chatModelProviders)[0];
+
+        chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
+
+        if (!chatModelProviders || Object.keys(chatModelProviders).length === 0)
+          return toast.error('No chat models available');
+      }
+
+      if (!embeddingModel || !embeddingModelProvider) {
+        const embeddingModelProviders = providers.embeddingModelProviders;
 
         if (
-          !chatModel ||
-          !chatModelProvider ||
-          !embeddingModel ||
-          !embeddingModelProvider
-        ) {
-          if (!chatModel || !chatModelProvider) {
-            const chatModelProviders = providers.chatModelProviders;
+          !embeddingModelProviders ||
+          Object.keys(embeddingModelProviders).length === 0
+        )
+          return toast.error('No embedding models available');
 
-            chatModelProvider = Object.keys(chatModelProviders)[0];
+        embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
+        embeddingModel = Object.keys(
+          embeddingModelProviders[embeddingModelProvider],
+        )[0];
+      }
 
-            if (chatModelProvider === 'custom_openai') {
-              toast.error(
-                'Seems like you are using the custom OpenAI provider, please open the settings and configure the API key and base URL',
-              );
-              setError(true);
-              return;
-            } else {
-              chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
-              if (
-                !chatModelProviders ||
-                Object.keys(chatModelProviders).length === 0
-              )
-                return toast.error('No chat models available');
-            }
-          }
+      localStorage.setItem('chatModel', chatModel!);
+      localStorage.setItem('chatModelProvider', chatModelProvider);
+      localStorage.setItem('embeddingModel', embeddingModel!);
+      localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
+    } else {
+      const chatModelProviders = providers.chatModelProviders;
+      const embeddingModelProviders = providers.embeddingModelProviders;
 
-          if (!embeddingModel || !embeddingModelProvider) {
-            const embeddingModelProviders = providers.embeddingModelProviders;
+      if (
+        Object.keys(chatModelProviders).length > 0 &&
+        !chatModelProviders[chatModelProvider]
+      ) {
+        const chatModelProvidersKeys = Object.keys(chatModelProviders);
+        chatModelProvider =
+          chatModelProvidersKeys.find(
+            (key) => Object.keys(chatModelProviders[key]).length > 0,
+          ) || chatModelProvidersKeys[0];
 
-            if (
-              !embeddingModelProviders ||
-              Object.keys(embeddingModelProviders).length === 0
-            )
-              return toast.error('No embedding models available');
+        localStorage.setItem('chatModelProvider', chatModelProvider);
+      }
 
-            embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
-            embeddingModel = Object.keys(
-              embeddingModelProviders[embeddingModelProvider],
-            )[0];
-          }
+      if (
+        chatModelProvider &&
+        !chatModelProviders[chatModelProvider][chatModel]
+      ) {
+        chatModel = Object.keys(
+          chatModelProviders[
+            Object.keys(chatModelProviders[chatModelProvider]).length > 0
+              ? chatModelProvider
+              : Object.keys(chatModelProviders)[0]
+          ],
+        )[0];
+        localStorage.setItem('chatModel', chatModel);
+      }
 
-          localStorage.setItem('chatModel', chatModel!);
-          localStorage.setItem('chatModelProvider', chatModelProvider);
-          localStorage.setItem('embeddingModel', embeddingModel!);
-          localStorage.setItem(
-            'embeddingModelProvider',
-            embeddingModelProvider,
-          );
-        } else {
-          const chatModelProviders = providers.chatModelProviders;
-          const embeddingModelProviders = providers.embeddingModelProviders;
+      if (
+        Object.keys(embeddingModelProviders).length > 0 &&
+        !embeddingModelProviders[embeddingModelProvider]
+      ) {
+        embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
+        localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
+      }
 
-          if (
-            Object.keys(chatModelProviders).length > 0 &&
-            !chatModelProviders[chatModelProvider]
-          ) {
-            chatModelProvider = Object.keys(chatModelProviders)[0];
-            localStorage.setItem('chatModelProvider', chatModelProvider);
-          }
-
-          if (
-            chatModelProvider &&
-            chatModelProvider != 'custom_openai' &&
-            !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);
-        const searchParams = new URLSearchParams({});
-
-        searchParams.append('chatModel', chatModel!);
-        searchParams.append('chatModelProvider', chatModelProvider);
-
-        if (chatModelProvider === 'custom_openai') {
-          searchParams.append(
-            'openAIApiKey',
-            localStorage.getItem('openAIApiKey')!,
-          );
-          searchParams.append(
-            'openAIBaseURL',
-            localStorage.getItem('openAIBaseURL')!,
-          );
-        }
-
-        searchParams.append('embeddingModel', embeddingModel!);
-        searchParams.append('embeddingModelProvider', embeddingModelProvider);
-
-        wsURL.search = searchParams.toString();
-
-        const ws = new WebSocket(wsURL.toString());
-
-        const timeoutId = setTimeout(() => {
-          if (ws.readyState !== 1) {
-            toast.error(
-              'Failed to connect to the server. Please try again later.',
-            );
-          }
-        }, 10000);
-
-        ws.addEventListener('message', (e) => {
-          const data = JSON.parse(e.data);
-          if (data.type === 'signal' && data.data === 'open') {
-            const interval = setInterval(() => {
-              if (ws.readyState === 1) {
-                setIsWSReady(true);
-                clearInterval(interval);
-              }
-            }, 5);
-            clearTimeout(timeoutId);
-            console.log('[DEBUG] opened');
-          }
-          if (data.type === 'error') {
-            toast.error(data.data);
-          }
-        });
-
-        ws.onerror = () => {
-          clearTimeout(timeoutId);
-          setError(true);
-          toast.error('WebSocket connection error.');
-        };
-
-        ws.onclose = () => {
-          clearTimeout(timeoutId);
-          setError(true);
-          console.log('[DEBUG] closed');
-        };
-
-        setWs(ws);
-      };
-
-      connectWs();
+      if (
+        embeddingModelProvider &&
+        !embeddingModelProviders[embeddingModelProvider][embeddingModel]
+      ) {
+        embeddingModel = Object.keys(
+          embeddingModelProviders[embeddingModelProvider],
+        )[0];
+        localStorage.setItem('embeddingModel', embeddingModel);
+      }
     }
-  }, [ws, url, setIsWSReady, setError]);
 
-  return ws;
+    setChatModelProvider({
+      name: chatModel!,
+      provider: chatModelProvider,
+    });
+
+    setEmbeddingModelProvider({
+      name: embeddingModel!,
+      provider: embeddingModelProvider,
+    });
+
+    setIsConfigReady(true);
+  } catch (err) {
+    console.error('An error occurred while checking the configuration:', err);
+    setIsConfigReady(false);
+    setHasError(true);
+  }
 };
 
 const loadMessages = async (
@@ -226,15 +189,12 @@ const loadMessages = async (
   setFiles: (files: File[]) => void,
   setFileIds: (fileIds: string[]) => void,
 ) => {
-  const res = await fetch(
-    `${process.env.NEXT_PUBLIC_API_URL}/chats/${chatId}`,
-    {
-      method: 'GET',
-      headers: {
-        'Content-Type': 'application/json',
-      },
+  const res = await fetch(`/api/chats/${chatId}`, {
+    method: 'GET',
+    headers: {
+      'Content-Type': 'application/json',
     },
-  );
+  });
 
   if (res.status === 404) {
     setNotFound(true);
@@ -257,7 +217,7 @@ const loadMessages = async (
     return [msg.role, msg.content];
   }) as [string, string][];
 
-  console.log('[DEBUG] messages loaded');
+  console.debug(new Date(), 'app:messages_loaded');
 
   document.title = messages[0].content;
 
@@ -284,15 +244,32 @@ const ChatWindow = ({ id }: { id?: string }) => {
   const [chatId, setChatId] = useState<string | undefined>(id);
   const [newChatCreated, setNewChatCreated] = useState(false);
 
+  const [chatModelProvider, setChatModelProvider] = useState<ChatModelProvider>(
+    {
+      name: '',
+      provider: '',
+    },
+  );
+
+  const [embeddingModelProvider, setEmbeddingModelProvider] =
+    useState<EmbeddingModelProvider>({
+      name: '',
+      provider: '',
+    });
+
+  const [isConfigReady, setIsConfigReady] = useState(false);
   const [hasError, setHasError] = useState(false);
   const [isReady, setIsReady] = useState(false);
 
-  const [isWSReady, setIsWSReady] = useState(false);
-  const ws = useSocket(
-    process.env.NEXT_PUBLIC_WS_URL!,
-    setIsWSReady,
-    setHasError,
-  );
+  useEffect(() => {
+    checkConfig(
+      setChatModelProvider,
+      setEmbeddingModelProvider,
+      setIsConfigReady,
+      setHasError,
+    );
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
 
   const [loading, setLoading] = useState(false);
   const [messageAppeared, setMessageAppeared] = useState(false);
@@ -335,16 +312,6 @@ const ChatWindow = ({ id }: { id?: string }) => {
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, []);
 
-  useEffect(() => {
-    return () => {
-      if (ws?.readyState === 1) {
-        ws.close();
-        console.log('[DEBUG] closed');
-      }
-    };
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, []);
-
   const messagesRef = useRef<Message[]>([]);
 
   useEffect(() => {
@@ -352,14 +319,20 @@ const ChatWindow = ({ id }: { id?: string }) => {
   }, [messages]);
 
   useEffect(() => {
-    if (isMessagesLoaded && isWSReady) {
+    if (isMessagesLoaded && isConfigReady) {
       setIsReady(true);
-      console.log('[DEBUG] ready');
+      console.debug(new Date(), 'app:ready');
+    } else {
+      setIsReady(false);
     }
-  }, [isMessagesLoaded, isWSReady]);
+  }, [isMessagesLoaded, isConfigReady]);
 
   const sendMessage = async (message: string, messageId?: string) => {
     if (loading) return;
+    if (!isConfigReady) {
+      toast.error('Cannot send message before the configuration is ready');
+      return;
+    }
 
     setLoading(true);
     setMessageAppeared(false);
@@ -370,21 +343,6 @@ const ChatWindow = ({ id }: { id?: string }) => {
 
     messageId = messageId ?? crypto.randomBytes(7).toString('hex');
 
-    ws?.send(
-      JSON.stringify({
-        type: 'message',
-        message: {
-          messageId: messageId,
-          chatId: chatId!,
-          content: message,
-        },
-        files: fileIds,
-        focusMode: focusMode,
-        optimizationMode: optimizationMode,
-        history: [...chatHistory, ['human', message]],
-      }),
-    );
-
     setMessages((prevMessages) => [
       ...prevMessages,
       {
@@ -396,9 +354,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
       },
     ]);
 
-    const messageHandler = async (e: MessageEvent) => {
-      const data = JSON.parse(e.data);
-
+    const messageHandler = async (data: any) => {
       if (data.type === 'error') {
         toast.error(data.data);
         setLoading(false);
@@ -461,11 +417,25 @@ const ChatWindow = ({ id }: { id?: string }) => {
           ['assistant', recievedMessage],
         ]);
 
-        ws?.removeEventListener('message', messageHandler);
         setLoading(false);
 
         const lastMsg = messagesRef.current[messagesRef.current.length - 1];
 
+        const autoImageSearch = localStorage.getItem('autoImageSearch');
+        const autoVideoSearch = localStorage.getItem('autoVideoSearch');
+
+        if (autoImageSearch === 'true') {
+          document
+            .getElementById(`search-images-${lastMsg.messageId}`)
+            ?.click();
+        }
+
+        if (autoVideoSearch === 'true') {
+          document
+            .getElementById(`search-videos-${lastMsg.messageId}`)
+            ?.click();
+        }
+
         if (
           lastMsg.role === 'assistant' &&
           lastMsg.sources &&
@@ -485,7 +455,59 @@ const ChatWindow = ({ id }: { id?: string }) => {
       }
     };
 
-    ws?.addEventListener('message', messageHandler);
+    const res = await fetch('/api/chat', {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({
+        content: message,
+        message: {
+          messageId: messageId,
+          chatId: chatId!,
+          content: message,
+        },
+        chatId: chatId!,
+        files: fileIds,
+        focusMode: focusMode,
+        optimizationMode: optimizationMode,
+        history: chatHistory,
+        chatModel: {
+          name: chatModelProvider.name,
+          provider: chatModelProvider.provider,
+        },
+        embeddingModel: {
+          name: embeddingModelProvider.name,
+          provider: embeddingModelProvider.provider,
+        },
+      }),
+    });
+
+    if (!res.body) throw new Error('No response body');
+
+    const reader = res.body?.getReader();
+    const decoder = new TextDecoder('utf-8');
+
+    let partialChunk = '';
+
+    while (true) {
+      const { value, done } = await reader.read();
+      if (done) break;
+
+      partialChunk += decoder.decode(value, { stream: true });
+
+      try {
+        const messages = partialChunk.split('\n');
+        for (const msg of messages) {
+          if (!msg.trim()) continue;
+          const json = JSON.parse(msg);
+          messageHandler(json);
+        }
+        partialChunk = '';
+      } catch (error) {
+        console.warn('Incomplete JSON, waiting for next chunk...');
+      }
+    }
   };
 
   const rewrite = (messageId: string) => {
@@ -506,25 +528,32 @@ const ChatWindow = ({ id }: { id?: string }) => {
   };
 
   useEffect(() => {
-    if (isReady && initialMessage && ws?.readyState === 1) {
+    if (isReady && initialMessage && isConfigReady) {
       sendMessage(initialMessage);
     }
     // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [ws?.readyState, isReady, initialMessage, isWSReady]);
+  }, [isConfigReady, isReady, initialMessage]);
 
   if (hasError) {
     return (
-      <div className="flex flex-col items-center justify-center min-h-screen">
-        <p className="dark:text-white/70 text-black/70 text-sm">
-          Failed to connect to the server. Please try again later.
-        </p>
+      <div className="relative">
+        <div className="absolute w-full flex flex-row items-center justify-end mr-5 mt-5">
+          <Link href="/settings">
+            <Settings className="cursor-pointer lg:hidden" />
+          </Link>
+        </div>
+        <div className="flex flex-col items-center justify-center min-h-screen">
+          <p className="dark:text-white/70 text-black/70 text-sm">
+            Failed to connect to the server. Please try again later.
+          </p>
+        </div>
       </div>
     );
   }
 
   return isReady ? (
     notFound ? (
-      <Error statusCode={404} />
+      <NextError statusCode={404} />
     ) : (
       <div>
         {messages.length > 0 ? (

src/components/DeleteChat.tsx

@@ -29,15 +29,12 @@ const DeleteChat = ({
   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',
-          },
+      const res = await fetch(`/api/chats/${chatId}`, {
+        method: 'DELETE',
+        headers: {
+          'Content-Type': 'application/json',
         },
-      );
+      });
 
       if (res.status != 200) {
         throw new Error('Failed to delete chat');

src/components/EmptyChat.tsx

@@ -1,8 +1,8 @@
 import { Settings } from 'lucide-react';
 import EmptyChatMessageInput from './EmptyChatMessageInput';
-import SettingsDialog from './SettingsDialog';
 import { useState } from 'react';
 import { File } from './ChatWindow';
+import Link from 'next/link';
 
 const EmptyChat = ({
   sendMessage,
@@ -29,12 +29,10 @@ const EmptyChat = ({
 
   return (
     <div className="relative">
-      <SettingsDialog isOpen={isSettingsOpen} setIsOpen={setIsSettingsOpen} />
       <div className="absolute w-full flex flex-row items-center justify-end mr-5 mt-5">
-        <Settings
-          className="cursor-pointer lg:hidden"
-          onClick={() => setIsSettingsOpen(true)}
-        />
+        <Link href="/settings">
+          <Settings className="cursor-pointer lg:hidden" />
+        </Link>
       </div>
       <div className="flex flex-col items-center justify-center min-h-screen max-w-screen-sm mx-auto p-2 space-y-8">
         <h2 className="text-black/70 dark:text-white/70 text-3xl font-medium -mt-8">

src/components/MessageBox.tsx

@@ -12,13 +12,18 @@ import {
   Layers3,
   Plus,
 } from 'lucide-react';
-import Markdown from 'markdown-to-jsx';
+import Markdown, { MarkdownToJSX } from 'markdown-to-jsx';
 import Copy from './MessageActions/Copy';
 import Rewrite from './MessageActions/Rewrite';
 import MessageSources from './MessageSources';
 import SearchImages from './SearchImages';
 import SearchVideos from './SearchVideos';
 import { useSpeech } from 'react-text-to-speech';
+import ThinkBox from './ThinkBox';
+
+const ThinkTagProcessor = ({ children }: { children: React.ReactNode }) => {
+  return <ThinkBox content={children as string} />;
+};
 
 const MessageBox = ({
   message,
@@ -44,31 +49,58 @@ const MessageBox = ({
 
   useEffect(() => {
     const regex = /\[(\d+)\]/g;
+    let processedMessage = message.content;
+
+    if (message.role === 'assistant' && message.content.includes('<think>')) {
+      const openThinkTag = processedMessage.match(/<think>/g)?.length || 0;
+      const closeThinkTag = processedMessage.match(/<\/think>/g)?.length || 0;
+
+      if (openThinkTag > closeThinkTag) {
+        processedMessage += '</think> <a> </a>'; // The extra <a> </a> is to prevent the the think component from looking bad
+      }
+    }
 
     if (
       message.role === 'assistant' &&
       message?.sources &&
       message.sources.length > 0
     ) {
-      return setParsedMessage(
-        message.content.replace(
+      setParsedMessage(
+        processedMessage.replace(
           regex,
           (_, number) =>
-            `<a href="${message.sources?.[number - 1]?.metadata?.url}" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${number}</a>`,
+            `<a href="${
+              message.sources?.[number - 1]?.metadata?.url
+            }" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${number}</a>`,
         ),
       );
+      return;
     }
 
     setSpeechMessage(message.content.replace(regex, ''));
-    setParsedMessage(message.content);
+    setParsedMessage(processedMessage);
   }, [message.content, message.sources, message.role]);
 
   const { speechStatus, start, stop } = useSpeech({ text: speechMessage });
 
+  const markdownOverrides: MarkdownToJSX.Options = {
+    overrides: {
+      think: {
+        component: ThinkTagProcessor,
+      },
+    },
+  };
+
   return (
     <div>
       {message.role === 'user' && (
-        <div className={cn('w-full', messageIndex === 0 ? 'pt-16' : 'pt-8')}>
+        <div
+          className={cn(
+            'w-full',
+            messageIndex === 0 ? 'pt-16' : 'pt-8',
+            'break-words',
+          )}
+        >
           <h2 className="text-black dark:text-white font-medium text-3xl lg:w-9/12">
             {message.content}
           </h2>
@@ -105,11 +137,13 @@ const MessageBox = ({
                   Answer
                 </h3>
               </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',
                 )}
+                options={markdownOverrides}
               >
                 {parsedMessage}
               </Markdown>
@@ -187,10 +221,12 @@ const MessageBox = ({
             <SearchImages
               query={history[messageIndex - 1].content}
               chatHistory={history.slice(0, messageIndex - 1)}
+              messageId={message.messageId}
             />
             <SearchVideos
               chatHistory={history.slice(0, messageIndex - 1)}
               query={history[messageIndex - 1].content}
+              messageId={message.messageId}
             />
           </div>
         </div>

src/components/MessageInputActions/Attach.tsx

@@ -41,7 +41,7 @@ const Attach = ({
     data.append('embedding_model_provider', embeddingModelProvider!);
     data.append('embedding_model', embeddingModel!);
 
-    const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/uploads`, {
+    const res = await fetch(`/api/uploads`, {
       method: 'POST',
       body: data,
     });
@@ -110,7 +110,7 @@ const Attach = ({
                 <button
                   type="button"
                   onClick={() => fileInputRef.current.click()}
-                  className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
+                  className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
                 >
                   <input
                     type="file"
@@ -128,7 +128,7 @@ const Attach = ({
                     setFiles([]);
                     setFileIds([]);
                   }}
-                  className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
+                  className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
                 >
                   <Trash size={14} />
                   <p className="text-xs">Clear</p>
@@ -145,7 +145,7 @@ const Attach = ({
                   <div className="bg-dark-100 flex items-center justify-center w-10 h-10 rounded-md">
                     <File size={16} className="text-white/70" />
                   </div>
-                  <p className="text-white/70 text-sm">
+                  <p className="text-black/70 dark:text-white/70 text-sm">
                     {file.fileName.length > 25
                       ? file.fileName.replace(/\.\w+$/, '').substring(0, 25) +
                         '...' +

src/components/MessageInputActions/AttachSmall.tsx

@@ -39,7 +39,7 @@ const AttachSmall = ({
     data.append('embedding_model_provider', embeddingModelProvider!);
     data.append('embedding_model', embeddingModel!);
 
-    const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/uploads`, {
+    const res = await fetch(`/api/uploads`, {
       method: 'POST',
       body: data,
     });
@@ -82,7 +82,7 @@ const AttachSmall = ({
                 <button
                   type="button"
                   onClick={() => fileInputRef.current.click()}
-                  className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
+                  className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
                 >
                   <input
                     type="file"
@@ -100,7 +100,7 @@ const AttachSmall = ({
                     setFiles([]);
                     setFileIds([]);
                   }}
-                  className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
+                  className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
                 >
                   <Trash size={14} />
                   <p className="text-xs">Clear</p>
@@ -117,7 +117,7 @@ const AttachSmall = ({
                   <div className="bg-dark-100 flex items-center justify-center w-10 h-10 rounded-md">
                     <File size={16} className="text-white/70" />
                   </div>
-                  <p className="text-white/70 text-sm">
+                  <p className="text-black/70 dark:text-white/70 text-sm">
                     {file.fileName.length > 25
                       ? file.fileName.replace(/\.\w+$/, '').substring(0, 25) +
                         '...' +

src/components/MessageInputActions/Focus.tsx

@@ -45,25 +45,13 @@ const focusModes = [
     key: 'youtubeSearch',
     title: 'Youtube',
     description: 'Search and watch videos',
-    icon: (
-      <SiYoutube
-        className="h-5 w-auto mr-0.5"
-        onPointerEnterCapture={undefined}
-        onPointerLeaveCapture={undefined}
-      />
-    ),
+    icon: <SiYoutube className="h-5 w-auto mr-0.5" />,
   },
   {
     key: 'redditSearch',
     title: 'Reddit',
     description: 'Search for discussions and opinions',
-    icon: (
-      <SiReddit
-        className="h-5 w-auto mr-0.5"
-        onPointerEnterCapture={undefined}
-        onPointerLeaveCapture={undefined}
-      />
-    ),
+    icon: <SiReddit className="h-5 w-auto mr-0.5" />,
   },
 ];
 
@@ -83,7 +71,7 @@ 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} className="-translate-x-1" />
@@ -91,7 +79,7 @@ const Focus = ({
         ) : (
           <div className="flex flex-row items-center space-x-1">
             <ScanEye size={20} />
-            <p className="text-xs font-medium">Focus</p>
+            <p className="text-xs font-medium hidden lg:block">Focus</p>
           </div>
         )}
       </PopoverButton>

src/components/MessageSources.tsx

@@ -69,11 +69,15 @@ const MessageSources = ({ sources }: { sources: Document[] }) => {
           <div className="flex flex-row items-center space-x-1">
             {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">
+                <div
+                  key={i}
+                  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
+                  key={i}
                   src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
                   width={16}
                   height={16}

src/components/SearchImages.tsx

@@ -14,9 +14,11 @@ type Image = {
 const SearchImages = ({
   query,
   chatHistory,
+  messageId,
 }: {
   query: string;
   chatHistory: Message[];
+  messageId: string;
 }) => {
   const [images, setImages] = useState<Image[] | null>(null);
   const [loading, setLoading] = useState(false);
@@ -27,6 +29,7 @@ const SearchImages = ({
     <>
       {!loading && images === null && (
         <button
+          id={`search-images-${messageId}`}
           onClick={async () => {
             setLoading(true);
 
@@ -36,27 +39,24 @@ const SearchImages = ({
             const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
             const customOpenAIKey = localStorage.getItem('openAIApiKey');
 
-            const res = await fetch(
-              `${process.env.NEXT_PUBLIC_API_URL}/images`,
-              {
-                method: 'POST',
-                headers: {
-                  'Content-Type': 'application/json',
-                },
-                body: JSON.stringify({
-                  query: query,
-                  chatHistory: chatHistory,
-                  chatModel: {
-                    provider: chatModelProvider,
-                    model: chatModel,
-                    ...(chatModelProvider === 'custom_openai' && {
-                      customOpenAIBaseURL: customOpenAIBaseURL,
-                      customOpenAIKey: customOpenAIKey,
-                    }),
-                  },
-                }),
+            const res = await fetch(`/api/images`, {
+              method: 'POST',
+              headers: {
+                'Content-Type': 'application/json',
               },
-            );
+              body: JSON.stringify({
+                query: query,
+                chatHistory: chatHistory,
+                chatModel: {
+                  provider: chatModelProvider,
+                  model: chatModel,
+                  ...(chatModelProvider === 'custom_openai' && {
+                    customOpenAIBaseURL: customOpenAIBaseURL,
+                    customOpenAIKey: customOpenAIKey,
+                  }),
+                },
+              }),
+            });
 
             const data = await res.json();
 

src/components/SearchVideos.tsx

@@ -1,6 +1,6 @@
 /* eslint-disable @next/next/no-img-element */
 import { PlayCircle, PlayIcon, PlusIcon, VideoIcon } from 'lucide-react';
-import { useState } from 'react';
+import { useRef, useState } from 'react';
 import Lightbox, { GenericSlide, VideoSlide } from 'yet-another-react-lightbox';
 import 'yet-another-react-lightbox/styles.css';
 import { Message } from './ChatWindow';
@@ -27,19 +27,24 @@ declare module 'yet-another-react-lightbox' {
 const Searchvideos = ({
   query,
   chatHistory,
+  messageId,
 }: {
   query: string;
   chatHistory: Message[];
+  messageId: string;
 }) => {
   const [videos, setVideos] = useState<Video[] | null>(null);
   const [loading, setLoading] = useState(false);
   const [open, setOpen] = useState(false);
   const [slides, setSlides] = useState<VideoSlide[]>([]);
+  const [currentIndex, setCurrentIndex] = useState(0);
+  const videoRefs = useRef<(HTMLIFrameElement | null)[]>([]);
 
   return (
     <>
       {!loading && videos === null && (
         <button
+          id={`search-videos-${messageId}`}
           onClick={async () => {
             setLoading(true);
 
@@ -49,27 +54,24 @@ const Searchvideos = ({
             const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
             const customOpenAIKey = localStorage.getItem('openAIApiKey');
 
-            const res = await fetch(
-              `${process.env.NEXT_PUBLIC_API_URL}/videos`,
-              {
-                method: 'POST',
-                headers: {
-                  'Content-Type': 'application/json',
-                },
-                body: JSON.stringify({
-                  query: query,
-                  chatHistory: chatHistory,
-                  chatModel: {
-                    provider: chatModelProvider,
-                    model: chatModel,
-                    ...(chatModelProvider === 'custom_openai' && {
-                      customOpenAIBaseURL: customOpenAIBaseURL,
-                      customOpenAIKey: customOpenAIKey,
-                    }),
-                  },
-                }),
+            const res = await fetch(`/api/videos`, {
+              method: 'POST',
+              headers: {
+                'Content-Type': 'application/json',
               },
-            );
+              body: JSON.stringify({
+                query: query,
+                chatHistory: chatHistory,
+                chatModel: {
+                  provider: chatModelProvider,
+                  model: chatModel,
+                  ...(chatModelProvider === 'custom_openai' && {
+                    customOpenAIBaseURL: customOpenAIBaseURL,
+                    customOpenAIKey: customOpenAIKey,
+                  }),
+                },
+              }),
+            });
 
             const data = await res.json();
 
@@ -182,18 +184,39 @@ const Searchvideos = ({
             open={open}
             close={() => setOpen(false)}
             slides={slides}
+            index={currentIndex}
+            on={{
+              view: ({ index }) => {
+                const previousIframe = videoRefs.current[currentIndex];
+                if (previousIframe?.contentWindow) {
+                  previousIframe.contentWindow.postMessage(
+                    '{"event":"command","func":"pauseVideo","args":""}',
+                    '*',
+                  );
+                }
+
+                setCurrentIndex(index);
+              },
+            }}
             render={{
-              slide: ({ slide }) =>
-                slide.type === 'video-slide' ? (
+              slide: ({ slide }) => {
+                const index = slides.findIndex((s) => s === slide);
+                return slide.type === 'video-slide' ? (
                   <div className="h-full w-full flex flex-row items-center justify-center">
                     <iframe
-                      src={slide.iframe_src}
+                      src={`${slide.iframe_src}${slide.iframe_src.includes('?') ? '&' : '?'}enablejsapi=1`}
+                      ref={(el) => {
+                        if (el) {
+                          videoRefs.current[index] = el;
+                        }
+                      }}
                       className="aspect-video max-h-[95vh] w-[95vw] rounded-2xl md:w-[80vw]"
                       allowFullScreen
                       allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
                     />
                   </div>
-                ) : null,
+                ) : null;
+              },
             }}
           />
         </>

src/components/Sidebar.tsx

@@ -6,7 +6,6 @@ import Link from 'next/link';
 import { useSelectedLayoutSegments } from 'next/navigation';
 import React, { useState, type ReactNode } from 'react';
 import Layout from './Layout';
-import SettingsDialog from './SettingsDialog';
 
 const VerticalIconContainer = ({ children }: { children: ReactNode }) => {
   return (
@@ -17,8 +16,6 @@ const VerticalIconContainer = ({ children }: { children: ReactNode }) => {
 const Sidebar = ({ children }: { children: React.ReactNode }) => {
   const segments = useSelectedLayoutSegments();
 
-  const [isSettingsOpen, setIsSettingsOpen] = useState(false);
-
   const navLinks = [
     {
       icon: Home,
@@ -67,15 +64,9 @@ const Sidebar = ({ children }: { children: React.ReactNode }) => {
             ))}
           </VerticalIconContainer>
 
-          <Settings
-            onClick={() => setIsSettingsOpen(!isSettingsOpen)}
-            className="cursor-pointer"
-          />
-
-          <SettingsDialog
-            isOpen={isSettingsOpen}
-            setIsOpen={setIsSettingsOpen}
-          />
+          <Link href="/settings">
+            <Settings className="cursor-pointer" />
+          </Link>
         </div>
       </div>
 

src/components/ThinkBox.tsx

@@ -0,0 +1,43 @@
+'use client';
+
+import { useState } from 'react';
+import { cn } from '@/lib/utils';
+import { ChevronDown, ChevronUp, BrainCircuit } from 'lucide-react';
+
+interface ThinkBoxProps {
+  content: string;
+}
+
+const ThinkBox = ({ content }: ThinkBoxProps) => {
+  const [isExpanded, setIsExpanded] = useState(false);
+
+  return (
+    <div className="my-4 bg-light-secondary/50 dark:bg-dark-secondary/50 rounded-xl border border-light-200 dark:border-dark-200 overflow-hidden">
+      <button
+        onClick={() => setIsExpanded(!isExpanded)}
+        className="w-full flex items-center justify-between px-4 py-1 text-black/90 dark:text-white/90 hover:bg-light-200 dark:hover:bg-dark-200 transition duration-200"
+      >
+        <div className="flex items-center space-x-2">
+          <BrainCircuit
+            size={20}
+            className="text-[#9C27B0] dark:text-[#CE93D8]"
+          />
+          <p className="font-medium text-sm">Thinking Process</p>
+        </div>
+        {isExpanded ? (
+          <ChevronUp size={18} className="text-black/70 dark:text-white/70" />
+        ) : (
+          <ChevronDown size={18} className="text-black/70 dark:text-white/70" />
+        )}
+      </button>
+
+      {isExpanded && (
+        <div className="px-4 py-3 text-black/80 dark:text-white/80 text-sm border-t border-light-200 dark:border-dark-200 bg-light-100/50 dark:bg-dark-100/50 whitespace-pre-wrap">
+          {content}
+        </div>
+      )}
+    </div>
+  );
+};
+
+export default ThinkBox;

src/components/theme/Switcher.tsx

@@ -1,8 +1,7 @@
 'use client';
 import { useTheme } from 'next-themes';
-import { SunIcon, MoonIcon, MonitorIcon } from 'lucide-react';
 import { useCallback, useEffect, useState } from 'react';
-import { Select } from '../SettingsDialog';
+import Select from '../ui/Select';
 
 type Theme = 'dark' | 'light' | 'system';
 

src/components/ui/Select.tsx

@@ -0,0 +1,28 @@
+import { cn } from '@/lib/utils';
+import { SelectHTMLAttributes } from 'react';
+
+interface SelectProps extends SelectHTMLAttributes<HTMLSelectElement> {
+  options: { value: string; label: string; disabled?: boolean }[];
+}
+
+export const Select = ({ className, options, ...restProps }: SelectProps) => {
+  return (
+    <select
+      {...restProps}
+      className={cn(
+        'bg-light-secondary dark:bg-dark-secondary px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
+        className,
+      )}
+    >
+      {options.map(({ label, value, disabled }) => {
+        return (
+          <option key={value} value={value} disabled={disabled}>
+            {label}
+          </option>
+        );
+      })}
+    </select>
+  );
+};
+
+export default Select;

src/config.ts

@@ -1,76 +0,0 @@
-import fs from 'fs';
-import path from 'path';
-import toml from '@iarna/toml';
-
-const configFileName = 'config.toml';
-
-interface Config {
-  GENERAL: {
-    PORT: number;
-    SIMILARITY_MEASURE: string;
-    KEEP_ALIVE: string;
-  };
-  API_KEYS: {
-    OPENAI: string;
-    GROQ: string;
-    ANTHROPIC: string;
-  };
-  API_ENDPOINTS: {
-    SEARXNG: string;
-    OLLAMA: string;
-  };
-}
-
-type RecursivePartial<T> = {
-  [P in keyof T]?: RecursivePartial<T[P]>;
-};
-
-const loadConfig = () =>
-  toml.parse(
-    fs.readFileSync(path.join(__dirname, `../${configFileName}`), 'utf-8'),
-  ) as any as Config;
-
-export const getPort = () => loadConfig().GENERAL.PORT;
-
-export const getSimilarityMeasure = () =>
-  loadConfig().GENERAL.SIMILARITY_MEASURE;
-
-export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
-
-export const getOpenaiApiKey = () => loadConfig().API_KEYS.OPENAI;
-
-export const getGroqApiKey = () => loadConfig().API_KEYS.GROQ;
-
-export const getAnthropicApiKey = () => loadConfig().API_KEYS.ANTHROPIC;
-
-export const getSearxngApiEndpoint = () =>
-  process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
-
-export const getOllamaApiEndpoint = () => loadConfig().API_ENDPOINTS.OLLAMA;
-
-export const updateConfig = (config: RecursivePartial<Config>) => {
-  const currentConfig = loadConfig();
-
-  for (const key in currentConfig) {
-    if (!config[key]) config[key] = {};
-
-    if (typeof currentConfig[key] === 'object' && currentConfig[key] !== null) {
-      for (const nestedKey in currentConfig[key]) {
-        if (
-          !config[key][nestedKey] &&
-          currentConfig[key][nestedKey] &&
-          config[key][nestedKey] !== ''
-        ) {
-          config[key][nestedKey] = currentConfig[key][nestedKey];
-        }
-      }
-    } else if (currentConfig[key] && config[key] !== '') {
-      config[key] = currentConfig[key];
-    }
-  }
-
-  fs.writeFileSync(
-    path.join(__dirname, `../${configFileName}`),
-    toml.stringify(config),
-  );
-};

src/lib/actions.ts

@@ -7,7 +7,7 @@ export const getSuggestions = async (chatHisory: Message[]) => {
   const customOpenAIKey = localStorage.getItem('openAIApiKey');
   const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
 
-  const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/suggestions`, {
+  const res = await fetch(`/api/suggestions`, {
     method: 'POST',
     headers: {
       'Content-Type': 'application/json',

src/lib/chains/imageSearchAgent.ts

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../lib/searxng';
+import { searchSearxng } from '../searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const imageSearchChainPrompt = `
@@ -36,6 +36,12 @@ type ImageSearchChainInput = {
   query: string;
 };
 
+interface ImageSearchResult {
+  img_src: string;
+  url: string;
+  title: string;
+}
+
 const strParser = new StringOutputParser();
 
 const createImageSearchChain = (llm: BaseChatModel) => {
@@ -52,11 +58,13 @@ const createImageSearchChain = (llm: BaseChatModel) => {
     llm,
     strParser,
     RunnableLambda.from(async (input: string) => {
+      input = input.replace(/<think>.*?<\/think>/g, '');
+
       const res = await searchSearxng(input, {
         engines: ['bing images', 'google images'],
       });
 
-      const images = [];
+      const images: ImageSearchResult[] = [];
 
       res.results.forEach((result) => {
         if (result.img_src && result.url && result.title) {

src/lib/chains/suggestionGeneratorAgent.ts

@@ -1,5 +1,5 @@
 import { RunnableSequence, RunnableMap } from '@langchain/core/runnables';
-import ListLineOutputParser from '../lib/outputParsers/listLineOutputParser';
+import ListLineOutputParser from '../outputParsers/listLineOutputParser';
 import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';

src/lib/chains/videoSearchAgent.ts

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../lib/searxng';
+import { searchSearxng } from '../searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const VideoSearchChainPrompt = `
@@ -36,6 +36,13 @@ type VideoSearchChainInput = {
   query: string;
 };
 
+interface VideoSearchResult {
+  img_src: string;
+  url: string;
+  title: string;
+  iframe_src: string;
+}
+
 const strParser = new StringOutputParser();
 
 const createVideoSearchChain = (llm: BaseChatModel) => {
@@ -52,11 +59,13 @@ const createVideoSearchChain = (llm: BaseChatModel) => {
     llm,
     strParser,
     RunnableLambda.from(async (input: string) => {
+      input = input.replace(/<think>.*?<\/think>/g, '');
+
       const res = await searchSearxng(input, {
         engines: ['youtube'],
       });
 
-      const videos = [];
+      const videos: VideoSearchResult[] = [];
 
       res.results.forEach((result) => {
         if (

src/lib/config.ts

@@ -0,0 +1,113 @@
+import fs from 'fs';
+import path from 'path';
+import toml from '@iarna/toml';
+
+const configFileName = 'config.toml';
+
+interface Config {
+  GENERAL: {
+    SIMILARITY_MEASURE: string;
+    KEEP_ALIVE: string;
+  };
+  MODELS: {
+    OPENAI: {
+      API_KEY: string;
+    };
+    GROQ: {
+      API_KEY: string;
+    };
+    ANTHROPIC: {
+      API_KEY: string;
+    };
+    GEMINI: {
+      API_KEY: string;
+    };
+    OLLAMA: {
+      API_URL: string;
+    };
+    CUSTOM_OPENAI: {
+      API_URL: string;
+      API_KEY: string;
+      MODEL_NAME: string;
+    };
+  };
+  API_ENDPOINTS: {
+    SEARXNG: string;
+  };
+}
+
+type RecursivePartial<T> = {
+  [P in keyof T]?: RecursivePartial<T[P]>;
+};
+
+const loadConfig = () =>
+  toml.parse(
+    fs.readFileSync(path.join(process.cwd(), `${configFileName}`), 'utf-8'),
+  ) as any as Config;
+
+export const getSimilarityMeasure = () =>
+  loadConfig().GENERAL.SIMILARITY_MEASURE;
+
+export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
+
+export const getOpenaiApiKey = () => loadConfig().MODELS.OPENAI.API_KEY;
+
+export const getGroqApiKey = () => loadConfig().MODELS.GROQ.API_KEY;
+
+export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
+
+export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
+
+export const getSearxngApiEndpoint = () =>
+  process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
+
+export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
+
+export const getCustomOpenaiApiKey = () =>
+  loadConfig().MODELS.CUSTOM_OPENAI.API_KEY;
+
+export const getCustomOpenaiApiUrl = () =>
+  loadConfig().MODELS.CUSTOM_OPENAI.API_URL;
+
+export const getCustomOpenaiModelName = () =>
+  loadConfig().MODELS.CUSTOM_OPENAI.MODEL_NAME;
+
+const mergeConfigs = (current: any, update: any): any => {
+  if (update === null || update === undefined) {
+    return current;
+  }
+
+  if (typeof current !== 'object' || current === null) {
+    return update;
+  }
+
+  const result = { ...current };
+
+  for (const key in update) {
+    if (Object.prototype.hasOwnProperty.call(update, key)) {
+      const updateValue = update[key];
+
+      if (
+        typeof updateValue === 'object' &&
+        updateValue !== null &&
+        typeof result[key] === 'object' &&
+        result[key] !== null
+      ) {
+        result[key] = mergeConfigs(result[key], updateValue);
+      } else if (updateValue !== undefined) {
+        result[key] = updateValue;
+      }
+    }
+  }
+
+  return result;
+};
+
+export const updateConfig = (config: RecursivePartial<Config>) => {
+  const currentConfig = loadConfig();
+  const mergedConfig = mergeConfigs(currentConfig, config);
+  fs.writeFileSync(
+    path.join(path.join(process.cwd(), `${configFileName}`)),
+    toml.stringify(mergedConfig),
+  );
+};

src/lib/db/index.ts

@@ -1,8 +1,9 @@
 import { drizzle } from 'drizzle-orm/better-sqlite3';
 import Database from 'better-sqlite3';
 import * as schema from './schema';
+import path from 'path';
 
-const sqlite = new Database('data/db.sqlite');
+const sqlite = new Database(path.join(process.cwd(), 'data/db.sqlite'));
 const db = drizzle(sqlite, {
   schema: schema,
 });

src/lib/huggingfaceTransformer.ts

@@ -28,7 +28,7 @@ export class HuggingFaceTransformersEmbeddings
 
   timeout?: number;
 
-  private pipelinePromise: Promise<any>;
+  private pipelinePromise: Promise<any> | undefined;
 
   constructor(fields?: Partial<HuggingFaceTransformersEmbeddingsParams>) {
     super(fields ?? {});

src/lib/outputParsers/lineOutputParser.ts

@@ -9,7 +9,7 @@ class LineOutputParser extends BaseOutputParser<string> {
 
   constructor(args?: LineOutputParserArgs) {
     super();
-    this.key = args.key ?? this.key;
+    this.key = args?.key ?? this.key;
   }
 
   static lc_name() {
@@ -19,6 +19,8 @@ class LineOutputParser extends BaseOutputParser<string> {
   lc_namespace = ['langchain', 'output_parsers', 'line_output_parser'];
 
   async parse(text: string): Promise<string> {
+    text = text.trim() || '';
+
     const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
     const startKeyIndex = text.indexOf(`<${this.key}>`);
     const endKeyIndex = text.indexOf(`</${this.key}>`);

src/lib/outputParsers/listLineOutputParser.ts

@@ -9,7 +9,7 @@ class LineListOutputParser extends BaseOutputParser<string[]> {
 
   constructor(args?: LineListOutputParserArgs) {
     super();
-    this.key = args.key ?? this.key;
+    this.key = args?.key ?? this.key;
   }
 
   static lc_name() {
@@ -19,11 +19,13 @@ class LineListOutputParser extends BaseOutputParser<string[]> {
   lc_namespace = ['langchain', 'output_parsers', 'line_list_output_parser'];
 
   async parse(text: string): Promise<string[]> {
+    text = text.trim() || '';
+
     const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
     const startKeyIndex = text.indexOf(`<${this.key}>`);
     const endKeyIndex = text.indexOf(`</${this.key}>`);
 
-    if (startKeyIndex === -1 && endKeyIndex === -1) {
+    if (startKeyIndex === -1 || endKeyIndex === -1) {
       return [];
     }
 

src/lib/prompts/academicSearch.ts

@@ -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}.
+`;

src/lib/prompts/redditSearch.ts

@@ -0,0 +1,65 @@
+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}.
+`;

src/lib/prompts/webSearch.ts

@@ -0,0 +1,106 @@
+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}.
+`;

src/lib/prompts/wolframAlpha.ts

@@ -0,0 +1,65 @@
+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}.
+`;

src/lib/prompts/youtubeSearch.ts

@@ -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}.
+`;

src/lib/providers/anthropic.ts

@@ -1,6 +1,38 @@
 import { ChatAnthropic } from '@langchain/anthropic';
-import { getAnthropicApiKey } from '../../config';
-import logger from '../../utils/logger';
+import { ChatModel } from '.';
+import { getAnthropicApiKey } from '../config';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+
+const anthropicChatModels: Record<string, string>[] = [
+  {
+    displayName: 'Claude 3.7 Sonnet',
+    key: 'claude-3-7-sonnet-20250219',
+  },
+  {
+    displayName: 'Claude 3.5 Haiku',
+    key: 'claude-3-5-haiku-20241022',
+  },
+  {
+    displayName: 'Claude 3.5 Sonnet v2',
+    key: 'claude-3-5-sonnet-20241022',
+  },
+  {
+    displayName: 'Claude 3.5 Sonnet',
+    key: 'claude-3-5-sonnet-20240620',
+  },
+  {
+    displayName: 'Claude 3 Opus',
+    key: 'claude-3-opus-20240229',
+  },
+  {
+    displayName: 'Claude 3 Sonnet',
+    key: 'claude-3-sonnet-20240229',
+  },
+  {
+    displayName: 'Claude 3 Haiku',
+    key: 'claude-3-haiku-20240307',
+  },
+];
 
 export const loadAnthropicChatModels = async () => {
   const anthropicApiKey = getAnthropicApiKey();
@@ -8,44 +40,22 @@ export const loadAnthropicChatModels = async () => {
   if (!anthropicApiKey) return {};
 
   try {
-    const chatModels = {
-      'claude-3-5-sonnet-20240620': {
-        displayName: 'Claude 3.5 Sonnet',
+    const chatModels: Record<string, ChatModel> = {};
+
+    anthropicChatModels.forEach((model) => {
+      chatModels[model.key] = {
+        displayName: model.displayName,
         model: new ChatAnthropic({
+          apiKey: anthropicApiKey,
+          modelName: model.key,
           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',
-        }),
-      },
-    };
+        }) as unknown as BaseChatModel,
+      };
+    });
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading Anthropic models: ${err}`);
+    console.error(`Error loading Anthropic models: ${err}`);
     return {};
   }
 };

src/lib/providers/gemini.ts

@@ -0,0 +1,97 @@
+import {
+  ChatGoogleGenerativeAI,
+  GoogleGenerativeAIEmbeddings,
+} from '@langchain/google-genai';
+import { getGeminiApiKey } from '../config';
+import { ChatModel, EmbeddingModel } from '.';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { Embeddings } from '@langchain/core/embeddings';
+
+const geminiChatModels: Record<string, string>[] = [
+  {
+    displayName: 'Gemini 2.5 Pro Experimental',
+    key: 'gemini-2.5-pro-exp-03-25',
+  },
+  {
+    displayName: 'Gemini 2.0 Flash',
+    key: 'gemini-2.0-flash',
+  },
+  {
+    displayName: 'Gemini 2.0 Flash-Lite',
+    key: 'gemini-2.0-flash-lite',
+  },
+  {
+    displayName: 'Gemini 2.0 Flash Thinking Experimental',
+    key: 'gemini-2.0-flash-thinking-exp-01-21',
+  },
+  {
+    displayName: 'Gemini 1.5 Flash',
+    key: 'gemini-1.5-flash',
+  },
+  {
+    displayName: 'Gemini 1.5 Flash-8B',
+    key: 'gemini-1.5-flash-8b',
+  },
+  {
+    displayName: 'Gemini 1.5 Pro',
+    key: 'gemini-1.5-pro',
+  },
+];
+
+const geminiEmbeddingModels: Record<string, string>[] = [
+  {
+    displayName: 'Gemini Embedding',
+    key: 'gemini-embedding-exp',
+  },
+];
+
+export const loadGeminiChatModels = async () => {
+  const geminiApiKey = getGeminiApiKey();
+
+  if (!geminiApiKey) return {};
+
+  try {
+    const chatModels: Record<string, ChatModel> = {};
+
+    geminiChatModels.forEach((model) => {
+      chatModels[model.key] = {
+        displayName: model.displayName,
+        model: new ChatGoogleGenerativeAI({
+          apiKey: geminiApiKey,
+          modelName: model.key,
+          temperature: 0.7,
+        }) as unknown as BaseChatModel,
+      };
+    });
+
+    return chatModels;
+  } catch (err) {
+    console.error(`Error loading Gemini models: ${err}`);
+    return {};
+  }
+};
+
+export const loadGeminiEmbeddingModels = async () => {
+  const geminiApiKey = getGeminiApiKey();
+
+  if (!geminiApiKey) return {};
+
+  try {
+    const embeddingModels: Record<string, EmbeddingModel> = {};
+
+    geminiEmbeddingModels.forEach((model) => {
+      embeddingModels[model.key] = {
+        displayName: model.displayName,
+        model: new GoogleGenerativeAIEmbeddings({
+          apiKey: geminiApiKey,
+          modelName: model.key,
+        }) as unknown as Embeddings,
+      };
+    });
+
+    return embeddingModels;
+  } catch (err) {
+    console.error(`Error loading OpenAI embeddings models: ${err}`);
+    return {};
+  }
+};

src/lib/providers/groq.ts

@@ -1,6 +1,78 @@
 import { ChatOpenAI } from '@langchain/openai';
-import { getGroqApiKey } from '../../config';
-import logger from '../../utils/logger';
+import { getGroqApiKey } from '../config';
+import { ChatModel } from '.';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+
+const groqChatModels: Record<string, string>[] = [
+  {
+    displayName: 'Gemma2 9B IT',
+    key: 'gemma2-9b-it',
+  },
+  {
+    displayName: 'Llama 3.3 70B Versatile',
+    key: 'llama-3.3-70b-versatile',
+  },
+  {
+    displayName: 'Llama 3.1 8B Instant',
+    key: 'llama-3.1-8b-instant',
+  },
+  {
+    displayName: 'Llama3 70B 8192',
+    key: 'llama3-70b-8192',
+  },
+  {
+    displayName: 'Llama3 8B 8192',
+    key: 'llama3-8b-8192',
+  },
+  {
+    displayName: 'Mixtral 8x7B 32768',
+    key: 'mixtral-8x7b-32768',
+  },
+  {
+    displayName: 'Qwen QWQ 32B (Preview)',
+    key: 'qwen-qwq-32b',
+  },
+  {
+    displayName: 'Mistral Saba 24B (Preview)',
+    key: 'mistral-saba-24b',
+  },
+  {
+    displayName: 'Qwen 2.5 Coder 32B (Preview)',
+    key: 'qwen-2.5-coder-32b',
+  },
+  {
+    displayName: 'Qwen 2.5 32B (Preview)',
+    key: 'qwen-2.5-32b',
+  },
+  {
+    displayName: 'DeepSeek R1 Distill Qwen 32B (Preview)',
+    key: 'deepseek-r1-distill-qwen-32b',
+  },
+  {
+    displayName: 'DeepSeek R1 Distill Llama 70B (Preview)',
+    key: 'deepseek-r1-distill-llama-70b',
+  },
+  {
+    displayName: 'Llama 3.3 70B SpecDec (Preview)',
+    key: 'llama-3.3-70b-specdec',
+  },
+  {
+    displayName: 'Llama 3.2 1B Preview (Preview)',
+    key: 'llama-3.2-1b-preview',
+  },
+  {
+    displayName: 'Llama 3.2 3B Preview (Preview)',
+    key: 'llama-3.2-3b-preview',
+  },
+  {
+    displayName: 'Llama 3.2 11B Vision Preview (Preview)',
+    key: 'llama-3.2-11b-vision-preview',
+  },
+  {
+    displayName: 'Llama 3.2 90B Vision Preview (Preview)',
+    key: 'llama-3.2-90b-vision-preview',
+  },
+];
 
 export const loadGroqChatModels = async () => {
   const groqApiKey = getGroqApiKey();
@@ -8,142 +80,25 @@ export const loadGroqChatModels = async () => {
   if (!groqApiKey) return {};
 
   try {
-    const chatModels = {
-      'llama-3.2-3b-preview': {
-        displayName: 'Llama 3.2 3B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-3b-preview',
-            temperature: 0.7,
-          },
-          {
+    const chatModels: Record<string, ChatModel> = {};
+
+    groqChatModels.forEach((model) => {
+      chatModels[model.key] = {
+        displayName: model.displayName,
+        model: new ChatOpenAI({
+          openAIApiKey: groqApiKey,
+          modelName: model.key,
+          temperature: 0.7,
+          configuration: {
             baseURL: 'https://api.groq.com/openai/v1',
           },
-        ),
-      },
-      'llama-3.2-11b-vision-preview': {
-        displayName: 'Llama 3.2 11B Vision',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-11b-vision-preview',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama-3.2-90b-vision-preview': {
-        displayName: 'Llama 3.2 90B Vision',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-90b-vision-preview',
-            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',
-          },
-        ),
-      },
-    };
+        }) as unknown as BaseChatModel,
+      };
+    });
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading Groq models: ${err}`);
+    console.error(`Error loading Groq models: ${err}`);
     return {};
   }
 };

src/lib/providers/index.ts

@@ -1,24 +1,51 @@
+import { Embeddings } from '@langchain/core/embeddings';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { loadOpenAIChatModels, loadOpenAIEmbeddingModels } from './openai';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '../config';
+import { ChatOpenAI } from '@langchain/openai';
+import { loadOllamaChatModels, loadOllamaEmbeddingModels } from './ollama';
 import { loadGroqChatModels } from './groq';
-import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
-import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
 import { loadAnthropicChatModels } from './anthropic';
+import { loadGeminiChatModels, loadGeminiEmbeddingModels } from './gemini';
 import { loadTransformersEmbeddingsModels } from './transformers';
 
-const chatModelProviders = {
+export interface ChatModel {
+  displayName: string;
+  model: BaseChatModel;
+}
+
+export interface EmbeddingModel {
+  displayName: string;
+  model: Embeddings;
+}
+
+export const chatModelProviders: Record<
+  string,
+  () => Promise<Record<string, ChatModel>>
+> = {
   openai: loadOpenAIChatModels,
-  groq: loadGroqChatModels,
   ollama: loadOllamaChatModels,
+  groq: loadGroqChatModels,
   anthropic: loadAnthropicChatModels,
+  gemini: loadGeminiChatModels,
 };
 
-const embeddingModelProviders = {
-  openai: loadOpenAIEmbeddingsModels,
-  local: loadTransformersEmbeddingsModels,
-  ollama: loadOllamaEmbeddingsModels,
+export const embeddingModelProviders: Record<
+  string,
+  () => Promise<Record<string, EmbeddingModel>>
+> = {
+  openai: loadOpenAIEmbeddingModels,
+  ollama: loadOllamaEmbeddingModels,
+  gemini: loadGeminiEmbeddingModels,
+  transformers: loadTransformersEmbeddingsModels,
 };
 
 export const getAvailableChatModelProviders = async () => {
-  const models = {};
+  const models: Record<string, Record<string, ChatModel>> = {};
 
   for (const provider in chatModelProviders) {
     const providerModels = await chatModelProviders[provider]();
@@ -27,13 +54,33 @@ export const getAvailableChatModelProviders = async () => {
     }
   }
 
-  models['custom_openai'] = {};
+  const customOpenAiApiKey = getCustomOpenaiApiKey();
+  const customOpenAiApiUrl = getCustomOpenaiApiUrl();
+  const customOpenAiModelName = getCustomOpenaiModelName();
+
+  models['custom_openai'] = {
+    ...(customOpenAiApiKey && customOpenAiApiUrl && customOpenAiModelName
+      ? {
+          [customOpenAiModelName]: {
+            displayName: customOpenAiModelName,
+            model: new ChatOpenAI({
+              openAIApiKey: customOpenAiApiKey,
+              modelName: customOpenAiModelName,
+              temperature: 0.7,
+              configuration: {
+                baseURL: customOpenAiApiUrl,
+              },
+            }) as unknown as BaseChatModel,
+          },
+        }
+      : {}),
+  };
 
   return models;
 };
 
 export const getAvailableEmbeddingModelProviders = async () => {
-  const models = {};
+  const models: Record<string, Record<string, EmbeddingModel>> = {};
 
   for (const provider in embeddingModelProviders) {
     const providerModels = await embeddingModelProviders[provider]();

src/lib/providers/ollama.ts

@@ -1,73 +1,73 @@
-import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
-import { getKeepAlive, getOllamaApiEndpoint } from '../../config';
-import logger from '../../utils/logger';
+import axios from 'axios';
+import { getKeepAlive, getOllamaApiEndpoint } from '../config';
+import { ChatModel, EmbeddingModel } from '.';
 import { ChatOllama } from '@langchain/community/chat_models/ollama';
+import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
 
 export const loadOllamaChatModels = async () => {
-  const ollamaEndpoint = getOllamaApiEndpoint();
-  const keepAlive = getKeepAlive();
+  const ollamaApiEndpoint = getOllamaApiEndpoint();
 
-  if (!ollamaEndpoint) return {};
+  if (!ollamaApiEndpoint) return {};
 
   try {
-    const response = await fetch(`${ollamaEndpoint}/api/tags`, {
+    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models: ollamaModels } = (await response.json()) as any;
+    const { models } = res.data;
 
-    const chatModels = ollamaModels.reduce((acc, model) => {
-      acc[model.model] = {
+    const chatModels: Record<string, ChatModel> = {};
+
+    models.forEach((model: any) => {
+      chatModels[model.model] = {
         displayName: model.name,
         model: new ChatOllama({
-          baseUrl: ollamaEndpoint,
+          baseUrl: ollamaApiEndpoint,
           model: model.model,
           temperature: 0.7,
-          keepAlive: keepAlive,
+          keepAlive: getKeepAlive(),
         }),
       };
-
-      return acc;
-    }, {});
+    });
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading Ollama models: ${err}`);
+    console.error(`Error loading Ollama models: ${err}`);
     return {};
   }
 };
 
-export const loadOllamaEmbeddingsModels = async () => {
-  const ollamaEndpoint = getOllamaApiEndpoint();
+export const loadOllamaEmbeddingModels = async () => {
+  const ollamaApiEndpoint = getOllamaApiEndpoint();
 
-  if (!ollamaEndpoint) return {};
+  if (!ollamaApiEndpoint) return {};
 
   try {
-    const response = await fetch(`${ollamaEndpoint}/api/tags`, {
+    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models: ollamaModels } = (await response.json()) as any;
+    const { models } = res.data;
 
-    const embeddingsModels = ollamaModels.reduce((acc, model) => {
-      acc[model.model] = {
+    const embeddingModels: Record<string, EmbeddingModel> = {};
+
+    models.forEach((model: any) => {
+      embeddingModels[model.model] = {
         displayName: model.name,
         model: new OllamaEmbeddings({
-          baseUrl: ollamaEndpoint,
+          baseUrl: ollamaApiEndpoint,
           model: model.model,
         }),
       };
+    });
 
-      return acc;
-    }, {});
-
-    return embeddingsModels;
+    return embeddingModels;
   } catch (err) {
-    logger.error(`Error loading Ollama embeddings model: ${err}`);
+    console.error(`Error loading Ollama embeddings models: ${err}`);
     return {};
   }
 };

src/lib/providers/openai.ts

@@ -1,89 +1,90 @@
 import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
-import { getOpenaiApiKey } from '../../config';
-import logger from '../../utils/logger';
+import { getOpenaiApiKey } from '../config';
+import { ChatModel, EmbeddingModel } from '.';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { Embeddings } from '@langchain/core/embeddings';
+
+const openaiChatModels: Record<string, string>[] = [
+  {
+    displayName: 'GPT-3.5 Turbo',
+    key: 'gpt-3.5-turbo',
+  },
+  {
+    displayName: 'GPT-4',
+    key: 'gpt-4',
+  },
+  {
+    displayName: 'GPT-4 turbo',
+    key: 'gpt-4-turbo',
+  },
+  {
+    displayName: 'GPT-4 omni',
+    key: 'gpt-4o',
+  },
+  {
+    displayName: 'GPT-4 omni mini',
+    key: 'gpt-4o-mini',
+  },
+];
+
+const openaiEmbeddingModels: Record<string, string>[] = [
+  {
+    displayName: 'Text Embedding 3 Small',
+    key: 'text-embedding-3-small',
+  },
+  {
+    displayName: 'Text Embedding 3 Large',
+    key: 'text-embedding-3-large',
+  },
+];
 
 export const loadOpenAIChatModels = async () => {
-  const openAIApiKey = getOpenaiApiKey();
+  const openaiApiKey = getOpenaiApiKey();
 
-  if (!openAIApiKey) return {};
+  if (!openaiApiKey) return {};
 
   try {
-    const chatModels = {
-      'gpt-3.5-turbo': {
-        displayName: 'GPT-3.5 Turbo',
+    const chatModels: Record<string, ChatModel> = {};
+
+    openaiChatModels.forEach((model) => {
+      chatModels[model.key] = {
+        displayName: model.displayName,
         model: new ChatOpenAI({
-          openAIApiKey,
-          modelName: 'gpt-3.5-turbo',
+          openAIApiKey: openaiApiKey,
+          modelName: model.key,
           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,
-        }),
-      },
-    };
+        }) as unknown as BaseChatModel,
+      };
+    });
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading OpenAI models: ${err}`);
+    console.error(`Error loading OpenAI models: ${err}`);
     return {};
   }
 };
 
-export const loadOpenAIEmbeddingsModels = async () => {
-  const openAIApiKey = getOpenaiApiKey();
+export const loadOpenAIEmbeddingModels = async () => {
+  const openaiApiKey = getOpenaiApiKey();
 
-  if (!openAIApiKey) return {};
+  if (!openaiApiKey) return {};
 
   try {
-    const embeddingModels = {
-      'text-embedding-3-small': {
-        displayName: 'Text Embedding 3 Small',
+    const embeddingModels: Record<string, EmbeddingModel> = {};
+
+    openaiEmbeddingModels.forEach((model) => {
+      embeddingModels[model.key] = {
+        displayName: model.displayName,
         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',
-        }),
-      },
-    };
+          openAIApiKey: openaiApiKey,
+          modelName: model.key,
+        }) as unknown as Embeddings,
+      };
+    });
 
     return embeddingModels;
   } catch (err) {
-    logger.error(`Error loading OpenAI embeddings model: ${err}`);
+    console.error(`Error loading OpenAI embeddings models: ${err}`);
     return {};
   }
 };

src/lib/providers/transformers.ts

@@ -1,4 +1,3 @@
-import logger from '../../utils/logger';
 import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
 
 export const loadTransformersEmbeddingsModels = async () => {
@@ -26,7 +25,7 @@ export const loadTransformersEmbeddingsModels = async () => {
 
     return embeddingModels;
   } catch (err) {
-    logger.error(`Error loading Transformers embeddings model: ${err}`);
+    console.error(`Error loading Transformers embeddings model: ${err}`);
     return {};
   }
 };

src/lib/search/index.ts

@@ -0,0 +1,59 @@
+import MetaSearchAgent from '@/lib/search/metaSearchAgent';
+import prompts from '../prompts';
+
+export const searchHandlers: Record<string, MetaSearchAgent> = {
+  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,
+  }),
+};

src/lib/search/metaSearchAgent.ts

@@ -13,18 +13,17 @@ import {
 } 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 LineListOutputParser from '../outputParsers/listLineOutputParser';
+import LineOutputParser from '../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 { searchSearxng } from '../searxng';
+import path from 'node:path';
+import fs from 'node: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: (
@@ -90,7 +89,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
             question = 'summarize';
           }
 
-          let docs = [];
+          let docs: Document[] = [];
 
           const linkDocs = await getDocumentsFromLinks({ links });
 
@@ -203,6 +202,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
           return { query: question, docs: docs };
         } else {
+          question = question.replace(/<think>.*?<\/think>/g, '');
+
           const res = await searchSearxng(question, {
             language: 'en',
             engines: this.config.activeEngines,
@@ -211,7 +212,11 @@ class MetaSearchAgent implements MetaSearchAgentType {
           const documents = res.results.map(
             (result) =>
               new Document({
-                pageContent: result.content,
+                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,
@@ -236,6 +241,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
       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,
@@ -306,7 +312,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
         const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
 
         const fileSimilaritySearchObject = content.contents.map(
-          (c: string, i) => {
+          (c: string, i: number) => {
             return {
               fileName: content.title,
               content: c,
@@ -409,16 +415,21 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
       return sortedDocs;
     }
+
+    return [];
   }
 
   private processDocs(docs: Document[]) {
     return docs
-      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
+      .map(
+        (_, index) =>
+          `${index + 1}. ${docs[index].metadata.title} ${docs[index].pageContent}`,
+      )
       .join('\n');
   }
 
   private async handleStream(
-    stream: IterableReadableStream<StreamEvent>,
+    stream: AsyncGenerator<StreamEvent, any, any>,
     emitter: eventEmitter,
   ) {
     for await (const event of stream) {

src/lib/searxng.ts

@@ -1,5 +1,5 @@
 import axios from 'axios';
-import { getSearxngApiEndpoint } from '../config';
+import { getSearxngApiEndpoint } from './config';
 
 interface SearxngSearchOptions {
   categories?: string[];
@@ -30,11 +30,12 @@ export const searchSearxng = async (
 
   if (opts) {
     Object.keys(opts).forEach((key) => {
-      if (Array.isArray(opts[key])) {
-        url.searchParams.append(key, opts[key].join(','));
+      const value = opts[key as keyof SearxngSearchOptions];
+      if (Array.isArray(value)) {
+        url.searchParams.append(key, value.join(','));
         return;
       }
-      url.searchParams.append(key, opts[key]);
+      url.searchParams.append(key, value as string);
     });
   }
 

src/lib/types/compute-dot.d.ts

@@ -0,0 +1,5 @@
+declare function computeDot(vectorA: number[], vectorB: number[]): number;
+
+declare module 'compute-dot' {
+  export default computeDot;
+}

src/lib/utils/computeSimilarity.ts

@@ -6,7 +6,7 @@ const computeSimilarity = (x: number[], y: number[]): number => {
   const similarityMeasure = getSimilarityMeasure();
 
   if (similarityMeasure === 'cosine') {
-    return cosineSimilarity(x, y);
+    return cosineSimilarity(x, y) as number;
   } else if (similarityMeasure === 'dot') {
     return dot(x, y);
   }

src/lib/utils/documents.ts

@@ -3,7 +3,6 @@ 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 './logger';
 
 export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
   const splitter = new RecursiveCharacterTextSplitter();
@@ -79,12 +78,13 @@ export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
 
         docs.push(...linkDocs);
       } catch (err) {
-        logger.error(
-          `Error at generating documents from links: ${err.message}`,
+        console.error(
+          'An error occurred while getting documents from links: ',
+          err,
         );
         docs.push(
           new Document({
-            pageContent: `Failed to retrieve content from the link: ${err.message}`,
+            pageContent: `Failed to retrieve content from the link: ${err}`,
             metadata: {
               title: 'Failed to retrieve content',
               url: link,