feat(package): bump version

feat(providers): add gpt-4-turbo provider

.env.example

@@ -1,5 +0,0 @@
-PORT=3001
-OLLAMA_URL=http://localhost:11434 # url of the ollama server
-SIMILARITY_MEASURE=cosine # cosine or dot
-SEARXNG_API_URL= # no need to fill this if using docker
-MODEL_NAME=llama2

.github/ISSUE_TEMPLATE/bug_report.md

@@ -4,7 +4,6 @@ about: Create an issue to help us fix bugs
 title: ''
 labels: bug
 assignees: ''
-
 ---
 
 **Describe the bug**
@@ -12,6 +11,7 @@ A clear and concise description of what the bug is.
 
 **To Reproduce**
 Steps to reproduce the behavior:
+
 1. Go to '...'
 2. Click on '....'
 3. Scroll down to '....'

.github/ISSUE_TEMPLATE/custom.md

@@ -4,7 +4,4 @@ about: Describe this issue template's purpose here.
 title: ''
 labels: ''
 assignees: ''
-
 ---
-
-

.github/ISSUE_TEMPLATE/feature_request.md

@@ -4,7 +4,6 @@ about: Suggest an idea for this project
 title: ''
 labels: enhancement
 assignees: ''
-
 ---
 
 **Is your feature request related to a problem? Please describe.**

.gitignore

@@ -19,6 +19,9 @@ yarn-error.log
 .env.test.local
 .env.production.local
 
+# Config files
+config.toml
+
 # Log files
 logs/
 *.log

.prettierignore

@@ -0,0 +1,38 @@
+# Ignore all files in the node_modules directory
+node_modules
+
+# Ignore all files in the .next directory (Next.js build output)
+.next
+
+# Ignore all files in the .out directory (TypeScript build output)
+.out
+
+# Ignore all files in the .cache directory (Prettier cache)
+.cache
+
+# Ignore all files in the .vscode directory (Visual Studio Code settings)
+.vscode
+
+# Ignore all files in the .idea directory (IntelliJ IDEA settings)
+.idea
+
+# Ignore all files in the dist directory (build output)
+dist
+
+# Ignore all files in the build directory (build output)
+build
+
+# Ignore all files in the coverage directory (test coverage reports)
+coverage
+
+# Ignore all files with the .log extension
+*.log
+
+# Ignore all files with the .tmp extension
+*.tmp
+
+# Ignore all files with the .swp extension
+*.swp
+
+# Ignore all files with the .DS_Store extension (macOS specific)
+.DS_Store

CONTRIBUTING.md

@@ -9,16 +9,14 @@ Perplexica's design consists of two main domains:
 - **Frontend (`ui` directory)**: This is a Next.js application holding all user interface components. It's a self-contained environment that manages everything the user interacts with.
 - **Backend (root and `src` directory)**: The backend logic is situated in the `src` folder, but the root directory holds the main `package.json` for backend dependency management.
 
-Both the root directory (for backend configurations outside `src`) and the `ui` folder come with an `.env.example` file. These are templates for environment variables that you need to set up manually for the application to run correctly.
-
 ## 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 `.env.example` file.
-2. Rename it to `.env` and fill in the necessary environment variables specific to the 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. Use `npm run dev` to start the backend in development mode.
 

README.md

@@ -10,34 +10,38 @@
 - [Installation](#installation)
   - [Getting Started with Docker (Recommended)](#getting-started-with-docker-recommended)
   - [Non-Docker Installation](#non-docker-installation)
+- [One-Click Deployment](#one-click-deployment)
 - [Upcoming Features](#upcoming-features)
 - [Support Us](#support-us)
 - [Contribution](#contribution)
-- [Acknowledgements](#acknowledgements)
+- [Help and Support](#help-and-support)
 
 ## Overview
 
 Perplexica is an open-source AI-powered searching tool or an AI-powered search engine that goes deep into the internet to find answers. Inspired by Perplexity AI, it's an open-source option that not just searches the web but understands your questions. It uses advanced machine learning algorithms like similarity searching and embeddings to refine results and provides clear answers with sources cited.
 
+Using SearxNG to stay current and fully open source, Perplexica ensures you always get the most up-to-date information without compromising your privacy.
+
+Want to know more about its architecture and how it works? You can read it [here](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/architecture/README.md).
+
 ## Preview
 
 ![video-preview](.assets/perplexica-preview.gif)
 
 ## Features
 
+- **Local LLMs**: You can make use local LLMs such as Llama3 and Mixtral using Ollama.
 - **Two Main Modes:**
   - **Copilot Mode:** (In development) Boosts search by generating different queries to find more relevant internet sources. Like normal search instead of just using the context by SearxNG, it visits the top matches and tries to find relevant sources to the user's query directly from the page.
   - **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:
-
-  1. **All Mode:** Searches the entire web to find the best results.
-  2. **Writing Assistant Mode:** Helpful for writing tasks that does not require searching the web.
-  3. **Academic Search Mode:** Finds articles and papers, ideal for academic research.
-  4. **YouTube Search Mode:** Finds YouTube videos based on the search query.
-  5. **Wolfram Alpha Search Mode:** Answers queries that need calculations or data analysis using Wolfram Alpha.
-  6. **Reddit Search Mode:** Searches Reddit for discussions and opinions related to the query.
-
-- **Current Information:** Some search tools might give you outdated info because they use data from crawling bots and convert them into embeddings and store them in a index (its like converting the web into embeddings which is quite expensive.). Unlike them, Perplexica uses SearxNG, a metasearch engine to get the results and rerank and get the most relevent source out of it, ensuring you always get the latest information without the overhead of daily data updates.
+  - **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.
+  - **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.
+  - **Reddit Search Mode:** Searches Reddit for discussions and opinions related to the query.
+- **Current Information:** Some search tools might give you outdated info because they use data from crawling bots and convert them into embeddings and store them in a index. Unlike them, Perplexica uses SearxNG, a metasearch engine to get the results and rerank and get the most relevant source out of it, ensuring you always get the latest information without the overhead of daily data updates.
 
 It has many more features like image and video search. Some of the planned features are mentioned in [upcoming features](#upcoming-features).
 
@@ -51,41 +55,50 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
 2. Clone the Perplexica repository:
 
    ```bash
-   git clone -b feat/ollama-support https://github.com/ItzCrazyKns/Perplexica.git
+   git clone https://github.com/ItzCrazyKns/Perplexica.git
    ```
 
 3. After cloning, navigate to the directory containing the project files.
 
-4. Rename the `.env.example` file to `.env`. For Docker setups, you need only fill in the following fields:
+4. Rename the `sample.config.toml` file to `config.toml`. For Docker setups, you need only fill in the following fields:
 
-   - `OLLAMA_URL` (It should be the URL where Ollama is running; it is also filled by default but you need to replace it if your Ollama URL is different.)
-   - `MODEL_NAME` (This is filled by default; you can change it if you want to use a different model.)
-   - `SIMILARITY_MEASURE` (This is filled by default; you can leave it as is if you are unsure about it.)
+   - `OPENAI`: Your OpenAI API key. **You only need to fill this if you wish to use OpenAI's models**.
+   - `OLLAMA`: Your Ollama API URL. You should enter it as `http://host.docker.internal:PORT_NUMBER`. If you installed Ollama on port 11434, use `http://host.docker.internal:11434`. For other ports, adjust accordingly. **You need to fill this if you wish to use Ollama's models instead of OpenAI's**.
+   - `GROQ`: Your Groq API key. **You only need to fill this if you wish to use Groq's hosted models**
+
+     **Note**: You can change these after starting Perplexica from the settings dialog.
+
+   - `SIMILARITY_MEASURE`: The similarity measure to use (This is filled by default; you can leave it as is if you are unsure about it.)
 
 5. Ensure you are in the directory containing the `docker-compose.yaml` file and execute:
 
    ```bash
-   docker compose up
+   docker compose up -d
    ```
 
 6. Wait a few minutes for the setup to complete. You can access Perplexica at http://localhost:3000 in your web browser.
 
-**Note**: Once the terminal is stopped, Perplexica will also stop. To restart it, you will need to open Docker Desktop and run Perplexica again.
+**Note**: After the containers are built, you can start Perplexica directly from Docker without having to open a terminal.
 
 ### Non-Docker Installation
 
-For setups without Docker:
-
-1. Follow the initial steps to clone the repository and rename the `.env.example` file to `.env` in the root directory. You will need to fill in all the fields in this file.
-2. Additionally, rename the `.env.example` file to `.env` in the `ui` folder and complete all fields.
-3. The non-Docker setup requires manual configuration of both the backend and frontend.
+1. Clone the repository and rename the `sample.config.toml` file to `config.toml` in the root directory. Ensure you complete all required fields in this file.
+2. Rename the `.env.example` file to `.env` in the `ui` folder and fill in all necessary fields.
+3. After populating the configuration and environment files, run `npm i` in both the `ui` folder and the root directory.
+4. Install the dependencies and then execute `npm run build` in both the `ui` folder and the root directory.
+5. Finally, start both the frontend and the backend by running `npm run start` in both the `ui` folder and the root directory.
 
 **Note**: Using Docker is recommended as it simplifies the setup process, especially for managing environment variables and dependencies.
 
+## One-Click Deployment
+
+[![Deploy to RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploylobe.svg)](https://repocloud.io/details/?app_id=267)
+
 ## Upcoming Features
 
 - [ ] Finalizing Copilot Mode
-- [ ] Adding support for multiple local LLMs and LLM providers such as Anthropic, Google, etc.
+- [x] Add settings page
+- [x] Adding support for local LLMs
 - [ ] Adding Discover and History Saving features
 - [x] Introducing various Focus Modes
 
@@ -97,8 +110,8 @@ If you find Perplexica useful, consider giving us a star on GitHub. This helps m
 
 Perplexica is built on the idea that AI and large language models should be easy for everyone to use. If you find bugs or have ideas, please share them in via GitHub Issues. For more information on contributing to Perplexica you can read the [CONTRIBUTING.md](CONTRIBUTING.md) file to learn more about Perplexica and how you can contribute to it.
 
-## Acknowledgements
+## Help and Support
 
-Inspired by Perplexity AI, Perplexica aims to provide a similar service but always up-to-date and fully open source, thanks to SearxNG.
+If you have any questions or feedback, please feel free to reach out to us. You can create an issue on GitHub or join our Discord server. There, you can connect with other users, share your experiences and reviews, and receive more personalized help. [Click here](https://discord.gg/EFwsmQDgAu) to join the Discord server. To discuss matters outside of regular support, feel free to contact me on Discord at `itzcrazykns`.
 
-If you have any queries you can reach me via my Discord - `itzcrazykns`. Thanks for checking out Perplexica.
+Thank you for exploring Perplexica, the AI-powered search engine designed to enhance your search experience. We are constantly working to improve Perplexica and expand its capabilities. We value your feedback and contributions which help us make Perplexica even better. Don't forget to check back for updates and new features!

backend.dockerfile

@@ -1,16 +1,17 @@
 FROM node:alpine
 
 ARG SEARXNG_API_URL
-ENV SEARXNG_API_URL=${SEARXNG_API_URL}
 
 WORKDIR /home/perplexica
 
 COPY src /home/perplexica/src
 COPY tsconfig.json /home/perplexica/
-COPY .env /home/perplexica/
+COPY config.toml /home/perplexica/
 COPY package.json /home/perplexica/
 COPY yarn.lock /home/perplexica/
 
+RUN sed -i "s|SEARXNG = \".*\"|SEARXNG = \"${SEARXNG_API_URL}\"|g" /home/perplexica/config.toml
+
 RUN yarn install
 RUN yarn build
 

docker-compose.yaml

@@ -9,6 +9,7 @@ services:
       - 4000:8080
     networks:
       - perplexica-network
+
   perplexica-backend:
     build:
       context: .

docs/architecture/README.md

@@ -0,0 +1,11 @@
+## Perplexica's Architecture
+
+Perplexica's architecture consists of the following key components:
+
+1. **User Interface**: A web-based interface that allows users to interact with Perplexica for searching images, videos, and much more.
+2. **Agent/Chains**: These components predict Perplexica's next actions, understand user queries, and decide whether a web search is necessary.
+3. **SearXNG**: A metadata search engine used by Perplexica to search the web for sources.
+4. **LLMs (Large Language Models)**: Utilized by agents and chains for tasks like understanding content, writing responses, and citing sources. Examples include Claude, GPTs, etc.
+5. **Embedding Models**: To improve the accuracy of search results, embedding models re-rank the results using similarity search algorithms such as cosine similarity and dot product distance.
+
+For a more detailed explanation of how these components work together, see [WORKING.md](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/architecture/WORKING.md).

docs/architecture/WORKING.md

@@ -0,0 +1,19 @@
+## 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.
+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 or 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?
+
+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 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.

package.json

@@ -1,12 +1,12 @@
 {
   "name": "perplexica-backend",
-  "version": "1.0.0",
+  "version": "1.3.3",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {
-    "start": "node --env-file=.env dist/app.js",
+    "start": "node dist/app.js",
     "build": "tsc",
-    "dev": "nodemon -r dotenv/config src/app.ts",
+    "dev": "nodemon src/app.ts",
     "format": "prettier . --check",
     "format:write": "prettier . --write"
   },
@@ -14,11 +14,13 @@
     "@types/cors": "^2.8.17",
     "@types/express": "^4.17.21",
     "@types/readable-stream": "^4.0.11",
+    "nodemon": "^3.1.0",
     "prettier": "^3.2.5",
     "ts-node": "^10.9.2",
     "typescript": "^5.4.3"
   },
   "dependencies": {
+    "@iarna/toml": "^2.2.5",
     "@langchain/openai": "^0.0.25",
     "axios": "^1.6.8",
     "compute-cosine-similarity": "^1.1.0",
@@ -27,6 +29,7 @@
     "dotenv": "^16.4.5",
     "express": "^4.19.2",
     "langchain": "^0.1.30",
+    "winston": "^3.13.0",
     "ws": "^8.16.0",
     "zod": "^3.22.4"
   }

sample.config.toml

@@ -0,0 +1,11 @@
+[GENERAL]
+PORT = 3001 # Port to run the server on
+SIMILARITY_MEASURE = "cosine" # "cosine" or "dot"
+
+[API_KEYS]
+OPENAI = "" # OpenAI API key - sk-1234567890abcdef1234567890abcdef
+GROQ = "" # Groq API key - gsk_1234567890abcdef1234567890abcdef
+
+[API_ENDPOINTS]
+SEARXNG = "http://localhost:32768" # SearxNG API URL
+OLLAMA = "" # Ollama API URL - http://host.docker.internal:11434

searxng-settings.yml

@@ -1071,25 +1071,6 @@ engines:
       require_api_key: false
       results: HTML
 
-  - name: azlyrics
-    shortcut: lyrics
-    engine: xpath
-    timeout: 4.0
-    disabled: true
-    categories: [music, lyrics]
-    paging: true
-    search_url: https://search.azlyrics.com/search.php?q={query}&w=lyrics&p={pageno}
-    url_xpath: //td[@class="text-left visitedlyr"]/a/@href
-    title_xpath: //span/b/text()
-    content_xpath: //td[@class="text-left visitedlyr"]/a/small
-    about:
-      website: https://azlyrics.com
-      wikidata_id: Q66372542
-      official_api_documentation:
-      use_official_api: false
-      require_api_key: false
-      results: HTML
-
   - name: mastodon users
     engine: mastodon
     mastodon_type: accounts
@@ -1569,11 +1550,6 @@ engines:
     shortcut: scc
     disabled: true
 
-  - name: framalibre
-    engine: framalibre
-    shortcut: frl
-    disabled: true
-
   # - name: searx
   #   engine: searx_engine
   #   shortcut: se

src/agents/academicSearchAgent.ts

@@ -9,33 +9,16 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
-import { Ollama } from '@langchain/community/llms/ollama';
-import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../core/searxng';
+import { searchSearxng } from '../lib/searxng';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import computeSimilarity from '../utils/computeSimilarity';
-
-const chatLLM = new ChatOllama({
-  baseUrl: process.env.OLLAMA_URL,
-  model: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
-
-const llm = new Ollama({
-  temperature: 0,
-  model: process.env.MODEL_NAME,
-  baseUrl: process.env.OLLAMA_URL,
-});
-
-const embeddings = new OllamaEmbeddings({
-  model: process.env.MODEL_NAME,
-  baseUrl: process.env.OLLAMA_URL,
-});
+import logger from '../utils/logger';
 
 const basicAcademicSearchRetrieverPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
@@ -59,7 +42,7 @@ Rephrased question:
 `;
 
 const basicAcademicSearchResponsePrompt = `
-    You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Acadedemic', this means you will be searching for academic papers and articles on the web.
+    You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
 
     Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containg a brief description of the content of that page).
     You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
@@ -114,122 +97,139 @@ const handleStream = async (
   }
 };
 
-const processDocs = async (docs: Document[]) => {
-  return docs
-    .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-    .join('\n');
-};
-
-const rerankDocs = async ({
-  query,
-  docs,
-}: {
-  query: string;
-  docs: Document[];
-}) => {
-  if (docs.length === 0) {
-    return docs;
-  }
-
-  const docsWithContent = docs.filter(
-    (doc) => doc.pageContent && doc.pageContent.length > 0,
-  );
-
-  const docEmbeddings = await embeddings.embedDocuments(
-    docsWithContent.map((doc) => doc.pageContent),
-  );
-
-  const queryEmbedding = await embeddings.embedQuery(query);
-
-  const similarity = docEmbeddings.map((docEmbedding, i) => {
-    const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-    return {
-      index: i,
-      similarity: sim,
-    };
-  });
-
-  const sortedDocs = similarity
-    .sort((a, b) => b.similarity - a.similarity)
-    .slice(0, 15)
-    .map((sim) => docsWithContent[sim.index]);
-
-  return sortedDocs;
-};
-
 type BasicChainInput = {
   chat_history: BaseMessage[];
   query: string;
 };
 
-const basicAcademicSearchRetrieverChain = RunnableSequence.from([
-  PromptTemplate.fromTemplate(basicAcademicSearchRetrieverPrompt),
-  llm,
-  strParser,
-  RunnableLambda.from(async (input: string) => {
-    if (input === 'not_needed') {
-      return { query: '', docs: [] };
+const createBasicAcademicSearchRetrieverChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    PromptTemplate.fromTemplate(basicAcademicSearchRetrieverPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      if (input === 'not_needed') {
+        return { query: '', docs: [] };
+      }
+
+      const res = await searchSearxng(input, {
+        language: 'en',
+        engines: [
+          'arxiv',
+          'google scholar',
+          'internetarchivescholar',
+          'pubmed',
+        ],
+      });
+
+      const documents = res.results.map(
+        (result) =>
+          new Document({
+            pageContent: result.content,
+            metadata: {
+              title: result.title,
+              url: result.url,
+              ...(result.img_src && { img_src: result.img_src }),
+            },
+          }),
+      );
+
+      return { query: input, docs: documents };
+    }),
+  ]);
+};
+
+const createBasicAcademicSearchAnsweringChain = (
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const basicAcademicSearchRetrieverChain =
+    createBasicAcademicSearchRetrieverChain(llm);
+
+  const processDocs = async (docs: Document[]) => {
+    return docs
+      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
+      .join('\n');
+  };
+
+  const rerankDocs = async ({
+    query,
+    docs,
+  }: {
+    query: string;
+    docs: Document[];
+  }) => {
+    if (docs.length === 0) {
+      return docs;
     }
 
-    const res = await searchSearxng(input, {
-      language: 'en',
-      engines: [
-        'arxiv',
-        'google_scholar',
-        'internet_archive_scholar',
-        'pubmed',
-      ],
-    });
-
-    const documents = res.results.map(
-      (result) =>
-        new Document({
-          pageContent: result.content,
-          metadata: {
-            title: result.title,
-            url: result.url,
-            ...(result.img_src && { img_src: result.img_src }),
-          },
-        }),
+    const docsWithContent = docs.filter(
+      (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    return { query: input, docs: documents };
-  }),
-]);
+    const [docEmbeddings, queryEmbedding] = await Promise.all([
+      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      embeddings.embedQuery(query),
+    ]);
 
-const basicAcademicSearchAnsweringChain = RunnableSequence.from([
-  RunnableMap.from({
-    query: (input: BasicChainInput) => input.query,
-    chat_history: (input: BasicChainInput) => input.chat_history,
-    context: RunnableSequence.from([
-      (input) => ({
-        query: input.query,
-        chat_history: formatChatHistoryAsString(input.chat_history),
-      }),
-      basicAcademicSearchRetrieverChain
-        .pipe(rerankDocs)
-        .withConfig({
-          runName: 'FinalSourceRetriever',
-        })
-        .pipe(processDocs),
+    const similarity = docEmbeddings.map((docEmbedding, i) => {
+      const sim = computeSimilarity(queryEmbedding, docEmbedding);
+
+      return {
+        index: i,
+        similarity: sim,
+      };
+    });
+
+    const sortedDocs = similarity
+      .sort((a, b) => b.similarity - a.similarity)
+      .slice(0, 15)
+      .map((sim) => docsWithContent[sim.index]);
+
+    return sortedDocs;
+  };
+
+  return RunnableSequence.from([
+    RunnableMap.from({
+      query: (input: BasicChainInput) => input.query,
+      chat_history: (input: BasicChainInput) => input.chat_history,
+      context: RunnableSequence.from([
+        (input) => ({
+          query: input.query,
+          chat_history: formatChatHistoryAsString(input.chat_history),
+        }),
+        basicAcademicSearchRetrieverChain
+          .pipe(rerankDocs)
+          .withConfig({
+            runName: 'FinalSourceRetriever',
+          })
+          .pipe(processDocs),
+      ]),
+    }),
+    ChatPromptTemplate.fromMessages([
+      ['system', basicAcademicSearchResponsePrompt],
+      new MessagesPlaceholder('chat_history'),
+      ['user', '{query}'],
     ]),
-  }),
-  ChatPromptTemplate.fromMessages([
-    ['system', basicAcademicSearchResponsePrompt],
-    new MessagesPlaceholder('chat_history'),
-    ['user', '{query}'],
-  ]),
-  chatLLM,
-  strParser,
-]).withConfig({
-  runName: 'FinalResponseGenerator',
-});
+    llm,
+    strParser,
+  ]).withConfig({
+    runName: 'FinalResponseGenerator',
+  });
+};
 
-const basicAcademicSearch = (query: string, history: BaseMessage[]) => {
+const basicAcademicSearch = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const basicAcademicSearchAnsweringChain =
+      createBasicAcademicSearchAnsweringChain(llm, embeddings);
+
     const stream = basicAcademicSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -246,14 +246,19 @@ const basicAcademicSearch = (query: string, history: BaseMessage[]) => {
       'error',
       JSON.stringify({ data: 'An error has occurred please try again later' }),
     );
-    console.error(err);
+    logger.error(`Error in academic search: ${err}`);
   }
 
   return emitter;
 };
 
-const handleAcademicSearch = (message: string, history: BaseMessage[]) => {
-  const emitter = basicAcademicSearch(message, history);
+const handleAcademicSearch = (
+  message: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const emitter = basicAcademicSearch(message, history, llm, embeddings);
   return emitter;
 };
 

src/agents/imageSearchAgent.ts

@@ -4,17 +4,11 @@ import {
   RunnableLambda,
 } from '@langchain/core/runnables';
 import { PromptTemplate } from '@langchain/core/prompts';
-import { Ollama } from '@langchain/community/llms/ollama';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../core/searxng';
-
-const llm = new Ollama({
-  temperature: 0,
-  model: process.env.MODEL_NAME,
-  baseUrl: process.env.OLLAMA_URL,
-});
+import { searchSearxng } from '../lib/searxng';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const imageSearchChainPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search the web for images.
@@ -44,38 +38,47 @@ type ImageSearchChainInput = {
 
 const strParser = new StringOutputParser();
 
-const imageSearchChain = RunnableSequence.from([
-  RunnableMap.from({
-    chat_history: (input: ImageSearchChainInput) => {
-      return formatChatHistoryAsString(input.chat_history);
-    },
-    query: (input: ImageSearchChainInput) => {
-      return input.query;
-    },
-  }),
-  PromptTemplate.fromTemplate(imageSearchChainPrompt),
-  llm,
-  strParser,
-  RunnableLambda.from(async (input: string) => {
-    const res = await searchSearxng(input, {
-      categories: ['images'],
-      engines: ['bing_images', 'google_images'],
-    });
+const createImageSearchChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    RunnableMap.from({
+      chat_history: (input: ImageSearchChainInput) => {
+        return formatChatHistoryAsString(input.chat_history);
+      },
+      query: (input: ImageSearchChainInput) => {
+        return input.query;
+      },
+    }),
+    PromptTemplate.fromTemplate(imageSearchChainPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      const res = await searchSearxng(input, {
+        engines: ['bing images', 'google images'],
+      });
 
-    const images = [];
+      const images = [];
 
-    res.results.forEach((result) => {
-      if (result.img_src && result.url && result.title) {
-        images.push({
-          img_src: result.img_src,
-          url: result.url,
-          title: result.title,
-        });
-      }
-    });
+      res.results.forEach((result) => {
+        if (result.img_src && result.url && result.title) {
+          images.push({
+            img_src: result.img_src,
+            url: result.url,
+            title: result.title,
+          });
+        }
+      });
 
-    return images.slice(0, 10);
-  }),
-]);
+      return images.slice(0, 10);
+    }),
+  ]);
+};
 
-export default imageSearchChain;
+const handleImageSearch = (
+  input: ImageSearchChainInput,
+  llm: BaseChatModel,
+) => {
+  const imageSearchChain = createImageSearchChain(llm);
+  return imageSearchChain.invoke(input);
+};
+
+export default handleImageSearch;

src/agents/redditSearchAgent.ts

@@ -9,33 +9,16 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
-import { Ollama } from '@langchain/community/llms/ollama';
-import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../core/searxng';
+import { searchSearxng } from '../lib/searxng';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import computeSimilarity from '../utils/computeSimilarity';
-
-const chatLLM = new ChatOllama({
-  baseUrl: process.env.OLLAMA_URL,
-  model: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
-
-const llm = new Ollama({
-  temperature: 0,
-  model: process.env.MODEL_NAME,
-  baseUrl: process.env.OLLAMA_URL,
-});
-
-const embeddings = new OllamaEmbeddings({
-  model: process.env.MODEL_NAME,
-  baseUrl: process.env.OLLAMA_URL,
-});
+import logger from '../utils/logger';
 
 const basicRedditSearchRetrieverPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
@@ -114,118 +97,134 @@ const handleStream = async (
   }
 };
 
-const processDocs = async (docs: Document[]) => {
-  return docs
-    .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-    .join('\n');
-};
-
-const rerankDocs = async ({
-  query,
-  docs,
-}: {
-  query: string;
-  docs: Document[];
-}) => {
-  if (docs.length === 0) {
-    return docs;
-  }
-
-  const docsWithContent = docs.filter(
-    (doc) => doc.pageContent && doc.pageContent.length > 0,
-  );
-
-  const docEmbeddings = await embeddings.embedDocuments(
-    docsWithContent.map((doc) => doc.pageContent),
-  );
-
-  const queryEmbedding = await embeddings.embedQuery(query);
-
-  const similarity = docEmbeddings.map((docEmbedding, i) => {
-    const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-    return {
-      index: i,
-      similarity: sim,
-    };
-  });
-
-  const sortedDocs = similarity
-    .sort((a, b) => b.similarity - a.similarity)
-    .slice(0, 15)
-    .filter((sim) => sim.similarity > 0.3)
-    .map((sim) => docsWithContent[sim.index]);
-
-  return sortedDocs;
-};
-
 type BasicChainInput = {
   chat_history: BaseMessage[];
   query: string;
 };
 
-const basicRedditSearchRetrieverChain = RunnableSequence.from([
-  PromptTemplate.fromTemplate(basicRedditSearchRetrieverPrompt),
-  llm,
-  strParser,
-  RunnableLambda.from(async (input: string) => {
-    if (input === 'not_needed') {
-      return { query: '', docs: [] };
+const createBasicRedditSearchRetrieverChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    PromptTemplate.fromTemplate(basicRedditSearchRetrieverPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      if (input === 'not_needed') {
+        return { query: '', docs: [] };
+      }
+
+      const res = await searchSearxng(input, {
+        language: 'en',
+        engines: ['reddit'],
+      });
+
+      const documents = res.results.map(
+        (result) =>
+          new Document({
+            pageContent: result.content ? result.content : result.title,
+            metadata: {
+              title: result.title,
+              url: result.url,
+              ...(result.img_src && { img_src: result.img_src }),
+            },
+          }),
+      );
+
+      return { query: input, docs: documents };
+    }),
+  ]);
+};
+
+const createBasicRedditSearchAnsweringChain = (
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const basicRedditSearchRetrieverChain =
+    createBasicRedditSearchRetrieverChain(llm);
+
+  const processDocs = async (docs: Document[]) => {
+    return docs
+      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
+      .join('\n');
+  };
+
+  const rerankDocs = async ({
+    query,
+    docs,
+  }: {
+    query: string;
+    docs: Document[];
+  }) => {
+    if (docs.length === 0) {
+      return docs;
     }
 
-    const res = await searchSearxng(input, {
-      language: 'en',
-      engines: ['reddit'],
-    });
-
-    const documents = res.results.map(
-      (result) =>
-        new Document({
-          pageContent: result.content ? result.content : result.title,
-          metadata: {
-            title: result.title,
-            url: result.url,
-            ...(result.img_src && { img_src: result.img_src }),
-          },
-        }),
+    const docsWithContent = docs.filter(
+      (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    return { query: input, docs: documents };
-  }),
-]);
+    const [docEmbeddings, queryEmbedding] = await Promise.all([
+      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      embeddings.embedQuery(query),
+    ]);
 
-const basicRedditSearchAnsweringChain = RunnableSequence.from([
-  RunnableMap.from({
-    query: (input: BasicChainInput) => input.query,
-    chat_history: (input: BasicChainInput) => input.chat_history,
-    context: RunnableSequence.from([
-      (input) => ({
-        query: input.query,
-        chat_history: formatChatHistoryAsString(input.chat_history),
-      }),
-      basicRedditSearchRetrieverChain
-        .pipe(rerankDocs)
-        .withConfig({
-          runName: 'FinalSourceRetriever',
-        })
-        .pipe(processDocs),
+    const similarity = docEmbeddings.map((docEmbedding, i) => {
+      const sim = computeSimilarity(queryEmbedding, docEmbedding);
+
+      return {
+        index: i,
+        similarity: sim,
+      };
+    });
+
+    const sortedDocs = similarity
+      .sort((a, b) => b.similarity - a.similarity)
+      .slice(0, 15)
+      .filter((sim) => sim.similarity > 0.3)
+      .map((sim) => docsWithContent[sim.index]);
+
+    return sortedDocs;
+  };
+
+  return RunnableSequence.from([
+    RunnableMap.from({
+      query: (input: BasicChainInput) => input.query,
+      chat_history: (input: BasicChainInput) => input.chat_history,
+      context: RunnableSequence.from([
+        (input) => ({
+          query: input.query,
+          chat_history: formatChatHistoryAsString(input.chat_history),
+        }),
+        basicRedditSearchRetrieverChain
+          .pipe(rerankDocs)
+          .withConfig({
+            runName: 'FinalSourceRetriever',
+          })
+          .pipe(processDocs),
+      ]),
+    }),
+    ChatPromptTemplate.fromMessages([
+      ['system', basicRedditSearchResponsePrompt],
+      new MessagesPlaceholder('chat_history'),
+      ['user', '{query}'],
     ]),
-  }),
-  ChatPromptTemplate.fromMessages([
-    ['system', basicRedditSearchResponsePrompt],
-    new MessagesPlaceholder('chat_history'),
-    ['user', '{query}'],
-  ]),
-  chatLLM,
-  strParser,
-]).withConfig({
-  runName: 'FinalResponseGenerator',
-});
+    llm,
+    strParser,
+  ]).withConfig({
+    runName: 'FinalResponseGenerator',
+  });
+};
 
-const basicRedditSearch = (query: string, history: BaseMessage[]) => {
+const basicRedditSearch = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const basicRedditSearchAnsweringChain =
+      createBasicRedditSearchAnsweringChain(llm, embeddings);
     const stream = basicRedditSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -242,14 +241,19 @@ const basicRedditSearch = (query: string, history: BaseMessage[]) => {
       'error',
       JSON.stringify({ data: 'An error has occurred please try again later' }),
     );
-    console.error(err);
+    logger.error(`Error in RedditSearch: ${err}`);
   }
 
   return emitter;
 };
 
-const handleRedditSearch = (message: string, history: BaseMessage[]) => {
-  const emitter = basicRedditSearch(message, history);
+const handleRedditSearch = (
+  message: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const emitter = basicRedditSearch(message, history, llm, embeddings);
   return emitter;
 };
 

src/agents/videoSearchAgent.ts

@@ -0,0 +1,90 @@
+import {
+  RunnableSequence,
+  RunnableMap,
+  RunnableLambda,
+} from '@langchain/core/runnables';
+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 type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+
+const VideoSearchChainPrompt = `
+  You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search Youtube for videos.
+  You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
+  
+  Example:
+  1. Follow up question: How does a car work?
+  Rephrased: How does a car work?
+  
+  2. Follow up question: What is the theory of relativity?
+  Rephrased: What is theory of relativity
+  
+  3. Follow up question: How does an AC work?
+  Rephrased: How does an AC work
+  
+  Conversation:
+  {chat_history}
+  
+  Follow up question: {query}
+  Rephrased question:
+  `;
+
+type VideoSearchChainInput = {
+  chat_history: BaseMessage[];
+  query: string;
+};
+
+const strParser = new StringOutputParser();
+
+const createVideoSearchChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    RunnableMap.from({
+      chat_history: (input: VideoSearchChainInput) => {
+        return formatChatHistoryAsString(input.chat_history);
+      },
+      query: (input: VideoSearchChainInput) => {
+        return input.query;
+      },
+    }),
+    PromptTemplate.fromTemplate(VideoSearchChainPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      const res = await searchSearxng(input, {
+        engines: ['youtube'],
+      });
+
+      const videos = [];
+
+      res.results.forEach((result) => {
+        if (
+          result.thumbnail &&
+          result.url &&
+          result.title &&
+          result.iframe_src
+        ) {
+          videos.push({
+            img_src: result.thumbnail,
+            url: result.url,
+            title: result.title,
+            iframe_src: result.iframe_src,
+          });
+        }
+      });
+
+      return videos.slice(0, 10);
+    }),
+  ]);
+};
+
+const handleVideoSearch = (
+  input: VideoSearchChainInput,
+  llm: BaseChatModel,
+) => {
+  const VideoSearchChain = createVideoSearchChain(llm);
+  return VideoSearchChain.invoke(input);
+};
+
+export default handleVideoSearch;

src/agents/webSearchAgent.ts

@@ -9,33 +9,16 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
-import { Ollama } from '@langchain/community/llms/ollama';
-import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../core/searxng';
+import { searchSearxng } from '../lib/searxng';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import computeSimilarity from '../utils/computeSimilarity';
-
-const chatLLM = new ChatOllama({
-  baseUrl: process.env.OLLAMA_URL,
-  model: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
-
-const llm = new Ollama({
-  temperature: 0,
-  model: process.env.MODEL_NAME,
-  baseUrl: process.env.OLLAMA_URL,
-});
-
-const embeddings = new OllamaEmbeddings({
-  model: process.env.MODEL_NAME,
-  baseUrl: process.env.OLLAMA_URL,
-});
+import logger from '../utils/logger';
 
 const basicSearchRetrieverPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
@@ -114,117 +97,135 @@ const handleStream = async (
   }
 };
 
-const processDocs = async (docs: Document[]) => {
-  return docs
-    .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-    .join('\n');
-};
-
-const rerankDocs = async ({
-  query,
-  docs,
-}: {
-  query: string;
-  docs: Document[];
-}) => {
-  if (docs.length === 0) {
-    return docs;
-  }
-
-  const docsWithContent = docs.filter(
-    (doc) => doc.pageContent && doc.pageContent.length > 0,
-  );
-
-  const docEmbeddings = await embeddings.embedDocuments(
-    docsWithContent.map((doc) => doc.pageContent),
-  );
-
-  const queryEmbedding = await embeddings.embedQuery(query);
-
-  const similarity = docEmbeddings.map((docEmbedding, i) => {
-    const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-    return {
-      index: i,
-      similarity: sim,
-    };
-  });
-
-  const sortedDocs = similarity
-    .sort((a, b) => b.similarity - a.similarity)
-    .filter((sim) => sim.similarity > 0.5)
-    .slice(0, 15)
-    .map((sim) => docsWithContent[sim.index]);
-
-  return sortedDocs;
-};
-
 type BasicChainInput = {
   chat_history: BaseMessage[];
   query: string;
 };
 
-const basicWebSearchRetrieverChain = RunnableSequence.from([
-  PromptTemplate.fromTemplate(basicSearchRetrieverPrompt),
-  llm,
-  strParser,
-  RunnableLambda.from(async (input: string) => {
-    if (input === 'not_needed') {
-      return { query: '', docs: [] };
+const createBasicWebSearchRetrieverChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    PromptTemplate.fromTemplate(basicSearchRetrieverPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      if (input === 'not_needed') {
+        return { query: '', docs: [] };
+      }
+
+      const res = await searchSearxng(input, {
+        language: 'en',
+      });
+
+      const documents = res.results.map(
+        (result) =>
+          new Document({
+            pageContent: result.content,
+            metadata: {
+              title: result.title,
+              url: result.url,
+              ...(result.img_src && { img_src: result.img_src }),
+            },
+          }),
+      );
+
+      return { query: input, docs: documents };
+    }),
+  ]);
+};
+
+const createBasicWebSearchAnsweringChain = (
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const basicWebSearchRetrieverChain = createBasicWebSearchRetrieverChain(llm);
+
+  const processDocs = async (docs: Document[]) => {
+    return docs
+      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
+      .join('\n');
+  };
+
+  const rerankDocs = async ({
+    query,
+    docs,
+  }: {
+    query: string;
+    docs: Document[];
+  }) => {
+    if (docs.length === 0) {
+      return docs;
     }
 
-    const res = await searchSearxng(input, {
-      language: 'en',
-    });
-
-    const documents = res.results.map(
-      (result) =>
-        new Document({
-          pageContent: result.content,
-          metadata: {
-            title: result.title,
-            url: result.url,
-            ...(result.img_src && { img_src: result.img_src }),
-          },
-        }),
+    const docsWithContent = docs.filter(
+      (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    return { query: input, docs: documents };
-  }),
-]);
+    const [docEmbeddings, queryEmbedding] = await Promise.all([
+      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      embeddings.embedQuery(query),
+    ]);
 
-const basicWebSearchAnsweringChain = RunnableSequence.from([
-  RunnableMap.from({
-    query: (input: BasicChainInput) => input.query,
-    chat_history: (input: BasicChainInput) => input.chat_history,
-    context: RunnableSequence.from([
-      (input) => ({
-        query: input.query,
-        chat_history: formatChatHistoryAsString(input.chat_history),
-      }),
-      basicWebSearchRetrieverChain
-        .pipe(rerankDocs)
-        .withConfig({
-          runName: 'FinalSourceRetriever',
-        })
-        .pipe(processDocs),
+    const similarity = docEmbeddings.map((docEmbedding, i) => {
+      const sim = computeSimilarity(queryEmbedding, docEmbedding);
+
+      return {
+        index: i,
+        similarity: sim,
+      };
+    });
+
+    const sortedDocs = similarity
+      .sort((a, b) => b.similarity - a.similarity)
+      .filter((sim) => sim.similarity > 0.5)
+      .slice(0, 15)
+      .map((sim) => docsWithContent[sim.index]);
+
+    return sortedDocs;
+  };
+
+  return RunnableSequence.from([
+    RunnableMap.from({
+      query: (input: BasicChainInput) => input.query,
+      chat_history: (input: BasicChainInput) => input.chat_history,
+      context: RunnableSequence.from([
+        (input) => ({
+          query: input.query,
+          chat_history: formatChatHistoryAsString(input.chat_history),
+        }),
+        basicWebSearchRetrieverChain
+          .pipe(rerankDocs)
+          .withConfig({
+            runName: 'FinalSourceRetriever',
+          })
+          .pipe(processDocs),
+      ]),
+    }),
+    ChatPromptTemplate.fromMessages([
+      ['system', basicWebSearchResponsePrompt],
+      new MessagesPlaceholder('chat_history'),
+      ['user', '{query}'],
     ]),
-  }),
-  ChatPromptTemplate.fromMessages([
-    ['system', basicWebSearchResponsePrompt],
-    new MessagesPlaceholder('chat_history'),
-    ['user', '{query}'],
-  ]),
-  chatLLM,
-  strParser,
-]).withConfig({
-  runName: 'FinalResponseGenerator',
-});
+    llm,
+    strParser,
+  ]).withConfig({
+    runName: 'FinalResponseGenerator',
+  });
+};
 
-const basicWebSearch = (query: string, history: BaseMessage[]) => {
+const basicWebSearch = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const basicWebSearchAnsweringChain = createBasicWebSearchAnsweringChain(
+      llm,
+      embeddings,
+    );
+
     const stream = basicWebSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -241,14 +242,19 @@ const basicWebSearch = (query: string, history: BaseMessage[]) => {
       'error',
       JSON.stringify({ data: 'An error has occurred please try again later' }),
     );
-    console.error(err);
+    logger.error(`Error in websearch: ${err}`);
   }
 
   return emitter;
 };
 
-const handleWebSearch = (message: string, history: BaseMessage[]) => {
-  const emitter = basicWebSearch(message, history);
+const handleWebSearch = (
+  message: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const emitter = basicWebSearch(message, history, llm, embeddings);
   return emitter;
 };
 

src/agents/wolframAlphaSearchAgent.ts

@@ -9,26 +9,15 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
-import { Ollama } from '@langchain/community/llms/ollama';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../core/searxng';
+import { searchSearxng } from '../lib/searxng';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
-
-const chatLLM = new ChatOllama({
-  baseUrl: process.env.OLLAMA_URL,
-  model: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
-
-const llm = new Ollama({
-  temperature: 0,
-  model: process.env.MODEL_NAME,
-  baseUrl: process.env.OLLAMA_URL,
-});
+import logger from '../utils/logger';
 
 const basicWolframAlphaSearchRetrieverPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
@@ -107,81 +96,94 @@ const handleStream = async (
   }
 };
 
-const processDocs = async (docs: Document[]) => {
-  return docs
-    .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-    .join('\n');
-};
-
 type BasicChainInput = {
   chat_history: BaseMessage[];
   query: string;
 };
 
-const basicWolframAlphaSearchRetrieverChain = RunnableSequence.from([
-  PromptTemplate.fromTemplate(basicWolframAlphaSearchRetrieverPrompt),
-  llm,
-  strParser,
-  RunnableLambda.from(async (input: string) => {
-    if (input === 'not_needed') {
-      return { query: '', docs: [] };
-    }
+const createBasicWolframAlphaSearchRetrieverChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    PromptTemplate.fromTemplate(basicWolframAlphaSearchRetrieverPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      if (input === 'not_needed') {
+        return { query: '', docs: [] };
+      }
 
-    const res = await searchSearxng(input, {
-      language: 'en',
-      engines: ['wolframalpha'],
-    });
+      const res = await searchSearxng(input, {
+        language: 'en',
+        engines: ['wolframalpha'],
+      });
 
-    const documents = res.results.map(
-      (result) =>
-        new Document({
-          pageContent: result.content,
-          metadata: {
-            title: result.title,
-            url: result.url,
-            ...(result.img_src && { img_src: result.img_src }),
-          },
+      const documents = res.results.map(
+        (result) =>
+          new Document({
+            pageContent: result.content,
+            metadata: {
+              title: result.title,
+              url: result.url,
+              ...(result.img_src && { img_src: result.img_src }),
+            },
+          }),
+      );
+
+      return { query: input, docs: documents };
+    }),
+  ]);
+};
+
+const createBasicWolframAlphaSearchAnsweringChain = (llm: BaseChatModel) => {
+  const basicWolframAlphaSearchRetrieverChain =
+    createBasicWolframAlphaSearchRetrieverChain(llm);
+
+  const processDocs = (docs: Document[]) => {
+    return docs
+      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
+      .join('\n');
+  };
+
+  return RunnableSequence.from([
+    RunnableMap.from({
+      query: (input: BasicChainInput) => input.query,
+      chat_history: (input: BasicChainInput) => input.chat_history,
+      context: RunnableSequence.from([
+        (input) => ({
+          query: input.query,
+          chat_history: formatChatHistoryAsString(input.chat_history),
         }),
-    );
-
-    return { query: input, docs: documents };
-  }),
-]);
-
-const basicWolframAlphaSearchAnsweringChain = RunnableSequence.from([
-  RunnableMap.from({
-    query: (input: BasicChainInput) => input.query,
-    chat_history: (input: BasicChainInput) => input.chat_history,
-    context: RunnableSequence.from([
-      (input) => ({
-        query: input.query,
-        chat_history: formatChatHistoryAsString(input.chat_history),
-      }),
-      basicWolframAlphaSearchRetrieverChain
-        .pipe(({ query, docs }) => {
-          return docs;
-        })
-        .withConfig({
-          runName: 'FinalSourceRetriever',
-        })
-        .pipe(processDocs),
+        basicWolframAlphaSearchRetrieverChain
+          .pipe(({ query, docs }) => {
+            return docs;
+          })
+          .withConfig({
+            runName: 'FinalSourceRetriever',
+          })
+          .pipe(processDocs),
+      ]),
+    }),
+    ChatPromptTemplate.fromMessages([
+      ['system', basicWolframAlphaSearchResponsePrompt],
+      new MessagesPlaceholder('chat_history'),
+      ['user', '{query}'],
     ]),
-  }),
-  ChatPromptTemplate.fromMessages([
-    ['system', basicWolframAlphaSearchResponsePrompt],
-    new MessagesPlaceholder('chat_history'),
-    ['user', '{query}'],
-  ]),
-  chatLLM,
-  strParser,
-]).withConfig({
-  runName: 'FinalResponseGenerator',
-});
+    llm,
+    strParser,
+  ]).withConfig({
+    runName: 'FinalResponseGenerator',
+  });
+};
 
-const basicWolframAlphaSearch = (query: string, history: BaseMessage[]) => {
+const basicWolframAlphaSearch = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const basicWolframAlphaSearchAnsweringChain =
+      createBasicWolframAlphaSearchAnsweringChain(llm);
     const stream = basicWolframAlphaSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -198,14 +200,19 @@ const basicWolframAlphaSearch = (query: string, history: BaseMessage[]) => {
       'error',
       JSON.stringify({ data: 'An error has occurred please try again later' }),
     );
-    console.error(err);
+    logger.error(`Error in WolframAlphaSearch: ${err}`);
   }
 
   return emitter;
 };
 
-const handleWolframAlphaSearch = (message: string, history: BaseMessage[]) => {
-  const emitter = basicWolframAlphaSearch(message, history);
+const handleWolframAlphaSearch = (
+  message: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const emitter = basicWolframAlphaSearch(message, history, llm);
   return emitter;
 };
 

src/agents/writingAssistant.ts

@@ -4,16 +4,12 @@ import {
   MessagesPlaceholder,
 } from '@langchain/core/prompts';
 import { RunnableSequence } from '@langchain/core/runnables';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
 import eventEmitter from 'events';
-
-const chatLLM = new ChatOllama({
-  baseUrl: process.env.OLLAMA_URL,
-  model: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
+import logger from '../utils/logger';
 
 const writingAssistantPrompt = `
 You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query. 
@@ -45,22 +41,30 @@ const handleStream = async (
   }
 };
 
-const writingAssistantChain = RunnableSequence.from([
-  ChatPromptTemplate.fromMessages([
-    ['system', writingAssistantPrompt],
-    new MessagesPlaceholder('chat_history'),
-    ['user', '{query}'],
-  ]),
-  chatLLM,
-  strParser,
-]).withConfig({
-  runName: 'FinalResponseGenerator',
-});
+const createWritingAssistantChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    ChatPromptTemplate.fromMessages([
+      ['system', writingAssistantPrompt],
+      new MessagesPlaceholder('chat_history'),
+      ['user', '{query}'],
+    ]),
+    llm,
+    strParser,
+  ]).withConfig({
+    runName: 'FinalResponseGenerator',
+  });
+};
 
-const handleWritingAssistant = (query: string, history: BaseMessage[]) => {
+const handleWritingAssistant = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const writingAssistantChain = createWritingAssistantChain(llm);
     const stream = writingAssistantChain.streamEvents(
       {
         chat_history: history,
@@ -77,7 +81,7 @@ const handleWritingAssistant = (query: string, history: BaseMessage[]) => {
       'error',
       JSON.stringify({ data: 'An error has occurred please try again later' }),
     );
-    console.error(err);
+    logger.error(`Error in writing assistant: ${err}`);
   }
 
   return emitter;

src/agents/youtubeSearchAgent.ts

@@ -9,33 +9,16 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
-import { Ollama } from '@langchain/community/llms/ollama';
-import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../core/searxng';
+import { searchSearxng } from '../lib/searxng';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import computeSimilarity from '../utils/computeSimilarity';
-
-const chatLLM = new ChatOllama({
-  baseUrl: process.env.OLLAMA_URL,
-  model: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
-
-const llm = new Ollama({
-  temperature: 0,
-  model: process.env.MODEL_NAME,
-  baseUrl: process.env.OLLAMA_URL,
-});
-
-const embeddings = new OllamaEmbeddings({
-  model: process.env.MODEL_NAME,
-  baseUrl: process.env.OLLAMA_URL,
-});
+import logger from '../utils/logger';
 
 const basicYoutubeSearchRetrieverPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
@@ -114,118 +97,135 @@ const handleStream = async (
   }
 };
 
-const processDocs = async (docs: Document[]) => {
-  return docs
-    .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-    .join('\n');
-};
-
-const rerankDocs = async ({
-  query,
-  docs,
-}: {
-  query: string;
-  docs: Document[];
-}) => {
-  if (docs.length === 0) {
-    return docs;
-  }
-
-  const docsWithContent = docs.filter(
-    (doc) => doc.pageContent && doc.pageContent.length > 0,
-  );
-
-  const docEmbeddings = await embeddings.embedDocuments(
-    docsWithContent.map((doc) => doc.pageContent),
-  );
-
-  const queryEmbedding = await embeddings.embedQuery(query);
-
-  const similarity = docEmbeddings.map((docEmbedding, i) => {
-    const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-    return {
-      index: i,
-      similarity: sim,
-    };
-  });
-
-  const sortedDocs = similarity
-    .sort((a, b) => b.similarity - a.similarity)
-    .slice(0, 15)
-    .filter((sim) => sim.similarity > 0.3)
-    .map((sim) => docsWithContent[sim.index]);
-
-  return sortedDocs;
-};
-
 type BasicChainInput = {
   chat_history: BaseMessage[];
   query: string;
 };
 
-const basicYoutubeSearchRetrieverChain = RunnableSequence.from([
-  PromptTemplate.fromTemplate(basicYoutubeSearchRetrieverPrompt),
-  llm,
-  strParser,
-  RunnableLambda.from(async (input: string) => {
-    if (input === 'not_needed') {
-      return { query: '', docs: [] };
+const createBasicYoutubeSearchRetrieverChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    PromptTemplate.fromTemplate(basicYoutubeSearchRetrieverPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      if (input === 'not_needed') {
+        return { query: '', docs: [] };
+      }
+
+      const res = await searchSearxng(input, {
+        language: 'en',
+        engines: ['youtube'],
+      });
+
+      const documents = res.results.map(
+        (result) =>
+          new Document({
+            pageContent: result.content ? result.content : result.title,
+            metadata: {
+              title: result.title,
+              url: result.url,
+              ...(result.img_src && { img_src: result.img_src }),
+            },
+          }),
+      );
+
+      return { query: input, docs: documents };
+    }),
+  ]);
+};
+
+const createBasicYoutubeSearchAnsweringChain = (
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const basicYoutubeSearchRetrieverChain =
+    createBasicYoutubeSearchRetrieverChain(llm);
+
+  const processDocs = async (docs: Document[]) => {
+    return docs
+      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
+      .join('\n');
+  };
+
+  const rerankDocs = async ({
+    query,
+    docs,
+  }: {
+    query: string;
+    docs: Document[];
+  }) => {
+    if (docs.length === 0) {
+      return docs;
     }
 
-    const res = await searchSearxng(input, {
-      language: 'en',
-      engines: ['youtube'],
-    });
-
-    const documents = res.results.map(
-      (result) =>
-        new Document({
-          pageContent: result.content ? result.content : result.title,
-          metadata: {
-            title: result.title,
-            url: result.url,
-            ...(result.img_src && { img_src: result.img_src }),
-          },
-        }),
+    const docsWithContent = docs.filter(
+      (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    return { query: input, docs: documents };
-  }),
-]);
+    const [docEmbeddings, queryEmbedding] = await Promise.all([
+      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      embeddings.embedQuery(query),
+    ]);
 
-const basicYoutubeSearchAnsweringChain = RunnableSequence.from([
-  RunnableMap.from({
-    query: (input: BasicChainInput) => input.query,
-    chat_history: (input: BasicChainInput) => input.chat_history,
-    context: RunnableSequence.from([
-      (input) => ({
-        query: input.query,
-        chat_history: formatChatHistoryAsString(input.chat_history),
-      }),
-      basicYoutubeSearchRetrieverChain
-        .pipe(rerankDocs)
-        .withConfig({
-          runName: 'FinalSourceRetriever',
-        })
-        .pipe(processDocs),
+    const similarity = docEmbeddings.map((docEmbedding, i) => {
+      const sim = computeSimilarity(queryEmbedding, docEmbedding);
+
+      return {
+        index: i,
+        similarity: sim,
+      };
+    });
+
+    const sortedDocs = similarity
+      .sort((a, b) => b.similarity - a.similarity)
+      .slice(0, 15)
+      .filter((sim) => sim.similarity > 0.3)
+      .map((sim) => docsWithContent[sim.index]);
+
+    return sortedDocs;
+  };
+
+  return RunnableSequence.from([
+    RunnableMap.from({
+      query: (input: BasicChainInput) => input.query,
+      chat_history: (input: BasicChainInput) => input.chat_history,
+      context: RunnableSequence.from([
+        (input) => ({
+          query: input.query,
+          chat_history: formatChatHistoryAsString(input.chat_history),
+        }),
+        basicYoutubeSearchRetrieverChain
+          .pipe(rerankDocs)
+          .withConfig({
+            runName: 'FinalSourceRetriever',
+          })
+          .pipe(processDocs),
+      ]),
+    }),
+    ChatPromptTemplate.fromMessages([
+      ['system', basicYoutubeSearchResponsePrompt],
+      new MessagesPlaceholder('chat_history'),
+      ['user', '{query}'],
     ]),
-  }),
-  ChatPromptTemplate.fromMessages([
-    ['system', basicYoutubeSearchResponsePrompt],
-    new MessagesPlaceholder('chat_history'),
-    ['user', '{query}'],
-  ]),
-  chatLLM,
-  strParser,
-]).withConfig({
-  runName: 'FinalResponseGenerator',
-});
+    llm,
+    strParser,
+  ]).withConfig({
+    runName: 'FinalResponseGenerator',
+  });
+};
 
-const basicYoutubeSearch = (query: string, history: BaseMessage[]) => {
+const basicYoutubeSearch = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const basicYoutubeSearchAnsweringChain =
+      createBasicYoutubeSearchAnsweringChain(llm, embeddings);
+
     const stream = basicYoutubeSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -242,14 +242,19 @@ const basicYoutubeSearch = (query: string, history: BaseMessage[]) => {
       'error',
       JSON.stringify({ data: 'An error has occurred please try again later' }),
     );
-    console.error(err);
+    logger.error(`Error in youtube search: ${err}`);
   }
 
   return emitter;
 };
 
-const handleYoutubeSearch = (message: string, history: BaseMessage[]) => {
-  const emitter = basicYoutubeSearch(message, history);
+const handleYoutubeSearch = (
+  message: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const emitter = basicYoutubeSearch(message, history, llm, embeddings);
   return emitter;
 };
 

src/app.ts

@@ -3,6 +3,10 @@ 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);
@@ -19,8 +23,8 @@ app.get('/api', (_, res) => {
   res.status(200).json({ status: 'ok' });
 });
 
-server.listen(process.env.PORT!, () => {
-  console.log(`API server started on port ${process.env.PORT}`);
+server.listen(port, () => {
+  logger.info(`Server is running on port ${port}`);
 });
 
 startWebSocketServer(server);

src/config.ts

@@ -0,0 +1,69 @@
+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;
+  };
+  API_KEYS: {
+    OPENAI: string;
+    GROQ: 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, `../${configFileName}`), 'utf-8'),
+  ) as any as Config;
+
+export const getPort = () => loadConfig().GENERAL.PORT;
+
+export const getSimilarityMeasure = () =>
+  loadConfig().GENERAL.SIMILARITY_MEASURE;
+
+export const getOpenaiApiKey = () => loadConfig().API_KEYS.OPENAI;
+
+export const getGroqApiKey = () => loadConfig().API_KEYS.GROQ;
+
+export const getSearxngApiEndpoint = () => loadConfig().API_ENDPOINTS.SEARXNG;
+
+export const getOllamaApiEndpoint = () => loadConfig().API_ENDPOINTS.OLLAMA;
+
+export const updateConfig = (config: RecursivePartial<Config>) => {
+  const currentConfig = loadConfig();
+
+  for (const key in currentConfig) {
+    if (!config[key]) config[key] = {};
+
+    if (typeof currentConfig[key] === 'object' && currentConfig[key] !== null) {
+      for (const nestedKey in currentConfig[key]) {
+        if (
+          !config[key][nestedKey] &&
+          currentConfig[key][nestedKey] &&
+          config[key][nestedKey] !== ''
+        ) {
+          config[key][nestedKey] = currentConfig[key][nestedKey];
+        }
+      }
+    } else if (currentConfig[key] && config[key] !== '') {
+      config[key] = currentConfig[key];
+    }
+  }
+
+  fs.writeFileSync(
+    path.join(__dirname, `../${configFileName}`),
+    toml.stringify(config),
+  );
+};

src/core/agentPicker.ts

@@ -1,69 +0,0 @@
-import { z } from 'zod';
-import { OpenAI } from '@langchain/openai';
-import { RunnableSequence } from '@langchain/core/runnables';
-import { StructuredOutputParser } from 'langchain/output_parsers';
-import { PromptTemplate } from '@langchain/core/prompts';
-
-const availableAgents = [
-  {
-    name: 'webSearch',
-    description:
-      'It is expert is searching the web for information and answer user queries',
-  },
-  /* {
-    name: 'academicSearch',
-    description:
-      'It is expert is searching the academic databases for information and answer user queries. It is particularly good at finding research papers and articles on topics like science, engineering, and technology. Use this instead of wolframAlphaSearch if the user query is not mathematical or scientific in nature',
-  },
-  {
-    name: 'youtubeSearch',
-    description:
-      'This model is expert at finding videos on youtube based on user queries',
-  },
-  {
-    name: 'wolframAlphaSearch',
-    description:
-      'This model is expert at finding answers to mathematical and scientific questions based on user queries.',
-  },
-  {
-    name: 'redditSearch',
-    description:
-      'This model is expert at finding posts and discussions on reddit based on user queries',
-  },
-  {
-    name: 'writingAssistant',
-    description:
-      'If there is no need for searching, this model is expert at generating text based on user queries',
-  }, */
-];
-
-const parser = StructuredOutputParser.fromZodSchema(
-  z.object({
-    agent: z.string().describe('The name of the selected agent'),
-  }),
-);
-
-const prompt = `
-    You are an AI model who is expert at finding suitable agents for user queries. The available agents are:
-    ${availableAgents.map((agent) => `- ${agent.name}: ${agent.description}`).join('\n')}
-
-    Your task is to find the most suitable agent for the following query: {query}
-
-    {format_instructions}
-`;
-
-const chain = RunnableSequence.from([
-  PromptTemplate.fromTemplate(prompt),
-  new OpenAI({ temperature: 0 }),
-  parser,
-]);
-
-const pickSuitableAgent = async (query: string) => {
-  const res = await chain.invoke({
-    query,
-    format_instructions: parser.getFormatInstructions(),
-  });
-  return res.agent;
-};
-
-export default pickSuitableAgent;

src/lib/providers.ts

@@ -0,0 +1,165 @@
+import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
+import { ChatOllama } from '@langchain/community/chat_models/ollama';
+import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
+import {
+  getGroqApiKey,
+  getOllamaApiEndpoint,
+  getOpenaiApiKey,
+} from '../config';
+import logger from '../utils/logger';
+
+export const getAvailableChatModelProviders = async () => {
+  const openAIApiKey = getOpenaiApiKey();
+  const groqApiKey = getGroqApiKey();
+  const ollamaEndpoint = getOllamaApiEndpoint();
+
+  const models = {};
+
+  if (openAIApiKey) {
+    try {
+      models['openai'] = {
+        'GPT-3.5 turbo': new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-3.5-turbo',
+          temperature: 0.7,
+        }),
+        'GPT-4': new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-4',
+          temperature: 0.7,
+        }),
+        'GPT-4 turbo': new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-4-turbo',
+          temperature: 0.7,
+        }),
+      };
+    } catch (err) {
+      logger.error(`Error loading OpenAI models: ${err}`);
+    }
+  }
+
+  if (groqApiKey) {
+    try {
+      models['groq'] = {
+        'LLaMA3 8b': new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama3-8b-8192',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+        'LLaMA3 70b': new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama3-70b-8192',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+        'Mixtral 8x7b': new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'mixtral-8x7b-32768',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+        'Gemma 7b': new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'gemma-7b-it',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      };
+    } catch (err) {
+      logger.error(`Error loading Groq models: ${err}`);
+    }
+  }
+
+  if (ollamaEndpoint) {
+    try {
+      const response = await fetch(`${ollamaEndpoint}/api/tags`, {
+        headers: {
+          'Content-Type': 'application/json',
+        },
+      });
+
+      const { models: ollamaModels } = (await response.json()) as any;
+
+      models['ollama'] = ollamaModels.reduce((acc, model) => {
+        acc[model.model] = new ChatOllama({
+          baseUrl: ollamaEndpoint,
+          model: model.model,
+          temperature: 0.7,
+        });
+        return acc;
+      }, {});
+    } catch (err) {
+      logger.error(`Error loading Ollama models: ${err}`);
+    }
+  }
+
+  models['custom_openai'] = {};
+
+  return models;
+};
+
+export const getAvailableEmbeddingModelProviders = async () => {
+  const openAIApiKey = getOpenaiApiKey();
+  const ollamaEndpoint = getOllamaApiEndpoint();
+
+  const models = {};
+
+  if (openAIApiKey) {
+    try {
+      models['openai'] = {
+        'Text embedding 3 small': new OpenAIEmbeddings({
+          openAIApiKey,
+          modelName: 'text-embedding-3-small',
+        }),
+        'Text embedding 3 large': new OpenAIEmbeddings({
+          openAIApiKey,
+          modelName: 'text-embedding-3-large',
+        }),
+      };
+    } catch (err) {
+      logger.error(`Error loading OpenAI embeddings: ${err}`);
+    }
+  }
+
+  if (ollamaEndpoint) {
+    try {
+      const response = await fetch(`${ollamaEndpoint}/api/tags`, {
+        headers: {
+          'Content-Type': 'application/json',
+        },
+      });
+
+      const { models: ollamaModels } = (await response.json()) as any;
+
+      models['ollama'] = ollamaModels.reduce((acc, model) => {
+        acc[model.model] = new OllamaEmbeddings({
+          baseUrl: ollamaEndpoint,
+          model: model.model,
+        });
+        return acc;
+      }, {});
+    } catch (err) {
+      logger.error(`Error loading Ollama embeddings: ${err}`);
+    }
+  }
+
+  return models;
+};

src/lib/searxng.ts

@@ -1,4 +1,5 @@
 import axios from 'axios';
+import { getSearxngApiEndpoint } from '../config';
 
 interface SearxngSearchOptions {
   categories?: string[];
@@ -12,15 +13,19 @@ interface SearxngSearchResult {
   url: string;
   img_src?: string;
   thumbnail_src?: string;
+  thumbnail?: string;
   content?: string;
   author?: string;
+  iframe_src?: string;
 }
 
 export const searchSearxng = async (
   query: string,
   opts?: SearxngSearchOptions,
 ) => {
-  const url = new URL(`${process.env.SEARXNG_API_URL}/search?format=json`);
+  const searxngURL = getSearxngApiEndpoint();
+
+  const url = new URL(`${searxngURL}/search?format=json`);
   url.searchParams.append('q', query);
 
   if (opts) {

src/routes/config.ts

@@ -0,0 +1,63 @@
+import express from 'express';
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '../lib/providers';
+import {
+  getGroqApiKey,
+  getOllamaApiEndpoint,
+  getOpenaiApiKey,
+  updateConfig,
+} from '../config';
+
+const router = express.Router();
+
+router.get('/', async (_, res) => {
+  const config = {};
+
+  const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+    getAvailableChatModelProviders(),
+    getAvailableEmbeddingModelProviders(),
+  ]);
+
+  config['chatModelProviders'] = {};
+  config['embeddingModelProviders'] = {};
+
+  for (const provider in chatModelProviders) {
+    config['chatModelProviders'][provider] = Object.keys(
+      chatModelProviders[provider],
+    );
+  }
+
+  for (const provider in embeddingModelProviders) {
+    config['embeddingModelProviders'][provider] = Object.keys(
+      embeddingModelProviders[provider],
+    );
+  }
+
+  config['openaiApiKey'] = getOpenaiApiKey();
+  config['ollamaApiUrl'] = getOllamaApiEndpoint();
+  config['groqApiKey'] = getGroqApiKey();
+
+  res.status(200).json(config);
+});
+
+router.post('/', async (req, res) => {
+  const config = req.body;
+
+  const updatedConfig = {
+    API_KEYS: {
+      OPENAI: config.openaiApiKey,
+      GROQ: config.groqApiKey,
+    },
+    API_ENDPOINTS: {
+      OLLAMA: config.ollamaApiUrl,
+    },
+  };
+
+  updateConfig(updatedConfig);
+
+  res.status(200).json({ message: 'Config updated' });
+});
+
+export default router;

src/routes/images.ts

@@ -1,21 +1,45 @@
 import express from 'express';
-import imageSearchChain from '../agents/imageSearchAgent';
+import handleImageSearch from '../agents/imageSearchAgent';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { getAvailableChatModelProviders } from '../lib/providers';
+import { HumanMessage, AIMessage } from '@langchain/core/messages';
+import logger from '../utils/logger';
 
 const router = express.Router();
 
 router.post('/', async (req, res) => {
   try {
-    const { query, chat_history } = req.body;
+    let { query, chat_history, chat_model_provider, chat_model } = req.body;
 
-    const images = await imageSearchChain.invoke({
-      query,
-      chat_history,
+    chat_history = chat_history.map((msg: any) => {
+      if (msg.role === 'user') {
+        return new HumanMessage(msg.content);
+      } else if (msg.role === 'assistant') {
+        return new AIMessage(msg.content);
+      }
     });
 
+    const chatModels = await getAvailableChatModelProviders();
+    const provider = chat_model_provider || Object.keys(chatModels)[0];
+    const chatModel = chat_model || Object.keys(chatModels[provider])[0];
+
+    let llm: BaseChatModel | undefined;
+
+    if (chatModels[provider] && chatModels[provider][chatModel]) {
+      llm = chatModels[provider][chatModel] as BaseChatModel | undefined;
+    }
+
+    if (!llm) {
+      res.status(500).json({ message: 'Invalid LLM model selected' });
+      return;
+    }
+
+    const images = await handleImageSearch({ query, chat_history }, llm);
+
     res.status(200).json({ images });
   } catch (err) {
     res.status(500).json({ message: 'An error has occurred.' });
-    console.log(err.message);
+    logger.error(`Error in image search: ${err.message}`);
   }
 });
 

src/routes/index.ts

@@ -1,8 +1,14 @@
 import express from 'express';
 import imagesRouter from './images';
+import videosRouter from './videos';
+import configRouter from './config';
+import modelsRouter from './models';
 
 const router = express.Router();
 
 router.use('/images', imagesRouter);
+router.use('/videos', videosRouter);
+router.use('/config', configRouter);
+router.use('/models', modelsRouter);
 
 export default router;

src/routes/models.ts

@@ -0,0 +1,24 @@
+import express from 'express';
+import logger from '../utils/logger';
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '../lib/providers';
+
+const router = express.Router();
+
+router.get('/', async (req, res) => {
+  try {
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
+
+    res.status(200).json({ chatModelProviders, embeddingModelProviders });
+  } catch (err) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(err.message);
+  }
+});
+
+export default router;

src/routes/videos.ts

@@ -0,0 +1,46 @@
+import express from 'express';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { getAvailableChatModelProviders } from '../lib/providers';
+import { HumanMessage, AIMessage } from '@langchain/core/messages';
+import logger from '../utils/logger';
+import handleVideoSearch from '../agents/videoSearchAgent';
+
+const router = express.Router();
+
+router.post('/', async (req, res) => {
+  try {
+    let { query, chat_history, chat_model_provider, chat_model } = req.body;
+
+    chat_history = chat_history.map((msg: any) => {
+      if (msg.role === 'user') {
+        return new HumanMessage(msg.content);
+      } else if (msg.role === 'assistant') {
+        return new AIMessage(msg.content);
+      }
+    });
+
+    const chatModels = await getAvailableChatModelProviders();
+    const provider = chat_model_provider || Object.keys(chatModels)[0];
+    const chatModel = chat_model || Object.keys(chatModels[provider])[0];
+
+    let llm: BaseChatModel | undefined;
+
+    if (chatModels[provider] && chatModels[provider][chatModel]) {
+      llm = chatModels[provider][chatModel] as BaseChatModel | undefined;
+    }
+
+    if (!llm) {
+      res.status(500).json({ message: 'Invalid LLM model selected' });
+      return;
+    }
+
+    const videos = await handleVideoSearch({ chat_history, query }, llm);
+
+    res.status(200).json({ videos });
+  } catch (err) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(`Error in video search: ${err.message}`);
+  }
+});
+
+export default router;

src/utils/computeSimilarity.ts

@@ -1,10 +1,13 @@
 import dot from 'compute-dot';
 import cosineSimilarity from 'compute-cosine-similarity';
+import { getSimilarityMeasure } from '../config';
 
 const computeSimilarity = (x: number[], y: number[]): number => {
-  if (process.env.SIMILARITY_MEASURE === 'cosine') {
+  const similarityMeasure = getSimilarityMeasure();
+
+  if (similarityMeasure === 'cosine') {
     return cosineSimilarity(x, y);
-  } else if (process.env.SIMILARITY_MEASURE === 'dot') {
+  } else if (similarityMeasure === 'dot') {
     return dot(x, y);
   }
 

src/utils/logger.ts

@@ -0,0 +1,22 @@
+import winston from 'winston';
+
+const logger = winston.createLogger({
+  level: 'info',
+  transports: [
+    new winston.transports.Console({
+      format: winston.format.combine(
+        winston.format.colorize(),
+        winston.format.simple(),
+      ),
+    }),
+    new winston.transports.File({
+      filename: 'app.log',
+      format: winston.format.combine(
+        winston.format.timestamp(),
+        winston.format.json(),
+      ),
+    }),
+  ],
+});
+
+export default logger;

src/websocket/connectionManager.ts

@@ -1,11 +1,87 @@
 import { WebSocket } from 'ws';
 import { handleMessage } from './messageHandler';
+import {
+  getAvailableEmbeddingModelProviders,
+  getAvailableChatModelProviders,
+} from '../lib/providers';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
+import type { IncomingMessage } from 'http';
+import logger from '../utils/logger';
+import { ChatOpenAI } from '@langchain/openai';
+
+export const handleConnection = async (
+  ws: WebSocket,
+  request: IncomingMessage,
+) => {
+  const searchParams = new URL(request.url, `http://${request.headers.host}`)
+    .searchParams;
+
+  const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+    getAvailableChatModelProviders(),
+    getAvailableEmbeddingModelProviders(),
+  ]);
+
+  const chatModelProvider =
+    searchParams.get('chatModelProvider') || Object.keys(chatModelProviders)[0];
+  const chatModel =
+    searchParams.get('chatModel') ||
+    Object.keys(chatModelProviders[chatModelProvider])[0];
+
+  const embeddingModelProvider =
+    searchParams.get('embeddingModelProvider') ||
+    Object.keys(embeddingModelProviders)[0];
+  const embeddingModel =
+    searchParams.get('embeddingModel') ||
+    Object.keys(embeddingModelProviders[embeddingModelProvider])[0];
+
+  let llm: BaseChatModel | undefined;
+  let embeddings: Embeddings | undefined;
+
+  if (
+    chatModelProviders[chatModelProvider] &&
+    chatModelProviders[chatModelProvider][chatModel] &&
+    chatModelProvider != 'custom_openai'
+  ) {
+    llm = chatModelProviders[chatModelProvider][chatModel] as
+      | BaseChatModel
+      | undefined;
+  } else if (chatModelProvider == 'custom_openai') {
+    llm = new ChatOpenAI({
+      modelName: chatModel,
+      openAIApiKey: searchParams.get('openAIApiKey'),
+      temperature: 0.7,
+      configuration: {
+        baseURL: searchParams.get('openAIBaseURL'),
+      },
+    });
+  }
+
+  if (
+    embeddingModelProviders[embeddingModelProvider] &&
+    embeddingModelProviders[embeddingModelProvider][embeddingModel]
+  ) {
+    embeddings = embeddingModelProviders[embeddingModelProvider][
+      embeddingModel
+    ] as Embeddings | undefined;
+  }
+
+  if (!llm || !embeddings) {
+    ws.send(
+      JSON.stringify({
+        type: 'error',
+        data: 'Invalid LLM or embeddings model selected, please refresh the page and try again.',
+        key: 'INVALID_MODEL_SELECTED',
+      }),
+    );
+    ws.close();
+  }
 
-export const handleConnection = (ws: WebSocket) => {
   ws.on(
     'message',
-    async (message) => await handleMessage(message.toString(), ws),
+    async (message) =>
+      await handleMessage(message.toString(), ws, llm, embeddings),
   );
 
-  ws.on('close', () => console.log('Connection closed'));
+  ws.on('close', () => logger.debug('Connection closed'));
 };

src/websocket/messageHandler.ts

@@ -6,6 +6,9 @@ import handleWritingAssistant from '../agents/writingAssistant';
 import handleWolframAlphaSearch from '../agents/wolframAlphaSearchAgent';
 import handleYoutubeSearch from '../agents/youtubeSearchAgent';
 import handleRedditSearch from '../agents/redditSearchAgent';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
+import logger from '../utils/logger';
 
 type Message = {
   type: string;
@@ -54,18 +57,33 @@ const handleEmitterEvents = (
   });
   emitter.on('error', (data) => {
     const parsedData = JSON.parse(data);
-    ws.send(JSON.stringify({ type: 'error', data: parsedData.data }));
+    ws.send(
+      JSON.stringify({
+        type: 'error',
+        data: parsedData.data,
+        key: 'CHAIN_ERROR',
+      }),
+    );
   });
 };
 
-export const handleMessage = async (message: string, ws: WebSocket) => {
+export const handleMessage = async (
+  message: string,
+  ws: WebSocket,
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   try {
     const parsedMessage = JSON.parse(message) as Message;
     const id = Math.random().toString(36).substring(7);
 
     if (!parsedMessage.content)
       return ws.send(
-        JSON.stringify({ type: 'error', data: 'Invalid message format' }),
+        JSON.stringify({
+          type: 'error',
+          data: 'Invalid message format',
+          key: 'INVALID_FORMAT',
+        }),
       );
 
     const history: BaseMessage[] = parsedMessage.history.map((msg) => {
@@ -83,14 +101,31 @@ export const handleMessage = async (message: string, ws: WebSocket) => {
     if (parsedMessage.type === 'message') {
       const handler = searchHandlers[parsedMessage.focusMode];
       if (handler) {
-        const emitter = handler(parsedMessage.content, history);
+        const emitter = handler(
+          parsedMessage.content,
+          history,
+          llm,
+          embeddings,
+        );
         handleEmitterEvents(emitter, ws, id);
       } else {
-        ws.send(JSON.stringify({ type: 'error', data: 'Invalid focus mode' }));
+        ws.send(
+          JSON.stringify({
+            type: 'error',
+            data: 'Invalid focus mode',
+            key: 'INVALID_FOCUS_MODE',
+          }),
+        );
       }
     }
-  } catch (error) {
-    console.error('Failed to handle message', error);
-    ws.send(JSON.stringify({ type: 'error', data: 'Invalid message format' }));
+  } catch (err) {
+    ws.send(
+      JSON.stringify({
+        type: 'error',
+        data: 'Invalid message format',
+        key: 'INVALID_FORMAT',
+      }),
+    );
+    logger.error(`Failed to handle message: ${err}`);
   }
 };

src/websocket/websocketServer.ts

@@ -1,15 +1,16 @@
 import { WebSocketServer } from 'ws';
 import { handleConnection } from './connectionManager';
 import http from 'http';
+import { getPort } from '../config';
+import logger from '../utils/logger';
 
 export const initServer = (
   server: http.Server<typeof http.IncomingMessage, typeof http.ServerResponse>,
 ) => {
+  const port = getPort();
   const wss = new WebSocketServer({ server });
 
-  wss.on('connection', (ws) => {
-    handleConnection(ws);
-  });
+  wss.on('connection', handleConnection);
 
-  console.log(`WebSocket server started on port ${process.env.PORT}`);
+  logger.info(`WebSocket server started on port ${port}`);
 };

ui/app/layout.tsx

@@ -3,6 +3,7 @@ import { Montserrat } from 'next/font/google';
 import './globals.css';
 import { cn } from '@/lib/utils';
 import Sidebar from '@/components/Sidebar';
+import { Toaster } from 'sonner';
 
 const montserrat = Montserrat({
   weight: ['300', '400', '500', '700'],
@@ -26,6 +27,15 @@ export default function RootLayout({
     <html className="h-full" lang="en">
       <body className={cn('h-full', montserrat.className)}>
         <Sidebar>{children}</Sidebar>
+        <Toaster
+          toastOptions={{
+            unstyled: true,
+            classNames: {
+              toast:
+                'bg-[#111111] text-white rounded-lg p-4 flex flex-row items-center space-x-2',
+            },
+          }}
+        />
       </body>
     </html>
   );

ui/components/Chat.tsx

@@ -77,7 +77,7 @@ const Chat = ({
           className="bottom-24 lg:bottom-10 fixed z-40"
           style={{ width: dividerWidth }}
         >
-          <MessageInput sendMessage={sendMessage} />
+          <MessageInput loading={loading} sendMessage={sendMessage} />
         </div>
       )}
     </div>

ui/components/ChatWindow.tsx

@@ -5,6 +5,7 @@ import { Document } from '@langchain/core/documents';
 import Navbar from './Navbar';
 import Chat from './Chat';
 import EmptyChat from './EmptyChat';
+import { toast } from 'sonner';
 
 export type Message = {
   id: string;
@@ -19,11 +20,102 @@ const useSocket = (url: string) => {
 
   useEffect(() => {
     if (!ws) {
-      const ws = new WebSocket(url);
-      ws.onopen = () => {
-        console.log('[DEBUG] open');
-        setWs(ws);
+      const connectWs = async () => {
+        let chatModel = localStorage.getItem('chatModel');
+        let chatModelProvider = localStorage.getItem('chatModelProvider');
+        let embeddingModel = localStorage.getItem('embeddingModel');
+        let embeddingModelProvider = localStorage.getItem(
+          'embeddingModelProvider',
+        );
+
+        if (
+          !chatModel ||
+          !chatModelProvider ||
+          !embeddingModel ||
+          !embeddingModelProvider
+        ) {
+          const providers = await fetch(
+            `${process.env.NEXT_PUBLIC_API_URL}/models`,
+            {
+              headers: {
+                'Content-Type': 'application/json',
+              },
+            },
+          ).then(async (res) => await res.json());
+
+          const chatModelProviders = providers.chatModelProviders;
+          const embeddingModelProviders = providers.embeddingModelProviders;
+
+          if (
+            !chatModelProviders ||
+            Object.keys(chatModelProviders).length === 0
+          )
+            return console.error('No chat models available');
+
+          if (
+            !embeddingModelProviders ||
+            Object.keys(embeddingModelProviders).length === 0
+          )
+            return console.error('No embedding models available');
+
+          chatModelProvider = Object.keys(chatModelProviders)[0];
+          chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
+
+          embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
+          embeddingModel = Object.keys(
+            embeddingModelProviders[embeddingModelProvider],
+          )[0];
+
+          localStorage.setItem('chatModel', chatModel!);
+          localStorage.setItem('chatModelProvider', chatModelProvider);
+          localStorage.setItem('embeddingModel', embeddingModel!);
+          localStorage.setItem(
+            'embeddingModelProvider',
+            embeddingModelProvider,
+          );
+        }
+
+        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());
+
+        ws.onopen = () => {
+          console.log('[DEBUG] open');
+          setWs(ws);
+        };
+
+        ws.onmessage = (e) => {
+          const parsedData = JSON.parse(e.data);
+          if (parsedData.type === 'error') {
+            toast.error(parsedData.data);
+            if (parsedData.key === 'INVALID_MODEL_SELECTED') {
+              localStorage.clear();
+            }
+          }
+        };
       };
+
+      connectWs();
     }
 
     return () => {
@@ -74,6 +166,12 @@ const ChatWindow = () => {
     const messageHandler = (e: MessageEvent) => {
       const data = JSON.parse(e.data);
 
+      if (data.type === 'error') {
+        toast.error(data.data);
+        setLoading(false);
+        return;
+      }
+
       if (data.type === 'sources') {
         sources = data.data;
         if (!added) {
@@ -152,7 +250,7 @@ const ChatWindow = () => {
     sendMessage(message.content);
   };
 
-  return (
+  return ws ? (
     <div>
       {messages.length > 0 ? (
         <>
@@ -173,6 +271,25 @@ const ChatWindow = () => {
         />
       )}
     </div>
+  ) : (
+    <div className="flex flex-row items-center justify-center min-h-screen">
+      <svg
+        aria-hidden="true"
+        className="w-8 h-8 text-[#202020] animate-spin fill-[#ffffff3b]"
+        viewBox="0 0 100 101"
+        fill="none"
+        xmlns="http://www.w3.org/2000/svg"
+      >
+        <path
+          d="M100 50.5908C100 78.2051 77.6142 100.591 50 100.591C22.3858 100.591 0 78.2051 0 50.5908C0 22.9766 22.3858 0.59082 50 0.59082C77.6142 0.59082 100 22.9766 100 50.5908ZM9.08144 50.5908C9.08144 73.1895 27.4013 91.5094 50 91.5094C72.5987 91.5094 90.9186 73.1895 90.9186 50.5908C90.9186 27.9921 72.5987 9.67226 50 9.67226C27.4013 9.67226 9.08144 27.9921 9.08144 50.5908Z"
+          fill="currentColor"
+        />
+        <path
+          d="M93.9676 39.0409C96.393 38.4038 97.8624 35.9116 97.0079 33.5539C95.2932 28.8227 92.871 24.3692 89.8167 20.348C85.8452 15.1192 80.8826 10.7238 75.2124 7.41289C69.5422 4.10194 63.2754 1.94025 56.7698 1.05124C51.7666 0.367541 46.6976 0.446843 41.7345 1.27873C39.2613 1.69328 37.813 4.19778 38.4501 6.62326C39.0873 9.04874 41.5694 10.4717 44.0505 10.1071C47.8511 9.54855 51.7191 9.52689 55.5402 10.0491C60.8642 10.7766 65.9928 12.5457 70.6331 15.2552C75.2735 17.9648 79.3347 21.5619 82.5849 25.841C84.9175 28.9121 86.7997 32.2913 88.1811 35.8758C89.083 38.2158 91.5421 39.6781 93.9676 39.0409Z"
+          fill="currentFill"
+        />
+      </svg>
+    </div>
   );
 };
 

ui/components/MessageBox.tsx

@@ -1,21 +1,17 @@
+'use client';
+
 /* eslint-disable @next/next/no-img-element */
 import React, { MutableRefObject, useEffect, useState } from 'react';
 import { Message } from './ChatWindow';
 import { cn } from '@/lib/utils';
-import {
-  BookCopy,
-  Disc3,
-  FilePen,
-  PlusIcon,
-  Share,
-  ThumbsDown,
-  VideoIcon,
-} from 'lucide-react';
+import { BookCopy, Disc3, Share, Volume2, StopCircle } from 'lucide-react';
 import Markdown 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';
 
 const MessageBox = ({
   message,
@@ -35,15 +31,16 @@ const MessageBox = ({
   rewrite: (messageId: string) => void;
 }) => {
   const [parsedMessage, setParsedMessage] = useState(message.content);
+  const [speechMessage, setSpeechMessage] = useState(message.content);
 
   useEffect(() => {
+    const regex = /\[(\d+)\]/g;
+
     if (
       message.role === 'assistant' &&
       message?.sources &&
       message.sources.length > 0
     ) {
-      const regex = /\[(\d+)\]/g;
-
       return setParsedMessage(
         message.content.replace(
           regex,
@@ -52,9 +49,13 @@ const MessageBox = ({
         ),
       );
     }
+
+    setSpeechMessage(message.content.replace(regex, ''));
     setParsedMessage(message.content);
   }, [message.content, message.sources, message.role]);
 
+  const { speechStatus, start, stop } = useSpeech({ text: speechMessage });
+
   return (
     <div>
       {message.role === 'user' && (
@@ -94,7 +95,7 @@ const MessageBox = ({
               <Markdown className="prose max-w-none break-words prose-invert prose-p:leading-relaxed prose-pre:p-0 text-white text-sm md:text-base font-medium">
                 {parsedMessage}
               </Markdown>
-              {!loading && (
+              {loading && isLast ? null : (
                 <div className="flex flex-row items-center justify-between w-full text-white py-4 -mx-2">
                   <div className="flex flex-row items-center space-x-1">
                     <button className="p-2 text-white/70 rounded-xl hover:bg-[#1c1c1c] transition duration-200 hover:text-white">
@@ -104,11 +105,21 @@ const MessageBox = ({
                   </div>
                   <div className="flex flex-row items-center space-x-1">
                     <Copy initialMessage={message.content} message={message} />
-                    <button className="p-2 text-white/70 rounded-xl hover:bg-[#1c1c1c] transition duration-200 hover:text-white">
-                      <FilePen size={18} />
-                    </button>
-                    <button className="p-2 text-white/70 rounded-xl hover:bg-[#1c1c1c] transition duration-200 hover:text-white">
-                      <ThumbsDown size={18} />
+                    <button
+                      onClick={() => {
+                        if (speechStatus === 'started') {
+                          stop();
+                        } else {
+                          start();
+                        }
+                      }}
+                      className="p-2 text-white/70 rounded-xl hover:bg-[#1c1c1c] transition duration-200 hover:text-white"
+                    >
+                      {speechStatus === 'started' ? (
+                        <StopCircle size={18} />
+                      ) : (
+                        <Volume2 size={18} />
+                      )}
                     </button>
                   </div>
                 </div>
@@ -116,14 +127,14 @@ const MessageBox = ({
             </div>
           </div>
           <div className="lg:sticky lg:top-20 flex flex-col items-center space-y-3 w-full lg:w-3/12 z-30 h-full pb-4">
-            <SearchImages query={history[messageIndex - 1].content} />
-            <div className="border border-dashed border-[#1C1C1C] px-4 py-2 flex flex-row items-center justify-between rounded-lg text-white text-sm w-full">
-              <div className="flex flex-row items-center space-x-2">
-                <VideoIcon size={17} />
-                <p>Search videos</p>
-              </div>
-              <PlusIcon className="text-[#24A0ED]" size={17} />
-            </div>
+            <SearchImages
+              query={history[messageIndex - 1].content}
+              chat_history={history.slice(0, messageIndex - 1)}
+            />
+            <SearchVideos
+              chat_history={history.slice(0, messageIndex - 1)}
+              query={history[messageIndex - 1].content}
+            />
           </div>
         </div>
       )}

ui/components/MessageInput.tsx

@@ -6,8 +6,10 @@ import { Attach, CopilotToggle } from './MessageInputActions';
 
 const MessageInput = ({
   sendMessage,
+  loading,
 }: {
   sendMessage: (message: string) => void;
+  loading: boolean;
 }) => {
   const [copilotEnabled, setCopilotEnabled] = useState(false);
   const [message, setMessage] = useState('');
@@ -25,12 +27,13 @@ const MessageInput = ({
   return (
     <form
       onSubmit={(e) => {
+        if (loading) return;
         e.preventDefault();
         sendMessage(message);
         setMessage('');
       }}
       onKeyDown={(e) => {
-        if (e.key === 'Enter' && !e.shiftKey) {
+        if (e.key === 'Enter' && !e.shiftKey && !loading) {
           e.preventDefault();
           sendMessage(message);
           setMessage('');
@@ -58,7 +61,7 @@ const MessageInput = ({
             setCopilotEnabled={setCopilotEnabled}
           />
           <button
-            disabled={message.trim().length === 0}
+            disabled={message.trim().length === 0 || loading}
             className="bg-[#24A0ED] text-white disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#ececec21] rounded-full p-2"
           >
             <ArrowUp className="bg-background" size={17} />
@@ -74,7 +77,7 @@ const MessageInput = ({
               setCopilotEnabled={setCopilotEnabled}
             />
             <button
-              disabled={message.trim().length === 0}
+              disabled={message.trim().length === 0 || loading}
               className="bg-[#24A0ED] text-white disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#ececec21] rounded-full p-2"
             >
               <ArrowUp className="bg-background" size={17} />

ui/components/MessageInputActions.tsx

@@ -109,7 +109,7 @@ export const Focus = ({
         leaveTo="opacity-0 translate-y-1"
       >
         <Popover.Panel className="absolute z-10 w-full">
-          <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-1 bg-[#0A0A0A] border rounded-lg  border-[#1c1c1c] w-full p-2">
+          <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-1 bg-[#0A0A0A] border rounded-lg border-[#1c1c1c] w-full p-2 max-h-[200px] md:max-h-none overflow-y-auto">
             {focusModes.map((mode, i) => (
               <Popover.Button
                 onClick={() => setFocusMode(mode.key)}

ui/components/MessageSources.tsx

@@ -1,21 +1,20 @@
 /* eslint-disable @next/next/no-img-element */
-import { cn } from '@/lib/utils';
 import { Dialog, Transition } from '@headlessui/react';
 import { Document } from '@langchain/core/documents';
-import Link from 'next/link';
 import { Fragment, useState } from 'react';
 
 const MessageSources = ({ sources }: { sources: Document[] }) => {
   const [isDialogOpen, setIsDialogOpen] = useState(false);
-  function closeModal() {
+
+  const closeModal = () => {
     setIsDialogOpen(false);
     document.body.classList.remove('overflow-hidden-scrollable');
-  }
+  };
 
-  function openModal() {
+  const openModal = () => {
     setIsDialogOpen(true);
     document.body.classList.add('overflow-hidden-scrollable');
-  }
+  };
 
   return (
     <div className="grid grid-cols-2 lg:grid-cols-4 gap-2">

ui/components/Navbar.tsx

@@ -38,12 +38,12 @@ const Navbar = ({ messages }: { messages: Message[] }) => {
   }, []);
 
   return (
-    <div className="fixed z-40 top-0 left-0 right-0 px-4 lg:pl-32 lg:pr-4 lg:px-8 flex flex-row items-center justify-between w-full py-4 text-sm text-white/70 border-b bg-[#0A0A0A] border-[#1C1C1C]">
+    <div className="fixed z-40 top-0 left-0 right-0 px-4 lg:pl-[104px] lg:pr-6 lg:px-8 flex flex-row items-center justify-between w-full py-4 text-sm text-white/70 border-b bg-[#0A0A0A] border-[#1C1C1C]">
       <Edit
         size={17}
         className="active:scale-95 transition duration-100 cursor-pointer lg:hidden"
       />
-      <div className="hidden lg:flex flex-row items-center space-x-2">
+      <div className="hidden lg:flex flex-row items-center justify-center space-x-2">
         <Clock size={17} />
         <p className="text-xs">{timeAgo} ago</p>
       </div>

ui/components/SearchImages.tsx

@@ -3,6 +3,7 @@ import { ImagesIcon, PlusIcon } from 'lucide-react';
 import { useState } from 'react';
 import Lightbox from 'yet-another-react-lightbox';
 import 'yet-another-react-lightbox/styles.css';
+import { Message } from './ChatWindow';
 
 type Image = {
   url: string;
@@ -10,7 +11,13 @@ type Image = {
   title: string;
 };
 
-const SearchImages = ({ query }: { query: string }) => {
+const SearchImages = ({
+  query,
+  chat_history,
+}: {
+  query: string;
+  chat_history: Message[];
+}) => {
   const [images, setImages] = useState<Image[] | null>(null);
   const [loading, setLoading] = useState(false);
   const [open, setOpen] = useState(false);
@@ -22,6 +29,10 @@ const SearchImages = ({ query }: { query: string }) => {
         <button
           onClick={async () => {
             setLoading(true);
+
+            const chatModelProvider = localStorage.getItem('chatModelProvider');
+            const chatModel = localStorage.getItem('chatModel');
+
             const res = await fetch(
               `${process.env.NEXT_PUBLIC_API_URL}/images`,
               {
@@ -31,7 +42,9 @@ const SearchImages = ({ query }: { query: string }) => {
                 },
                 body: JSON.stringify({
                   query: query,
-                  chat_history: [],
+                  chat_history: chat_history,
+                  chat_model_provider: chatModelProvider,
+                  chat_model: chatModel,
                 }),
               },
             );
@@ -59,7 +72,7 @@ const SearchImages = ({ query }: { query: string }) => {
         </button>
       )}
       {loading && (
-        <div className="grid grid-cols-1 lg:grid-cols-2 gap-2">
+        <div className="grid grid-cols-2 gap-2">
           {[...Array(4)].map((_, i) => (
             <div
               key={i}
@@ -85,7 +98,7 @@ const SearchImages = ({ query }: { query: string }) => {
                     key={i}
                     src={image.img_src}
                     alt={image.title}
-                    className="h-full w-full aspect-video object-cover rounded-lg transition duration-200 active:scale-95 cursor-pointer"
+                    className="h-full w-full aspect-video object-cover rounded-lg transition duration-200 active:scale-95 hover:scale-[1.02] cursor-zoom-in"
                   />
                 ))
               : images.map((image, i) => (
@@ -101,13 +114,13 @@ const SearchImages = ({ query }: { query: string }) => {
                     key={i}
                     src={image.img_src}
                     alt={image.title}
-                    className="h-full w-full aspect-video object-cover rounded-lg transition duration-200 active:scale-95 cursor-pointer"
+                    className="h-full w-full aspect-video object-cover rounded-lg transition duration-200 active:scale-95 hover:scale-[1.02] cursor-zoom-in"
                   />
                 ))}
             {images.length > 4 && (
               <button
                 onClick={() => setOpen(true)}
-                className="bg-[#111111] hover:bg-[#1c1c1c] transition duration-200 active:scale-95 h-auto w-full rounded-lg flex flex-col justify-between text-white p-2"
+                className="bg-[#111111] hover:bg-[#1c1c1c] transition duration-200 active:scale-95 hover:scale-[1.02] h-auto w-full rounded-lg flex flex-col justify-between text-white p-2"
               >
                 <div className="flex flex-row items-center space-x-1">
                   {images.slice(3, 6).map((image, i) => (
@@ -120,7 +133,7 @@ const SearchImages = ({ query }: { query: string }) => {
                   ))}
                 </div>
                 <p className="text-white/70 text-xs">
-                  View {images.slice(0, 2).length} more
+                  View {images.length - 3} more
                 </p>
               </button>
             )}

ui/components/SearchVideos.tsx

@@ -0,0 +1,196 @@
+/* eslint-disable @next/next/no-img-element */
+import { PlayCircle, PlayIcon, PlusIcon, VideoIcon } from 'lucide-react';
+import { useState } from 'react';
+import Lightbox, { GenericSlide, VideoSlide } from 'yet-another-react-lightbox';
+import 'yet-another-react-lightbox/styles.css';
+import { Message } from './ChatWindow';
+
+type Video = {
+  url: string;
+  img_src: string;
+  title: string;
+  iframe_src: string;
+};
+
+declare module 'yet-another-react-lightbox' {
+  export interface VideoSlide extends GenericSlide {
+    type: 'video-slide';
+    src: string;
+    iframe_src: string;
+  }
+
+  interface SlideTypes {
+    'video-slide': VideoSlide;
+  }
+}
+
+const Searchvideos = ({
+  query,
+  chat_history,
+}: {
+  query: string;
+  chat_history: Message[];
+}) => {
+  const [videos, setVideos] = useState<Video[] | null>(null);
+  const [loading, setLoading] = useState(false);
+  const [open, setOpen] = useState(false);
+  const [slides, setSlides] = useState<VideoSlide[]>([]);
+
+  return (
+    <>
+      {!loading && videos === null && (
+        <button
+          onClick={async () => {
+            setLoading(true);
+
+            const chatModelProvider = localStorage.getItem('chatModelProvider');
+            const chatModel = localStorage.getItem('chatModel');
+
+            const res = await fetch(
+              `${process.env.NEXT_PUBLIC_API_URL}/videos`,
+              {
+                method: 'POST',
+                headers: {
+                  'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({
+                  query: query,
+                  chat_history: chat_history,
+                  chat_model_provider: chatModelProvider,
+                  chat_model: chatModel,
+                }),
+              },
+            );
+
+            const data = await res.json();
+
+            const videos = data.videos;
+            setVideos(videos);
+            setSlides(
+              videos.map((video: Video) => {
+                return {
+                  type: 'video-slide',
+                  iframe_src: video.iframe_src,
+                  src: video.img_src,
+                };
+              }),
+            );
+            setLoading(false);
+          }}
+          className="border border-dashed border-[#1C1C1C] hover:bg-[#1c1c1c] active:scale-95 duration-200 transition px-4 py-2 flex flex-row items-center justify-between rounded-lg text-white text-sm w-full"
+        >
+          <div className="flex flex-row items-center space-x-2">
+            <VideoIcon size={17} />
+            <p>Search videos</p>
+          </div>
+          <PlusIcon className="text-[#24A0ED]" size={17} />
+        </button>
+      )}
+      {loading && (
+        <div className="grid grid-cols-2 gap-2">
+          {[...Array(4)].map((_, i) => (
+            <div
+              key={i}
+              className="bg-[#1C1C1C] h-32 w-full rounded-lg animate-pulse aspect-video object-cover"
+            />
+          ))}
+        </div>
+      )}
+      {videos !== null && videos.length > 0 && (
+        <>
+          <div className="grid grid-cols-2 gap-2">
+            {videos.length > 4
+              ? videos.slice(0, 3).map((video, i) => (
+                  <div
+                    onClick={() => {
+                      setOpen(true);
+                      setSlides([
+                        slides[i],
+                        ...slides.slice(0, i),
+                        ...slides.slice(i + 1),
+                      ]);
+                    }}
+                    className="relative transition duration-200 active:scale-95 hover:scale-[1.02] cursor-pointer"
+                    key={i}
+                  >
+                    <img
+                      src={video.img_src}
+                      alt={video.title}
+                      className="relative h-full w-full aspect-video object-cover rounded-lg"
+                    />
+                    <div className="absolute bg-black/70 text-white/70 px-2 py-1 flex flex-row items-center space-x-1 bottom-1 right-1 rounded-md">
+                      <PlayCircle size={15} />
+                      <p className="text-xs">Video</p>
+                    </div>
+                  </div>
+                ))
+              : videos.map((video, i) => (
+                  <div
+                    onClick={() => {
+                      setOpen(true);
+                      setSlides([
+                        slides[i],
+                        ...slides.slice(0, i),
+                        ...slides.slice(i + 1),
+                      ]);
+                    }}
+                    className="relative transition duration-200 active:scale-95 hover:scale-[1.02] cursor-pointer"
+                    key={i}
+                  >
+                    <img
+                      src={video.img_src}
+                      alt={video.title}
+                      className="relative h-full w-full aspect-video object-cover rounded-lg"
+                    />
+                    <div className="absolute bg-black/70 text-white/70 px-2 py-1 flex flex-row items-center space-x-1 bottom-1 right-1 rounded-md">
+                      <PlayCircle size={15} />
+                      <p className="text-xs">Video</p>
+                    </div>
+                  </div>
+                ))}
+            {videos.length > 4 && (
+              <button
+                onClick={() => setOpen(true)}
+                className="bg-[#111111] hover:bg-[#1c1c1c] transition duration-200 active:scale-95 hover:scale-[1.02] h-auto w-full rounded-lg flex flex-col justify-between text-white p-2"
+              >
+                <div className="flex flex-row items-center space-x-1">
+                  {videos.slice(3, 6).map((video, i) => (
+                    <img
+                      key={i}
+                      src={video.img_src}
+                      alt={video.title}
+                      className="h-6 w-12 rounded-md lg:h-3 lg:w-6 lg:rounded-sm aspect-video object-cover"
+                    />
+                  ))}
+                </div>
+                <p className="text-white/70 text-xs">
+                  View {videos.length - 3} more
+                </p>
+              </button>
+            )}
+          </div>
+          <Lightbox
+            open={open}
+            close={() => setOpen(false)}
+            slides={slides}
+            render={{
+              slide: ({ slide }) =>
+                slide.type === 'video-slide' ? (
+                  <div className="h-full w-full flex flex-row items-center justify-center">
+                    <iframe
+                      src={slide.iframe_src}
+                      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,
+            }}
+          />
+        </>
+      )}
+    </>
+  );
+};
+
+export default Searchvideos;

ui/components/SettingsDialog.tsx

@@ -0,0 +1,402 @@
+import { Dialog, Transition } from '@headlessui/react';
+import { CloudUpload, RefreshCcw, RefreshCw } from 'lucide-react';
+import React, { Fragment, useEffect, useState } from 'react';
+
+interface SettingsType {
+  chatModelProviders: {
+    [key: string]: string[];
+  };
+  embeddingModelProviders: {
+    [key: string]: string[];
+  };
+  openaiApiKey: string;
+  groqApiKey: string;
+  ollamaApiUrl: string;
+}
+
+const SettingsDialog = ({
+  isOpen,
+  setIsOpen,
+}: {
+  isOpen: boolean;
+  setIsOpen: (isOpen: boolean) => void;
+}) => {
+  const [config, setConfig] = useState<SettingsType | null>(null);
+  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 [customOpenAIApiKey, setCustomOpenAIApiKey] = useState<string | null>(
+    null,
+  );
+  const [customOpenAIBaseURL, setCustomOpenAIBaseURL] = useState<string | null>(
+    null,
+  );
+  const [isLoading, setIsLoading] = useState(false);
+  const [isUpdating, setIsUpdating] = useState(false);
+
+  useEffect(() => {
+    if (isOpen) {
+      const fetchConfig = async () => {
+        setIsLoading(true);
+        const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/config`, {
+          headers: {
+            'Content-Type': 'application/json',
+          },
+        });
+        const data = await res.json();
+        setConfig(data);
+        setSelectedChatModelProvider(localStorage.getItem('chatModelProvider'));
+        setSelectedChatModel(localStorage.getItem('chatModel'));
+        setSelectedEmbeddingModelProvider(
+          localStorage.getItem('embeddingModelProvider'),
+        );
+        setSelectedEmbeddingModel(localStorage.getItem('embeddingModel'));
+        setCustomOpenAIApiKey(localStorage.getItem('openAIApiKey'));
+        setCustomOpenAIBaseURL(localStorage.getItem('openAIBaseUrl'));
+        setIsLoading(false);
+      };
+
+      fetchConfig();
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [isOpen]);
+
+  const handleSubmit = async () => {
+    setIsUpdating(true);
+
+    try {
+      await fetch(`${process.env.NEXT_PUBLIC_API_URL}/config`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify(config),
+      });
+
+      localStorage.setItem('chatModelProvider', selectedChatModelProvider!);
+      localStorage.setItem('chatModel', selectedChatModel!);
+      localStorage.setItem(
+        'embeddingModelProvider',
+        selectedEmbeddingModelProvider!,
+      );
+      localStorage.setItem('embeddingModel', selectedEmbeddingModel!);
+      localStorage.setItem('openAIApiKey', customOpenAIApiKey!);
+      localStorage.setItem('openAIBaseURL', customOpenAIBaseURL!);
+    } catch (err) {
+      console.log(err);
+    } finally {
+      setIsUpdating(false);
+      setIsOpen(false);
+
+      window.location.reload();
+    }
+  };
+
+  return (
+    <Transition appear show={isOpen} as={Fragment}>
+      <Dialog
+        as="div"
+        className="relative z-50"
+        onClose={() => setIsOpen(false)}
+      >
+        <Transition.Child
+          as={Fragment}
+          enter="ease-out duration-300"
+          enterFrom="opacity-0"
+          enterTo="opacity-100"
+          leave="ease-in duration-200"
+          leaveFrom="opacity-100"
+          leaveTo="opacity-0"
+        >
+          <div className="fixed inset-0 bg-black/50" />
+        </Transition.Child>
+        <div className="fixed inset-0 overflow-y-auto">
+          <div className="flex min-h-full items-center justify-center p-4 text-center">
+            <Transition.Child
+              as={Fragment}
+              enter="ease-out duration-200"
+              enterFrom="opacity-0 scale-95"
+              enterTo="opacity-100 scale-100"
+              leave="ease-in duration-100"
+              leaveFrom="opacity-100 scale-200"
+              leaveTo="opacity-0 scale-95"
+            >
+              <Dialog.Panel className="w-full max-w-md transform rounded-2xl bg-[#111111] border border-[#1c1c1c] p-6 text-left align-middle shadow-xl transition-all">
+                <Dialog.Title className="text-xl font-medium leading-6 text-white">
+                  Settings
+                </Dialog.Title>
+                {config && !isLoading && (
+                  <div className="flex flex-col space-y-4 mt-6">
+                    {config.chatModelProviders && (
+                      <div className="flex flex-col space-y-1">
+                        <p className="text-white/70 text-sm">
+                          Chat model Provider
+                        </p>
+                        <select
+                          onChange={(e) => {
+                            setSelectedChatModelProvider(e.target.value);
+                            setSelectedChatModel(
+                              config.chatModelProviders[e.target.value][0],
+                            );
+                          }}
+                          className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
+                        >
+                          {Object.keys(config.chatModelProviders).map(
+                            (provider) => (
+                              <option
+                                key={provider}
+                                value={provider}
+                                selected={
+                                  provider === selectedChatModelProvider
+                                }
+                              >
+                                {provider.charAt(0).toUpperCase() +
+                                  provider.slice(1)}
+                              </option>
+                            ),
+                          )}
+                        </select>
+                      </div>
+                    )}
+                    {selectedChatModelProvider &&
+                      selectedChatModelProvider != 'custom_openai' && (
+                        <div className="flex flex-col space-y-1">
+                          <p className="text-white/70 text-sm">Chat Model</p>
+                          <select
+                            onChange={(e) =>
+                              setSelectedChatModel(e.target.value)
+                            }
+                            className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
+                          >
+                            {config.chatModelProviders[
+                              selectedChatModelProvider
+                            ] ? (
+                              config.chatModelProviders[
+                                selectedChatModelProvider
+                              ].length > 0 ? (
+                                config.chatModelProviders[
+                                  selectedChatModelProvider
+                                ].map((model) => (
+                                  <option
+                                    key={model}
+                                    value={model}
+                                    selected={model === selectedChatModel}
+                                  >
+                                    {model}
+                                  </option>
+                                ))
+                              ) : (
+                                <option value="" disabled selected>
+                                  No models available
+                                </option>
+                              )
+                            ) : (
+                              <option value="" disabled selected>
+                                Invalid provider, please check backend logs
+                              </option>
+                            )}
+                          </select>
+                        </div>
+                      )}
+                    {selectedChatModelProvider &&
+                      selectedChatModelProvider === 'custom_openai' && (
+                        <>
+                          <div className="flex flex-col space-y-1">
+                            <p className="text-white/70 text-sm">Model name</p>
+                            <input
+                              type="text"
+                              placeholder="Model name"
+                              defaultValue={selectedChatModel!}
+                              onChange={(e) =>
+                                setSelectedChatModel(e.target.value)
+                              }
+                              className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
+                            />
+                          </div>
+                          <div className="flex flex-col space-y-1">
+                            <p className="text-white/70 text-sm">
+                              Custom OpenAI API Key (optional)
+                            </p>
+                            <input
+                              type="text"
+                              placeholder="Custom OpenAI API Key"
+                              defaultValue={customOpenAIApiKey!}
+                              onChange={(e) =>
+                                setCustomOpenAIApiKey(e.target.value)
+                              }
+                              className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
+                            />
+                          </div>
+                          <div className="flex flex-col space-y-1">
+                            <p className="text-white/70 text-sm">
+                              Custom OpenAI Base URL
+                            </p>
+                            <input
+                              type="text"
+                              placeholder="Custom OpenAI Base URL"
+                              defaultValue={customOpenAIBaseURL!}
+                              onChange={(e) =>
+                                setCustomOpenAIBaseURL(e.target.value)
+                              }
+                              className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
+                            />
+                          </div>
+                        </>
+                      )}
+                    {/* Embedding models */}
+                    {config.embeddingModelProviders && (
+                      <div className="flex flex-col space-y-1">
+                        <p className="text-white/70 text-sm">
+                          Embedding model Provider
+                        </p>
+                        <select
+                          onChange={(e) => {
+                            setSelectedEmbeddingModelProvider(e.target.value);
+                            setSelectedEmbeddingModel(
+                              config.embeddingModelProviders[e.target.value][0],
+                            );
+                          }}
+                          className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
+                        >
+                          {Object.keys(config.embeddingModelProviders).map(
+                            (provider) => (
+                              <option
+                                key={provider}
+                                value={provider}
+                                selected={
+                                  provider === selectedEmbeddingModelProvider
+                                }
+                              >
+                                {provider.charAt(0).toUpperCase() +
+                                  provider.slice(1)}
+                              </option>
+                            ),
+                          )}
+                        </select>
+                      </div>
+                    )}
+                    {selectedEmbeddingModelProvider && (
+                      <div className="flex flex-col space-y-1">
+                        <p className="text-white/70 text-sm">Embedding Model</p>
+                        <select
+                          onChange={(e) =>
+                            setSelectedEmbeddingModel(e.target.value)
+                          }
+                          className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
+                        >
+                          {config.embeddingModelProviders[
+                            selectedEmbeddingModelProvider
+                          ] ? (
+                            config.embeddingModelProviders[
+                              selectedEmbeddingModelProvider
+                            ].length > 0 ? (
+                              config.embeddingModelProviders[
+                                selectedEmbeddingModelProvider
+                              ].map((model) => (
+                                <option
+                                  key={model}
+                                  value={model}
+                                  selected={model === selectedEmbeddingModel}
+                                >
+                                  {model}
+                                </option>
+                              ))
+                            ) : (
+                              <option value="" disabled selected>
+                                No embedding models available
+                              </option>
+                            )
+                          ) : (
+                            <option value="" disabled selected>
+                              Invalid provider, please check backend logs
+                            </option>
+                          )}
+                        </select>
+                      </div>
+                    )}
+                    <div className="flex flex-col space-y-1">
+                      <p className="text-white/70 text-sm">OpenAI API Key</p>
+                      <input
+                        type="text"
+                        placeholder="OpenAI API Key"
+                        defaultValue={config.openaiApiKey}
+                        onChange={(e) =>
+                          setConfig({
+                            ...config,
+                            openaiApiKey: e.target.value,
+                          })
+                        }
+                        className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
+                      />
+                    </div>
+                    <div className="flex flex-col space-y-1">
+                      <p className="text-white/70 text-sm">Ollama API URL</p>
+                      <input
+                        type="text"
+                        placeholder="Ollama API URL"
+                        defaultValue={config.ollamaApiUrl}
+                        onChange={(e) =>
+                          setConfig({
+                            ...config,
+                            ollamaApiUrl: e.target.value,
+                          })
+                        }
+                        className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
+                      />
+                    </div>
+                    <div className="flex flex-col space-y-1">
+                      <p className="text-white/70 text-sm">GROQ API Key</p>
+                      <input
+                        type="text"
+                        placeholder="GROQ API Key"
+                        defaultValue={config.groqApiKey}
+                        onChange={(e) =>
+                          setConfig({
+                            ...config,
+                            groqApiKey: e.target.value,
+                          })
+                        }
+                        className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
+                      />
+                    </div>
+                  </div>
+                )}
+                {isLoading && (
+                  <div className="w-full flex items-center justify-center mt-6 text-white/70 py-6">
+                    <RefreshCcw className="animate-spin" />
+                  </div>
+                )}
+                <div className="w-full mt-6 space-y-2">
+                  <p className="text-xs text-white/50">
+                    We&apos;ll refresh the page after updating the settings.
+                  </p>
+                  <button
+                    onClick={handleSubmit}
+                    className="bg-[#24A0ED] flex flex-row items-center space-x-2 text-white disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#ececec21] rounded-full px-4 py-2"
+                    disabled={isLoading || isUpdating}
+                  >
+                    {isUpdating ? (
+                      <RefreshCw size={20} className="animate-spin" />
+                    ) : (
+                      <CloudUpload size={20} />
+                    )}
+                  </button>
+                </div>
+              </Dialog.Panel>
+            </Transition.Child>
+          </div>
+        </div>
+      </Dialog>
+    </Transition>
+  );
+};
+
+export default SettingsDialog;

ui/components/Sidebar.tsx

@@ -1,16 +1,19 @@
 'use client';
 
 import { cn } from '@/lib/utils';
-import { BookOpenText, Home, Search, SquarePen } from 'lucide-react';
-import { SiGithub } from '@icons-pack/react-simple-icons';
+import { BookOpenText, Home, Search, SquarePen, Settings } from 'lucide-react';
 import Link from 'next/link';
 import { useSelectedLayoutSegments } from 'next/navigation';
-import React from 'react';
+import React, { Fragment, useState } from 'react';
 import Layout from './Layout';
+import { Dialog, Transition } from '@headlessui/react';
+import SettingsDialog from './SettingsDialog';
 
 const Sidebar = ({ children }: { children: React.ReactNode }) => {
   const segments = useSelectedLayoutSegments();
 
+  const [isSettingsOpen, setIsSettingsOpen] = useState(false);
+
   const navLinks = [
     {
       icon: Home,
@@ -56,16 +59,14 @@ const Sidebar = ({ children }: { children: React.ReactNode }) => {
               </Link>
             ))}
           </div>
-          <Link
-            href="https://github.com/ItzCrazyKns/Perplexica"
-            className="flex flex-col items-center text-center justify-center"
-          >
-            <SiGithub
-              className="text-white"
-              onPointerEnterCapture={undefined}
-              onPointerLeaveCapture={undefined}
-            />
-          </Link>
+          <Settings
+            onClick={() => setIsSettingsOpen(!isSettingsOpen)}
+            className="text-white cursor-pointer"
+          />
+          <SettingsDialog
+            isOpen={isSettingsOpen}
+            setIsOpen={setIsSettingsOpen}
+          />
         </div>
       </div>
 

ui/package.json

@@ -1,6 +1,6 @@
 {
   "name": "perplexica-frontend",
-  "version": "1.0.0",
+  "version": "1.3.3",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {
@@ -22,7 +22,9 @@
     "next": "14.1.4",
     "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",
     "yet-another-react-lightbox": "^3.17.2",
     "zod": "^3.22.4"

ui/yarn.lock

@@ -2632,6 +2632,11 @@ react-is@^16.13.1:
   resolved "https://registry.yarnpkg.com/react-is/-/react-is-16.13.1.tgz#789729a4dc36de2999dc156dd6c1d9c18cea56a4"
   integrity sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ==
 
+react-text-to-speech@^0.14.5:
+  version "0.14.5"
+  resolved "https://registry.yarnpkg.com/react-text-to-speech/-/react-text-to-speech-0.14.5.tgz#f918786ab283311535682011045bd49777193300"
+  integrity sha512-3brr/IrK/5YTtOZSTo+Y8b+dnWelzfZiDZvkXnOct1e7O7fgA/h9bYAVrtwSRo/VxKfdw+wh6glkj6M0mlQuQQ==
+
 react-textarea-autosize@^8.5.3:
   version "8.5.3"
   resolved "https://registry.yarnpkg.com/react-textarea-autosize/-/react-textarea-autosize-8.5.3.tgz#d1e9fe760178413891484847d3378706052dd409"
@@ -2834,6 +2839,11 @@ slash@^3.0.0:
   resolved "https://registry.yarnpkg.com/slash/-/slash-3.0.0.tgz#6539be870c165adbd5240220dbe361f1bc4d4634"
   integrity sha512-g9Q1haeby36OSStwb4ntCGGGaKsaVSjQ68fBxoQcutl5fS1vuY18H3wSt3jFyFtrkx+Kz0V1G85A4MyAdDMi2Q==
 
+sonner@^1.4.41:
+  version "1.4.41"
+  resolved "https://registry.yarnpkg.com/sonner/-/sonner-1.4.41.tgz#ff085ae4f4244713daf294959beaa3e90f842d2c"
+  integrity sha512-uG511ggnnsw6gcn/X+YKkWPo5ep9il9wYi3QJxHsYe7yTZ4+cOd1wuodOUmOpFuXL+/RE3R04LczdNCDygTDgQ==
+
 source-map-js@^1.0.2, source-map-js@^1.2.0:
   version "1.2.0"
   resolved "https://registry.yarnpkg.com/source-map-js/-/source-map-js-1.2.0.tgz#16b809c162517b5b8c3e7dcd315a2a5c2612b2af"
@@ -2845,6 +2855,7 @@ streamsearch@^1.1.0:
   integrity sha512-Mcc5wHehp9aXz1ax6bZUyY5afg9u2rv5cqQI3mRrYkGC8rW2hM02jWuwjtL++LS5qinSyhj2QfLyNsuc+VsExg==
 
 "string-width-cjs@npm:string-width@^4.2.0", string-width@^4.1.0:
+  name string-width-cjs
   version "4.2.3"
   resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.3.tgz#269c7117d27b05ad2e536830a8ec895ef9c6d010"
   integrity sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==

yarn.lock

@@ -17,6 +17,11 @@
     node-fetch "^2.6.7"
     web-streams-polyfill "^3.2.1"
 
+"@colors/colors@1.6.0", "@colors/colors@^1.6.0":
+  version "1.6.0"
+  resolved "https://registry.yarnpkg.com/@colors/colors/-/colors-1.6.0.tgz#ec6cd237440700bc23ca23087f513c75508958b0"
+  integrity sha512-Ir+AOibqzrIsL6ajt3Rz3LskB7OiMVHqltZmspbW/TJuTVuyOMirVqAkjfY6JISiLHgyNqicAC8AyHHGzNd/dA==
+
 "@cspotcode/source-map-support@^0.8.0":
   version "0.8.1"
   resolved "https://registry.yarnpkg.com/@cspotcode/source-map-support/-/source-map-support-0.8.1.tgz#00629c35a688e05a88b1cda684fb9d5e73f000a1"
@@ -24,6 +29,20 @@
   dependencies:
     "@jridgewell/trace-mapping" "0.3.9"
 
+"@dabh/diagnostics@^2.0.2":
+  version "2.0.3"
+  resolved "https://registry.yarnpkg.com/@dabh/diagnostics/-/diagnostics-2.0.3.tgz#7f7e97ee9a725dffc7808d93668cc984e1dc477a"
+  integrity sha512-hrlQOIi7hAfzsMqlGSFyVucrx38O+j6wiGOf//H2ecvIEqYN4ADBSS2iLMh5UFyDunCNniUIPk/q3riFv45xRA==
+  dependencies:
+    colorspace "1.1.x"
+    enabled "2.0.x"
+    kuler "^2.0.0"
+
+"@iarna/toml@^2.2.5":
+  version "2.2.5"
+  resolved "https://registry.yarnpkg.com/@iarna/toml/-/toml-2.2.5.tgz#b32366c89b43c6f8cefbdefac778b9c828e3ba8c"
+  integrity sha512-trnsAYxU3xnS1gPHPyU961coFyLkh4gAD/0zQ5mymY4yOZ+CYvsPqUbOFSw0aDM4y0tV7tiFxL/1XfXPNC6IPg==
+
 "@jridgewell/resolve-uri@^3.0.3":
   version "3.1.2"
   resolved "https://registry.yarnpkg.com/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz#7a0ee601f60f99a20c7c7c5ff0c80388c1189bd6"
@@ -217,11 +236,21 @@
     "@types/node" "*"
     "@types/send" "*"
 
+"@types/triple-beam@^1.3.2":
+  version "1.3.5"
+  resolved "https://registry.yarnpkg.com/@types/triple-beam/-/triple-beam-1.3.5.tgz#74fef9ffbaa198eb8b588be029f38b00299caa2c"
+  integrity sha512-6WaYesThRMCl19iryMYP7/x2OVgCtbIVflDGFpWnb9irXI3UjYE4AzmYuiUKY1AJstGijoY+MgUszMgRxIYTYw==
+
 "@types/uuid@^9.0.1":
   version "9.0.8"
   resolved "https://registry.yarnpkg.com/@types/uuid/-/uuid-9.0.8.tgz#7545ba4fc3c003d6c756f651f3bf163d8f0f29ba"
   integrity sha512-jg+97EGIcY9AGHJJRaaPVgetKDsrTgbRjQ5Msgjh/DQKEFl0DtyRr/VCOyD1T2R1MNeWPK/u7JoGhlDZnKBAfA==
 
+abbrev@1:
+  version "1.1.1"
+  resolved "https://registry.yarnpkg.com/abbrev/-/abbrev-1.1.1.tgz#f8f2c887ad10bf67f634f005b6987fed3179aac8"
+  integrity sha512-nne9/IiQ/hzIhY6pdDnbBtz7DjPTKrY00P/zvPSm5pOFkl6xuGrGnXn/VtTNNfNtAfZ9/1RtehkszU9qcTii0Q==
+
 abort-controller@^3.0.0:
   version "3.0.0"
   resolved "https://registry.yarnpkg.com/abort-controller/-/abort-controller-3.0.0.tgz#eaf54d53b62bae4138e809ca225c8439a6efb392"
@@ -259,6 +288,14 @@ ansi-styles@^5.0.0:
   resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-5.2.0.tgz#07449690ad45777d1924ac2abb2fc8895dba836b"
   integrity sha512-Cxwpt2SfTzTtXcfOlzGEee8O+c+MmUgGrNiBcXnuWxuFJHe6a5Hz7qwhwe5OgaSYI0IJvkLqWX1ASG+cJOkEiA==
 
+anymatch@~3.1.2:
+  version "3.1.3"
+  resolved "https://registry.yarnpkg.com/anymatch/-/anymatch-3.1.3.tgz#790c58b19ba1720a84205b57c618d5ad8524973e"
+  integrity sha512-KMReFUr0B4t+D+OBkjR3KYqvocp2XaSzO55UcB6mgQMd3KbcE+mWTyvVV7D/zsdEbNnV6acZUutkiHQXvTr1Rw==
+  dependencies:
+    normalize-path "^3.0.0"
+    picomatch "^2.0.4"
+
 arg@^4.1.0:
   version "4.1.3"
   resolved "https://registry.yarnpkg.com/arg/-/arg-4.1.3.tgz#269fc7ad5b8e42cb63c896d5666017261c144089"
@@ -274,6 +311,11 @@ array-flatten@1.1.1:
   resolved "https://registry.yarnpkg.com/array-flatten/-/array-flatten-1.1.1.tgz#9a5f699051b1e7073328f2a008968b64ea2955d2"
   integrity sha512-PCVAQswWemu6UdxsDFFX/+gVeYqKAod3D3UVm91jHwynguOwAvYPhx8nNlM++NqRcK6CxxpUafjmhIdKiHibqg==
 
+async@^3.2.3:
+  version "3.2.5"
+  resolved "https://registry.yarnpkg.com/async/-/async-3.2.5.tgz#ebd52a8fdaf7a2289a24df399f8d8485c8a46b66"
+  integrity sha512-baNZyqaaLhyLVKm/DlvdW051MSgO6b8eVfIezl9E5PqWxFgzLm/wQntEW4zOytVburDEr0JlALEpdOFwvErLsg==
+
 asynckit@^0.4.0:
   version "0.4.0"
   resolved "https://registry.yarnpkg.com/asynckit/-/asynckit-0.4.0.tgz#c79ed97f7f34cb8f2ba1bc9790bcc366474b4b79"
@@ -288,6 +330,11 @@ axios@^1.6.8:
     form-data "^4.0.0"
     proxy-from-env "^1.1.0"
 
+balanced-match@^1.0.0:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee"
+  integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==
+
 base-64@^0.1.0:
   version "0.1.0"
   resolved "https://registry.yarnpkg.com/base-64/-/base-64-0.1.0.tgz#780a99c84e7d600260361511c4877613bf24f6bb"
@@ -298,7 +345,7 @@ base64-js@^1.5.1:
   resolved "https://registry.yarnpkg.com/base64-js/-/base64-js-1.5.1.tgz#1b1b440160a5bf7ad40b650f095963481903930a"
   integrity sha512-AKpaYlHn8t4SVbOHCy+b5+KKgvR4vrsD8vbvrbiQJps7fKDTkjkDry6ji0rUJjC0kzbNePLwzxq8iypo41qeWA==
 
-binary-extensions@^2.2.0:
+binary-extensions@^2.0.0, binary-extensions@^2.2.0:
   version "2.3.0"
   resolved "https://registry.yarnpkg.com/binary-extensions/-/binary-extensions-2.3.0.tgz#f6e14a97858d327252200242d4ccfe522c445522"
   integrity sha512-Ceh+7ox5qe7LJuLHoY0feh3pHuUDHAcRUeyL2VYghZwfpkNIy/+8Ocg0a3UuSoYzavmylwuLWQOf3hl0jjMMIw==
@@ -326,6 +373,21 @@ body-parser@1.20.2:
     type-is "~1.6.18"
     unpipe "1.0.0"
 
+brace-expansion@^1.1.7:
+  version "1.1.11"
+  resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-1.1.11.tgz#3c7fcbf529d87226f3d2f52b966ff5271eb441dd"
+  integrity sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA==
+  dependencies:
+    balanced-match "^1.0.0"
+    concat-map "0.0.1"
+
+braces@~3.0.2:
+  version "3.0.2"
+  resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.2.tgz#3454e1a462ee8d599e236df336cd9ea4f8afe107"
+  integrity sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==
+  dependencies:
+    fill-range "^7.0.1"
+
 bytes@3.1.2:
   version "3.1.2"
   resolved "https://registry.yarnpkg.com/bytes/-/bytes-3.1.2.tgz#8b0beeb98605adf1b128fa4386403c009e0221a5"
@@ -352,6 +414,62 @@ charenc@0.0.2:
   resolved "https://registry.yarnpkg.com/charenc/-/charenc-0.0.2.tgz#c0a1d2f3a7092e03774bfa83f14c0fc5790a8667"
   integrity sha512-yrLQ/yVUFXkzg7EDQsPieE/53+0RlaWTs+wBrvW36cyilJ2SaDWfl4Yj7MtLTXleV9uEKefbAGUPv2/iWSooRA==
 
+chokidar@^3.5.2:
+  version "3.6.0"
+  resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.6.0.tgz#197c6cc669ef2a8dc5e7b4d97ee4e092c3eb0d5b"
+  integrity sha512-7VT13fmjotKpGipCW9JEQAusEPE+Ei8nl6/g4FBAmIm0GOOLMua9NDDo/DWp0ZAxCr3cPq5ZpBqmPAQgDda2Pw==
+  dependencies:
+    anymatch "~3.1.2"
+    braces "~3.0.2"
+    glob-parent "~5.1.2"
+    is-binary-path "~2.1.0"
+    is-glob "~4.0.1"
+    normalize-path "~3.0.0"
+    readdirp "~3.6.0"
+  optionalDependencies:
+    fsevents "~2.3.2"
+
+color-convert@^1.9.3:
+  version "1.9.3"
+  resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-1.9.3.tgz#bb71850690e1f136567de629d2d5471deda4c1e8"
+  integrity sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg==
+  dependencies:
+    color-name "1.1.3"
+
+color-name@1.1.3:
+  version "1.1.3"
+  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.3.tgz#a7d0558bd89c42f795dd42328f740831ca53bc25"
+  integrity sha512-72fSenhMw2HZMTVHeCA9KCmpEIbzWiQsjN+BHcBbS9vr1mtt+vJjPdksIBNUmKAW8TFUDPJK5SUU3QhE9NEXDw==
+
+color-name@^1.0.0:
+  version "1.1.4"
+  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
+  integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
+
+color-string@^1.6.0:
+  version "1.9.1"
+  resolved "https://registry.yarnpkg.com/color-string/-/color-string-1.9.1.tgz#4467f9146f036f855b764dfb5bf8582bf342c7a4"
+  integrity sha512-shrVawQFojnZv6xM40anx4CkoDP+fZsw/ZerEMsW/pyzsRbElpsL/DBVW7q3ExxwusdNXI3lXpuhEZkzs8p5Eg==
+  dependencies:
+    color-name "^1.0.0"
+    simple-swizzle "^0.2.2"
+
+color@^3.1.3:
+  version "3.2.1"
+  resolved "https://registry.yarnpkg.com/color/-/color-3.2.1.tgz#3544dc198caf4490c3ecc9a790b54fe9ff45e164"
+  integrity sha512-aBl7dZI9ENN6fUGC7mWpMTPNHmWUSNan9tuWN6ahh5ZLNk9baLJOnSMlrQkHcrfFgz2/RigjUVAjdx36VcemKA==
+  dependencies:
+    color-convert "^1.9.3"
+    color-string "^1.6.0"
+
+colorspace@1.1.x:
+  version "1.1.4"
+  resolved "https://registry.yarnpkg.com/colorspace/-/colorspace-1.1.4.tgz#8d442d1186152f60453bf8070cd66eb364e59243"
+  integrity sha512-BgvKJiuVu1igBUF2kEjRCZXol6wiiGbY5ipL/oVPwm0BL9sIpMIzM8IK7vwuxIIzOXMV3Ey5w+vxhm0rR/TN8w==
+  dependencies:
+    color "^3.1.3"
+    text-hex "1.0.x"
+
 combined-stream@^1.0.8:
   version "1.0.8"
   resolved "https://registry.yarnpkg.com/combined-stream/-/combined-stream-1.0.8.tgz#c3d45a8b34fd730631a110a8a2520682b31d5a7f"
@@ -390,6 +508,11 @@ compute-l2norm@^1.1.0:
     validate.io-array "^1.0.3"
     validate.io-function "^1.0.2"
 
+concat-map@0.0.1:
+  version "0.0.1"
+  resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
+  integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==
+
 content-disposition@0.5.4:
   version "0.5.4"
   resolved "https://registry.yarnpkg.com/content-disposition/-/content-disposition-0.5.4.tgz#8b82b4efac82512a02bb0b1dcec9d2c5e8eb5bfe"
@@ -437,6 +560,13 @@ debug@2.6.9:
   dependencies:
     ms "2.0.0"
 
+debug@^4:
+  version "4.3.4"
+  resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.4.tgz#1319f6579357f2338d3337d2cdd4914bb5dcc865"
+  integrity sha512-PRWFHuSU3eDtQJPvnNY7Jcket1j0t5OuOsFzPPzsekD52Zl8qUfFIPEiswXqIvHWGVHOgX+7G/vCNNhehwxfkQ==
+  dependencies:
+    ms "2.1.2"
+
 decamelize@1.2.0:
   version "1.2.0"
   resolved "https://registry.yarnpkg.com/decamelize/-/decamelize-1.2.0.tgz#f6534d15148269b20352e7bee26f501f9a191290"
@@ -489,6 +619,11 @@ ee-first@1.1.1:
   resolved "https://registry.yarnpkg.com/ee-first/-/ee-first-1.1.1.tgz#590c61156b0ae2f4f0255732a158b266bc56b21d"
   integrity sha512-WMwm9LhRUo+WUaRN+vRuETqG89IgZphVSNkdFgeb6sS/E4OrDIN7t48CAewSHXc6C8lefD8KKfr5vY61brQlow==
 
+enabled@2.0.x:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/enabled/-/enabled-2.0.0.tgz#f9dd92ec2d6f4bbc0d5d1e64e21d61cd4665e7c2"
+  integrity sha512-AKrN98kuwOzMIdAizXGI86UFBoo26CL21UM763y1h/GMSJ4/OHU9k2YlsmBpyScFo/wbLzWQJBMCW4+IO3/+OQ==
+
 encodeurl@~1.0.2:
   version "1.0.2"
   resolved "https://registry.yarnpkg.com/encodeurl/-/encodeurl-1.0.2.tgz#ad3ff4c86ec2d029322f5a02c3a9a606c95b3f59"
@@ -568,6 +703,18 @@ express@^4.19.2:
     utils-merge "1.0.1"
     vary "~1.1.2"
 
+fecha@^4.2.0:
+  version "4.2.3"
+  resolved "https://registry.yarnpkg.com/fecha/-/fecha-4.2.3.tgz#4d9ccdbc61e8629b259fdca67e65891448d569fd"
+  integrity sha512-OP2IUU6HeYKJi3i0z4A19kHMQoLVs4Hc+DPqqxI2h/DPZHTm/vjsfC6P0b4jCMy14XizLBqvndQ+UilD7707Jw==
+
+fill-range@^7.0.1:
+  version "7.0.1"
+  resolved "https://registry.yarnpkg.com/fill-range/-/fill-range-7.0.1.tgz#1919a6a7c75fe38b2c7c77e5198535da9acdda40"
+  integrity sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ==
+  dependencies:
+    to-regex-range "^5.0.1"
+
 finalhandler@1.2.0:
   version "1.2.0"
   resolved "https://registry.yarnpkg.com/finalhandler/-/finalhandler-1.2.0.tgz#7d23fe5731b207b4640e4fcd00aec1f9207a7b32"
@@ -586,6 +733,11 @@ flat@^5.0.2:
   resolved "https://registry.yarnpkg.com/flat/-/flat-5.0.2.tgz#8ca6fe332069ffa9d324c327198c598259ceb241"
   integrity sha512-b6suED+5/3rTpUBdG1gupIl8MPFCAMA0QXwmljLhvCUKcUvdE4gWky9zpuGCcXHOsz4J9wPGNWq6OKpmIzz3hQ==
 
+fn.name@1.x.x:
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/fn.name/-/fn.name-1.1.0.tgz#26cad8017967aea8731bc42961d04a3d5988accc"
+  integrity sha512-GRnmB5gPyJpAhTQdSZTSp9uaPSvl09KoYcMQtsB9rQoOmzs9dH6ffeccH+Z+cv6P68Hu5bC6JjRh4Ah/mHSNRw==
+
 follow-redirects@^1.15.6:
   version "1.15.6"
   resolved "https://registry.yarnpkg.com/follow-redirects/-/follow-redirects-1.15.6.tgz#7f815c0cda4249c74ff09e95ef97c23b5fd0399b"
@@ -623,6 +775,11 @@ fresh@0.5.2:
   resolved "https://registry.yarnpkg.com/fresh/-/fresh-0.5.2.tgz#3d8cadd90d976569fa835ab1f8e4b23a105605a7"
   integrity sha512-zJ2mQYM18rEFOudeV4GShTGIQ7RbzA7ozbU9I/XBpm7kqgMywgmylMwXHxZJmkVoYkna9d2pVXVXPdYTP9ej8Q==
 
+fsevents@~2.3.2:
+  version "2.3.3"
+  resolved "https://registry.yarnpkg.com/fsevents/-/fsevents-2.3.3.tgz#cac6407785d03675a2a5e1a5305c697b347d90d6"
+  integrity sha512-5xoDfX+fL7faATnagmWPpbFtwh/R77WmMMqqHGS65C3vvB0YHrgF+B1YmZ3441tMj5n63k0212XNoJwzlhffQw==
+
 function-bind@^1.1.2:
   version "1.1.2"
   resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.2.tgz#2c02d864d97f3ea6c8830c464cbd11ab6eab7a1c"
@@ -639,6 +796,13 @@ get-intrinsic@^1.1.3, get-intrinsic@^1.2.4:
     has-symbols "^1.0.3"
     hasown "^2.0.0"
 
+glob-parent@~5.1.2:
+  version "5.1.2"
+  resolved "https://registry.yarnpkg.com/glob-parent/-/glob-parent-5.1.2.tgz#869832c58034fe68a4093c17dc15e8340d8401c4"
+  integrity sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow==
+  dependencies:
+    is-glob "^4.0.1"
+
 gopd@^1.0.1:
   version "1.0.1"
   resolved "https://registry.yarnpkg.com/gopd/-/gopd-1.0.1.tgz#29ff76de69dac7489b7c0918a5788e56477c332c"
@@ -646,6 +810,11 @@ gopd@^1.0.1:
   dependencies:
     get-intrinsic "^1.1.3"
 
+has-flag@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-3.0.0.tgz#b5d454dc2199ae225699f3467e5a07f3b955bafd"
+  integrity sha512-sKJf1+ceQBr4SMkvQnBDNDtf4TXpVhVGateu0t918bl30FnbE2m4vNLX+VWe/dpjlb+HugGYzW7uQXH98HPEYw==
+
 has-property-descriptors@^1.0.2:
   version "1.0.2"
   resolved "https://registry.yarnpkg.com/has-property-descriptors/-/has-property-descriptors-1.0.2.tgz#963ed7d071dc7bf5f084c5bfbe0d1b6222586854"
@@ -695,7 +864,12 @@ iconv-lite@0.4.24:
   dependencies:
     safer-buffer ">= 2.1.2 < 3"
 
-inherits@2.0.4:
+ignore-by-default@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/ignore-by-default/-/ignore-by-default-1.0.1.tgz#48ca6d72f6c6a3af00a9ad4ae6876be3889e2b09"
+  integrity sha512-Ius2VYcGNk7T90CppJqcIkS5ooHUZyIQK+ClZfMfMNFEF9VSE73Fq+906u/CWu92x4gzZMWOwfFYckPObzdEbA==
+
+inherits@2.0.4, inherits@^2.0.3:
   version "2.0.4"
   resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c"
   integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==
@@ -710,11 +884,45 @@ is-any-array@^2.0.0:
   resolved "https://registry.yarnpkg.com/is-any-array/-/is-any-array-2.0.1.tgz#9233242a9c098220290aa2ec28f82ca7fa79899e"
   integrity sha512-UtilS7hLRu++wb/WBAw9bNuP1Eg04Ivn1vERJck8zJthEvXCBEBpGR/33u/xLKWEQf95803oalHrVDptcAvFdQ==
 
+is-arrayish@^0.3.1:
+  version "0.3.2"
+  resolved "https://registry.yarnpkg.com/is-arrayish/-/is-arrayish-0.3.2.tgz#4574a2ae56f7ab206896fb431eaeed066fdf8f03"
+  integrity sha512-eVRqCvVlZbuw3GrM63ovNSNAeA1K16kaR/LRY/92w0zxQ5/1YzwblUX652i4Xs9RwAGjW9d9y6X88t8OaAJfWQ==
+
+is-binary-path@~2.1.0:
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/is-binary-path/-/is-binary-path-2.1.0.tgz#ea1f7f3b80f064236e83470f86c09c254fb45b09"
+  integrity sha512-ZMERYes6pDydyuGidse7OsHxtbI7WVeUEozgR/g7rd0xUimYNlvZRE/K2MgZTjWy725IfelLeVcEM97mmtRGXw==
+  dependencies:
+    binary-extensions "^2.0.0"
+
 is-buffer@~1.1.6:
   version "1.1.6"
   resolved "https://registry.yarnpkg.com/is-buffer/-/is-buffer-1.1.6.tgz#efaa2ea9daa0d7ab2ea13a97b2b8ad51fefbe8be"
   integrity sha512-NcdALwpXkTm5Zvvbk7owOUSvVvBKDgKP5/ewfXEznmQFfs4ZRmanOeKBTjRVjka3QFoN6XJ+9F3USqfHqTaU5w==
 
+is-extglob@^2.1.1:
+  version "2.1.1"
+  resolved "https://registry.yarnpkg.com/is-extglob/-/is-extglob-2.1.1.tgz#a88c02535791f02ed37c76a1b9ea9773c833f8c2"
+  integrity sha512-SbKbANkN603Vi4jEZv49LeVJMn4yGwsbzZworEoyEiutsN3nJYdbO36zfhGJ6QEDpOZIFkDtnq5JRxmvl3jsoQ==
+
+is-glob@^4.0.1, is-glob@~4.0.1:
+  version "4.0.3"
+  resolved "https://registry.yarnpkg.com/is-glob/-/is-glob-4.0.3.tgz#64f61e42cbbb2eec2071a9dac0b28ba1e65d5084"
+  integrity sha512-xelSayHH36ZgE7ZWhli7pW34hNbNl8Ojv5KVmkJD4hBdD3th8Tfk9vYasLM+mXWOZhFkgZfxhLSnrwRr4elSSg==
+  dependencies:
+    is-extglob "^2.1.1"
+
+is-number@^7.0.0:
+  version "7.0.0"
+  resolved "https://registry.yarnpkg.com/is-number/-/is-number-7.0.0.tgz#7535345b896734d5f80c4d06c50955527a14f12b"
+  integrity sha512-41Cifkg6e8TylSpdtTpeLVMqvSBEVzTttHvERD741+pnZ8ANv0004MRL43QKPDlK9cGvNp6NZWZUBlbGXYxxng==
+
+is-stream@^2.0.0:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-2.0.1.tgz#fac1e3d53b97ad5a9d0ae9cef2389f5810a5c077"
+  integrity sha512-hFoiJiTl63nn+kstHGBtewWSKnQLpyb155KHheA1l39uvtO9nWIop1p3udqPcUd/xbF1VLMO4n7OI6p7RbngDg==
+
 js-tiktoken@^1.0.7, js-tiktoken@^1.0.8:
   version "1.0.10"
   resolved "https://registry.yarnpkg.com/js-tiktoken/-/js-tiktoken-1.0.10.tgz#2b343ec169399dcee8f9ef9807dbd4fafd3b30dc"
@@ -734,6 +942,11 @@ jsonpointer@^5.0.1:
   resolved "https://registry.yarnpkg.com/jsonpointer/-/jsonpointer-5.0.1.tgz#2110e0af0900fd37467b5907ecd13a7884a1b559"
   integrity sha512-p/nXbhSEcu3pZRdkW1OfJhpsVtW1gd4Wa1fnQc9YLiTfAjn0312eMKimbdIQzuZl9aa9xUGaRlP9T/CJE/ditQ==
 
+kuler@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/kuler/-/kuler-2.0.0.tgz#e2c570a3800388fb44407e851531c1d670b061b3"
+  integrity sha512-Xq9nH7KlWZmXAtodXDDRE7vs6DU1gTU8zYDHDiWLSip45Egwq3plLHzPn27NgvzL2r1LMPC1vdqh98sQxtqj4A==
+
 langchain@^0.1.30:
   version "0.1.30"
   resolved "https://registry.yarnpkg.com/langchain/-/langchain-0.1.30.tgz#e1adb3f1849fcd5c596c668300afd5dc8cb37a97"
@@ -773,6 +986,25 @@ langsmith@~0.1.1, langsmith@~0.1.7:
     p-retry "4"
     uuid "^9.0.0"
 
+logform@^2.3.2, logform@^2.4.0:
+  version "2.6.0"
+  resolved "https://registry.yarnpkg.com/logform/-/logform-2.6.0.tgz#8c82a983f05d6eaeb2d75e3decae7a768b2bf9b5"
+  integrity sha512-1ulHeNPp6k/LD8H91o7VYFBng5i1BDE7HoKxVbZiGFidS1Rj65qcywLxX+pVfAPoQJEjRdvKcusKwOupHCVOVQ==
+  dependencies:
+    "@colors/colors" "1.6.0"
+    "@types/triple-beam" "^1.3.2"
+    fecha "^4.2.0"
+    ms "^2.1.1"
+    safe-stable-stringify "^2.3.1"
+    triple-beam "^1.3.0"
+
+lru-cache@^6.0.0:
+  version "6.0.0"
+  resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-6.0.0.tgz#6d6fe6570ebd96aaf90fcad1dafa3b2566db3a94"
+  integrity sha512-Jo6dJ04CmSjuznwJSS3pUeWmd/H0ffTlkXXgwZi+eq1UCmqQwCh+eLsYOYCwY991i2Fah4h1BEMCx4qThGbsiA==
+  dependencies:
+    yallist "^4.0.0"
+
 make-error@^1.1.1:
   version "1.3.6"
   resolved "https://registry.yarnpkg.com/make-error/-/make-error-1.3.6.tgz#2eb2e37ea9b67c4891f684a1394799af484cf7a2"
@@ -819,6 +1051,13 @@ mime@1.6.0:
   resolved "https://registry.yarnpkg.com/mime/-/mime-1.6.0.tgz#32cd9e5c64553bd58d19a568af452acff04981b1"
   integrity sha512-x0Vn8spI+wuJ1O6S7gnbaQg8Pxh4NNHb7KSINmEWKiPE4RKOplvijn+NkmYmmRgP68mc70j2EbeTFRsrswaQeg==
 
+minimatch@^3.1.2:
+  version "3.1.2"
+  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.1.2.tgz#19cd194bfd3e428f049a70817c038d89ab4be35b"
+  integrity sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==
+  dependencies:
+    brace-expansion "^1.1.7"
+
 ml-array-mean@^1.1.6:
   version "1.1.6"
   resolved "https://registry.yarnpkg.com/ml-array-mean/-/ml-array-mean-1.1.6.tgz#d951a700dc8e3a17b3e0a583c2c64abd0c619c56"
@@ -860,7 +1099,12 @@ ms@2.0.0:
   resolved "https://registry.yarnpkg.com/ms/-/ms-2.0.0.tgz#5608aeadfc00be6c2901df5f9861788de0d597c8"
   integrity sha512-Tpp60P6IUJDTuOq/5Z8cdskzJujfwqfOTkrwIwj7IRISpnkJnT6SyJ4PCPnGMoFjC9ddhal5KVIYtAt97ix05A==
 
-ms@2.1.3, ms@^2.0.0:
+ms@2.1.2:
+  version "2.1.2"
+  resolved "https://registry.yarnpkg.com/ms/-/ms-2.1.2.tgz#d09d1f357b443f493382a8eb3ccd183872ae6009"
+  integrity sha512-sGkPx+VjMtmA6MX27oA4FBFELFCZZ4S4XqeGOXCv68tT+jb3vk/RyaKWP0PTKyWtmLSM0b+adUTEvbs1PEaH2w==
+
+ms@2.1.3, ms@^2.0.0, ms@^2.1.1:
   version "2.1.3"
   resolved "https://registry.yarnpkg.com/ms/-/ms-2.1.3.tgz#574c8138ce1d2b5861f0b44579dbadd60c6615b2"
   integrity sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==
@@ -882,6 +1126,34 @@ node-fetch@^2.6.7:
   dependencies:
     whatwg-url "^5.0.0"
 
+nodemon@^3.1.0:
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/nodemon/-/nodemon-3.1.0.tgz#ff7394f2450eb6a5e96fe4180acd5176b29799c9"
+  integrity sha512-xqlktYlDMCepBJd43ZQhjWwMw2obW/JRvkrLxq5RCNcuDDX1DbcPT+qT1IlIIdf+DhnWs90JpTMe+Y5KxOchvA==
+  dependencies:
+    chokidar "^3.5.2"
+    debug "^4"
+    ignore-by-default "^1.0.1"
+    minimatch "^3.1.2"
+    pstree.remy "^1.1.8"
+    semver "^7.5.3"
+    simple-update-notifier "^2.0.0"
+    supports-color "^5.5.0"
+    touch "^3.1.0"
+    undefsafe "^2.0.5"
+
+nopt@~1.0.10:
+  version "1.0.10"
+  resolved "https://registry.yarnpkg.com/nopt/-/nopt-1.0.10.tgz#6ddd21bd2a31417b92727dd585f8a6f37608ebee"
+  integrity sha512-NWmpvLSqUrgrAC9HCuxEvb+PSloHpqVu+FqcO4eeF2h5qYRhA7ev6KvelyQAKtegUbC6RypJnlEOhd8vloNKYg==
+  dependencies:
+    abbrev "1"
+
+normalize-path@^3.0.0, normalize-path@~3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/normalize-path/-/normalize-path-3.0.0.tgz#0dcd69ff23a1c9b11fd0978316644a0388216a65"
+  integrity sha512-6eZs5Ls3WtCisHWp9S2GUy8dqkpGi4BVSz3GaqiE6ezub0512ESztXUwUB6C6IKbQkY2Pnb/mD4WYojCRwcwLA==
+
 num-sort@^2.0.0:
   version "2.1.0"
   resolved "https://registry.yarnpkg.com/num-sort/-/num-sort-2.1.0.tgz#1cbb37aed071329fdf41151258bc011898577a9b"
@@ -904,6 +1176,13 @@ on-finished@2.4.1:
   dependencies:
     ee-first "1.1.1"
 
+one-time@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/one-time/-/one-time-1.0.0.tgz#e06bc174aed214ed58edede573b433bbf827cb45"
+  integrity sha512-5DXOiRKwuSEcQ/l0kGCF6Q3jcADFv5tSmRaJck/OqkVFcOzutB134KRSfF0xDrL39MNnqxbHBbUUcjZIhTgb2g==
+  dependencies:
+    fn.name "1.x.x"
+
 openai@^4.26.0:
   version "4.31.0"
   resolved "https://registry.yarnpkg.com/openai/-/openai-4.31.0.tgz#5d96045c4eb244fa21f0fff0981043a2c9f09e8c"
@@ -962,6 +1241,11 @@ path-to-regexp@0.1.7:
   resolved "https://registry.yarnpkg.com/path-to-regexp/-/path-to-regexp-0.1.7.tgz#df604178005f522f15eb4490e7247a1bfaa67f8c"
   integrity sha512-5DFkuoqlv1uYQKxy8omFBeJPQcdoE07Kv2sferDCrAq1ohOU+MSDswDIbnx3YAM60qIOnYa53wBhXW0EbMonrQ==
 
+picomatch@^2.0.4, picomatch@^2.2.1:
+  version "2.3.1"
+  resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.3.1.tgz#3ba3833733646d9d3e4995946c1365a67fb07a42"
+  integrity sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA==
+
 prettier@^3.2.5:
   version "3.2.5"
   resolved "https://registry.yarnpkg.com/prettier/-/prettier-3.2.5.tgz#e52bc3090586e824964a8813b09aba6233b28368"
@@ -980,6 +1264,11 @@ proxy-from-env@^1.1.0:
   resolved "https://registry.yarnpkg.com/proxy-from-env/-/proxy-from-env-1.1.0.tgz#e102f16ca355424865755d2c9e8ea4f24d58c3e2"
   integrity sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg==
 
+pstree.remy@^1.1.8:
+  version "1.1.8"
+  resolved "https://registry.yarnpkg.com/pstree.remy/-/pstree.remy-1.1.8.tgz#c242224f4a67c21f686839bbdb4ac282b8373d3a"
+  integrity sha512-77DZwxQmxKnu3aR542U+X8FypNzbfJ+C5XQDk3uWjWxn6151aIMGthWYRXTqT1E5oJvg+ljaa2OJi+VfvCOQ8w==
+
 qs@6.11.0:
   version "6.11.0"
   resolved "https://registry.yarnpkg.com/qs/-/qs-6.11.0.tgz#fd0d963446f7a65e1367e01abd85429453f0c37a"
@@ -1002,12 +1291,28 @@ raw-body@2.5.2:
     iconv-lite "0.4.24"
     unpipe "1.0.0"
 
+readable-stream@^3.4.0, readable-stream@^3.6.0:
+  version "3.6.2"
+  resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-3.6.2.tgz#56a9b36ea965c00c5a93ef31eb111a0f11056967"
+  integrity sha512-9u/sniCrY3D5WdsERHzHE4G2YCXqoG5FTHUiCC4SIbr6XcLZBY05ya9EKjYek9O5xOAwjGq+1JdGBAS7Q9ScoA==
+  dependencies:
+    inherits "^2.0.3"
+    string_decoder "^1.1.1"
+    util-deprecate "^1.0.1"
+
+readdirp@~3.6.0:
+  version "3.6.0"
+  resolved "https://registry.yarnpkg.com/readdirp/-/readdirp-3.6.0.tgz#74a370bd857116e245b29cc97340cd431a02a6c7"
+  integrity sha512-hOS089on8RduqdbhvQ5Z37A0ESjsqz6qnRcffsMU3495FuTdqSm+7bhJ29JvIOsBDEEnan5DPu9t3To9VRlMzA==
+  dependencies:
+    picomatch "^2.2.1"
+
 retry@^0.13.1:
   version "0.13.1"
   resolved "https://registry.yarnpkg.com/retry/-/retry-0.13.1.tgz#185b1587acf67919d63b357349e03537b2484658"
   integrity sha512-XQBQ3I8W1Cge0Seh+6gjj03LbmRFWuoszgK9ooCpwYIrhhoO80pfq4cUkU5DkknwfOfFteRwlZ56PYOGYyFWdg==
 
-safe-buffer@5.2.1:
+safe-buffer@5.2.1, safe-buffer@~5.2.0:
   version "5.2.1"
   resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.2.1.tgz#1eaf9fa9bdb1fdd4ec75f58f9cdb4e6b7827eec6"
   integrity sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==
@@ -1017,11 +1322,23 @@ safe-buffer@~5.1.1:
   resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.1.2.tgz#991ec69d296e0313747d59bdfd2b745c35f8828d"
   integrity sha512-Gd2UZBJDkXlY7GbJxfsE8/nvKkUEU1G38c1siN6QP6a9PT9MmHB8GnpscSmMJSoF8LOIrt8ud/wPtojys4G6+g==
 
+safe-stable-stringify@^2.3.1:
+  version "2.4.3"
+  resolved "https://registry.yarnpkg.com/safe-stable-stringify/-/safe-stable-stringify-2.4.3.tgz#138c84b6f6edb3db5f8ef3ef7115b8f55ccbf886"
+  integrity sha512-e2bDA2WJT0wxseVd4lsDP4+3ONX6HpMXQa1ZhFQ7SU+GjvORCmShbCMltrtIDfkYhVHrOcPtj+KhmDBdPdZD1g==
+
 "safer-buffer@>= 2.1.2 < 3":
   version "2.1.2"
   resolved "https://registry.yarnpkg.com/safer-buffer/-/safer-buffer-2.1.2.tgz#44fa161b0187b9549dd84bb91802f9bd8385cd6a"
   integrity sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg==
 
+semver@^7.5.3:
+  version "7.6.0"
+  resolved "https://registry.yarnpkg.com/semver/-/semver-7.6.0.tgz#1a46a4db4bffcccd97b743b5005c8325f23d4e2d"
+  integrity sha512-EnwXhrlwXMk9gKu5/flx5sv/an57AkRplG3hTK68W7FRDN+k+OWBj65M7719OkA82XLBxrcX0KSHj+X5COhOVg==
+  dependencies:
+    lru-cache "^6.0.0"
+
 send@0.18.0:
   version "0.18.0"
   resolved "https://registry.yarnpkg.com/send/-/send-0.18.0.tgz#670167cc654b05f5aa4a767f9113bb371bc706be"
@@ -1078,21 +1395,78 @@ side-channel@^1.0.4:
     get-intrinsic "^1.2.4"
     object-inspect "^1.13.1"
 
+simple-swizzle@^0.2.2:
+  version "0.2.2"
+  resolved "https://registry.yarnpkg.com/simple-swizzle/-/simple-swizzle-0.2.2.tgz#a4da6b635ffcccca33f70d17cb92592de95e557a"
+  integrity sha512-JA//kQgZtbuY83m+xT+tXJkmJncGMTFT+C+g2h2R9uxkYIrE2yy9sgmcLhCnw57/WSD+Eh3J97FPEDFnbXnDUg==
+  dependencies:
+    is-arrayish "^0.3.1"
+
+simple-update-notifier@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/simple-update-notifier/-/simple-update-notifier-2.0.0.tgz#d70b92bdab7d6d90dfd73931195a30b6e3d7cebb"
+  integrity sha512-a2B9Y0KlNXl9u/vsW6sTIu9vGEpfKu2wRV6l1H3XEas/0gUIzGzBoP/IouTcUQbm9JWZLH3COxyn03TYlFax6w==
+  dependencies:
+    semver "^7.5.3"
+
+stack-trace@0.0.x:
+  version "0.0.10"
+  resolved "https://registry.yarnpkg.com/stack-trace/-/stack-trace-0.0.10.tgz#547c70b347e8d32b4e108ea1a2a159e5fdde19c0"
+  integrity sha512-KGzahc7puUKkzyMt+IqAep+TVNbKP+k2Lmwhub39m1AsTSkaDutx56aDCo+HLDzf/D26BIHTJWNiTG1KAJiQCg==
+
 statuses@2.0.1:
   version "2.0.1"
   resolved "https://registry.yarnpkg.com/statuses/-/statuses-2.0.1.tgz#55cb000ccf1d48728bd23c685a063998cf1a1b63"
   integrity sha512-RwNA9Z/7PrK06rYLIzFMlaF+l73iwpzsqRIFgbMLbTcLD6cOao82TaWefPXQvB2fOC4AjuYSEndS7N/mTCbkdQ==
 
+string_decoder@^1.1.1:
+  version "1.3.0"
+  resolved "https://registry.yarnpkg.com/string_decoder/-/string_decoder-1.3.0.tgz#42f114594a46cf1a8e30b0a84f56c78c3edac21e"
+  integrity sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA==
+  dependencies:
+    safe-buffer "~5.2.0"
+
+supports-color@^5.5.0:
+  version "5.5.0"
+  resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-5.5.0.tgz#e2e69a44ac8772f78a1ec0b35b689df6530efc8f"
+  integrity sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==
+  dependencies:
+    has-flag "^3.0.0"
+
+text-hex@1.0.x:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/text-hex/-/text-hex-1.0.0.tgz#69dc9c1b17446ee79a92bf5b884bb4b9127506f5"
+  integrity sha512-uuVGNWzgJ4yhRaNSiubPY7OjISw4sw4E5Uv0wbjp+OzcbmVU/rsT8ujgcXJhn9ypzsgr5vlzpPqP+MBBKcGvbg==
+
+to-regex-range@^5.0.1:
+  version "5.0.1"
+  resolved "https://registry.yarnpkg.com/to-regex-range/-/to-regex-range-5.0.1.tgz#1648c44aae7c8d988a326018ed72f5b4dd0392e4"
+  integrity sha512-65P7iz6X5yEr1cwcgvQxbbIw7Uk3gOy5dIdtZ4rDveLqhrdJP+Li/Hx6tyK0NEb+2GCyneCMJiGqrADCSNk8sQ==
+  dependencies:
+    is-number "^7.0.0"
+
 toidentifier@1.0.1:
   version "1.0.1"
   resolved "https://registry.yarnpkg.com/toidentifier/-/toidentifier-1.0.1.tgz#3be34321a88a820ed1bd80dfaa33e479fbb8dd35"
   integrity sha512-o5sSPKEkg/DIQNmH43V0/uerLrpzVedkUh8tGNvaeXpfpuwjKenlSox/2O/BTlZUtEe+JG7s5YhEz608PlAHRA==
 
+touch@^3.1.0:
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/touch/-/touch-3.1.0.tgz#fe365f5f75ec9ed4e56825e0bb76d24ab74af83b"
+  integrity sha512-WBx8Uy5TLtOSRtIq+M03/sKDrXCLHxwDcquSP2c43Le03/9serjQBIztjRz6FkJez9D/hleyAXTBGLwwZUw9lA==
+  dependencies:
+    nopt "~1.0.10"
+
 tr46@~0.0.3:
   version "0.0.3"
   resolved "https://registry.yarnpkg.com/tr46/-/tr46-0.0.3.tgz#8184fd347dac9cdc185992f3a6622e14b9d9ab6a"
   integrity sha512-N3WMsuqV66lT30CrXNbEjx4GEwlow3v6rr4mCcv6prnfwhS01rkgyFdjPNBYd9br7LpXV1+Emh01fHnq2Gdgrw==
 
+triple-beam@^1.3.0:
+  version "1.4.1"
+  resolved "https://registry.yarnpkg.com/triple-beam/-/triple-beam-1.4.1.tgz#6fde70271dc6e5d73ca0c3b24e2d92afb7441984"
+  integrity sha512-aZbgViZrg1QNcG+LULa7nhZpJTZSLm/mXnHXnbAbjmN5aSa0y7V+wvv6+4WaBtpISJzThKy+PIPxc1Nq1EJ9mg==
+
 ts-node@^10.9.2:
   version "10.9.2"
   resolved "https://registry.yarnpkg.com/ts-node/-/ts-node-10.9.2.tgz#70f021c9e185bccdca820e26dc413805c101c71f"
@@ -1125,6 +1499,11 @@ typescript@^5.4.3:
   resolved "https://registry.yarnpkg.com/typescript/-/typescript-5.4.3.tgz#5c6fedd4c87bee01cd7a528a30145521f8e0feff"
   integrity sha512-KrPd3PKaCLr78MalgiwJnA25Nm8HAmdwN3mYUYZgG/wizIo9EainNVQI9/yDavtVFRN2h3k8uf3GLHuhDMgEHg==
 
+undefsafe@^2.0.5:
+  version "2.0.5"
+  resolved "https://registry.yarnpkg.com/undefsafe/-/undefsafe-2.0.5.tgz#38733b9327bdcd226db889fb723a6efd162e6e2c"
+  integrity sha512-WxONCrssBM8TSPRqN5EmsjVrsv4A8X12J4ArBiiayv3DyyG3ZlIg6yysuuSYdZsVz3TKcTg2fd//Ujd4CHV1iA==
+
 undici-types@~5.26.4:
   version "5.26.5"
   resolved "https://registry.yarnpkg.com/undici-types/-/undici-types-5.26.5.tgz#bcd539893d00b56e964fd2657a4866b221a65617"
@@ -1135,6 +1514,11 @@ unpipe@1.0.0, unpipe@~1.0.0:
   resolved "https://registry.yarnpkg.com/unpipe/-/unpipe-1.0.0.tgz#b2bf4ee8514aae6165b4817829d21b2ef49904ec"
   integrity sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ==
 
+util-deprecate@^1.0.1:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
+  integrity sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==
+
 utils-merge@1.0.1:
   version "1.0.1"
   resolved "https://registry.yarnpkg.com/utils-merge/-/utils-merge-1.0.1.tgz#9f95710f50a267947b2ccc124741c1028427e713"
@@ -1188,11 +1572,42 @@ whatwg-url@^5.0.0:
     tr46 "~0.0.3"
     webidl-conversions "^3.0.0"
 
+winston-transport@^4.7.0:
+  version "4.7.0"
+  resolved "https://registry.yarnpkg.com/winston-transport/-/winston-transport-4.7.0.tgz#e302e6889e6ccb7f383b926df6936a5b781bd1f0"
+  integrity sha512-ajBj65K5I7denzer2IYW6+2bNIVqLGDHqDw3Ow8Ohh+vdW+rv4MZ6eiDvHoKhfJFZ2auyN8byXieDDJ96ViONg==
+  dependencies:
+    logform "^2.3.2"
+    readable-stream "^3.6.0"
+    triple-beam "^1.3.0"
+
+winston@^3.13.0:
+  version "3.13.0"
+  resolved "https://registry.yarnpkg.com/winston/-/winston-3.13.0.tgz#e76c0d722f78e04838158c61adc1287201de7ce3"
+  integrity sha512-rwidmA1w3SE4j0E5MuIufFhyJPBDG7Nu71RkZor1p2+qHvJSZ9GYDA81AyleQcZbh/+V6HjeBdfnTZJm9rSeQQ==
+  dependencies:
+    "@colors/colors" "^1.6.0"
+    "@dabh/diagnostics" "^2.0.2"
+    async "^3.2.3"
+    is-stream "^2.0.0"
+    logform "^2.4.0"
+    one-time "^1.0.0"
+    readable-stream "^3.4.0"
+    safe-stable-stringify "^2.3.1"
+    stack-trace "0.0.x"
+    triple-beam "^1.3.0"
+    winston-transport "^4.7.0"
+
 ws@^8.16.0:
   version "8.16.0"
   resolved "https://registry.yarnpkg.com/ws/-/ws-8.16.0.tgz#d1cd774f36fbc07165066a60e40323eab6446fd4"
   integrity sha512-HS0c//TP7Ina87TfiPUz1rQzMhHrl/SG2guqRcTOIUYD2q8uhUdNHZYJUaQ8aTGPzCh+c6oawMKW35nFl1dxyQ==
 
+yallist@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
+  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
+
 yaml@^2.2.1:
   version "2.4.1"
   resolved "https://registry.yarnpkg.com/yaml/-/yaml-2.4.1.tgz#2e57e0b5e995292c25c75d2658f0664765210eed"