/llm-answer-engine

Build a Perplexity-Inspired Answer Engine Using Next.js, Groq, Llama-3, Langchain, OpenAI, Upstash, Brave & Serper

Primary LanguageTypeScriptMIT LicenseMIT

Perplexity-Inspired LLM Answer Engine

developersdigest%2Fllm-answer-engine | Trendshift

This repository contains the code and instructions needed to build a sophisticated answer engine that leverages the capabilities of Groq, Mistral AI's Mixtral, Langchain.JS, Brave Search, Serper API, and OpenAI. Designed to efficiently return sources, answers, images, videos, and follow-up questions based on user queries, this project is an ideal starting point for developers interested in natural language processing and search technologies.

YouTube Tutorials

Tutorial 2 Tutorial 1 Tutorial 3 Tutorial 4

Technologies Used

  • Next.js: A React framework for building server-side rendered and static web applications.
  • Tailwind CSS: A utility-first CSS framework for rapidly building custom user interfaces.
  • Vercel AI SDK: The Vercel AI SDK is a library for building AI-powered streaming text and chat UIs.
  • Groq & Mixtral: Technologies for processing and understanding user queries.
  • Langchain.JS: A JavaScript library focused on text operations, such as text splitting and embeddings.
  • Brave Search: A privacy-focused search engine used for sourcing relevant content and images.
  • Serper API: Used for fetching relevant video and image results based on the user's query.
  • OpenAI Embeddings: Used for creating vector representations of text chunks.
  • Cheerio: Utilized for HTML parsing, allowing the extraction of content from web pages.
  • Ollama (Optional): Used for streaming inference and embeddings.
  • Upstash Redis Rate Limiting (Optional): Used for setting up rate limiting for the application.
  • Upstash Semantic Cache (Optional): Used for caching data for faster response times.

Getting Started

Prerequisites

  • Ensure Node.js and npm are installed on your machine.
  • Obtain API keys from OpenAI, Groq, Brave Search, and Serper.

Obtaining API Keys

Installation

  1. Clone the repository: 
    git clone https://github.com/developersdigest/llm-answer-engine.git
  2. Install the required dependencies: 
    npm install
    or 
    bun install
  3. Create a .env file in the root of your project and add your API keys: 
    OPENAI_API_KEY=your_openai_api_key
GROQ_API_KEY=your_groq_api_key
BRAVE_SEARCH_API_KEY=your_brave_search_api_key
SERPER_API=your_serper_api_key

Running the Server

To start the server, execute:

npm run dev

or

bun run dev

the server will be listening on the specified port.

Editing the Configuration

The configuration file is located in the app/config.tsx file. You can modify the following values

  • useOllamaInference: false,
  • useOllamaEmbeddings: false,
  • inferenceModel: 'mixtral-8x7b-32768',
  • inferenceAPIKey: process.env.GROQ_API_KEY,
  • embeddingsModel: 'text-embedding-3-small',
  • textChunkSize: 800,
  • textChunkOverlap: 200,
  • numberOfSimilarityResults: 2,
  • numberOfPagesToScan: 10,
  • nonOllamaBaseURL: 'https://api.groq.com/openai/v1'
  • useFunctionCalling: true
  • useRateLimiting: false
  • useSemanticCache: false
  • usePortkey: false

Function Calling Support (Beta)

Currently, function calling is supported with the following capabilities:

  • Maps and Locations (Serper Locations API)
  • Shopping (Serper Shopping API)
  • TradingView Stock Data (Free Widget)
  • Spotify (Free API)
  • Any functionality that you would like to see here, please open an issue or submit a PR.
  • To enable function calling and conditional streaming UI (currently in beta), ensure useFunctionCalling is set to true in the config file.

Ollama Support (Partially supported)

Currently, streaming text responses are supported for Ollama, but follow-up questions are not yet supported.

Embeddings are supported, however, time-to-first-token can be quite long when using both a local embedding model as well as a local model for the streaming inference. I recommended decreasing a number of the RAG values specified in the app/config.tsx file to decrease the time-to-first-token when using Ollama.

To get started, make sure you have the Ollama running model on your local machine and set within the config the model you would like to use and set use OllamaInference and/or useOllamaEmbeddings to true.

Note: When 'useOllamaInference' is set to true, the model will be used for both text generation, but it will skip the follow-up questions inference step when using Ollama.

More info: https://ollama.com/blog/openai-compatibility

Roadmap

  • [] Add document upload + RAG for document search/retrieval
  • [] Add a settings component to allow users to select the model, embeddings model, and other parameters from the UI
  • [] Add support for follow-up questions when using Ollama
  • [Complete] Add support diffusion models (Fal.AI SD3 to start), accessible via '@ mention'
  • [Complete] Add AI Gateway to support multiple models and embeddings. (OpenAI, Azure OpenAI, Anyscale, Google Gemini & Palm, Anthropic, Cohere, Together AI, Perplexity, Mistral, Nomic, AI21, Stability AI, DeepInfra, Ollama, etc) https://github.com/Portkey-AI/gateway
  • [Complete] Add support for semantic caching to improve response times
  • [Complete] Add support for dynamic and conditionally rendered UI components based on the user's query

Example

  • [Completed] Add dark mode support based on the user's system preference

Backend + Node Only Express API

Watch the express tutorial here for a detailed guide on setting up and running this project. In addition to the Next.JS version of the project, there is a backend only version that uses Node.js and Express. Which is located in the 'express-api' directory. This is a standalone version of the project that can be used as a reference for building a similar API. There is also a readme file in the 'express-api' directory that explains how to run the backend version.

Upstash Redis Rate Limiting

Watch the Upstash Redis Rate Limiting tutorial here for a detailed guide on setting up and running this project. Upstash Redis Rate Limiting is a free tier service that allows you to set up rate limiting for your application. It provides a simple and easy-to-use interface for configuring and managing rate limits. With Upstash, you can easily set limits on the number of requests per user, IP address, or other criteria. This can help prevent abuse and ensure that your application is not overwhelmed with requests.

Contributing

Contributions to the project are welcome. Feel free to fork the repository, make your changes, and submit a pull request. You can also open issues to suggest improvements or report bugs.

License

This project is licensed under the MIT License.

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