Llm-Answer-Engine

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llm-answer-engine is an open-source AI Agents & Automation tool that builds a Perplexity-inspired answer engine. It leverages multiple AI models and search APIs to provide comprehensive answers with sources, images, and videos.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is llm-answer-engine?

llm-answer-engine is an open-source project designed to create a sophisticated answer engine similar to Perplexity. It integrates powerful AI technologies such as Groq, Mistral AI's Mixtral, Langchain.JS, and OpenAI for processing queries and generating responses. For comprehensive search results, it utilizes Brave Search and Serper API to fetch relevant content, images, and videos. The engine is capable of returning detailed answers, along with their sources, and suggesting follow-up questions. It also supports advanced features like function calling, Ollama integration for local inference, and optional rate limiting and semantic caching with Upstash Redis, making it a robust starting point for developers interested in natural language processing and search technologies.

Best used for

Ideal for developers who need to build custom AI-powered answer engines, integrate advanced search capabilities, and leverage multiple AI models and APIs. Especially valuable for creating applications that require comprehensive, sourced answers with multimedia elements and follow-up questions.

Common actions

build answer engine

integrate AI search

process natural language

retrieve multi-modal content

face swappinggithub copilot"AI Agents"automated workflowlow-code/no-codeopen-sourcecollaborationworkflowsdeepfake

Capabilities

Key features

Perplexity-inspired answer engine
Multi-source content retrieval
Image and video search
Follow-up question generation
Function calling support
Ollama integration
Semantic caching

Target Audience

developerstartup founder

Integrations

openaigroqmistral-aibrave-searchserper-apiollamaupstash-redis

Pricing & Plans

Open Source

Free

FAQs

What AI models and search APIs does llm-answer-engine integrate with?

The engine integrates with Groq, Mistral AI's Mixtral, Langchain.JS, and OpenAI for AI processing. For search capabilities, it uses Brave Search and Serper API to retrieve relevant content, images, and videos. It also supports Ollama for local inference and embeddings.

Can llm-answer-engine provide images and videos in its answers?

Yes, the llm-answer-engine is designed to efficiently return sources, answers, images, videos, and follow-up questions based on user queries. It leverages the Brave Search and Serper API to fetch relevant multimedia content.

Does llm-answer-engine support function calling?

Yes, function calling is supported in beta, enabling capabilities like maps and locations (Serper Locations API), shopping (Serper Shopping API), TradingView Stock Data, and Spotify. Users can enable this feature in the configuration file.

Is there a Docker setup available for llm-answer-engine?

Yes, the project provides a Docker installation guide. Users can set up the engine using Docker and docker-compose, which simplifies the deployment process by containerizing the application and its dependencies.

What are the prerequisites for running llm-answer-engine?

To run the engine, you need API keys from OpenAI, Groq, Brave Search, and Serper. For non-Docker installations, Node.js and npm are required. Docker and docker-compose are needed for Docker-based deployments.

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