Module 1: Intro to Generative AI and Gooey.AI

Welcome! This guide will provide an overview of the core concepts behind building generative AI Agent using Gooey.AI, and outline the components and workflow involved in setting up, testing, and deploying your own AI assistant.

What we'll cover:

• Understanding Large Language Models (LLMs)

• Introduction to Retrieval Augmented Generation (RAG)

• The Role of Vector Databases (VectorDB)

• Speech-to-Text and Text-to-Speech Overview

• Building and Deploying Your AI Agent

• Using Knowledge Bases and Tools

• Evaluation and Observability

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1. Core Concepts

Large Language Models (LLMs)

LLMs, such as GPT-4, are AI models trained to generate natural language responses to user queries. They work by taking user input (e.g., “What is the capital of India?”) and generating an answer. However, LLMs can sometimes produce incorrect answers (hallucinations) if they lack relevant training data.

Retrieval Augmented Generation (RAG)

RAG enhances LLMs by integrating external knowledge sources:

• User queries are matched against an indexed knowledge base (documents, PDFs, web pages, etc.).

• Relevant snippets are retrieved and summarized by the LLM to form an accurate response.

• This is akin to an “open book exam,” allowing the AI to reference source material for answers.

Vector Databases (VectorDB)

A VectorDB indexes and stores document “embeddings”—numerical representations that map semantic similarity between pieces of text. For example, the word “bunny” is represented by its proximity to related concepts, allowing for smarter information retrieval.

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2. Speech and Language Processing

• Speech-to-Text: Converts user audio inputs into transcribed text using models like Google Speech, Azure, Deepgram, Whisper (open source), or regional APIs like Bhashini.

• Text-to-Speech: Converts AI-generated text responses back into audio, allowing users to hear the answers.

• Translation & Lip Sync: Supports multilingual scenarios by translating answers and optionally generating video avatar responses.

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3. AI Agent Interaction Flow

Typical flow for a AI Agent:

1. User submits a query (text, voice, or image).

2. AI Agent searches the knowledge base for relevant information (including conversation history, if applicable).

3. LLM synthesizes a response, optionally calling special functions/tools or APIs as needed (tool calling).

4. The answer is returned in text, audio, and/or video format, translated as required.

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4. Tools, APIs, and Customization

• Choose appropriate models/APIs for each component (e.g., open source or commercial options for speech, embedding, translation, etc.).

• Configure tool calling for simple code-based functions or external API/database access, supporting “agentic” LLM behavior.

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5. Deployment and Evaluation

• Deploy AI Agent via channels like web, WhatsApp, or IVR.

• Use built-in evaluation and observability tools to monitor AI Agent performance, ensure answer accuracy, and analyze user interactions.

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6. Getting Started

To set up a AI Agent:

1. Select language and speech models for input processing.

2. Upload and index your knowledge base (documents, PDFs, CSVs, etc.).

3. Configure your LLM and give it appropriate instructions.

4. Integrate tools/APIs as needed for additional functionality.

5. Set up output options (text-to-speech, avatars, etc.).

6. Deploy and monitor your AI Agent through your chosen channels.

Throughout this documentation, you will find detailed modules explaining each step, with practical guides and demos to help you build, test, and refine your own generative AI Agent.