Build Your Own AI Stock Analyst Avatar: A Step-by-Step Guide

Introduction: The Future of Financial Analysis is Here

Imagine having an AI that talks to you about stocks in real time. This isn't science fiction anymore; it's a project you can build today. We're creating an AI avatar that can analyze market performance and chat with you about it. It uses LiveKit for voice, Tavly for data, and Hedra for the avatar itself. This tool offers personalized financial insights and a conversational way to understand market data. Get ready, because we'll guide you through every step to build this amazing AI assistant.

Section 1: Understanding the Core Components for Your AI Stock Expert

To build your AI stock analyst, you’ll need a few key technologies. Each plays a vital role in bringing your intelligent assistant to life. Let's break down the essential tools.

LiveKit: Powering Real-Time Voice Communication

LiveKit is the engine for your AI's voice. It's an open-source platform. This means you can configure voice agents easily. LiveKit provides the space, or "room," where your AI avatar will interact with you. Its ability to handle real-time communication is crucial for a smooth conversation.

Tavly Search API: Your Window to Market Data

Tavly Search API is your data source. It lets your AI access the web for research. You can get stock data, market performance, and news. Tavly helps your AI gather up-to-date information on any stock. It’s like giving your AI a research assistant. For example, you can ask it about Tesla's performance, and Tavly will find the details.

Hedra: Bringing Your AI Avatar to Life

Hedra is what makes your AI visual. It’s the platform for creating your AI avatar. Hedra transforms data into an interactive experience. Your avatar will have a face and a voice, making the interaction more engaging. It’s how your AI goes from just talking to actually appearing before you.

Section 2: Setting Up Your Voice Agent with LiveKit

First, let's get your basic voice agent up and running with LiveKit. This is the foundation for our stock expert.

Follow these steps in the documents - https://docs.livekit.io/agents/start/voice-ai/

Project Initialization and API Key Generation

Start by heading over to LiveKit.io. You'll need to create a new project. Give it a name, like "Stock Expert." Once created, find the settings section. There, you'll generate an API key. Copy these keys carefully; they're needed later.

Environment Setup and Dependency Installation

Next, set up a Python virtual environment. Open your terminal and run python -m venv venv. Then, activate it with source venv/bin/activate. You'll need specific packages for voice processing. Install them using pip install python-dotenv openai deepgram-sdk cartesia. These libraries enable speech-to-text, language processing, and text-to-speech.

Configuring the Agent.py File

Create a file named agent.py. This file is the heart of your voice agent. It connects all the voice components. You'll also paste your API keys into a .env file. This file will store your LiveKit, OpenAI, Deepgram, and Cartisia keys. Then, you'll run python agent.py download files. Finally, start your agent with python3 agent.py console. You can then talk to it!

Section 3: Integrating Tavly API for Stock Analysis

Now, let's give your AI the power to analyze stocks using the Tavly API.

Obtaining Tavly API Credentials and Installation

Go to Tavly.com and create an account. Tavly offers 1,000 free credits each month, which is great for starting. Once logged in, find your API key. Install the Tavly client in your Python environment by running pip install Tavly-Python.

Understanding Tavly Search Parameters

The Tavly API has useful search options. You'll use the search method. Key parameters include query (what you're asking about, like "Meta stock performance"), search_depth (basic or advanced), and include_answer=True. Setting include_answer to true tells Tavly to use an LLM to summarize the search results. This gives you a direct answer. You can also set topic='finance' for better results.

Creating a Test Script for Tavly Integration

Create a new file called test.py. Import the Tavly client and load your environment variables. Use os.getenv("TAVLY_API_KEY") to get your key. Then, make a search request, for example, tavly.search(query="how is Tesla stock doing?", include_answer=True). Print the answer from the response: print(response["answer"]). Run this script using python3 test.py to see how it works.

Section 4: Developing the tools.py for Stock Analysis Functionality

We need to create a specific tool for our AI to use. This tool will call the Tavly API.

Importing Necessary Libraries and Setting Up Environment Variables

Create a file named tools.py. You'll need imports like os and load_dotenv. Import the Tavly client too. Also, bring in function_tool from LiveKit. This helps define functions your AI can use. Make sure your .env file has your Tavly API key.

Defining the get_stock_analysis Function

Define a function called get_stock_analysis. It will take a stock_name as input. Inside, use the Tavly client to search. Set the query to include the stock name, price, and performance. Use search_depth='basic' and topic='finance'. Crucially, set include_answer=True. The function should then return just the answer from the Tavly response.

Integrating the Tool into the LiveKit Agent

Now, link this tool to your main agent. Open agent.py. Import your new function: from tools import get_stock_analysis. When you initialize your agent class, add tools=[get_stock_analysis] to the list. Update the agent's instructions. Tell it to be a stock expert. For example, "You are a helpful AI stock expert. Ask users what stock they want." Also, tell it to say "Okay, let me check the market stats for today" before using the tool.

Section 5: Building and Testing Your AI Stock Avatar with Hedra

Finally, let's bring your AI to life with an avatar using Hedra.

Obtaining Hedra API Key and Installation

Go to Hedra.com. Find your API key in your profile settings. Copy it and add it to your .env file as HEDRA_API_KEY. Install the Hedra plugin for LiveKit: pip install hedra-livkit-plugin. This connects Hedra to LiveKit.

Setting Up the Avatar Image and Session

In agent.py, import Image from PIL and hedra from livekit.plugins. Load your avatar image file, like stalker.png, using Image.open("path/to/your/image.png"). Then, create the avatar: avatar = hedra.avatar(session=session, avatar_image=avatar_image). You also need to manage room output options. Set room_output_options=livekit.RoomOutputOptions(audio_enabled=False) so the avatar streams audio correctly.

Testing the Avatar in the LiveKit Playground

Instead of running the console, start the LiveKit development server with livekit dev. Open your browser and search for "LiveKit Playground." Connect to your project, which you named "Stock Expert." Your AI avatar should appear. Now, ask it about a stock, like Tesla. It will use Tavly to get data and then respond with the information, all while showing its avatar.

Conclusion: Your Real-Time AI Financial Advisor

You've now built an AI stock analyst avatar! It uses LiveKit for voice, Tavly for data, and Hedra for the visual avatar. This system provides real-time stock insights through conversation. You can make it even better. Try tweaking the instructions to make the AI sound more human. You could also add a custom front-end interface. Experiment and explore what else you can build with these powerful tools!