Build an Agentic AI App with Vector Databases: Tutorial

In this comprehensive guide, we will explore how to build an agentic AI application that communicates with vector databases to answer specific queries. This setup enhances AI capabilities, enabling agents to retrieve and process information from external databases more efficiently and provide targeted responses. We will also cover how to move beyond simple integrations and incorporate external vector databases to enhance our learning experience.

What are Agentic AI Applications?

Agentic AI applications involve creating AI agents that can perform tasks autonomously. These agents can interact with various tools and data sources to achieve specific goals. A key aspect of these applications is the ability of the agents to reason, plan, and execute actions based on the information they Gather. This makes them highly versatile and capable of handling complex tasks. Integrating these with vector databases improves data handling, particularly when dealing with large volumes of unstructured data. This powerful combination allows for enhanced data processing and retrieval.

Agentic AI applications can be used in various fields, such as:

• Customer Service: AI agents can handle customer inquiries, provide support, and resolve issues without human intervention.
• Financial Analysis: Agents can analyze financial data, generate reports, and provide investment recommendations.

  With data retrieval from external sources, these AI models can perform a variety of financial services.

• Content Creation: Agents can assist in writing articles, generating marketing copy, or creating other forms of content.
• Data Analysis: They assist in data processing and analysis, with added features like retrieval augmented generation(RAG).

Understanding Vector Databases

Vector databases are specialized databases designed to store and manage data represented as vectors. Unlike traditional databases that store structured data in rows and columns, vector databases focus on storing high-dimensional vectors that capture the semantic meaning of data. These vectors are often generated using embedding models, which transform text, images, and other types of data into numerical representations that can be easily compared and analyzed.

This is the main appeal of vector databases, since they are easy to use with AI models and are more dynamic that a regular sql database.

Key features of vector databases include:

• Semantic Similarity Search: Vector databases enable efficient similarity searches based on semantic meaning. By comparing the vectors, it’s possible to find data points that are semantically similar, even if they don't share exact keywords.
• High-Dimensional Data Support: Vector databases are designed to handle high-dimensional data, making them suitable for storing embeddings generated from complex models.
• Scalability and Performance: They are optimized for handling large volumes of vector data and providing fast query responses.

Setting Up the Development Environment

To begin, ensure that your development environment is properly set up. This involves installing the necessary libraries and tools, configuring API keys, and setting up a virtual environment to manage dependencies. A crucial step is to make sure you have Python 3.6 or higher installed, along with Docker Desktop for running vector databases locally.

Key steps for setting up the environment:

1. Install Python and Pip: Verify that Python is installed correctly by running python --version. Ensure Pip, the Python Package installer, is also available.
2. Set Up a Virtual Environment: Create a virtual environment to isolate project dependencies using python -m venv venv. Activate it via source venv/bin/activate on Linux/macOS or venv\Scripts\activate on Windows.
3. Install Dependencies: Use pip install -r requirements.txt to install required packages from a requirements.txt file, which should include libraries like phidata, typer, and others related to AI agent development and vector database interaction.

   This makes sure all the Relevant information to set up the app is available.

4. Configure API Keys: Obtain necessary API keys, such as those for Groq, and set them as environment variables. This often involves modifying .env files and ensuring the keys are securely accessed within the application code.

To properly set up, you will also need to know about some external tools for this app:

External Tools to Setup

When building a agentic ai application, there are some tools to consider and have properly setup for future use. Some are:

• Phidata
• Typer
• Python-Dotenv
• Postgresql

Building the AI Agents

The core of our agentic AI application involves creating AI agents that can communicate with vector databases. These agents need to be designed to perform specific tasks, such as searching for information, analyzing data, and generating responses.

Steps to build the AI agents:

1. Define Agent Roles: Determine the roles each agent will play in the application. For example, create a web search agent to find information online and a financial analysis agent to analyze financial data.
2. Implement Agent Logic: Implement the logic for each agent using libraries like phidata and OpenAI. This involves defining functions that allow the agent to interact with external tools and data sources.
3. Integrate with Vector Databases: Configure the agents to communicate with vector databases to retrieve and store information. This typically involves using a vector database client library to connect to the database and perform queries.

Code Snippet:

from phi.agent import Agent

web_search_agent = Agent(
 name="Web Search Agent",
 role="Search the web for information",
 model=Groq(id="llama3-groq-70b-8192-tool-use-preview"),
 tools=[DuckDuckGo()],
 instructions="Always include sources",
 show_tool_calls=True,
 markdown=True,
)

This code snippet shows how to define a web search agent that uses DuckDuckGo to search the web and the Groq model for natural language processing.

This will be the model running your tools.

Interacting with Vector Databases

To enable AI agents to interact with vector databases, we need to set up a database and configure the agents to store and retrieve data. This involves using a client library to connect to the vector database and defining functions to perform common operations.

Steps to interact with vector databases:

1. Run Vector Database Locally: Utilize Docker to run a vector database locally. This provides a controlled environment for development and testing.

   Since this AI model will retrieve information from data stored, having a docker that runs it will allow you to edit the data.

2. Upload PDF Content: Use the Knowledge Base to upload content from PDF URLs into the vector database. This involves extracting text from PDFs and converting it into vector embeddings. It reads the content and tries to store that to the new vector database.

Code Snippet:

from phi.knowledge import PDFUrlKnowledgeBase

knowledge_base = PDFUrlKnowledgeBase(
 urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
)
knowledge_base.load()

This code snippet demonstrates how to load content from a PDF URL into the vector database, enabling the AI agent to retrieve and use this information.

Asking Queries and Generating Responses

Once the AI agents are set up and the vector database is populated with content, we can start asking queries and generating responses. This involves sending a query to the agent and retrieving relevant information from the vector database to construct a response.

Steps to ask queries and generate responses:

1. Define a Query: Create a clear and specific query to ask the AI agent. For example, “List down all the dishes.”
2. Retrieve Information: Use the agent to search the knowledge base and retrieve relevant information from the vector database.
3. Generate a Response: Construct a response based on the retrieved information. This may involve summarizing, paraphrasing, or combining information from multiple sources.

Code Snippet:

assistant.cli_app(markdown=True)

Step 1: Setup and Installation

Before diving into the application, setting up the foundation is essential. Ensure you have Python installed on your system. Create a virtual environment to manage dependencies, keeping your project organized and avoiding conflicts with other Python projects. Next, clone the repository containing the code, and install the necessary dependencies using pip. This ensures all the required libraries and tools are available for your application.

Step 2: Configure API Keys

Authentication is vital for secure operation. Obtain your Groq API key by signing up on their platform. Once you have the key, securely store it as an environment variable. This prevents accidental exposure of the key in your code and allows the application to access it safely during runtime.

Without your API keys, the AI application is rendered useless since it would be unable to retrieve data.

Step 3: Run Vector Database Locally

A vector database needs to be running to hold and manage the data your agent will access. Use Docker Desktop to pull and run a compatible vector database image, such as PgVector. Docker streamlines the deployment process by encapsulating the database and its dependencies, making it easy to set up and manage. This helps maintain consistency across development and production environments.

Step 4: Upload PDF Content

With the database set up, populate it with your data. The next step involves writing script to extract content from PDF URLs and store it in your vector database. This process typically includes reading the PDFs, converting the text into vector embeddings, and loading them into the database.

When your AI agent retrieves relevant data to the users, they should be relevant and useful.

Step 5: Ask Queries and Generate Responses

With the data loaded, it's time to test the agent's capabilities. Use the provided command-line interface to interact with your agent. Construct queries that target specific information in the knowledge base. The agent then searches for relevant data, retrieves it from the vector database, and formulates a response. This interactive testing allows you to evaluate and refine the agent's accuracy and effectiveness. You can test all kinds of prompts and see which ones provide the optimal results.

Cost Factors

When building and deploying an agentic AI application, there are several cost factors to consider. These costs can vary depending on the complexity of the application, the amount of data processed, and the infrastructure used.

Key cost factors include:

• API Usage: The cost of using external APIs, such as those provided by Groq, can be significant. These costs are typically based on the number of requests made and the amount of data processed.
• Infrastructure: The cost of hosting and maintaining the application can also be substantial. This includes the cost of servers, storage, and networking.
• Development and Maintenance: The cost of developing and maintaining the application should also be considered. This includes the cost of developer time, testing, and ongoing support.

It’s essential to carefully evaluate these cost factors and develop a budget that accounts for all expenses. By doing so, you can ensure that the application is financially sustainable and provides a positive return on investment.

Information Retrieval and Analysis

The core function of our agentic AI application is the ability to retrieve and analyze information from vector databases. This involves using semantic similarity searches to find relevant data and extracting key insights to answer user queries. By leveraging vector databases, the application can handle large volumes of unstructured data and provide targeted responses based on the semantic meaning of the data.

External API Integration

Our application integrates seamlessly with external APIs such as Groq, enabling it to access a wide range of resources and tools. This includes accessing pre-trained models, searching the web, and analyzing financial data. By integrating with external APIs, the application can leverage the capabilities of other systems and provide a more comprehensive and versatile solution.

Customizable AI Agents

The architecture of our application allows for the creation of customizable AI agents tailored to specific tasks. Each agent can be configured with a unique role, set of tools, and instructions, enabling it to perform its task efficiently.

This modular approach makes it easy to add new capabilities and adapt the application to changing needs.

Local Vector Database Support

Our application supports local vector databases, enabling users to run the database locally for development and testing purposes. This is particularly useful for users who want to maintain control over their data and avoid relying on external services. By providing local vector database support, we make it easier for users to get started with agentic AI and explore the capabilities of vector databases.

Customer Service Automation

Agentic AI can transform Customer Service by automating routine tasks such as answering queries, providing support, and resolving issues. By integrating with vector databases, AI agents can access a vast knowledge base of customer information and provide personalized and accurate responses. This helps reduce wait times, improve customer satisfaction, and free up human agents to focus on complex and sensitive issues.

Financial Analysis and Reporting

The world of finance is now moving faster and faster, and AI has become a crutial ingredient for all successful investments. Agentic AI can analyze financial data, generate reports, and provide investment recommendations. By integrating with external APIs and vector databases, AI agents can access real-time financial data and extract key insights to help investors make informed decisions. This helps improve investment performance, reduce risk, and streamline financial processes.

Content Creation and Curation

Agentic AI can also generate marketing copy, or curate content. By analyzing user preferences and accessing relevant information, AI agents can create content that is tailored to specific audiences and interests. This helps improve engagement, increase conversions, and reduce the cost of content creation.