Hi everyone, today we're diving into the fascinating features of Text AI 8.0 and its powerful agent functionality. We'll be setting up a Text AI agent right within Google Colab, ensuring it's accessible to anyone online. We chose Colab for this demo because it provides

access to GPU resources. Before we start, let's quickly cover what Text AI 8 is. Text AI is an all-in-one embeddings database for semantic search, LLM orchestration, and language model workflows. Version 8 introduces a game-changing feature: agents. These agents can automatically

create workflows to answer complex user requests, interfacing with various tools and databases to provide comprehensive answers. Text AI 8's agent functionality is particularly useful for creating search agents because it can integrate multiple data sources seamlessly. It uses advanced language models for understanding and generating responses. It can perform iterative

searches and reasoning to answer complex queries. It's highly customizable and can be tailored to specific use cases. Start by opening a new Google Colab notebook, which provides a free cloud-based Python environment for our project. We're using Colab primarily because it offers access to GPU resources, which are

essential for running Text AI effectively. Make sure to select GPU as the hardware accelerator by navigating to Runtime > Change runtime type and setting the hardware accelerator to GPU. This step is crucial for optimizing Text AI's performance. In the first code cell, install Text AI along with its

dependencies by running the following command: The command installs Text AI for AI-powered search and indexing, Auto-AWQ for optimizing models with quantization, and Model VC for embedding models or data into vector space, enhancing performance and functionality for AI tasks. Next, we'll install the DuckDuckGo

search library for performing web searches. DuckDuckGo search enables integrating DuckDuckGo search results into Python projects, making it useful for retrieving web information programmatically. Next, we'll generate Hugging Face access tokens to authenticate with the Hugging Face Hub. To do this, visit huggingface.co.

Navigate to settings and create an access token under the access token section. Once you have generated your token, add it to your Google Colab environment by clicking on secrets in the sidebar, then selecting add a new secret and entering your token. Now let's create a sample agent using this code.

This setup allows the agent to dynamically retrieve information from multiple sources, such as Wikipedia, scientific papers, and web searches, or to generate responses using the specified large language model (LLM). Let's execute the code and evaluate its performance. The output highlights the agent's ability to reason and take a

step-by-step approach to answering the query, leveraging its tools and logic effectively. Here's a clearer explanation: The agent tackled the query on Quantum Computing by dynamically using tools such as web search and Wikipedia. It began with a broad search to identify relevant articles, then

refined its method to gather more specific and detailed information. Finally, it synthesized the data into a well-structured response. The answer provided insights into advancements like superconducting Quantum Computing, adiabatic Quantum computation, and Quantum image processing, while also mentioning key contributors

such as Google, IBM, and QuEra Computing. The agent showcased strong decision making and effective tool usage, resulting in a concise, accurate, and informative summary of the latest developments in Quantum Computing. We've successfully created a text AI agent in Google Colab. This agent can

Now perform complex searches and answer questions using multiple data sources. Experiment with different queries and see how the agent performs. Don't forget to explore Text AI's documentation for more advanced features and customizations.

🚀 Build Your Own AI Search Agent with txtai 8 in Google Colab 🤖

🚀 Build Your Own AI Search Agent with txtai 8 in Google Colab 🤖

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