Building Custom LLM Researcher Agents with Telegram, LlamaIndex & Python
In this video, we're going to build a simple automation system that integrates OpenAI's capabilities with Telegram for managing tasks, leveraging llama-index for creating useful agents. We start by creating a custom distributed pipeline capable of assigning different tools like web searching, reading, and writing files to custom agents created during runtime. We guide you step-by-step on setting up this amazing iteration from scratch, demonstrating live coding sessions, debugging errors along the way, and offering useful tips. Perfect for tech enthusiasts looking to explore the realms of automation. Join us, explore these cool capabilities, and enhance your productivity toolkit!.
📚 Chapters
00:00 - Introduction to the Automation Project
01:09 - Overview of Automation Process
01:22 - Discussing Basic Tools and Setup
02:05 - Importing Required Libraries
03:23 - Setting Up the OpenAI Client
05:19 - Initializing Models and Folder for Artifacts
06:46 - Setting Up API Parameters
08:05 - Task Extraction Function Implementation
12:54 - Parsing Tasks and Tools from Input
15:22 - Testing the Extraction Code
18:24 - Implementing Additional Agent Tools
22:54 - Performing Web Search with SerpAPI
25:14 - Handling API Key Issues and Demonstrating Web Search
27:26 - Writing Function to Create Agents
29:29 - Main Function and Command-line Execution
36:07 - Integrating with Telegram Bot
38:19 - Writing Telegram Bot Start Function
41:06 - Handling Messages for Telegram Bot
44:25 - Setting Up and Running the Telegram Bot
45:28 - Demonstrating the Telegram Bot in Action
47:12 - Conclusion and Final Remarks
🔗 Links:
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