Can You Tell Me About LLM Agents : Its About Time We Dis-Assembled the Langchain Agents & Tools
LLM Agents are made of Prompts that are fed into LLMs, and their outputs are parsed in the way to reach the final objective. Its a Sequence or Chain of actions taken one step after another. How the Agents are able to do this sequential steps?
We will dive into LC Functions, Classes and understand the prompts and how they are linked with the tools, and custom functions that we want Agents to call.
Chapter Navigation:
0:00 Intro
0:25 Experiencing Json Agent
1:25 Create New VEnv & Install Dependencies
3:45 Instantiating the Json Agents
4:45 Observation of Agent output
7:10 Reviewing the create_json_agent code
8:30 Dynamic Prompt of the create_json_agent
10:20 Brief Intro on Agents
11:50 Creating AgentExecutor
12:10 Intro to TavilyTool
13:35 Creating Agent from Prompt
15:15 Locating the Prompt Create_json_chat_agent
18:15 Reviewing the Tool Prompt
19:30 Creating AgentExecutor to Call Tools
21:20 Where are the Prompts
23:10 Invoke Method of Chain
24:10 Answering The Question on Agents
25:10 Langchain Hub Intro
26:20 Agent is LLM Chain with Python Logic
28:10 Disassembling Tools
29:25 Recap and outro
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