From 40ea980419b3c4002aabbb7e3c9c64fac8ad6987 Mon Sep 17 00:00:00 2001 From: Danizord Date: Thu, 28 Sep 2023 10:47:00 -0300 Subject: [PATCH] Remove duplicated sentence --- website/docs/Use-Cases/agent_chat.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/Use-Cases/agent_chat.md b/website/docs/Use-Cases/agent_chat.md index 5dcc7d8e070..55bfb7ef02d 100644 --- a/website/docs/Use-Cases/agent_chat.md +++ b/website/docs/Use-Cases/agent_chat.md @@ -23,7 +23,7 @@ We have designed a generic `ConversableAgent` class for Agents that are capable - The `AssistantAgent` is designed to act as an AI assistant, using LLMs by default but not requiring human input or code execution. It could write Python code (in a Python coding block) for a user to execute when a message (typically a description of a task that needs to be solved) is received. Under the hood, the Python code is written by LLM (e.g., GPT-4). It can also receive the execution results and suggest code with bug fix. Its behavior can be altered by passing a new system message. The LLM [inference](#enhanced-inference) configuration can be configured via `llm_config`. -- The `UserProxyAgent` is conceptually a proxy agent for humans, soliciting human input as the agent's reply at each interaction turn by default and also having the capability to execute code and call functions. The `UserProxyAgent` triggers code execution automatically when it detects an executable code block in the received message and no human user input is provided. Code execution can be disabled by setting `code_execution_config` to False. LLM-based response is disabled by default. It can be enabled by setting `llm_config` to a dict corresponding to the [inference](/docs/Use-Cases/enhanced_inference) configuration. When `llm_config` is set to a dict, `UserProxyAgent` can generate replies using an LLM when code execution is not performed. When `llm_config` is set to a dict, `UserProxyAgent` can generate replies using an LLM when code execution is not performed. +- The `UserProxyAgent` is conceptually a proxy agent for humans, soliciting human input as the agent's reply at each interaction turn by default and also having the capability to execute code and call functions. The `UserProxyAgent` triggers code execution automatically when it detects an executable code block in the received message and no human user input is provided. Code execution can be disabled by setting `code_execution_config` to False. LLM-based response is disabled by default. It can be enabled by setting `llm_config` to a dict corresponding to the [inference](/docs/Use-Cases/enhanced_inference) configuration. When `llm_config` is set to a dict, `UserProxyAgent` can generate replies using an LLM when code execution is not performed. The auto-reply capability of `ConversableAgent` allows for more autonomous multi-agent communication while retaining the possibility of human intervention. One can also easily extend it by registering reply functions with the `register_reply()` method.