Merge pull request #581 from pchalasani/langroid-example

shorter langroid example, update section title

docs/my-website/docs/proxy_server.md

@@ -137,7 +137,7 @@ $ litellm --model command-nightly
 
 [**Jump to Code**](https://github.com/BerriAI/litellm/blob/fef4146396d5d87006259e00095a62e3900d6bb4/litellm/proxy.py#L36)
 
-## [Tutorial]: Use with Aider/AutoGen/Continue-Dev
+## [Tutorial]: Use with Aider/AutoGen/Continue-Dev/Langroid
 
 Here's how to use the proxy to test codellama/mistral/etc. models for different github repos 
 
@@ -217,26 +217,19 @@ pip install langroid
 ```python
 from langroid.language_models.openai_gpt import OpenAIGPTConfig, OpenAIGPT
 
-# create the (Pydantic-derived) config class: Allows setting params via MYLLM_XXX env vars
-MyLLMConfig = OpenAIGPTConfig.create(prefix="myllm") 
-
-# instantiate the class, with the model name and context length
-my_llm_config = MyLLMConfig(
-    chat_model="local/localhost:8000", # "local/[URL where LiteLLM proxy is listening]
+# configure the LLM
+my_llm_config = OpenAIGPTConfig(
+    #format: "local/[URL where LiteLLM proxy is listening]
+    chat_model="local/localhost:8000", 
     chat_context_length=2048,  # adjust based on model
 )
 
-# create llm and interact with it 
-from langroid.language_models.base import LLMMessage, Role
-
+# create llm, one-off interaction
 llm = OpenAIGPT(my_llm_config)
-messages = [
-    LLMMessage(content="You are a helpful assistant",  role=Role.SYSTEM),
-    LLMMessage(content="What is the capital of Ontario?",  role=Role.USER),
-],
-response = mdl.chat(messages, max_tokens=50)
+response = mdl.chat("What is the capital of China?", max_tokens=50)
 
-# Create an Agent with this LLM, wrap it in a Task, and run it as an interactive chat app:
+# Create an Agent with this LLM, wrap it in a Task, and 
+# run it as an interactive chat app:
 from langroid.agent.base import ChatAgent, ChatAgentConfig
 from langroid.agent.task import Task