Updated integration docs (#3392)

docs/integrations/autogen.mdx

@@ -1,3 +1,7 @@
+---
+title: AutoGen
+---
+
 Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.
 
 ## Overview
@@ -10,7 +14,7 @@ In this guide, we'll explore an example of creating a conversational AI system w
 Install necessary libraries:
 
 ```bash
-pip install pyautogen mem0ai openai
+pip install autogen mem0ai openai python-dotenv
 ```
 
 First, we'll import the necessary libraries and set up our configurations.
@@ -22,15 +26,18 @@ import os
 from autogen import ConversableAgent
 from mem0 import MemoryClient
 from openai import OpenAI
+from dotenv import load_dotenv
+
+load_dotenv()
 
 # Configuration
-OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
-MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key from https://app.mem0.ai
-USER_ID = "customer_service_bot"
+# OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
+# MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key from https://app.mem0.ai
+USER_ID = "alice"
 
 # Set up OpenAI API key
-os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
-os.environ['MEM0_API_KEY'] = MEM0_API_KEY
+OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
+# os.environ['MEM0_API_KEY'] = MEM0_API_KEY
 
 # Initialize Mem0 and AutoGen agents
 memory_client = MemoryClient()
@@ -55,7 +62,7 @@ conversation = [
     {"role": "assistant", "content": "Thank you for the information. Let's troubleshoot this issue..."}
 ]
 
-memory_client.add(messages=conversation, user_id=USER_ID)
+memory_client.add(messages=conversation, user_id=USER_ID, output_format="v1.1")
 print("Conversation added to memory.")
 ```
 
@@ -65,7 +72,7 @@ Create a function to get context-aware responses based on user's question and pr
 
 ```python
 def get_context_aware_response(question):
-    relevant_memories = memory_client.search(question, user_id=USER_ID)
+    relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
     context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""Answer the user question considering the previous interactions:
@@ -97,7 +104,7 @@ manager = ConversableAgent(
 )
 
 def escalate_to_manager(question):
-    relevant_memories = memory_client.search(question, user_id=USER_ID)
+    relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
     context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""