Updated integration docs (#3392)

docs/integrations/langchain.mdx

@@ -16,7 +16,7 @@ In this guide, we'll create a Travel Agent AI that:
 Install necessary libraries:
 
 ```bash
-pip install langchain langchain_openai mem0ai
+pip install langchain langchain_openai mem0ai python-dotenv
 ```
 
 Import required modules and set up configurations:
@@ -30,10 +30,13 @@ from langchain_openai import ChatOpenAI
 from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from mem0 import MemoryClient
+from dotenv import load_dotenv
+
+load_dotenv()
 
 # Configuration
-os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
-os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+# os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+# os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
 
 # Initialize LangChain and Mem0
 llm = ChatOpenAI(model="gpt-4o-mini")
@@ -61,19 +64,26 @@ Create functions to handle context retrieval, response generation, and addition
 ```python
 def retrieve_context(query: str, user_id: str) -> List[Dict]:
     """Retrieve relevant context from Mem0"""
-    memories = mem0.search(query, user_id=user_id)
-    serialized_memories = ' '.join([mem["memory"] for mem in memories.get('results', [])])
-    context = [
-        {
-            "role": "system", 
-            "content": f"Relevant information: {serialized_memories}"
-        },
-        {
-            "role": "user",
-            "content": query
-        }
-    ]
-    return context
+    try:
+        memories = mem0.search(query, user_id=user_id, output_format='v1.1')
+        memory_list = memories['results']
+        
+        serialized_memories = ' '.join([mem["memory"] for mem in memory_list])
+        context = [
+            {
+                "role": "system", 
+                "content": f"Relevant information: {serialized_memories}"
+            },
+            {
+                "role": "user",
+                "content": query
+            }
+        ]
+        return context
+    except Exception as e:
+        print(f"Error retrieving memories: {e}")
+        # Return empty context if there's an error
+        return [{"role": "user", "content": query}]
 
 def generate_response(input: str, context: List[Dict]) -> str:
     """Generate a response using the language model"""
@@ -86,17 +96,21 @@ def generate_response(input: str, context: List[Dict]) -> str:
 
 def save_interaction(user_id: str, user_input: str, assistant_response: str):
     """Save the interaction to Mem0"""
-    interaction = [
-        {
-          "role": "user",
-          "content": user_input
-        },
-        {
-            "role": "assistant",
-            "content": assistant_response
-        }
-    ]
-    mem0.add(interaction, user_id=user_id)
+    try:
+        interaction = [
+            {
+              "role": "user",
+              "content": user_input
+            },
+            {
+                "role": "assistant",
+                "content": assistant_response
+            }
+        ]
+        result = mem0.add(interaction, user_id=user_id, output_format='v1.1')
+        print(f"Memory saved successfully: {len(result.get('results', []))} memories added")
+    except Exception as e:
+        print(f"Error saving interaction: {e}")
 ```
 
 ## Create Chat Turn Function
@@ -124,7 +138,7 @@ Set up the main program loop for user interaction:
 ```python
 if __name__ == "__main__":
     print("Welcome to your personal Travel Agent Planner! How can I assist you with your travel plans today?")
-    user_id = "john"
+    user_id = "alice"
     
     while True:
         user_input = input("You: ")