From fd065fe9ccc07fdbd8b3781ce1d195ab73ce7d96 Mon Sep 17 00:00:00 2001
From: Antaripa Saha <antaripasaha2000@gmail.com>
Date: Thu, 10 Apr 2025 20:42:39 +0530
Subject: [PATCH] Personal Study Buddy (#2531)

---
 examples/misc/study_buddy.py | 84 ++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 examples/misc/study_buddy.py

diff --git a/examples/misc/study_buddy.py b/examples/misc/study_buddy.py
new file mode 100644
index 00000000..4b4b6a68
--- /dev/null
+++ b/examples/misc/study_buddy.py
@@ -0,0 +1,84 @@
+"""
+Create your personal AI Study Buddy that remembers what you’ve studied (and where you struggled),
+helps  with spaced repetition and topic review, personalizes responses using your past interactions.
+Supports both text and PDF/image inputs.
+
+In order to run this file, you need to set up your Mem0 API at Mem0 platform and also need a OpenAI API key.
+export OPENAI_API_KEY="your_openai_api_key"
+export MEM0_API_KEY="your_mem0_api_key"
+"""
+import asyncio
+
+from mem0 import MemoryClient
+from agents import Agent, Runner
+
+
+client = MemoryClient()
+
+# Define your study buddy agent
+study_agent = Agent(
+    name="StudyBuddy",
+    instructions="""You are a helpful study coach. You:
+- Track what the user has studied before
+- Identify topics the user has struggled with (e.g., "I'm confused", "this is hard")
+- Help with spaced repetition by suggesting topics to revisit based on last review time
+- Personalize answers using stored memories
+- Summarize PDFs or notes the user uploads""")
+
+
+# Upload and store PDF to Mem0
+def upload_pdf(pdf_url: str, user_id: str):
+    pdf_message = {
+        "role": "user",
+        "content": {
+            "type": "pdf_url",
+            "pdf_url": {"url": pdf_url}
+        }
+    }
+    client.add([pdf_message], user_id=user_id)
+    print("✅ PDF uploaded and processed into memory.")
+
+
+# Main interaction loop with your personal study buddy
+async def study_buddy(user_id: str, topic: str, user_input: str):
+
+    memories = client.search(f"{topic}", user_id=user_id)
+    memory_context = "n".join(f"- {m['memory']}" for m in memories)
+
+    prompt = f"""
+You are helping the user study the topic: {topic}.
+Here are past memories from previous sessions:
+{memory_context}
+
+Now respond to the user's new question or comment:
+{user_input}
+"""
+    result = await Runner.run(study_agent, prompt)
+    response = result.final_output
+
+    client.add([
+        {"role": "user", "content": f'''Topic: {topic}nUser: {user_input}nnStudy Assistant: {response}'''}
+    ], user_id=user_id, metadata={"topic": topic})
+
+    return response
+
+
+# Example usage
+async def main():
+    user_id = "Ajay"
+    pdf_url = "https://pages.physics.ua.edu/staff/fabi/ph101/classnotes/8RotD101.pdf"
+    upload_pdf(pdf_url, user_id)  # Upload a relevant lecture PDF to memory
+
+    topic = "Lagrangian Mechanics"
+    # Demonstrate tracking previously learned topics
+    print(await study_buddy(user_id, topic, "Can you remind me of what we discussed about generalized coordinates?"))
+
+    # Demonstrate weakness detection
+    print(await study_buddy(user_id, topic, "I still don’t get what frequency domain really means."))
+
+    # Demonstrate spaced repetition prompting
+    topic = "Momentum Conservation"
+    print(await study_buddy(user_id, topic, "I think we covered this last week. Is it time to review momentum conservation again?"))
+
+if __name__ == "__main__":
+    asyncio.run(main())