my-project-public

repository

loading code, commits, and activity

repositories

loading repo index

#1	"""
#2	Memory-Powered Movie Recommendation Assistant (Grok 3 + Mem0)
#3	This script builds a personalized movie recommender that remembers your preferences
#4	(e.g. dislikes horror, loves romcoms) using Mem0 as a memory layer and Grok 3 for responses.
#5
#6	In order to run this file, you need to set up your Mem0 API at Mem0 platform and also need an XAI API key.
#7	export XAI_API_KEY="your_xai_api_key"
#8	export MEM0_API_KEY="your_mem0_api_key"
#9	"""
#10
#11	import os
#12
#13	from openai import OpenAI
#14
#15	from mem0 import Memory
#16
#17	# Configure Mem0 with Grok 3 and Qdrant
#18	config = {
#19	"vector_store": {"provider": "qdrant", "config": {"embedding_model_dims": 384}},
#20	"llm": {
#21	"provider": "xai",
#22	"config": {
#23	"model": "grok-3-beta",
#24	"temperature": 0.1,
#25	"max_tokens": 2000,
#26	},
#27	},
#28	"embedder": {
#29	"provider": "huggingface",
#30	"config": {
#31	"model": "all-MiniLM-L6-v2" # open embedding model
#32	},
#33	},
#34	}
#35
#36	# Instantiate memory layer
#37	memory = Memory.from_config(config)
#38
#39	# Initialize Grok 3 client
#40	grok_client = OpenAI(
#41	api_key=os.getenv("XAI_API_KEY"),
#42	base_url="https://api.x.ai/v1",
#43	)
#44
#45
#46	def recommend_movie_with_memory(user_id: str, user_query: str):
#47	# Retrieve prior memory about movies
#48	past_memories = memory.search("movie preferences", user_id=user_id)
#49
#50	prompt = user_query
#51	if past_memories:
#52	prompt += f"\nPreviously, the user mentioned: {past_memories}"
#53
#54	# Generate movie recommendation using Grok 3
#55	response = grok_client.chat.completions.create(model="grok-3-beta", messages=[{"role": "user", "content": prompt}])
#56	recommendation = response.choices[0].message.content
#57
#58	# Store conversation in memory
#59	memory.add(
#60	[{"role": "user", "content": user_query}, {"role": "assistant", "content": recommendation}],
#61	user_id=user_id,
#62	metadata={"category": "movie"},
#63	)
#64
#65	return recommendation
#66
#67
#68	# Example Usage
#69	if __name__ == "__main__":
#70	user_id = "arshi"
#71	recommend_movie_with_memory(user_id, "I'm looking for a movie to watch tonight. Any suggestions?")
#72	# OUTPUT: You have watched Intersteller last weekend and you don't like horror movies, maybe you can watch "Purple Hearts" today.
#73	recommend_movie_with_memory(
#74	user_id, "Can we skip the tearjerkers? I really enjoyed Notting Hill and Crazy Rich Asians."
#75	)
#76	# OUTPUT: Got it — no sad endings! You might enjoy "The Proposal" or "Love, Rosie". They’re both light-hearted romcoms with happy vibes.
#77	recommend_movie_with_memory(user_id, "Any light-hearted movie I can watch after work today?")
#78	# OUTPUT: Since you liked Crazy Rich Asians and The Proposal, how about "The Intern" or "Isn’t It Romantic"? Both are upbeat, funny, and perfect for relaxing.
#79	recommend_movie_with_memory(user_id, "I’ve already watched The Intern. Something new maybe?")
#80	# OUTPUT: No problem! Try "Your Place or Mine" - romcoms that match your taste and are tear-free!
#81

z6Mkq5mY3JWtxoxUobWcfNHm7AkRubgSWEZTkBVqZXJviFZ5/my-project-public