|
1 year ago | |
---|---|---|
.github | 1 year ago | |
docs | 1 year ago | |
embedchain | 1 year ago | |
mem0 | 1 year ago | |
tests | 1 year ago | |
.gitignore | 1 year ago | |
Makefile | 1 year ago | |
README.md | 1 year ago | |
poetry.lock | 1 year ago | |
pyproject.toml | 1 year ago |
Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.
Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain ❤️. You can find the Embedchain codebase in the embedchain directory.
🚀 Quick Start
pip install mem0ai
from mem0 import Memory
# Initialize Mem0
m = Memory()
# Store a memory from any unstructured text
result = m.add("I am working on improving my tennis skills. Suggest some online courses.", user_id="alice", metadata={"category": "hobbies"})
print(result)
# Created memory: Improving her tennis skills. Looking for online suggestions.
# Retrieve memories
all_memories = m.get_all()
print(all_memories)
# Search memories
related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
print(related_memories)
# Update a memory
result = m.update(memory_id="m1", data="Likes to play tennis on weekends")
print(result)
# Get memory history
history = m.history(memory_id="m1")
print(history)
For detailed usage instructions and API reference, visit our documentation at docs.mem0.ai.
For production environments, you can use Qdrant as a vector store:
from mem0 import Memory
config = {
"vector_store": {
"provider": "qdrant",
"config": {
"host": "localhost",
"port": 6333,
}
},
}
m = Memory.from_config(config)
Join our Slack or Discord community for support and discussions. If you have any questions, feel free to reach out to us using one of the following methods: