llama-index memory mem0 integration
Project description
LlamaIndex Memory Integration: Mem0
Installation
To install the required package, run:
%pip install llama-index-memory-mem0
Setup with Mem0 Platform
- Set your Mem0 Platform API key as an environment variable. You can replace
<your-mem0-api-key>
with your actual API key:
Note: You can obtain your Mem0 Platform API key from the Mem0 Platform.
os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
- Import the necessary modules and create a Mem0Memory instance:
from llama_index.memory.mem0 import Mem0Memory
context = {"user_id": "user_1"}
memory = Mem0Memory.from_client(
context=context,
api_key="<your-mem0-api-key>",
search_msg_limit=4, # optional, default is 5
)
Mem0 Context is used to identify the user, agent or the conversation in the Mem0. It is required to be passed in the at least one of the fields in the Mem0Memory
constructor. It can be any of the following:
context = {
"user_id": "user_1",
"agent_id": "agent_1",
"run_id": "run_1",
}
search_msg_limit
is optional, default is 5. It is the number of messages from the chat history to be used for memory retrieval from Mem0. More number of messages will result in more context being used for retrieval but will also increase the retrieval time and might result in some unwanted results.
Setup with Mem0 OSS
- Set your Mem0 OSS by providing configuration details:
Note: To know more about Mem0 OSS, read Mem0 OSS Quickstart.
config = {
"vector_store": {
"provider": "qdrant",
"config": {
"collection_name": "test_9",
"host": "localhost",
"port": 6333,
"embedding_model_dims": 1536, # Change this according to your local model's dimensions
},
},
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4o",
"temperature": 0.2,
"max_tokens": 1500,
},
},
"embedder": {
"provider": "openai",
"config": {"model": "text-embedding-3-small"},
},
"version": "v1.1",
}
- Create a Mem0Memory instance:
memory = Mem0Memory.from_config(
context=context,
config=config,
search_msg_limit=4, # optional, default is 5
)
Basic Usage
Currently, Mem0 Memory is supported in the SimpleChatEngine
, FunctionCallingAgent
and ReActAgent
.
Intilaize the LLM
import os
from llama_index.llms.openai import OpenAI
os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
llm = OpenAI(model="gpt-4o")
SimpleChatEngine
from llama_index.core import SimpleChatEngine
agent = SimpleChatEngine.from_defaults(
llm=llm, memory=memory # set you memory here
)
# Start the chat
response = agent.chat("Hi, My name is Mayank")
print(response)
Initialize the tools
from llama_index.core.tools import FunctionTool
def call_fn(name: str):
"""Call the provided name.
Args:
name: str (Name of the person)
"""
print(f"Calling... {name}")
def email_fn(name: str):
"""Email the provided name.
Args:
name: str (Name of the person)
"""
print(f"Emailing... {name}")
call_tool = FunctionTool.from_defaults(fn=call_fn)
email_tool = FunctionTool.from_defaults(fn=email_fn)
FunctionCallingAgent
from llama_index.core.agent import FunctionCallingAgent
agent = FunctionCallingAgent.from_tools(
[call_tool, email_tool],
llm=llm,
memory=memory,
verbose=True,
)
# Start the chat
response = agent.chat("Hi, My name is Mayank")
print(response)
ReActAgent
from llama_index.core.agent import ReActAgent
agent = ReActAgent.from_tools(
[call_tool, email_tool],
llm=llm,
memory=memory,
verbose=True,
)
# Start the chat
response = agent.chat("Hi, My name is Mayank")
print(response)
Note: For more examples refer to: Notebooks
References
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