Começar com a integração com o MongoDB Mem0

mkdir mem0-mongodb-project
cd mem0-mongodb-project
pip install mem0ai pymongo openai python-dotenv

OPENAI_API_KEY="<openai-api-key>"
MONGODB_URI="<connection-string>"

from mem0 import Memory
from openai import OpenAI
from dotenv import load_dotenv
import os
load_dotenv()

config = {
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4o-mini",
            "temperature": 0.1,
            "max_tokens": 2000,
        },
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-3-small",
            "embedding_dims": 1536,
        },
    },
    "vector_store": {
        "provider": "mongodb",
        "config": {
            "mongo_uri": os.environ["MONGODB_URI"],
            "db_name": "mem0_db",
            "collection_name": "agent_memory",
            "embedding_model_dims": 1536,
        },
    },
}
m = Memory.from_config(config)

messages = [
    {"role": "user", "content": "I'm moving to Berlin next month."},
    {"role": "assistant", "content": "I'll remember that you're relocating to Berlin."},
]
result = m.add(messages, user_id="alice")
print(result)

messages = [
    {"role": "user", "content": "I prefer concise answers and I use Python daily."},
    {"role": "assistant", "content": "Noted. I'll keep my responses brief and Python-focused."},
]
result = m.add(messages, user_id="alice")
print(result)

print("\nWaiting for the MongoDB Vector Search index to finish building...")
time.sleep(60)
results = m.search(
    query="When is Alice moving?",
    filters={"user_id": "alice"},
    top_k=5,
)
for mem in results["results"]:
    print(mem["memory"], "- score:", mem["score"])

all_memories = m.get_all(filters={"user_id": "alice"})
for mem in all_memories["results"]:
    print(mem["id"], mem["memory"])

openai_client = OpenAI()
USER_ID = "alice"
def build_system_prompt(user_message):
    # Retrieve the memories that are most relevant to the message
    relevant = m.search(
        query=user_message,
        filters={"user_id": USER_ID},
        top_k=5,
    )
    memory_text = "\n".join(f"- {mem['memory']}" for mem in relevant["results"])
    # Add the retrieved memories to the system prompt
    system_prompt = "You are a helpful personal assistant."
    if memory_text:
        system_prompt += (
            " Use the following facts to personalize your response:\n"
            f"{memory_text}"
        )
    return system_prompt

def chat(user_message):
    # Generate a response
    response = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": build_system_prompt(user_message)},
            {"role": "user", "content": user_message},
        ],
    )
    assistant_message = response.choices[0].message.content
    # Store the new conversation turn as memories
    m.add(
        [
            {"role": "user", "content": user_message},
            {"role": "assistant", "content": assistant_message},
        ],
        user_id=USER_ID,
    )
    return assistant_message
print(chat("Can you recommend a programming meetup in my city?"))