Profiles
User Profiles
Build rich user profiles through dialectic conversations. Hebbrix learns user preferences, interests, and personality traits to provide personalized AI experiences.
How Profiles Work
Automatic Learning
Profiles are built automatically from conversations. Every interaction adds to the user's profile.
Dialectic Chat
Use the dialectic endpoint to have focused conversations that extract specific profile information.
Profile Fields
Profiles can store any structured data. Common fields include:
namepreferencesinterestsexpertisecommunication_stylegoalstimezonelanguageindustryrolepersonality_traitscustom_fieldsEndpoints
Code Examples
Get User Profile
Python
from hebbrix import Hebbrix
client = Hebbrix()
# Get current user's profile
profile = client.profile.get()
print(f"Name: {profile.name}")
print(f"Interests: {', '.join(profile.interests)}")
print(f"Preferences: {profile.preferences}")
print(f"Communication style: {profile.communication_style}")Dialectic Conversation
Python (Dialectic)
# Start a dialectic chat to learn about the user
response = client.profile.dialectic(
message="I'm a software engineer interested in AI",
topic="interests"
)
print(response.assistant_message)
# "That's great! What aspects of AI interest you most -
# machine learning, NLP, or something else?"
# Continue the conversation
response = client.profile.dialectic(
message="I'm particularly interested in LLMs and memory systems",
session_id=response.session_id
)
# Profile is automatically updated with learned interestsUpdate Profile Manually
Python (Manual Update)
# Set or update profile fields
client.profile.update(
name="John Doe",
preferences={
"theme": "dark",
"notifications": True,
"language": "en"
},
custom_fields={
"company": "Acme Inc",
"role": "Senior Engineer"
}
)cURL Example
GET
/v1/profilecurl -X GET "https://api.hebbrix.com/v1/profile" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json"Profile Injection in Chat
When using the chat completions endpoint, profile data is automatically injected:
Python
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Recommend a book for me"}],
features={
"memory": True,
"user_profile": True # Injects profile context
}
)
# AI knows user's interests and preferences for personalized recommendations