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title Chat
desc An end-to-end RAG loop integrating retrieval, generation, and storage — supporting personalized responses with MemCube and automatic memory crystallization.

:::note For a complete reference of API fields and formats, see the Chat API Documentation. :::

Endpoints:

  • Complete Response: POST /product/chat/complete
  • Streaming Response (SSE): POST /product/chat/stream

Description: The core business orchestration entry point of MemOS. It automatically recalls relevant memories from specified readable_cube_ids, generates contextual responses, and optionally writes conversation results back to writable_cube_ids for continuous AI self-evolution.

1. Core Architecture: ChatHandler Orchestration

  1. Memory Retrieval: Calls SearchHandler based on readable_cube_ids to extract relevant facts, preferences, and tool context from isolated Cubes.
  2. Context-Augmented Generation: Injects recalled memory fragments into the prompt, then calls the specified LLM (via model_name_or_path) to generate a targeted response.
  3. Automatic Memory Loop: If add_message_on_answer=true, the system calls AddHandler to asynchronously store the conversation in the specified Cubes — no manual add call required.

2. Key Parameters

2.1 Identity & Context

Parameter Type Required Description
query str Yes The user's current question.
user_id str Yes Unique user identifier for auth and data isolation.
history list No Short-term conversation history for maintaining session coherence.
session_id str No Session ID. Acts as a "soft signal" to boost recall weight for in-session memories.

2.2 MemCube Read/Write Control

Parameter Type Default Description
readable_cube_ids list - Read: Memory Cubes allowed for retrieval (can span personal and shared Cubes).
writable_cube_ids list - Write: Target Cubes for auto-generated memories after conversation.
add_message_on_answer bool true Whether to enable auto-writeback. Recommended to keep enabled.

2.3 Algorithm & Model Configuration

Parameter Type Default Description
mode str fast Retrieval mode: fast, fine, mixture.
model_name_or_path str - LLM model name or path.
system_prompt str - Override the default system prompt.
temperature float - Sampling temperature for controlling creativity.
threshold float 0.5 Relevance threshold — memories below this score are filtered out.

3. Response Modes

3.1 Complete Response (/complete)

  • Returns the full JSON response after the model finishes generation.
  • Best for non-interactive tasks, background processing, or simple applications.

3.2 Streaming Response (/stream)

  • Uses Server-Sent Events (SSE) to push tokens in real time.
  • Best for chatbots and assistants requiring typewriter-style UI feedback.

4. Quick Start

from memos.api.client import MemOSClient

client = MemOSClient(api_key="...", base_url="...")

res = client.chat(
    user_id="dev_user_01",
    query="Based on my preferences, suggest an R data cleaning workflow",
    readable_cube_ids=["private_cube_01", "public_kb_r_lang"],
    writable_cube_ids=["private_cube_01"],
    add_message_on_answer=True,
    mode="fine"
)

if res:
    print(f"AI response: {res.data}")

:::note Developer Tip: For debugging in a Playground environment, use the dedicated stream endpoint /product/chat/stream/playground. :::