Mastering Dify’s Multi‑Turn Context: From Short‑Term Memory to Knowledge‑Enhanced RAG

1. Multi‑turn conversation context

Dify organizes context using a Conversation object that contains multiple Message entries. The Context Window size determines how many recent messages are kept.

Conversation: a full dialogue session with many messages.

Message: a single user input or AI reply.

Context Window: configurable history size.

Configuration is done in the application orchestration UI under “Context”, defaulting to the last 10 rounds (customizable 1‑50).

2. Context compression strategies

When the dialogue history grows, Dify offers four handling options:

Full history – retain all messages.

Sliding window – keep only the most recent N rounds.

Summary mode – compress early turns into a summary.

Key‑info extraction – retain only entities and crucial memories.

3. Long‑term memory

From version 1.0 onward Dify adds a Memory feature with three capabilities:

Automatic memory – AI extracts key facts from the conversation.

Manual memory – developers explicitly write memory entries.

Memory retrieval – semantic similarity is used to fetch relevant memories.

Memory expiration – memories can be given a TTL.

4. Knowledge‑base context enhancement (RAG)

Dify’s Retrieval‑Augmented Generation (RAG) pipeline merges retrieved knowledge‑base snippets with the conversation context before prompting the LLM:

User question
↓
[Retrieve knowledge base] → relevant document fragments
↓
[Assemble context] → system prompt + knowledge fragments + history + current query
↓
LLM generates answer

Key parameters include TopK (default 3‑5) and a relevance Score threshold, plus optional re‑ranking.

5. System‑level prompt orchestration

Prompt templates are written in YAML. An example system prompt forces the assistant to answer based on the knowledge base, acknowledge missing information, and keep a consistent tone. Placeholders such as {{#memory}}…{{/memory}}, {{#context}}…{{/context}}, and {{#history}}…{{/history}} inject memory, retrieved context, and prior dialogue into the prompt.

6. API‑level context control

Developers can start a fresh conversation or continue an existing one via the Chat API. Example curl commands illustrate how to create a new session (empty conversation_id) or resume a session by supplying the previous conversation_id.

# New session
curl -X POST 'https://your-dify/v1/chat-messages' \
  --header 'Authorization: Bearer {api_key}' \
  --data '{
    "inputs": {},
    "query": "你好",
    "response_mode": "streaming",
    "conversation_id": ""
}'

# Continue session
curl -X POST 'https://your-dify/v1/chat-messages' \
  --data '{
    "query": "刚才说的详细解释一下",
    "conversation_id": "abc-123-xyz"
}'

7. Best‑practice recommendations

Typical configurations per scenario:

Customer‑service bot – sliding window 5‑10 rounds + knowledge‑base RAG.

Personal assistant – enable long‑term memory and summary mode.

Code assistant – keep full history because code context is critical.

Multi‑step tasks – key‑info extraction plus conversation variables.

Images illustrate the UI settings for context rounds, memory configuration, and prompt orchestration.