RAG Engine
Context Aware

Conversation Memory

Build AI assistants that remember context and maintain coherent, multi-turn conversations.

The Challenge

Without conversation memory, AI assistants treat each message as independent, leading to repetitive questions and loss of important context from earlier in the conversation.

How It Works

1

Capture Context

Automatically store relevant information from each conversation turn.

2

Smart Summarization

Intelligently compress long conversations while preserving key details.

3

Context Injection

Seamlessly include relevant history in each new query for coherent responses.

Benefits

Natural Conversations

Users can reference earlier parts of the conversation naturally without repeating themselves.

Reduced Frustration

Eliminate the need for users to re-explain context when conversations span multiple turns.

Better Recommendations

Use conversation history to provide increasingly personalized and relevant suggestions.

Session Persistence

Allow users to continue conversations across sessions without losing context.

Comparison

FeatureRAG EngineChatbaseCustomGPTDify
Unlimited Conversation History
Partial
Smart Context Summarization
Cross-Session Memory
Selective Memory Control
Partial

Based on publicly available feature lists as of 2024

Use Cases

Customer Support

Remember customer issues and preferences across support interactions.

Educational Tutoring

Track student progress and adapt teaching based on previous conversations.

Personal Assistants

Build assistants that learn user preferences and habits over time.

Sales Conversations

Maintain context about prospect needs and objections throughout the sales cycle.

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