By effectively deploying and integrating Database, Memory, and RAG technologies, this platform will possess robust data management capabilities and intelligent interaction performance, delivering a more efficient, precise, and personalized AI automation experience.
The database stores and manages core platform data, including user data, task data, interaction records of AI agents, multimodal content (text, images, videos), and logs.
- Efficient Storage
- Data Security
- Fast Retrieval

Relational Databases
Suitable for structured data management.
- Tools
PostgreSQL, MySQL.
- Uses
User information, task allocation, workflow settings, etc.

NoSQL Databases
Suitable for unstructured or semi-structured data.
- Tools
MongoDB, Cassandra, DynamoDB.
- Uses
Multimodal data storage (e.g., metadata for images, audio, videos) and log records.

Time-Series Databases
Designed for storing and processing real-time data.
- Tools
InfluxDB, TimescaleDB.
- Uses
Monitoring AI agent operations and recording real-time interaction data.

Graph Databases
For storing and analyzing complex relational data.
- Tools
Neo4j, TigerGraph.
- Uses
Building knowledge graphs to support semantic search and reasoning.
Memory systems store contextual information from AI agent interactions, supporting both short-term and long-term memory to enhance the continuity and intelligence of conversations and task handling.
- Enhanced Context Understanding
- Personalized Interaction
- Knowledge Reuse

Short-Term Memory
Stores contextual information for current conversations or tasks.
- Tools
Redis, Memcached.
- Uses
Quickly access task states, user requests, and immediate interaction data.

Long-Term Memory
Stores long-term data such as user preferences, task history, and domain knowledge.
- Tools
Integrated with NoSQL databases (e.g., MongoDB) or dedicated memory systems.
- Uses
User profiling, task history records, personalized recommendations.

Vector Memory
Stores embedding vectors to support semantic search and knowledge enhancement.
- Tools
Pinecone, Weaviate, Milvus.
- Uses
Combined with RAG to achieve efficient knowledge retrieval.
RAG combines knowledge retrieval with generation capabilities, allowing AI agents to reference external data sources or knowledge bases in real time when generating responses, thus delivering more accurate and rich outputs.
- Knowledge Enhancement
- Real-Time Updates
- Efficient Responses

Document Store
Stores and indexes unstructured documents.
- Tools
ElasticSearch, Qdrant, Weaviate.
- Uses
Stores enterprise knowledge bases, industry documents, and user-submitted reference materials.

Vector Search Engine
Supports semantic retrieval.
- Tools
Pinecone, FAISS, Milvus.
- Uses
Converts text or multimodal data into vector representations for fast content retrieval.

Retrieval-Generation Integration
Combines language models with retrieval modules.
- Tools
LangChain, Haystack, LLamaIndex.
- Uses
Retrieves relevant data before generating responses to improve accuracy and contextual relevance.
Data-Driven Intelligence
Efficiently manages and retrieves diverse data types to support real-time and accurate decision-making.
Contextual Sensitivity
Enhances AI agents' understanding of user interactions and provides personalized services.
Dynamic Knowledge Expansion
Enables the platform to acquire and utilize up-to-date external knowledge, ensuring intelligent upgrades.
Workflow Efficiency
Establishes a stable infrastructure for storage and knowledge management, supporting the automation of complex tasks.