Intelligent Interaction

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.

Database

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.

Relational Databases

Suitable for structured data management.

PostgreSQL, MySQL.

User information, task allocation, workflow settings, etc.

NoSQL Databases

Suitable for unstructured or semi-structured data.

MongoDB, Cassandra, DynamoDB.

Multimodal data storage (e.g., metadata for images, audio, videos) and log records.

Time-Series Databases

Designed for storing and processing real-time data.

InfluxDB, TimescaleDB.

Monitoring AI agent operations and recording real-time interaction data.

Graph Databases

For storing and analyzing complex relational data.

Neo4j, TigerGraph.

Building knowledge graphs to support semantic search and reasoning.

Memory Systems

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.

Short-Term Memory

Stores contextual information for current conversations or tasks.

Redis, Memcached.

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.

Integrated with NoSQL databases (e.g., MongoDB) or dedicated memory systems.

User profiling, task history records, personalized recommendations.

Vector Memory

Stores embedding vectors to support semantic search and knowledge enhancement.

Pinecone, Weaviate, Milvus.

Combined with RAG to achieve efficient knowledge retrieval.

RAG (Retrieval-Augmented Generation)

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.

Document Store

Stores and indexes unstructured documents.

ElasticSearch, Qdrant, Weaviate.

Stores enterprise knowledge bases, industry documents, and user-submitted reference materials.

Vector Search Engine

Supports semantic retrieval.

Pinecone, FAISS, Milvus.

Converts text or multimodal data into vector representations for fast content retrieval.

Retrieval-Generation Integration

Combines language models with retrieval modules.

LangChain, Haystack, LLamaIndex.

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.

AI Agents not only simulate human behaviors but also automate tasks, make decisions, and drive innovation across various fields.

Looking into the Future of AI Agents