Retrieval-augmented generation (RAG) extends LLM responses with your own data by retrieving relevant content from an external knowledge base before generating an answer. This reduces hallucination and lets LLMs answer questions about private or domain-specific data without fine-tuning.
PAI provides tools to build, deploy, and manage RAG applications. Deploy a RAG service on EAS with your choice of vector databases and LLMs, or use LangStudio to build RAG application flows for specialized domains.
Capabilities
|
Capability |
Description |
|
Deploy a RAG chatbot on EAS |
Combine vector retrieval with LLM generation in a single service. Access the service through WebUI or API. WebUI lets you configure inference parameters and upload knowledge base files. |
|
Build RAG flows in LangStudio |
Design and deploy RAG application flows in a visual, flow-based environment. Tailor RAG solutions for specific domains such as finance and healthcare. |
|
Connect to messaging platforms |
Use AppFlow to link a PAI RAG service to third-party messaging platforms and build AI-powered chatbots and intelligent customer service agents. |
Supported components
EAS-based RAG services support flexible configuration of both retrieval and generation components.
|
Component type |
Supported options |
|
Vector databases |
Faiss, Elasticsearch, Hologres, OpenSearch, and RDS PostgreSQL |
|
LLMs |
Deploy models from Model Gallery, or connect to any LLM service that supports the OpenAI API. |
|
Access methods |
WebUI, API |
Get started
Choose an approach based on how much control you need:
|
Approach |
When to use |
Documentation |
|
LangStudio |
You want a visual, flow-based interface to build domain-specific RAG applications. |
Use LangStudio to create a DeepSeek- and RAG-based Q&A application flow for finance and healthcare |
|
EAS scenario-based deployment |
You need full control over which vector database and LLM to use. |