Spring AI

Level up your Java code and explore what Spring can do for you.

集成了常见 ai 生文、生图、翻译、向量存储基础功能

是一个丰富的 ai 开发工具

Features

Portable API support across AI providers for Chat, text-to-image, and Embedding models. Both synchronous and stream API options are supported. Dropping down to access model-specific features is also supported.

Chat Models

OpenAI

Azure Open AI

Amazon Bedrock

Cohere's Command
AI21 Labs' Jurassic-2
Meta's LLama 2
Amazon's Titan

Google Vertex AI Palm

Google Gemini

HuggingFace - access thousands of models, including those from Meta such as Llama2

Ollama - run AI models on your local machine

MistralAI

Text-to-image Models

OpenAI with DALL-E

StabilityAI

Transcription (audio to text) Models

OpenAI

Embedding Models

OpenAI

Azure OpenAI

Ollama

ONNX

PostgresML

Bedrock Cohere

Bedrock Titan

Google VertexAI

Mistal AI

The Vector Store API provides portability across different providers, featuring a novel SQL-like metadata filtering API that maintains portability.

Vector Databases

Azure Vector Search

Chroma

Milvus

Neo4j

PostgreSQL/PGVector

PineCone

Redis

Weaviate

Qdrant

Spring Boot Auto Configuration and Starters for AI Models and Vector Stores.

Function calling You can declare java.util.Function implementations to OpenAI models for use in their prompt responses. You can directly provide these functions as objects or refer to their names if registered as a @Bean within the application context. This feature minimizes unnecessary code and enables the AI model to ask for more information to fulfill its response.

Models supported are

OpenAI

Azure OpenAI

VertexAI

Mistral AI

Anthropic Claude

ETL framework for Data Engineering

The core functionality of our ETL framework is to facilitate the transfer of documents to model providers using a Vector Store. The ETL framework is based on Java functional programming concepts, helping you chain together multiple steps.

We support reading documents in various formats, including PDF, JSON, and more.

The framework allows for data manipulation to suit your needs. This often involves splitting documents to adhere to context window limitations and enhancing them with keywords for improved document retrieval effectiveness.

Finally, processed documents are stored in the Vector Database, making them accessible for future retrieval.

Extensive reference documentation, sample applications, and workshop/course material.

Future releases will build upon this foundation to provide access to additional AI Models, for example, the Gemini multi-modal modal just released by Google, a framework for evaluating the effectiveness of your AI application, more convenience APIs, and features to help solve the “query/summarize my documents” use cases. Check GitHub for details on upcoming releases.