一、什么是Spring AI
二、Spring AI的主要特点
Spring AI提供的API支持跨人工智能提供商的聊天、文本到图像、嵌入模型等，同时支持同步喝流API选项；
1、Chat Models 聊天模型
- 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
2、Text-to-image Models
- OpenAI with DALL-E
- StabilityAI
3、Transcription (audio to text) Models
- OpenAI
4、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.
5、Vector Databases
- Azure Vector Search
- Chroma
- Milvus
- Neo4j
- PostgreSQL/PGVector
- PineCone
- Redis
- Weaviate
- Qdrant
6、Spring Boot Auto Configuration and Starters for AI Models and Vector Stores.
7、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
8、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.
9、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.
标签：documents,framework,AI,Sping,初步,Models,Vector,OpenAI From： https://www.cnblogs.com/jhdhl/p/18293587