committed by
Ilayaperumal Gopinathan
parent
1280c2a3ba
commit
1c41c6a802
@@ -7,7 +7,7 @@ The `Spring AI` project aims to streamline the development of applications that
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The project draws inspiration from notable Python projects, such as LangChain and LlamaIndex, but Spring AI is not a direct port of those projects.
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The project was founded with the belief that the next wave of Generative AI applications will not be only for Python developers but will be ubiquitous across many programming languages.
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NOTE: Spring AI addresses the fundamental challenge of AI integration: `Connecting your enterprise Data and APIs with the AI Models`.
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NOTE: Spring AI addresses the fundamental challenge of AI integration: `Connecting your enterprise Data and APIs with AI Models`.
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image::spring-ai-integration-diagram-3.svg[Interactive,500,opts=interactive]
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@@ -29,14 +29,14 @@ Spring AI provides the following features:
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* Portable API across Vector Store providers, including a novel SQL-like metadata filter API.
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* xref:api/functions.adoc[Tools/Function Calling] - permits the model to request the execution of client-side tools and functions, thereby accessing necessary real-time information as required.
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* xref:observability/index.adoc[Observability] - Provides insights into AI-related operations.
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* Document injection xref:api/etl-pipeline.adoc[ETL framework] for Data Engineering.
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* Document ingestion xref:api/etl-pipeline.adoc[ETL framework] for Data Engineering.
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* xref:api/testing.adoc[AI Model Evaluation] - Utilities to help evaluate generated content and protect against hallucinated response.
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* Spring Boot Auto Configuration and Starters for AI Models and Vector Stores.
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* xref:api/chatclient.adoc[ChatClient API] - Fluent API for communicating with AI Chat Models, idiomatically similar to the WebClient and RestClient APIs.
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* xref:api/advisors.adoc[Advisors API] - Encapsulates recurring Generative AI patterns, transforms data sent to and from Language Models (LLMs), and provides portability across various models and use cases.
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* Support for xref:api/chatclient.adoc#_chat_memory[Chat Conversation Memory] and xref:api/chatclient.adoc#_retrieval_augmented_generation[Retrieval Augmented Generation (RAG)].
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This feature set lets you implement common use cases such as "`Q&A over your documentation`" or "`Chat with your documentation.`"
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This feature set lets you implement common use cases, such as "`Q&A over your documentation`" or "`Chat with your documentation.`"
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The xref:concepts.adoc[concepts section] provides a high-level overview of AI concepts and their representation in Spring AI.
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