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Spring AI Chat Bot

This project demonstrates various AI functionalities using Spring AI. It includes capabilities for generating chat responses, handling JSON formatted data, creating images, describing images, loading real-time data via APIs with Open AI Functions, generating speech from text, and utilizing a vector store for custom data.

API POST-Requests:

General Message to AI

  • URL: http://localhost:8080/api/generalMessage
  • Description: Sends a general message to AI for a response. General Message

Get Biggest Customers

  • URL: http://localhost:8080/api/biggestCustomers
  • Description: Retrieves the biggest customers for a given company in JSON format. Biggest Customers

Get Headquarters for a Company

  • URL: http://localhost:8080/api/companyHeadquarters
  • Description: Gets the headquarters of a given company in JSON format. Company Headquarters

Generate an Image from Text

  • URL: http://localhost:8080/api/image
  • Description: Generates an image from a given text using DALLE. Image Generation

Load Real-Time Data via API

  • URL: http://localhost:8080/api/weatherService
  • Description: Uses AI functions to load actual data from an API request and generate a response. Weather Service

Describe an Image

  • URL: http://localhost:8080/api/describeImage
  • Description: Uses OpenAI vision to describe the content of an image. Image Description

Generate Speech from Text

  • URL: http://localhost:8080/api/talk
  • Description: Generates speech from a given text. Text to Speech

Describe an Image with Speech

  • URL: http://localhost:8080/api/describeImageWithSpeech
  • Description: Describes an image with generated speech. Image Description with Speech

Use a Vector Store for Custom Data

  • URL: http://localhost:8080/vectorstore/message
  • Description: Uses a vector store to build embeddings on custom data. Vector Store Message

Vector Store Setup in PostgreSQL

CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS hstore;
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

CREATE TABLE IF NOT EXISTS vector_store (
    id uuid DEFAULT uuid_generate_v4() PRIMARY KEY,
    content text,
    metadata json,
    embedding vector(1536)
);

CREATE INDEX ON vector_store USING HNSW (embedding vector_cosine_ops);