Merge branch 'master' of https://github.com/Angular2Guy/AIDocumentLib…

…raryChat.git

README.md

@@ -9,7 +9,7 @@ The project uses Spring Boot with Spring AI to access OpenAI and the vector data
 Ollama is used to use locally run AI/LLM models. Ollama can be installed or run as a Docker image in a local machine. Ollama supports a library of AI/LLM models for different use cases. Models like LLava for image description, falcon for RAG based question answering, sqlcoder for Sql generation and mixtral for function calling can be used. Ollama can use GPUs if they are available and works on CPU without them. Spring AI has Ollama support that make the use similar to using an AI service. On current CPUs there are often performance issues. The CPU providers want to add AI engines to their CPUs in the future to solve these issues. 
 
 ### Image library search
-The project uses Spring Boot with Spring AI to question a image database that uses Postgresql with the PGVector extension and Ollama. The for the imported and resized images are descriptions generated with the LLava model that runs locally on Ollama. These descriptons are turned in Embeddings and are stored with the description in the vector database. The image and metadata is stored in the relational database. A question to the image database is turned in Embeddings and the vector database is queried for the nearest neighbor. The best matches are returned with image and description. The result is displayed in the frontend. This enables are new kind of image search that was not possible before AI/LLMs became usable. 
+The project uses Spring Boot with Spring AI to question a image database that uses Postgresql with the PGVector extension and Ollama. The for the imported and resized images are descriptions generated with the LLava model that runs locally on Ollama. These descriptions are turned in Embeddings and are stored with the description in the vector database. The image and metadata is stored in the relational database. A question to the image database is turned in Embeddings and the vector database is queried for the nearest neighbor. The best matches are returned with image and description. The result is displayed in the frontend. This enables are new kind of image search that was not possible before AI/LLMs became usable. 
 
 ### Relational database search
 The project uses Spring AI to turn questions with a LLMs into Sql queries and display the result in the frontend. Based on metadata that is provided for the relational dataset the LLM is able to create embeddings for the metadata of the tables and columns. The project also creates embeddings for the content of certain columns to be able to decide if the column should be used as join in the Sql query. With that embedding metadata the LLM is able to turn a question in a reasonable Sql query and display the result in the frontend. The frontend uses a Angular Material Table with a flexible amount of columns and rows. 
@@ -77,4 +77,4 @@ NodeJs 18.13.x or newer
 
 Npm 8.19.x or newer
 
-Angular Cli 17 or newer
+Angular Cli 17 or newer