Merge pull request #115 from LD-Reborn/52-fix-documentation-to-show-that-we-have-a-front-end

52 fix documentation to show that we have a front end

README.md

@@ -1,92 +1,59 @@
-# embeddingsearch
+# embeddingsearch<img src="docs/logo.png" alt="Logo" width="50" align="left">
-<img src="https://github.com/LD-Reborn/embeddingsearch/blob/main/logo.png" alt="Logo" width="100">
+embeddingsearch is a self-hosted semantic search server built on vector embeddings.<br/>It lets you index and semantically search text using modern embedding models.
+<br/><br/>
+It's designed to be flexible, extensible, and easy to use.
 
-embeddingsearch is a search server that uses Embedding Similarity Search (similiarly to [Magna](https://github.com/yousef-rafat/Magna/tree/main)) to semantically compare a given input to a database of indexed entries.
+# Project outline
+<img src="docs/ProjectOutline/ProjectOutlineDiagram.excalidraw.svg" alt="Logo">
 
-embeddingsearch offers:
+## What embeddingsearch offers:
-- Privacy and flexibility through self-hosted solutions like:
+- Privacy and flexibility by allowing one to self-host everything, including:
-  - ollama
+  - Ollama
   - OpenAI-compatible APIs (like LocalAI)
-- Great flexibility through deep control over
+- Astonishing accuracy when using multiple models for single indices
-  - the amount of datapoints per entity (i.e. the thing you're trying to find)
+- Ease-of-use and ease-of-implementation
-  - which models are used (multiple per datapoint possible to improve accuracy)
+  - The server offers a front-end for management and status information, as well as a decorated swagger back-end
-  - which models are sourced from where (multiple Ollama/OpenAI-compatible sources possible)
+  - The indexer can also be self-hosted and serves as a host for executing indexing scripts
-  - similarity calculation methods
+  - The client library can be used to develop your own client software that posts queries or creates indices
-  - aggregation of results (when multiple models are used per datapoint)
+- Caching & persistency
+  - Generating embeddings is expensive. So why not cache AND store them?
+  - Query results can also be cached.
+  - "Doesn't that eat a lot of precious RAM?" - My own testing showed: embeddings take up around 4200-5200 bytes each depending on the request string size. So around 4-5 GB per million cached embeddings.
 
-This repository comes with a
+This repository comes with a:
-- server (accessible via API calls & swagger)
+- Server
-- clientside library (C#)
+- Client library (C#)
-- scripting based indexer service that supports the use of
+- Scripting based indexer service that supports the use of
   - Python
-  - CSharp (Roslyn)
+  - CSharp (Roslyn - at-runtime evaluation)
-  - Golang (Planned)
+  - CSharp (Reflection - compiled)
+  - Lua (Planned)
   - Javascript (Planned)
 
-# How to set up / use
+# How to set up
 ## Server
-(Docker now available! See [Docker installation](docs/Server.md#docker-installation))
+(Docker also available! See [Docker installation](docs/Server.md#docker-installation))
-1. Install [ollama](https://ollama.com/download)
+1. Install the inferencing tool of your choice, (e.g. [ollama](https://ollama.com/download)) and pull a few models that support generating embeddings.
-2. Pull a few models using ollama (e.g. `paraphrase-multilingual`, `bge-m3`, `mxbai-embed-large`, `nomic-embed-text`)
+2. [Install the depencencies](docs/Server.md#installing-the-dependencies)
-3. [Install the depencencies](docs/Server.md#installing-the-dependencies)
+3. [Set up a mysql database](docs/Server.md#mysql-database-setup)
-4. [Set up a local mysql database](docs/Server.md#mysql-database-setup)
+4. [Set up the configuration](docs/Server.md#configuration)
-5. [Set up the configuration](docs/Server.md#setup)
+5. In `src/Server` execute `dotnet build && dotnet run` to start the server
-6. In `src/server` execute `dotnet build && dotnet run` to start the server
+6. (optional) Create a searchdomain using the web interface
-7. (optional) [Create a searchdomain using the web interface](docs/Server.md#accessing-the-api)
-## Client
-1. Download the package and add it to your project (TODO: NuGet)
-2. Create a new client by either:
-    1. By injecting IConfiguration (e.g. `services.AddSingleton<Client>();`)
-    2. By specifying the baseUri, apiKey, and searchdomain (e.g. `new Client.Client(baseUri, apiKey, searchdomain)`)
 ## Indexer
 (Docker now available! See [Docker installation](docs/Indexer.md#docker-installation))
 1. [Install the dependencies](docs/Indexer.md#installing-the-dependencies)
-2. [Set up the server](#server)
+2. [Configure the indexer](docs/Indexer.md#configuration)
-3. [Configure the indexer](docs/Indexer.md#configuration)
+3. [Set up your indexing script(s)](docs/Indexer.md#scripting)
-4. [Set up your indexing script(s)](docs/Indexer.md#scripting)
+4. In `src/Indexer` execute `dotnet build && dotnet run` to start the indexer
-5. Run with `dotnet build && dotnet run` (Or `/usr/bin/dotnet build && /usr/bin/dotnet run`)
 # Known issues
 | Issue | Solution |
 | --- | --- |
-| Unhandled exception. MySql.Data.MySqlClient.MySqlException (0x80004005): Invalid attempt to access a field before calling Read() | The searchdomain you entered does not exist |
+| System.DllNotFoundException: Could not load libpython3.13.so with flags RTLD_NOW \| RTLD_GLOBAL: libpython3.12.so: cannot open shared object file: No such file or directory | Install python3.13-dev via apt. Also: try running the indexer using `/usr/bin/dotnet` instead of `dotnet` (to make sure dotnet is not running as a snap) |
-| Unhandled exception. MySql.Data.MySqlClient.MySqlException (0x80004005): Authentication to host 'localhost' for user 'embeddingsearch' using method 'caching_sha2_password' failed with message: Access denied for user 'embeddingsearch'@'localhost' (using password: YES) | TBD |
-| System.DllNotFoundException: Could not load libpython3.12.so with flags RTLD_NOW \| RTLD_GLOBAL: libpython3.12.so: cannot open shared object file: No such file or directory | Install python3.12-dev via apt. Also: try running the indexer using `/usr/bin/dotnet` instead of `dotnet` (make sure dotnet is installed via apt) |
-# To-do
-- (High priority) Add default indexer
-  - Library
-    - Processing:
-      - Text / Markdown documents: file name, full text, paragraphs
-      - Documents
-        - PDF: file name, full text, headline?, paragraphs, images?
-        - odt/docx: file name, full text, headline?, images?
-        - msg/eml: file name, title, recipients, cc, text
-      - Images: file name, OCR, image description?
-      - Videos?
-      - Presentations (Impress/Powerpoint): file name, full text, first slide title, titles, slide texts
-      - Tables (Calc / Excel): file name, tab/page names?, full text (per tab/page)
-      - Other? (TBD)
-  - Server
-    - ~~Scripting capability (Python; perhaps also lua)~~ (Done with the latest commits)
-      - ~~Intended sourcing possibilities:~~
-        - ~~Local/Remote files (CIFS, SMB, FTP)~~
-        - ~~Database contents (MySQL, MSSQL)~~
-        - ~~Web requests (E.g. manual crawling)~~
-    - ~~Script call management (interval based & event based)~~
-- Implement [ReaderWriterLock](https://learn.microsoft.com/en-us/dotnet/api/system.threading.readerwriterlockslim?view=net-9.0&redirectedfrom=MSDN) for entityCache to allow for multithreaded read access while retaining single-threaded write access.
-- NuGet packaging and corresponding README documentation
-- Add option for query result detail levels. e.g.:
-  - Level 0: `{"Name": "...", "Value": 0.53}`
-  - Level 1: `{"Name": "...", "Value": 0.53, "Datapoints": [{"Name": "title", "Value": 0.65}, {...}]}`
-  - Level 2: `{"Name": "...", "Value": 0.53, "Datapoints": [{"Name": "title", "Value": 0.65, "Embeddings": [{"Model": "bge-m3", "Value": 0.87}, {...}]}, {...}]}`
-- Add "Click-Through" result evaluation (For each entity: store a list of queries that led to the entity being chosen by the user. Then at query-time choose the best-fitting entry and maybe use it as another datapoint? Or use a separate weight function?)
-- Reranker/Crossencoder/RAG (or anything else beyond initial retrieval) support
-- Remove the `id` collumns from the database tables where the table is actually identified (and should be unique by) the name, which should become the new primary key.
-- Improve performance & latency (Create ready-to-go processes where each contain an n'th share of the entity cache, ready to perform a query. Prepare it after creating the entity cache.)
-- Implement dynamic invocation based database migrations
-
-# Future features
-- Support for other database types (MSSQL, SQLite)
 
+# Planned features and support
+- Document processor with automatic chunking (e.g.: .md, .pdf, .docx, .xlsx, .png, .mp4)
+- Indexer front-end
+- Support for other database types (MSSQL, SQLite, PostgreSQL, MongoDB, Redis)
 
 # Community
 <a href="https://discord.gg/MUKeZM3k"><img src="https://img.shields.io/badge/Join%20Discord-7289DA?style=flat&logo=discord&logoColor=whiteServer" alt="Discord"></img></a>

docs/Indexer.md

@@ -8,15 +8,18 @@ The indexer by default
 - Uses HealthChecks (endpoint: `/healthz`)
 ## Docker installation
 (On Linux you might need root privileges, thus use `sudo` where necessary)
-1. Navigate to the `src` directory
+1. [Configure the indexer](docs/Indexer.md#configuration)
-2. Build the docker container: `docker build -t embeddingsearch-indexer -f Indexer/Dockerfile .`
+2. [Set up your indexing script(s)](docs/Indexer.md#scripting)
-3. Run the docker container: `docker run --net=host -t embeddingsearch-indexer` (the `-t` is optional, but you get more meaningful output. Or use `-d` to run it in the background)
+3. Navigate to the `src` directory
+4. Build the docker container: `docker build -t embeddingsearch-indexer -f Indexer/Dockerfile .`
+5. Run the docker container: `docker run --net=host -t embeddingsearch-indexer` (the `-t` is optional, but you get more meaningful output. Or use `-d` to run it in the background)
 ## Installing the dependencies
 ## Ubuntu 24.04
-1. Install the .NET SDK: `sudo apt update && sudo apt install dotnet-sdk-8.0 -y`
+1. Install the .NET SDK: `sudo apt update && sudo apt install dotnet-sdk-10.0 -y`
-2. Install the python SDK: `sudo apt install python3 python3.12 python3.12-dev`
+2. Install the python SDK: `sudo apt install python3 python3.13 python3.13-dev`
+    - Note: Python 3.14 is not supported yet
 ## Windows
-Download the [.NET SDK](https://dotnet.microsoft.com/en-us/download) or follow these steps to use WSL:
+Download and install the [.NET SDK](https://dotnet.microsoft.com/en-us/download) or follow these steps to use WSL:
 1. Install Ubuntu in WSL (`wsl --install` and `wsl --install -d Ubuntu`)
 2. Enter your WSL environment `wsl.exe` and configure it
 3. Update via `sudo apt update && sudo apt upgrade -y && sudo snap refresh`
@@ -26,15 +29,15 @@ The configuration is located in `src/Indexer` and conforms to the [ASP.NET confi
 
 If you plan to use multiple environments, create any `appsettings.{YourEnvironment}.json` (e.g. `Development`, `Staging`, `Prod`) and set the environment variable `DOTNET_ENVIRONMENT` accordingly on the target machine.
 ## Setup
-If you just installed the server and want to configure it:
+If you just installed the indexer and want to configure it:
-1. Open `src/Server/appsettings.Development.json`
+1. Open `src/Indexer/appsettings.Development.json`
 2. If your search server is not on the same machine as the indexer, update "BaseUri" to reflect the URL to the server.
-3. If your search server requires API keys, (i.e. it's operating outside of the "Development" environment) set `"ApiKey": "<your key here>"` beneath `"BaseUri"` in the `"Embeddingsearch"` section.
+3. If you configured API keys for the search server, set `"ApiKey": "<your key here>"` beneath `"BaseUri"` in the `"Server"` section.
-4. Create your own indexing script(s) in `src/Indexer/Scripts/` and configure their use as 
+4. Create your own indexing script(s) in `src/Indexer/Scripts/` and configure them as shown below
 ## Structure
 ```json
-  "EmbeddingsearchIndexer": {
+  "Indexer": {
-    "Worker":
+    "Workers":
     [ // This is a list; you can have as many "workers" as you want
       {
         "Name": "example",
@@ -50,7 +53,12 @@ If you just installed the server and want to configure it:
         "Name": "secondWorker",
         /* ... */
       }
-    ]
+    ],
+    "ApiKeys": ["YourApiKeysHereForTheIndexer"], // API Keys for if you want to protect the indexer's API
+    "Server": {
+      "BaseUri": "http://localhost:5000", // URL to the embeddingsearch server
+      "ApiKey": "ServerApiKeyHere" // API Key set in the server
+    }
   }
 ```
 ## Call types
@@ -71,6 +79,13 @@ If you just installed the server and want to configure it:
     - Parameters:
       - Path (e.g. "Scripts/example_content")
 # Scripting
+Scripts should be put in `src/Indexer/Scripts/`. If you look there, by default you will find some example scripts that can be taken as reference when building your own.
+
+For configuration of the scripts see: [Structure](#structure)
+
+The next few sections explain some core concepts/patterns. If you want to skip to explicit code examples, look here:
+- [Python](#python)
+- [Roslyn](#c-roslyn)
 ## General
 Scripts need to define the following functions:
 - `init()`
@@ -186,7 +201,7 @@ from tools import * # Import all tools that are provided for ease of scripting
 
 def init(toolset: Toolset): # defining an init() function with 1 parameter is required.
     pass # Your code would go here.
-    # DO NOT put a main loop here! Why?
+    # Don't put a main loop here! Why?
     # This function prevents the application from initializing and maintains exclusive control over the GIL
 
 def update(toolset: Toolset): # defining an update() function with 1 parameter is required.
@@ -261,7 +276,7 @@ public class ExampleScript : Indexer.Models.IScript
 // Required: return an instance of your IScript-extending class
 return new ExampleScript();
 ```
-## Golang
+## Lua
 TODO
 ## Javascript
 TODO

docs/ProjectOutline/ProjectOutlineDiagram.excalidraw.md

@@ -0,0 +1,190 @@
+---
+
+excalidraw-plugin: parsed
+tags: [excalidraw]
+
+---
+==⚠  Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠== You can decompress Drawing data with the command palette: 'Decompress current Excalidraw file'. For more info check in plugin settings under 'Saving'
+
+
+# Excalidraw Data
+
+## Text Elements
+Server ^TJzgO4nS
+
+Indexer ^rgrd8gyy
+
+embeddingsearch ^jB1B7xr7
+
+Client ^ZttcBOXC
+
+embeddings
+provider ^mEIPhpn1
+
+✔️ Ollama
+✔️ OpenAI-compatible
+    (e.g. LocalAI) ^o6rED2fi
+
+uses ^QkKnkGvS
+
+Database ^yaSaChsK
+
+✔️ MySQL
+⚒️ SQLite
+⚒️ MSSQL
+⚒️ PostgreSQL
+⚒️ MongoDB
+⚒️ Redis ^LHP4PU6V
+
+Stores
+data in ^FP2xPhxz
+
+Listens on port 5146
+ ^CJG2peC6
+
+Listens on port 5210 ^iLZT5hca
+
+Workers ^33rXJfFb
+
+- example.py
+- example.csx
+- ... ^e1BVqXa2
+
+✔️ Front-end
+✔️ Swagger
+✔️ Elmah ^6UTNDntp
+
+⚒️ Front-end
+✔️ Swagger
+✔️ Elmah ^tlLF3R27
+
+✔️ Caches embeddings
+✔️ Caches queries ^I2lN1U82
+
+✔️ C# library
+⚒️ NuGet
+✔️ Searchdomain CRUD
+✔️ Entity CRUD
+✔️ Management operations ^4Ab3XHhK
+
+Uses ^KvuBRV2K
+
+Accesses ^ikhSH5rs
+
+✔️ Multiple provider
+configuration ^ipkoadg8
+
+%%
+## Drawing
+```compressed-json
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+```
+%%

docs/Server.md

@@ -1,21 +1,21 @@
 # Overview
 The server by default
 - runs on port 5146
-- Uses Swagger UI in development mode (`/swagger/index.html`)
+- Uses Swagger UI (`/swagger/index.html`)
-- Ignores API keys when in development mode
 - Uses Elmah error logging (endpoint: `/elmah`, local files: `~/logs`)
 - Uses serilog logging (local files: `~/logs`)
 - Uses HealthChecks (endpoint: `/healthz`)
 ## Docker installation
-(On Linux you might need root privileges, thus use `sudo` where necessary)
+(On Linux you might need root privileges. Use `sudo` where necessary)
-1. Navigate to the `src/server` directory
+1. [Set up the configuration](docs/Server.md#setup)
-2. Build the docker container: `docker build -t embeddingsearch-server -f /Dockerfile .`
+2. Navigate to the `src` directory
-3. Run the docker container: `docker run --net=host -t embeddingsearch-server` (the `-t` is optional, but you get more meaningful output. Or use `-d` to run it in the background)
+3. Build the docker container: `docker build -t embeddingsearch-server -f Server/Dockerfile .`
+4. Run the docker container: `docker run --net=host -t embeddingsearch-server` (the `-t` is optional, but you get more meaningful output. Or use `-d` to run it in the background)
 # Installing the dependencies
 ## Ubuntu 24.04
-1. Install the .NET SDK: `sudo apt update && sudo apt install dotnet-sdk-8.0 -y`
+1. Install the .NET SDK: `sudo apt update && sudo apt install dotnet-sdk-10.0 -y`
 ## Windows
-Download the [.NET SDK](https://dotnet.microsoft.com/en-us/download) or follow these steps to use WSL:
+Download and install the [.NET SDK](https://dotnet.microsoft.com/en-us/download) or follow these steps to use WSL:
 1. Install Ubuntu in WSL (`wsl --install` and `wsl --install -d Ubuntu`)
 2. Enter your WSL environment `wsl.exe` and configure it
 3. Update via `sudo apt update && sudo apt upgrade -y && sudo snap refresh`
@@ -30,6 +30,9 @@ Download the [.NET SDK](https://dotnet.microsoft.com/en-us/download) or follow t
 `CREATE DATABASE embeddingsearch; use embeddingsearch;`
 4. Create the user (replace "somepassword! with a secure password):
 `CREATE USER 'embeddingsearch'@'%' identified by "somepassword!"; GRANT ALL ON embeddingsearch.* TO embeddingsearch; FLUSH PRIVILEGES;`
+    - Caution: The symbol "%" in the command means that this user can be logged into from outside of the machine.
+    - Replace `'%'` with `'localhost'` or with the IP of your embeddingsearch server machine if that is a concern.
+5. Exit mysql: `exit`
 
 # Configuration
 ## Environments
@@ -43,34 +46,39 @@ If you just installed the server and want to configure it:
 3. Check the "AiProviders" section. If your Ollama/LocalAI/etc. instance does not run locally, update the "baseURL" to point to the correct URL.
 4. If you plan on using the server in production:
     1. Set the environment variable `DOTNET_ENVIRONMENT` to something that is not "Development". (e.g. "Prod")
-    2. Rename the `appsettings.Development.json` - replace "Development" with whatever you chose. (e.g. "Prod")
+    2. Rename the `appsettings.Development.json` - replace "Development" with what you chose for `DOTNET_ENVIRONMENT`
     3. Set API keys in the "ApiKeys" section (generate keys using the `uuid` command on Linux)
 ## Structure
 ```json
   "Embeddingsearch": {
     "ConnectionStrings": {
-      "SQL": "server=localhost;database=embeddingsearch;uid=embeddingsearch;pwd=somepassword!;"
+      "SQL": "server=localhost;database=embeddingsearch;uid=embeddingsearch;pwd=somepassword!;",
+      "Cache": "Data Source=embeddings.db;Mode=ReadWriteCreate;Cache=Shared" // Name of the sqlite cache file
     },
     "Elmah": {
-      "AllowedHosts": [ // Specify which IP addresses can access /elmah
+      "LogPath": "~/logs" // Where the logs are stored
-        "127.0.0.1",
-        "::1",
-        "172.17.0.1"
-      ]
     },
     "AiProviders": {
-      "ollama": { // Name of the provider. Used when defining models for a datapoint, e.g. "ollama:mxbai-embed-large"
+      "ollama": { // Name for the provider. Used when defining models for a datapoint, e.g. "ollama:mxbai-embed-large"
         "handler": "ollama", // The type of API located at baseURL
-        "baseURL": "http://localhost:11434" // Location of the API
+        "baseURL": "http://localhost:11434", // Location of the API
+        "Allowlist": [".*"], // Allow- and Denylist. Filter out non-embeddings models using regular expressions
+        "Denylist": ["qwen3-coder:latest", "qwen3:0.6b", "deepseek-v3.1:671b-cloud", "qwen3-vl", "deepseek-ocr"]
       },
-      "localAI": {
+      "localAI": { // e.g. model name: "localAI:bert-embeddings"
         "handler": "openai",
         "baseURL": "http://localhost:8080",
-        "ApiKey": "Some API key here"
+        "ApiKey": "Some API key here",
+        "Allowlist": [".*"],
+        "Denylist": ["cross-encoder", "..."]
       }
     },
-    "ApiKeys": ["Some UUID here", "Another UUID here"], // Restrict access in non-development environments to the server's API using your own generated API keys
+    "ApiKeys": ["Some UUID here", "Another UUID here"], // (optional) Restrict access using API keys
-    "UseHttpsRedirection": true // tbh I don't even know why this is still here. // TODO implement HttpsRedirection or remove this line
+    "Cache": {
+      "CacheTopN": 10000, // Only cache this number of queries. (Eviction policy: LRU)
+      "StoreEmbeddingCache": true, // If set to true, the SQLite database will be used to store the embeddings
+      "StoreTopN": 10000 // Only write the top n number of queries to the SQLite database
+    }
   }
 ```
 ## AiProviders
@@ -91,9 +99,9 @@ One can even specify multiple Ollama instances and name them however one pleases
 ```
 ### handler
 Currently two handlers are implemented for embeddings generation:
-- ollama
+- `ollama`
     - requests embeddings from `/api/embed`
-- localai
+- `openai`
     - requests embeddings from `/v1/embeddings`
 ### baseURL
 Specified by `scheme://host:port`. E.g.: `"baseUrl": "http://localhost:11434"`
@@ -105,7 +113,7 @@ Any specified absolute path will be disregarded. (e.g. "http://x.x.x.x/any/subro
 
 # API
 ## Accessing the api
-Once started, the server's API can be comfortably be viewed and manipulated via swagger.
+Once started, the server's API can be viewed and manipulated via swagger.
 
 By default it is accessible under: `http://localhost:5146/swagger/index.html`
 
@@ -114,7 +122,7 @@ To make an API request from within swagger:
 2. Click the "Try it out" button. The input fields (if there are any for your action) should now be editable.
 3. Fill in the necessary information
 4. Click "Execute"
-## Restricting access
+## Authorization
-API keys do **not** get checked in Development environment!
+Being logged in has priority over API Key requirement (if api keys are set).
 
-Set up a non-development environment as described in [Configuration>Setup](#setup) to enable API key authentication.
+So being logged in automatically authorizes endpoint usage.

src/Indexer/Dockerfile

@@ -1,7 +1,7 @@
-FROM ubuntu:24.04 AS ubuntu
+FROM ubuntu:25.10 AS ubuntu
 WORKDIR /app
 RUN apt-get update
-RUN apt-get install -y python3.12 python3.12-venv python3.12-dev dotnet-sdk-8.0
+RUN apt-get install -y python3.13 python3.13-venv python3.13-dev dotnet-sdk-10.0
 RUN apt-get clean
 COPY . /src/
 ENV ASPNETCORE_ENVIRONMENT Docker

src/Indexer/Program.cs

@@ -80,8 +80,6 @@ else
     app.UseMiddleware<Shared.ApiKeyMiddleware>();
 }
 
-// app.UseHttpsRedirection();
-
 app.MapControllers();
 
 app.Run();

src/Indexer/appsettings.Development.json

@@ -21,7 +21,8 @@
     "ApiKeys": ["xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"],
     "Server": {
       "BaseUri": "http://localhost:5146",
-      "ApiKey": "yyyyyyyy-yyyy-yyyy-yyyy-yyyyyyyyyyyy"
+      "ApiKey": "APIKeyForTheServer"
-    }
+    },
+    "PythonRuntime": "libpython3.13.so"
   }
 }

src/Indexer/appsettings.Docker.json

@@ -5,26 +5,8 @@
       "Microsoft.AspNetCore": "Warning"
     }
   },
-  "Kestrel":{
+  "Indexer": {
-    "Endpoints": {
+    "Workers":
-      "http":{
-        "Url": "http://0.0.0.0:5120"
-      }
-    }
-  },
-  "Embeddingsearch": {
-    "BaseUri": "http://172.17.0.1:5146",
-    "ApiKeys": ["b54ea868-496e-11f0-9cc7-f79f06b160e5", "bbdeedf0-496e-11f0-9744-97e28c221f67"]
-  },
-  "EmbeddingsearchIndexer": {
-    "Elmah": {
-      "AllowedHosts": [
-        "127.0.0.1",
-        "::1",
-        "172.17.0.1"
-      ]
-    },
-    "Worker":
     [
       {
         "Name": "pythonExample",
@@ -36,6 +18,12 @@
           }
         ]
       }
-    ]
+    ],
+    "ApiKeys": ["APIKeyOfYourChoice", "AnotherOneIfYouLike"],
+    "Server": {
+      "BaseUri": "http://172.17.0.1:5146",
+      "ApiKey": "APIKeyForTheServer"
+    },
+    "PythonRuntime": "libpython3.13.so"
   }
 }

src/Server/Dockerfile

@@ -1,10 +1,10 @@
-FROM mcr.microsoft.com/dotnet/sdk:8.0 AS build
+FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
 WORKDIR /build
 COPY . .
-RUN dotnet restore ./Server.csproj
+RUN dotnet restore Server/Server.csproj
-RUN dotnet publish ./Server.csproj -c Release -o /output
+RUN dotnet publish Server/Server.csproj -c Release -o /output
 
-FROM mcr.microsoft.com/dotnet/aspnet:8.0 AS final
+FROM mcr.microsoft.com/dotnet/aspnet:10.0 AS final
 WORKDIR /app
 COPY --from=build /output .
 ENV ASPNETCORE_ENVIRONMENT Docker

src/Server/appsettings.Docker.json

@@ -15,27 +15,41 @@
   "UseSwagger": true,
   "Embeddingsearch": {
     "ConnectionStrings": {
-      "SQL": "server=localhost;database=embeddingsearch;uid=embeddingsearch;pwd=somepassword!;"
+      "SQL": "server=localhost;database=embeddingsearch;uid=embeddingsearch;pwd=somepassword!;",
+      "Cache": "Data Source=embeddings.db;Mode=ReadWriteCreate;Cache=Shared"
     },
     "Elmah": {
-      "AllowedHosts": [
+      "LogPath": "~/logs"
-        "127.0.0.1",
-        "::1",
-        "172.17.0.1"
-      ]
     },
     "AiProviders": {
       "ollama": {
         "handler": "ollama",
-        "baseURL": "http://localhost:11434"
+        "baseURL": "http://localhost:11434",
+        "Allowlist": [".*"],
+        "Denylist": ["qwen3-coder:latest", "qwen3:0.6b", "qwen3-vl", "deepseek-ocr"]
       },
       "localAI": {
         "handler": "openai",
         "baseURL": "http://localhost:8080",
-        "ApiKey": "Some API key here"
+        "ApiKey": "Some API key here",
+        "Allowlist": [".*"],
+        "Denylist": ["cross-encoder", "jina-reranker-v1-tiny-en", "whisper-small"]
       }
     },
-    "ApiKeys": ["Some UUID here", "Another UUID here"],
+    "SimpleAuth": {
-    "UseHttpsRedirection": true
+      "Users": [
+        {
+          "Username": "admin",
+          "Password": "UnsafePractice.67",
+          "Roles": ["Admin"]
+        }
+      ]
+    },
+    "ApiKeys": ["APIKeyOfYourChoice", "AnotherOneIfYouLike"],
+    "Cache": {
+      "CacheTopN": 10000,
+      "StoreEmbeddingCache": true,
+      "StoreTopN": 10000
+    }
   }
 }

src/Server/wwwroot/css/site.css

@@ -80,3 +80,7 @@ td.btn-group {
   display: revert;
   min-width: 15rem;
 }
+
+[data-bs-theme="light"] img[alt="Logo"] {
+  filter: invert(100%);
+}