embeddingsearch

Embeddingsearch is a DotNet C# library that uses Embedding Similarity Search (similiarly to Magna) to semantically compare a given input to a database of pre-processed entries.

This repository comes with

• a server (accessible via API calls & swagger)
• a clientside library
• a CLI module (deprecated)
• a scripting based indexer service that supports
  • Python
  • Golang (WIP)
  • Javascript (WIP)

How to set up / use

server

1. Install ollama
2. Pull a few models using ollama (e.g. paraphrase-multilingual, bge-m3, mxbai-embed-large, nomic-embed-text)
3. Install the depencencies
4. Set up a local mysql database
5. Set up the configuration
6. In src/server execute dotnet build && dotnet run to start the server
7. (optional) Create a searchdomain using the web interface

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

1. Install the dependencies
2. Set up the server
3. Configure the indexer
4. Set up your indexing script(s)
5. Run with dotnet build && dotnet run (Or /usr/bin/dotnet build && /usr/bin/dotnet run)

CLI

Before anything follow these steps:

1. Enter the project's src directory (used as the working directory in all examples)
2. Build the project: dotnet build All user-defined parameters are denoted using the $ symbol. I.e. $mysql_ip means: replace this with your MySQL IP address or set it as a local variable in your terminal session.

All commands, parameters and examples are documented here: docs/CLI.md

Known issues

Issue	Solution
Failed to load /usr/lib/dotnet/host/fxr/8.0.15/libhostfxr.so, error: /snap/core20/current/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by /usr/lib/dotnet/host/fxr/8.0.15/libhostfxr.so)	You likely installed dotnet via snap instead of apt. Try running the CLI using /usr/bin/dotnet instead of dotnet.
Unhandled exception. MySql.Data.MySqlClient.MySqlException (0x80004005): Invalid attempt to access a field before calling Read()	The searchdomain you entered does not exist
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

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 hash value to reduce wasteful re-indexing (Perhaps as a default property for an entity, set by the default indexer)
• Implement Healthz check
• Implement ReaderWriterLock 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 CLI
• Improve error messaging for when retrieving a searchdomain fails.
• 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.)
• Make the API server (and indexer, once it is done) a docker container
• Implement dynamic invocation based database migrations

Future features

• Support for other database types (MSSQL, SQLite)

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