• Docker: Install Docker
• Docker Compose: Install Docker Compose
• Windows or linux, will not work on OSX

Clone the repository:

git clone https://github.com/AI-Empower-Labs/Studio
cd ./Studio/docker/minimum

This section is key and should provide detailed instructions on how to use Docker Compose to start the system.

Notice, this example run on CPU only, so LLM inference may be slow. See GPU section on how to run with GPU if available. To start all services defined in the docker-compose.yml file:

docker compose pull
docker compose up

Start Studio UI on http:https://localhost:8080

To run in detached mode, use the -d flag:

docker compose logs

To follow logs for a specific service:

docker compose logs -f service_name

To stop all services:

docker compose down

• registry.aiempowerlabs.com/aistudio
• registry.aiempowerlabs.com/llama3
• registry.aiempowerlabs.com/translation
• registry.aiempowerlabs.com/transcription
• registry.aiempowerlabs.com/multilingual-e5-large

AI Empowerlabs offers flexible hosting solutions for Large Language Models (LLMs) using Docker images based on ollama specifications. This document provides guidelines for local hosting and configuring GPU support when using custom models.

• Service Provider: ollama Specifications
• Resource: Docker images based on ollama
• Usage: For deployment of LLMs, AI Empowerlabs uses Docker images developed based on ollama specifications. These images facilitate quick setup and reliable performance tailored to ollama's standards.

• Reference Guide: ollama GPU Acceleration & NVidia - https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html
• Details: To optimize the performance of LLM hosting, particularly with custom models, configuring GPU support is recommended. Detailed instructions and configurations can be accessed on the ollama GPU acceleration page linked above.

• Compatibility: AI Empowerlabs supports any OpenAI compatible endpoints.
• Custom Model Creation: Users can create their own custom models that adhere to OpenAI specifications. For instance, models based on ollama can be integrated seamlessly.

For additional information on custom model integration and other advanced configurations, users are encouraged to refer to the specific documentation provided by ollama or contact AI Empowerlabs support.

The appsettings.json file serves as a central configuration file for settings in an AI Empowerlabs Studio. Here are guidelines on how to create a new configuration or customize the existing settings specifically for an AI application.

1. ConnectionStrings
2. AiStudioOptions
3. KernelMemory
4. Serilog
5. Columns

• Purpose: Manages database and external service connections.
• Includes: PostgreSQL database and an external API.

To customize:

• Postgres: Update credentials and host details.
• libraTranslate: Change the URL if using a different service.

"ConnectionStrings": {
    "postgres": "Host=myhost;Port=5432;User Id=myuser;Database=mydb;Password=mypassword",
    "libraTranslate": "http:https://api.example.com"
}

This section configures and manages the integration of various AI models and services. Each subsection here represents a specific AI capability or service, and adjusting these settings allows you to tailor the AI functionalities to meet specific needs.

• Purpose: This key is used to validate your license for using specific AI services or models.
• Customization: Replace the existing key with your new license key if it has been updated or changed.

"AiStudioOptions": {
    "LicenceKey": "Your_New_License_Key_Here"
}

This subsection configures the settings for various large language models used in your application.

• Models: Configuration options for multiple models like phi-2, llama2-70b-4096, mixtral-8x7b-32768, etc.
• Attributes:
  • Default: Specifies if the model is the default choice for operations.
  • Address: API endpoint for the model.
  • AuthorizationToken: Security token for accessing the model API.
  • ContextSize: Defines the maximum context size (in tokens) that the model can handle.
  • DisplayName: A friendly name for the model used for identification in logs or user interfaces.
  • SupportsChatCompletionsResponseFormat: Indicates if the model supports a specific response format for chat completions.

To customize: Select the model and update its attributes according to your operational requirements or credentials update.

"AiStudioOptions": {
    "Llm": {
        "gemma-7b-it": {
            "Default": "false",
            "Address": "https://newapi.example.com",
            "AuthorizationToken": "new_authorization_token_here",
            "ContextSize": "8192",
            "DisplayName": "New DisplayName"
        }
    }
}

Manages the embedding models for transforming text into numerical vectors, which are useful in various machine learning and AI tasks.

• Models such as multilingual-e5-large, mxbai-embed-large-v1, etc.
• Attributes:
  • Default: Indicates if this is the default embedding model.
  • Type: The type or source of the embedding model.
  • Address: API endpoint for accessing the model.
  • MaxTokens: Maximum number of tokens that the model can process in one request.
  • Dimensions: The dimensionality of the embeddings generated.

To customize: Modify properties such as MaxTokens or Dimensions to tune the model's performance according to your requirements.

"AiStudioOptions": {
    "Embedding": {
        "multilingual-e5-large": {
            "Default": true,
            "Address": "http:https://127.0.0.1:3000",
            "MaxTokens": 1024,
            "Dimensions": 2048
        }
    }
}

Configures transcription services that convert speech to text.

• Default: Whether this service is selected by default.
• Address: API endpoint for the transcription service.

To customize: Update the Address to point to a new transcription service if changed.

"AiStudioOptions": {
    "Transcription": {
        "AiEmpowerLabs": {
            "Default": "true",
            "Address": "http:https://new_transcription_service.example.com"
        }
    }
}

After editing the appsettings.json with the new configurations, save the file and ensure to restart your ASP.NET application to apply the changes. It's crucial to validate the settings for correctness to prevent any disruptions in the functionality of your application.

The KernelMemory section of the appsettings.json file is essential for defining and managing the data processing, ingestion, retrieval, and storage strategies of your AI application. This section controls how data is handled throughout its lifecycle in the application, from initial ingestion to retrieval for AI processes.

The KernelMemory subsections allow for detailed control over various stages of data management. Here’s how you can tailor each to better fit the operational needs of your AI systems.

• Purpose: Configures the steps and parameters for initial data processing workflows.
• DefaultSteps: Lists the sequential operations applied to data as it is ingested.
• TextPartitioning: Controls how text data is segmented into manageable parts.

To customize:

1. DefaultSteps: Modify this array to include or exclude steps such as 'extract', 'sanitize', etc., depending on your data processing requirements.

2. TextPartitioning: Adjust settings like MaxTokensPerParagraph, MaxTokensPerLine, and OverlappingTokens to optimize how text data is broken down.

"KernelMemory": {
    "DataIngestion": {
        "DefaultSteps": ["extract", "sanitize", "transform", "save_records"],
        "TextPartitioning": {
            "MaxTokensPerParagraph": 1000,
            "MaxTokensPerLine": 250,
            "OverlappingTokens": 100
        }
    }
}

• Purpose: Defines how data is retrieved for use in AI tasks, specifically in generating responses from large language models.
• SearchClient: Customizes the behavior of the retrieval system in terms of prompt size, matches count, and the tokens reserved for answers.

To customize:

• MaxAskPromptSize: Set the maximum number of tokens for the query prompts.
• MaxMatchesCount: Define the upper limit of data points to consider for generating a response.
• AnswerTokens: Specify how many tokens should be reserved for the answers, influencing the length and detail of responses.
• EmptyAnswer: Customize the response when no suitable answer can be generated.

"KernelMemory": {
    "Retrieval": {
        "SearchClient": {
            "MaxAskPromptSize": 5000,
            "MaxMatchesCount": 50,
            "AnswerTokens": 400,
            "EmptyAnswer": "No relevant information available."
        }
    }
}

• Purpose: Manages database-specific configurations, particularly for services like PostgreSQL.
• Postgres: Custom settings for table management in PostgreSQL, useful for AI data storage and retrieval.

To customize:

• TableNamePrefix: Define a prefix for all database tables used by the application.
• Columns: Customize database column definitions to align with the data structure used by your AI models.
• CreateTableSql: Adjust the SQL script for table creation to include additional fields or modify indexing strategies.

"KernelMemory": {
    "Services": {
        "Postgres": {
            "TableNamePrefix": "ai_memory_",
            "Columns": {
                "id": "pk",
                "embedding": "vector(2048)",
                "tags": "labels",
                "content": "chunk",
                "payload": "extras"
            },
            "CreateTableSql": [
                "BEGIN;",
                "CREATE TABLE IF NOT EXISTS %%table_name%% (",
                "  pk TEXT NOT NULL PRIMARY KEY,",
                "  embedding vector(2048),",
                "  labels TEXT[] DEFAULT '{}'::TEXT[] NOT NULL,",
                "  chunk TEXT DEFAULT '' NOT NULL,",
                "  extras JSONB DEFAULT '{}'::JSONB NOT NULL,",
                "  my_field1 TEXT DEFAULT '',",
                "_update TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP",
                ");",
                "CREATE INDEX ON %%table_name%% USING GIN(labels);",
                "CREATE INDEX ON %%table_name%% USING hnsw (embedding vector_cosine_ops);",
                "COMMIT;"
            ]
        }
    }
}

Configures logging via Serilog.

To customize:

• MinimumLevel: Adjust log levels.
• WriteTo: Modify outputs (e.g., Console, PostgreSQL).

"Serilog": {
    "MinimumLevel": {
        "Default": "Warning"
    },
    "WriteTo": [
        {
            "Name": "Console",
            "Args": {
                "restrictedToMinimumLevel": "Error"
            }
        }
    ]
}

After making changes to the appsettings.json, save the file and restart your ASP.NET application to apply these adjustments. It is essential to validate the settings to prevent configuration errors that could disrupt the operation of your application.