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The available settings and configurations may vary depending on the project selected. Select the appropriate project from the project dropdown menu to access specific configurations relevant to that project.

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Accessing AI Configuration

To access the AI Configuration section:
  1. Click on the Settings icon in the main navigation sidebar.
  2. Select AI Configuration from the settings menu.
  3. The AI Configuration interface will display with two tabs available:
    • AI Configuration - Main configuration and integrations management
    • OpenAI Template - Code examples and templates
AI Configuration

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AI Configuration Tab

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Server Configuration Fields

The top section displays essential server and project information with click-to-copy functionality: Settings menu
FieldDescription
OpenAI-BaseURLThe API endpoint URL for OpenAI-compatible services (formatted as {server_url}/llm/v2)
OpenAI-ProjectThe project identifier used for OpenAI API compatibility
Server URLThe base server URL for your ELITEA instance
Project IDThe unique identifier for your current project
  • Click to Copy - Click any field value to copy it to your clipboard
  • Copy All - Use the copy icon in the top-right corner to copy all configuration details at once

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Integrations

The Integrations section displays all configured AI service integrations organized by type. Click the + button in the header to create new integrations.

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LLM Models

Download config example Displays configured Large Language Model providers and their models. Default Model Settings:
SettingDescriptionExamples
Default ⓘUsed for most activities by default. Start here; switch to Low-tier or High-tier when neededGeneral purpose models
High-tier ⓘMore capable (and more expensive) models for complex workflows (multi-step reasoning, heavy tool usage)Anthropic Sonnet 4.5/Opus, OpenAI GPT-5.2, Google Gemini 3 Pro
Low-tier ⓘCheaper/faster models for routine tasks (diagram fixing, formatting, simple edits)GPT mini from OpenAI, Gemini Flash from Google, Anthropic Haiku
Each LLM configuration displays:
  • Integration Icon - Visual indicator of the provider (OpenAI, Azure, Vertex AI, Claude, Amazon Bedrock, Ollama, HuggingFace, AI Dial, etc.)
  • Model Name - Display name of the configured model
  • Status Indicator - Shows “OK • Shared” or “OK • Local” depending on sharing status
  • Tier Badges - If applicable, displays “High-Tier”, “Low-Tier”, or “Default” badge
  • Click to Edit - Click on any card to view or edit configuration details (if permissions allow)

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Creating a New LLM Model:

  1. Ensure Credentials Exist - Before adding a model, create AI credentials for your provider (see AI Credentials section)
  2. Click the + Button - In the Integrations section header (or main navigation sidebar), click the + button
  3. Select Integration Type - Choose LLM Model from the list of available integration types
  4. Fill Required Fields:
    FieldDescription
    Display NameEnter a friendly name for the model (e.g., “GPT-4o Production”)
    ID*Auto-populated from the Display Name
    Name*Enter the exact model identifier from the provider (e.g., “gpt-4o”, “claude-3-sonnet-20240229”)
    Context WindowSpecify the maximum context window size in tokens (e.g., 128000)
    Max Output TokensSet the maximum output token limit (e.g., 16000)
    Supports ReasoningEnable if the model supports reasoning capabilities (e.g., for GPT-5.1 o3-mini)
    Supports VisionEnable if the model can process and understand images
    Low TierMark this model as suitable for routine, cost-effective tasks
    High TierMark this model as suitable for complex workflows requiring advanced capabilities
    CredentialsSelect the AI credential configuration from the dropdown
  5. Click Save - The model will be created and appear in the LLM Models section
  6. Set as Default (Optional) - Use the dropdown in Default Model Settings to assign this model as the Default model
New LLM
The model name must exactly match the provider’s identifier. Refer to your provider’s documentation:
  • OpenAI: Model names
  • Anthropic: Claude models
  • Azure OpenAI: Deployment name from your Azure portal
  • Vertex AI: Model names from Google Cloud console

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Embedding Models

Embedding Models Embedding models convert text into numerical vector representations (embeddings) that capture semantic meaning. These vectors enable similarity comparisons, semantic search, and retrieval-augmented generation (RAG) workflows. Embedding models are essential for features like document indexing, knowledge base search, and context retrieval. Default embedding model ⓘ Generates embeddings (vectors) used for indexing and semantic search (RAG). Affects retrieval quality and performance
Each embedding model configuration displays:
  • Integration Icon - Visual indicator of the provider (OpenAI, Azure, HuggingFace, etc.)
  • Model Name - Display name of the configured embedding model
  • Status Indicator - Shows “OK • Shared” or “OK • Local” depending on sharing status
  • Click to Edit - Click on any card to view or edit configuration details (if permissions allow)

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Creating a New Embedding Model:

  1. Ensure Credentials Exist - Before adding a model, create AI credentials for your provider (see AI Credentials section)
  2. Click the + Button - In the Integrations section header (or main navigation sidebar), click the + button
  3. Select Integration Type - Choose Embedding Model from the list of available integration types
  4. Fill Required Fields:
    FieldDescription
    Display NameEnter a descriptive name (e.g., “OpenAI Embeddings Large”)
    ID*Auto-populated from the Display Name
    Name*Enter the exact model identifier (e.g., “text-embedding-3-large”, “text-embedding-ada-002”)
    CredentialsSelect credentials from the dropdown
  5. Click Save - The embedding model will be created and available for selection
  6. Set as Default (Optional) - Use the “Default embedding model” dropdown to make this the default for RAG operations
Embedding Model
  • OpenAI: text-embedding-3-large (3072 dims), text-embedding-3-small (1536 dims), text-embedding-ada-002 (1536 dims)
  • Azure OpenAI: Same models as OpenAI, use your Azure deployment name
  • HuggingFace: sentence-transformers/all-MiniLM-L6-v2 (384 dims), BAAI/bge-large-en-v1.5 (1024 dims)
  • Vertex AI: textembedding-gecko@003 (768 dims)

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Vector Storage

Vector Storage Vector storage systems (vector databases) store and manage embedding vectors generated by embedding models. These specialized databases enable efficient similarity searches across large collections of embeddings, supporting features like semantic search, document retrieval, and knowledge base queries. Vector databases optimize storage and retrieval of high-dimensional vector data. Default vector storage ⓘ Where embeddings are stored for retrieval/search. Choose based on persistence and scale requirements
Each vector storage configuration displays:
  • Integration Icon - Visual indicator of the provider (PGVector, Chroma, etc.)
  • Storage Name - Display name of the configured vector storage
  • Status Indicator - Shows “OK • Shared” or “OK • Local” depending on sharing status
  • Click to Edit - Click on any card to view or edit configuration details (if permissions allow)

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Creating a New Vector Storage Configuration:

  1. Click the + Button - In the Integrations section header (or main navigation sidebar), click the + button
  2. Select Integration Type - Choose Vector Storage from the list of available integration types
  3. Select Vector Storage Provider - Choose PGVector (PostgreSQL extension for vector storage)
  4. Fill Required Fields:
    FieldDescription
    Display NameEnter a descriptive name (e.g., “Production PGVector”)
    ID*Auto-populated from the Display Name
    Connection StringPostgreSQL connection string
  5. Click Save - The vector storage configuration will be created
  6. Set as Default (Optional) - Use the “Default vector storage” dropdown to make this the default
Vector Storage
For PGVector configurations, ensure the PostgreSQL database has the pgvector extension installed:
CREATE EXTENSION IF NOT EXISTS vector;

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Image Generation

Image Generation Image generation models create images from text descriptions (text-to-image) or modify existing images based on prompts. These AI models enable creative workflows, visual content creation, and automated image production based on natural language instructions. Default image generation model Select the default model for image generation tasks
Each image generation model configuration displays:
  • Integration Icon - Visual indicator of the provider (OpenAI DALL-E, Azure, etc.)
  • Model Name - Display name of the configured image generation model
  • Status Indicator - Shows “OK • Shared” or “OK • Local” depending on sharing status
  • Click to Edit - Click on any card to view or edit configuration details (if permissions allow)

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Creating a New Image Generation Model:

  1. Ensure Credentials Exist - Before adding a model, create AI credentials for your provider (see AI Credentials section)
  2. Click the + Button - In the Integrations section header (or main navigation sidebar), click the + button
  3. Select Integration Type - Choose Image Generation Model from the list of available integration types
  4. Fill Required Fields:
    FieldDescription
    Display NameEnter a descriptive name (e.g., “DALL-E 3 Standard”)
    ID*Auto-populated from the Display Name
    Name*Enter the exact model identifier (e.g., “dall-e-3”, “dall-e-2”)
    CredentialsSelect the appropriate AI credential from the dropdown
  5. Click Save - The image generation model will be created and available
  6. Set as Default (Optional) - Use the “Default image generation model” dropdown to set as default
New Image Generation Model
  • OpenAI DALL-E 3: High-quality image generation with text rendering capabilities
  • OpenAI DALL-E 2: Fast image generation with good quality
  • Azure OpenAI: DALL-E models through Azure endpoints
  • Other Providers: Stability AI, Midjourney (if configured with appropriate integrations)

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AI Credentials

The AI Credentials section only appears when credentials are configured in your project. If no credentials exist, this section will be hidden.
AI Credentials are authentication configurations that store API keys, tokens, endpoints, and connection strings for accessing AI service providers. These credentials serve as the foundation for other integrations, enabling secure communication with external AI platforms without exposing sensitive authentication details in individual model configurations. Multiple models and integrations can reference the same credentials, providing centralized credential management.
Each AI Credentials configuration displays:
  • Integration Icon - Visual indicator of the provider (OpenAI, Azure, Anthropic, Vertex AI, etc.)
  • Credential Name - Display name of the configured credentials
  • Status Indicator - Shows “OK • Shared” or “OK • Local” depending on sharing status
  • Click to Edit - Click on any card to view or edit configuration details (if permissions allow)

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Creating New AI Credentials:

  1. Click the + Button - In the Integrations section header (or main navigation sidebar), click the + button
  2. Select Integration Type - Choose AI Credentials from the list of available integration types
  3. Select Provider Type - Choose the AI service provider you want to configure:
    ProviderDescription
    AI DialFor EPAM AI Dial platform
    Amazon BedrockFor AWS AI services
    Azure OpenAIFor Azure-hosted OpenAI services
    OllamaFor local/self-hosted models
    OpenAIFor OpenAI API access
    Vertex AIFor Google Cloud AI services
  4. Fill Provider-Specific Fields: For OpenAI:
    FieldDescriptionExample
    Display Name*Enter a descriptive name”OpenAI Production Key”
    ID*Auto-populated from the Display Name
    Api Base*OpenAI API endpoint URLhttps://api.openai.com/v1
    Api KeyYour OpenAI API key from platform.openai.comsk-proj-...
    For Azure OpenAI:
    FieldDescriptionExample
    Display Name*Enter a descriptive name”Azure OpenAI East US”
    ID*Auto-populated from the Display Name
    Api Base*Azure OpenAI endpoint URLhttps://your-resource.openai.azure.com
    Api KeyAzure OpenAI API key from Azure portala1b2c3d4e5f6...
    Api VersionAzure API version2024-02-15-preview
    For Vertex AI:
    FieldDescriptionExample
    Display NameEnter a descriptive name”GCP Vertex AI”
    ID*Auto-populated from the Display Name
    Vertex Project*Google Cloud project IDmy-gcp-project-123456
    Vertex Location*GCP regionus-central1
    Vertex Credentials*Paste the complete JSON content from downloaded service account key file{"type": "service_account", "project_id": "...", ...}
    For Amazon Bedrock:
    FieldDescriptionExample
    Display Name*Enter a descriptive name”AWS Bedrock US-East-1”
    ID*Auto-populated from the Display Name
    Aws Access Key IdIAM access keyAKIAIOSFODNN7EXAMPLE
    Aws Secret Access KeyIAM secret access keywJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
    Aws Region NameAWS regionus-east-1
    For AI Dial:
    FieldDescriptionExample
    Display NameEnter a descriptive name”EPAM AI Dial Personal”
    ID*Auto-populated from the Display Name
    Api Base*AI Dial endpoint URLhttps://ai-proxy.lab.epam.com
    Api KeyYour personal AI Dial tokenaidial_...
    Api VersionAPI version for AI Dial2025-04-01-preview
    For Ollama:
    FieldDescriptionExample
    Display Name*Enter a descriptive name”Local Ollama Server”
    ID*Auto-populated from the Display Name
    Api Base*Ollama server URLhttp://localhost:11434
  5. Click Save - The credential configuration will be created and available for use
AI Credentials
  • Never share API keys in public channels or commit them to version control
  • Use environment-specific credentials (separate keys for dev, staging, production)
  • Regularly rotate API keys for enhanced security
  • Use “Local” credentials for personal testing, “Shared” only when necessary
  • Monitor API usage to detect unauthorized access
One credential configuration can be used by multiple models. For example, a single OpenAI credential can support GPT-4o, GPT-3.5, and multiple embedding models, reducing the need for duplicate credential management.

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Default Model Selection

The default model settings in the Integrations section control which models are automatically selected for new chats and agents:
  • Default Model - Used for most activities (new chats, agents, general operations)
  • High-tier Model - Selected when high-capability processing is needed
  • Low-tier Model - Used for routine, cost-effective tasks
  • Default Embedding Model - Used for text embedding operations
  • Default Vector Storage - Used for vector database operations
  • Default Image Generation Model - Used for image generation tasks
Usage Impact:
  • New chat conversations use the default model unless manually changed
  • New agents use the default model unless manually configured
  • Existing conversations and agents retain their previously configured models
  • Changes apply immediately without requiring save
Do not modify the default PG Vector or LLM models in the project, as this may cause improper project functionality and system instability. These default models are essential for core platform operations.

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OpenAI Template Tab

The OpenAI Template tab generates ready-to-use code examples for integrating applications with ELITEA’s AI models using OpenAI-compatible API format. Templates are pre-configured with server settings and support cURL, Node.js, and Python, enabling quick implementation without manual setup. How to Access: Click “OpenAI Template” in the tab bar at the top of the AI Configuration page. OpenAI Model and Language Selection:
  • Model dropdown - Select from available configured LLM models
  • Code dropdown - Choose programming language (cURL, Node.js, or Python)
Action Buttons:
  • Copy Button - Copy the code example to clipboard
  • Download Button - Download code example file with appropriate extension:
    • cURL → api_example.sh
    • Node.js → api_example.js
    • Python → api_example.py
Code Example Features:
  • Pre-configured with server URL, project ID, and model settings
  • Dynamic updates based on selected model and language
  • Includes authentication placeholder (Your_Personal_Token)
  • Syntax highlighting for better readability

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Troubleshooting

Problem: “No access to LLM” message is displayed when using custom LLM configurations. The exact message may vary depending on the provider.Solution Steps:
  1. Verify Provider Access: Use your model provider’s platform help center or documentation pages to confirm you have access to the specific LLM mentioned in your configuration
  2. Test Credentials: Use a CURL request to verify that your credentials can successfully access the model
  3. Check Model Availability: Ensure the model name exactly matches the provider’s model identifier
  4. Validate Authentication: Confirm your API keys, tokens, or authentication credentials are valid and not expired
Problem: Receiving 401 (Unauthorized) or 403 (Forbidden) errors when testing credentials.Solution Steps:
  1. Check Credential Values:
    • Verify API keys, tokens, or passwords are correct
    • Ensure there are no extra spaces or hidden characters
    • Confirm credentials haven’t expired
  2. Verify Permissions:
    • Ensure your account has necessary permissions for the integration
    • Check if you need specific scopes or roles enabled
    • Verify service URLs are accurate and accessible
  3. Provider-Specific Checks:
    • OpenAI: Verify your API key starts with sk- and is from the correct organization
    • Azure OpenAI: Confirm the API version matches your deployment and endpoint URL is correct
    • Vertex AI: Ensure the service account JSON has the required IAM permissions
    • Amazon Bedrock: Verify IAM user has bedrock:InvokeModel permissions
Problem: Unable to connect to AI provider endpoints (Connection refused, timeout, SSL errors).Solution Steps:
  1. Network Connectivity:
    • Verify you can reach the endpoint URL from your network
    • Check if there are firewall or proxy restrictions
    • Ensure the endpoint URL is correct and accessible
  2. SSL/Certificate Issues:
    • Verify the endpoint uses a valid SSL certificate
    • Check if your organization uses custom certificates
    • For self-hosted solutions, ensure certificates are properly configured
  3. URL Format:
    • Ensure URLs start with http:// or https://
    • Remove trailing slashes from base URLs
    • Verify the hostname is correct (no typos)
Problem: Errors indicating the specified model doesn’t exist or isn’t available.Solution Steps:
  1. Verify Model Name:
    • Check the exact model identifier from provider documentation
    • Model names are case-sensitive
    • Ensure no extra spaces or characters in the model name
  2. Check Model Availability:
    • Confirm the model is available in your region
    • Verify your subscription/account has access to the model
    • Check if the model has been deprecated or renamed
  3. Provider Documentation:
Problem: EPAM AI Dial integration failures with personal tokens.Common Issues:
  • Limited Model Access: EPAM AI Dial personal tokens provided to EPAMers have access to a very limited model list
  • Permission Issues: Token may lack necessary permissions for specific models
  • Token Scope: Personal tokens may have restricted capabilities
Resolution Steps:
  1. Verify Model Access:
    • Review the AI Dial documentation for permission check procedures
    • Execute permission verification requests for your specific model
    • Confirm your token has access to the model you’re trying to use
  2. Token Management:
    • Check token expiration date
    • Verify token permissions and scopes
    • Consider using project-level tokens if available for broader model access
  3. Contact Support:
    • Contact EPAM AI Dial administrators if additional model access is required
    • Request specific model permissions if needed
Problem: Newly created credentials don’t show up in dropdown menus or integration lists.Solution Steps:
  1. Verify Creation:
    • Confirm the credential was successfully saved
    • Check for any error messages during creation
    • Refresh the page or credential list
  2. Check Permissions:
    • Ensure you have permissions to view the credential
    • Verify you’re viewing the correct project scope
    • Check if credential is marked as “Local” vs “Shared”
  3. Credential Type:
    • Confirm the credential type matches the integration requirement
    • Verify the credential hasn’t been deleted by another user
Problem: Receiving rate limit or quota exceeded errors from AI providers.Solution Steps:
  1. Check Usage Limits:
    • Review your provider’s rate limits and quotas
    • Check your current usage in the provider’s dashboard
    • Verify your subscription tier and limits
  2. Implement Best Practices:
    • Use appropriate tier models (Low-tier for routine tasks)
    • Implement retry logic with exponential backoff
    • Consider upgrading your plan if consistently hitting limits
  3. Monitor Usage:
    • Track API usage across your team
    • Set up alerts for approaching quota limits
    • Consider using project-level credentials for better visibility