presenton Agent工作流

⚙️ Deployment Configurations

The lists below match the environment variables forwarded in this repository’s docker-compose.yml (production, production-gpu, development, and development-gpu). Put values in a .env file next to the compose file, or export them before docker compose up. The Electron app backend can read the same names when run outside Docker.

Other optional variables exist in code (for example advanced Mem0 paths, LiteParse runners, or FAST_API_INTERNAL_URL when Next.js and FastAPI are not same-origin); they are not wired in docker-compose.yml. Supported names are discoverable from servers/fastapi/utils/get_env.py and the Next.js server utilities under servers/nextjs/.

LLM and API keys

- CAN_CHANGE_KEYS=[true/false]: Set to false if you want to keep API keys hidden and make them unmodifiable. - PRESENTON_PUBLIC_URL: Optional browser-reachable Presenton origin, for example http://localhost:5001 or https://slides.example.com. Set this when an MCP client reaches Presenton through a Docker-internal hostname; generated download, edit, and preview links use this origin instead. - PRESENTATION_GENERATION_MODE=[both/standard/smart]: Controls which presentation generation mode is available in the UI and which generation tools the MCP server exposes. Defaults to both. A single-mode value hides the mode selector and forces that mode; smart also hides template and custom-template features and tools. See the presentation generation modes guide for configuration examples and mode behavior. - LLM=[openai/deepseek/google/vertex/azure/bedrock/openrouter/fireworks/together/cerebras/anthropic/litellm/lmstudio/ollama/custom/codex]: Select the text LLM. - OPENAI_API_KEY: Required if LLM is openai. - OPENAI_MODEL: Required if LLM is openai (default: gpt-4.1). - DEEPSEEK_API_KEY: Required if LLM is deepseek. - DEEPSEEK_MODEL: Required if LLM is deepseek (default: deepseek-chat). - DEEPSEEK_BASE_URL: Optional if LLM is deepseek (default: https://api.deepseek.com). - GOOGLE_API_KEY: Required if LLM is google. - GOOGLE_MODEL: Required if LLM is google (default: models/gemini-2.0-flash). - VERTEX_MODEL: Required if LLM is vertex (default: gemini-2.5-flash). - VERTEX_API_KEY: Optional auth path for LLM=vertex (Vertex Express). - VERTEX_PROJECT / VERTEX_LOCATION: Optional auth path for LLM=vertex when using GCP project credentials (do not combine with VERTEX_API_KEY). - VERTEX_BASE_URL: Optional Vertex gateway/base URL override. - AZURE_OPENAI_MODEL: Required if LLM is azure (deployment/model name). - AZURE_OPENAI_API_KEY: Required if LLM is azure. - AZURE_OPENAI_API_VERSION: Required if LLM is azure (for example 2024-10-21). - AZURE_OPENAI_ENDPOINT / AZURE_OPENAI_BASE_URL: At least one is required if LLM is azure. - AZURE_OPENAI_DEPLOYMENT: Optional deployment override for LLM is azure. - BEDROCK_REGION: Optional if LLM is bedrock (default: us-east-1). - BEDROCK_MODEL: Required if LLM is bedrock. Use a standard model ID (example: us.anthropic.claude-3-5-haiku-20241022-v1:0) or a full inference profile ARN for newer models (example: Claude Sonnet 4.6). Passed through to Bedrock Converse as modelId. See Amazon Bedrock guide. - BEDROCK_API_KEY: Optional if LLM is bedrock (API key auth; alternative to AWS keys). - BEDROCK_AWS_ACCESS_KEY_ID / BEDROCK_AWS_SECRET_ACCESS_KEY: Required together if LLM is bedrock and BEDROCK_API_KEY is not set. - BEDROCK_AWS_SESSION_TOKEN: Optional session token for LLM is bedrock. - BEDROCK_PROFILE_NAME: Optional AWS profile name for LLM is bedrock. - OPENROUTER_API_KEY: Required if LLM is openrouter. - OPENROUTER_MODEL: Required if LLM is openrouter (default: openai/gpt-4o). - OPENROUTER_BASE_URL: Optional if LLM is openrouter (default: https://openrouter.ai/api/v1). - OPENROUTER_PROVIDER_ORDER: Optional comma-separated OpenRouter provider routing order. - OPENROUTER_ALLOW_FALLBACKS=[true/false]: Optional OpenRouter fallback override. - OPENROUTER_REQUIRE_PARAMETERS=[true/false]: Only route to providers supporting every request parameter. - OPENROUTER_DATA_COLLECTION=[allow/deny]: Optional OpenRouter data-collection policy. - OPENROUTER_ZDR=[true/false]: Optional OpenRouter zero-data-retention requirement. - FIREWORKS_API_KEY: Required if LLM is fireworks. - FIREWORKS_MODEL: Required if LLM is fireworks (example: accounts/fireworks/models/llama-v3p1-8b-instruct). - FIREWORKS_BASE_URL: Optional if LLM is fireworks (default: https://api.fireworks.ai/inference/v1). - TOGETHER_API_KEY: Required if LLM is together. - TOGETHER_MODEL: Required if LLM is together (example: openai/gpt-oss-20b). - TOGETHER_BASE_URL: Optional if LLM is together (default: https://api.together.ai/v1). - CEREBRAS_API_KEY: Required if LLM is cerebras. - CEREBRAS_MODEL: Required if LLM is cerebras (default: llama-3.3-70b). - CEREBRAS_BASE_URL: Optional if LLM is cerebras (default: https://api.cerebras.ai/v1). - ANTHROPIC_API_KEY: Required if LLM is anthropic. - ANTHROPIC_MODEL: Required if LLM is anthropic (default: claude-3-5-sonnet-20241022). - CODEX_MODEL: Required if LLM is codex (Codex OAuth flow; compose maps host port 1455 for the callback). - CUSTOM_LLM_URL: OpenAI-compatible base URL if LLM is custom. The server must implement POST /v1/chat/completions; a provider-specific completion endpoint is not sufficient. - CUSTOM_LLM_API_KEY: API key if LLM is custom. - CUSTOM_MODEL: Model id if LLM is custom. - LITELLM_BASE_URL: LiteLLM proxy or gateway base URL if LLM is litellm. - LITELLM_API_KEY: Optional API key if LLM is litellm. - LITELLM_MODEL: Required if LLM is litellm (default: gpt-4.1). - LMSTUDIO_BASE_URL: Optional LM Studio base URL if LLM is lmstudio (default: http://localhost:1234/v1; /v1 is auto-appended when omitted). - LMSTUDIO_API_KEY: Optional API key if LLM is lmstudio. - LMSTUDIO_MODEL: Required if LLM is lmstudio (example: openai/gpt-oss-20b). - DISABLE_THINKING=[true/false]: If true, disables “thinking” for providers that support it (including DeepSeek). - WEB_GROUNDING=[true/false]: If true, enables web search by default. - WEB_SEARCH_PROVIDER=[auto/native/searxng/tavily/exa]: Selects the web search mode. auto uses native search for OpenAI, Google, and Anthropic, and otherwise leaves web search off unless you choose an external provider.

• WEB_SEARCH_MAX_RESULTS: Maximum external search results to add to model context (default 5, maximum 10).
• SEARXNG_BASE_URL: Base URL for a self-hosted SearXNG instance.
• TAVILY_API_KEY, EXA_API_KEY: Credentials for optional hosted search APIs.

• EXTENDED_REASONING=[true/false]: Enables extended reasoning where supported by the configured stack.
• LLM_GENERATION_PROFILE=[fast/balanced/deep/model_max]: Optional global generation profile (default: balanced).
• LLM_MAX_OUTPUT_TOKENS: Optional positive output-token override for every text provider.
• MAX_OUTLINE_WORDS: Maximum words allowed in each generated or edited slide outline (default: 100). Must be a positive integer.
• LLM_REASONING_MODE=[auto/enabled/disabled]: Optional global reasoning-mode override.
• LLM_REASONING_EFFORT=[default/none/minimal/low/medium/high/xhigh/max]: Optional reasoning-effort override.
• LLM_REASONING_BUDGET_TOKENS: Optional non-negative reasoning token budget.

All advanced text-provider settings are optional. Use Reset advanced settings in Settings or onboarding to remove overrides and inherit application/provider defaults.

Ollama

Use when LLM is ollama:

• OLLAMA_URL: Base URL of the Ollama HTTP API (e.g. http://host.docker.internal:11434 from Docker).
• OLLAMA_MODEL: Model name in Ollama (e.g. llama3.2:3b).
• START_OLLAMA=[true/false]: Container entrypoint (start.js): optional install + ollama serve. Default false (development / production compose).

Presentation memory (Mem0 OSS)

Mem0 uses local Qdrant + SQLite (OSS); memory is scoped per presentation.

By default the Docker runtime now points Mem0 at a local Ollama-compatible LLM endpoint, so it no longer needs an OpenAI key just to initialize. If you want to use OpenAI instead, set MEM0_LLM_BASE_URL/MEM0_LLM_API_KEY to your OpenAI-compatible endpoint and key. Docker images install the default spaCy model (en_core_web_sm) during build so Mem0 can start without extra setup on each run.

Document parsing (LiteParse)

Database

• DATABASE_URL: SQLAlchemy URL; if unset, the app falls back to SQLite under app data.
• MIGRATE_DATABASE_ON_STARTUP: Compose sets true for all services so migrations run on startup.

Image generation

These variables match docker-compose.yml. IMAGE_PROVIDER selects the backend (pexels, pixabay, gemini_flash, nanobanana_pro, dall-e-3, gpt-image-1.5, comfyui, open_webui). Use OPENAI_API_KEY for OpenAI image modes and GOOGLE_API_KEY for Gemini image modes (same keys as the LLM section).

• DISABLE_IMAGE_GENERATION=[true/false]: Disable slide image generation.
• ENABLE_PARALLEL_IMAGE_GENERATION=[true/false]: Allow concurrent image provider requests (default true). Set to false to generate images one at a time when the provider has strict rate limits.
• IMAGE_PROVIDER: Provider id (see enum above).
• PEXELS_API_KEY: Pexels stock images.
• PIXABAY_API_KEY: Pixabay stock images.
• DALL_E_3_QUALITY=[standard/hd]: Optional for dall-e-3 (default standard).
• GPT_IMAGE_1_5_QUALITY=[low/medium/high]: Optional for gpt-image-1.5 (default medium).
• COMFYUI_URL / COMFYUI_WORKFLOW: Self-hosted ComfyUI workflow JSON.
• OPEN_WEBUI_IMAGE_URL / OPEN_WEBUI_IMAGE_API_KEY: Open WebUI–compatible image endpoint.
• OPENAI_COMPAT_IMAGE_BASE_URL / OPENAI_COMPAT_IMAGE_API_KEY / OPENAI_COMPAT_IMAGE_MODEL: Required if using openai_compatible to send image requests to any OpenAI-compatible /v1/images/* endpoint (LiteLLM, Azure, vLLM Gateways, etc.).

The parallel image generation option applies everywhere images are generated: initial presentation generation, slide editing and regeneration, direct image requests, and assistant image tools.

Telemetry

• DISABLE_ANONYMOUS_TRACKING=[true/false]: Set to true to disable anonymous telemetry.

Multi-user authentication

Presenton supports multiple accounts with a private workspace for each user. The first account becomes the primary administrator and can create, reset, or remove other accounts from Admin → Users.

Existing single-user installations are upgraded automatically: the current account becomes the primary administrator, while its presentations, templates, tasks, and other owned data stay attached to the same account.

Set up the primary administrator

On a new installation, open Presenton and follow the account setup screen. For an unattended Docker deployment, you can create the primary administrator on first boot with environment variables:

docker run -it --name presenton \
  -p 5001:80 \
  -e AUTH_USERNAME=admin \
  -e AUTH_PASSWORD=change-this-password \
  -v "./app_data:/app_data" \
  ghcr.io/presenton/presenton:latest

Usernames must contain at least 3 characters, and new passwords must contain at least 8 characters. Older six- or seven-character passwords remain valid after an upgrade.

Authentication environment variables

Presenton Cloud provider

Presenton Cloud is an optional, installation-wide generation provider. It is not an authentication method for the self-hosted instance. Create or sign in to the local administrator account first, then connect Presenton from provider onboarding.

Only the local administrator can connect, replace, or disconnect the provider. The browser displays a short device code and opens the hosted Presenton approval page. After approval, the delegated access and rotating refresh tokens are encrypted at rest and stored as one global provider credential; they are never returned to the browser or stored per local user.

Selecting Presenton as the text provider saves LLM=presenton. Presentation generation and document uploads then use the Presenton Cloud API and its token. Connecting the provider alone does not change generation: when another provider is selected, the existing local generation pipeline remains unchanged. Disconnecting revokes and removes the global credentials and deselects Presenton.

No OAuth client registration, client secret, or environment configuration is required. Official builds contain the cloud URL and first-party public device-flow client ID.

To rotate credentials from the environment:

docker stop presenton
docker rm presenton
docker run -it --name presenton \
  -p 5001:80 \
  -e AUTH_USERNAME=admin \
  -e AUTH_PASSWORD=new-secure-password \
  -e AUTH_OVERRIDE_FROM_ENV=true \
  -v "./app_data:/app_data" \
  ghcr.io/presenton/presenton:latest

For account recovery, use the same command with RESET_AUTH=true instead of AUTH_OVERRIDE_FROM_ENV=true. Both operations preserve the administrator's user ID and owned data, and invalidate existing browser sessions and API keys. Remove the one-time flag after the successful startup.

[!IMPORTANT] Do not remove authentication fields from app_data/userConfig.json to reset access. Presenton stores a hashed recovery copy of the primary administrator credentials and the session-signing secret there. Use the recovery variables above to preserve the database account and its ownership links.

To sign out, open Settings → Other → Sign out.

API and MCP authentication

Presenton exposes a client-neutral MCP 2025-11-25 Streamable HTTP endpoint at /mcp. When authentication is enabled, REST API and MCP clients use the same administrator-provisioned, user-scoped API key. Browser JWT cookies are not accepted as MCP credentials.

1. An administrator creates the target Presenton user, then calls POST /api/v1/admin/api-keys from an authenticated admin session with {"user_id":"...","label":"My API client","expiry_days":90}. The response contains the new key. Listing and revocation use GET /api/v1/admin/api-keys and POST /api/v1/admin/api-keys/{api_key_id}/revoke. An administrator can securely reveal a key again with GET /api/v1/admin/api-keys/{api_key_id}/token.

2. Configure any Streamable HTTP MCP client to send the generated sk-presenton-... key on every request:

{
  "servers": {
    "presenton": {
      "url": "http://localhost:5001/mcp",
      "type": "http",
      "headers": {
        "Authorization": "Bearer sk-presenton-0123456789abcdef.REPLACE_WITH_SECRET"
      }
    }
  },
  "inputs": []
}

Notes:

- This example uses VS Code's .vscode/mcp.json format. Use the equivalent Streamable HTTP and bearer-header configuration in Open WebUI or any other conforming MCP client. - API keys retain an Argon2 authentication hash and an encrypted, admin-only recoverable copy. They expire by default after 90 days and represent exactly the selected Presenton user. The same key authenticates general REST endpoints and MCP, but cannot sign in to the browser or change admin/provider settings. - The API key is exchanged inside the MCP process for a short-lived internal user session. The long-lived key is never forwarded to presentation APIs. - Revoking the key through the admin API takes effect immediately. - Standard, Smart Mode, and template generation tools return an async task ID. Poll get_job_status until completed or error; template listing is immediate. - MCP exposes only the polling Smart workflow (start_smart_presentation followed by get_job_status) so Standard and Smart generation use the same job model. - upload_template_assets accepts one PPTX and its replacement fonts in a single call, then returns the data needed by start_template_generation. - upload_files accepts reference documents and images as base64 and returns user-scoped paths that can be passed unchanged to Standard or Smart generation. - MCP is not available in the Electron desktop app (PRESENTON_ELECTRON=true). Electron runs with DISABLE_AUTH=true by default, and the MCP server is disabled there to avoid auth conflicts.

Note: LLM and image variables above are forwarded from docker-compose.yml when set in .env.

<br> <br>

Docker Run Examples by Provider

Same variables as compose; use -e instead of .env when running docker run directly.

- Using OpenAI <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="openai" -e OPENAI_API_KEY="******" -e IMAGE_PROVIDER="dall-e-3" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using Google <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="google" -e GOOGLE_API_KEY="******" -e IMAGE_PROVIDER="gemini_flash" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using Vertex AI (API key mode) <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="vertex" -e VERTEX_API_KEY="******" -e VERTEX_MODEL="gemini-2.5-flash" -e IMAGE_PROVIDER="gemini_flash" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using Azure OpenAI <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="azure" -e AZURE_OPENAI_API_KEY="****" -e AZURE_OPENAI_MODEL="gpt-4.1" -e AZURE_OPENAI_API_VERSION="2024-10-21" -e AZURE_OPENAI_ENDPOINT="https://YOUR-RESOURCE.openai.azure.com" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="****" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using Amazon Bedrock (on-demand model ID) — see docs/amazon-bedrock.md for inference profiles, IAM, and troubleshooting. <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="bedrock" -e BEDROCK_REGION="us-east-1" -e BEDROCK_AWS_ACCESS_KEY_ID="****" -e BEDROCK_AWS_SECRET_ACCESS_KEY="**" -e BEDROCK_MODEL="us.anthropic.claude-3-5-haiku-20241022-v1:0" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="****" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using Amazon Bedrock (inference profile ARN, e.g. Claude Sonnet 4.6) <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="bedrock" -e BEDROCK_REGION="us-east-1" -e BEDROCK_AWS_ACCESS_KEY_ID="****" -e BEDROCK_AWS_SECRET_ACCESS_KEY="**" -e BEDROCK_MODEL="arn:aws:bedrock:us-east-1:YOUR_ACCOUNT_ID:inference-profile/us.anthropic.claude-sonnet-4-6" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="****" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using Fireworks <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="fireworks" -e FIREWORKS_API_KEY="****" -e FIREWORKS_MODEL="accounts/fireworks/models/llama-v3p1-8b-instruct" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="****" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using Together AI <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="together" -e TOGETHER_API_KEY="****" -e TOGETHER_MODEL="openai/gpt-oss-20b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="****" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using Ollama <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="ollama" -e OLLAMA_MODEL="llama3.2:3b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="*******" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using Anthropic <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="anthropic" -e ANTHROPIC_API_KEY="****" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="****" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using LM Studio (local) <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e LLM="lmstudio" -e LMSTUDIO_BASE_URL="http://host.docker.internal:1234" -e LMSTUDIO_MODEL="openai/gpt-oss-20b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="******" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Using OpenAI Compatible LLM API <pre><code class="language-bash">docker run -it -p 5001:80 -e CAN_CHANGE_KEYS="false" -e LLM="custom" -e CUSTOM_LLM_URL="http://***" -e CUSTOM_LLM_API_KEY="" -e CUSTOM_MODEL="llama3.2:3b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="*****" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

- Running Presenton with GPU Support To use GPU acceleration with Ollama models, you need to install and configure the NVIDIA Container Toolkit. This allows Docker containers to access your NVIDIA GPU. Once the NVIDIA Container Toolkit is installed and configured, you can run Presenton with GPU support by adding the --gpus=all flag: <pre><code class="language-bash">docker run -it --name presenton --gpus=all -p 5001:80 -e LLM="ollama" -e OLLAMA_MODEL="llama3.2:3b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="*******" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

• Using an OpenAI-Compatible Image Provider

This routes all slide image requests through your OpenAI-compatible gateway (LiteLLM, Azure, vLLM, etc.) while keeping the text LLM configuration independent: <pre><code class="language-bash">docker run -it --name presenton -p 5001:80 -e IMAGE_PROVIDER="openai_compatible" -e OPENAI_COMPAT_IMAGE_BASE_URL="https://proxy.example.com/v1" -e OPENAI_COMPAT_IMAGE_API_KEY="******" -e OPENAI_COMPAT_IMAGE_MODEL="gpt-image-1" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest</code></pre>

#

✨ Generate Presentation via API

Generate Presentation

<p> <strong>Endpoint:</strong> <code>/api/v1/ppt/presentation/generate</code><br> <strong>Method:</strong> <code>POST</code><br> <strong>Content-Type:</strong> <code>application/json</code> </p>

<p> <strong>Authentication (API key):</strong><br> All <code>/api/v1/</code> routes except the public authentication endpoints require authentication. An administrator creates an access key under <strong>Admin → API keys</strong>. Send that <code>sk-presenton-...</code> key as <code>Authorization: Bearer YOUR_KEY</code>. API keys act as their owning user and cannot call browser-session-only administrator endpoints. </p>

Request Body

<tr> <td><code>content</code></td> <td>string</td> <td>Yes</td> <td>Main content used to generate the presentation.</td> </tr>

<tr> <td><code>slides_markdown</code></td> <td>string[] | null</td> <td>No</td> <td>Provide custom slide markdown instead of auto-generation.</td> </tr>

<tr> <td><code>instructions</code></td> <td>string | null</td> <td>No</td> <td>Additional generation instructions.</td> </tr>

<tr> <td><code>tone</code></td> <td>string</td> <td>No</td> <td> Text tone (default: <code>"default"</code>). Options: <code>default</code>, <code>casual</code>, <code>professional</code>, <code>funny</code>, <code>educational</code>, <code>sales_pitch</code> </td> </tr>

<tr> <td><code>verbosity</code></td> <td>string</td> <td>No</td> <td> Content density (default: <code>"standard"</code>). Options: <code>concise</code>, <code>standard</code>, <code>text-heavy</code> </td> </tr>

<tr> <td><code>web_search</code></td> <td>boolean</td> <td>No</td> <td>Enable web search grounding (default: <code>false</code>).</td> </tr>

<tr> <td><code>n_slides</code></td> <td>integer</td> <td>No</td> <td>Number of slides to generate (default: <code>8</code>).</td> </tr>

<tr> <td><code>language</code></td> <td>string</td> <td>No</td> <td>Presentation language (default: <code>"English"</code>).</td> </tr>

<tr> <td><code>template</code></td> <td>string</td> <td>No</td> <td>Template name (default: <code>"general"</code>).</td> </tr>

<tr> <td><code>include_table_of_contents</code></td> <td>boolean</td> <td>No</td> <td>Include table of contents slide (default: <code>false</code>).</td> </tr>

<tr> <td><code>include_title_slide</code></td> <td>boolean</td> <td>No</td> <td>Include title slide (default: <code>true</code>).</td> </tr>

<tr> <td><code>files</code></td> <td>string[] | null</td> <td>No</td> <td> Files to use in generation. Upload first via <code>/api/v1/ppt/files/upload</code>. </td> </tr>

<tr> <td><code>export_as</code></td> <td>string</td> <td>No</td> <td> Export format (default: <code>"pptx"</code>). Options: <code>pptx</code>, <code>pdf</code> </td> </tr>

</tbody> </table>

Response

{
  "presentation_id": "string",
  "path": "string",
  "edit_path": "string"
}

Example (curl + API key)

curl \
  -X POST http://localhost:5001/api/v1/ppt/presentation/generate \
  -H "Authorization: Bearer sk-presenton-YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
   "content": "Introduction to Machine Learning",
    "n_slides": 5,
    "language": "English",
    "template": "general",
    "export_as": "pptx"
  }'

Example Response

{
  "presentation_id": "d3000f96-096c-4768-b67b-e99aed029b57",
  "path": "/app_data/d3000f96-096c-4768-b67b-e99aed029b57/Introduction_to_Machine_Learning.pptx",
  "edit_path": "/presentation?id=d3000f96-096c-4768-b67b-e99aed029b57"
}

<blockquote> <strong>Note:</strong> Prepend your server’s root URL to <code>path</code> and <code>edit_path</code> to construct valid links. </blockquote>

Documentation & Tutorials

• Deploy Presenton
• Full API Documentation
• Generate Presentations via API in 5 Minutes
• Create Presentations from CSV using AI
• Create Data Reports Using AI

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