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.
• 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).
• 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.
• 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.

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.
• 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.).

Telemetry

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

Authentication (web login)

Presenton uses a single admin account per instance. Credentials live in app_data (hashed; see userConfig.json). Pass these with -e or via .env for compose:

• AUTH_USERNAME / AUTH_PASSWORD — Preseed the admin login on first boot (password at least 6 characters). Ignored if a user already exists unless AUTH_OVERRIDE_FROM_ENV is set.
• AUTH_OVERRIDE_FROM_ENV=[true/false] — If true, replace stored credentials from the env vars on every FastAPI startup and rotate the session signing secret (invalidates existing sessions). Remove after a one-off rotation.
• RESET_AUTH=[true/false] — If true, clear stored credentials on startup. Use for a single boot to recover access, then unset.

Examples

docker run -it --name presenton -p 5001:80 -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest

docker run -it --name presenton -p 5001:80 -e AUTH_USERNAME=admin -e AUTH_PASSWORD=changeme123 -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest

docker run -it --name presenton -p 5001:80 -e AUTH_USERNAME=admin -e AUTH_PASSWORD=changeme123 -v "${PWD}\app_data:/app_data" ghcr.io/presenton/presenton:latest

docker stop presenton && docker rm presenton && docker run -it --name presenton -p 5001:80 -e AUTH_USERNAME=admin -e AUTH_PASSWORD=newcred456 -e AUTH_OVERRIDE_FROM_ENV=true -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest

docker stop presenton && docker rm presenton && docker run -it --name presenton -p 5001:80 -e RESET_AUTH=true -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest

docker stop presenton && docker rm presenton && docker run -it --name presenton -p 5001:80 -e AUTH_USERNAME=admin -e AUTH_PASSWORD=changeme123 -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest

Manual reset: stop the container, edit ./app_data/userConfig.json, delete AUTH_USERNAME, AUTH_PASSWORD_HASH, and AUTH_SECRET_KEY, save, and start again.

Sign out from the app: Settings → Other → Sign out.

MCP authentication

When auth is configured (AUTH_USERNAME / AUTH_PASSWORD), the MCP endpoint at /mcp now requires authentication as well.

1. Log in once to get a bearer token:

curl -s -X POST http://localhost:5001/api/v1/auth/login \
  -H "Content-Type: application/json" \
  -d '{"username":"admin","password":"changeme123"}'

The response includes:

• access_token (session token)
• token_type (bearer)

1. Configure your MCP client to send that token on every request:

{
  "mcpServers": {
    "presenton": {
      "url": "http://localhost:5001/mcp",
      "headers": {
        "Authorization": "Bearer <access_token>"
      }
    }
  }
}

Notes:

• If you rotate credentials with AUTH_OVERRIDE_FROM_ENV=true, previously issued session tokens are invalidated.
• 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 (HTTP Basic):</strong><br> All <code>/api/v1/</code> routes except <code>/api/v1/auth/*</code> require authentication. Send your Presenton admin username and password (same as the web UI, or <strong>AUTH_USERNAME</strong> / <strong>AUTH_PASSWORD</strong> when preseeding Docker). With <code>curl</code>, put them right after <code>-u</code> as <code>-u USERNAME:PASSWORD</code> — that is HTTP Basic auth and sets <code>Authorization: Basic …</code> for you. Replace the sample <code>username:password</code> below with your real credentials. </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 + HTTP Basic auth with <code>-u</code>)

curl -u username:password \
  -X POST http://localhost:5001/api/v1/ppt/presentation/generate \
  -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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