> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pipeshub.com/llms.txt
> Use this file to discover all available pages before exploring further.
# AI Models Overview
> Connect PipesHub to LLM and embedding providers to power AI features across your workspace
The AI Models page is where workspace admins connect PipesHub to one or more AI providers. Once configured, these models power every AI feature in the platform — answering questions in chat, summarising documents, and running AI agents. You can connect as many providers as you need and switch between them at any time.
You need at least one **LLMs** provider configured before any AI feature in your workspace will work. Embedding models are used for document search and are configured separately.
## How to navigate to this page
Open the **left sidebar** → **Workspace Settings** → **AI Models**. The page opens on the Providers tab by default. Click the **LLM** tab to configure language models.
Only workspace **admins** and **owners** can access the AI Models settings page.
## Add your first LLMs model
Open the left sidebar → **Workspace Settings** → **AI Models**.
The page opens on the Providers tab. Click the **LLMs** tab.
Browse the provider cards or use the search box to filter by name.
Click **Configure** on the provider card. A configuration panel slides open.
Enter the credentials required for your provider (API key, endpoint URL, etc.). See the [provider reference](#provider-reference) below.
* **Model Friendly Name** — a human-readable label shown in chat (e.g. "My GPT-5"). Optional.
* **Context Length** — token window override (1–1,000,000). Leave blank to use the provider default.
* **Multimodal** toggle — enable if this model can accept image input. On by default.
* **Reasoning** toggle — enable for chain-of-thought / o-series style models. Off by default.
Click **Add Model**. PipesHub runs a live health check to validate your credentials.
If this is the first LLMs model you've added, it is automatically set as the default model used by all AI features.
## Provider reference
The table below lists every supported LLMs provider, the fields you must fill in, and example model name strings.
| Provider | Required fields | Optional fields | Example model names |
| --------------------- | -------------------------------------------------- | ---------------------------------------- | ---------------------------------------------------------- |
| **OpenAI** | API Key, Model Name | — | `gpt-5`, `gpt-5-mini`, `gpt-5-nano` |
| **Anthropic** | API Key, Model Name | — | `claude-opus-4-7`, `claude-sonnet-4-6`, `claude-haiku-4-6` |
| **Gemini** | API Key, Model Name | — | `gemini-3-flash-preview`, `gemini-3.1-pro-preview` |
| **Azure OpenAI** | Endpoint URL, API Key, Deployment Name, Model Name | — | `gpt-5`, `gpt-5-mini`, `gpt-5-nano` |
| **Azure AI** | Endpoint URL, API Key, Model Name | — | `gpt-5.1`, `claude-sonnet-4-5`, `DeepSeek-V3.1` |
| **Cohere** | API Key, Model Name | — | `command-a-03-2025` |
| **Mistral** | API Key, Model Name | — | `mistral-large-latest` |
| **Groq** | API Key, Model Name | — | `meta-llama/llama-4-scout-17b-16e-instruct` |
| **xAI** | API Key, Model Name | — | `grok-3-latest` |
| **MiniMax** | API Key, Model Name | — | `MiniMax-M2.7`, `MiniMax-M2.7-highspeed` |
| **Fireworks** | API Key, Model Name | — | `accounts/fireworks/models/kimi-k2-instruct` |
| **Together** | API Key, Model Name | Endpoint URL | `deepseek-ai/DeepSeek-V3` |
| **OpenAI Compatible** | Endpoint URL, API Key, Model Name | — | `deepseek-ai/DeepSeek-V3` |
| **Ollama** | Model Name | API Key, Endpoint URL | `gemma4:latest`, `hf.co/unsloth/gpt-oss-20b-GGUF:F16` |
| **Amazon Bedrock** | AWS Region, Model Name, Provider | AWS Access Key ID, AWS Secret Access Key | `us.anthropic.claude-sonnet-4-20250514-v1:0` |
> **Azure OpenAI — Deployment Name:** This is the name you assigned when deploying the model in Azure AI Studio, not the model ID itself.
>
> **Azure AI — Endpoint URL format:** Use `https://.inference.ai.azure.com/anthropic` for Claude models, or `https://.cognitiveservices.azure.com/openai/v1/` for OpenAI, DeepSeek, and other models.
>
> **OpenAI Compatible:** Use this for any provider that speaks the OpenAI API format (e.g. `https://api.together.xyz/v1/`).
>
> **Ollama:** No API key is required for a local Ollama instance. The endpoint defaults to `http://host.docker.internal:11434`. Ensure the Ollama server is reachable from the PipesHub Docker network.
>
> **Amazon Bedrock:** If PipesHub is running on an EC2 instance with an appropriate IAM role attached, you can leave the AWS Access Key ID and Secret Access Key blank.
## Managing configured models
After saving, your model appears under the **Configured** tab. Each row has the following actions:
* **Edit** — update credentials or settings without deleting the configuration.
* **Set as Default** — make this model the one used by all AI features by default.
* **Delete** — permanently remove the configuration. You will be asked to type the model's name to confirm.
## Troubleshooting
If the health check fails when saving, double-check:
* The API key is correct and has not expired.
* For Azure providers, the endpoint URL matches the correct region and resource.
* For Ollama, the server is running and the Docker internal hostname resolves correctly.
* The model name string exactly matches what the provider API expects (check the provider's documentation for valid model IDs).
## Embedding models
Embedding models convert documents and search queries into numerical vectors so PipesHub can find semantically similar content. They power document indexing, semantic search, and retrieval across your workspace. At least one embedding model must be configured before you can index documents or use search.
PipesHub ships with **BAAI/bge-large-en-v1.5** built-in as the system default — it works out of the box with no configuration required. Connect an external provider only if you need a different model.
### How to navigate to this page
Open the **left sidebar** → **Workspace Settings** → **AI Models**. Click the **Embedding** tab.
Only workspace **admins** and **owners** can access the AI Models settings page.
### Add an embedding model
Open the left sidebar → **Workspace Settings** → **AI Models**.
Click the **Embedding** tab in the capability row.
Browse the provider cards or use the search box to filter by name.
Click **Configure** on the provider card. A configuration panel slides open.
Enter the credentials required for your provider. See the [provider reference](#embedding-provider-reference) below.
* **Model Friendly Name** — a human-readable label for this configuration.
* **Output Dimensions** — override the vector size (1–65,536). Leave blank to use the model default. Only supported by select models (e.g. OpenAI text-embedding-3-\* series).
* **Multimodal** toggle — enable if this model can embed images as well as text (off by default).
Click **Add Model**. PipesHub runs a live health check to validate your credentials.
If this is the first embedding model you have added, it is automatically set as the default used for all document indexing and search.
### Embedding provider reference
| Provider | Required fields | Optional fields | Example model names |
| ------------------------- | -------------------------------------------------- | ---------------------------------------- | -------------------------------------------------- |
| **Default (System)** | None — works out of the box | — | `BAAI/bge-large-en-v1.5` |
| **OpenAI** | API Key, Model Name | Output Dimensions | `text-embedding-3-small`, `text-embedding-3-large` |
| **Gemini** | API Key, Model Name | — | `gemini-embedding-001` |
| **Azure OpenAI** | Endpoint URL, API Key, Deployment Name, Model Name | — | `text-embedding-3-small` |
| **Azure AI** | Endpoint URL, API Key, Model Name | — | `text-embedding-ada-002`, `embed-v-4-0` |
| **Cohere** | API Key, Model Name | — | `embed-v4.0` |
| **Mistral** | API Key, Model Name | — | `mistral-embed` |
| **Together** | API Key, Model Name | Endpoint URL | `togethercomputer/m2-bert-80M-32k-retrieval` |
| **OpenAI Compatible** | Endpoint URL, API Key, Model Name | — | `text-embedding-3-small` |
| **Ollama** | Model Name | Endpoint URL | `mxbai-embed-large` |
| **Sentence Transformers** | Model Name | — | `all-MiniLM-L6-v2` |
| **HuggingFace** | Model Name | — | `sentence-transformers/all-MiniLM-L6-v2` |
| **Jina AI** | API Key, Model Name | — | `jina-embeddings-v3` |
| **Voyage** | API Key, Model Name | — | `voyage-3.5` |
| **Amazon Bedrock** | AWS Region, Model Name, Provider | AWS Access Key ID, AWS Secret Access Key | `cohere2.embed-multilingual-v3` |
> **Azure OpenAI — Deployment Name:** This is the name you assigned when deploying the model in Azure, not the model ID itself.
>
> **Azure AI — Endpoint URL format:** `https://.services.ai.azure.com/openai/v1/`
>
> **OpenAI Compatible:** Use this for any provider that speaks the OpenAI embeddings API format.
>
> **Ollama:** No API key required for a local instance. Endpoint defaults to `http://host.docker.internal:11434`.
>
> **Sentence Transformers / HuggingFace:** Run locally inside PipesHub. No external API or API key needed.
>
> **Amazon Bedrock:** Leave AWS keys blank when running PipesHub on EC2 with an appropriate IAM role. Provider dropdown: Cohere, Amazon (Titan), Other.
### Managing configured embedding models
After saving, your model appears under the **Configured** tab. Each row has the following actions:
* **Edit** — update credentials or settings without deleting the configuration.
* **Set as Default** — make this model the one used for all new document indexing and search.
* **Delete** — permanently remove the configuration. You will be asked to type the model's name to confirm.
Changing the default embedding model does not automatically re-index existing documents.
Documents indexed with the old model and queries run with the new model use different vector spaces, which will degrade search quality. Re-index your documents after switching embedding models.
### Troubleshooting
If the health check fails when saving, double-check:
* The API key is correct and has not expired.
* For Azure providers, the endpoint URL matches the correct region and resource.
* For Ollama, Sentence Transformers, and HuggingFace, confirm the model name is available locally.
* The model name exactly matches what the provider API expects (check provider docs for valid IDs).
* Output Dimensions is only supported by models that expose a dimensions parameter (e.g. OpenAI text-embedding-3-\* series). Setting it on unsupported models will cause errors.
Keep your API keys secure. PipesHub stores these credentials securely, but you should never share them publicly or commit them to version control.
## Image Generation models
Image generation models allow AI agents and workflows in PipesHub to generate images from text prompts. Once configured, these models are available to agents that support image creation tasks. At least one image generation model must be configured before image generation features are available in your workspace.
Currently two providers are supported: **OpenAI** (DALL-E / GPT-Image models) and **Gemini** (Imagen / Gemini Image models).
### How to navigate to this page
Open the **left sidebar** → **Workspace Settings** → **AI Models**. Click the **Image Generation** tab.
Only workspace **admins** and **owners** can access the AI Models settings page.
### Add an image generation model
Open the left sidebar → **Workspace Settings** → **AI Models**.
Click the **Image Generation** tab in the capability row.
Click **Configure** on the provider card you want to use.
Enter your **API Key** and the **Model Name** (exact model identifier string). See the [provider reference](#image-generation-provider-reference) below.
Optionally set a **Model Friendly Name** — a human-readable label displayed in the UI.
Click **Add Model**. PipesHub runs a live health check to validate your credentials.
If this is the first image generation model you have added, it is automatically set as the default used when image generation is triggered.
### Image generation provider reference
| Provider | Required fields | Example model names |
| ---------- | ------------------- | --------------------------------------------------- |
| **OpenAI** | API Key, Model Name | `gpt-image-1`, `dall-e-3` |
| **Gemini** | API Key, Model Name | `gemini-2.5-flash-image`, `imagen-4.0-generate-001` |
> **OpenAI:** Get your API key at [platform.openai.com/api-keys](https://platform.openai.com/api-keys).
>
> **Gemini:** Get your API key at [aistudio.google.com/app/apikey](https://aistudio.google.com/app/apikey). Imagen models require a Google Cloud project with the Vertex AI API enabled, or access via Google AI Studio.
### Managing configured image generation models
After saving, your model appears under the **Configured** tab. Each row has the following actions:
* **Edit** — update credentials or the model name.
* **Set as Default** — make this the model used when image generation is triggered.
* **Delete** — permanently remove the configuration. You will be asked to type the model's name to confirm.
### Troubleshooting
If the health check fails when saving, double-check:
* The API key is correct and has not expired.
* The model name exactly matches what the provider API expects (e.g. `dall-e-3`, not `DALL-E-3`).
* For Gemini Imagen models, confirm your API key has access to image generation — not all Google AI Studio keys have Imagen enabled by default.
## Text to Speech (TTS) models
Text to Speech models convert AI text responses into spoken audio, enabling voice output in AI workflows and agents. Once configured, these models are available wherever PipesHub produces audio output. At least one TTS model must be configured before voice features are available in your workspace.
Currently two providers are supported: **OpenAI** (tts-1, tts-1-hd, gpt-4o-mini-tts) and **Gemini** (gemini-2.5-flash-preview-tts and related models).
### How to navigate to this page
Open the **left sidebar** → **Workspace Settings** → **AI Models**. Click the **TTS** tab.
Only workspace **admins** and **owners** can access the AI Models settings page.
### Add a TTS model
Open the left sidebar → **Workspace Settings** → **AI Models**.
Click the **TTS** tab in the capability row.
Click **Configure** on the provider card you want to use.
Enter your **API Key** and the **Model Name** (exact model identifier string). See the [provider reference](#tts-provider-reference) below.
* **Voice** — select a default voice for audio output.
* **Audio Format** — select the output audio format.
* **Model Friendly Name** — a custom display label for this configuration.
Click **Add Model**. PipesHub validates your credentials with a live health check.
If this is the first TTS model you have added, it is automatically set as the default used for all TTS output.
### TTS provider reference
| Provider | Required fields | Example model names | Default voice | Default format |
| ---------- | ------------------- | -------------------------------------------------------------------------------------------- | ------------- | -------------- |
| **OpenAI** | API Key, Model Name | `tts-1`, `tts-1-hd`, `gpt-4o-mini-tts` | Alloy | MP3 |
| **Gemini** | API Key, Model Name | `gemini-3.1-flash-tts-preview`, `gemini-2.5-flash-preview-tts`, `gemini-2.5-pro-preview-tts` | Kore | WAV |
> **OpenAI voices:** Alloy, Echo, Fable, Onyx, Nova, Shimmer. Get your API key at [platform.openai.com/api-keys](https://platform.openai.com/api-keys).
>
> **OpenAI audio formats:** MP3, Opus, AAC, FLAC, WAV.
>
> **Gemini voices:** 30 prebuilt voices named after astronomical objects — Zephyr, Puck, Charon, Kore, Fenrir, Leda, Orus, Aoede, Callirrhoe, Autonoe, Enceladus, Iapetus, Umbriel, Algieba, Despina, Erinome, Algenib, Rasalgethi, Laomedeia, Achernar, Alnilam, Schedar, Gacrux, Pulcherrima, Achird, Zubenelgenubi, Vindemiatrix, Sadachbia, Sadaltager, Sulafat. Get your API key at [aistudio.google.com/app/apikey](https://aistudio.google.com/app/apikey).
>
> **Gemini audio formats:** WAV and PCM work without any extra dependencies. MP3, Opus, AAC, and FLAC require **ffmpeg** installed on the PipesHub backend host.
### Managing configured TTS models
After saving, your model appears under the **Configured** tab. Each row has the following actions:
* **Edit** — update credentials, voice, or format settings.
* **Set as Default** — make this the model used for all TTS output.
* **Delete** — permanently remove the configuration. You will be asked to type the model's name to confirm.
### Troubleshooting
If the health check fails when saving, double-check:
* The API key is correct and has not expired.
* The model name exactly matches what the provider API expects (e.g. `tts-1`, not `TTS-1`).
* For Gemini compressed formats (MP3, Opus, AAC, FLAC), ensure ffmpeg is installed on the machine running the PipesHub backend. WAV and PCM work without any additional dependencies.
## Speech to Text (STT) models
Speech to Text models transcribe spoken audio into text, enabling voice input in AI workflows and agents. Once configured, these models power voice-driven interactions across your PipesHub workspace.
Four providers are supported: **OpenAI** and **Gemini** (cloud APIs), **Whisper** (self-hosted open-source via faster-whisper), and **Wispr Flow** (hosted specialist transcription service). At least one STT model must be configured before voice input features are available.
### How to navigate to this page
Open the **left sidebar** → **Workspace Settings** → **AI Models**. Click the **STT** tab.
Only workspace **admins** and **owners** can access the AI Models settings page.
### Add an STT model
Open the left sidebar → **Workspace Settings** → **AI Models**.
Click the **STT** tab in the capability row.
Click **Configure** on the provider card you want to use.
Enter the required fields for your provider (API Key and Model Name for cloud providers, or select a Model size for Whisper local). See the [provider reference](#stt-provider-reference) below.
Configure any optional settings such as language, device, compute type, or app type depending on your provider.
Optionally set a **Model Friendly Name** — a custom label shown in the UI.
Click **Add Model**. PipesHub validates your configuration with a live health check.
If this is the first STT model you have added, it is automatically set as the default used for all speech transcription.
### STT provider reference
| Provider | Required fields | Example model names / sizes |
| ------------------- | --------------------- | -------------------------------------------------------------- |
| **OpenAI** | API Key, Model Name | `whisper-1`, `gpt-4o-transcribe`, `gpt-4o-mini-transcribe` |
| **Gemini** | API Key, Model Name | `gemini-2.5-flash`, `gemini-2.5-pro`, `gemini-3-flash-preview` |
| **Whisper (local)** | Model size (dropdown) | tiny, base, small, medium, large-v2, large-v3, distil-large-v3 |
| **Wispr Flow** | API Key | flow-v1 (selected automatically) |
> **OpenAI:** Get your API key at [platform.openai.com/api-keys](https://platform.openai.com/api-keys).
>
> **Gemini:** Get your API key at [aistudio.google.com/app/apikey](https://aistudio.google.com/app/apikey).
>
> **Whisper (local):** No API key required. Runs on your own infrastructure using faster-whisper. Model weights are downloaded from HuggingFace on first use. Optional: Device (Auto/CPU/CUDA), Compute Type (int8/float16/float32), Model Cache Directory.
>
> **Wispr Flow:** Contact [enterprise@wisprflow.ai](mailto:enterprise@wisprflow.ai) to obtain an API key. Requires ffmpeg on the backend host. Optional: Default Language, App Type, API Endpoint override.
### Managing configured STT models
After saving, your model appears under the **Configured** tab. Each row has the following actions:
* **Edit** — update credentials or settings.
* **Set as Default** — make this the model used for all speech transcription.
* **Delete** — permanently remove the configuration. You will be asked to type the model's name to confirm.
### Troubleshooting
If the health check fails when saving, double-check:
* For OpenAI and Gemini: the API key is correct and has not expired.
* For Whisper (local): the faster-whisper package is installed on the backend host. On first use, model weights are downloaded — ensure internet access is available.
* For Wispr Flow: ffmpeg must be installed on the backend host. Confirm your API key is active (contact [enterprise@wisprflow.ai](mailto:enterprise@wisprflow.ai) if unsure).