---
title: "AI Model Selection"
description: "The AI Model Selection feature allows you to configure, switch, and customize the underlying Large Language Models (LLMs), embedding providers, and vector stores that power your memory engine. Whet..."
last_updated: "2026-10-11T17:38:36.498751+00:00"
canonical_url: "https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/guide/configuration/ai-model-selection"
---

## Overview

The AI Model Selection feature allows you to configure, switch, and customize the underlying Large Language Models (LLMs), embedding providers, and vector stores that power your memory engine. Whether you want to leverage cloud-powered intelligence using hosted APIs or maintain complete data privacy with fully local execution, you can easily select the providers that best fit your infrastructure and workflow requirements.

---

## Supported Providers

### LLM Providers
Supported LLM providers: `openai`, `openai_structured`, `anthropic`, `groq`, `ollama`, `lmstudio`, `google` (or `gemini`), `azure_openai`, `mistral`, `langchain`, `deepseek`, `xai`, `sarvam`, `aws_bedrock`, `litellm`, `minimax`, `together`, `vllm`, `azure_openai_structured`.

### Embedder Providers
Supported embedder providers: `openai`, `aws_bedrock`, `ollama`, `lmstudio`, `together`, `google` (or `gemini`), `azure_openai`, `fastembed`, `langchain`, `vertexai`, `huggingface`.

### Vector Store Providers
Supported vector store providers: `qdrant`, `chroma`, `pgvector`, `pinecone`, `mongodb`, `milvus`, `baidu`, `cassandra`, `neptune`, `upstash_vector`, `azure_ai_search`, `azure_mysql`, `redis`, `valkey`, `databricks`, `elasticsearch`, `vertex_ai_vector_search`, `opensearch`, `supabase`, `weaviate`, `faiss`, `langchain`, `s3_vectors`, `turbopuffer`, `oracledb`, `memory`.

> [!NOTE]
> When switching embedding providers or changing model dimensions, ensure your vector store collection matches the new dimension size to avoid compatibility errors.

---

## Configuring Providers via Dashboard

You can update your LLM and embedding providers directly from the web dashboard.

1. Open your self-hosted dashboard and navigate to the **Configuration** page.
2. Locate the **LLM Provider** section.
3. Select your desired provider from the **Provider** dropdown menu.
4. Enter your preferred **Model** identifier (e.g., `gpt-5-mini`) and input your corresponding **API Key** if required.
5. Scroll down to the **Embedding Model** section and select your preferred embedding provider and model.
6. Click **Save Configuration** to apply your changes.

> [!TIP]
> If you are using an OpenAI-compatible local provider or Ollama, you can often reuse the base URL and authentication settings across both LLM and embedding configurations.

---

## Configuring Providers via Interactive CLI Wizard

### Wizard Steps
If you are setting up your environment via the command line, the interactive configuration wizard will guide you through provider selection step-by-step.

1. **LLM Provider Selection:** The wizard displays a list of available LLM options (such as OpenAI, Anthropic, or Ollama). Type the number corresponding to your choice and press Enter. If prompted, supply your API key or custom server URL.
2. **Embedding Provider Selection:** Choose your embedding provider. If you select the same provider as your LLM, the wizard will automatically reuse your API key and connection URL from the previous step.
3. **Vector Store Selection:** Choose a vector database backend (such as Qdrant or PGVector). Follow the on-screen prompts to input connection details such as host, port, and credentials.

---

## Configuration Reference

The following table summarizes the primary configuration options available when setting up providers manually in your configuration files:

| Option Key | Description | Example Value |
| :--- | :--- | :--- |
| `llm.provider` | The active LLM provider identifier | `"openai"`, `"ollama"` |
| `llm.config.model` | The specific model name to generate responses | `"gpt-5-mini"`, `"llama3.1:8b"` |
| `llm.config.apiKey` | Authentication API key for cloud providers | `"sk-..."` |
| `llm.config.baseURL` or `ollama_base_url` | Custom API endpoint or local server URL | `"http://localhost:11434"` |
| `embedder.provider` | The active embedding provider identifier | `"openai"`, `"ollama"` |
| `embedder.config.model` | The specific embedding model name | `"text-embedding-3-small"` |
| `vectorStore.provider` | The active vector database provider | `"qdrant"`, `"pgvector"` |

> [!WARNING]
> Storing API keys in plaintext configuration files is discouraged. Always use environment variables or secure secret reference objects (such as `${OPENAI_API_KEY}` or `SecretRef`) where supported.

## Related

- [Storage Provider Settings](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/guide/configuration/storage-provider-settings)
- [Quickstart Overview](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/guide/getting-started/quickstart-overview)


## Sitemap

See the full [sitemap](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/llms.txt) for all pages in this wiki.