Embedding Providers
Vector embedding services for knowledge base retrieval — configuration, model selection, and consistency rules.
Embedding Providers
An embedding provider converts text into numerical vectors. OpenAgent uses it at two moments: when a File is uploaded (each chunk is embedded and stored as a Vector), and when a user sends a message (the query is embedded so OpenAgent can find the most similar chunks). The same provider must be used for both — mixing providers produces meaningless similarity scores.
Recommended defaults
If you're not sure what to pick:
- Most teams:
text-embedding-3-small(OpenAI) as a fast, affordable default - Multilingual or highest accuracy:
text-embedding-3-large(OpenAI) or Cohere multilingual embeddings - Offline / local:
nomic-embed-textvia Ollama
Supported providers
| Provider | Notable models |
|---|---|
| OpenAI | text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002 |
| Gemini (Google) | text-embedding-004, embedding-001 |
| Cohere | embed-english-v3.0, embed-multilingual-v3.0 |
| Ollama | nomic-embed-text, mxbai-embed-large, all-minilm, and others |
| Azure OpenAI | OpenAI embedding models deployed to your Azure subscription |
| Alibaba Cloud (Qwen) | text-embedding-v1, text-embedding-v2, text-embedding-v3 |
| Baidu Cloud (Ernie) | ERNIE embedding models |
| MiniMax | MiniMax embedding models |
| Tencent Cloud (Hunyuan) | Hunyuan embedding models |
| Jina | jina-embeddings-v3, jina-embeddings-v2-base-en |
| Hugging Face | Embedding models via the Inference API |
| Word2Vec | Classic word vector models for legacy or offline use |
| Local | Any OpenAI-compatible local embedding endpoint (LM Studio, Infinity, TEI) |
Adding a provider
- Providers → Add Provider, category Embedding
- Choose a Type from the table above
- Enter API Key (and Provider URL for Azure, Jina, or local servers)
- Set Sub Type to the model name (e.g.
text-embedding-3-small) - Save — the provider appears immediately in the Embedding Provider dropdown on Stores
Provider-specific configuration
The simplest setup. OpenAI offers three generations of embedding models:
- Type: OpenAI
- API Key: your OpenAI API key
- Sub Type: model name
| Model | Dimensions | Best for |
|---|---|---|
text-embedding-3-small | 1536 | General use, low cost |
text-embedding-3-large | 3072 | Higher accuracy, multilingual |
text-embedding-ada-002 | 1536 | Legacy — prefer 3-small for new stores |
text-embedding-3-small is the recommended default. It is fast, affordable, and outperforms ada-002 on most benchmarks.
Both 3-small and 3-large support dimension reduction — you can specify a lower dimension count to reduce storage requirements while retaining most accuracy. OpenAgent uses the model's default dimension unless you configure otherwise.
Use when you need OpenAI embeddings through your Azure subscription (compliance, VNet access, cost management):
- Type: Azure
- API Key: your Azure OpenAI key
- Provider URL:
https://your-resource.openai.azure.com - Sub Type: your deployment name (the name you gave the model in Azure — not the model family name)
The API version is handled automatically. Make sure the model deployed in Azure is an embedding model, not a chat completion model.
Ollama lets you run embedding models locally with no external API calls:
- Type: Ollama
- Provider URL:
http://localhost:11434(or wherever Ollama is running) - Sub Type: model name as it appears in
ollama list - API Key: not required
Pull the model before creating the provider:
ollama pull nomic-embed-textCommonly used embedding models with Ollama:
| Model | Dimensions | Notes |
|---|---|---|
nomic-embed-text | 768 | Fast, good quality, recommended default |
mxbai-embed-large | 1024 | Higher quality, slower |
all-minilm | 384 | Lightweight, lower accuracy |
Ollama embedding models work on CPU; GPU is not required for most sizes.
Jina provides high-quality embedding models with strong multilingual support:
- Type: Jina
- API Key: your Jina API key
- Sub Type: model name (e.g.
jina-embeddings-v3)
jina-embeddings-v3 supports 89 languages and late-interaction features useful for long documents. If your knowledge base contains documents in multiple languages and you're not using Cohere's multilingual model, Jina is a strong alternative.
Any server that implements the OpenAI embeddings API format works:
- Type: Local
- Provider URL: base URL of your server (e.g.
http://localhost:8080) - Sub Type: model identifier your server expects
- API Key: leave empty or set to
none
Compatible servers: Infinity, TEI (Text Embeddings Inference), LM Studio (embedding mode).
This is the fully offline option that doesn't depend on Ollama — useful if you already have an embedding inference stack.
Choosing an embedding model
For most English-language knowledge bases: text-embedding-3-small (OpenAI). Low cost, reliable, integrates in minutes.
For multilingual content: Cohere embed-multilingual-v3.0 or Jina jina-embeddings-v3. Both handle non-English documents and cross-language queries (English query matching a Chinese document, for example).
For fully offline or on-premise deployments: Ollama with nomic-embed-text or mxbai-embed-large. No data leaves your server. Works on CPU.
For high-accuracy retrieval on large knowledge bases: text-embedding-3-large or Cohere embed-english-v3.0. Higher dimensionality improves discrimination between similar chunks, at higher cost and slightly higher latency.
For legacy or research workflows: Word2Vec is available for compatibility with pre-existing vector pipelines, but modern neural embedding models outperform it significantly on retrieval tasks.
The consistency rule
All Vectors in a Store must be embedded by the same model. If you change the Store's Embedding Provider after Files have been uploaded, existing Vectors are no longer comparable to new query embeddings — retrieval will silently return wrong results.
After changing the Embedding Provider, delete all Files in the Store and re-upload them. Processing runs automatically with the new provider.
The reason: embedding models produce vectors in their own coordinate space. A vector from text-embedding-3-small and a vector from nomic-embed-text exist in completely different spaces — cosine similarity between them has no meaning.
Embedding provider vs. chat model
The embedding provider is entirely independent of the chat model. You can freely mix:
- OpenAI embeddings + Ollama chat model (send no data to OpenAI during conversations, only during indexing)
- Ollama embeddings + Claude chat model (fully offline indexing, cloud-based reasoning)
- Jina embeddings + a strong chat model (e.g. OpenAI
gpt-4.1) for multilingual retrieval with high-quality generation
The Store has separate fields for Model Provider (chat) and Embedding Provider — configure each independently.
Pricing and token counts
Most embedding providers charge per token, but at rates far lower than chat models. For cost estimation:
text-embedding-3-small(OpenAI): about $0.02 / 1M tokens (about $0.01 / 1M via Batch API)text-embedding-3-large(OpenAI): about $0.13 / 1M tokens (about $0.065 / 1M via Batch API)- Ollama and local: free (compute cost only)
Embedding costs are incurred at upload time (once per chunk) and at query time (once per user message). Query-time costs are typically small compared to upload-time costs for large knowledge bases.
Prices change over time. The numbers above are a rough reference as of April 2026 — check your provider’s current pricing page for production budgeting (for OpenAI: https://platform.openai.com/pricing).
The embedding provider does not need to be from the same vendor as your chat model. Mixing providers is normal — choose each independently based on your requirements for retrieval quality, language coverage, and cost.