RAGFlow

RAGFlow is an open-source engine that answers questions from your own documents. Add the exchange as an OpenAI-API-Compatible provider. RAGFlow then buys its chat answers and its embeddings from the exchange. The exchange auctions each request, sets a cap before the job starts and charges for the measured use.

Before you start

A workspace with a balance and a spend key. See API keys.
A RAGFlow server that you can configure.
A chat model and an embedding model from the book. See Market data and Embeddings.

1. Add a provider

Open User settings → Model providers. Add the OpenAI-API-Compatible provider and create an instance. Enter:

https://router.inference.ai.exchange/v1

The URL ends in /v1.

2. Paste the key

sk-liquid-...

3. Add the chat model

Add a model to the instance. Set the model type to chat. The model name must be an exact name from GET /v1/models.

liquid.auto

Turn on Tool calling only for a model that supports tools. To list those models, use GET /v1/models?feature=tools.

4. Add the embedding model

Add a second model to the same instance. Set the model type to embedding. Use a name from GET /v1/models?kind=embedding.

text-embedding-3-small

5. Set the defaults

In Set default models, choose the chat model and the embedding model. RAGFlow needs both. Then use Verify on each model. Verify sends a real request, so each check is a small purchase.

Notes

Keep one embedding model for one dataset. RAGFlow ties the vectors of a dataset to the model that made them. Choose the model before you parse documents. A change of model needs a new dataset. See Embeddings.
Name the embedding model. The exchange does not choose one for you, and Auto Router cannot choose one.
The exchange has no rerank endpoint. Leave the rerank model empty, or use the rerank model of another provider.
Parsing a large set of documents makes many embedding requests. A request can have up to 2,048 texts.
The exchange sets a cap on each request. It is in the x-liquid-cap-usd response header. To limit total spend, set a spending limit on the key. See API keys.
Use a preset for other purchasing rules. See Purchasing rules.

Troubleshooting

Symptom	Cause and fix
`401`	The key is wrong, revoked or expired. Paste it again.
`404`	Keep `/v1` at the end of the base URL.
`model_not_found`	The model is not on the book. Use an exact name from `GET /v1/models`.
A request is refused for lack of funds.	The workspace balance cannot cover the cap. Add funds on the Credits page.
Verify fails for the embedding model.	The name is not an embedding model. Use a name from `?kind=embedding`.

See Errors for the error codes.

Next steps

Embeddings: the request, the price and the limits.
LlamaIndex: the same task in code.
Purchasing rules: models, strategy and limits, and presets.