Reranker

The MariaDB AI RAG reranker applies a cross-encoder second pass to re-score candidate chunks, with FlashRank, Sentence-Transformers, and Cohere backends and configurable presets.

Reranking improves retrieval quality in the RAG pipeline.

Vector search is fast, but it can miss deeper contextual relevance. A reranker adds a second pass that rescans the retrieved documents and sorts them by how well they answer the exact query.

In practice, retrieval may return 10 candidate documents. The reranker scores those candidates more precisely and returns the best few results to the next stage.

Rerank endpoint

Use POST /rerank to rerank raw text documents against a query.

This endpoint uses a cross-encoder model to re-score and sort documents. It improves relevance over vector similarity alone.

Request headers

• Authorization: Bearer <your_jwt_token>

• Content-Type: application/json

Request body

Field	Type	Required	Default	Description
query	string	Yes	-	Query used to score each document.
documents	Array<string>	Yes	-	Document texts to rerank. Must contain at least one item.
doc_ids	Array<Any>	No	null	Optional IDs for mapping results back to source records.
metadata	Array<dict>	No	null	Optional metadata entries aligned with each document.
top_k	integer	No	null	Maximum number of results to return. If omitted, all documents are returned in ranked order.
model_type	string	No	"flashrank"	Backend to use. Valid values: "flashrank", "sentence-transformers", "cohere", "hybrid".
model_name	string	No	"ms-marco-MiniLM-L-12-v2"	Specific reranker model.
alpha	float	No	0.7	Hybrid-only weight for the cross-encoder score.
beta	float	No	0.3	Hybrid-only weight for the TF-IDF score.
api_key	string	No	null	Required only when model_type is "cohere".

Example request

{
  "query": "What are the financial results for Q3 2023?",
  "documents": [
    "The company released a new product in Q3.",
    "In Q3 2023, the company saw a 20% increase in revenue reaching $5M.",
    "Our marketing budget for 2023 was approved.",
    "Q3 2023 financial statements indicate a strong profit margin."
  ],
  "doc_ids": [101, 102, 103, 104],
  "top_k": 2,
  "model_type": "flashrank",
  "model_name": "ms-marco-MiniLM-L-12-v2"
}

Response format

The response is a JSON array of RerankResponse objects, sorted from highest score to lowest score.

Field	Type	Description
doc_id	Any	Document ID, if provided in the request.
text	string	Document text.
score	float	Relevance score. Higher is better.
rank	integer	Final rank. 1 is best.
metadata	dict	Metadata entry, if provided in the request.

Example response

[
  {
    "doc_id": 102,
    "text": "In Q3 2023, the company saw a 20% increase in revenue reaching $5M.",
    "score": 0.985,
    "rank": 1,
    "metadata": null
  },
  {
    "doc_id": 104,
    "text": "Q3 2023 financial statements indicate a strong profit margin.",
    "score": 0.892,
    "rank": 2,
    "metadata": null
  }
]

Reranking in the full pipeline

You will usually enable reranking inside POST /orchestrate/generation.

Include a reranking object in the request body:

{
  "query": "What are the key findings?",
  "document_ids": [1, 2, 3],
  "retrieval_method": "hybrid",
  "top_k": 5,
  "reranking": {
    "enabled": true,
    "model_type": "flashrank",
    "model_name": "ms-marco-MiniLM-L-12-v2",
    "top_k": 5
  },
  "llm_provider": "openai",
  "llm_model": "gpt-4"
}

If reranking.enabled is true, the system fetches extra documents first, such as top_k * 2.0. It reranks them and forwards only the best top_k results to the LLM.

Supported models and providers

The reranking layer supports multiple backends and models.

By default, the system uses FlashRank with ms-marco-MiniLM-L-12-v2.

Why this default:

• It is fast.

• It runs well on CPUs.

• It does not require a GPU.

• It is cached in memory for faster repeated requests.

Supported backends:

1. flashrank: Default option for fast local CPU deployments. Example: ms-marco-MiniLM-L-12-v2

2. sentence-transformers: Local Python ML environments. Example: cross-encoder/ms-marco-TinyBERT-L-2-v2

3. cohere: External cloud API. Example: rerank-english-v3.0

Configuration

You can control default reranker behavior through config.env.

Core settings

• RERANKING_ENABLED=true: Enables or disables reranking globally.

• RERANKING_MODEL_TYPE=flashrank: Selects the backend.

• RERANKING_MODEL_NAME=ms-marco-MiniLM-L-12-v2: Selects the model.

• RERANKING_API_KEY=: Required only for cloud providers such as Cohere.

Search strategy settings

• RERANKING_TOP_K_MULTIPLIER=2.0: Number of extra documents to fetch before reranking.

• RERANKING_DEFAULT_TOP_K=5: Default number of reranked results to return.

With a multiplier of 2.0, a request for 5 final documents pulls 10 candidates before reranking.

Performance and caching settings

• RERANKING_CACHE_MODELS=true: Keeps models loaded in memory.

• RERANKING_CACHE_MAX_SIZE=3: Maximum number of cached models.

• RERANKING_ENABLE_BATCHING=true: Processes documents in batches.

• RERANKING_BATCH_SIZE=32: Number of documents scored per batch.

Presets

You can also use built-in presets through RERANKING_PRESET.

fast

• Model: ms-marco-TinyBERT-L-2-v2 with FlashRank

• Best for lowest latency

• Uses a 1.5x multiplier

balanced

• Model: ms-marco-MiniLM-L-12-v2 with FlashRank

• Default preset

• Balances speed and quality

• Uses a 2.0x multiplier

quality

• Model: BAAI/bge-reranker-v2-m3 with Sentence-Transformers

• Best for highest accuracy

• Better suited to GPU-backed deployments

• Uses a 3.0x multiplier

Summary

• For standard use, keep the default FlashRank setup.

• For custom workflows, use POST /rerank.

• For higher accuracy on strong hardware, use RERANKING_PRESET=quality.

• For fully managed cloud reranking, use RERANKING_MODEL_TYPE=cohere and provide RERANKING_API_KEY.

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