Reranking

Vector search is fast, but it only ranks by embedding similarity. Reranking takes the retrieved candidates, scores each candidate against the query, and returns the best subset. Use reranking when precision matters more than latency. Skip it when results are already good, latency is tight, or an extra model call is not worth the quality gain.

Retrieval reranking

Retrieve wide, rerank down.

result = rag.retrieve(
    "How do I authenticate?",
    top_k=20,
    use_rerank=True,
    reranker=rag.reranker(provider="voyage", model="rerank-2-lite"),
    rerank_top_k=5,
)

for item in result.items:
    print(item.rerank_score, item.score, item.text)

ragrails retrieve "How do I authenticate?" \
  --vector-db qdrant \
  --collection docs \
  --url http://localhost:6333 \
  --provider voyage \
  --model voyage-3 \
  --top-k 20 \
  --rerank \
  --reranker voyage \
  --reranker-model rerank-2-lite \
  --rerank-top-k 5

curl -X POST http://127.0.0.1:8000/v1/retrieve \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How do I authenticate?",
    "provider": "voyage",
    "model": "voyage-3",
    "vector_db": "qdrant",
    "collection": "docs",
    "url": "http://localhost:6333",
    "top_k": 20,
    "use_rerank": true,
    "reranker": "voyage",
    "reranker_model": "rerank-2-lite",
    "rerank_top_k": 5
  }'

Result shape:

{
  "query": "How do I authenticate?",
  "search_query": "How do I authenticate?",
  "retrieved": 20,
  "items": [
    {
      "id": "auth#overview",
      "chunk_id": "auth#overview",
      "score": 0.72,
      "rerank_score": 0.94,
      "text": "Create an API key and send it as a Bearer token.",
      "metadata": {"title": "Authentication"}
    }
  ],
  "failed": 0,
  "errors": []
}

Query pipeline reranking

query() wraps query embedding and retrieval in one stable pipeline shape.

result = rag.query(
    "How do I authenticate?",
    retrieval={
        "top_k": 20,
        "rerank": {
            "enabled": True,
            "provider": "voyage",
            "model": "rerank-2-lite",
            "top_k": 5,
        },
    },
)

ragrails query "How do I authenticate?" \
  --vector-db qdrant \
  --collection docs \
  --url http://localhost:6333 \
  --top-k 20 \
  --rerank \
  --rerank-top-k 5

curl -X POST http://127.0.0.1:8000/v1/pipelines/query \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How do I authenticate?",
    "retrieval": {
      "vector_db": "qdrant",
      "collection": "docs",
      "url": "http://localhost:6333",
      "top_k": 20,
      "rerank": {
        "enabled": true,
        "provider": "voyage",
        "model": "rerank-2-lite",
        "top_k": 5
      }
    }
  }'

Chat reranking

Chat can rerank before building the prompt context. Use Streaming if the UI should show retrieval and generation progress while reranked chat runs.

from ragrails.core.stg_05_retriever import RetrieverConfig

result = rag.chat(
    "How do I authenticate?",
    history=[],
    retrieval_config=RetrieverConfig(top_k=20, use_rerank=True, rerank_top_k=5),
)

ragrails chat "How do I authenticate?" \
  --vector-db qdrant \
  --collection docs \
  --url http://localhost:6333 \
  --rerank \
  --rerank-top-k 5

curl -X POST http://127.0.0.1:8000/v1/chat \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How do I authenticate?",
    "collection": "docs",
    "url": "http://localhost:6333",
    "rerank": true,
    "rerank_top_k": 5,
    "history": []
  }'

Choosing a reranker

Provider	Models	Use when
`voyage`	`rerank-2-lite`, `rerank-2`	You want semantic reranking and can pay for an API call.
`bm25`	`bm25`	You want local, no-network, exact-term-oriented reranking.

# Local reranking, useful for offline or cost-sensitive setups.
result = rag.retrieve(
    "refund deadline",
    top_k=20,
    use_rerank=True,
    reranker=rag.reranker(provider="bm25"),
    rerank_top_k=5,
)

Practical defaults

Setting	Starting point	Why
`top_k`	`20`	Gives the reranker enough candidates.
`rerank_top_k`	`5`	Keeps answer context focused.
`min_rerank_score` in chat quality	`0.50`	Filters weak reranked context before generation.

If use_rerank=True, the SDK retrieval path needs an actual reranker object or a configured reranker. REST and CLI create the reranker from provider/model fields.

Get Started

Features

Capabilities

Use Cases

Walkthroughs

Retrieval reranking

Query pipeline reranking

Chat reranking

Choosing a reranker

Practical defaults

​Retrieval reranking

​Query pipeline reranking

​Chat reranking

​Choosing a reranker

​Practical defaults

​Related pages

Retrieval reranking

Query pipeline reranking

Chat reranking

Choosing a reranker

Practical defaults

Related pages