Reranking

def create(
    model: str,
    query: str,
    documents: Union[List[str], List[Dict[str, Any]]],
    top_n: Optional[int] = None,
    return_documents: bool = False,
    rank_fields: Optional[List[str]] = None,
    **kwargs
) -> RerankResponse:
    """
    Rerank documents based on query relevance.

    Args:
        model: Reranking model identifier
        query: Search query for relevance comparison
        documents: List of documents to rerank (strings or objects)
        top_n: Number of top documents to return
        return_documents: Include original documents in response
        rank_fields: Fields to rank when documents are objects

    Returns:
        RerankResponse with relevance scores and rankings
    """

async def create(
    model: str,
    query: str,
    documents: Union[List[str], List[Dict[str, Any]]],
    **kwargs
) -> RerankResponse:
    """
    Asynchronously rerank documents.

    Returns:
        RerankResponse with relevance scores and rankings
    """

from together import Together

client = Together()

query = "What is the capital of the United States?"
documents = [
    "New York is the most populous city in the United States.",
    "Washington, D.C. is the capital of the United States.",
    "Los Angeles is known for its entertainment industry.",
    "The United States has 50 states and a federal district.",
    "Chicago is the third-largest city in the United States."
]

response = client.rerank.create(
    model="Salesforce/Llama-Rank-V1",
    query=query,
    documents=documents,
    top_n=3
)

print("Reranked documents by relevance:")
for result in response.results:
    print(f"Rank {result.index + 1}: {documents[result.index]}")
    print(f"Relevance score: {result.relevance_score:.4f}")
    print("---")

# Documents with metadata
documents = [
    {
        "title": "Python Programming Basics",
        "content": "Python is a high-level programming language known for its simplicity.",
        "category": "programming"
    },
    {
        "title": "Machine Learning Introduction", 
        "content": "Machine learning is a subset of artificial intelligence.",
        "category": "ai"
    },
    {
        "title": "Data Science with Python",
        "content": "Python is widely used in data science for analysis and visualization.",
        "category": "data-science"
    }
]

query = "Python programming for beginners"

response = client.rerank.create(
    model="Salesforce/Llama-Rank-V1",
    query=query,
    documents=documents,
    top_n=2,
    return_documents=True,
    rank_fields=["title", "content"]
)

print("Top relevant documents:")
for result in response.results:
    doc = documents[result.index]
    print(f"Title: {doc['title']}")
    print(f"Relevance: {result.relevance_score:.4f}")
    print(f"Content: {doc['content'][:100]}...")
    print("---")

class RerankRequest:
    model: str
    query: str
    documents: Union[List[str], List[Dict[str, Any]]]
    top_n: Optional[int] = None
    return_documents: bool = False
    rank_fields: Optional[List[str]] = None

class RerankResponse:
    id: str
    results: List[RerankResult]
    meta: RerankMeta

class RerankResult:
    index: int
    relevance_score: float
    document: Optional[Dict[str, Any]] = None

class RerankMeta:
    api_version: Dict[str, str]
    billed_units: Dict[str, int]