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# Fireworks AI Rerank
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"""
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Fireworks AI Rerank API transformation
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Reference: https://docs.fireworks.ai/inference-api-reference/rerank
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"""
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from typing import Any, Dict, List, Optional, Union
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import httpx
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from litellm._uuid import uuid
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
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from litellm.llms.fireworks_ai.common_utils import FireworksAIMixin
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from litellm.types.rerank import (
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RerankBilledUnits,
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RerankResponse,
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RerankResponseDocument,
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RerankResponseMeta,
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RerankResponseResult,
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RerankTokens,
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)
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class FireworksAIRerankConfig(FireworksAIMixin, BaseRerankConfig):
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"""
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Fireworks AI Rerank API configuration
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"""
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def get_complete_url(
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self,
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api_base: Optional[str],
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model: str,
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optional_params: Optional[dict] = None,
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) -> str:
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if api_base:
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# Remove trailing slashes and ensure clean base URL
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api_base = api_base.rstrip("/")
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if not api_base.endswith("/rerank"):
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if api_base.endswith("/v1"):
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api_base = f"{api_base}/rerank"
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elif api_base.endswith("/inference/v1"):
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api_base = f"{api_base}/rerank"
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else:
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api_base = f"{api_base}/inference/v1/rerank"
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return api_base
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return "https://api.fireworks.ai/inference/v1/rerank"
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def get_supported_cohere_rerank_params(self, model: str) -> list:
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return [
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"query",
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"documents",
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"top_n",
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"return_documents",
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]
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def map_cohere_rerank_params(
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self,
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non_default_params: Optional[dict],
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model: str,
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drop_params: bool,
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query: str,
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documents: List[Union[str, Dict[str, Any]]],
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custom_llm_provider: Optional[str] = None,
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top_n: Optional[int] = None,
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rank_fields: Optional[List[str]] = None,
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return_documents: Optional[bool] = True,
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max_chunks_per_doc: Optional[int] = None,
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max_tokens_per_doc: Optional[int] = None,
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) -> Dict[str, Any]:
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"""
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Map Cohere rerank params to Fireworks AI rerank params
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"""
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params: Dict[str, Any] = {
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"query": query,
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"documents": documents,
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}
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if top_n is not None:
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params["top_n"] = top_n
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if return_documents is not None:
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params["return_documents"] = return_documents
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# Fireworks AI doesn't support these params
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if rank_fields is not None:
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# Silently ignore rank_fields as Fireworks AI doesn't support it
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pass
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if max_chunks_per_doc is not None:
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# Silently ignore max_chunks_per_doc as Fireworks AI doesn't support it
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pass
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if max_tokens_per_doc is not None:
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# Silently ignore max_tokens_per_doc as Fireworks AI doesn't support it
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pass
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return params
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def validate_environment( # type: ignore[override]
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self,
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headers: dict,
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model: str,
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api_key: Optional[str] = None,
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optional_params: Optional[dict] = None,
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) -> dict:
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api_key = self._get_api_key(api_key)
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if api_key is None:
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raise ValueError(
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"FIREWORKS_API_KEY is not set. Please set 'FIREWORKS_API_KEY' or 'FIREWORKS_AI_API_KEY' in your environment"
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)
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default_headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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# If 'Authorization' is provided in headers, it overrides the default.
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if "Authorization" in headers:
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default_headers["Authorization"] = headers["Authorization"]
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# Merge other headers, overriding any default ones except Authorization
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return {**default_headers, **headers}
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def transform_rerank_request(
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self,
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model: str,
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optional_rerank_params: Dict,
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headers: dict,
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) -> dict:
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"""
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Transform request to Fireworks AI rerank format
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"""
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if "query" not in optional_rerank_params:
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raise ValueError("query is required for Fireworks AI rerank")
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if "documents" not in optional_rerank_params:
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raise ValueError("documents is required for Fireworks AI rerank")
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# Handle model name - Fireworks AI expects model name like "fireworks/qwen3-reranker-8b"
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# Remove fireworks_ai/ prefix if present
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if model.startswith("fireworks_ai/"):
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model = model.replace("fireworks_ai/", "")
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# If model doesn't start with "fireworks/", add it
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# But don't add if it already has the prefix
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if not model.startswith("fireworks/"):
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model = f"fireworks/{model}"
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request_data = {
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"model": model,
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"query": optional_rerank_params["query"],
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"documents": optional_rerank_params["documents"],
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}
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if (
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"top_n" in optional_rerank_params
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and optional_rerank_params["top_n"] is not None
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):
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request_data["top_n"] = optional_rerank_params["top_n"]
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if (
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"return_documents" in optional_rerank_params
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and optional_rerank_params["return_documents"] is not None
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):
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request_data["return_documents"] = optional_rerank_params[
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"return_documents"
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]
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return request_data
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def transform_rerank_response(
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self,
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model: str,
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raw_response: httpx.Response,
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model_response: RerankResponse,
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logging_obj: LiteLLMLoggingObj,
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api_key: Optional[str] = None,
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request_data: dict = {},
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optional_params: dict = {},
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litellm_params: dict = {},
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) -> RerankResponse:
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"""
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Transform Fireworks AI rerank response to LiteLLM RerankResponse format
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"""
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try:
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raw_response_json = raw_response.json()
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except Exception as e:
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raise self.get_error_class(
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error_message=f"Failed to parse response: {str(e)}",
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status_code=raw_response.status_code,
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headers=raw_response.headers,
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)
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# Fireworks AI response format:
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# {
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# "object": "list",
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# "model": "accounts/fireworks/models/qwen3-reranker-8b",
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# "data": [
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# {
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# "index": 0,
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# "relevance_score": 0.95,
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# "document": "..."
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# }
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# ],
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# "usage": {
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# "total_tokens": 100,
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# "prompt_tokens": 50,
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# "completion_tokens": 50
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# }
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# }
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# Extract usage information
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usage = raw_response_json.get("usage", {})
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_billed_units = RerankBilledUnits(search_units=usage.get("total_tokens", 0))
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_tokens = RerankTokens(
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input_tokens=usage.get("prompt_tokens", 0),
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output_tokens=usage.get("completion_tokens", 0),
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)
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rerank_meta = RerankResponseMeta(billed_units=_billed_units, tokens=_tokens)
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# Extract results - Fireworks AI uses "data" instead of "results"
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_results: Optional[List[dict]] = raw_response_json.get(
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"data"
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) or raw_response_json.get("results")
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if _results is None:
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raise ValueError(f"No results found in the response={raw_response_json}")
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rerank_results: List[RerankResponseResult] = []
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for result in _results:
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# Validate required fields exist
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if not all(key in result for key in ["index", "relevance_score"]):
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raise ValueError(f"Missing required fields in the result={result}")
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# Get document data - Fireworks AI returns document as a string directly
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document_text = result.get("document")
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document = None
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if document_text:
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# Handle both string and object formats
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if isinstance(document_text, str):
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document = RerankResponseDocument(text=document_text)
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elif isinstance(document_text, dict):
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# Handle object format if it exists
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text = document_text.get("text", "")
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if text:
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document = RerankResponseDocument(text=str(text))
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# Create typed result
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rerank_result = RerankResponseResult(
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index=int(result["index"]),
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relevance_score=float(result["relevance_score"]),
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)
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# Only add document if it exists
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if document:
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rerank_result["document"] = document
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rerank_results.append(rerank_result)
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# Use model name as id if no id is provided
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response_id = (
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raw_response_json.get("id")
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or raw_response_json.get("model")
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or str(uuid.uuid4())
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)
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return RerankResponse(
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id=response_id,
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results=rerank_results,
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meta=rerank_meta,
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)
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