File size: 11,846 Bytes
e3278e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 |
import asyncio
import contextvars
from functools import partial
from typing import Any, Coroutine, Dict, List, Literal, Optional, Union
import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
from litellm.llms.bedrock.rerank.handler import BedrockRerankHandler
from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
from litellm.llms.jina_ai.rerank.handler import JinaAIRerank
from litellm.llms.together_ai.rerank.handler import TogetherAIRerank
from litellm.rerank_api.rerank_utils import get_optional_rerank_params
from litellm.secret_managers.main import get_secret, get_secret_str
from litellm.types.rerank import OptionalRerankParams, RerankResponse
from litellm.types.router import *
from litellm.utils import ProviderConfigManager, client, exception_type
####### ENVIRONMENT VARIABLES ###################
# Initialize any necessary instances or variables here
together_rerank = TogetherAIRerank()
jina_ai_rerank = JinaAIRerank()
bedrock_rerank = BedrockRerankHandler()
base_llm_http_handler = BaseLLMHTTPHandler()
#################################################
@client
async def arerank(
model: str,
query: str,
documents: List[Union[str, Dict[str, Any]]],
custom_llm_provider: Optional[Literal["cohere", "together_ai"]] = None,
top_n: Optional[int] = None,
rank_fields: Optional[List[str]] = None,
return_documents: Optional[bool] = None,
max_chunks_per_doc: Optional[int] = None,
**kwargs,
) -> Union[RerankResponse, Coroutine[Any, Any, RerankResponse]]:
"""
Async: Reranks a list of documents based on their relevance to the query
"""
try:
loop = asyncio.get_event_loop()
kwargs["arerank"] = True
func = partial(
rerank,
model,
query,
documents,
custom_llm_provider,
top_n,
rank_fields,
return_documents,
max_chunks_per_doc,
**kwargs,
)
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
init_response = await loop.run_in_executor(None, func_with_context)
if asyncio.iscoroutine(init_response):
response = await init_response
else:
response = init_response
return response
except Exception as e:
raise e
@client
def rerank( # noqa: PLR0915
model: str,
query: str,
documents: List[Union[str, Dict[str, Any]]],
custom_llm_provider: Optional[
Literal["cohere", "together_ai", "azure_ai", "infinity"]
] = None,
top_n: Optional[int] = None,
rank_fields: Optional[List[str]] = None,
return_documents: Optional[bool] = True,
max_chunks_per_doc: Optional[int] = None,
**kwargs,
) -> Union[RerankResponse, Coroutine[Any, Any, RerankResponse]]:
"""
Reranks a list of documents based on their relevance to the query
"""
headers: Optional[dict] = kwargs.get("headers") # type: ignore
litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
proxy_server_request = kwargs.get("proxy_server_request", None)
model_info = kwargs.get("model_info", None)
metadata = kwargs.get("metadata", {})
user = kwargs.get("user", None)
client = kwargs.get("client", None)
try:
_is_async = kwargs.pop("arerank", False) is True
optional_params = GenericLiteLLMParams(**kwargs)
model, _custom_llm_provider, dynamic_api_key, dynamic_api_base = (
litellm.get_llm_provider(
model=model,
custom_llm_provider=custom_llm_provider,
api_base=optional_params.api_base,
api_key=optional_params.api_key,
)
)
rerank_provider_config: BaseRerankConfig = (
ProviderConfigManager.get_provider_rerank_config(
model=model,
provider=litellm.LlmProviders(_custom_llm_provider),
)
)
optional_rerank_params: OptionalRerankParams = get_optional_rerank_params(
rerank_provider_config=rerank_provider_config,
model=model,
drop_params=kwargs.get("drop_params") or litellm.drop_params or False,
query=query,
documents=documents,
custom_llm_provider=_custom_llm_provider,
top_n=top_n,
rank_fields=rank_fields,
return_documents=return_documents,
max_chunks_per_doc=max_chunks_per_doc,
non_default_params=kwargs,
)
if isinstance(optional_params.timeout, str):
optional_params.timeout = float(optional_params.timeout)
model_response = RerankResponse()
litellm_logging_obj.update_environment_variables(
model=model,
user=user,
optional_params=dict(optional_rerank_params),
litellm_params={
"litellm_call_id": litellm_call_id,
"proxy_server_request": proxy_server_request,
"model_info": model_info,
"metadata": metadata,
"preset_cache_key": None,
"stream_response": {},
**optional_params.model_dump(exclude_unset=True),
},
custom_llm_provider=_custom_llm_provider,
)
# Implement rerank logic here based on the custom_llm_provider
if _custom_llm_provider == "cohere":
# Implement Cohere rerank logic
api_key: Optional[str] = (
dynamic_api_key or optional_params.api_key or litellm.api_key
)
api_base: Optional[str] = (
dynamic_api_base
or optional_params.api_base
or litellm.api_base
or get_secret("COHERE_API_BASE") # type: ignore
or "https://api.cohere.com"
)
if api_base is None:
raise Exception(
"Invalid api base. api_base=None. Set in call or via `COHERE_API_BASE` env var."
)
response = base_llm_http_handler.rerank(
model=model,
custom_llm_provider=_custom_llm_provider,
optional_rerank_params=optional_rerank_params,
logging_obj=litellm_logging_obj,
timeout=optional_params.timeout,
api_key=dynamic_api_key or optional_params.api_key,
api_base=api_base,
_is_async=_is_async,
headers=headers or litellm.headers or {},
client=client,
model_response=model_response,
)
elif _custom_llm_provider == "azure_ai":
api_base = (
dynamic_api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
or optional_params.api_base
or litellm.api_base
or get_secret("AZURE_AI_API_BASE") # type: ignore
)
response = base_llm_http_handler.rerank(
model=model,
custom_llm_provider=_custom_llm_provider,
optional_rerank_params=optional_rerank_params,
logging_obj=litellm_logging_obj,
timeout=optional_params.timeout,
api_key=dynamic_api_key or optional_params.api_key,
api_base=api_base,
_is_async=_is_async,
headers=headers or litellm.headers or {},
client=client,
model_response=model_response,
)
elif _custom_llm_provider == "infinity":
# Implement Infinity rerank logic
api_key = dynamic_api_key or optional_params.api_key or litellm.api_key
api_base = (
dynamic_api_base
or optional_params.api_base
or litellm.api_base
or get_secret_str("INFINITY_API_BASE")
)
if api_base is None:
raise Exception(
"Invalid api base. api_base=None. Set in call or via `INFINITY_API_BASE` env var."
)
response = base_llm_http_handler.rerank(
model=model,
custom_llm_provider=_custom_llm_provider,
optional_rerank_params=optional_rerank_params,
logging_obj=litellm_logging_obj,
timeout=optional_params.timeout,
api_key=dynamic_api_key or optional_params.api_key,
api_base=api_base,
_is_async=_is_async,
headers=headers or litellm.headers or {},
client=client,
model_response=model_response,
)
elif _custom_llm_provider == "together_ai":
# Implement Together AI rerank logic
api_key = (
dynamic_api_key
or optional_params.api_key
or litellm.togetherai_api_key
or get_secret("TOGETHERAI_API_KEY") # type: ignore
or litellm.api_key
)
if api_key is None:
raise ValueError(
"TogetherAI API key is required, please set 'TOGETHERAI_API_KEY' in your environment"
)
response = together_rerank.rerank(
model=model,
query=query,
documents=documents,
top_n=top_n,
rank_fields=rank_fields,
return_documents=return_documents,
max_chunks_per_doc=max_chunks_per_doc,
api_key=api_key,
_is_async=_is_async,
)
elif _custom_llm_provider == "jina_ai":
if dynamic_api_key is None:
raise ValueError(
"Jina AI API key is required, please set 'JINA_AI_API_KEY' in your environment"
)
response = jina_ai_rerank.rerank(
model=model,
api_key=dynamic_api_key,
query=query,
documents=documents,
top_n=top_n,
rank_fields=rank_fields,
return_documents=return_documents,
max_chunks_per_doc=max_chunks_per_doc,
_is_async=_is_async,
)
elif _custom_llm_provider == "bedrock":
api_base = (
dynamic_api_base
or optional_params.api_base
or litellm.api_base
or get_secret("BEDROCK_API_BASE") # type: ignore
)
response = bedrock_rerank.rerank(
model=model,
query=query,
documents=documents,
top_n=top_n,
rank_fields=rank_fields,
return_documents=return_documents,
max_chunks_per_doc=max_chunks_per_doc,
_is_async=_is_async,
optional_params=optional_params.model_dump(exclude_unset=True),
api_base=api_base,
logging_obj=litellm_logging_obj,
)
else:
raise ValueError(f"Unsupported provider: {_custom_llm_provider}")
# Placeholder return
return response
except Exception as e:
verbose_logger.error(f"Error in rerank: {str(e)}")
raise exception_type(
model=model, custom_llm_provider=custom_llm_provider, original_exception=e
)
|