chore: initial snapshot for gitea/github upload
This commit is contained in:
Your Name
@@ -0,0 +1,148 @@
|
||||
from typing import List, Optional, Union
|
||||
|
||||
import httpx
|
||||
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
|
||||
from litellm.llms.base_llm.chat.transformation import BaseLLMException
|
||||
from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
|
||||
from litellm.types.utils import EmbeddingResponse, Usage
|
||||
|
||||
|
||||
class VoyageError(BaseLLMException):
|
||||
def __init__(
|
||||
self,
|
||||
status_code: int,
|
||||
message: str,
|
||||
headers: Union[dict, httpx.Headers] = {},
|
||||
):
|
||||
self.status_code = status_code
|
||||
self.message = message
|
||||
self.request = httpx.Request(
|
||||
method="POST", url="https://api.voyageai.com/v1/embeddings"
|
||||
)
|
||||
self.response = httpx.Response(status_code=status_code, request=self.request)
|
||||
super().__init__(
|
||||
status_code=status_code,
|
||||
message=message,
|
||||
headers=headers,
|
||||
)
|
||||
|
||||
|
||||
class VoyageEmbeddingConfig(BaseEmbeddingConfig):
|
||||
"""
|
||||
Reference: https://docs.voyageai.com/reference/embeddings-api
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def get_complete_url(
|
||||
self,
|
||||
api_base: Optional[str],
|
||||
api_key: Optional[str],
|
||||
model: str,
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
stream: Optional[bool] = None,
|
||||
) -> str:
|
||||
if api_base:
|
||||
if not api_base.endswith("/embeddings"):
|
||||
api_base = f"{api_base}/embeddings"
|
||||
return api_base
|
||||
return "https://api.voyageai.com/v1/embeddings"
|
||||
|
||||
def get_supported_openai_params(self, model: str) -> list:
|
||||
return [
|
||||
"encoding_format",
|
||||
"dimensions",
|
||||
]
|
||||
|
||||
def map_openai_params(
|
||||
self,
|
||||
non_default_params: dict,
|
||||
optional_params: dict,
|
||||
model: str,
|
||||
drop_params: bool,
|
||||
) -> dict:
|
||||
"""
|
||||
Map OpenAI params to Voyage params
|
||||
|
||||
Reference: https://docs.voyageai.com/reference/embeddings-api
|
||||
"""
|
||||
if "encoding_format" in non_default_params:
|
||||
optional_params["encoding_format"] = non_default_params["encoding_format"]
|
||||
if "dimensions" in non_default_params:
|
||||
optional_params["output_dimension"] = non_default_params["dimensions"]
|
||||
return optional_params
|
||||
|
||||
def validate_environment(
|
||||
self,
|
||||
headers: dict,
|
||||
model: str,
|
||||
messages: List[AllMessageValues],
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
api_key: Optional[str] = None,
|
||||
api_base: Optional[str] = None,
|
||||
) -> dict:
|
||||
if api_key is None:
|
||||
api_key = (
|
||||
get_secret_str("VOYAGE_API_KEY")
|
||||
or get_secret_str("VOYAGE_AI_API_KEY")
|
||||
or get_secret_str("VOYAGE_AI_TOKEN")
|
||||
)
|
||||
return {
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
}
|
||||
|
||||
def transform_embedding_request(
|
||||
self,
|
||||
model: str,
|
||||
input: AllEmbeddingInputValues,
|
||||
optional_params: dict,
|
||||
headers: dict,
|
||||
) -> dict:
|
||||
return {
|
||||
"input": input,
|
||||
"model": model,
|
||||
**optional_params,
|
||||
}
|
||||
|
||||
def transform_embedding_response(
|
||||
self,
|
||||
model: str,
|
||||
raw_response: httpx.Response,
|
||||
model_response: EmbeddingResponse,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
api_key: Optional[str] = None,
|
||||
request_data: dict = {},
|
||||
optional_params: dict = {},
|
||||
litellm_params: dict = {},
|
||||
) -> EmbeddingResponse:
|
||||
try:
|
||||
raw_response_json = raw_response.json()
|
||||
except Exception:
|
||||
raise VoyageError(
|
||||
message=raw_response.text, status_code=raw_response.status_code
|
||||
)
|
||||
|
||||
# model_response.usage
|
||||
model_response.model = raw_response_json.get("model")
|
||||
model_response.data = raw_response_json.get("data")
|
||||
model_response.object = raw_response_json.get("object")
|
||||
|
||||
usage = Usage(
|
||||
prompt_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
|
||||
total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
|
||||
)
|
||||
model_response.usage = usage
|
||||
return model_response
|
||||
|
||||
def get_error_class(
|
||||
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
|
||||
) -> BaseLLMException:
|
||||
return VoyageError(
|
||||
message=error_message, status_code=status_code, headers=headers
|
||||
)
|
||||
@@ -0,0 +1,153 @@
|
||||
"""
|
||||
This module is used to transform the request and response for the Voyage contextualized embeddings API.
|
||||
This would be used for all the contextualized embeddings models in Voyage.
|
||||
"""
|
||||
from typing import List, Optional, Union
|
||||
|
||||
import httpx
|
||||
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
|
||||
from litellm.llms.base_llm.chat.transformation import BaseLLMException
|
||||
from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
|
||||
from litellm.types.utils import EmbeddingResponse, Usage
|
||||
|
||||
|
||||
class VoyageError(BaseLLMException):
|
||||
def __init__(
|
||||
self,
|
||||
status_code: int,
|
||||
message: str,
|
||||
headers: Union[dict, httpx.Headers] = {},
|
||||
):
|
||||
self.status_code = status_code
|
||||
self.message = message
|
||||
self.request = httpx.Request(
|
||||
method="POST", url="https://api.voyageai.com/v1/contextualizedembeddings"
|
||||
)
|
||||
self.response = httpx.Response(status_code=status_code, request=self.request)
|
||||
super().__init__(
|
||||
status_code=status_code,
|
||||
message=message,
|
||||
headers=headers,
|
||||
)
|
||||
|
||||
|
||||
class VoyageContextualEmbeddingConfig(BaseEmbeddingConfig):
|
||||
"""
|
||||
Reference: https://docs.voyageai.com/reference/embeddings-api
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def get_complete_url(
|
||||
self,
|
||||
api_base: Optional[str],
|
||||
api_key: Optional[str],
|
||||
model: str,
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
stream: Optional[bool] = None,
|
||||
) -> str:
|
||||
if api_base:
|
||||
if not api_base.endswith("/contextualizedembeddings"):
|
||||
api_base = f"{api_base}/contextualizedembeddings"
|
||||
return api_base
|
||||
return "https://api.voyageai.com/v1/contextualizedembeddings"
|
||||
|
||||
def get_supported_openai_params(self, model: str) -> list:
|
||||
return ["encoding_format", "dimensions"]
|
||||
|
||||
def map_openai_params(
|
||||
self,
|
||||
non_default_params: dict,
|
||||
optional_params: dict,
|
||||
model: str,
|
||||
drop_params: bool,
|
||||
) -> dict:
|
||||
"""
|
||||
Map OpenAI params to Voyage params
|
||||
|
||||
Reference: https://docs.voyageai.com/reference/contextualized-embeddings-api
|
||||
"""
|
||||
if "encoding_format" in non_default_params:
|
||||
optional_params["encoding_format"] = non_default_params["encoding_format"]
|
||||
if "dimensions" in non_default_params:
|
||||
optional_params["output_dimension"] = non_default_params["dimensions"]
|
||||
return optional_params
|
||||
|
||||
def validate_environment(
|
||||
self,
|
||||
headers: dict,
|
||||
model: str,
|
||||
messages: List[AllMessageValues],
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
api_key: Optional[str] = None,
|
||||
api_base: Optional[str] = None,
|
||||
) -> dict:
|
||||
if api_key is None:
|
||||
api_key = (
|
||||
get_secret_str("VOYAGE_API_KEY")
|
||||
or get_secret_str("VOYAGE_AI_API_KEY")
|
||||
or get_secret_str("VOYAGE_AI_TOKEN")
|
||||
)
|
||||
return {
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
}
|
||||
|
||||
def transform_embedding_request(
|
||||
self,
|
||||
model: str,
|
||||
input: Union[AllEmbeddingInputValues, List[List[str]]],
|
||||
optional_params: dict,
|
||||
headers: dict,
|
||||
) -> dict:
|
||||
return {
|
||||
"inputs": input,
|
||||
"model": model,
|
||||
**optional_params,
|
||||
}
|
||||
|
||||
def transform_embedding_response(
|
||||
self,
|
||||
model: str,
|
||||
raw_response: httpx.Response,
|
||||
model_response: EmbeddingResponse,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
api_key: Optional[str] = None,
|
||||
request_data: dict = {},
|
||||
optional_params: dict = {},
|
||||
litellm_params: dict = {},
|
||||
) -> EmbeddingResponse:
|
||||
try:
|
||||
raw_response_json = raw_response.json()
|
||||
except Exception:
|
||||
raise VoyageError(
|
||||
message=raw_response.text, status_code=raw_response.status_code
|
||||
)
|
||||
|
||||
# model_response.usage
|
||||
model_response.model = raw_response_json.get("model")
|
||||
model_response.data = raw_response_json.get("data")
|
||||
model_response.object = raw_response_json.get("object")
|
||||
|
||||
usage = Usage(
|
||||
prompt_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
|
||||
total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
|
||||
)
|
||||
model_response.usage = usage
|
||||
return model_response
|
||||
|
||||
def get_error_class(
|
||||
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
|
||||
) -> BaseLLMException:
|
||||
return VoyageError(
|
||||
message=error_message, status_code=status_code, headers=headers
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def is_contextualized_embeddings(model: str) -> bool:
|
||||
return "context" in model.lower()
|
||||
@@ -0,0 +1,5 @@
|
||||
"""
|
||||
Voyage AI Rerank Handler
|
||||
|
||||
HTTP calling is handled by `litellm/llms/custom_httpx/llm_http_handler.py`
|
||||
"""
|
||||
@@ -0,0 +1,175 @@
|
||||
"""
|
||||
Transformation logic for Voyage AI's /v1/rerank endpoint.
|
||||
|
||||
Docs - https://docs.voyageai.com/docs/reranker
|
||||
"""
|
||||
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import httpx
|
||||
|
||||
from litellm.llms.base_llm.chat.transformation import LiteLLMLoggingObj
|
||||
from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
from litellm.types.rerank import (
|
||||
RerankBilledUnits,
|
||||
RerankResponse,
|
||||
RerankResponseMeta,
|
||||
RerankTokens,
|
||||
)
|
||||
from litellm.types.utils import ModelInfo
|
||||
|
||||
from ..embedding.transformation import VoyageError
|
||||
|
||||
|
||||
class VoyageRerankConfig(BaseRerankConfig):
|
||||
def get_supported_cohere_rerank_params(self, model: str) -> list:
|
||||
return ["query", "documents", "top_n", "return_documents"]
|
||||
|
||||
def map_cohere_rerank_params(
|
||||
self,
|
||||
non_default_params: dict,
|
||||
model: str,
|
||||
drop_params: bool,
|
||||
query: str,
|
||||
documents: List[Union[str, Dict[str, Any]]],
|
||||
custom_llm_provider: Optional[str] = None,
|
||||
top_n: Optional[int] = None,
|
||||
rank_fields: Optional[List[str]] = None,
|
||||
return_documents: Optional[bool] = True,
|
||||
max_chunks_per_doc: Optional[int] = None,
|
||||
max_tokens_per_doc: Optional[int] = None,
|
||||
) -> Dict:
|
||||
# Voyage AI uses 'top_k' instead of 'top_n'
|
||||
optional_params: Dict[str, Any] = {"query": query, "documents": documents}
|
||||
if top_n is not None:
|
||||
optional_params["top_k"] = top_n
|
||||
if return_documents is not None:
|
||||
optional_params["return_documents"] = return_documents
|
||||
# Return as dict - OptionalRerankParams is a TypedDict with total=False
|
||||
# so all fields are optional and we can return the dict directly
|
||||
return optional_params
|
||||
|
||||
def get_complete_url(
|
||||
self,
|
||||
api_base: Optional[str],
|
||||
model: str,
|
||||
optional_params: Optional[dict] = None,
|
||||
) -> str:
|
||||
if api_base is None:
|
||||
return "https://api.voyageai.com/v1/rerank"
|
||||
api_base = api_base.rstrip("/")
|
||||
if not api_base.endswith("/v1/rerank"):
|
||||
if api_base.endswith("/v1"):
|
||||
api_base = f"{api_base}/rerank"
|
||||
else:
|
||||
api_base = f"{api_base}/v1/rerank"
|
||||
return api_base
|
||||
|
||||
def transform_rerank_request(
|
||||
self, model: str, optional_rerank_params: Dict, headers: Dict
|
||||
) -> Dict:
|
||||
return {"model": model, **optional_rerank_params}
|
||||
|
||||
def transform_rerank_response(
|
||||
self,
|
||||
model: str,
|
||||
raw_response: httpx.Response,
|
||||
model_response: RerankResponse,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
api_key: Optional[str] = None,
|
||||
request_data: Dict = {},
|
||||
optional_params: Dict = {},
|
||||
litellm_params: Dict = {},
|
||||
) -> RerankResponse:
|
||||
if raw_response.status_code != 200:
|
||||
raise VoyageError(
|
||||
message=raw_response.text, status_code=raw_response.status_code
|
||||
)
|
||||
|
||||
logging_obj.post_call(original_response=raw_response.text)
|
||||
|
||||
try:
|
||||
_json_response = raw_response.json()
|
||||
except Exception:
|
||||
raise VoyageError(
|
||||
message=f"Failed to parse response: {raw_response.text}",
|
||||
status_code=raw_response.status_code,
|
||||
)
|
||||
|
||||
# Voyage AI returns results in "data" key, not "results"
|
||||
_results: Optional[List[dict]] = _json_response.get("data")
|
||||
if _results is None:
|
||||
raise ValueError(f"No results found in the response={_json_response}")
|
||||
|
||||
# Transform to LiteLLM format
|
||||
transformed_results = []
|
||||
for result in _results:
|
||||
transformed_result: Dict[str, Any] = {
|
||||
"index": result["index"],
|
||||
"relevance_score": result["relevance_score"],
|
||||
}
|
||||
if "document" in result:
|
||||
if isinstance(result["document"], str):
|
||||
transformed_result["document"] = {"text": result["document"]}
|
||||
else:
|
||||
transformed_result["document"] = result["document"]
|
||||
transformed_results.append(transformed_result)
|
||||
|
||||
usage = _json_response.get("usage", {})
|
||||
total_tokens = usage.get("total_tokens", 0)
|
||||
_billed_units = RerankBilledUnits(total_tokens=total_tokens)
|
||||
_tokens = RerankTokens(input_tokens=total_tokens, output_tokens=0)
|
||||
rerank_meta = RerankResponseMeta(billed_units=_billed_units, tokens=_tokens)
|
||||
|
||||
return RerankResponse(
|
||||
id=_json_response.get("id", f"voyage-rerank-{model}"),
|
||||
results=transformed_results, # type: ignore
|
||||
meta=rerank_meta,
|
||||
)
|
||||
|
||||
def validate_environment(
|
||||
self,
|
||||
headers: Dict,
|
||||
model: str,
|
||||
api_key: Optional[str] = None,
|
||||
optional_params: Optional[dict] = None,
|
||||
) -> Dict:
|
||||
if api_key is None:
|
||||
api_key = get_secret_str("VOYAGE_API_KEY") or get_secret_str(
|
||||
"VOYAGE_AI_API_KEY"
|
||||
)
|
||||
if api_key is None:
|
||||
raise ValueError(
|
||||
"Voyage AI API key is required. Set via `api_key` parameter or `VOYAGE_API_KEY` env var."
|
||||
)
|
||||
return {
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"content-type": "application/json",
|
||||
}
|
||||
|
||||
def calculate_rerank_cost(
|
||||
self,
|
||||
model: str,
|
||||
custom_llm_provider: Optional[str] = None,
|
||||
billed_units: Optional[RerankBilledUnits] = None,
|
||||
model_info: Optional[ModelInfo] = None,
|
||||
) -> Tuple[float, float]:
|
||||
if (
|
||||
model_info is None
|
||||
or "input_cost_per_token" not in model_info
|
||||
or model_info["input_cost_per_token"] is None
|
||||
or billed_units is None
|
||||
):
|
||||
return 0.0, 0.0
|
||||
total_tokens = billed_units.get("total_tokens")
|
||||
if total_tokens is None:
|
||||
return 0.0, 0.0
|
||||
return model_info["input_cost_per_token"] * total_tokens, 0.0
|
||||
|
||||
def get_error_class(
|
||||
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
|
||||
):
|
||||
return VoyageError(
|
||||
message=error_message, status_code=status_code, headers=headers
|
||||
)
|
||||
Reference in New Issue
Block a user