import logging
import math
import os
from copy import copy
from typing import Any
import litellm
import requests
from aiohttp import ClientError, ClientSession
from litellm import AuthenticationError, aembedding, embedding
from core.base import (
ChunkSearchResult,
EmbeddingConfig,
EmbeddingProvider,
EmbeddingPurpose,
R2RException,
)
logger = logging.getLogger()
class LiteLLMEmbeddingProvider(EmbeddingProvider):
def __init__(
self,
config: EmbeddingConfig,
*args,
**kwargs,
) -> None:
super().__init__(config)
self.litellm_embedding = embedding
self.litellm_aembedding = aembedding
provider = config.provider
if not provider:
raise ValueError(
"Must set provider in order to initialize `LiteLLMEmbeddingProvider`."
)
if provider != "litellm":
raise ValueError(
"LiteLLMEmbeddingProvider must be initialized with provider `litellm`."
)
self.rerank_url = None
if config.rerank_model:
if "huggingface" not in config.rerank_model:
raise ValueError(
"LiteLLMEmbeddingProvider only supports re-ranking via the HuggingFace text-embeddings-inference API"
)
url = os.getenv("HUGGINGFACE_API_BASE") or config.rerank_url
if not url:
raise ValueError(
"LiteLLMEmbeddingProvider requires a valid reranking API url to be set via `embedding.rerank_url` in the r2r.toml, or via the environment variable `HUGGINGFACE_API_BASE`."
)
self.rerank_url = url
self.base_model = config.base_model
if "amazon" in self.base_model:
logger.warn("Amazon embedding model detected, dropping params")
litellm.drop_params = True
self.base_dimension = config.base_dimension
def _get_embedding_kwargs(self, **kwargs):
embedding_kwargs = {
"model": self.base_model,
"dimensions": self.base_dimension,
}
embedding_kwargs.update(kwargs)
return embedding_kwargs
async def _execute_task(self, task: dict[str, Any]) -> list[list[float]]:
texts = task["texts"]
kwargs = self._get_embedding_kwargs(**task.get("kwargs", {}))
if "dimensions" in kwargs and math.isnan(kwargs["dimensions"]):
kwargs.pop("dimensions")
logger.warning("Dropping nan dimensions from kwargs")
try:
response = await self.litellm_aembedding(
input=texts,
**kwargs,
)
return [data["embedding"] for data in response.data]
except AuthenticationError:
logger.error(
"Authentication error: Invalid API key or credentials."
)
raise
except Exception as e:
error_msg = f"Error getting embeddings: {str(e)}"
logger.error(error_msg)
raise R2RException(error_msg, 400) from e
def _execute_task_sync(self, task: dict[str, Any]) -> list[list[float]]:
texts = task["texts"]
kwargs = self._get_embedding_kwargs(**task.get("kwargs", {}))
try:
response = self.litellm_embedding(
input=texts,
**kwargs,
)
return [data["embedding"] for data in response.data]
except AuthenticationError:
logger.error(
"Authentication error: Invalid API key or credentials."
)
raise
except Exception as e:
error_msg = f"Error getting embeddings: {str(e)}"
logger.error(error_msg)
raise R2RException(error_msg, 400) from e
async def async_get_embedding(
self,
text: str,
stage: EmbeddingProvider.Step = EmbeddingProvider.Step.BASE,
purpose: EmbeddingPurpose = EmbeddingPurpose.INDEX,
**kwargs,
) -> list[float]:
if stage != EmbeddingProvider.Step.BASE:
raise ValueError(
"LiteLLMEmbeddingProvider only supports search stage."
)
task = {
"texts": [text],
"stage": stage,
"purpose": purpose,
"kwargs": kwargs,
}
return (await self._execute_with_backoff_async(task))[0]
def get_embedding(
self,
text: str,
stage: EmbeddingProvider.Step = EmbeddingProvider.Step.BASE,
purpose: EmbeddingPurpose = EmbeddingPurpose.INDEX,
**kwargs,
) -> list[float]:
if stage != EmbeddingProvider.Step.BASE:
raise ValueError(
"Error getting embeddings: LiteLLMEmbeddingProvider only supports search stage."
)
task = {
"texts": [text],
"stage": stage,
"purpose": purpose,
"kwargs": kwargs,
}
return self._execute_with_backoff_sync(task)[0]
async def async_get_embeddings(
self,
texts: list[str],
stage: EmbeddingProvider.Step = EmbeddingProvider.Step.BASE,
purpose: EmbeddingPurpose = EmbeddingPurpose.INDEX,
**kwargs,
) -> list[list[float]]:
if stage != EmbeddingProvider.Step.BASE:
raise ValueError(
"LiteLLMEmbeddingProvider only supports search stage."
)
task = {
"texts": texts,
"stage": stage,
"purpose": purpose,
"kwargs": kwargs,
}
return await self._execute_with_backoff_async(task)
def get_embeddings(
self,
texts: list[str],
stage: EmbeddingProvider.Step = EmbeddingProvider.Step.BASE,
purpose: EmbeddingPurpose = EmbeddingPurpose.INDEX,
**kwargs,
) -> list[list[float]]:
if stage != EmbeddingProvider.Step.BASE:
raise ValueError(
"LiteLLMEmbeddingProvider only supports search stage."
)
task = {
"texts": texts,
"stage": stage,
"purpose": purpose,
"kwargs": kwargs,
}
return self._execute_with_backoff_sync(task)
def rerank(
self,
query: str,
results: list[ChunkSearchResult],
stage: EmbeddingProvider.Step = EmbeddingProvider.Step.RERANK,
limit: int = 10,
):
if self.config.rerank_model is not None:
if not self.rerank_url:
raise ValueError(
"Error, `rerank_url` was expected to be set inside LiteLLMEmbeddingProvider"
)
texts = [result.text for result in results]
payload = {
"query": query,
"texts": texts,
"model-id": self.config.rerank_model.split("huggingface/")[1],
}
headers = {"Content-Type": "application/json"}
try:
response = requests.post(
self.rerank_url, json=payload, headers=headers
)
response.raise_for_status()
reranked_results = response.json()
# Copy reranked results into new array
scored_results = []
for rank_info in reranked_results:
original_result = results[rank_info["index"]]
copied_result = copy(original_result)
# Inject the reranking score into the result object
copied_result.score = rank_info["score"]
scored_results.append(copied_result)
# Return only the ChunkSearchResult objects, limited to specified count
return scored_results[:limit]
except requests.RequestException as e:
logger.error(f"Error during reranking: {str(e)}")
# Fall back to returning the original results if reranking fails
return results[:limit]
else:
return results[:limit]
async def arerank(
self,
query: str,
results: list[ChunkSearchResult],
stage: EmbeddingProvider.Step = EmbeddingProvider.Step.RERANK,
limit: int = 10,
) -> list[ChunkSearchResult]:
"""Asynchronously rerank search results using the configured rerank
model.
Args:
query: The search query string
results: List of ChunkSearchResult objects to rerank
limit: Maximum number of results to return
Returns:
List of reranked ChunkSearchResult objects, limited to specified count
"""
if self.config.rerank_model is not None:
if not self.rerank_url:
raise ValueError(
"Error, `rerank_url` was expected to be set inside LiteLLMEmbeddingProvider"
)
texts = [result.text for result in results]
payload = {
"query": query,
"texts": texts,
"model-id": self.config.rerank_model.split("huggingface/")[1],
}
headers = {"Content-Type": "application/json"}
try:
async with ClientSession() as session:
async with session.post(
self.rerank_url, json=payload, headers=headers
) as response:
response.raise_for_status()
reranked_results = await response.json()
# Copy reranked results into new array
scored_results = []
for rank_info in reranked_results:
original_result = results[rank_info["index"]]
copied_result = copy(original_result)
# Inject the reranking score into the result object
copied_result.score = rank_info["score"]
scored_results.append(copied_result)
# Return only the ChunkSearchResult objects, limited to specified count
return scored_results[:limit]
except (ClientError, Exception) as e:
logger.error(f"Error during async reranking: {str(e)}")
# Fall back to returning the original results if reranking fails
return results[:limit]
else:
return results[:limit]
