"""
Ensemble retriever that ensemble the results of
multiple retrievers by using weighted Reciprocal Rank Fusion
"""
import asyncio
from collections import defaultdict
from collections.abc import Hashable
from itertools import chain
from typing import (
Any,
Callable,
Dict,
Iterable,
Iterator,
List,
Optional,
TypeVar,
cast,
)
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever, RetrieverLike
from langchain_core.runnables import RunnableConfig
from langchain_core.runnables.config import ensure_config, patch_config
from langchain_core.runnables.utils import (
ConfigurableFieldSpec,
get_unique_config_specs,
)
from pydantic import model_validator
T = TypeVar("T")
H = TypeVar("H", bound=Hashable)
[docs]
def unique_by_key(iterable: Iterable[T], key: Callable[[T], H]) -> Iterator[T]:
"""Yield unique elements of an iterable based on a key function.
Args:
iterable: The iterable to filter.
key: A function that returns a hashable key for each element.
Yields:
Unique elements of the iterable based on the key function.
"""
seen = set()
for e in iterable:
if (k := key(e)) not in seen:
seen.add(k)
yield e
[docs]
class EnsembleRetriever(BaseRetriever):
"""Retriever that ensembles the multiple retrievers.
It uses a rank fusion.
Args:
retrievers: A list of retrievers to ensemble.
weights: A list of weights corresponding to the retrievers. Defaults to equal
weighting for all retrievers.
c: A constant added to the rank, controlling the balance between the importance
of high-ranked items and the consideration given to lower-ranked items.
Default is 60.
id_key: The key in the document's metadata used to determine unique documents.
If not specified, page_content is used.
"""
retrievers: List[RetrieverLike]
weights: List[float]
c: int = 60
id_key: Optional[str] = None
@property
def config_specs(self) -> List[ConfigurableFieldSpec]:
"""List configurable fields for this runnable."""
return get_unique_config_specs(
spec for retriever in self.retrievers for spec in retriever.config_specs
)
@model_validator(mode="before")
@classmethod
def set_weights(cls, values: Dict[str, Any]) -> Any:
if not values.get("weights"):
n_retrievers = len(values["retrievers"])
values["weights"] = [1 / n_retrievers] * n_retrievers
return values
[docs]
def invoke(
self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any
) -> List[Document]:
from langchain_core.callbacks import CallbackManager
config = ensure_config(config)
callback_manager = CallbackManager.configure(
config.get("callbacks"),
None,
verbose=kwargs.get("verbose", False),
inheritable_tags=config.get("tags", []),
local_tags=self.tags,
inheritable_metadata=config.get("metadata", {}),
local_metadata=self.metadata,
)
run_manager = callback_manager.on_retriever_start(
None,
input,
name=config.get("run_name") or self.get_name(),
**kwargs,
)
try:
result = self.rank_fusion(input, run_manager=run_manager, config=config)
except Exception as e:
run_manager.on_retriever_error(e)
raise e
else:
run_manager.on_retriever_end(
result,
**kwargs,
)
return result
[docs]
async def ainvoke(
self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any
) -> List[Document]:
from langchain_core.callbacks import AsyncCallbackManager
config = ensure_config(config)
callback_manager = AsyncCallbackManager.configure(
config.get("callbacks"),
None,
verbose=kwargs.get("verbose", False),
inheritable_tags=config.get("tags", []),
local_tags=self.tags,
inheritable_metadata=config.get("metadata", {}),
local_metadata=self.metadata,
)
run_manager = await callback_manager.on_retriever_start(
None,
input,
name=config.get("run_name") or self.get_name(),
**kwargs,
)
try:
result = await self.arank_fusion(
input, run_manager=run_manager, config=config
)
except Exception as e:
await run_manager.on_retriever_error(e)
raise e
else:
await run_manager.on_retriever_end(
result,
**kwargs,
)
return result
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
"""
Get the relevant documents for a given query.
Args:
query: The query to search for.
Returns:
A list of reranked documents.
"""
# Get fused result of the retrievers.
fused_documents = self.rank_fusion(query, run_manager)
return fused_documents
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
) -> List[Document]:
"""
Asynchronously get the relevant documents for a given query.
Args:
query: The query to search for.
Returns:
A list of reranked documents.
"""
# Get fused result of the retrievers.
fused_documents = await self.arank_fusion(query, run_manager)
return fused_documents
[docs]
def rank_fusion(
self,
query: str,
run_manager: CallbackManagerForRetrieverRun,
*,
config: Optional[RunnableConfig] = None,
) -> List[Document]:
"""
Retrieve the results of the retrievers and use rank_fusion_func to get
the final result.
Args:
query: The query to search for.
Returns:
A list of reranked documents.
"""
# Get the results of all retrievers.
retriever_docs = [
retriever.invoke(
query,
patch_config(
config, callbacks=run_manager.get_child(tag=f"retriever_{i+1}")
),
)
for i, retriever in enumerate(self.retrievers)
]
# Enforce that retrieved docs are Documents for each list in retriever_docs
for i in range(len(retriever_docs)):
retriever_docs[i] = [
Document(page_content=cast(str, doc)) if isinstance(doc, str) else doc
for doc in retriever_docs[i]
]
# apply rank fusion
fused_documents = self.weighted_reciprocal_rank(retriever_docs)
return fused_documents
[docs]
async def arank_fusion(
self,
query: str,
run_manager: AsyncCallbackManagerForRetrieverRun,
*,
config: Optional[RunnableConfig] = None,
) -> List[Document]:
"""
Asynchronously retrieve the results of the retrievers
and use rank_fusion_func to get the final result.
Args:
query: The query to search for.
Returns:
A list of reranked documents.
"""
# Get the results of all retrievers.
retriever_docs = await asyncio.gather(
*[
retriever.ainvoke(
query,
patch_config(
config, callbacks=run_manager.get_child(tag=f"retriever_{i+1}")
),
)
for i, retriever in enumerate(self.retrievers)
]
)
# Enforce that retrieved docs are Documents for each list in retriever_docs
for i in range(len(retriever_docs)):
retriever_docs[i] = [
Document(page_content=doc) if not isinstance(doc, Document) else doc # type: ignore[arg-type]
for doc in retriever_docs[i]
]
# apply rank fusion
fused_documents = self.weighted_reciprocal_rank(retriever_docs)
return fused_documents
[docs]
def weighted_reciprocal_rank(
self, doc_lists: List[List[Document]]
) -> List[Document]:
"""
Perform weighted Reciprocal Rank Fusion on multiple rank lists.
You can find more details about RRF here:
https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf
Args:
doc_lists: A list of rank lists, where each rank list contains unique items.
Returns:
list: The final aggregated list of items sorted by their weighted RRF
scores in descending order.
"""
if len(doc_lists) != len(self.weights):
raise ValueError(
"Number of rank lists must be equal to the number of weights."
)
# Associate each doc's content with its RRF score for later sorting by it
# Duplicated contents across retrievers are collapsed & scored cumulatively
rrf_score: Dict[str, float] = defaultdict(float)
for doc_list, weight in zip(doc_lists, self.weights):
for rank, doc in enumerate(doc_list, start=1):
rrf_score[
(
doc.page_content
if self.id_key is None
else doc.metadata[self.id_key]
)
] += weight / (rank + self.c)
# Docs are deduplicated by their contents then sorted by their scores
all_docs = chain.from_iterable(doc_lists)
sorted_docs = sorted(
unique_by_key(
all_docs,
lambda doc: (
doc.page_content
if self.id_key is None
else doc.metadata[self.id_key]
),
),
reverse=True,
key=lambda doc: rrf_score[
doc.page_content if self.id_key is None else doc.metadata[self.id_key]
],
)
return sorted_docs
