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304 | class QueryFusionRetriever(BaseRetriever):
def __init__(
self,
retrievers: List[BaseRetriever],
llm: Optional[LLMType] = None,
query_gen_prompt: Optional[str] = None,
mode: FUSION_MODES = FUSION_MODES.SIMPLE,
similarity_top_k: int = DEFAULT_SIMILARITY_TOP_K,
num_queries: int = 4,
use_async: bool = True,
verbose: bool = False,
callback_manager: Optional[CallbackManager] = None,
objects: Optional[List[IndexNode]] = None,
object_map: Optional[dict] = None,
retriever_weights: Optional[List[float]] = None,
) -> None:
self.num_queries = num_queries
self.query_gen_prompt = query_gen_prompt or QUERY_GEN_PROMPT
self.similarity_top_k = similarity_top_k
self.mode = mode
self.use_async = use_async
self._retrievers = retrievers
if retriever_weights is None:
self._retriever_weights = [1.0 / len(retrievers)] * len(retrievers)
else:
# Sum of retriever_weights must be 1
total_weight = sum(retriever_weights)
self._retriever_weights = [w / total_weight for w in retriever_weights]
self._llm = (
resolve_llm(llm, callback_manager=callback_manager) if llm else Settings.llm
)
super().__init__(
callback_manager=callback_manager,
object_map=object_map,
objects=objects,
verbose=verbose,
)
def _get_prompts(self) -> PromptDictType:
"""Get prompts."""
return {"query_gen_prompt": PromptTemplate(self.query_gen_prompt)}
def _update_prompts(self, prompts: PromptDictType) -> None:
"""Update prompts."""
if "query_gen_prompt" in prompts:
self.query_gen_prompt = cast(
PromptTemplate, prompts["query_gen_prompt"]
).template
def _get_queries(self, original_query: str) -> List[QueryBundle]:
prompt_str = self.query_gen_prompt.format(
num_queries=self.num_queries - 1,
query=original_query,
)
response = self._llm.complete(prompt_str)
# assume LLM proper put each query on a newline
queries = response.text.split("\n")
queries = [q.strip() for q in queries if q.strip()]
if self._verbose:
queries_str = "\n".join(queries)
print(f"Generated queries:\n{queries_str}")
# The LLM often returns more queries than we asked for, so trim the list.
return [QueryBundle(q) for q in queries[: self.num_queries - 1]]
def _reciprocal_rerank_fusion(
self, results: Dict[Tuple[str, int], List[NodeWithScore]]
) -> List[NodeWithScore]:
"""
Apply reciprocal rank fusion.
The original paper uses k=60 for best results:
https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf
"""
k = 60.0 # `k` is a parameter used to control the impact of outlier rankings.
fused_scores = {}
hash_to_node = {}
# compute reciprocal rank scores
for nodes_with_scores in results.values():
for rank, node_with_score in enumerate(
sorted(nodes_with_scores, key=lambda x: x.score or 0.0, reverse=True)
):
hash = node_with_score.node.hash
hash_to_node[hash] = node_with_score
if hash not in fused_scores:
fused_scores[hash] = 0.0
fused_scores[hash] += 1.0 / (rank + k)
# sort results
reranked_results = dict(
sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)
)
# adjust node scores
reranked_nodes: List[NodeWithScore] = []
for hash, score in reranked_results.items():
reranked_nodes.append(hash_to_node[hash])
reranked_nodes[-1].score = score
return reranked_nodes
def _relative_score_fusion(
self,
results: Dict[Tuple[str, int], List[NodeWithScore]],
dist_based: Optional[bool] = False,
) -> List[NodeWithScore]:
"""Apply relative score fusion."""
# MinMax scale scores of each result set (highest value becomes 1, lowest becomes 0)
# then scale by the weight of the retriever
min_max_scores = {}
for query_tuple, nodes_with_scores in results.items():
if not nodes_with_scores:
min_max_scores[query_tuple] = (0.0, 0.0)
continue
scores = [
node_with_score.score or 0.0 for node_with_score in nodes_with_scores
]
if dist_based:
# Set min and max based on mean and std dev
mean_score = sum(scores) / len(scores)
std_dev = (
sum((x - mean_score) ** 2 for x in scores) / len(scores)
) ** 0.5
min_score = mean_score - 3 * std_dev
max_score = mean_score + 3 * std_dev
else:
min_score = min(scores)
max_score = max(scores)
min_max_scores[query_tuple] = (min_score, max_score)
for query_tuple, nodes_with_scores in results.items():
for node_with_score in nodes_with_scores:
min_score, max_score = min_max_scores[query_tuple]
# Scale the score to be between 0 and 1
if max_score == min_score:
node_with_score.score = 1.0 if max_score > 0 else 0.0
else:
node_with_score.score = (node_with_score.score - min_score) / (
max_score - min_score
)
# Scale by the weight of the retriever
retriever_idx = query_tuple[1]
existing_score = node_with_score.score or 0.0
node_with_score.score = (
existing_score * self._retriever_weights[retriever_idx]
)
# Divide by the number of queries
node_with_score.score /= self.num_queries
# Use a dict to de-duplicate nodes
all_nodes: Dict[str, NodeWithScore] = {}
# Sum scores for each node
for nodes_with_scores in results.values():
for node_with_score in nodes_with_scores:
hash = node_with_score.node.hash
if hash in all_nodes:
cur_score = all_nodes[hash].score or 0.0
all_nodes[hash].score = cur_score + (node_with_score.score or 0.0)
else:
all_nodes[hash] = node_with_score
return sorted(all_nodes.values(), key=lambda x: x.score or 0.0, reverse=True)
def _simple_fusion(
self, results: Dict[Tuple[str, int], List[NodeWithScore]]
) -> List[NodeWithScore]:
"""Apply simple fusion."""
# Use a dict to de-duplicate nodes
all_nodes: Dict[str, NodeWithScore] = {}
for nodes_with_scores in results.values():
for node_with_score in nodes_with_scores:
hash = node_with_score.node.hash
if hash in all_nodes:
max_score = max(
node_with_score.score or 0.0, all_nodes[hash].score or 0.0
)
all_nodes[hash].score = max_score
else:
all_nodes[hash] = node_with_score
return sorted(all_nodes.values(), key=lambda x: x.score or 0.0, reverse=True)
def _run_nested_async_queries(
self, queries: List[QueryBundle]
) -> Dict[Tuple[str, int], List[NodeWithScore]]:
tasks, task_queries = [], []
for query in queries:
for i, retriever in enumerate(self._retrievers):
tasks.append(retriever.aretrieve(query))
task_queries.append((query.query_str, i))
task_results = run_async_tasks(tasks)
results = {}
for query_tuple, query_result in zip(task_queries, task_results):
results[query_tuple] = query_result
return results
async def _run_async_queries(
self, queries: List[QueryBundle]
) -> Dict[Tuple[str, int], List[NodeWithScore]]:
tasks, task_queries = [], []
for query in queries:
for i, retriever in enumerate(self._retrievers):
tasks.append(retriever.aretrieve(query))
task_queries.append((query.query_str, i))
task_results = await asyncio.gather(*tasks)
results = {}
for query_tuple, query_result in zip(task_queries, task_results):
results[query_tuple] = query_result
return results
def _run_sync_queries(
self, queries: List[QueryBundle]
) -> Dict[Tuple[str, int], List[NodeWithScore]]:
results = {}
for query in queries:
for i, retriever in enumerate(self._retrievers):
results[(query.query_str, i)] = retriever.retrieve(query)
return results
def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
queries: List[QueryBundle] = [query_bundle]
if self.num_queries > 1:
queries.extend(self._get_queries(query_bundle.query_str))
if self.use_async:
results = self._run_nested_async_queries(queries)
else:
results = self._run_sync_queries(queries)
if self.mode == FUSION_MODES.RECIPROCAL_RANK:
return self._reciprocal_rerank_fusion(results)[: self.similarity_top_k]
elif self.mode == FUSION_MODES.RELATIVE_SCORE:
return self._relative_score_fusion(results)[: self.similarity_top_k]
elif self.mode == FUSION_MODES.DIST_BASED_SCORE:
return self._relative_score_fusion(results, dist_based=True)[
: self.similarity_top_k
]
elif self.mode == FUSION_MODES.SIMPLE:
return self._simple_fusion(results)[: self.similarity_top_k]
else:
raise ValueError(f"Invalid fusion mode: {self.mode}")
async def _aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
queries: List[QueryBundle] = [query_bundle]
if self.num_queries > 1:
queries.extend(self._get_queries(query_bundle.query_str))
results = await self._run_async_queries(queries)
if self.mode == FUSION_MODES.RECIPROCAL_RANK:
return self._reciprocal_rerank_fusion(results)[: self.similarity_top_k]
elif self.mode == FUSION_MODES.RELATIVE_SCORE:
return self._relative_score_fusion(results)[: self.similarity_top_k]
elif self.mode == FUSION_MODES.DIST_BASED_SCORE:
return self._relative_score_fusion(results, dist_based=True)[
: self.similarity_top_k
]
elif self.mode == FUSION_MODES.SIMPLE:
return self._simple_fusion(results)[: self.similarity_top_k]
else:
raise ValueError(f"Invalid fusion mode: {self.mode}")
|
