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 | 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 = {}
text_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)
):
text = node_with_score.node.get_content()
text_to_node[text] = node_with_score
if text not in fused_scores:
fused_scores[text] = 0.0
fused_scores[text] += 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 text, score in reranked_results.items():
reranked_nodes.append(text_to_node[text])
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 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]
node_with_score.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:
text = node_with_score.node.get_content()
if text in all_nodes:
all_nodes[text].score += node_with_score.score
else:
all_nodes[text] = 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:
text = node_with_score.node.get_content()
if text in all_nodes:
max_score = max(node_with_score.score, all_nodes[text].score)
all_nodes[text].score = max_score
else:
all_nodes[text] = 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}")
|