File size: 6,628 Bytes
b9fe2b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 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 |
#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import logging
import re
import umap
import numpy as np
from sklearn.mixture import GaussianMixture
import trio
from graphrag.utils import (
get_llm_cache,
get_embed_cache,
set_embed_cache,
set_llm_cache,
chat_limiter,
)
from rag.utils import truncate
class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
def __init__(
self, max_cluster, llm_model, embd_model, prompt, max_token=512, threshold=0.1
):
self._max_cluster = max_cluster
self._llm_model = llm_model
self._embd_model = embd_model
self._threshold = threshold
self._prompt = prompt
self._max_token = max_token
async def _chat(self, system, history, gen_conf):
response = get_llm_cache(self._llm_model.llm_name, system, history, gen_conf)
if response:
return response
response = await trio.to_thread.run_sync(
lambda: self._llm_model.chat(system, history, gen_conf)
)
response = re.sub(r"<think>.*</think>", "", response, flags=re.DOTALL)
if response.find("**ERROR**") >= 0:
raise Exception(response)
set_llm_cache(self._llm_model.llm_name, system, response, history, gen_conf)
return response
async def _embedding_encode(self, txt):
response = get_embed_cache(self._embd_model.llm_name, txt)
if response is not None:
return response
embds, _ = await trio.to_thread.run_sync(lambda: self._embd_model.encode([txt]))
if len(embds) < 1 or len(embds[0]) < 1:
raise Exception("Embedding error: ")
embds = embds[0]
set_embed_cache(self._embd_model.llm_name, txt, embds)
return embds
def _get_optimal_clusters(self, embeddings: np.ndarray, random_state: int):
max_clusters = min(self._max_cluster, len(embeddings))
n_clusters = np.arange(1, max_clusters)
bics = []
for n in n_clusters:
gm = GaussianMixture(n_components=n, random_state=random_state)
gm.fit(embeddings)
bics.append(gm.bic(embeddings))
optimal_clusters = n_clusters[np.argmin(bics)]
return optimal_clusters
async def __call__(self, chunks, random_state, callback=None):
layers = [(0, len(chunks))]
start, end = 0, len(chunks)
if len(chunks) <= 1:
return []
chunks = [(s, a) for s, a in chunks if s and len(a) > 0]
async def summarize(ck_idx: list[int]):
nonlocal chunks
texts = [chunks[i][0] for i in ck_idx]
len_per_chunk = int(
(self._llm_model.max_length - self._max_token) / len(texts)
)
cluster_content = "\n".join(
[truncate(t, max(1, len_per_chunk)) for t in texts]
)
async with chat_limiter:
cnt = await self._chat(
"You're a helpful assistant.",
[
{
"role": "user",
"content": self._prompt.format(
cluster_content=cluster_content
),
}
],
{"temperature": 0.3, "max_tokens": self._max_token},
)
cnt = re.sub(
"(路路路路路路\n鐢变簬闀垮害鐨勫師鍥狅紝鍥炵瓟琚埅鏂簡锛岃缁х画鍚楋紵|For the content length reason, it stopped, continue?)",
"",
cnt,
)
logging.debug(f"SUM: {cnt}")
embds = await self._embedding_encode(cnt)
chunks.append((cnt, embds))
labels = []
while end - start > 1:
embeddings = [embd for _, embd in chunks[start:end]]
if len(embeddings) == 2:
await summarize([start, start + 1])
if callback:
callback(
msg="Cluster one layer: {} -> {}".format(
end - start, len(chunks) - end
)
)
labels.extend([0, 0])
layers.append((end, len(chunks)))
start = end
end = len(chunks)
continue
n_neighbors = int((len(embeddings) - 1) ** 0.8)
reduced_embeddings = umap.UMAP(
n_neighbors=max(2, n_neighbors),
n_components=min(12, len(embeddings) - 2),
metric="cosine",
).fit_transform(embeddings)
n_clusters = self._get_optimal_clusters(reduced_embeddings, random_state)
if n_clusters == 1:
lbls = [0 for _ in range(len(reduced_embeddings))]
else:
gm = GaussianMixture(n_components=n_clusters, random_state=random_state)
gm.fit(reduced_embeddings)
probs = gm.predict_proba(reduced_embeddings)
lbls = [np.where(prob > self._threshold)[0] for prob in probs]
lbls = [lbl[0] if isinstance(lbl, np.ndarray) else lbl for lbl in lbls]
async with trio.open_nursery() as nursery:
for c in range(n_clusters):
ck_idx = [i + start for i in range(len(lbls)) if lbls[i] == c]
assert len(ck_idx) > 0
async with chat_limiter:
nursery.start_soon(lambda: summarize(ck_idx))
assert len(chunks) - end == n_clusters, "{} vs. {}".format(
len(chunks) - end, n_clusters
)
labels.extend(lbls)
layers.append((end, len(chunks)))
if callback:
callback(
msg="Cluster one layer: {} -> {}".format(
end - start, len(chunks) - end
)
)
start = end
end = len(chunks)
return chunks
|