File size: 6,418 Bytes
2436df2 8bc2fc9 2436df2 758538f 22fe41e 2436df2 0d756a3 2436df2 758538f 9c8f077 2436df2 9c8f077 2436df2 0404a52 ab87187 2436df2 9c8f077 2436df2 758538f 9c8f077 8bc2fc9 2436df2 758538f 2436df2 8bc2fc9 2436df2 9c8f077 2436df2 9c8f077 2436df2 9c8f077 2436df2 75a07ce 2436df2 9c8f077 2436df2 4ac524c 8bc2fc9 2436df2 9c8f077 2436df2 9c8f077 |
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 |
#
# 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
from concurrent.futures import ThreadPoolExecutor, ALL_COMPLETED, wait
from threading import Lock
import umap
import numpy as np
from sklearn.mixture import GaussianMixture
from graphrag.utils import get_llm_cache, get_embed_cache, set_embed_cache, set_llm_cache
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
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 = self._llm_model.chat(system, history, gen_conf)
if response.find("**ERROR**") >= 0:
raise Exception(response)
set_llm_cache(self._llm_model.llm_name, system, response, history, gen_conf)
return response
def _embedding_encode(self, txt):
response = get_embed_cache(self._embd_model.llm_name, txt)
if response:
return response
embds, _ = 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
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 len(a) > 0]
def summarize(ck_idx, lock):
nonlocal chunks
try:
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])
cnt = 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, _ = self._embd_model.encode([cnt])
with lock:
chunks.append((cnt, self._embedding_encode(cnt)))
except Exception as e:
logging.exception("summarize got exception")
return e
labels = []
while end - start > 1:
embeddings = [embd for _, embd in chunks[start: end]]
if len(embeddings) == 2:
summarize([start, start + 1], Lock())
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]
lock = Lock()
with ThreadPoolExecutor(max_workers=12) as executor:
threads = []
for c in range(n_clusters):
ck_idx = [i + start for i in range(len(lbls)) if lbls[i] == c]
threads.append(executor.submit(summarize, ck_idx, lock))
wait(threads, return_when=ALL_COMPLETED)
for th in threads:
if isinstance(th.result(), Exception):
raise th.result()
logging.debug(str([t.result() for t in threads]))
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
|