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30f6421760
### What problem does this PR solve? Use consistent log file names, introduced initLogger ### Type of change - [ ] Bug Fix (non-breaking change which fixes an issue) - [ ] New Feature (non-breaking change which adds functionality) - [ ] Documentation Update - [x] Refactoring - [ ] Performance Improvement - [ ] Other (please describe):
117 lines
5.2 KiB
Python
117 lines
5.2 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import re
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from concurrent.futures import ThreadPoolExecutor, ALL_COMPLETED, wait
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from threading import Lock
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from typing import Tuple
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import umap
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import numpy as np
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from sklearn.mixture import GaussianMixture
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from rag.utils import truncate
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class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
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def __init__(self, max_cluster, llm_model, embd_model, prompt, max_token=256, threshold=0.1):
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self._max_cluster = max_cluster
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self._llm_model = llm_model
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self._embd_model = embd_model
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self._threshold = threshold
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self._prompt = prompt
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self._max_token = max_token
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def _get_optimal_clusters(self, embeddings: np.ndarray, random_state:int):
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max_clusters = min(self._max_cluster, len(embeddings))
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n_clusters = np.arange(1, max_clusters)
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bics = []
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for n in n_clusters:
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gm = GaussianMixture(n_components=n, random_state=random_state)
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gm.fit(embeddings)
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bics.append(gm.bic(embeddings))
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optimal_clusters = n_clusters[np.argmin(bics)]
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return optimal_clusters
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def __call__(self, chunks: Tuple[str, np.ndarray], random_state, callback=None):
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layers = [(0, len(chunks))]
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start, end = 0, len(chunks)
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if len(chunks) <= 1: return
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chunks = [(s, a) for s, a in chunks if len(a) > 0]
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def summarize(ck_idx, lock):
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nonlocal chunks
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try:
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texts = [chunks[i][0] for i in ck_idx]
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len_per_chunk = int((self._llm_model.max_length - self._max_token)/len(texts))
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cluster_content = "\n".join([truncate(t, max(1, len_per_chunk)) for t in texts])
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cnt = self._llm_model.chat("You're a helpful assistant.",
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[{"role": "user", "content": self._prompt.format(cluster_content=cluster_content)}],
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{"temperature": 0.3, "max_tokens": self._max_token}
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)
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cnt = re.sub("(······\n由于长度的原因，回答被截断了，要继续吗？|For the content length reason, it stopped, continue?)", "", cnt)
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logging.debug(f"SUM: {cnt}")
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embds, _ = self._embd_model.encode([cnt])
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with lock:
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if not len(embds[0]): return
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chunks.append((cnt, embds[0]))
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except Exception as e:
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logging.exception("summarize got exception")
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return e
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labels = []
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while end - start > 1:
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embeddings = [embd for _, embd in chunks[start: end]]
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if len(embeddings) == 2:
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summarize([start, start+1], Lock())
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if callback:
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callback(msg="Cluster one layer: {} -> {}".format(end-start, len(chunks)-end))
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labels.extend([0,0])
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layers.append((end, len(chunks)))
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start = end
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end = len(chunks)
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continue
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n_neighbors = int((len(embeddings) - 1) ** 0.8)
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reduced_embeddings = umap.UMAP(
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n_neighbors=max(2, n_neighbors), n_components=min(12, len(embeddings)-2), metric="cosine"
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).fit_transform(embeddings)
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n_clusters = self._get_optimal_clusters(reduced_embeddings, random_state)
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if n_clusters == 1:
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lbls = [0 for _ in range(len(reduced_embeddings))]
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else:
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gm = GaussianMixture(n_components=n_clusters, random_state=random_state)
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gm.fit(reduced_embeddings)
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probs = gm.predict_proba(reduced_embeddings)
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lbls = [np.where(prob > self._threshold)[0] for prob in probs]
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lbls = [lbl[0] if isinstance(lbl, np.ndarray) else lbl for lbl in lbls]
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lock = Lock()
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with ThreadPoolExecutor(max_workers=12) as executor:
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threads = []
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for c in range(n_clusters):
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ck_idx = [i+start for i in range(len(lbls)) if lbls[i] == c]
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threads.append(executor.submit(summarize, ck_idx, lock))
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wait(threads, return_when=ALL_COMPLETED)
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logging.debug(str([t.result() for t in threads]))
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assert len(chunks) - end == n_clusters, "{} vs. {}".format(len(chunks) - end, n_clusters)
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labels.extend(lbls)
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layers.append((end, len(chunks)))
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if callback:
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callback(msg="Cluster one layer: {} -> {}".format(end-start, len(chunks)-end))
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start = end
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end = len(chunks)
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