mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-01 07:00:15 +01:00
376 lines
15 KiB
Python
376 lines
15 KiB
Python
import threading
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import chromadb
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import posthog
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import torch
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import math
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import numpy as np
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import extensions.superboogav2.parameters as parameters
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from chromadb.config import Settings
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from sentence_transformers import SentenceTransformer
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from modules.logging_colors import logger
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from modules.text_generation import encode, decode
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logger.debug('Intercepting all calls to posthog.')
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posthog.capture = lambda *args, **kwargs: None
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class Collecter():
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def __init__(self):
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pass
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def add(self, texts: list[str], texts_with_context: list[str], starting_indices: list[int]):
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pass
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def get(self, search_strings: list[str], n_results: int) -> list[str]:
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pass
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def clear(self):
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pass
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class Embedder():
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def __init__(self):
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pass
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def embed(self, text: str) -> list[torch.Tensor]:
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pass
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class Info:
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def __init__(self, start_index, text_with_context, distance, id):
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self.text_with_context = text_with_context
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self.start_index = start_index
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self.distance = distance
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self.id = id
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def calculate_distance(self, other_info):
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if parameters.get_new_dist_strategy() == parameters.DIST_MIN_STRATEGY:
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# Min
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return min(self.distance, other_info.distance)
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elif parameters.get_new_dist_strategy() == parameters.DIST_HARMONIC_STRATEGY:
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# Harmonic mean
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return 2 * (self.distance * other_info.distance) / (self.distance + other_info.distance)
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elif parameters.get_new_dist_strategy() == parameters.DIST_GEOMETRIC_STRATEGY:
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# Geometric mean
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return (self.distance * other_info.distance) ** 0.5
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elif parameters.get_new_dist_strategy() == parameters.DIST_ARITHMETIC_STRATEGY:
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# Arithmetic mean
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return (self.distance + other_info.distance) / 2
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else: # Min is default
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return min(self.distance, other_info.distance)
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def merge_with(self, other_info):
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s1 = self.text_with_context
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s2 = other_info.text_with_context
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s1_start = self.start_index
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s2_start = other_info.start_index
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new_dist = self.calculate_distance(other_info)
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if self.should_merge(s1, s2, s1_start, s2_start):
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if s1_start <= s2_start:
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if s1_start + len(s1) >= s2_start + len(s2): # if s1 completely covers s2
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return Info(s1_start, s1, new_dist, self.id)
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else:
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overlap = max(0, s1_start + len(s1) - s2_start)
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return Info(s1_start, s1 + s2[overlap:], new_dist, self.id)
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else:
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if s2_start + len(s2) >= s1_start + len(s1): # if s2 completely covers s1
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return Info(s2_start, s2, new_dist, other_info.id)
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else:
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overlap = max(0, s2_start + len(s2) - s1_start)
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return Info(s2_start, s2 + s1[overlap:], new_dist, other_info.id)
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return None
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@staticmethod
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def should_merge(s1, s2, s1_start, s2_start):
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# Check if s1 and s2 are adjacent or overlapping
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s1_end = s1_start + len(s1)
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s2_end = s2_start + len(s2)
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return not (s1_end < s2_start or s2_end < s1_start)
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class ChromaCollector(Collecter):
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def __init__(self, embedder: Embedder):
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super().__init__()
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self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
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self.embedder = embedder
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self.collection = self.chroma_client.create_collection(name="context", embedding_function=self.embedder.embed)
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self.ids = []
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self.id_to_info = {}
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self.embeddings_cache = {}
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self.lock = threading.Lock() # Locking so the server doesn't break.
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def add(self, texts: list[str], texts_with_context: list[str], starting_indices: list[int], metadatas: list[dict] = None):
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with self.lock:
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assert metadatas is None or len(metadatas) == len(texts), "metadatas must be None or have the same length as texts"
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if len(texts) == 0:
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return
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new_ids = self._get_new_ids(len(texts))
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(existing_texts, existing_embeddings, existing_ids, existing_metas), \
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(non_existing_texts, non_existing_ids, non_existing_metas) = self._split_texts_by_cache_hit(texts, new_ids, metadatas)
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# If there are any already existing texts, add them all at once.
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if existing_texts:
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logger.info(f'Adding {len(existing_embeddings)} cached embeddings.')
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args = {'embeddings': existing_embeddings, 'documents': existing_texts, 'ids': existing_ids}
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if metadatas is not None:
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args['metadatas'] = existing_metas
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self.collection.add(**args)
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# If there are any non-existing texts, compute their embeddings all at once. Each call to embed has significant overhead.
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if non_existing_texts:
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non_existing_embeddings = self.embedder.embed(non_existing_texts).tolist()
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for text, embedding in zip(non_existing_texts, non_existing_embeddings):
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self.embeddings_cache[text] = embedding
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logger.info(f'Adding {len(non_existing_embeddings)} new embeddings.')
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args = {'embeddings': non_existing_embeddings, 'documents': non_existing_texts, 'ids': non_existing_ids}
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if metadatas is not None:
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args['metadatas'] = non_existing_metas
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self.collection.add(**args)
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# Create a dictionary that maps each ID to its context and starting index
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new_info = {
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id_: {'text_with_context': context, 'start_index': start_index}
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for id_, context, start_index in zip(new_ids, texts_with_context, starting_indices)
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}
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self.id_to_info.update(new_info)
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self.ids.extend(new_ids)
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def _split_texts_by_cache_hit(self, texts: list[str], new_ids: list[str], metadatas: list[dict]):
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existing_texts, non_existing_texts = [], []
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existing_embeddings = []
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existing_ids, non_existing_ids = [], []
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existing_metas, non_existing_metas = [], []
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for i, text in enumerate(texts):
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id_ = new_ids[i]
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metadata = metadatas[i] if metadatas is not None else None
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embedding = self.embeddings_cache.get(text)
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if embedding:
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existing_texts.append(text)
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existing_embeddings.append(embedding)
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existing_ids.append(id_)
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existing_metas.append(metadata)
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else:
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non_existing_texts.append(text)
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non_existing_ids.append(id_)
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non_existing_metas.append(metadata)
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return (existing_texts, existing_embeddings, existing_ids, existing_metas), \
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(non_existing_texts, non_existing_ids, non_existing_metas)
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def _get_new_ids(self, num_new_ids: int):
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if self.ids:
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max_existing_id = max(int(id_) for id_ in self.ids)
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else:
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max_existing_id = -1
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return [str(i + max_existing_id + 1) for i in range(num_new_ids)]
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def _find_min_max_start_index(self):
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max_index, min_index = 0, float('inf')
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for _, val in self.id_to_info.items():
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if val['start_index'] > max_index:
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max_index = val['start_index']
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if val['start_index'] < min_index:
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min_index = val['start_index']
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return min_index, max_index
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# NB: Does not make sense to weigh excerpts from different documents.
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# But let's say that's the user's problem. Perfect world scenario:
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# Apply time weighing to different documents. For each document, then, add
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# separate time weighing.
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def _apply_sigmoid_time_weighing(self, infos: list[Info], document_len: int, time_steepness: float, time_power: float):
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sigmoid = lambda x: 1 / (1 + np.exp(-x))
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weights = sigmoid(time_steepness * np.linspace(-10, 10, document_len))
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# Scale to [0,time_power] and shift it up to [1-time_power, 1]
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weights = weights - min(weights)
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weights = weights * (time_power / max(weights))
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weights = weights + (1 - time_power)
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# Reverse the weights
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weights = weights[::-1]
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for info in infos:
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index = info.start_index
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info.distance *= weights[index]
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def _filter_outliers_by_median_distance(self, infos: list[Info], significant_level: float):
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# Ensure there are infos to filter
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if not infos:
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return []
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# Find info with minimum distance
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min_info = min(infos, key=lambda x: x.distance)
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# Calculate median distance among infos
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median_distance = np.median([inf.distance for inf in infos])
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# Filter out infos that have a distance significantly greater than the median
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filtered_infos = [inf for inf in infos if inf.distance <= significant_level * median_distance]
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# Always include the info with minimum distance
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if min_info not in filtered_infos:
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filtered_infos.append(min_info)
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return filtered_infos
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def _merge_infos(self, infos: list[Info]):
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merged_infos = []
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current_info = infos[0]
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for next_info in infos[1:]:
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merged = current_info.merge_with(next_info)
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if merged is not None:
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current_info = merged
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else:
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merged_infos.append(current_info)
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current_info = next_info
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merged_infos.append(current_info)
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return merged_infos
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# Main function for retrieving chunks by distance. It performs merging, time weighing, and mean filtering.
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def _get_documents_ids_distances(self, search_strings: list[str], n_results: int):
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n_results = min(len(self.ids), n_results)
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if n_results == 0:
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return [], [], []
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if isinstance(search_strings, str):
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search_strings = [search_strings]
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infos = []
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min_start_index, max_start_index = self._find_min_max_start_index()
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for search_string in search_strings:
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result = self.collection.query(query_texts=search_string, n_results=math.ceil(n_results / len(search_strings)), include=['distances'])
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curr_infos = [Info(start_index=self.id_to_info[id]['start_index'],
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text_with_context=self.id_to_info[id]['text_with_context'],
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distance=distance, id=id)
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for id, distance in zip(result['ids'][0], result['distances'][0])]
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self._apply_sigmoid_time_weighing(infos=curr_infos, document_len=max_start_index - min_start_index + 1, time_steepness=parameters.get_time_steepness(), time_power=parameters.get_time_power())
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curr_infos = self._filter_outliers_by_median_distance(curr_infos, parameters.get_significant_level())
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infos.extend(curr_infos)
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infos.sort(key=lambda x: x.start_index)
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infos = self._merge_infos(infos)
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texts_with_context = [inf.text_with_context for inf in infos]
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ids = [inf.id for inf in infos]
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distances = [inf.distance for inf in infos]
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return texts_with_context, ids, distances
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# Get chunks by similarity
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def get(self, search_strings: list[str], n_results: int) -> list[str]:
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with self.lock:
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documents, _, _ = self._get_documents_ids_distances(search_strings, n_results)
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return documents
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# Get ids by similarity
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def get_ids(self, search_strings: list[str], n_results: int) -> list[str]:
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with self.lock:
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_, ids, _ = self._get_documents_ids_distances(search_strings, n_results)
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return ids
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# Cutoff token count
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def _get_documents_up_to_token_count(self, documents: list[str], max_token_count: int):
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# TODO: Move to caller; We add delimiters there which might go over the limit.
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current_token_count = 0
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return_documents = []
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for doc in documents:
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doc_tokens = encode(doc)[0]
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doc_token_count = len(doc_tokens)
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if current_token_count + doc_token_count > max_token_count:
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# If adding this document would exceed the max token count,
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# truncate the document to fit within the limit.
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remaining_tokens = max_token_count - current_token_count
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truncated_doc = decode(doc_tokens[:remaining_tokens], skip_special_tokens=True)
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return_documents.append(truncated_doc)
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break
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else:
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return_documents.append(doc)
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current_token_count += doc_token_count
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return return_documents
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# Get chunks by similarity and then sort by ids
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def get_sorted_by_ids(self, search_strings: list[str], n_results: int, max_token_count: int) -> list[str]:
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with self.lock:
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documents, ids, _ = self._get_documents_ids_distances(search_strings, n_results)
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sorted_docs = [x for _, x in sorted(zip(ids, documents))]
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return self._get_documents_up_to_token_count(sorted_docs, max_token_count)
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# Get chunks by similarity and then sort by distance (lowest distance is last).
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def get_sorted_by_dist(self, search_strings: list[str], n_results: int, max_token_count: int) -> list[str]:
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with self.lock:
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documents, _, distances = self._get_documents_ids_distances(search_strings, n_results)
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sorted_docs = [doc for doc, _ in sorted(zip(documents, distances), key=lambda x: x[1])] # sorted lowest -> highest
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# If a document is truncated or competely skipped, it would be with high distance.
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return_documents = self._get_documents_up_to_token_count(sorted_docs, max_token_count)
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return_documents.reverse() # highest -> lowest
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return return_documents
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def delete(self, ids_to_delete: list[str], where: dict):
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with self.lock:
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ids_to_delete = self.collection.get(ids=ids_to_delete, where=where)['ids']
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self.collection.delete(ids=ids_to_delete, where=where)
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# Remove the deleted ids from self.ids and self.id_to_info
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ids_set = set(ids_to_delete)
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self.ids = [id_ for id_ in self.ids if id_ not in ids_set]
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for id_ in ids_to_delete:
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self.id_to_info.pop(id_, None)
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logger.info(f'Successfully deleted {len(ids_to_delete)} records from chromaDB.')
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def clear(self):
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with self.lock:
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self.chroma_client.reset()
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self.collection = self.chroma_client.create_collection("context", embedding_function=self.embedder.embed)
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self.ids = []
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self.id_to_info = {}
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logger.info('Successfully cleared all records and reset chromaDB.')
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class SentenceTransformerEmbedder(Embedder):
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def __init__(self) -> None:
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logger.debug('Creating Sentence Embedder...')
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self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
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self.embed = self.model.encode
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def make_collector():
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return ChromaCollector(SentenceTransformerEmbedder()) |