File size: 15,476 Bytes
0eeee8c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
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
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
import threading
import chromadb
import posthog
import torch
import math

import numpy as np
import extensions.superboogav2.parameters as parameters

from chromadb.config import Settings
from sentence_transformers import SentenceTransformer

from modules.logging_colors import logger
from modules.text_generation import encode, decode

logger.debug('Intercepting all calls to posthog.')
posthog.capture = lambda *args, **kwargs: None


class Collecter():
    def __init__(self):
        pass

    def add(self, texts: list[str], texts_with_context: list[str], starting_indices: list[int]):
        pass

    def get(self, search_strings: list[str], n_results: int) -> list[str]:
        pass

    def clear(self):
        pass


class Embedder():
    def __init__(self):
        pass

    def embed(self, text: str) -> list[torch.Tensor]:
        pass

class Info:
    def __init__(self, start_index, text_with_context, distance, id):
        self.text_with_context = text_with_context
        self.start_index = start_index
        self.distance = distance
        self.id = id

    def calculate_distance(self, other_info):
        if parameters.get_new_dist_strategy() == parameters.DIST_MIN_STRATEGY:
            # Min
            return min(self.distance, other_info.distance)
        elif parameters.get_new_dist_strategy() == parameters.DIST_HARMONIC_STRATEGY:
            # Harmonic mean
            return 2 * (self.distance * other_info.distance) / (self.distance + other_info.distance)
        elif parameters.get_new_dist_strategy() == parameters.DIST_GEOMETRIC_STRATEGY:
            # Geometric mean
            return (self.distance * other_info.distance) ** 0.5
        elif parameters.get_new_dist_strategy() == parameters.DIST_ARITHMETIC_STRATEGY:
            # Arithmetic mean
            return (self.distance + other_info.distance) / 2
        else: # Min is default
            return min(self.distance, other_info.distance)

    def merge_with(self, other_info):
        s1 = self.text_with_context
        s2 = other_info.text_with_context
        s1_start = self.start_index
        s2_start = other_info.start_index
        
        new_dist = self.calculate_distance(other_info)

        if self.should_merge(s1, s2, s1_start, s2_start):
            if s1_start <= s2_start:
                if s1_start + len(s1) >= s2_start + len(s2):  # if s1 completely covers s2
                    return Info(s1_start, s1, new_dist, self.id)
                else:
                    overlap = max(0, s1_start + len(s1) - s2_start)
                    return Info(s1_start, s1 + s2[overlap:], new_dist, self.id)
            else:
                if s2_start + len(s2) >= s1_start + len(s1):  # if s2 completely covers s1
                    return Info(s2_start, s2, new_dist, other_info.id)
                else:
                    overlap = max(0, s2_start + len(s2) - s1_start)
                    return Info(s2_start, s2 + s1[overlap:], new_dist, other_info.id)

        return None
    
    @staticmethod
    def should_merge(s1, s2, s1_start, s2_start):
        # Check if s1 and s2 are adjacent or overlapping
        s1_end = s1_start + len(s1)
        s2_end = s2_start + len(s2)
        
        return not (s1_end < s2_start or s2_end < s1_start)

class ChromaCollector(Collecter):
    def __init__(self, embedder: Embedder):
        super().__init__()
        self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
        self.embedder = embedder
        self.collection = self.chroma_client.create_collection(name="context", embedding_function=self.embedder.embed)
        self.ids = []
        self.id_to_info = {}
        self.embeddings_cache = {}
        self.lock = threading.Lock() # Locking so the server doesn't break.

    def add(self, texts: list[str], texts_with_context: list[str], starting_indices: list[int], metadatas: list[dict] = None):
        with self.lock:
            assert metadatas is None or len(metadatas) == len(texts), "metadatas must be None or have the same length as texts"
            
            if len(texts) == 0: 
                return

            new_ids = self._get_new_ids(len(texts))

            (existing_texts, existing_embeddings, existing_ids, existing_metas), \
            (non_existing_texts, non_existing_ids, non_existing_metas) = self._split_texts_by_cache_hit(texts, new_ids, metadatas)

            # If there are any already existing texts, add them all at once.
            if existing_texts:
                logger.info(f'Adding {len(existing_embeddings)} cached embeddings.')
                args = {'embeddings': existing_embeddings, 'documents': existing_texts, 'ids': existing_ids}
                if metadatas is not None: 
                    args['metadatas'] = existing_metas
                self.collection.add(**args)

            # If there are any non-existing texts, compute their embeddings all at once. Each call to embed has significant overhead.
            if non_existing_texts:
                non_existing_embeddings = self.embedder.embed(non_existing_texts).tolist()
                for text, embedding in zip(non_existing_texts, non_existing_embeddings):
                    self.embeddings_cache[text] = embedding

                logger.info(f'Adding {len(non_existing_embeddings)} new embeddings.')
                args = {'embeddings': non_existing_embeddings, 'documents': non_existing_texts, 'ids': non_existing_ids}
                if metadatas is not None: 
                    args['metadatas'] = non_existing_metas
                self.collection.add(**args)

            # Create a dictionary that maps each ID to its context and starting index
            new_info = {
                id_: {'text_with_context': context, 'start_index': start_index}
                for id_, context, start_index in zip(new_ids, texts_with_context, starting_indices)
            }

            self.id_to_info.update(new_info)
            self.ids.extend(new_ids)

    
    def _split_texts_by_cache_hit(self, texts: list[str], new_ids: list[str], metadatas: list[dict]):
        existing_texts, non_existing_texts = [], []
        existing_embeddings = []
        existing_ids, non_existing_ids = [], []
        existing_metas, non_existing_metas = [], []

        for i, text in enumerate(texts):
            id_ = new_ids[i]
            metadata = metadatas[i] if metadatas is not None else None
            embedding = self.embeddings_cache.get(text)
            if embedding:
                existing_texts.append(text)
                existing_embeddings.append(embedding)
                existing_ids.append(id_)
                existing_metas.append(metadata)
            else:
                non_existing_texts.append(text)
                non_existing_ids.append(id_)
                non_existing_metas.append(metadata)

        return (existing_texts, existing_embeddings, existing_ids, existing_metas), \
               (non_existing_texts, non_existing_ids, non_existing_metas)


    def _get_new_ids(self, num_new_ids: int):
        if self.ids:
            max_existing_id = max(int(id_) for id_ in self.ids)
        else:
            max_existing_id = -1

        return [str(i + max_existing_id + 1) for i in range(num_new_ids)]

    
    def _find_min_max_start_index(self):
        max_index, min_index = 0, float('inf')
        for _, val in self.id_to_info.items():
            if val['start_index'] > max_index:
                max_index = val['start_index']
            if val['start_index'] < min_index:
                min_index = val['start_index']
        return min_index, max_index


    # NB: Does not make sense to weigh excerpts from different documents. 
    # But let's say that's the user's problem. Perfect world scenario:
    # Apply time weighing to different documents. For each document, then, add
    # separate time weighing.
    def _apply_sigmoid_time_weighing(self, infos: list[Info], document_len: int, time_steepness: float, time_power: float):
        sigmoid = lambda x: 1 / (1 + np.exp(-x))
        
        weights = sigmoid(time_steepness * np.linspace(-10, 10, document_len))

        # Scale to [0,time_power] and shift it up to [1-time_power, 1]
        weights = weights - min(weights) 
        weights = weights * (time_power / max(weights))
        weights = weights + (1 - time_power) 

        # Reverse the weights
        weights = weights[::-1]  

        for info in infos:
            index = info.start_index
            info.distance *= weights[index]


    def _filter_outliers_by_median_distance(self, infos: list[Info], significant_level: float):
        # Ensure there are infos to filter
        if not infos:
            return []
            
        # Find info with minimum distance
        min_info = min(infos, key=lambda x: x.distance)

        # Calculate median distance among infos
        median_distance = np.median([inf.distance for inf in infos])

        # Filter out infos that have a distance significantly greater than the median
        filtered_infos = [inf for inf in infos if inf.distance <= significant_level * median_distance]

        # Always include the info with minimum distance
        if min_info not in filtered_infos:
            filtered_infos.append(min_info)

        return filtered_infos


    def _merge_infos(self, infos: list[Info]):
        merged_infos = []
        current_info = infos[0]

        for next_info in infos[1:]:
            merged = current_info.merge_with(next_info)
            if merged is not None:
                current_info = merged
            else:
                merged_infos.append(current_info)
                current_info = next_info

        merged_infos.append(current_info)
        return merged_infos


    # Main function for retrieving chunks by distance. It performs merging, time weighing, and mean filtering.
    def _get_documents_ids_distances(self, search_strings: list[str], n_results: int):
        n_results = min(len(self.ids), n_results)
        if n_results == 0:
            return [], [], []

        if isinstance(search_strings, str):
            search_strings = [search_strings]

        infos = []
        min_start_index, max_start_index = self._find_min_max_start_index()

        for search_string in search_strings:
            result = self.collection.query(query_texts=search_string, n_results=math.ceil(n_results / len(search_strings)), include=['distances'])
            curr_infos = [Info(start_index=self.id_to_info[id]['start_index'], 
                               text_with_context=self.id_to_info[id]['text_with_context'], 
                               distance=distance, id=id) 
                          for id, distance in zip(result['ids'][0], result['distances'][0])]
            
            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())
            curr_infos = self._filter_outliers_by_median_distance(curr_infos, parameters.get_significant_level())
            infos.extend(curr_infos)

        infos.sort(key=lambda x: x.start_index)
        infos = self._merge_infos(infos)

        texts_with_context = [inf.text_with_context for inf in infos]
        ids = [inf.id for inf in infos]
        distances = [inf.distance for inf in infos]

        return texts_with_context, ids, distances
    

    # Get chunks by similarity
    def get(self, search_strings: list[str], n_results: int) -> list[str]:
        with self.lock:
            documents, _, _ = self._get_documents_ids_distances(search_strings, n_results)
            return documents
    

    # Get ids by similarity
    def get_ids(self, search_strings: list[str], n_results: int) -> list[str]:
        with self.lock:
            _, ids, _ = self._get_documents_ids_distances(search_strings, n_results)
            return ids
    
    
    # Cutoff token count
    def _get_documents_up_to_token_count(self, documents: list[str], max_token_count: int):
        # TODO: Move to caller; We add delimiters there which might go over the limit.
        current_token_count = 0
        return_documents = []

        for doc in documents:
            doc_tokens = encode(doc)[0]
            doc_token_count = len(doc_tokens)
            if current_token_count + doc_token_count > max_token_count:
                # If adding this document would exceed the max token count,
                # truncate the document to fit within the limit.
                remaining_tokens = max_token_count - current_token_count
                
                truncated_doc = decode(doc_tokens[:remaining_tokens], skip_special_tokens=True)
                return_documents.append(truncated_doc)
                break
            else:
                return_documents.append(doc)
                current_token_count += doc_token_count

        return return_documents
    

    # Get chunks by similarity and then sort by ids
    def get_sorted_by_ids(self, search_strings: list[str], n_results: int, max_token_count: int) -> list[str]:
        with self.lock:
            documents, ids, _ = self._get_documents_ids_distances(search_strings, n_results)
            sorted_docs = [x for _, x in sorted(zip(ids, documents))]

            return self._get_documents_up_to_token_count(sorted_docs, max_token_count)
    
    
    # Get chunks by similarity and then sort by distance (lowest distance is last).
    def get_sorted_by_dist(self, search_strings: list[str], n_results: int, max_token_count: int) -> list[str]:
        with self.lock:
            documents, _, distances = self._get_documents_ids_distances(search_strings, n_results)
            sorted_docs = [doc for doc, _ in sorted(zip(documents, distances), key=lambda x: x[1])] # sorted lowest -> highest
            
            # If a document is truncated or competely skipped, it would be with high distance.
            return_documents = self._get_documents_up_to_token_count(sorted_docs, max_token_count)
            return_documents.reverse() # highest -> lowest

            return return_documents
    

    def delete(self, ids_to_delete: list[str], where: dict):
        with self.lock:
            ids_to_delete = self.collection.get(ids=ids_to_delete, where=where)['ids']
            self.collection.delete(ids=ids_to_delete, where=where)

            # Remove the deleted ids from self.ids and self.id_to_info
            ids_set = set(ids_to_delete)
            self.ids = [id_ for id_ in self.ids if id_ not in ids_set]
            for id_ in ids_to_delete:
                self.id_to_info.pop(id_, None)

            logger.info(f'Successfully deleted {len(ids_to_delete)} records from chromaDB.')


    def clear(self):
        with self.lock:
            self.chroma_client.reset()
            self.collection = self.chroma_client.create_collection("context", embedding_function=self.embedder.embed)
            self.ids = []
            self.id_to_info = {}

            logger.info('Successfully cleared all records and reset chromaDB.')


class SentenceTransformerEmbedder(Embedder):
    def __init__(self) -> None:
        logger.debug('Creating Sentence Embedder...')
        self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
        self.embed = self.model.encode


def make_collector():
    return ChromaCollector(SentenceTransformerEmbedder())