Spaces:
Running
Running
File size: 3,855 Bytes
19dc0f3 |
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 |
import random
import chromadb
import posthog
from chromadb.config import Settings
from chromadb.utils import embedding_functions
# Intercept calls to posthog
posthog.capture = lambda *args, **kwargs: None
embedder = embedding_functions.SentenceTransformerEmbeddingFunction("sentence-transformers/all-mpnet-base-v2")
class ChromaCollector():
def __init__(self):
name = ''.join(random.choice('ab') for _ in range(10))
self.name = name
self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
self.collection = self.chroma_client.create_collection(name=name, embedding_function=embedder)
self.ids = []
def add(self, texts: list[str]):
if len(texts) == 0:
return
self.ids = [f"id{i}" for i in range(len(texts))]
self.collection.add(documents=texts, ids=self.ids)
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 [], [], []
result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents', 'distances'])
documents = result['documents'][0]
ids = list(map(lambda x: int(x[2:]), result['ids'][0]))
distances = result['distances'][0]
return documents, ids, distances
# Get chunks by similarity
def get(self, search_strings: list[str], n_results: int) -> list[str]:
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]:
_, ids, _ = self.get_documents_ids_distances(search_strings, n_results)
return ids
# Get chunks by similarity and then sort by insertion order
def get_sorted(self, search_strings: list[str], n_results: int) -> list[str]:
documents, ids, _ = self.get_documents_ids_distances(search_strings, n_results)
return [x for _, x in sorted(zip(ids, documents))]
# Multiply distance by factor within [0, time_weight] where more recent is lower
def apply_time_weight_to_distances(self, ids: list[int], distances: list[float], time_weight: float = 1.0) -> list[float]:
if len(self.ids) <= 1:
return distances.copy()
return [distance * (1 - _id / (len(self.ids) - 1) * time_weight) for _id, distance in zip(ids, distances)]
# Get ids by similarity and then sort by insertion order
def get_ids_sorted(self, search_strings: list[str], n_results: int, n_initial: int = None, time_weight: float = 1.0) -> list[str]:
do_time_weight = time_weight > 0
if not (do_time_weight and n_initial is not None):
n_initial = n_results
elif n_initial == -1:
n_initial = len(self.ids)
if n_initial < n_results:
raise ValueError(f"n_initial {n_initial} should be >= n_results {n_results}")
_, ids, distances = self.get_documents_ids_distances(search_strings, n_initial)
if do_time_weight:
distances_w = self.apply_time_weight_to_distances(ids, distances, time_weight=time_weight)
results = zip(ids, distances, distances_w)
results = sorted(results, key=lambda x: x[2])[:n_results]
results = sorted(results, key=lambda x: x[0])
ids = [x[0] for x in results]
return sorted(ids)
def clear(self):
self.ids = []
self.chroma_client.delete_collection(name=self.name)
self.collection = self.chroma_client.create_collection(name=self.name, embedding_function=embedder)
def make_collector():
return ChromaCollector()
def add_chunks_to_collector(chunks, collector):
collector.clear()
collector.add(chunks)
|