mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-01 07:00:15 +01:00
126 lines
4.2 KiB
Python
126 lines
4.2 KiB
Python
import chromadb
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import posthog
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import torch
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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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logger.info('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]):
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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 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=embedder.embed)
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self.ids = []
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def add(self, texts: list[str]):
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if len(texts) == 0:
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return
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self.ids = [f"id{i}" for i in range(len(texts))]
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self.collection.add(documents=texts, ids=self.ids)
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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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result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents', 'distances'])
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documents = result['documents'][0]
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ids = list(map(lambda x: int(x[2:]), result['ids'][0]))
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distances = result['distances'][0]
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return documents, 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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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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_, ids, _ = self.get_documents_ids_distances(search_strings, n_results)
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return ids
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# Get chunks by similarity and then sort by insertion order
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def get_sorted(self, search_strings: list[str], n_results: int) -> list[str]:
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documents, ids, _ = self.get_documents_ids_distances(search_strings, n_results)
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return [x for _, x in sorted(zip(ids, documents))]
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# Multiply distance by factor within [0, time_weight] where more recent is lower
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def apply_time_weight_to_distances(self, ids: list[int], distances: list[float], time_weight: float = 1.0) -> list[float]:
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if len(self.ids) <= 1:
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return distances.copy()
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return [distance * (1 - _id / (len(self.ids) - 1) * time_weight) for _id, distance in zip(ids, distances)]
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# Get ids by similarity and then sort by insertion order
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def get_ids_sorted(self, search_strings: list[str], n_results: int, n_initial: int = None, time_weight: float = 1.0) -> list[str]:
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do_time_weight = time_weight > 0
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if not (do_time_weight and n_initial is not None):
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n_initial = n_results
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elif n_initial == -1:
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n_initial = len(self.ids)
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if n_initial < n_results:
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raise ValueError(f"n_initial {n_initial} should be >= n_results {n_results}")
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_, ids, distances = self.get_documents_ids_distances(search_strings, n_initial)
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if do_time_weight:
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distances_w = self.apply_time_weight_to_distances(ids, distances, time_weight=time_weight)
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results = zip(ids, distances, distances_w)
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results = sorted(results, key=lambda x: x[2])[:n_results]
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results = sorted(results, key=lambda x: x[0])
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ids = [x[0] for x in results]
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return sorted(ids)
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def clear(self):
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self.collection.delete(ids=self.ids)
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self.ids = []
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class SentenceTransformerEmbedder(Embedder):
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def __init__(self) -> None:
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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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global embedder
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return ChromaCollector(embedder)
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def add_chunks_to_collector(chunks, collector):
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collector.clear()
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collector.add(chunks)
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embedder = SentenceTransformerEmbedder()
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