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"""TODO""" |
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from __future__ import annotations |
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import os.path |
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from typing import TYPE_CHECKING, Any |
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import numpy as np |
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import tiktoken |
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from openai import AzureOpenAI # Try using huggingface client |
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# import annif.util |
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from annif.exception import NotInitializedException, NotSupportedException |
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from annif.suggestion import vector_to_suggestions |
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from . import backend |
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if TYPE_CHECKING: |
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from collections.abc import Iterator |
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from annif.corpus.document import DocumentCorpus |
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class Vectorizer: |
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def __init__(self, endpoint, model): |
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self.model = model |
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self.client = AzureOpenAI( # TODO Try AsyncAzureOpenAI( |
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azure_endpoint=endpoint, |
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api_key=os.getenv("AZURE_OPENAI_KEY"), |
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api_version="2024-02-15-preview", |
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) |
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def transform(self, text): |
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response = self.client.embeddings.create( |
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input=text, |
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model=self.model, |
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# dimensions=dimensions, # TODO Try with reduced dimensions |
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) |
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return response.data[0].embedding |
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class EmbeddingsBackend(backend.AnnifBackend): |
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"""Semantic vector space similarity based backend for Annif""" |
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name = "embeddings" |
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_index = None |
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INDEX_FILE = "emdeddings-index.npy" |
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BASE_MODEL = "text-embedding-3-large" |
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VECTOR_DIMENSIONS = 3072 # For text-embedding-3-large |
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MAX_TOKENS = 8192 # For text-embedding-3-large |
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encoding = tiktoken.encoding_for_model(BASE_MODEL) |
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def _initialize_index(self) -> None: |
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if self._index is None: |
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path = os.path.join(self.datadir, self.INDEX_FILE) |
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self.debug("loading similarity index from {}".format(path)) |
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if os.path.exists(path): |
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self._index = np.load(path, allow_pickle=True) |
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else: |
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raise NotInitializedException( |
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"similarity index {} not found".format(path), |
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backend_id=self.backend_id, |
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) |
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def initialize( |
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self, |
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parallel: bool = False, |
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) -> None: |
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self.vectorizer = Vectorizer(self.params["endpoint"], self.params["model"]) |
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self._initialize_index() |
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def _create_index(self, corpus) -> None: |
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self.vectorizer = Vectorizer(self.params["endpoint"], self.params["model"]) |
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self.info("creating similarity index") |
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path = os.path.join(self.datadir, self.INDEX_FILE) |
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subject_vectors = np.zeros((len(self.project.subjects), self.VECTOR_DIMENSIONS)) |
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for doc in corpus.documents: |
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vec = self.vectorizer.transform(self._truncate_text(doc.text)) |
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for sid in doc.subject_set: |
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subject_vectors[sid, :] = subject_vectors[sid, :] + vec |
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row_norms = np.linalg.norm(subject_vectors, axis=1, keepdims=True) |
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# Avoid division by zero: Only normalize non-zero rows |
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self._index = np.where(row_norms == 0, 0, subject_vectors / row_norms) |
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np.save(path, self._index, allow_pickle=True) |
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def _truncate_text(self, text): |
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"""truncate text so it contains at most MAX_TOKENS according to the OpenAI |
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tokenizer""" |
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tokens = self.encoding.encode(text) |
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return self.encoding.decode(tokens[: self.MAX_TOKENS]) |
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def _train( |
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self, |
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corpus: DocumentCorpus, |
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params: dict[str, Any], |
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jobs: int = 0, |
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) -> None: |
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if corpus == "cached": |
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raise NotSupportedException( |
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"Training embeddings project from cached data not supported." |
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) |
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if corpus.is_empty(): |
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raise NotSupportedException( |
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"Cannot train embeddings project with no documents" |
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) |
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self.info("transforming subject corpus") |
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self._create_index(corpus) |
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def _suggest(self, text: str, params: dict[str, Any]) -> Iterator: |
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self.debug( |
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'Suggesting subjects for text "{}..." (len={})'.format(text[:20], len(text)) |
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) |
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truncated_text = self._truncate_text(text) |
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vector = self.vectorizer.transform(truncated_text) |
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cosine_similarity = np.dot(self._index, np.array(vector)) |
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return vector_to_suggestions(cosine_similarity, int(params["limit"])) |
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NatLibFi /
Annif
