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"""TODO""" |
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from __future__ import annotations |
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import os.path |
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import tempfile |
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from typing import TYPE_CHECKING, Any |
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from openai import AzureOpenAI |
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from qdrant_client import QdrantClient |
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from qdrant_client.models import Distance, VectorParams, Batch |
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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 scipy.sparse._csr import csr_matrix |
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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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"""TODO xxx cector space similarity based backend for Annif""" |
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name = "embeddings" |
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is_trained = True |
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# defaults for uninitialized instances |
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_index = None |
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DB_FILE = "qdrant-db" |
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VECTOR_DIMENSIONS = 3072 # For text-embedding-3-large |
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COLLECTION_NAME = "index-collection" |
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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.DB_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.qdclient = QdrantClient(path=self.DB_FILE) |
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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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) -> 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.DB_FILE) |
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self.qdclient = QdrantClient(path=path) |
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self.qdclient.create_collection( |
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collection_name=self.COLLECTION_NAME, |
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vectors_config=VectorParams( |
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size=self.VECTOR_DIMENSIONS, distance=Distance.DOT |
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), |
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) |
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veccorpus = ( |
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(doc.subject_set, self.vectorizer.transform(doc.text)) |
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for doc in corpus.documents |
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) |
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subject_sets, vectors = zip(*veccorpus) |
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payloads = [{"subjects": [sid for sid in ss]} for ss in subject_sets] |
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ids = list(range(len(vectors))) |
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self.qdclient.upsert( |
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collection_name=self.COLLECTION_NAME, |
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points=Batch( |
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ids=ids, |
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vectors=vectors, |
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payloads=payloads, |
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), |
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) |
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print(self.qdclient.get_collection(collection_name=self.COLLECTION_NAME)) |
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# print( |
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# self.qdclient.count( |
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# collection_name=self.COLLECTION_NAME, |
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# exact=True, |
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# ).count |
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# ) |
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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.initialize() |
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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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vector = self.vectorizer.transform([" ".join(text)]) |
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self.debug( |
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f"Collection info: {self.qdclient.get_collection(collection_name=self.COLLECTION_NAME)}" |
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) |
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print(vector[:5]) |
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# get the most similar document from qdrant in here |
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return vector_to_suggestions(self._index[vector], int(params["limit"])) |
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NatLibFi /
Annif
