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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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from qdrant_client import QdrantClient, models |
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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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"""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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COLLECTION_NAME = "index-collection" |
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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.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=path) |
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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.recreate_collection( |
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collection_name=self.COLLECTION_NAME, |
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vectors_config=models.VectorParams( |
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size=self.VECTOR_DIMENSIONS, |
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distance=models.Distance.COSINE, |
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), |
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) |
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veccorpus = ( |
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( |
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doc.subject_set, |
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self.vectorizer.transform(self._truncate_text(" ".join(doc.text))), |
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) |
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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=models.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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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(" ".join(text)) |
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vector = self.vectorizer.transform(truncated_text) |
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# print(vector[:5]) |
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info = self.qdclient.get_collection(collection_name=self.COLLECTION_NAME) |
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self.debug(f"Collection info: {info}") |
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results = self._search(vector, params) |
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# print(results) |
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return self._prediction_to_result(results, params) |
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def _search(self, vector, params): |
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result = self.qdclient.search( |
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collection_name=self.COLLECTION_NAME, |
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query_vector=vector, |
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# score_threshold=1.0, # TODO parameterize this |
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limit=int(params["limit"]), |
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# search_params=models.SearchParams(hnsw_ef=128, exact=False), # TODO This |
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) |
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return [(sp.payload["subjects"], sp.score) for sp in result] |
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def _combine_search_results(self, results): |
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combined = [] |
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for res in results: |
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sids, weight = res[0], res[1] |
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combined.extend([sid * weight for sid in sids]) |
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return combined |
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# From backend/mllm.py |
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def _prediction_to_result( |
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self, |
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results, |
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params, |
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) -> Iterator: |
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vector = np.zeros(len(self.project.subjects), dtype=np.float32) |
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for subject_ids, score in results: |
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for sid in subject_ids: |
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vector[sid] += score |
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return vector_to_suggestions(vector, int(params["limit"])) |
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NatLibFi /
Annif
