350 lines
7.7 KiB
Python
350 lines
7.7 KiB
Python
from __future__ import annotations
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import os
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import hashlib
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import json
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from functools import lru_cache
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from typing import Any
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import numpy as np
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from tools.text_budget import split_to_fit
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from tools.runtime_assets import local_model
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DEFAULT_EMBEDDING_MODEL = (
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"intfloat/multilingual-e5-small"
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)
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DEFAULT_BATCH_SIZE = 32
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def embedding_model_name() -> str:
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return (
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os.getenv(
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"EMBEDDING_MODEL",
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DEFAULT_EMBEDDING_MODEL,
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).strip()
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or DEFAULT_EMBEDDING_MODEL
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)
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def embedding_batch_size() -> int:
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raw_value = os.getenv(
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"EMBEDDING_BATCH_SIZE",
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str(DEFAULT_BATCH_SIZE),
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).strip()
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try:
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value = int(
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raw_value
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)
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except ValueError:
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return DEFAULT_BATCH_SIZE
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return max(
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1,
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value,
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)
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def get_embedding_model(
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model_name: str,
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):
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return _load_embedding_model(model_name, os.getenv('EMBEDDING_REVISION') or None)
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@lru_cache(maxsize=2)
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def _load_embedding_model(model_name: str, revision: str | None):
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from sentence_transformers import (
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SentenceTransformer,
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)
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bundled = local_model(model_name, revision)
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return SentenceTransformer(
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bundled or model_name,
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revision=None if bundled else revision,
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local_files_only=bool(bundled) or os.getenv('HF_HUB_OFFLINE') == '1',
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)
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def model_profile(model_name: str) -> dict:
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"""Fingerprint the loaded weights, tokenizer and pooling, not a mutable alias."""
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return _model_profile(model_name, get_embedding_model(model_name))
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@lru_cache(maxsize=2)
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def _model_profile(model_name, model):
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import torch
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weights = hashlib.sha256()
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for name, tensor in sorted(model.state_dict().items()):
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weights.update(name.encode())
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weights.update(str((tuple(tensor.shape), tensor.dtype)).encode())
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weights.update(tensor.detach().cpu().contiguous().view(torch.uint8).numpy().tobytes())
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tokenizer_config = json.loads(model.tokenizer.backend_tokenizer.to_str())
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# Fast tokenizers store the last call's padding/truncation in the backend.
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# These mutable execution settings must not change the vocabulary fingerprint.
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tokenizer_config.pop('padding', None)
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tokenizer_config.pop('truncation', None)
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tokenizer = json.dumps(tokenizer_config, sort_keys=True, separators=(',', ':'), ensure_ascii=False)
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modules = {name: module.get_config_dict() for name, module in model._modules.items()
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if hasattr(module, 'get_config_dict')}
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profile = {'model': model_name, 'weights_sha256': weights.hexdigest(),
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'tokenizer_sha256': hashlib.sha256(tokenizer.encode()).hexdigest(),
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'max_seq_length': int(model.max_seq_length), 'modules': modules,
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'passage_prefix': 'passage: ', 'query_prefix': 'query: ',
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'normalize_embeddings': True, 'dtype': 'float32', 'segment_policy': 'lossless-v1'}
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profile['fingerprint'] = hashlib.sha256(json.dumps(profile, sort_keys=True).encode()).hexdigest()
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return profile
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def passage_token_count(text: str, *, model_name: str | None = None) -> int:
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model = get_embedding_model(model_name or embedding_model_name())
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return len(model.tokenizer('passage: ' + text, truncation=False, add_special_tokens=True,
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verbose=False)['input_ids'])
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def split_passage(text: str, *, model_name: str | None = None) -> list[str]:
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selected = model_name or embedding_model_name()
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limit = int(get_embedding_model(selected).max_seq_length)
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return split_to_fit(text, lambda value: passage_token_count(value, model_name=selected) <= limit)
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def build_embedding_text(
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chunk: dict[str, Any],
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) -> str:
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parts: list[str] = []
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title = str(
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chunk.get("title")
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or ""
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).strip()
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author = str(
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chunk.get("author")
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or ""
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).strip()
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tags = [
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str(value).strip()
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for value in (
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chunk.get("tags")
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or []
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)
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if str(value).strip()
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]
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categories = [
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str(value).strip()
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for value in (
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chunk.get("categories")
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or []
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)
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if str(value).strip()
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]
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text = str(
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chunk.get("text")
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or ""
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).strip()
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if title:
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parts.append(
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f"Názov: {title}"
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)
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if author:
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parts.append(
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f"Autor: {author}"
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)
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if tags:
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parts.append(
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"Tagy: "
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+ ", ".join(
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tags
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)
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)
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if categories:
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parts.append(
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"Kategórie: "
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+ ", ".join(
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categories
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)
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)
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if text:
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parts.append(
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text
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)
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return "\n".join(
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parts
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)
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def embed_passages(
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texts: list[str],
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*,
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model_name: str | None = None,
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) -> np.ndarray:
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if not texts:
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return np.empty(
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(
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0,
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0,
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),
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dtype=np.float32,
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)
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selected_model = (
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model_name
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or embedding_model_name()
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)
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model = get_embedding_model(
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selected_model
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)
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prepared = [
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"passage: "
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+ text
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for text in texts
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]
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if any(passage_token_count(text, model_name=selected_model) > model.max_seq_length for text in texts):
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raise ValueError('Embedding input exceeds model limit; split it before encoding.')
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vectors = model.encode(
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prepared,
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batch_size=(
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embedding_batch_size()
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),
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show_progress_bar=False,
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convert_to_numpy=True,
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normalize_embeddings=True,
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)
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return np.asarray(
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vectors,
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dtype=np.float32,
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)
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def embed_query(
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query: str,
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*,
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model_name: str | None = None,
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) -> np.ndarray:
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clean_query = query.strip()
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if not clean_query:
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raise ValueError(
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"Query nesmie byť prázdny"
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)
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selected_model = (
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model_name
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or embedding_model_name()
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)
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model = get_embedding_model(
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selected_model
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)
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length = len(model.tokenizer('query: ' + clean_query, truncation=False,
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add_special_tokens=True, verbose=False)['input_ids'])
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if length > model.max_seq_length:
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raise ValueError('Query prekračuje tokenový limit embeddingového modelu')
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vector = model.encode(
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[
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"query: "
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+ clean_query
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],
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batch_size=1,
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show_progress_bar=False,
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convert_to_numpy=True,
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normalize_embeddings=True,
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)[0]
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return np.asarray(
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vector,
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dtype=np.float32,
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)
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def vector_to_blob(
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vector: np.ndarray,
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) -> bytes:
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normalized = np.asarray(
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vector,
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dtype=np.float32,
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)
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return normalized.tobytes()
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def blob_to_vector(
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blob: bytes,
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dimensions: int,
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) -> np.ndarray:
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vector = np.frombuffer(
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blob,
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dtype=np.float32,
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)
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if (
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vector.shape[0]
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!= dimensions
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):
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raise RuntimeError(
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"Neplatný rozmer "
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"uloženého embeddingu"
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)
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return vector
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def cosine_similarity(
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first: np.ndarray,
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second: np.ndarray,
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) -> float:
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if (
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first.shape
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!= second.shape
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):
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raise ValueError(
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"Embeddingy majú "
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"rozdielny rozmer"
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)
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first_norm = float(
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np.linalg.norm(
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first
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)
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)
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second_norm = float(
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np.linalg.norm(
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second
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)
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)
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if (
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first_norm == 0.0
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or second_norm == 0.0
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):
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return 0.0
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return float(
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np.dot(
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first,
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second,
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)
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/ (
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first_norm
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* second_norm
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)
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)
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