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"""Language model based ensemble backend that combines results from multiple |
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projects.""" |
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from __future__ import annotations |
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import json |
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import os |
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from typing import TYPE_CHECKING, Any |
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import tiktoken |
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from openai import AzureOpenAI, BadRequestError |
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import annif.eval |
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import annif.parallel |
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import annif.util |
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from annif.exception import NotSupportedException |
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from annif.suggestion import SubjectSuggestion, SuggestionBatch |
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from . import backend, ensemble |
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# from openai import AsyncAzureOpenAI |
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if TYPE_CHECKING: |
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from annif.corpus.document import DocumentCorpus |
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class BaseLLMBackend(backend.AnnifBackend): |
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# """Base class for TODO backends""" |
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def initialize(self, parallel: bool = False) -> None: |
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# initialize all the source projects |
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params = self._get_backend_params(None) |
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for project_id, _ in annif.util.parse_sources(params["sources"]): |
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project = self.project.registry.get_project(project_id) |
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project.initialize(parallel) |
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# self.client = AsyncAzureOpenAI( |
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self.client = AzureOpenAI( |
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azure_endpoint=params["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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class LLMEnsembleBackend(BaseLLMBackend, ensemble.BaseEnsembleBackend): |
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# """TODO backend that combines results from multiple projects""" |
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name = "llm_ensemble" |
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system_prompt = """ |
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You will be given text and a list of keywords to describe it. Your task is to |
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score the keywords with a value between 0.0 and 1.0. The score value |
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should depend on how well the keyword represents the text: a perfect |
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keyword should have score 1.0 and completely unrelated keyword score |
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0.0. You must output JSON with keywords as field names and add their scores |
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as field values. |
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There must be the same number of objects in the JSON as there are lines in the |
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intput keyword list; do not skip scoring any keywords. |
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""" |
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# Give zero or very low score to the keywords that do not describe the text. |
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def _train(self, corpus: DocumentCorpus, params: dict[str, Any], jobs: int = 0): |
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raise NotSupportedException("Training LM ensemble backend is not possible.") |
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def _suggest_batch( |
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self, texts: list[str], params: dict[str, Any] |
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) -> SuggestionBatch: |
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sources = annif.util.parse_sources(params["sources"]) |
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batch_by_source = self._suggest_with_sources(texts, sources) |
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merged_source_batch = self._merge_source_batches( |
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batch_by_source, sources, params |
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) |
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# Add LLM suggestions to the source batches |
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batch_by_source[self.project.project_id] = self._llm_suggest_batch( |
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texts, merged_source_batch, params |
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) |
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new_sources = sources + [(self.project.project_id, float(params["llm_weight"]))] |
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return self._merge_source_batches(batch_by_source, new_sources, params) |
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def _llm_suggest_batch( |
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self, |
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texts: list[str], |
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suggestion_batch: SuggestionBatch, |
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params: dict[str, Any], |
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) -> SuggestionBatch: |
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model = params["model"] |
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encoding = tiktoken.encoding_for_model(model.rsplit("-", 1)[0]) |
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labels_batch = self._get_labels_batch(suggestion_batch) |
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llm_batch_suggestions = [] |
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for text, labels in zip(texts, labels_batch): |
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prompt = "Here are the keywords:\n" + "\n".join(labels) + "\n" * 3 |
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text = self._truncate_text(text, encoding) |
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prompt += "Here is the text:\n" + text + "\n" |
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response = self._call_llm(prompt, model) |
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try: |
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llm_result = json.loads(response) |
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except (TypeError, json.decoder.JSONDecodeError) as err: |
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print(err) |
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llm_result = None |
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continue # TODO: handle this error |
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llm_suggestions = [ |
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SubjectSuggestion( |
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subject_id=self.project.subjects.by_label(llm_label, "en"), |
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score=score, |
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) |
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for llm_label, score in llm_result.items() |
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] |
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llm_batch_suggestions.append(llm_suggestions) |
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return SuggestionBatch.from_sequence( |
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llm_batch_suggestions, |
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self.project.subjects, |
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) |
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def _get_labels_batch(self, suggestion_batch: SuggestionBatch) -> list[list[str]]: |
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return [ |
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[ |
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self.project.subjects[suggestion.subject_id].labels[ |
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"en" |
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] # TODO: make language selectable |
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for suggestion in suggestion_result |
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] |
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for suggestion_result in suggestion_batch |
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] |
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def _truncate_text(self, text, encoding): |
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"""truncate text so it contains at most MAX_PROMPT_TOKENS according to the |
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OpenAI tokenizer""" |
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MAX_PROMPT_TOKENS = 14000 |
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tokens = encoding.encode(text) |
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return encoding.decode(tokens[:MAX_PROMPT_TOKENS]) |
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def _call_llm(self, prompt: str, model: str): |
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messages = [ |
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{"role": "system", "content": self.system_prompt}, |
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{"role": "user", "content": prompt}, |
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] |
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try: |
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completion = self.client.chat.completions.create( |
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model=model, |
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messages=messages, |
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temperature=0.0, |
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seed=0, |
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max_tokens=1800, |
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top_p=0.95, |
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frequency_penalty=0, |
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presence_penalty=0, |
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stop=None, |
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response_format={"type": "json_object"}, |
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) |
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completion = completion.choices[0].message.content |
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return completion |
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except BadRequestError as err: # openai.RateLimitError |
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print(err) |
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return "{}" |
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NatLibFi /
Annif
