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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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DEFAULT_PARAMETERS = { |
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"api_version": "2024-10-21", |
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"temperature": 0.0, |
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"top_p": 1.0, |
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"seed": 0, |
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} |
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def initialize(self, parallel: bool = False) -> None: |
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super().initialize(parallel) |
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self.client = AzureOpenAI( |
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azure_endpoint=self.params["endpoint"], |
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api_version=self.params["api_version"], |
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api_key=os.getenv("AZURE_OPENAI_KEY"), |
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) |
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# TODO: Verify the connection? |
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def default_params(self): |
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params = backend.AnnifBackend.DEFAULT_PARAMETERS.copy() |
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params.update(BaseLLMBackend.DEFAULT_PARAMETERS.copy()) |
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params.update(self.DEFAULT_PARAMETERS) |
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return params |
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class LLMEnsembleBackend(BaseLLMBackend, ensemble.EnsembleBackend): |
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# """TODO backend that combines results from multiple projects""" |
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name = "llm_ensemble" |
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DEFAULT_PARAMETERS = { |
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"max_prompt_tokens": 127000, |
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"llm_weight": 0.7, |
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"labels_language": "en", |
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"sources_limit": 10, |
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} |
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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 get_hp_optimizer(self, corpus: DocumentCorpus, metric: str) -> None: |
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raise NotSupportedException( |
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"Hyperparameter optimization for LLM ensemble backend is not possible." |
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) |
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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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llm_weight = float(params["llm_weight"]) |
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if llm_weight < 0.0 or llm_weight > 1.0: |
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raise ValueError("llm_weight must be between 0.0 and 1.0") |
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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, {"limit": params["sources_limit"]} |
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) |
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# Score the suggestion labels with the LLM |
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llm_results_batch = self._llm_suggest_batch(texts, merged_source_batch, params) |
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batches = [merged_source_batch, llm_results_batch] |
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weights = [1.0 - llm_weight, llm_weight] |
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return SuggestionBatch.from_averaged(batches, weights).filter( |
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limit=int(params["limit"]) |
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) |
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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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max_prompt_tokens = int(params["max_prompt_tokens"]) |
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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, max_prompt_tokens) |
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prompt += "Here is the text:\n" + text + "\n" |
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response = self._call_llm(prompt, model, params) |
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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(f"Error decoding JSON response from LLM: {response}") |
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print(f"Error: {err}") |
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llm_batch_suggestions.append( |
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[SubjectSuggestion(subject_id=None, score=0.0) for _ in labels] |
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) |
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continue |
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llm_batch_suggestions.append( |
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[ |
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( |
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SubjectSuggestion( |
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subject_id=self.project.subjects.by_label( |
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llm_label, self.params["labels_language"] |
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), |
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score=score, |
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) |
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if llm_label in labels |
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else SubjectSuggestion(subject_id=None, score=0.0) |
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) |
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for llm_label, score in llm_result.items() |
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] |
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) |
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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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self.params["labels_language"] |
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] |
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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, max_prompt_tokens): |
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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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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, params: dict[str, Any]) -> str: |
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temperature = float(params["temperature"]) |
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top_p = float(params["top_p"]) |
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seed = int(params["seed"]) |
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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=temperature, |
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seed=seed, |
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top_p=top_p, |
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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
