From 61cc2740e1cb64718aa82c04cb4bb8568f21d2e0 Mon Sep 17 00:00:00 2001 From: Dmitriy Jarosh Date: Sun, 21 Jul 2024 17:51:33 +0300 Subject: [PATCH 1/3] Return m3m to production --- app/main.py | 4 +- poetry.lock | 1999 +++++++++++++++++++++++++++--------------------- pyproject.toml | 7 +- 3 files changed, 1115 insertions(+), 895 deletions(-) diff --git a/app/main.py b/app/main.py index 54f987d..90500f6 100644 --- a/app/main.py +++ b/app/main.py @@ -13,7 +13,7 @@ from app.models.base import WikidataSSPRequest from app.pipelines import seq2seq from app.pipelines import act_selection -# from app.pipelines import m3m +from app.pipelines import m3m from fastapi.middleware.cors import CORSMiddleware app = FastAPI() @@ -38,7 +38,7 @@ app.include_router(act_selection.router) app.include_router(seq2seq.router) -# app.include_router(m3m.router) +app.include_router(m3m.router) @app.get("/", response_class=HTMLResponse) diff --git a/poetry.lock b/poetry.lock index c6d4fa8..afc8cf2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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"pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "e46b0f76da20ea0165de3cc47f27c53086252b8a228590d5fbeccdbe413a8448" +content-hash = "44c6355471689c48f6462e6aacbe0134a3cdbd8469d3c6679a36e911c4f8b7e2" diff --git a/pyproject.toml b/pyproject.toml index 02f442d..dc352b5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,6 +8,7 @@ packages = [{include = "kgqa_act_selection"}] [tool.poetry.dependencies] python = "^3.9" +numpy = "1.26.4" fastapi = "^0.89.1" pydantic = "^1.10.4" uvicorn = "^0.20.0" @@ -16,12 +17,12 @@ requests = "^2.28.2" joblib = "^1.2.0" sentencepiece = "^0.1.97" protobuf = "^4.21.12" -spacy = "^3.5.0" +spacy = "3.7.5" nltk = "^3.8.1" -sentence-transformers = "^2.2.2" +sentence-transformers = "2.2.2" scikit-learn = "^1.2.1" torch = "^1.13.1" -transformers = "^4.26.1" +transformers = "4.30.2" natasha = "^1.5.0" wikidata = "^0.7.0" graphviz = "^0.20.1" From f8f92985e4e32cbfa5a9bc2e28e2bd49e3ed2226 Mon Sep 17 00:00:00 2001 From: Dmitriy Jarosh Date: Sun, 21 Jul 2024 20:50:47 +0300 Subject: [PATCH 2/3] Support graph visualization in seq2seq --- app/kgqa/entity_linking.py | 27 ++++++ app/models/base.py | 4 + app/pipelines/act_selection.py | 22 +---- app/pipelines/seq2seq.py | 11 ++- app/templates/index_search.html | 157 ++++++++++++++++++-------------- 5 files changed, 128 insertions(+), 93 deletions(-) diff --git a/app/kgqa/entity_linking.py b/app/kgqa/entity_linking.py index 1e15b62..51b3302 100644 --- a/app/kgqa/entity_linking.py +++ b/app/kgqa/entity_linking.py @@ -2,6 +2,8 @@ from nltk.stem.porter import PorterStemmer from .ner import NerToSentenceInsertion +from .utils.utils import get_wd_search_results +from .mgenre import build_mgenre_pipeline, MGENREPipeline class EntitiesSelection: @@ -45,3 +47,28 @@ def _check_label_fn(self, label, entities_list): if label == entity: return True return False + + +class EntityLinker: + def __init__(self, ner: NerToSentenceInsertion, mgenre: MGENREPipeline, entity_selection: EntitiesSelection): + self.ner = ner + self.mgenre = mgenre + self.entity_selection = entity_selection + + def extract_entities_from_question(self, question_text: str): + question_wit_ner, all_question_entities = self.ner.entity_labeling(question_text, True) + mgenre_predicted_entities = self.mgenre(question_wit_ner) + question_entities = self.entity_selection( + all_question_entities, mgenre_predicted_entities + ) + + question_entities = [get_wd_search_results(label, 1)[0] for label in question_entities] + question_entities = [idx for idx in question_entities if idx is not None] + + return question_entities + + +ner = NerToSentenceInsertion("/data/ner/") +mgenre = build_mgenre_pipeline() +entity_selection = EntitiesSelection(ner.model) +entity_linker = EntityLinker(ner, mgenre, entity_selection) diff --git a/app/models/base.py b/app/models/base.py index 53a4ca8..cf3a1bb 100644 --- a/app/models/base.py +++ b/app/models/base.py @@ -20,6 +20,10 @@ class PipelineResponce(BaseModel): answers: list[str] +class Seq2SeqPipelineResponce(PipelineResponce): + question_entities: list[str] + + class EntityNeighboursResponce(BaseModel): entity: str property: str diff --git a/app/pipelines/act_selection.py b/app/pipelines/act_selection.py index e1f4f62..bcd3944 100644 --- a/app/pipelines/act_selection.py +++ b/app/pipelines/act_selection.py @@ -2,22 +2,14 @@ from fastapi import APIRouter from joblib import Parallel, delayed -from pywikidata import Entity from app.kgqa.act_selection import QuestionToRankInstanceOf, QuestionToRankInstanceOfSimple, QuestionToRankInstanceOfSimpleWithDescriptionMatching -from app.kgqa.entity_linking import EntitiesSelection -from app.kgqa.mgenre import build_mgenre_pipeline -from app.kgqa.ner import NerToSentenceInsertion +from app.kgqa.entity_linking import ner, mgenre, entity_selection, entity_linker from app.kgqa.utils.utils import get_wd_search_results -from app.models.base import Entity as EntityResponce from app.models.base import Question as QuestionRequest from app.models.base import ACTPipelineResponce, ACTPipelineResponceWithDescriptionScore, QuestionEntitiesResponce, EntityNeighboursResponce from app.pipelines.seq2seq import seq2seq -ner = NerToSentenceInsertion("/data/ner/") -mgenre = build_mgenre_pipeline() -entity_selection = EntitiesSelection(ner.model) - router = APIRouter( prefix="/pipeline/act_selection", @@ -53,15 +45,7 @@ def raw_seq2seq(question: str) -> list[str]: def _prepare_question_entities_and_answer_candidate_helper(question: QuestionRequest): - question_wit_ner, all_question_entities = ner.entity_labeling(question.text, True) - mgenre_predicted_entities = mgenre(question_wit_ner) - question_entities = entity_selection( - all_question_entities, mgenre_predicted_entities - ) - - question_entities = [get_wd_search_results(label, 1)[0] for label in question_entities] - question_entities = [idx for idx in question_entities if idx is not None] - + question_entities = entity_linker.extract_entities_from_question(question.text) seq2seq_results = seq2seq(question.text) answers_candidates = Parallel(n_jobs=-2)( delayed(get_wd_search_results)(label, 1) for label in seq2seq_results @@ -72,7 +56,7 @@ def _prepare_question_entities_and_answer_candidate_helper(question: QuestionReq @lru_cache(maxsize=1024) -@router.post("/main") +@router.post("/main/") def pipeline(question: QuestionRequest) -> ACTPipelineResponce: question_entities, answers_candidates = _prepare_question_entities_and_answer_candidate_helper(question) diff --git a/app/pipelines/seq2seq.py b/app/pipelines/seq2seq.py index 586fd76..cabbb00 100644 --- a/app/pipelines/seq2seq.py +++ b/app/pipelines/seq2seq.py @@ -4,13 +4,12 @@ import torch from fastapi import APIRouter from joblib import Parallel, delayed -from pywikidata import Entity from app.config import seq2seq as seq2seq_config +from app.kgqa.entity_linking import entity_linker from app.kgqa.seq2seq import build_seq2seq_pipeline from app.kgqa.utils.utils import label_to_entity_idx -from app.models.base import Entity as EntityResponce -from app.models.base import PipelineResponce +from app.models.base import Seq2SeqPipelineResponce from app.models.base import Question as QuestionRequest router = APIRouter( @@ -23,13 +22,15 @@ torch.device(seq2seq_config["device"]), ) + @lru_cache(maxsize=1024) @router.post("/") -def seq2seq_pipeline(question: QuestionRequest) -> PipelineResponce: +def seq2seq_pipeline(question: QuestionRequest) -> Seq2SeqPipelineResponce: + question_entities = entity_linker.extract_entities_from_question(question.text) seq2seq_results = seq2seq(question.text) corr_entities = Parallel(n_jobs=-2)( delayed(label_to_entity_idx)(label) for label in seq2seq_results ) corr_entities = [e for e in corr_entities if e is not None] - return PipelineResponce(answers=corr_entities[:60]) + return Seq2SeqPipelineResponce(answers=corr_entities[:60], question_entities=question_entities) diff --git a/app/templates/index_search.html b/app/templates/index_search.html index 2fb6b99..11733d5 100644 --- a/app/templates/index_search.html +++ b/app/templates/index_search.html @@ -90,25 +90,20 @@ } } - function process_pipeline_data(pipeline_data) { - const queryString = window.location.search; - const urlParams = new URLSearchParams(queryString); - const pipeline = urlParams.get('pipeline'); - + function process_pipeline_data(pipeline_data, pipeline) { graph_svg = $(document.createElement('div')).addClass("my-3"); $("#details").append(graph_svg); - if (pipeline == 'm3m' || pipeline == 'm3m_subj_question_matching') { + if (pipeline === 'm3m' || pipeline === 'm3m_subj_question_matching') { $("#details").append( ""+m3m_uncertanity_to_decision(pipeline_data['uncertenity'])+"" ); } - - if (pipeline == 'act_selection' || pipeline == 'act_selection_simple' || pipeline == 'act_selection_simple_description') { + if (pipeline === 'seq2seq' || pipeline === 'act_selection' || pipeline === 'act_selection_simple' || pipeline === 'act_selection_simple_description') { question_entities = $(document.createElement('div')); question_entities.append('Question entities: '); pipeline_data['question_entities'].forEach(obj => { - let entity_idx = obj['entity']; + let entity_idx = pipeline === 'seq2seq' ? obj : obj['entity']; $.ajax({ url: 'wikidata/entities/' + entity_idx + '/label', method: 'GET', @@ -121,43 +116,45 @@ }); $("#details").append(question_entities); - answer_instance_of = $('
Final Instance Of:
'); - answer_instance_of_count = $('
'); - th = $(document.createElement('thead')) - th.append('Instance Of EntityCount'); - answer_instance_of_count.append(th); - answer_instance_of_count_body = $(""); - answer_instance_of_count.append(answer_instance_of_count_body); + if (pipeline !== 'seq2seq') { + answer_instance_of = $('
Final Instance Of:
'); + answer_instance_of_count = $('
'); + th = $(document.createElement('thead')) + th.append('Instance Of EntityCount'); + answer_instance_of_count.append(th); + answer_instance_of_count_body = $(""); + answer_instance_of_count.append(answer_instance_of_count_body); - $("#details").append(answer_instance_of); - $("#details").append(answer_instance_of_count); + $("#details").append(answer_instance_of); + $("#details").append(answer_instance_of_count); - Object.entries(pipeline_data['answer_instance_of_count']).forEach(function(entry, idx, _) { - const [entity_idx, count] = entry; - $.ajax({ - url: 'wikidata/entities/' + entity_idx + '/label', - method: 'GET', - dataType: 'json', - cache: true, - success: function(edata) { - tr = $(document.createElement('tr')); - tr.addClass('border-b'); - tr.addClass('answer_instance_of_item'); - tr.append("" + one_instace_of_to_html(entity_idx, edata['label']) + ""); - tr.append("" + String(count) + ""); - tr.attr('order', 10000000 - count); - answer_instance_of_count_body.append(tr); - - if (pipeline_data['answer_instance_of'].includes(entity_idx)) { - answer_instance_of.append(one_instace_of_to_html(entity_idx, edata['label'])); + Object.entries(pipeline_data['answer_instance_of_count']).forEach(function (entry, idx, _) { + const [entity_idx, count] = entry; + $.ajax({ + url: 'wikidata/entities/' + entity_idx + '/label', + method: 'GET', + dataType: 'json', + cache: true, + success: function (edata) { + tr = $(document.createElement('tr')); + tr.addClass('border-b'); + tr.addClass('answer_instance_of_item'); + tr.append("" + one_instace_of_to_html(entity_idx, edata['label']) + ""); + tr.append("" + String(count) + ""); + tr.attr('order', 10000000 - count); + answer_instance_of_count_body.append(tr); + + if (pipeline_data['answer_instance_of'].includes(entity_idx)) { + answer_instance_of.append(one_instace_of_to_html(entity_idx, edata['label'])); + } + }, + complete: function (edata) { + let instance_of_count_objcts = getSorted('.answer_instance_of_item', 'order'); + instance_of_count_objcts.detach().appendTo(answer_instance_of_count_body); } - }, - complete: function(edata) { - let instance_of_count_objcts = getSorted('.answer_instance_of_item', 'order'); - instance_of_count_objcts.detach().appendTo(answer_instance_of_count_body); - } - }) - }); + }) + }); + } } answers = []; @@ -213,39 +210,55 @@ $("#top_answer_card").append(body); if ( - pipeline == 'act_selection' || - pipeline == 'act_selection_simple' || - pipeline == 'act_selection_simple_description' || - pipeline == 'm3m' || - pipeline == 'm3m_subj_question_matching' + pipeline === 'seq2seq' || + pipeline === 'act_selection' || + pipeline === 'act_selection_simple' || + pipeline === 'act_selection_simple_description' || + pipeline === 'm3m' || + pipeline === 'm3m_subj_question_matching' ) { let question_entities_idx = []; - if (pipeline == 'act_selection' || pipeline == 'act_selection_simple' || pipeline == 'act_selection_simple_description') { + if (pipeline === 'act_selection' || pipeline === 'act_selection_simple' || pipeline === 'act_selection_simple_description') { pipeline_data['question_entities'].forEach(obj => { question_entities_idx.push(String(obj["entity"])); }) + } else if (pipeline === 'seq2seq') { + pipeline_data['question_entities'].forEach(idx => { + question_entities_idx.push(String(idx)); + }) } else { question_entities_idx.push(pipeline_data['triples'][0][0]); } - $.ajax({ - url: '/wikidata/entities/ssp/graph/svg', - method: 'POST', - data: JSON.stringify({question_entities_idx: question_entities_idx, answer_idx: String(data['idx'])}), - contentType: "application/json", - dataType: 'json', - cache: true, - beforeSend: function() { - if ( $( "#loading_spinner2" ).length < 1) { - graph_svg.append(loading_spinner(2)); - } - }, - success: function (svg_data) { - graph_svg.append(String(svg_data)); - }, - complete: function() { - $("#loading_spinner2").remove(); - }, - }); + const problem_text = question_entities_idx.length === 0 ? "Can't visualize graph\nNo question entities detected" : ""; + if (problem_text) { + pre = $(document.createElement('pre')); + pre.text(problem_text); + graph_svg.append(pre); + } else { + graph_svg.empty(); + $.ajax({ + url: '/wikidata/entities/ssp/graph/svg', + method: 'POST', + data: JSON.stringify({ + question_entities_idx: question_entities_idx, + answer_idx: String(data['idx']) + }), + contentType: "application/json", + dataType: 'json', + cache: true, + beforeSend: function () { + if ($("#loading_spinner2").length < 1) { + graph_svg.append(loading_spinner(2)); + } + }, + success: function (svg_data) { + graph_svg.append(String(svg_data)); + }, + complete: function () { + $("#loading_spinner2").remove(); + }, + }); + } } } @@ -301,6 +314,12 @@ } $(document).ready(function() { + const queryString = window.location.search; + const urlParams = new URLSearchParams(queryString); + const pipeline = urlParams.get('pipeline'); + if (!pipeline) { + window.location.replace(`${window.location.href}&pipeline=seq2seq`); + } $.ajax({ url: '/pipeline/' + pipeline_switcher('{{pipeline}}') + '/', method: 'POST', @@ -309,7 +328,7 @@ dataType: 'json', cache: true, success: function (data) { - process_pipeline_data(data); + process_pipeline_data(data, pipeline); }, }) }) From ac8b0f6664414419d4a4a22fe23e5dad7c6a6d20 Mon Sep 17 00:00:00 2001 From: Dmitriy Jarosh Date: Thu, 22 May 2025 08:50:01 +0300 Subject: [PATCH 3/3] Fix problems with old model and poetry --- Dockerfile | 2 +- app/config.py | 2 +- docker-compose.yaml | 2 ++ 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/Dockerfile b/Dockerfile index dcd7649..f49beb2 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,7 +1,7 @@ FROM python:3.10 as requirements-stage WORKDIR /tmp -RUN pip install poetry +RUN pip install poetry==1.8.5 COPY ./pyproject.toml ./poetry.lock* /tmp/ RUN poetry export -f requirements.txt --output requirements.txt --without-hashes diff --git a/app/config.py b/app/config.py index 74ac020..19ab4d5 100644 --- a/app/config.py +++ b/app/config.py @@ -4,7 +4,7 @@ "model": { "path": os.environ.get( "SEQ2SEQ_MODEL_PATH", - "msalnikov/kgqa_sqwd-tunned_t5-large-ssm-nq", + "s-nlp/t5_large_ssm_nq_mintaka", ), "route_postfix": "sqwd_tunned/t5_large_ssm_nq", }, diff --git a/docker-compose.yaml b/docker-compose.yaml index c06cc40..9212c85 100644 --- a/docker-compose.yaml +++ b/docker-compose.yaml @@ -23,3 +23,5 @@ services: build: ./gap environment: - TOKENIZERS_PARALLELISM=false + ports: + - 8084:8082