diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..2838efe3d7c9a37b1954461a72a621da9b365fb0 --- /dev/null +++ b/.gitignore @@ -0,0 +1,251 @@ +# For experiment of counting tokens +src/AI/tokens.py + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# ignore training data +src/data/tt-content-postprocess +src/data/tt-content-debug +src/data/tt-content +src/scraper/tt-content-postprocess +src/scraper/tt-content-debug +src/scraper/tt-content +src/scraper/tt-klever-content + +# staticfile +src/backend/TTChatBot/staticfiles/ +src/backend/TTChatBot/static/ + +# MacOS +.DS_Store + +# Frontend +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +src/frontend/node_modules/ +/.pnp +.pnp.js + +# next.js +src/frontend/.next/ +/out/ + +# misc +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# vercel +.vercel + +# Pycharm +.idea + +# Env vars - to be updated +/infra/ci/secret.yaml + +# Local .terraform directories +**/.terraform/* + +# .tfstate files +*.tfstate +*.tfstate.* + +# Exclude all .tfvars files, which are likely to contain sensitive data, such as +# password, private keys, and other secrets. These should not be part of version +# control as they are data points which are potentially sensitive and subject +# to change depending on the environment. +*.tfvars +*.tfvars.json + +# Ignore override files as they are usually used to override resources locally and so +# are not checked in +override.tf +override.tf.json +*_override.tf +*_override.tf.json + +# Include override files you do wish to add to version control using negated pattern +# !example_override.tf + +# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan +# example: *tfplan* + +# Ignore CLI configuration files +.terraformrc +terraform.rc +.terraform.lock.hcl +# ignore .vscode +.vscode + +# Ignore sensitive data - k8s env vars +infra/environments/chatbot-dev/dev_secret.yaml +infra/environments/chatbot-prod/prod_secret.yaml +infra/environments/tt-chatbot-prod/prod_secret.yaml + +# yarn file +yarn.lock diff --git a/models/freemind/embeddings/freemind.csv b/models/freemind/embeddings/freemind.csv new file mode 100644 index 0000000000000000000000000000000000000000..cde42bdcf55bfff9a1aba86d4bd5eeefe8f7c2ff --- /dev/null +++ b/models/freemind/embeddings/freemind.csv @@ -0,0 +1,248 @@ +text,embedding +"テーマ：試験情報 +Q: JPTはいつありますか？ +A: 日本のJPTの試験日程は以下のページをご覧ください。 +https://www.jptest.jp/examination/japan/","[-0.015543349087238312, 0.00041726569179445505, 0.01982133463025093, -0.02321779727935791, -0.018823139369487762, 0.017280470579862595, 0.002615405013784766, -0.00944397784769535, -0.010714410804212093, -0.007382766809314489, -0.006180393509566784, 0.016178565099835396, -0.009450459852814674, -0.005794726777821779, -0.0016658218810334802, -0.010999609716236591, 0.027897654101252556, -0.014246989972889423, 0.021234367042779922, -0.03839816525578499, 0.017941614612936974, 0.001785735134035349, -0.019587989896535873, -0.004644207656383514, 0.005460914224386215, 0.018265703693032265, 0.0020061160903424025, -0.023347433656454086, 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+受験申込から試験結果の確認まで、専用アプリで行います。 +https://www.jptest.jp/apps/ +申込方法を詳しく説明している動画もありますので、参照してください。 + +STEP1：アプリのダウンロード +STEP2：アカウント作成（※氏名はパスポートと同じ表記で記入してください） +STEP3：受験日、受験地選択 +STEP4：ご本人写真登録 +STEP5：受験料のお支払い（クレジットカードもしくはコンビニ決済） + +申込時や試験時の注意は以下のページに詳しく載っていますので、必ずご確認ください。 +https://www.jptest.jp/examination/japan/","[-0.0067566512152552605, 0.02211742475628853, 0.021007636561989784, -0.030134012922644615, -0.018631383776664734, 0.023736409842967987, 0.00835605151951313, -0.0282277874648571, 0.008467030711472034, -0.017991624772548676, -0.01162013504654169, -0.004507698118686676, -0.004713335540145636, -0.011319839395582676, -0.015341188758611679, -0.020955411717295647, 0.022026030346751213, -0.02185629867017269, 0.01361775305122137, -0.03543488308787346, 0.0020106749143451452, 0.012410042807459831, -0.021973805502057076, -0.006528165657073259, 0.007696707267314196, 0.006028760690242052, 0.007585728541016579, -0.006280095316469669, 0.003335892688483, 0.003136783605441451, -0.002449694089591503, 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0.008878305554389954, -0.013193422928452492, 0.010921620763838291, -0.014975612051784992]" +"テーマ：申し込み関連 +Q: アプリがダウンロードできない +A: アプリ（JPT APP）は日本国内でのみ、ダウンロード可能です。 +アプリをダウンロードするGoogle Playなどの、ストアのアカウント設定が日本以外の地域となっている場合は、ダウンロードすることはできません。各ストアの国設定をご確認ください。 +国設定を日本に変更すれば、ダウンロード可能となるかと思いますが、変更には制約が色々あるようです。各ストアのヘルプをご覧いただきまして、ご検討ください。 +国設定を変更できない、または国設定を変更してもアプリをダウンロードできない場合は、アプリを使わずに別の方法でお申込を承ります。 +JPT実施委員会までメールにてご相談ください。","[-0.016468890011310577, 0.004020932596176863, 0.016522016376256943, -0.01634935848414898, -0.020758770406246185, 0.01969626173377037, 0.008885231800377369, -0.01593763567507267, 0.002342500491067767, -0.019855637103319168, -0.0068465424701571465, -0.008161397650837898, -0.006932871416211128, -0.011096579022705555, -0.0218611229211092, 0.005979933775961399, 0.01876656524837017, -0.025154901668429375, 0.01820874959230423, -0.01962985470890999, 0.020878301933407784, 0.020161109045147896, -0.009004764258861542, -0.018115779384970665, 0.003343583084642887, 0.005176411475986242, 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+A: 受験料を支払う前であれば、一度申込をキャンセルし、正しい情報で申込をやり直してください。 + +受験料を支払った後は、アプリで名前などの申込情報を修正することはできません。 +（試験日の2週間より前の場合） +・申し込んだ試験日 +・名前 +・申込会場 +を書いたうえで、正しい申込情報と、それを証明するもの（在留カードなどの本人確認書類）の画像を、JPT実施委員会までメールで送ってください。JPT実施委員会にて修正します。 +（試験まで2週間を切っている場合） +試験当日、受付に申込情報を変更したいことを申し出てください。","[-0.0228887889534235, 0.022024810314178467, 0.020775364711880684, -0.024085067212581635, -0.02924235537648201, 0.012368187308311462, -0.0012328311568126082, -0.018316349014639854, -0.010580415837466717, -0.008347363211214542, 0.004419583361595869, 0.017784669995307922, 0.011743464507162571, -0.014554718509316444, -0.0005404020776040852, -0.02671688050031662, 0.020974744111299515, 0.00011723941861419007, 0.01387682743370533, -0.02549401856958866, -0.0037051395047456026, 0.009676561690866947, -0.036739032715559006, 0.01075985748320818, -0.0026484269183129072, -0.0007393663981929421, 0.002276251558214426, -0.005409835837781429, 0.021679218858480453, 0.0073105888441205025, 0.007549844682216644, 0.012241913937032223, 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-0.013504651375114918, -0.0018243242520838976, 0.00100520602427423, 0.009836065582931042, -0.02302170917391777]" +"テーマ：試験情報 +Q: 試験会場、試験時間が知りたい +A: 試験の案内（受験番号や会場、時間、持ち物など）は、試験日の約2週間前に、アプリで見られるようになります。 +ご案内メールが届くまで、今しばらくお待ちくださいませ。 + +もし、試験日まで1週間になってもメールが届かない・アプリで受験票が見られないなどの場合は、JPT実施委員会までメールにてご連絡ください。","[-0.02590807154774666, 0.003054787637665868, 0.013314798474311829, -0.018492404371500015, -0.018933335319161415, 0.004790120758116245, -0.0024234536103904247, -0.020309576764702797, -0.008183958008885384, -0.0013971185544505715, 0.005438156425952911, 0.014216704294085503, -0.007502518128603697, -0.00545151811093092, -0.010582358576357365, -0.01503175962716341, 0.020576808601617813, -0.0013069279957562685, 0.004172148182988167, -0.028433408588171005, 0.005665303207933903, 0.0007503353408537805, -0.032735832035541534, 0.009152671322226524, -0.009333052672445774, 0.017129525542259216, -0.0013670549960806966, -0.01181162241846323, -0.0025754414964467287, 0.007809834089130163, 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+その他の日本語試験に関するお問い合わせは、各試験の主催者（JLPT運営団体、EJU運営団体など）へ問い合わせてください。","[-0.018875986337661743, -0.010399867780506611, 0.007785376161336899, -0.03008279576897621, -0.019934693351387978, 0.0262998528778553, 0.019766850396990776, -0.0057938434183597565, -0.013543713837862015, -0.01930205151438713, -0.01202020701020956, -0.004160592798143625, -0.012452728115022182, -0.008275996893644333, -0.014202178455889225, -0.00596814276650548, 0.028146134689450264, -0.025254055857658386, 0.02179388888180256, -0.024389013648033142, -0.007959675043821335, -0.014240912161767483, -0.02518950030207634, 0.0015743437688797712, -5.8553727285470814e-05, 0.008263085968792439, 0.002288487507030368, -0.01631959341466427, -0.00918622687458992, 0.02212957665324211, 0.021135423332452774, 0.019237495958805084, -0.023756371811032295, -0.021690599620342255, 0.003954015672206879, -0.021535666659474373, -0.0033036204986274242, -0.020128360018134117, 0.023227017372846603, -0.0198830496519804, 0.02577049843966961, 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JPTは日本語でのコミュニケーション能力のレベルを測るために作られたテストです。結果は合格・不合格ではなく、10点～990点のスコアで出ます。そのため合格・不合格では表せない能力の比較が正確にできます。また、JPTは毎月実施されるので受験機会が多く、結果発表も2週間後と早いので便利です。 +いっぽう、JLPTは受験レベルがN1～N5の5つに分かれていて、それぞれのレベルで合格・不合格の結果が出ます。実施は毎年7月と12月の年2回で、結果発表まで2ヵ月かかります。 +JPTとJLPTのレベル比較はこちらを見てください。 +[JLPTとの違い（レベル比較）](https://www.jptest.jp/about/jlpt.php)","[-0.01670619286596775, 0.00423419987782836, 0.0306451003998518, -0.019294114783406258, -0.008859150111675262, 0.014336065389215946, -0.001426880480721593, -0.003018708899617195, -0.02145925723016262, -0.02388063073158264, -0.014169516041874886, 0.02145925723016262, -0.0229069571942091, -0.01577095314860344, -0.0019617604557424784, -0.008590108714997768, 0.02764721028506756, -0.009416450746357441, 0.0065658921375870705, -0.03566720709204674, 0.0016879147151485085, -0.008763063699007034, -0.036845866590738297, -0.0009608622640371323, 0.0008679789025336504, 0.0101338941603899, 0.012106864713132381, -0.01131255179643631, -0.004045230336487293, 0.027467848733067513, 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0.0020716337021440268, -0.012904072180390358]" +"テーマ：申し込み関連 +Q: 写真の服装に決まりはありますか？ +A: 服装は自由ですが、スーツなどの清潔感のある服装での撮影をおすすめします。 +マスク・帽子をつけたまま撮影した写真や、在留カード・パスポートなどの証明書に貼られた写真を撮影したもの、小さすぎる写真など、本人確認が適切にできない場合は受験できないことがあります。 +![サンプル画像](https://usercontent.obotai.com/file_content/182/1692284764-172961df-91fe-4339-843d-3912f98546ed.png)","[0.00012990635877940804, -0.0025744133163243532, 0.0027508113998919725, -0.03243061900138855, -0.00017858223873190582, 0.030300527811050415, -0.007075892295688391, -0.018691537901759148, 0.0009984796633943915, -0.02586727775633335, 0.0012589163379743695, -0.0008037760853767395, 0.006014175713062286, -0.017639806494116783, -0.009458930231630802, -0.010397501289844513, 0.029741378501057625, -0.011555736884474754, 0.01710728369653225, -0.040924351662397385, 0.011409293860197067, 0.020661871880292892, -0.04779389128088951, -0.011948472820222378, -0.01926399953663349, 0.0007492757285945117, 0.01305345632135868, -0.01922406069934368, 0.006220527924597263, 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-0.018837982788681984, 0.0116422725841403, -0.0038308333605527878, 0.01566947251558304, -0.015988986939191818]" +"テーマ：申し込み関連 +Q: 申し込み後に、写真を変えることはできますか？ +A: お申し込み後に写真を変更することはできません。適切な服装と背景で撮影した写真の使用をおすすめします。","[-0.015148142352700233, -0.0009064712794497609, 0.009475646540522575, -0.03032206930220127, -0.006491140462458134, 0.01941540651023388, -0.009353172034025192, -0.031482353806495667, -0.003990085329860449, 0.0005813502357341349, -0.003548532957211137, 0.004421968478709459, 0.01666940376162529, -0.02210984192788601, 0.0017903815023601055, -0.013394826091825962, 0.02498476579785347, -0.007354906760156155, 0.01283402182161808, -0.043497730046510696, 0.009785055182874203, 0.025951668620109558, -0.035324178636074066, -0.009469200856983662, -0.021387888118624687, -0.0009757659863680601, 0.01205405406653881, -0.01305963285267353, 0.00874724704772234, 0.00037245897692628205, 0.016604943200945854, 0.014323052018880844, -0.003944963216781616, -0.03429281711578369, -0.005927113816142082, 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『クレジットカード決済』、または『コンビニ決済』でお支払いいただけます。 +※タブレットはご使用いただけません","[-0.010273020714521408, 0.00470437528565526, 0.01742943376302719, -0.04355394095182419, -0.018948452547192574, 0.0169187281280756, -0.002879261039197445, -0.029437540099024773, -0.012197984382510185, -0.007647474762052298, 0.015137809328734875, 0.011530139483511448, 0.00956589076668024, -0.020611515268683434, -0.01094086468219757, -0.018961546942591667, 0.02734234184026718, 0.0022212378680706024, 0.005856734700500965, -0.03933735191822052, -0.012734878808259964, 0.009598628617823124, -0.02408168837428093, -0.012774163857102394, -0.01982581615447998, -0.0023014445323497057, 0.024540012702345848, -0.007968301884829998, 0.010384327732026577, 0.011300977319478989, 0.012433693744242191, 0.020271046087145805, -0.00394813995808363, -0.031218457967042923, -0.003200088394805789, -0.003493088763207197, -0.007634379900991917, 0.015111619606614113, 0.02063770592212677, -0.012990230694413185, 0.023335274308919907, -0.00595494732260704, 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-0.016787124797701836, 0.02794012799859047, 0.011172209866344929, 0.0012324644485488534, 0.017196880653500557, 0.009123438037931919, -0.023701731115579605, -0.015660300850868225, -0.02778646908700466, 0.0049010468646883965, -0.022331614047288895, 0.010295079089701176, -0.016992002725601196]" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6a782eb466352e356ee78b219b34d2d7b46b144b --- /dev/null +++ b/requirements.txt @@ -0,0 +1,33 @@ +# AI packages +gradio==3.40.1 # for demo with interface +llama-index==0.7.4 # enables the LLM to access the outside information that serves as our knowledge base +openai==0.27.8 # for generating embeddings +pandas==2.0.3 # for DataFrames to store article sections and embeddings +scipy==1.11.1 # for calculating vector similarities for search +tiktoken==0.4.0 # for counting tokens +typing_extensions==4.5.0 # for TypeError: issubclass() arg 1 must be a class of langchain + +# BE/FE packages +requests==2.31.0 +tqdm==4.65.0 +django==4.2.4 +python-dotenv==1.0.0 +beautifulsoup4==4.12.2 +# django rest API +djangorestframework==3.14.0 +drf-yasg==1.21.7 +celery==5.3.1 +django-celery-results==2.5.1 +psycopg2-binary==2.9.6 +# message broker for celery +redis==4.6.0 +# threading library for celery +gevent==23.7.0 +# production +gunicorn==21.2.0 +# cors +django-cors-headers==4.2.0 +# convert html to text +html2text==2020.1.16 +# serving staticfiles without using ngnix +whitenoise==6.5.0 diff --git a/src/AI/__init__.py b/src/AI/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/AI/ai_configs.py b/src/AI/ai_configs.py new file mode 100644 index 0000000000000000000000000000000000000000..120d4fd72cf1e9f8031621a0044be2bce2bd1171 --- /dev/null +++ b/src/AI/ai_configs.py @@ -0,0 +1,84 @@ +""" +Author: Khanh Phan +Date: 2023-04-20 +""" +import os +import sys + +# MODEL PARAMETERS: https://platform.openai.com/docs/models/gpt-3-5 +MODEL_NAME = "gpt-3.5-turbo" # Must select from MODEL_NAMES +MODEL_NAMES = ["gpt-4", "text-davinci-003", "gpt-3.5-turbo"] +EMBEDDING_MODEL = ( + "text-embedding-ada-002" # OpenAI's best embeddings as of Apr 2023 +) + +# CHATBOT SERVICE +SERVICE = "freemind" # Must select from SERVICES +SERVICES = ["TokyoTechies", "Klever", "Test", "freemind"] + +# DATA FORMATTING +DELIMITER_TOKYOTECHIES = "Sub Section:" +FILE_TYPE = ".txt" +FILE_ENCODING = "utf-8" +INTRODUCTION_MESSAGE = ( + f"You are a chatbot of {SERVICE}. " + f"Use the below articles on the {SERVICE} to answer the subsequent question. " # noqa: E501 + "If an answer cannot be found in the articles, write sorry that I cannot answer your request, please contact our support team for further assistance." # noqa: E501 + r'If an answer is found, add embedding title in this format "[Title](URL)" to the end of an answer and ignore the same title.' # noqa: E501 +) +SYSTEM_CONTENT = "You answer questions about {SERVICE}" + +# CALCULATE EMBEDDING PARAMETERS +MAX_TOKENS = 1600 # maximum tokens for a section +BATCH_SIZE = 1000 # up to 2048 embedding inputs per request +TOKEN_BUDGET = 4096 - 500 + +# TRAINING PARAMETERS +CONTEXT_WINDOW = 4096 # Context window for the LLM. +NUM_OUTPUTS = 512 # Number of outputs for the LLM. +CHUNK_OVERLAP_RATIO = 0.1 # Chunk overlap as a ratio of chunk size +TEMPERATURE = 0.0 # A parameter that controls the “creativity” or +# randomness of the text generated. A higher temperature (e.g., 0.7) +# results in more diverse and creative output, while a lower temperature +# (e.g., 0.2) makes the output more deterministic and focused. + +sys.path.append(os.path.abspath(os.path.join("..", "data"))) + +# PATH +if SERVICE in SERVICES: + if MODEL_NAME in MODEL_NAMES: + # Path to training files: + FOLDERPATH_DOCUMENTS = os.path.join( + "data", + SERVICE, + "training_files", + ) + # Path to model + FOLDERPATH_INDEXES = os.path.join( + "models", + SERVICE, + MODEL_NAME, + ) + FILEPATH_EMBEDDINGS = os.path.join( + "models", + SERVICE, + "embeddings", + f"{SERVICE}.csv", + ) + # For evaluation + FOLDERPATH_QUESTION = os.path.join( + "data", + SERVICE, + "evaluation", + "questions", + ) + FOLDERPATH_QA = os.path.join( + "data", + SERVICE, + "evaluation", + "QA_" + MODEL_NAME, + ) + else: + raise ValueError("MODEL_NAME must be in MODEL_NAMES") +else: + raise ValueError("SERVICE must be in SERVICES") diff --git a/src/AI/chatbot_demo.py b/src/AI/chatbot_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..fa0fb606bbc4c4042a5d7936a7d42fc1ec5eae12 --- /dev/null +++ b/src/AI/chatbot_demo.py @@ -0,0 +1,112 @@ +""" +Author: Khanh Phan +Date: 2023-04-20 +""" +import configparser +import os + +import gradio as gr +import openai +from ai_configs import ( + FOLDERPATH_INDEXES, + MODEL_NAME, +) +from llama_index import ( + StorageContext, + load_index_from_storage, +) + +env = configparser.ConfigParser() +env.read(".env") +os.environ["OPENAI_API_KEY"] = env["OpenAI"]["OPENAI_KEY_TT"] +openai.api_key = os.environ["OPENAI_API_KEY"] + + +def format_response(responses: dict): + """ + (Optional) Format one or multiple responses from version(s) of chatbot + + Parameters: + responses (dict): chatbot response with the name of model + + Returns: + output (str): formatted reponse + """ + output = "" + for response in responses: + output += response + (responses[response]) + "\n\n" + return output + + +def chat(message, history): + """ + Load index to chatbot and get response + + Parameters: + message (dict): question to chatbot + history (list): history of the whole conversation + + Returns: + history (list): history of the whole conversation (for displaying) + history (list): state of the chatbot + """ + history = history or [] + # rebuild storage context + FOLDERPATH_INDEXES_EN = FOLDERPATH_INDEXES + "_en" + storage_context = StorageContext.from_defaults( + persist_dir=FOLDERPATH_INDEXES_EN, + ) + + # load index to memory + index = load_index_from_storage(storage_context) + + # open QA engine + query_engine = index.as_query_engine() + + # Get the response from OpenAI + response_en = query_engine.query(message) + print("Q: ", message) + print("A: ", response_en.response, "\n") + + # ---------- JAPANESE + # rebuild storage context + FOLDERPATH_INDEXES_JA = FOLDERPATH_INDEXES + "_ja" + storage_context_ja = StorageContext.from_defaults( + persist_dir=FOLDERPATH_INDEXES_JA, + ) + + # load index to memory + index_ja = load_index_from_storage(storage_context_ja) + + # open QA engine + query_engine_ja = index_ja.as_query_engine() + + # Get the response from OpenAI + response_ja = query_engine_ja.query(message) + print("Q: ", message) + print("A: ", response_ja.response, "\n") + ###### + + # Format the response + responses = { + f"---{MODEL_NAME} (English)---": response_en.response, + f"---{MODEL_NAME} (Japanese)---": response_ja.response, + } + + response = format_response(responses) + + # Append the response to history (to show in the UI) + history.append((message, response)) + + return history, history + + +# Call the chat using gradio which supports UI for chatbot and is shareable +chatgpt = gr.Interface( + chat, + ["text", "state"], + ["chatbot", "state"], + allow_flagging="never", +) + +chatgpt.launch(share=True) # share=True to share the chat publicly diff --git a/src/AI/embedding.py b/src/AI/embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..0573641187e373b7d69ce3dcdcb2e440bd8e45ce --- /dev/null +++ b/src/AI/embedding.py @@ -0,0 +1,323 @@ +""" +Author: Khanh Phan +Date: 2023-07-20 +""" + +import configparser +import os + +import openai +import pandas as pd +import tiktoken +from ai_configs import ( + BATCH_SIZE, + DELIMITER_TOKYOTECHIES, + EMBEDDING_MODEL, + FILE_ENCODING, + FILE_TYPE, + FILEPATH_EMBEDDINGS, + FOLDERPATH_DOCUMENTS, + MAX_TOKENS, + MODEL_NAME, + SERVICE, +) + + +def list_files(directory: str) -> list: + files = [] + for file in os.listdir(directory): + # check only text files + if file.endswith(FILE_TYPE): + files.append(file) + return files + + +def read_file(file_path: str) -> str: + # Open a file: file + file = open(file_path, encoding=FILE_ENCODING) + + # read all lines at once + file_content = file.read() + + # close the file + file.close() + return file_content + + +def num_tokens(text: str, model: str = MODEL_NAME) -> int: + """Return the number of tokens in a string.""" + encoding = tiktoken.encoding_for_model(model) + return len(encoding.encode(text)) + + +def truncated_string( + string: str, + model: str, + max_tokens: int, + print_warning: bool = True, +) -> str: + """Truncate a string to a maximum number of tokens.""" + encoding = tiktoken.encoding_for_model(model) + encoded_string = encoding.encode(string) + truncated_string = encoding.decode(encoded_string[:max_tokens]) + if print_warning and len(encoded_string) > max_tokens: + print( + f"Warning: Truncated string from {len(encoded_string)} tokens to {max_tokens} tokens.", # noqa: E501 + ) + return truncated_string + + +def determine_delimiter( + strings: str, + service: str = SERVICE, +) -> str: + """ + Determine the delimiter of the file + """ + if service == "TokyoTechies": + return DELIMITER_TOKYOTECHIES + elif service == "Klever": + if "# " in strings: + return "# " + elif "## " in strings: + return "## " + elif "### " in strings: + return "### " + else: + return False + raise ValueError( + f"No delimiter found in Klever file: {strings[0:20]}", + ) + elif service == "freemind": + return "-----" + else: + raise ValueError(f"Unknown service: {service}") + + +def format_content_Tokyo_Techies( + strings: str, + content: str, + max_tokens: int = 1000, + model: str = MODEL_NAME, +): + """ + Format content for Tokyo Techies + """ + chunks = content.split(determine_delimiter(content)) + # TODO: add to config + if "URL:" and "Language:" in chunks[0]: + url = ( + "" + + (content.split("URL:"))[1].split("Language")[0].strip() + + "" + ) # get url + else: + url = "No URL" + + for chunk in chunks[1:]: + chunk = ( + chunk.strip() + ) # remove leading and trailing whitespace and newline + if not chunk: + continue + + # get section title (first row) and content (from 2nd row) + section_title = chunk.split("\n")[0] + titles = [url, section_title] + section_content = chunk.split("\n")[1:] + section_content = "\n".join(section_content) + + if num_tokens(section_content) > max_tokens: + print( + f"{titles} ({num_tokens(section_content)}) has more than {max_tokens} tokens", # noqa: E501 + ) + section_content = truncated_string( + section_content, + model=model, + max_tokens=max_tokens, + ) + + string = "\n\n".join(titles + [section_content]) + strings.extend([string]) + print(string) + return strings + + +def format_content_klever( + strings: str, + content: str, + max_tokens: int = 1000, + model: str = MODEL_NAME, +): + """ + Format content for Klever + """ + + # Add images tag to image link + content = content.replace("![](", "![image](") + + delimiter = determine_delimiter(content) + if delimiter: + chunks = content.split(delimiter) + else: + chunks = [content] + + # TODO: add to config + url = "" + if "Title:" and "URL:" in chunks[0]: + title = "Title: " + ( + (content.split("Title:"))[1].split("URL")[0].strip() + ) + if "Language:" in chunks[0]: + url = ( + "" + + (content.split("URL:"))[1].split("Language:")[0].strip() + + "" + ) + else: + title = "" + + # Extract content between title and the first sub-section + section_content = (chunks[0].split("-----"))[1].strip() + if section_content != "": + titles = [title, url] + string = "\n\n".join(titles + [section_content]) + # print(f"----------\n{string}\n") + strings.extend([string]) + + # Extract contentin every sub-section + for chunk in chunks[1:]: + chunk = ( + chunk.strip() + ) # remove leading and trailing whitespace and newline + if not chunk: + continue + + # get section title (first row) and content (from 2nd row) + section_title = chunk.split("\n")[0] + titles = [title + " > " + section_title, url] + section_content = chunk.split("\n")[1:] + section_content = "\n".join(section_content) + + if num_tokens(section_content) > max_tokens: + print( + f"{titles} ({num_tokens(section_content)}) has more than {max_tokens} tokens", # noqa: E501 + ) + section_content = truncated_string( + section_content, + model=model, + max_tokens=max_tokens, + ) + + string = "\n\n".join(titles + [section_content]) + # print(f"----------\n{string}\n") + strings.extend([string]) + return strings + + +def format_content_freemind( + strings: str, + content: str, + max_tokens: int = 1000, + model: str = MODEL_NAME, +): + """ + Format content for freemind + """ + chunks = content.split(determine_delimiter(content)) + for chunk in chunks: + chunk = ( + chunk.strip() + ) # remove leading and trailing whitespace and newline + if not chunk: + continue + + if num_tokens(chunk) > max_tokens: + print( + f"{chunk} ({num_tokens(section_content)}) has more than {max_tokens} tokens", # noqa: E501 + ) + section_content = truncated_string( + section_content, + model=model, + max_tokens=max_tokens, + ) + + string = chunk + # print(f"----------\n{string}\n") + strings.extend([string]) + return strings + + +def format_content( + directory: str, + max_tokens: int = 1000, + model: str = MODEL_NAME, +) -> list[str]: + strings = [] + + # read files + files = list_files(directory) + for file in files: + print(f"File: {file}") + file_content = read_file( + os.path.join( + FOLDERPATH_DOCUMENTS, + file, + ), + ) + + if SERVICE == "TokyoTechies": + strings = format_content_Tokyo_Techies( + strings, + file_content, + max_tokens, + model, + ) + elif SERVICE == "Klever": + strings = format_content_klever( + strings, + file_content, + max_tokens, + model, + ) + elif SERVICE == "freemind": + strings = format_content_freemind( + strings, + file_content, + max_tokens, + model, + ) + + return strings + + +def embed_data(): + # read config + env = configparser.ConfigParser() + env.read(".env") + os.environ["OPENAI_API_KEY"] = env["OpenAI"]["OPENAI_KEY_TT"] + openai.api_key = os.environ["OPENAI_API_KEY"] + + formatted_strings = format_content(FOLDERPATH_DOCUMENTS, MAX_TOKENS) + + embeddings = [] + for batch_start in range(0, len(formatted_strings), BATCH_SIZE): + batch_end = batch_start + BATCH_SIZE + batch = formatted_strings[batch_start:batch_end] + print(f"Batch {batch_start} to {batch_end-1}") + response = openai.Embedding.create(model=EMBEDDING_MODEL, input=batch) + for i, be in enumerate(response["data"]): + assert ( + i == be["index"] + ) # double check embeddings are in same order as input + batch_embeddings = [e["embedding"] for e in response["data"]] + embeddings.extend(batch_embeddings) + + df = pd.DataFrame({"text": formatted_strings, "embedding": embeddings}) + + # save document chunks and embeddings + SAVE_PATH = FILEPATH_EMBEDDINGS + df.to_csv(SAVE_PATH, index=False) + + +embed_data() diff --git a/src/AI/evaluation.py b/src/AI/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..55e9a79e4268876198e2afbdfb265d5316b959e7 --- /dev/null +++ b/src/AI/evaluation.py @@ -0,0 +1,101 @@ +""" +Author: Khanh Phan +Date: 2023-06-20 +""" +import configparser +import os + +import openai +from ai_configs import ( + FOLDERPATH_INDEXES, + FOLDERPATH_QA, + FOLDERPATH_QUESTION, +) +from llama_index import ( + StorageContext, + load_index_from_storage, +) + +env = configparser.ConfigParser() +env.read(".env") +os.environ["OPENAI_API_KEY"] = env["OpenAI"]["OPENAI_KEY_TT"] +openai.api_key = os.environ["OPENAI_API_KEY"] + + +def get_question_files(path: str = FOLDERPATH_QUESTION) -> None: + """ + Get the directory. + Check whether it is valid (a folder or a text file) or invalid. + If valid, generate the answer from the questions in the directory. + + Parameters: + path (str): Path to a question file/folder + + Returns: + None + """ + if os.path.isdir(path) is True: + for file in os.listdir(path): + if file.endswith(".txt"): + print("Generating answer from: ", os.path.join(path, file)) + generate_answers(os.path.join(path, file)) + elif os.path.isfile(path) is True and path.endswith(".txt"): + print("Generating answer from:", path) + generate_answers(path) + else: + raise Exception("Input is not a directory of a folder or a text file") + + +def generate_answers( + file_directory: str, + output_path=FOLDERPATH_QA, +) -> None: + """ + Get the list of questions from file(s), + then generate the answers and write to file(s). + These answers are used for evaluation + + Parameters: + file_directory (str): Path to a question file + output_path (str): folder to write the answers + + Returns: + None + """ + + # Load the questions + question_file = open(file_directory) + lines = question_file.readlines() + + # Create a file to write the answers + file_name = os.path.basename(file_directory) + qa_file = open(os.path.join(output_path, file_name), "w") + + count = 0 + for line in lines: # for each question + count += 1 + # generate the answer + response = query_engine.query(line) + + # format the output + question = "Q" + str(count) + ": " + str(line) + answer = "A" + str(count) + ": " + str(response.response) + + response = question + answer + "\n" + print(response) + + # write Q&A to file + qa_file.writelines(response) + + question_file.close() + qa_file.close() + + +# rebuild storage context +storage_context = StorageContext.from_defaults(persist_dir=FOLDERPATH_INDEXES) + +# load index +index = load_index_from_storage(storage_context) +query_engine = index.as_query_engine() + +get_question_files() diff --git a/src/AI/klever_search.py b/src/AI/klever_search.py new file mode 100644 index 0000000000000000000000000000000000000000..2829e939d76d9b91398a4964b408719c4937a820 --- /dev/null +++ b/src/AI/klever_search.py @@ -0,0 +1,73 @@ +import ast # for converting embeddings saved as strings back to arrays +import configparser +import os +import time + +import gradio as gr +import openai # for calling the OpenAI API +import pandas as pd # for storing text and embeddings data +import tiktoken # for counting tokens +from ai_configs import ( + EMBEDDING_MODEL, + FILEPATH_EMBEDDINGS, + INTRODUCTION_MESSAGE, + MODEL_NAME, + SYSTEM_CONTENT, + TOKEN_BUDGET, +) +from scipy import spatial # for calculating vector similarities for search + +env = configparser.ConfigParser() +env.read(".env") +os.environ["OPENAI_API_KEY"] = env["OpenAI"]["OPENAI_KEY_TT"] +openai.api_key = os.environ["OPENAI_API_KEY"] + +model_name = MODEL_NAME +# Read embbeding file +embedding_data = pd.read_csv(FILEPATH_EMBEDDINGS) +# Convert embeddings from CSV str type back to list type +embedding_data["embedding"] = embedding_data["embedding"].apply( + ast.literal_eval, +) +print("Finished loading embedding data!") + + +# search function +def strings_ranked_by_relatedness( + query: str, + df: pd.DataFrame, + relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y), + top_n: int = 3, +) -> tuple[list[str], list[float]]: + """Returns a list of strings and relatednesses, + sorted from most related to least. + """ + query_embedding_response = openai.Embedding.create( + model=EMBEDDING_MODEL, + input=query, + ) + query_embedding = query_embedding_response["data"][0]["embedding"] + strings_and_relatednesses = [ + (row["text"], relatedness_fn(query_embedding, row["embedding"])) + for i, row in df.iterrows() + ] + strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True) + strings, relatednesses = zip(*strings_and_relatednesses) + + return strings[:top_n], relatednesses[:top_n] + + +embedding_data = pd.read_csv(FILEPATH_EMBEDDINGS) +# Convert embeddings from CSV str type back to list type +embedding_data["embedding"] = embedding_data["embedding"].apply( + ast.literal_eval, +) + +query = "what is Klever?" +strings, relatedness = strings_ranked_by_relatedness(query, embedding_data) +for string in strings: + if "" in string: + string = string.split("")[0].replace("", "URL: ") + print(string) + print("----------------") +print(relatedness) \ No newline at end of file diff --git a/src/AI/parsing.py b/src/AI/parsing.py new file mode 100644 index 0000000000000000000000000000000000000000..495174caae8c47c6b1534e6d7babf72f7f3af533 --- /dev/null +++ b/src/AI/parsing.py @@ -0,0 +1,7 @@ +import aspose.words as aw + +# Load the PDF document from the disc. +doc = aw.Document("TestDocument.pdf") + +# Save the document to DOCX format. +doc.save("output.md") \ No newline at end of file diff --git a/src/AI/search.py b/src/AI/search.py new file mode 100644 index 0000000000000000000000000000000000000000..36b09a7e36ff4a8a9a821ef0b5e4e69cf1b2f361 --- /dev/null +++ b/src/AI/search.py @@ -0,0 +1,219 @@ +import ast # for converting embeddings saved as strings back to arrays +import configparser +import os +import time + +import gradio as gr +import openai # for calling the OpenAI API +import pandas as pd # for storing text and embeddings data +import tiktoken # for counting tokens +from ai_configs import ( + EMBEDDING_MODEL, + FILEPATH_EMBEDDINGS, + INTRODUCTION_MESSAGE, + MODEL_NAME, + SYSTEM_CONTENT, + TOKEN_BUDGET, +) +from scipy import spatial # for calculating vector similarities for search + +env = configparser.ConfigParser() +env.read(".env") +os.environ["OPENAI_API_KEY"] = env["OpenAI"]["OPENAI_KEY_TT"] +openai.api_key = os.environ["OPENAI_API_KEY"] + +print(openai.api_key) + +model_name = MODEL_NAME +# Read embbeding file +embedding_data = pd.read_csv(FILEPATH_EMBEDDINGS) +# Convert embeddings from CSV str type back to list type +embedding_data["embedding"] = embedding_data["embedding"].apply( + ast.literal_eval, +) +print("Finished loading embedding data!") + + +# search function +def strings_ranked_by_relatedness( + query: str, + df: pd.DataFrame, + relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y), + top_n: int = 3, +) -> tuple[list[str], list[float]]: + """Returns a list of strings and relatednesses, + sorted from most related to least. + """ + query_embedding_response = openai.Embedding.create( + model=EMBEDDING_MODEL, + input=query, + ) + query_embedding = query_embedding_response["data"][0]["embedding"] + strings_and_relatednesses = [ + (row["text"], relatedness_fn(query_embedding, row["embedding"])) + for i, row in df.iterrows() + ] + strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True) + strings, relatednesses = zip(*strings_and_relatednesses) + + return strings[:top_n], relatednesses[:top_n] + + +def num_tokens(text: str, model: str = MODEL_NAME) -> int: + """Return the number of tokens in a string.""" + encoding = tiktoken.encoding_for_model(model) + return len(encoding.encode(text)) + + +def query_message( + query: str, + df: pd.DataFrame, + model: str, + token_budget: int, +) -> str: + """Return a message for GPT, + with relevant source texts pulled from a dataframe. + """ + strings, _ = strings_ranked_by_relatedness(query, df) + + """ example: + #strings, relatednesses = strings_ranked_by_relatedness( + # "what solutions that TT provides?", + # df, + # top_n=5, + # ) + #for string, relatedness in zip(strings, relatednesses): + # print(f"{relatedness=:.3f}\n{string}\n") + """ + + question = f"\n\nQuestion: {query}" + message = INTRODUCTION_MESSAGE + for string in strings: + next_article = f"\nTT article section:\n--\n{string}\n--" + next_article = f"\nFreemind article section:\n--\n{string}\n--" + if ( + num_tokens(message + next_article + question, model=model) + > token_budget + ): + break + else: + message += next_article + return message + question + + +def get_response( + query: str, + df: pd.DataFrame, + model: str = MODEL_NAME, + token_budget: int = TOKEN_BUDGET, + print_message: bool = False, +) -> str: + """Answers a query using GPT and a dataframe of + relevant texts and embeddings. + """ + message = query_message(query, df, model=model, token_budget=token_budget) + + if print_message: + print(message) + messages = [ + {"role": "system", "content": SYSTEM_CONTENT}, + {"role": "user", "content": message}, + ] + + response = openai.ChatCompletion.create( + model=model, + messages=messages, + temperature=0, + ) + response_message = response["choices"][0]["message"]["content"] + print(f'Total used tokens: {response["usage"]["total_tokens"]}') + return response_message, message + + +# Code for getting chatbot's response ends here. Below code is for UI only. +def format_response(responses: dict): + """ + (Optional) Format one or multiple responses from version(s) of chatbot + + Parameters: + responses (dict): chatbot response with the name of model + + Returns: + output (str): formatted reponse + """ + output = "" + for response in responses: + output += response + (responses[response]) + "\n\n" + return output + + +with gr.Blocks() as chatgpt: + chatbot = gr.Chatbot(label="Freemind Bot", height=500) + message = gr.Textbox( + label="Enter your chat here", + placeholder="Press enter to send a message", + show_copy_button=True, + ) + radio = gr.Radio( + [ + "Full model (most capable but slow & expensive)", + "Lite model (Capable but fast & cheap)", + ], + label="Choose a chatbot model", + value="Lite model (Capable but fast & cheap)", + ) + clear = gr.Button("Clear all chat") + + def choice_model(choice): + if choice == "Full model (most capable but slow & expensive)": + return "gpt-4" + else: + return "gpt-3.5-turbo" + + def get_user_message(user_message, history): + return "", history + [[user_message, None]] + + def show_response(history, model): + message = history[-1][0] + model = choice_model(model) + print(f"model: {model}") + # Get the response from OpenAI + response, _ = get_response( + query=message, + df=embedding_data, + model=model, + ) + + # Correct URL + # I will remove this function after BE/FE fixing this bug + response = response.replace("help/document/", "wiki/1-") + response = response.replace(">>", ">") + print("Q: ", message, "\nA: ", response, "\n") + + # Format the response + # responses = { + # f"[{MODEL_NAME}] → ": response, + # } + # response = format_response(responses) + + history[-1][1] = "" + for character in response: + history[-1][1] += character + time.sleep(0.01) + yield history + + message.submit( + get_user_message, + [message, chatbot], + [message, chatbot], + queue=False, + ).then( + show_response, + [chatbot, radio], + chatbot, + ) + clear.click(lambda: None, None, chatbot, queue=False) + + +chatgpt.queue() +chatgpt.launch(share=True) # share=True to share the chat publicly diff --git a/src/AI/training.py b/src/AI/training.py new file mode 100644 index 0000000000000000000000000000000000000000..0561b48029d90556300a74338d28a649e2c325aa --- /dev/null +++ b/src/AI/training.py @@ -0,0 +1,95 @@ +""" +Author: Khanh Phan +Date: 2023-04-20 +""" + +import configparser +import os + +import openai +from ai_configs import ( # CHUNK_SIZE_LIMIT, + CHUNK_OVERLAP_RATIO, + CONTEXT_WINDOW, + FOLDERPATH_DOCUMENTS, + FOLDERPATH_INDEXES, + MODEL_NAME, + NUM_OUTPUTS, + TEMPERATURE, +) +from langchain import OpenAI +from llama_index import ( + GPTVectorStoreIndex, + LLMPredictor, + PromptHelper, + ServiceContext, + SimpleDirectoryReader, +) + +env = configparser.ConfigParser() +env.read(".env") +os.environ["OPENAI_API_KEY"] = env["OpenAI"]["OPENAI_KEY_TT"] +openai.api_key = os.environ["OPENAI_API_KEY"] + + +def construct_index( + folderpath_documents: str, + folderpath_index: str, +) -> GPTVectorStoreIndex: + """ + Construsted index for all the documents. + + Parameters: + folderpath_documents (str): Path to a training folder + folderpath_index (str): Path to a folder to save the model + + Returns: + document_index (GPTVectorStoreIndex): the model + """ + + # Create a prompt helper with initial parameters for the chatbot + prompt_helper = PromptHelper( + context_window=CONTEXT_WINDOW, + num_output=NUM_OUTPUTS, + chunk_overlap_ratio=CHUNK_OVERLAP_RATIO, + # chunk_size_limit=CHUNK_SIZE_LIMIT, + ) + + # Configure the LLM provider and model. + llm_predictor = LLMPredictor( + llm=OpenAI( + temperature=TEMPERATURE, + model_name=MODEL_NAME, + max_tokens=NUM_OUTPUTS, + ), + ) + + # Create the service context + service_context = ServiceContext.from_defaults( + llm_predictor=llm_predictor, + prompt_helper=prompt_helper, + ) + + # Load the documents + documents = SimpleDirectoryReader(folderpath_documents).load_data() + + # Generate the index from documents + document_index = GPTVectorStoreIndex.from_documents( + documents, + service_context=service_context, + ) + + # Save index to disk + document_index.storage_context.persist(persist_dir=folderpath_index) + + return document_index + + +document_index = construct_index(FOLDERPATH_DOCUMENTS, FOLDERPATH_INDEXES) + +""" +# These lines are for testing purposes only. +query = input("What do you want to ask? ") +query_engine = document_index.as_query_engine() +response = query_engine.query("what are the articles about?") +print(response) +""" diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/backend/TTChatBot/.sample-env b/src/backend/TTChatBot/.sample-env new file mode 100644 index 0000000000000000000000000000000000000000..2e7cc8a386c329fd57830f5b7882fa813f953a20 --- /dev/null +++ b/src/backend/TTChatBot/.sample-env @@ -0,0 +1,24 @@ +ENV_NAME='local' + +DJANGO_SETTINGS_MODULE=config.settings.local +DJANGO_SECRET_KEY='django-insecure-xfs(py=^axctf8(#5yd-svkffy3ft0u0z6^*&vx@g#)fttc#sl' +DJANGO_DEBUG=True + +# Database +# DB_NAME='postgres' +# DB_USER='postgres' +# DB_PASSWORD='postgres' +# DB_HOST='127.0.0.1' +# DB_PORT=5678 + +# Celery +# CELERY_BROKER_URL = 'redis://localhost:6379/0' +# CELERY_RESULT_BACKEND = 'redis://localhost:6379/0' + +# Other API +OPEN_AI_KEY='KEY' + +# Redis config +REDIS_HOST = redis +REDIS_PORT = 6380 +BROKER_URL = redis://${REDIS_HOST}:${REDIS_PORT}/0 diff --git a/src/backend/TTChatBot/chatbot/__init__.py b/src/backend/TTChatBot/chatbot/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/backend/TTChatBot/chatbot/admin.py b/src/backend/TTChatBot/chatbot/admin.py new file mode 100644 index 0000000000000000000000000000000000000000..4185d360e9a725a190ddfd9b8cce8aeb0df64cc3 --- /dev/null +++ b/src/backend/TTChatBot/chatbot/admin.py @@ -0,0 +1,3 @@ +# from django.contrib import admin + +# Register your models here. diff --git a/src/backend/TTChatBot/chatbot/apps.py b/src/backend/TTChatBot/chatbot/apps.py new file mode 100644 index 0000000000000000000000000000000000000000..58ebb23cb5d6f4bfc1321615318c780a2d164df0 --- /dev/null +++ b/src/backend/TTChatBot/chatbot/apps.py @@ -0,0 +1,242 @@ +import ast +import logging +import os + +import openai +import pandas as pd +from django.apps import AppConfig +from django.conf import settings +from llama_index import ( + StorageContext, + load_index_from_storage, +) +from scipy import spatial + +from .utils import num_tokens_from_messages + +# set OpenAI API key +openai.api_key = os.environ["OPENAI_API_KEY"] +logger = logging.getLogger(__name__) + + +def load_chatgpt_index(apps_names: str, index_file: str): + """Functions to load chatGPT index + + Args: + apps_names (str): TokyoTechies or Klever + index_file (str): Storage index path + """ + # build storage context + logger.info("Building %s storage context", apps_names) + storage_context = StorageContext.from_defaults( + persist_dir=index_file, + ) + + # load index + index = load_index_from_storage(storage_context) + + query_engine = index.as_query_engine() + logger.info("Loading index from %s storage completed", apps_names) + return query_engine + + +class ChatGPTEmbeddingSearchBased: + """ChatGPT embedding search based method for Kleverbot""" + + def __init__( + self, + service, + embedding_model, + chat_model, + filepath_embedding, + ): + self.embedding_model = embedding_model + self.chat_model = chat_model + self.filepath_embedding = filepath_embedding + self.service = service + # model related config + self.token_budget = settings.TOKEN_BUDGET + self.introduction_message = settings.INTRODUCTION_MESSAGE.format( + service=self.service, + ) + self.system_content = settings.SYSTEM_CONTENT.format( + service=self.service, + ) + self.next_article = settings.NEXT_ARTICLE + self.embedding_data = self.load_embedding_data() + + def load_embedding_data(self): + """Loading embedding data from csv""" + df_data = pd.read_csv(self.filepath_embedding) + df_data["embedding"] = df_data["embedding"].apply( + ast.literal_eval, + ) + logger.info( + "Loading embeddings from %s storage completed", + self.filepath_embedding, + ) + return df_data + + # search function + def strings_ranked_by_relatedness( + self, + query: str, + df_data: pd.DataFrame, + relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y), + top_n: int = 3, + ) -> tuple[list[str], list[float]]: + """Returns a list of strings and relatednesses, + sorted from most related to least. + """ + query_embedding_response = openai.Embedding.create( + model=self.embedding_model, + input=query, + ) + query_embedding = query_embedding_response["data"][0]["embedding"] + strings_and_relatednesses = [ + (row["text"], relatedness_fn(query_embedding, row["embedding"])) + for i, row in df_data.iterrows() + ] + strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True) + strings, relatednesses = zip(*strings_and_relatednesses) + + return strings[:top_n], relatednesses[:top_n] + + def query_message( + self, + query: str, + dataframe: pd.DataFrame, + model: str, + token_budget: int, + ) -> str: + """Return a message for GPT, + with relevant source texts pulled from a dataframe. + """ + strings, _ = self.strings_ranked_by_relatedness(query, dataframe) + question = f"\n\nQuestion: {query}" + message = self.introduction_message + for string in strings: + next_article = self.next_article.format( + service=self.service, + string=string, + ) + if ( + num_tokens_from_messages( + message + next_article + question, + model=model, + ) + > token_budget + ): + break + else: + message += next_article + return message + question + + def get_response( + self, + query: str, + data: pd.DataFrame, + model: str = "gpt-3.5-turbo", + token_budget: int = 4096 - 500, + log_message: bool = False, + ): + """Answers a query using GPT and a dataframe of + relevant texts and embeddings. + """ + message = self.query_message( + query=query, + dataframe=data, + model=model, + token_budget=token_budget, + ) + + if log_message: + logging.info(message) + + messages = [ + {"role": "system", "content": self.system_content}, + {"role": "user", "content": message}, + ] + + response = openai.ChatCompletion.create( + model=model, + messages=messages, + temperature=0, + ) + response_message = response["choices"][0]["message"]["content"] + + if log_message: + logging.info( + "Total used tokens: %s", + response["usage"]["total_tokens"], + ) + + return response_message, message + + def chat(self, message): + """Chat with Kleverbot with message, return response from OpenAI""" + res, _ = self.get_response( + query=message, + data=self.embedding_data, + model=self.chat_model, + token_budget=self.token_budget, + ) + + # TODO: fix this one after Klever FE fix -> scraper + return ( + res.replace("help/document/", "wiki/1-") + .replace(">>", ">") + .replace("https://tokyotechies.kleversuite.net", "{ORG_URL}") + ) + + +class TTChatbotConfig(AppConfig): + """TokyoTechies Chatbot Init""" + + name = "chatbot" + label = "tt_chatbot" + + # old method + # INDEXES_FILE = os.path.join( + # settings.TT_MODELS_PATH, + # settings.TT_MODEL_NAME, + # ) + + # QUERY_ENGINE = load_chatgpt_index( + # apps_names="TokyoTechies", + # index_file=INDEXES_FILE, + # ) + + # new method + QUERY_ENGINE = ChatGPTEmbeddingSearchBased( + service="TokyoTechies", + embedding_model=settings.TT_EMBEDDING_MODEL, + chat_model=settings.TT_EMBEDDING_CHAT_MODEL, + filepath_embedding=settings.TT_FILEPATH_EMBEDDING, + ) + + +class KleverChatbotConfig(AppConfig): + """Klever Chatbot Init""" + + name = "chatbot" + label = "klever_chatbot" + + # old method + # INDEXES_FILE = os.path.join( + # settings.KLEVER_MODELS_PATH, + # settings.KLEVER_MODEL_NAME, + # ) + + # QUERY_ENGINE = load_chatgpt_index( + # apps_names="Klever", + # index_file=INDEXES_FILE, + # ) + + # new method + QUERY_ENGINE = ChatGPTEmbeddingSearchBased( + service="Klever", + embedding_model=settings.KLEVER_EMBEDDING_MODEL, + chat_model=settings.KLEVER_EMBEDDING_CHAT_MODEL, + filepath_embedding=settings.KLEVER_FILEPATH_EMBEDDING, + ) diff --git a/src/backend/TTChatBot/chatbot/exceptions.py b/src/backend/TTChatBot/chatbot/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..f986a9af4e5857c1a3eb251741b87cffe31ee50e --- /dev/null +++ b/src/backend/TTChatBot/chatbot/exceptions.py @@ -0,0 +1,10 @@ +class TTChatBotConnectException(Exception): + pass + + +class TTChatBotEngineException(Exception): + pass + + +class ChatbotVersionException(Exception): + pass diff --git a/src/backend/TTChatBot/chatbot/migrations/__init__.py b/src/backend/TTChatBot/chatbot/migrations/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/backend/TTChatBot/chatbot/serializers.py b/src/backend/TTChatBot/chatbot/serializers.py new file mode 100644 index 0000000000000000000000000000000000000000..2f704e517f85bcd0e411ab658734978077d205f9 --- /dev/null +++ b/src/backend/TTChatBot/chatbot/serializers.py @@ -0,0 +1,25 @@ +from rest_framework import serializers + + +class ConversationSerializer(serializers.Serializer): + """Conversation content when user interact with the Chatbot""" + + user_chat = serializers.CharField(required=True, max_length=1000) + + +class MessageSerializer(ConversationSerializer): + """Response content when Chabot outputs to the user""" + + chatbot_answer = serializers.CharField(required=True, max_length=1000) + + +class TaskSerializer(ConversationSerializer): + """Response content when Chabot outputs to the user""" + + task_id = serializers.CharField(required=True) + + +class VersionSerializer(serializers.Serializer): + """Trained version of the chatbot""" + + version = serializers.CharField(required=True) diff --git a/src/backend/TTChatBot/chatbot/tasks.py b/src/backend/TTChatBot/chatbot/tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..37a06beb423dab5241e188d78f0f99fdcca4edf2 --- /dev/null +++ b/src/backend/TTChatBot/chatbot/tasks.py @@ -0,0 +1,79 @@ +import logging + +from celery import shared_task +from celery.utils.log import get_task_logger +from django.conf import settings + +from .apps import ( + KleverChatbotConfig, + TTChatbotConfig, +) +from .exceptions import ( + TTChatBotConnectException, + TTChatBotEngineException, +) +from .utils import num_tokens_from_messages + +# TODO: fix the logger does not goes into log file +logger = get_task_logger(__name__) + + +def tt_sync_chat_website(message): + return _chat_tt(message, engine="TokyoTechies") + + +@shared_task(max_retries=0) +def tt_async_chat_website(message): + max_token = settings.MAX_TOKEN + + if ( + num_tokens_from_messages( + messages=message, + model=settings.TT_MODEL_NAME, + ) + >= max_token + ): + logging.warning( + "Maximum token %s reached for user messages: %s", + max_token, + message, + ) + return settings.MAX_TOKEN_RESPONSE + else: + return _chat_tt(message, TTChatbotConfig) + + +@shared_task(max_retries=0) +def tt_async_chat_klever(message): + max_token = settings.MAX_TOKEN + + if ( + num_tokens_from_messages( + messages=message, + model=settings.KLEVER_MODEL_NAME, + ) + >= max_token + ): + logging.warning( + "Maximum token %s reached for user messages: %s", + max_token, + message, + ) + return settings.MAX_TOKEN_RESPONSE + else: + return _chat_tt(message, KleverChatbotConfig) + + +def _chat_tt(message, engine=None): + try: + # TODO: check for query retries number of time when disconnected + if engine: + response = engine.QUERY_ENGINE.chat(message) + return response + else: + raise TTChatBotEngineException("Connect engine failed") + except Exception as exc: + logging.error("OpenAI error: ", exc_info=exc) + raise TTChatBotConnectException( + "Connect OpenAPI engine failed", + ) from exc diff --git a/src/backend/TTChatBot/chatbot/urls.py b/src/backend/TTChatBot/chatbot/urls.py new file mode 100644 index 0000000000000000000000000000000000000000..bb487da09f05de3f687a8d9a0ad381cfdef83992 --- /dev/null +++ b/src/backend/TTChatBot/chatbot/urls.py @@ -0,0 +1,31 @@ +from django.urls import path + +# from rest_framework.urlpatterns import format_suffix_patterns +from . import views + +app_name = "chatbot" + +urlpatterns = [ + # List and create conversations + path("chat/sync", views.ConversationSyncView.as_view()), + path( + "chat/tokyotechies/async", + views.TokyoTechiesConversationAsyncView.as_view(), + ), + path("chat/klever/async", views.KleverConversationAsyncView.as_view()), + path( + "chat//", + views.ChatTaskStatus.as_view(), + name="gpt_task_status", + ), + path( + "chat/tokyotechies/version", + views.TTBotVerion.as_view(), + name="ttbot_version", + ), + path( + "chat/klever/version", + views.KleverBotVerion.as_view(), + name="kleverbot_version", + ), +] diff --git a/src/backend/TTChatBot/chatbot/utils.py b/src/backend/TTChatBot/chatbot/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..adde30063294ff359d9471d5535fab215105b9be --- /dev/null +++ b/src/backend/TTChatBot/chatbot/utils.py @@ -0,0 +1,108 @@ +import logging +import os +import re +from datetime import datetime + +import tiktoken + +from .exceptions import ChatbotVersionException + +logger = logging.getLogger(__name__) + + +def num_tokens_from_messages( + messages: str, + model: str = "gpt-3.5-turbo-0613", +) -> int: + """ + Return the number of tokens used by a list of messages. + """ + try: + if model in ["text-davinci-003", "text-davinci-002"]: + encoding = tiktoken.get_encoding("p50k_base") + else: # gpt-4, "gpt-3.5-turbo" + encoding = tiktoken.encoding_for_model(model) + except KeyError: + logger.warning("Warning: model not found. Using cl100k_base encoding.") + encoding = tiktoken.get_encoding("cl100k_base") + + num_tokens = len(encoding.encode(messages)) + return num_tokens + + +def get_datetime_from_file(file_path: str) -> str: + """Get datetime from file + + Args: + file_path (str): file path + + Raises: + ChatbotVersionException: expcetion when file is not found + + Returns: + str: version of the chatbot in format + """ + try: + # Get the modification timestamp of the file + file_ts = os.path.getmtime(file_path) + return datetime.fromtimestamp(file_ts).strftime("%Y%m%d") + + except FileNotFoundError as exc: + logging.error("File %s not found error: ", file_path, exc_info=exc) + raise ChatbotVersionException("File not found", exc) from exc + + +def extract_datetime_from_file( + version_file_path: str, + weight_file_path: str, +) -> str: + r"""Extract date from + "- Training data includes information up until (\w{3} \d{2})" + + Args: + version_file_path (str): file path of version file + weight_file_path (str): file path of embedding file + to be used when can not get file path of version file + + Raises: + ChatbotVersionException: expcetion when file is not found + + Returns: + str: version of the chatbot in format + """ + current_year = datetime.now().year + target_line_format = ( + r"- Training data includes information up until (\w{3} \d{2})" + ) + + try: + # Open the input file for reading + with open(version_file_path, encoding="utf-8") as infile: + # Iterate through each line in the input file + for line in infile: + # Use regex to search for the date format in the line + match = re.search(target_line_format, line.strip()) + if match: + # If a match is found, extract the date + extracted_date = match.group(1) + break # Exit the loop after finding the first date + + # Check if a date was extracted and print it + if extracted_date: + return datetime.strptime( + f"{extracted_date} {current_year}", + "%b %d %Y", + ).strftime("%Y%m%d") + else: + logging.warning( + "Date not found in the file, fallback to deployment date", + ) + return get_datetime_from_file(weight_file_path) + + except FileNotFoundError as exc: + logging.error( + "File %s not found error: ", + version_file_path, + exc_info=exc, + ) + raise ChatbotVersionException("File not found", exc) from exc diff --git a/src/backend/TTChatBot/chatbot/views.py b/src/backend/TTChatBot/chatbot/views.py new file mode 100644 index 0000000000000000000000000000000000000000..7fcad9bffbc3bcad896584857bb3b78dcd6980b8 --- /dev/null +++ b/src/backend/TTChatBot/chatbot/views.py @@ -0,0 +1,199 @@ +import json +import logging + +from celery.result import AsyncResult +from django.conf import settings +from rest_framework import ( + generics, + status, +) +from rest_framework.response import Response + +from .exceptions import ( + ChatbotVersionException, + TTChatBotConnectException, + TTChatBotEngineException, +) +from .serializers import ( + ConversationSerializer, + MessageSerializer, + TaskSerializer, + VersionSerializer, +) +from .tasks import ( + tt_async_chat_klever, + tt_async_chat_website, + tt_sync_chat_website, +) +from .utils import ( + extract_datetime_from_file, + num_tokens_from_messages, +) + +# add logger +logger = logging.getLogger(__name__) + + +class ConversationSyncView(generics.GenericAPIView): + serializer_class = ConversationSerializer + + def post(self, request, *args, **kwargs): + data = json.loads(request.body.decode("utf-8")) + question = data.get("user_chat", None) + max_token = settings.MAX_TOKEN + + try: + if ( + num_tokens_from_messages( + messages=question, + model=settings.TT_MODEL_NAME, + ) + >= max_token + ): + logging.warning( + "Maximum token %s reached for user messages: %s", + max_token, + question, + ) + res = MessageSerializer( + { + "user_chat": question, + "chatbot_answer": settings.MAX_TOKEN_RESPONSE, + }, + ) + else: + answer = tt_sync_chat_website(question) + res = MessageSerializer( + { + "user_chat": question, + "chatbot_answer": answer, + }, + ) + + except (TTChatBotConnectException, TTChatBotEngineException) as exc: + logger.error("Failed to send request to ChatGPT: %s", exc) + res = MessageSerializer( + { + "user_chat": question, + "chatbot_answer": settings.DEFAULT_RESPONSE, + }, + ) + return Response(res.data, status=status.HTTP_400_BAD_REQUEST) + + return Response(res.data, status=status.HTTP_200_OK) + + +class TokyoTechiesConversationAsyncView(generics.GenericAPIView): + serializer_class = ConversationSerializer + + def post(self, request, *args, **kwargs): + data = json.loads(request.body.decode("utf-8")) + question = data.get("user_chat", None) + + try: + answer = tt_async_chat_website.delay(question) + res = TaskSerializer( + { + "user_chat": question, + "task_id": answer.id, + }, + ) + + except (TTChatBotConnectException, TTChatBotEngineException) as exc: + logger.error("Failed to send request to ChatGPT: %s", exc) + res = TaskSerializer( + { + "user_chat": question, + "task_id": None, + }, + ) + return Response(res.data, status=status.HTTP_400_BAD_REQUEST) + + return Response(res.data, status=status.HTTP_200_OK) + + +class KleverConversationAsyncView(generics.GenericAPIView): + serializer_class = ConversationSerializer + + def post(self, request, *args, **kwargs): + data = json.loads(request.body.decode("utf-8")) + question = data.get("user_chat", None) + + try: + answer = tt_async_chat_klever.delay(question) + res = TaskSerializer( + { + "user_chat": question, + "task_id": answer.id, + }, + ) + + except (TTChatBotConnectException, TTChatBotEngineException) as exc: + logger.error("Failed to send request to ChatGPT: %s", exc) + res = TaskSerializer( + { + "user_chat": question, + "task_id": None, + }, + ) + return Response(res.data, status=status.HTTP_400_BAD_REQUEST) + + return Response(res.data, status=status.HTTP_200_OK) + + +class ChatTaskStatus(generics.GenericAPIView): + """ + Check the status of ChatGPT task + """ + + serializer_class = TaskSerializer + + def get(self, request, task_id, *args, **kwargs): + task = AsyncResult(task_id) + + if task.ready(): + response = task.result + logging.info("Task reponse: %s", response) + return Response({"status": "READY", "response": response}) + else: + return Response({"status": "PENDING"}) + + +class TTBotVerion(generics.GenericAPIView): + """Get version of TTBot""" + + serializer_class = VersionSerializer + + def get(self, *args, **kwargs): + try: + ttbot_version = extract_datetime_from_file( + version_file_path=settings.TT_TRAINING_VERSION, + weight_file_path=settings.TT_FILEPATH_EMBEDDING, + ) + res = VersionSerializer({"version": ttbot_version}) + return Response(res.data, status=status.HTTP_200_OK) + + except ChatbotVersionException as exc: + logger.error("Failed to check version: %s", exc) + res = VersionSerializer({"version": None}) + return Response(res.data, status=status.HTTP_400_BAD_REQUEST) + + +class KleverBotVerion(generics.GenericAPIView): + """Get version of Klever""" + + serializer_class = VersionSerializer + + def get(self, *args, **kwargs): + try: + kleverbot_version = extract_datetime_from_file( + version_file_path=settings.KLEVER_TRAINING_VERSION, + weight_file_path=settings.KLEVER_FILEPATH_EMBEDDING, + ) + res = VersionSerializer({"version": kleverbot_version}) + return Response(res.data, status=status.HTTP_200_OK) + + except ChatbotVersionException as exc: + logger.error("Failed to check version: %s", exc) + res = VersionSerializer({"version": None}) + return Response(res.data, status=status.HTTP_400_BAD_REQUEST) diff --git a/src/backend/TTChatBot/config/__init__.py b/src/backend/TTChatBot/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..370372af0d6acb6929b3ba5151d02b8c695b9d65 --- /dev/null +++ b/src/backend/TTChatBot/config/__init__.py @@ -0,0 +1,3 @@ +from .celery import app as celery_app + +__all__ = ["celery_app"] diff --git a/src/backend/TTChatBot/config/asgi.py b/src/backend/TTChatBot/config/asgi.py new file mode 100644 index 0000000000000000000000000000000000000000..9d22fb8e52f043bce011d858f597b682ad100dd4 --- /dev/null +++ b/src/backend/TTChatBot/config/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for TTChatBot project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.2/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings") + +application = get_asgi_application() diff --git a/src/backend/TTChatBot/config/celery.py b/src/backend/TTChatBot/config/celery.py new file mode 100644 index 0000000000000000000000000000000000000000..98f021dfe63b50e6b1a48dd7cd84703575b612b5 --- /dev/null +++ b/src/backend/TTChatBot/config/celery.py @@ -0,0 +1,24 @@ +import os + +from celery import Celery +from django.conf import settings + +# Set the default Django settings module for the 'celery' program. +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings") + +app = Celery("TTChatBot") + +app.conf.update( + broker_connection_retry_on_startup=True, + broker_connection_max_retries=10, + result_expires=60, + task_acks_late=True, +) + +app.config_from_object("django.conf:settings", namespace="CELERY") +app.autodiscover_tasks(lambda: settings.INSTALLED_APPS) + +# TODO: convention celery: +# https://qiita.com/hankehly/items/c3e0496eb04327a53ac4 +# TODO: crontab for celery: +# https://www.codingforentrepreneurs.com/blog/celery-redis-django/ diff --git a/src/backend/TTChatBot/config/settings/__init__.py b/src/backend/TTChatBot/config/settings/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b13c5bbe7e97fe82bde304b891117536c7d88f29 --- /dev/null +++ b/src/backend/TTChatBot/config/settings/__init__.py @@ -0,0 +1,19 @@ +import os + +from dotenv import load_dotenv + +load_dotenv() + +# Open AI key +OPENAI_API_KEY = os.getenv("OPEN_AI_KEY") +# settings keys for model +os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY + +env_name = os.getenv("ENV_NAME", "prod") + +if env_name == "local": + from .local import * # noqa +elif env_name == "staging": + from .staging import * # noqa +else: + from .prod import * # noqa diff --git a/src/backend/TTChatBot/config/settings/common.py b/src/backend/TTChatBot/config/settings/common.py new file mode 100644 index 0000000000000000000000000000000000000000..687f5b7bc40b23845a641584892d5b3b5450cc3c --- /dev/null +++ b/src/backend/TTChatBot/config/settings/common.py @@ -0,0 +1,132 @@ +import os +from pathlib import Path + +BASE_DIR = Path(__file__).resolve().parent.parent.parent + +# Static files (CSS, JavaScript, Images) +STORAGE_URL = BASE_DIR / "storage" + +# Swagger HTTPS +USE_X_FORWARDED_HOST = True +SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https") + +# Application definition + +DJANGO_APPS = [ + "django.contrib.admin", + "django.contrib.auth", + "django.contrib.contenttypes", + "django.contrib.sessions", + "django.contrib.messages", + "django.contrib.staticfiles", +] + +THIRD_PARTY_APPS = [ + "gunicorn", + "rest_framework", + "drf_yasg", # another way to swagger + "django_celery_results", # Store Celery Result and cache +] + +LOCAL_APPS = [ + "chatbot.apps", + # 'users.apps.UsersConfig', + # 'site_settings.apps.SiteSettingsConfig', + # 'training_model.apps.TrainingModelConfig', +] + +INSTALLED_APPS = DJANGO_APPS + THIRD_PARTY_APPS + LOCAL_APPS + +MIDDLEWARE = [ + "django.middleware.security.SecurityMiddleware", + "django.contrib.sessions.middleware.SessionMiddleware", + "django.middleware.common.CommonMiddleware", + "django.middleware.csrf.CsrfViewMiddleware", + "django.contrib.auth.middleware.AuthenticationMiddleware", + "django.contrib.messages.middleware.MessageMiddleware", + "django.middleware.clickjacking.XFrameOptionsMiddleware", + "whitenoise.middleware.WhiteNoiseMiddleware", +] + + +SECRET_KEY = os.getenv("DJANGO_SECRET_KEY") +ROOT_URLCONF = "config.urls" +WSGI_APPLICATION = "config.wsgi.application" +ASGI_APPLICATION = "config.asgi.application" + +TEMPLATES = [ + { + "BACKEND": "django.template.backends.django.DjangoTemplates", + "DIRS": [BASE_DIR / "templates"], + "APP_DIRS": True, + "OPTIONS": { + "context_processors": [ + "django.template.context_processors.debug", + "django.template.context_processors.request", + "django.contrib.auth.context_processors.auth", + "django.contrib.messages.context_processors.messages", + ], + }, + }, +] + +# Logging +LOGGING = { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "format": "%(asctime)s %(levelname)s: %(message)s", + }, + }, + "filters": { + "require_debug_false": { + "()": "django.utils.log.RequireDebugFalse", + }, + "require_debug_true": { + "()": "django.utils.log.RequireDebugTrue", + }, + }, + "handlers": { + "console": { + "class": "logging.StreamHandler", + "formatter": "default", + "level": "INFO", + }, + "common": { + "class": "logging.FileHandler", + "filename": STORAGE_URL / "common.log", + "formatter": "default", + "level": "INFO", + }, + }, + "loggers": { + "": { + "handlers": ["console", "common"], + "level": 1, + }, + }, +} + +# Internationalization +# https://docs.djangoproject.com/en/4.2/topics/i18n/ + +LANGUAGE_CODE = "en-us" + +TIME_ZONE = "UTC" + +USE_I18N = True + +USE_TZ = True + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.2/howto/static-files/ + +STATIC_URL = "/static/" +STATIC_ROOT = os.path.join(BASE_DIR, "static") +STATICFILES_STORAGE = "whitenoise.storage.CompressedManifestStaticFilesStorage" + +# Default primary key field type +# https://docs.djangoproject.com/en/4.2/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField" diff --git a/src/backend/TTChatBot/config/settings/local.py b/src/backend/TTChatBot/config/settings/local.py new file mode 100644 index 0000000000000000000000000000000000000000..d47b92d052590dfb9628b37a082511a49a730913 --- /dev/null +++ b/src/backend/TTChatBot/config/settings/local.py @@ -0,0 +1,98 @@ +import os + +from .common import * # noqa + +ALLOWED_HOSTS = ["*"] + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = os.getenv("DJANGO_DEBUG") + +# Chatbot default answer +DEFAULT_RESPONSE = "Sorry, I'm having trouble understanding you." +DEFAULT_RESPONSE_JP = "申し訳ございません、ご質問を理解いたしかねます" + +MAX_TOKEN_RESPONSE = ( + "Sorry, I'm having trouble processing all that information. " + "Could you summarize a bit more concisely?" +) +MAX_TOKEN_RESPONSE_JP = "申し訳ございません、いただいた全ての情報を処理することができません。もう少し簡潔にしてください。" + +# --Postgres-- +# DATABASES = { +# 'default': { +# 'ENGINE': 'django.db.backends.postgresql', +# 'NAME': os.getenv('DB_NAME'), +# 'USER': os.getenv('DB_USER'), +# 'PASSWORD': os.getenv('DB_PASSWORD'), +# 'HOST': os.getenv('DB_HOST', 'localhost'), +# 'PORT': os.getenv('DB_PORT'), +# } +# } + +# --Celery-- +# List of modules to import when celery starts. +# --Worker settings-- +# If you're doing mostly I/O you can have more processes, +# but if mostly spending CPU, try to keep it close to the +# number of CPUs on your machine. If not set, the number of CPUs/cores +# available will be used. +CELERY_WORKER_CONCURRENCY = 1 +# CELERYD_LOG_FILE = "celeryd.log" +# CELERYD_LOG_LEVEL = "INFO" +REDIS_HOST = os.getenv("REDIS_HOST") +REDIS_PORT = os.getenv("REDIS_PORT") +BROKER_URL = os.getenv("BROKER_URL") + +CELERY_BROKER_URL = BROKER_URL +CELERY_RESULT_BACKEND = BROKER_URL +CELERY_ACCEPT_CONTENT = ["application/json"] +CELERY_RESULT_SERIALIZER = "json" +CELERY_TASK_SERIALIZER = "json" + +# Config for old query methods +# TT Websites Models path +TT_MODELS_PATH = "../../../models/TokyoTechies/" +# text-davinci-003 or gpt-4 +TT_MODEL_NAME = "text-davinci-003" +# Klever Models path +KLEVER_MODELS_PATH = "../../../models/Klever/" +# text-davinci-003 or gpt-4 +KLEVER_MODEL_NAME = "text-davinci-003" + + +# Config for new embedding methods +KLEVER_EMBEDDING_MODEL = ( + "text-embedding-ada-002" # OpenAI's best embeddings as of Apr 2023 +) +KLEVER_EMBEDDING_CHAT_MODEL = "gpt-3.5-turbo" # "gpt-4" +KLEVER_FILEPATH_EMBEDDING = "../../../models/Klever/embeddings/Klever.csv" +KLEVER_TRAINING_VERSION = "../../../models/Klever/_version.txt" + +# Config for new embedding methods +TT_EMBEDDING_MODEL = ( + "text-embedding-ada-002" # OpenAI's best embeddings as of Apr 2023 +) +TT_EMBEDDING_CHAT_MODEL = "gpt-3.5-turbo" # "gpt-4" +TT_FILEPATH_EMBEDDING = ( + "../../../models/TokyoTechies/embeddings/TokyoTechies.csv" +) +TT_TRAINING_VERSION = "../../../models/TokyoTechies/_version.txt" + +INTRODUCTION_MESSAGE = ( + "You are a chatbot of {service}. " + "Use the below articles on the {service} to answer the subsequent question. " # noqa: E501 + "If the answer cannot be found in the articles, write sorry that I cannot answer your request, please contact our support team for further assistance." # noqa: E501 + r'If an answer is found, add embedding title in this format "[Title](URL)" to the end of an answer and ignore the same title.' # noqa: E501 +) +SYSTEM_CONTENT = "You answer questions about {service}" +NEXT_ARTICLE = "\n{service}" + "article section:\n--\n{string}\n--" +TOKEN_BUDGET = 4096 - 500 + +# max token +MAX_TOKEN = 200 + +# CELERYD_TASK_SOFT_TIME_LIMIT = 3 +# Kill anything longer than 10 seconds: +# CELERYD_TASK_TIME_LIMIT = 10 +# After 2 hours remove the task result: +# CELERY_TASK_RESULT_EXPIRES = 60 * 60 * 2 diff --git a/src/backend/TTChatBot/config/settings/prod.py b/src/backend/TTChatBot/config/settings/prod.py new file mode 100644 index 0000000000000000000000000000000000000000..c2b5451fdb7db8028cee1a3935489bcd2e5cef90 --- /dev/null +++ b/src/backend/TTChatBot/config/settings/prod.py @@ -0,0 +1,98 @@ +import os + +from .common import * # noqa + +ALLOWED_HOSTS = ["*"] + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = False + +# Chatbot default answer +DEFAULT_RESPONSE = "Sorry, I'm having trouble understanding you." +DEFAULT_RESPONSE_JP = "申し訳ございません、ご質問を理解いたしかねます" + +MAX_TOKEN_RESPONSE = ( + "Sorry, I'm having trouble processing all that information. " + "Could you summarize a bit more concisely?" +) +MAX_TOKEN_RESPONSE_JP = "申し訳ございません、いただいた全ての情報を処理することができません。もう少し簡潔にしてください。" + +# --Postgres-- +# DATABASES = { +# 'default': { +# 'ENGINE': 'django.db.backends.postgresql', +# 'NAME': os.getenv('DB_NAME'), +# 'USER': os.getenv('DB_USER'), +# 'PASSWORD': os.getenv('DB_PASSWORD'), +# 'HOST': os.getenv('DB_HOST', 'localhost'), +# 'PORT': os.getenv('DB_PORT'), +# } +# } + +# --Celery-- +# List of modules to import when celery starts. +# --Worker settings-- +# If you're doing mostly I/O you can have more processes, +# but if mostly spending CPU, try to keep it close to the +# number of CPUs on your machine. If not set, the number of CPUs/cores +# available will be used. +CELERY_WORKER_CONCURRENCY = 20 +# CELERYD_LOG_FILE = "celeryd.log" +# CELERYD_LOG_LEVEL = "INFO" +REDIS_HOST = os.getenv("REDIS_HOST") +REDIS_PORT = os.getenv("REDIS_PORT") +BROKER_URL = os.getenv("BROKER_URL") + +CELERY_BROKER_URL = BROKER_URL +CELERY_RESULT_BACKEND = BROKER_URL +CELERY_ACCEPT_CONTENT = ["application/json"] +CELERY_RESULT_SERIALIZER = "json" +CELERY_TASK_SERIALIZER = "json" + +# Config for old query methods +# TT Websites Models path +TT_MODELS_PATH = "../../../models/TokyoTechies/" +# text-davinci-003 or gpt-4 +TT_MODEL_NAME = "text-davinci-003" +# Klever Models path +KLEVER_MODELS_PATH = "../../../models/Klever/" +# text-davinci-003 or gpt-4 +KLEVER_MODEL_NAME = "text-davinci-003" + + +# Config for new embedding methods +KLEVER_EMBEDDING_MODEL = ( + "text-embedding-ada-002" # OpenAI's best embeddings as of Apr 2023 +) +KLEVER_EMBEDDING_CHAT_MODEL = "gpt-3.5-turbo" # "gpt-4" +KLEVER_FILEPATH_EMBEDDING = "../../../models/Klever/embeddings/Klever.csv" +KLEVER_TRAINING_VERSION = "../../../models/Klever/_version.txt" + +# Config for new embedding methods +TT_EMBEDDING_MODEL = ( + "text-embedding-ada-002" # OpenAI's best embeddings as of Apr 2023 +) +TT_EMBEDDING_CHAT_MODEL = "gpt-3.5-turbo" # "gpt-4" +TT_FILEPATH_EMBEDDING = ( + "../../../models/TokyoTechies/embeddings/TokyoTechies.csv" +) +TT_TRAINING_VERSION = "../../../models/TokyoTechies/_version.txt" + +INTRODUCTION_MESSAGE = ( + "You are a chatbot of {service}. " + "Use the below articles on the {service} to answer the subsequent question. " # noqa: E501 + "If the answer cannot be found in the articles, write sorry that I cannot answer your request, please contact our support team for further assistance." # noqa: E501 + r'If an answer is found, add embedding title in this format "[Title](URL)" to the end of an answer and ignore the same title.' # noqa: E501 +) +SYSTEM_CONTENT = "You answer questions about {service}" +NEXT_ARTICLE = "\n{service}" + "article section:\n--\n{string}\n--" +TOKEN_BUDGET = 4096 - 500 + +# max token +MAX_TOKEN = 200 + +# CELERYD_TASK_SOFT_TIME_LIMIT = 3 +# Kill anything longer than 10 seconds: +# CELERYD_TASK_TIME_LIMIT = 10 +# After 2 hours remove the task result: +# CELERY_TASK_RESULT_EXPIRES = 60 * 60 * 2 diff --git a/src/backend/TTChatBot/config/settings/staging.py b/src/backend/TTChatBot/config/settings/staging.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/backend/TTChatBot/config/urls.py b/src/backend/TTChatBot/config/urls.py new file mode 100644 index 0000000000000000000000000000000000000000..fef284654cfedc443448f2505939a982f99d208f --- /dev/null +++ b/src/backend/TTChatBot/config/urls.py @@ -0,0 +1,62 @@ +""" +URL configuration for TTChatBot project. + +The `urlpatterns` list routes URLs to views. For more information please see: + https://docs.djangoproject.com/en/4.2/topics/http/urls/ +Examples: +Function views + 1. Add an import: from my_app import views + 2. Add a URL to urlpatterns: path('', views.home, name='home') +Class-based views + 1. Add an import: from other_app.views import Home + 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') +Including another URLconf + 1. Import the include() function: from django.urls import include, path + 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) +""" +from django.conf import settings +from django.conf.urls.static import static +from django.urls import ( + include, + path, +) +from drf_yasg import openapi +from drf_yasg.generators import OpenAPISchemaGenerator +from drf_yasg.views import get_schema_view +from rest_framework import permissions + + +class BothHttpAndHttpsSchemaGenerator(OpenAPISchemaGenerator): + def get_schema(self, request=None, public=False): + schema = super().get_schema(request, public) + schema.schemes = ["http", "https"] + return schema + + +schema_view = get_schema_view( + openapi.Info( + title="Tokyo Techies Chatbot", + default_version="v1", + description="API documentation for Toyko Techies Chatbot API", + ), + public=True, + generator_class=BothHttpAndHttpsSchemaGenerator, + permission_classes=[permissions.AllowAny], +) + +urlpatterns = [ + path("api/v1/", include("chatbot.urls")), + # Swagger URLs + path( + "", + schema_view.with_ui("swagger", cache_timeout=0), + name="schema-swagger-ui", + ), +] + +# Include static files serving only during development +if settings.DEBUG: + urlpatterns += static( + settings.STATIC_URL, + document_root=settings.STATIC_ROOT, + ) diff --git a/src/backend/TTChatBot/config/wsgi.py b/src/backend/TTChatBot/config/wsgi.py new file mode 100644 index 0000000000000000000000000000000000000000..d43f20e2cb2b9763b998f3e788a3625cff926d1f --- /dev/null +++ b/src/backend/TTChatBot/config/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for TTChatBot project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.2/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings") + +application = get_wsgi_application() diff --git a/src/backend/TTChatBot/manage.py b/src/backend/TTChatBot/manage.py new file mode 100644 index 0000000000000000000000000000000000000000..5f5ad714c0bc7da5d6579c0c92c2a595c6360b91 --- /dev/null +++ b/src/backend/TTChatBot/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings") + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?", + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == "__main__": + main() diff --git a/src/backend/TTChatBot/storage/.gitkeep b/src/backend/TTChatBot/storage/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/frontend/.gitkeep b/src/frontend/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/frontend/.prettierignore b/src/frontend/.prettierignore new file mode 100644 index 0000000000000000000000000000000000000000..18480ed61e141dc5a9599da1cc2027ba901baa73 --- /dev/null +++ b/src/frontend/.prettierignore @@ -0,0 +1 @@ +public/**/* diff --git a/src/frontend/.prettierrc b/src/frontend/.prettierrc new file mode 100644 index 0000000000000000000000000000000000000000..b2095be81e4e40b736dfc9b3559d0250e4becd02 --- /dev/null +++ b/src/frontend/.prettierrc @@ -0,0 +1,4 @@ +{ + "semi": false, + "singleQuote": true +} diff --git a/src/frontend/.sample-env b/src/frontend/.sample-env new file mode 100644 index 0000000000000000000000000000000000000000..3c6c3abe81005f3d3f63128bfe5284b6c776b154 --- /dev/null +++ b/src/frontend/.sample-env @@ -0,0 +1 @@ +BACKEND_API_URL=http://localhost:8000/ diff --git a/src/frontend/Dockerfile b/src/frontend/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..3468115cd947d1eb6e953676cc2fb81feef6da76 --- /dev/null +++ b/src/frontend/Dockerfile @@ -0,0 +1,22 @@ +ARG NODE_VERSION=18.16.0 +ARG ALPINE_VERSION=3.17.2 + +FROM node:${NODE_VERSION}-alpine AS node +FROM alpine:${ALPINE_VERSION} + +COPY --from=node /usr/lib /usr/lib +COPY --from=node /usr/local/lib /usr/local/lib +COPY --from=node /usr/local/include /usr/local/include +COPY --from=node /usr/local/bin /usr/local/bin + +# create destination directory +RUN mkdir -p /src/frontend +WORKDIR /src/frontend + +# copy the app +COPY . /src/frontend +RUN npm install + +EXPOSE 3000 + +CMD [ "npm", "start" ] diff --git a/src/frontend/environments/dev/build.args b/src/frontend/environments/dev/build.args new file mode 100644 index 0000000000000000000000000000000000000000..45f171878b20caea76091cd8c74cf7ff84b66037 --- /dev/null +++ b/src/frontend/environments/dev/build.args @@ -0,0 +1 @@ +BACKEND_API_URL=https://www.chatbot-api.dev.aws.tokyotechies.co.jp/ diff --git a/src/frontend/environments/prod/build.args b/src/frontend/environments/prod/build.args new file mode 100644 index 0000000000000000000000000000000000000000..ec8e65b5ade4f1f445b18dcc98a43daaacdbdfed --- /dev/null +++ b/src/frontend/environments/prod/build.args @@ -0,0 +1 @@ +BACKEND_API_URL=https://www.chatbot-api.tokyotechies.com/ diff --git a/src/frontend/next-env.d.ts b/src/frontend/next-env.d.ts new file mode 100644 index 0000000000000000000000000000000000000000..4f11a03dc6cc37f2b5105c08f2e7b24c603ab2f4 --- /dev/null +++ b/src/frontend/next-env.d.ts @@ -0,0 +1,5 @@ +/// +/// + +// NOTE: This file should not be edited +// see https://nextjs.org/docs/basic-features/typescript for more information. diff --git a/src/frontend/next.config.js b/src/frontend/next.config.js new file mode 100644 index 0000000000000000000000000000000000000000..cf597f2567fb532562c37a8b0f95f3cdbad74e5b --- /dev/null +++ b/src/frontend/next.config.js @@ -0,0 +1,11 @@ +/** @type {import('next').NextConfig} */ +const nextConfig = { + reactStrictMode: true, + i18n: { + locales: ['en', 'ja'], + defaultLocale: 'en', + localeDetection: false, + }, +} + +module.exports = nextConfig diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..30deadd7d2f3ede90d7b27812f1b67823355f943 --- /dev/null +++ b/src/frontend/package-lock.json @@ -0,0 +1,5034 @@ +{ + "name": "tokyo-techies-chatbot", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": 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"https://github.com/sponsors/colinhacks" + } + } + } +} diff --git a/src/frontend/package.json b/src/frontend/package.json new file mode 100644 index 0000000000000000000000000000000000000000..3ed6a48e761dc2c5edda4567404fc3f6bda2e589 --- /dev/null +++ b/src/frontend/package.json @@ -0,0 +1,30 @@ +{ + "name": "tokyo-techies-chatbot", + "version": "0.1.0", + "private": true, + "scripts": { + "dev": "next dev", + "build": "next build", + "start": "next start", + "lint": "next lint" + }, + "dependencies": { + "@types/node": "20.5.3", + "@types/react": "18.2.21", + "@types/react-dom": "18.2.7", + "autoprefixer": "10.4.15", + "axios": "^0.27.2", + "eslint": "8.47.0", + "eslint-config-next": "13.4.19", + "eslint-config-prettier": "^8.5.0", + "eslint-plugin-prettier": "^4.0.0", + "next": "13.4.19", + "postcss": "8.4.28", + "prettier": "^2.7.0", + "react": "18.2.0", + "react-dom": "18.2.0", + "react-markdown": "^8.0.4", + "tailwindcss": "3.3.3", + "typescript": "5.1.6" + } +} diff --git a/src/frontend/postcss.config.js b/src/frontend/postcss.config.js new file mode 100644 index 0000000000000000000000000000000000000000..33ad091d26d8a9dc95ebdf616e217d985ec215b8 --- /dev/null +++ b/src/frontend/postcss.config.js @@ -0,0 +1,6 @@ +module.exports = { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/src/frontend/public/favicon.webp b/src/frontend/public/favicon.webp new file mode 100644 index 0000000000000000000000000000000000000000..f75f7988ca83fffb084bc140e524c6b96dd81613 Binary files /dev/null and b/src/frontend/public/favicon.webp differ diff --git a/src/frontend/public/locales/en.ts b/src/frontend/public/locales/en.ts new file mode 100644 index 0000000000000000000000000000000000000000..98c73fb5078efb4504309b093f28d0a4249c617a --- /dev/null +++ b/src/frontend/public/locales/en.ts @@ -0,0 +1,15 @@ +export default { + name: 'Techie ', + askMe: 'Ask me anything', + connected: 'You are connected with a virtual assistant', + greeting: 'Hi there! 😊 \n' + + 'I\'m Techie - a virtual assistant here to help you with anything related to Tokyo Techies.\n' + + 'If you have any questions, need information, or just want to chat, feel free to ask me!\n' + + 'How can I help you?', + placeholder: 'Type your question...', + maintenance: 'Sorry, we are under maintenance!', + year: '/', + month: '/', + day: '', + edition: ' version' +} diff --git a/src/frontend/public/locales/ja.ts b/src/frontend/public/locales/ja.ts new file mode 100644 index 0000000000000000000000000000000000000000..1c5ae88bd1377b9ffe66ed9c04dd2227def8b0b8 --- /dev/null +++ b/src/frontend/public/locales/ja.ts @@ -0,0 +1,16 @@ +export default { + name: 'Techie', + askMe: ' チャットで質問する', + connected: 'バーチャルアシスタントと接続しました', + greeting: 'こんにちは！\n' + + '私はTechieです！😊\n' + + 'Tokyo Techiesに関するあらゆることをお手伝いします。\n' + + '質問がある、情報が必要、またはただおしゃべりしたい場合は、どうぞお気軽にお尋ねください。\n' + + '今日はどのようにお手伝いできますか？ ', + placeholder: 'メッセージを入力してください...', + maintenance: '申し訳ございません、只今メンテナンス中です', + year: '年', + month: '月', + day: '日', + edition: '版' +} diff --git a/src/frontend/src/components/Chatbot/Header.tsx b/src/frontend/src/components/Chatbot/Header.tsx new file mode 100644 index 0000000000000000000000000000000000000000..855e6853fa9de3b6b1e5e2f0c84b18b215bb5b6f --- /dev/null +++ b/src/frontend/src/components/Chatbot/Header.tsx @@ -0,0 +1,20 @@ +import Headphones from '@/components/Icons/Headphones' +import useTranslation from '@/hooks/useTranslation' + +export function Header() { + const t = useTranslation() + + return ( +
+
+ +

{t.askMe}

+
+
+
+ ) +} diff --git a/src/frontend/src/components/Chatbot/Inputs.tsx b/src/frontend/src/components/Chatbot/Inputs.tsx new file mode 100644 index 0000000000000000000000000000000000000000..c1a6662926af187aded3e5fb50be6c64260d4d43 --- /dev/null +++ b/src/frontend/src/components/Chatbot/Inputs.tsx @@ -0,0 +1,129 @@ +import { SendIcon } from '@/components/Icons/SendIcon' +import React, { useEffect, useRef, useState } from 'react' +import { IMessage, setBooleanType, setStringType } from '@/utils/interfaces' +import useTranslation from '@/hooks/useTranslation' + +interface IProps { + loading: boolean + userInput: string + setUserInput: setStringType + setMessages: any + setLoading: setBooleanType + history: [string, string][] +} + +export function Inputs({ + loading, + userInput, + setUserInput, + setMessages, + setLoading, + history, +}: IProps) { + const textAreaRef = useRef(null) + const t = useTranslation() + const [isComposing, setIsComposing] = useState(false) + // Handle errors + const handleError = () => { + setMessages((prevMessages: IMessage[]) => [ + ...prevMessages, + { + message: t.maintenance, + type: 'apiMessage', + }, + ]) + setLoading(false) + setUserInput('') + } + + // Handle form submission + const handleSubmit = async () => { + if (userInput.trim() === '') { + return + } + + setLoading(true) + setMessages((prevMessages: IMessage[]) => [ + ...prevMessages, + { message: userInput, type: 'userMessage' }, + ]) + // Reset user input + setUserInput('') + + // MULTI chat + const response = await fetch('/api/multi-chat', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ question: userInput, history: history }), + }) + + if (!response.ok) { + handleError() + return + } + + const data = await response.json() + + if (data.result.error === 'Unauthorized') { + handleError() + return + } + + setMessages((prevMessages: IMessage[]) => [ + ...prevMessages, + { message: data.result, type: 'apiMessage' }, + ]) + setLoading(false) + } + + useEffect(() => { + //tracking if user uses japanese language composing + textAreaRef.current?.addEventListener('compositionstart', () => { + setIsComposing(true) + }) + textAreaRef.current?.addEventListener('compositionend', () => { + setIsComposing(false) + }) + }, []) + // Prevent blank submissions and allow for multiline input + const handleEnter = (e: React.KeyboardEvent) => { + if (e.key === 'Enter' && userInput) { + if (!e.shiftKey && userInput && !loading && !isComposing) { + e.preventDefault() + handleSubmit() + } + } else if (e.key === 'Enter') { + e.preventDefault() + } + } + + return ( +
+