diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..8aa299b --- /dev/null +++ b/.env.example @@ -0,0 +1,59 @@ +# -- DEV MODE if true, log debugs and traces +DEV_MODE=True + +# Ollama and ollamazure models to emulate openai or azure_openai +# run make run-ollama or make run-ollamazure to emulate openai or azure_openai locally +OLLAMA_MODEL_NAME="phi3:3.8b-mini-4k-instruct-q4_K_M" +OLLAMA_EMBEDDING_MODEL_NAME="all-minilm:l6-v2" + + +# LLM_PROVIDER : openai or azure_openai +LLM_PROVIDER="openai" + +# if provider is OPENAI +OPENAI_DEPLOYMENT_NAME="phi3:3.8b-mini-4k-instruct-q4_K_M" # or gpt-4o-mini if you use openai +OPENAI_BASE_URL="http://localhost:11434/v1" # ollama endpoint or https://api.openai.com/v1 if use use openai +OPENAI_API_KEY="t" +# if provider is AZURE +AZURE_OPENAI_DEPLOYMENT_NAME="phi3:3.8b-mini-4k-instruct-q4_K_M" # or your azure model name +AZURE_OPENAI_API_KEY="t" # +AZURE_OPENAI_BASE_URL="http://localhost:4041" # ollamazure endpoint or your azure endpoint +AZURE_OPENAI_API_VERSION="2024-10-01-preview" # fake api version + +# -- FASTAPI +FASTAPI_HOST="localhost" +FASTAPI_PORT=8080 +# -- Streamlit +STREAMLIT_PORT=8501 + +####################### EVALUATION ############################ +# (Optional) If you want to use Promptfoo and ragas, the eval tool +ENABLE_EVALUATION=false # if true, you need to set the following +# LLMAAJ stands for LLM as a judge +LLMAAJ_PROVIDER="openai" # or azure_openai +#if openai +LLMAAJ_OPENAI_DEPLOYMENT_NAME="phi3:3.8b-mini-4k-instruct-q4_K_M" # or gpt-4o-mini if you use openai +LLMAAJ_OPENAI_BASE_URL="http://localhost:11434/v1" # ollama endpoint or https://api.openai.com/v1 if use use openai +LLMAAJ_OPENAI_API_KEY="t" +LLMAAJ_OPENAI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-ada-002" +#if azure_openai +LLMAAJ_AZURE_OPENAI_DEPLOYMENT_NAME="phi3:3.8b-mini-4k-instruct-q4_K_M" # or your azure model name +LLMAAJ_AZURE_OPENAI_API_KEY="t" +LLMAAJ_AZURE_OPENAI_BASE_URL="http://localhost:4041" # ollamazure endpoint or your azure endpoint +LLMAAJ_AZURE_OPENAI_API_VERSION="2024-10-01-preview" +LLMAAJ_AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME="all-minilm:l6-v2" # or your azure embedding model name + + +####################### AI SEARCH ############################ +# (Optional) If you want to use Azure Search AI +ENABLE_AZURE_SEARCH=false # if true, you need to set the following +AZURE_SEARCH_TOP_K=3 +AZURE_SEARCH_API_KEY="" +AZURE_SEARCH_INDEX_NAME="" +AZURE_SEARCH_INDEXER_NAME="" +AZURE_SEARCH_SERVICE_ENDPOINT="" +SEMENTIC_CONFIGURATION_NAME="" +# -- AZURE BLOB STORAGE +AZURE_STORAGE_ACCOUNT_NAME="" +AZURE_STORAGE_ACCOUNT_KEY="" +AZURE_CONTAINER_NAME="" diff --git a/.github/workflows/ai_evaluation.yaml b/.github/workflows/ai_evaluation.yaml new file mode 100644 index 0000000..0233de1 --- /dev/null +++ b/.github/workflows/ai_evaluation.yaml @@ -0,0 +1,62 @@ +name: 'LLM Evaluation' + +on: + pull_request: + push: +#on: +# pull_request: +# paths: +# - src/ml +# - src/evaluation + +env: + OLLAMA_MODEL_NAME: "phi3:3.8b-mini-4k-instruct-q4_K_M" + OLLAMA_EMBEDDING_MODEL_NAME: "all-minilm:l6-v2" + LLM_PROVIDER: "openai" + OPENAI_DEPLOYMENT_NAME: "phi3:3.8b-mini-4k-instruct-q4_K_M" # or gpt-4o-mini if you use openai + OPENAI_BASE_URL: "http://localhost:11434/v1" # ollama endpoint or https://api.openai.com/v1 if use use openai + OPENAI_API_KEY: "t" + + ENABLE_EVALUATION: true # if true, you need to set the following + # LLMAAJ stands for LLM as a judge + LLMAAJ_PROVIDER: "openai" # or azure_openai + #if openai + LLMAAJ_OPENAI_DEPLOYMENT_NAME: "phi3:3.8b-mini-4k-instruct-q4_K_M" # or gpt-4o-mini if you use openai + LLMAAJ_OPENAI_BASE_URL: "http://localhost:11434/v1" # ollama endpoint or https://api.openai.com/v1 if use use openai + LLMAAJ_OPENAI_API_KEY: "t" + LLMAAJ_OPENAI_EMBEDDING_DEPLOYMENT_NAME: "text-embedding-ada-002" + +jobs: + evaluate: + runs-on: ubuntu-latest + container: + image: aminedjeghri/python3.11-nvm22.08:latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 +# - name: Install NVM +# shell: bash +# run : make install_nvm_node + - name: Install Python dependencies + shell: bash + run: | + make install-dev + - name: Install NPM dependencies + shell: bash + run: | + make install-npm-dependencies + - name: Download local models + shell: bash + run: | + make download-ollama-model + - name: Run tests + shell: bash + run: | + ollama serve & + make test +# - name: Run promptfoo evaluation +# shell: bash +# run: | +# source ${{ env.VENV_PATH }}/bin/activate +# make eval diff --git a/.github/workflows/deploy-pages.yaml b/.github/workflows/deploy-pages.yaml new file mode 100644 index 0000000..1c73ceb --- /dev/null +++ b/.github/workflows/deploy-pages.yaml @@ -0,0 +1,31 @@ +name: 'Deploy Github Pages' + +#on: +# pull_request: +# push: + +jobs: + # Build job + build: + + # Deploy job + deploy: + # Add a dependency to the build job + needs: build + + # Grant GITHUB_TOKEN the permissions required to make a Pages deployment + permissions: + pages: write # to deploy to Pages + id-token: write # to verify the deployment originates from an appropriate source + + # Deploy to the github-pages environment + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + + # Specify runner + deployment step + runs-on: ubuntu-latest + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 # or specific "vX.X.X" version tag for this action diff --git a/.github/workflows/pre-commit.yaml b/.github/workflows/pre-commit.yaml new file mode 100644 index 0000000..f12b888 --- /dev/null +++ b/.github/workflows/pre-commit.yaml @@ -0,0 +1,15 @@ +name: pre-commit + +on: + pull_request: + push: + +jobs: + pre-commit: + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: '3.11' + - uses: pre-commit/action@v3.0.1 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..acf7d59 --- /dev/null +++ b/.gitignore @@ -0,0 +1,192 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$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/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# 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 +.envd +.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/ + +# environment var +.env +*.secrets + +# node +node_modules/ + +# github actions local +bin/act + +#gitlab ci local +.gitlab-ci-local/ +.gitlab-ci-local-env +.gitlab-ci-local.yml + +# Windows specific files +*Zone.Identifier + + +.cache* + +**/*tmp*.py +**/*temp*.py + + + +src/evaluation/data/* +!src/evaluation/data/test_json.csv +!src/evaluation/data/test_simple.csv diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml new file mode 100644 index 0000000..3d65174 --- /dev/null +++ b/.gitlab-ci.yml @@ -0,0 +1,48 @@ +default: + image: aminedjeghri/python3.11-nvm22.08:latest + +stages: + - evaluate + + +variables: + VENV_PATH: "$CI_PROJECT_DIR/.venv" # Define virtual environment path + OLLAMA_MODEL_NAME: "phi3:3.8b-mini-4k-instruct-q4_K_M" + OLLAMA_EMBEDDING_MODEL_NAME: "all-minilm:l6-v2" + LLM_PROVIDER: "openai" + OPENAI_DEPLOYMENT_NAME: "phi3:3.8b-mini-4k-instruct-q4_K_M" # or gpt-4o-mini if you use openai + OPENAI_BASE_URL: "http://localhost:11434/v1" # ollama endpoint or https://api.openai.com/v1 if use use openai + OPENAI_API_KEY: "t" + + ENABLE_EVALUATION: true # if true, you need to set the following + # LLMAAJ stands for LLM as a judge + LLMAAJ_PROVIDER: "openai" # or azure_openai + #if openai + LLMAAJ_OPENAI_DEPLOYMENT_NAME: "phi3:3.8b-mini-4k-instruct-q4_K_M" # or gpt-4o-mini if you use openai + LLMAAJ_OPENAI_BASE_URL: "http://localhost:11434/v1" # ollama endpoint or https://api.openai.com/v1 if use use openai + LLMAAJ_OPENAI_API_KEY: "t" + LLMAAJ_OPENAI_EMBEDDING_DEPLOYMENT_NAME: "text-embedding-ada-002" + +cache: + key: + files: # can't use more than two files in a single cache key. + - pyproject.toml #or if you have a folder /requirements/* + - package.json + + paths: + - node_modules/ + - $VENV_PATH # Cache the virtual environment + +evaluate: + stage: evaluate # Reference the correct stage + script: + - if [ ! -d node_modules ]; then make install-npm-dependencies; fi + - | + if [ ! -d $VENV_PATH ]; then + echo "Virtual environment does not exist. Creating..." + make install-dev + fi + + # Run evaluation + - make download-ollama-model + - ollama serve && make test diff --git a/.nvmrc b/.nvmrc new file mode 100644 index 0000000..9d67327 --- /dev/null +++ b/.nvmrc @@ -0,0 +1 @@ +22.8.0 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..48e59b5 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,32 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +# ruff is for python files and is used with ruff.toml +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 + hooks: + - id: check-added-large-files + args: ['--maxkb=3000'] + - id: check-toml + - id: check-yaml + - id: check-json + - id: detect-private-key + - id: end-of-file-fixer + # - id: requirements-txt-fixer + # exclude: ^requirements/.*$ + - id: trailing-whitespace + - id: name-tests-test + args: [ --pytest-test-first ] + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.8.1 + hooks: + - id: ruff + args: ['--fix'] + - id: ruff-format + + - repo: https://github.com/Yelp/detect-secrets + rev: v1.5.0 + hooks: + - id: detect-secrets + args: ['--exclude-files', '.*[^i][^p][^y][^n][^b]$', '--exclude-lines', '"(hash|id|image/\w+)":.*', ] diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..f47aa92 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,70 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, or to ban +temporarily or permanently any contributor for other behaviors that they deem +inappropriate, threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at <>. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant](https://contributor-covenant.org/), version +[1.4](https://www.contributor-covenant.org/version/1/4/code-of-conduct/code_of_conduct.md) and +[2.0](https://www.contributor-covenant.org/version/2/0/code_of_conduct/code_of_conduct.md), +and was generated by [contributing-gen](https://github.com/bttger/contributing-gen). diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..1223191 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,52 @@ + + +# Contributing to ai-project-template + +First off, thanks for taking the time to contribute! ❤️ + +## Code of Conduct + +This project and everyone participating in it is governed by the [Code of Conduct](CODE_OF_CONDUCT.md). +By participating, you are expected to uphold this code. Please report unacceptable behavior. + +## Contributing + +### Team members: +- Amine Djeghri + +### ⚙️ Steps for Installation (Contributors and maintainers) + +- The first step is [to install to read and test the project as a user](README.md#-steps-for-installation-users) +- To install the dev dependencies (pre-commit, pytest, ruff...), run ``make install-dev`` +- run ``make pre-commit install`` to install pre-commit hooks +- To install the GitHub actions locally, run ``make install-act`` +- To install the gitlab ci locally, run ``make install-ci`` + +- Before you start working on an issue, please comment on (or create) the issue and wait for it to be assigned to you. If +someone has already been assigned but didn't have the time to work on it lately, please communicate with them and ask if +they're still working on it. This is to avoid multiple people working on the same issue. +Once you have been assigned an issue, you can start working on it. When you are ready to submit your changes, open a +pull request. For a detailed pull request tutorial, see this guide. + +1. Create a branch from the dev branch and respect the naming convention: `feature/your-feature-name` + or `bugfix/your-bug-name`. +2. Before commiting your code : + - Run ``make test`` to run the tests + - Run ``make pre-commit`` to check the code style & linting. If it fails because gitguardien api key is not in your secret, add it in .secret. ggshield provides it for free. + - Run `make deploy-doc-local` to update the documentation + - (optional) Commit Messages: This project uses [Gitmoji](https://gitmoji.dev/) for commit messages. It helps to + understand the purpose of the commit through emojis. For example, a commit message with a bug fix can be prefixed with + 🐛. There are also [Emojis in GitHub](https://github.com/ikatyang/emoji-cheat-sheet/blob/master/README.md) + - Manually, merge dev branch into your branch to solve and avoid any conflicts. Merging strategy: merge : dev → + your_branch + - After merging, run ``make test`` and ``make pre-commit`` again to ensure that the tests are still passing. + - (if your project is a python package) Update the package’s version, in pyproject.toml & build the wheel +3. Depending on the platform you use, run `make act` for GitHub Actions or `make gitlab-ci-local` for GitLab CI. +4. Create a pull request. If the GitHub actions pass, the PR will be accepted and merged to dev. + +### (For repository maintainers) Merging strategies & GitHub actions guidelines** + +- Once the dev branch is tested, the pipeline is green, and the PR has been accepted, you can merge with a 'merge' + strategy. +- DEV → MAIN: Then, you should create a merge from dev to main with Squash strategy. +- MAIN → RELEASE: The status of the ticket will change then to 'done.' diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..72766ac --- /dev/null +++ b/Dockerfile @@ -0,0 +1,29 @@ +# Base image +FROM python:3.11 + +LABEL authors="aminedjeghri" + +# Set environment variables +ENV APP_DIR=/generative-ai-project-template +# Set working directory +WORKDIR $APP_DIR + +# Copy the necessary files for installation +COPY package.json ./ +COPY package-lock.json ./ +COPY Makefile ./ +COPY .nvmrc ./ + + +# Install system dependencies +RUN apt-get update && apt-get upgrade -y + +# Install UV +RUN make install-uv + +# Install NVM, Node.js and set up the environment +RUN make install-nvm +#RUN apt-get clean && apt-get autoremove --purge && apt-get autoremove + +# Define default entrypoint if needed (Optional) +CMD ["/bin/bash"] diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..7837782 --- /dev/null +++ b/Makefile @@ -0,0 +1,263 @@ +ENV_FILE_PATH := .env +-include $(ENV_FILE_PATH) # keep the '-' to ignore this file if it doesn't exist.(Used in gitlab ci) + +# Colors +GREEN=\033[0;32m +YELLOW=\033[0;33m +NC=\033[0m + +NVM_USE := export NVM_DIR="$$HOME/.nvm" && . "$$NVM_DIR/nvm.sh" && nvm use +UV := "$$HOME/.local/bin/uv" # keep the quotes incase the path contains spaces + +# installation +install-uv: + @echo "${YELLOW}=========> installing uv ${NC}" + @if [ -f $(UV) ]; then \ + echo "${GREEN}uv exists at $(UV) ${NC}"; \ + $(UV) self update; \ + else \ + echo "${YELLOW}Installing uv${NC}"; \ + curl -LsSf https://astral.sh/uv/install.sh | env UV_INSTALL_DIR="$$HOME/.local/bin" sh ; \ + fi + +install-prod: + @echo "${YELLOW}=========> Installing dependencies...${NC}" + @$(UV) sync --no-group dev --no-group docs + @echo "${GREEN}Dependencies installed.${NC}" + +install-dev: + @echo "${YELLOW}=========> Installing dependencies...\n \ + Development dependencies (dev & docs) will be installed by default in install-dev.${NC}" + @$(UV) sync + @echo "${GREEN}Dependencies installed.${NC}" + +STREAMLIT_PORT ?= 8501 +run-frontend: + @echo "Running frontend" + cd src; $(UV) run streamlit run main_frontend.py --server.port $(STREAMLIT_PORT) --server.headless True; + +run-backend: + @echo "Running backend" + cd src; $(UV) run main_backend.py; + +run-app: + make frontend backend -j2 + +pre-commit-install: + @echo "${YELLOW}=========> Installing pre-commit...${NC}" + pre-commit install +pre-commit: + @echo "${YELLOW}=========> Running pre-commit...${NC}" + pre-commit run --all-files + +###### NVM & npm packages ######## +install-nvm: + echo "${YELLOW}=========> Installing Evaluation app $(NC)" + + @if [ -d "$$HOME/.nvm" ]; then \ + echo "${YELLOW}NVM is already installed.${NC}"; \ + $(NVM_USE) --version; \ + else \ + echo "${YELLOW}=========> Installing NVM...${NC}"; \ + curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash; \ + fi + + # Activate NVM (makefile runs in a subshell, always use this) + @echo "${YELLOW}Restart your terminal to use nvm. If you are on MacOS, run nvm ls, if there is no node installed, run nvm install ${NC}" + @bash -c ". $$HOME/.nvm/nvm.sh; nvm install" + +install-npm-dependencies: install-nvm + @echo "${YELLOW}=========> Installing npm packages...${NC}" + @$(NVM_USE) && npm ci + @echo "${GREEN} Installation complete ${NC}" + + +#check-npm-dependencies: +# @echo "${YELLOW}Checking npm dependencies...${NC}" && \ +# $(NVM_USE) && npm outdated || true # since the - flag is not working and we need to ignore the outdated return code +# @echo "${GREEN}If there are outdated dependencies, update the package.json and run ${YELLOW}npm install${NC}" + + +####### local CI / CD ######## +# uv caching : +cache-uv: + @echo "${YELLOW}=========> Caching uv...${NC}" + @$(UV) cache prune --ci +# Github actions locally +install-act: + @echo "${YELLOW}=========> Installing github actions act to test locally${NC}" + curl --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/nektos/act/master/install.sh | bash + @echo -e "${YELLOW}Github act version is :" + @./bin/act --version + +act: + @echo "${YELLOW}Running Github Actions locally...${NC}" + @./bin/act --env-file .env --secret-file .secrets + + +# Gitlab CI locally +GITLAB_CI_LOCAL_CMD=./node_modules/.bin/gitlab-ci-local +install-gitlab-ci-local: install-npm-packages + # gitlab-ci-local is an npm package and is installed within the install-evaluation-app step + @echo "${GREEN}Installed gitlab-ci-local${NC}" + @echo "${GREEN}gitlab-ci-local version is $$($(NVM_USE) > /dev/null && $(GITLAB_CI_LOCAL_CMD) --version | tail -n 1) ${NC}" +gitlab-ci-local: + @echo "${YELLOW}Running Gitlab Runner locally...${NC}" + @$(NVM_USE) && $(GITLAB_CI_LOCAL_CMD) --network=host --variables-file .env + +# clear GitHub and Gitlab CI local caches +clear_ci_cache: + @echo "${YELLOW}Clearing CI cache...${NC}" + @echo "${YELLOW}Clearing gitlab ci local cache...${NC}" + rm -rf .gitlab-ci-local/cache + @echo "${YELLOW}Clearing Github ACT local cache...${NC}" + rm -rf ~/.cache/act ~/.cache/actcache + + +########## evaluation framework ########### +PROMPTFOO_CMD=.././node_modules/.bin/promptfoo +install-promptfoo:install-nvm + @echo "${YELLOW}=========> Installing promptfoo...${NC}" + @echo "${GREEN}Promptfoo version is $$($(NVM_USE) > /dev/null && cd src && $(PROMPTFOO_CMD) --version | tail -n 1) ${NC}" + +# promptfoo eval and metrics +eval: + # requires export of environment variables + @echo "${YELLOW}Running evaluation...${NC}" + @$(NVM_USE) && \ + cd src && PYTHONPATH='.' $(PROMPTFOO_CMD) eval --config evaluation/configs/config_simple.yaml + +eval-env-file: + # requires .env file + @echo "${YELLOW}Running evaluation, reading variables from .env file...${NC}" + @#$(NVM_USE) && \ +# cd src && PYTHONPATH='.' $(PROMPTFOO_CMD) eval --no-cache --env-file ../.env --config evaluation/configs/config_simple.yaml +# @$(NVM_USE) && \ +# cd src && PYTHONPATH='.' $(PROMPTFOO_CMD) eval --no-cache --env-file ../.env --config evaluation/configs/config_json.yaml + @$(NVM_USE) && \ + cd src && PYTHONPATH='.' $(PROMPTFOO_CMD) eval --no-cache --env-file ../.env --config evaluation/configs/config_simple.yaml +eval-view: + @$(NVM_USE) ; \ + cd src && $(PROMPTFOO_CMD) view + +eval-share: + @$(NVM_USE) ; \ + cd src && $(PROMPTFOO_CMD) share + +# promptfoo redteam +redteam: + # requires export of environment variables + @echo "${YELLOW}Running redteaming.${NC}" + @$(NVM_USE) && \ + cd src && PYTHONPATH='.' $(PROMPTFOO_CMD) redteam run --config evaluation/configs/redteam_config.yaml + + +redteam-env-file: + # requires .env file + @echo "${YELLOW}Running redteaming.${NC}" +# @$(NVM_USE) && \ +# cd src && PYTHONPATH='.' $(PROMPTFOO_CMD) redteam run --env-file ../.env --config evaluation/configs/redteam_config.yaml + + @$(NVM_USE) && \ + cd src && PYTHONPATH='.' $(PROMPTFOO_CMD) redteam run --force --env-file ../.env --config evaluation/configs/redteam_config.yaml --verbose + +redteam-view: +# requires .env file + @echo "${YELLOW}Running redteaming.${NC}" + @$(NVM_USE) && \ + cd src && $(PROMPTFOO_CMD) redteam report + +######### Langfuse + + + + +######## Ollamazure +install-ollama: + @echo "${YELLOW}=========> Installing ollama first...${NC}" + @if [ "$$(uname)" = "Darwin" ]; then \ + echo "Detected macOS. Installing Ollama with Homebrew..."; \ + brew install --cask ollama; \ + elif [ "$$(uname)" = "Linux" ]; then \ + echo "Detected Linux. Installing Ollama with curl..."; \ + curl -fsSL https://ollama.com/install.sh | sh; \ + else \ + echo "Unsupported OS. Please install Ollama manually."; \ + exit 1; \ + fi + +download-ollama-model: install-ollama + @echo "Starting Ollama in the background..." + @echo "${YELLOW}Downloading local model ${OLLAMA_MODEL_NAME} and ${OLLAMA_EMBEDDING_MODEL_NAME} ...${NC}" + @ollama serve & + @sleep 5 + @ollama pull ${OLLAMA_EMBEDDING_MODEL_NAME} + @ollama pull ${OLLAMA_MODEL_NAME} + +run-ollama: + @echo "${YELLOW}Running ollama...${NC}" + @ollama serve + +# replace the model with the env variable +OLLAMAZURE_CMD=./node_modules/.bin/ollamazure +install-ollamazure:install-nvm install-ollama + @echo "${YELLOW}=========> Installing ollamazure...${NC}" + @echo "${GREEN}ollamazure version is $$($(NVM_USE) > /dev/null && $(OLLAMAZURE_CMD) --version | tail -n 1) ${NC}" + +run-ollamazure: + @echo "${YELLOW}Running ollama...${NC}" + @ollama serve & + @echo "${YELLOW}Running ollamazure...${NC}" + @#$(NVM_USE) && $(OLLAMAZURE_CMD) --model ${OLLAMA_MODEL_NAME} --embeddings ${OLLAMA_EMBEDDING_MODEL_NAME} + @$(NVM_USE) && $(OLLAMAZURE_CMD) --model phi3:3.8b-mini-4k-instruct-q4_K_M --embeddings all-minilm:l6-v2 + + + + +######## Tests ######## +test: + # pytest runs from the root directory + @echo "${YELLOW}Running tests...${NC}" + @$(UV) run pytest tests + +test-ollama: + curl -X POST http://localhost:11434/api/generate -H "Content-Type: application/json" -d '{"model": "phi3:3.8b-mini-4k-instruct-q4_K_M", "prompt": "Hello", "stream": false}' + +test-llm-client: + # llm that generate answers (used in chat, rag and promptfoo) + @echo "${YELLOW}=========> Testing LLM client...${NC}" + @$(UV) run pytest tests/test_llm_endpoint.py -k test_llm_client --disable-warnings + + +test-llmaaj-client: + # stands for llm as a judge client, used in promptfoo and ragas + @echo "${YELLOW}=========> Testing LLM As a judge client...${NC}" + @$(UV) run pytest tests/test_llm_endpoint.py -k test_llmaaj_client --disable-warnings + + +run-langfuse: + @echo "${YELLOW}Running langfuse...${NC}" + @if [ "$$(uname)" = "Darwin" ]; then \ + echo "Detected macOS running postgresql with Homebrew..."; \ + colima start + brew services start postgresql@17; \ + + elif [ "$$(uname)" = "Linux" ]; then \ + echo "Detected Linux running postgresql with systemctl..."; \ + else \ + echo "Unsupported OS. Please start postgres manually."; \ + exit 1; \ + fi + + + +# This build the documentation based on current code 'src/' and 'docs/' directories +# This is to run the documentation locally to see how it looks +deploy-doc-local: + @echo "${YELLOW}Deploying documentation locally...${NC}" + @$(UV) run mkdocs build && $(UV) run mkdocs serve + +# Deploy it to the gh-pages branch in your GitHub repository (you need to setup the GitHub Pages in github settings to use the gh-pages branch) +deploy-doc-gh: + @echo "${YELLOW}Deploying documentation in github actions..${NC}" + @$(UV) run mkdocs build && $(UV) run mkdocs gh-deploy diff --git a/README.md b/README.md new file mode 100644 index 0000000..e8bc874 --- /dev/null +++ b/README.md @@ -0,0 +1,183 @@ + + +

Generative AI Project Template

+ + +[![python](https://img.shields.io/badge/python-3.11+-blue?logo=python)](https://www.python.org/downloads/release/python-3110/) +[![Debian](https://img.shields.io/badge/Debian-A81D33?logo=debian&logoColor=fff)](https://www.debian.org/) +[![macOS](https://img.shields.io/badge/macOS-000000?logo=apple&logoColor=F0F0F0)](#) + +[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-FFD21E?logo=huggingface&logoColor=000)](#) +[![OpenAI](https://img.shields.io/badge/OpenAI-%23412991?logo=openai&logoColor=white)](https://pytorch.org/get-started/locally/) +[![Microsoft Azure](https://custom-icon-badges.demolab.com/badge/Microsoft%20Azure-0089D6?logo=msazure&logoColor=white)](#) + +[![streamlit](https://img.shields.io/badge/-Streamlit-FF4B4B?style=flat&logo=streamlit&logoColor=white)](#) +[![FastAPI](https://img.shields.io/badge/FastAPI-009485.svg?logo=fastapi&logoColor=white)](#) + +[![Style: Ruff](https://img.shields.io/badge/style-ruff-41B5BE?style=flat)](https://github.com/charliermarsh/ruff) +[![MkDocs](https://img.shields.io/badge/MkDocs-526CFE?logo=materialformkdocs&logoColor=fff)](#) +[![mkdocs-material](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/juftin/mkdocs-material/66d65cf/src/templates/assets/images/badge.json)]() +[![Docker](https://img.shields.io/badge/Docker-2496ED?logo=docker&logoColor=fff)](#) +[![GitLab CI](https://img.shields.io/badge/GitLab%20CI-FC6D26?logo=gitlab&logoColor=fff)](#) +[![GitHub Actions](https://img.shields.io/badge/GitHub_Actions-2088FF?logo=github-actions&logoColor=white)](#) + +Template for a new AI Cloud project. + +Click on [Use this template](https://github.com/aminedjeghri/ai-cloud-project-template/generate) to start your own project! + + + +This project is a generative ai template. It contains the following features: LLMs, information extraction, chat, rag & evaluation. +It uses LLMs(local or cloud),streamlit (with and without fastapi) & Promptfoo as an evaluation and redteam framework for your AI system. + + +# table with three columns for three photos + +| Evaluation with promptfoo | Streamlit | +|------------------------------------------------------|------------------------------------------------------| +| | | + +**Engineering tools:** + +- [x] Use UV to manage packages +- [x] pre-commit hooks: use ``ruff`` to ensure the code quality & ``detect-secrets`` to scan the secrets in the code. +- [x] Logging using loguru (with colors) +- [x] Pytest for unit tests +- [x] Dockerized project (Dockerfile & docker-compose): for the evaluation pipeline. +- [x] Streamlit (frontend) & FastAPI (backend) +- [x] Make commands to handle everything for you: install, run, test + +**AI tools:** + +- [x] LLM running locally (Ollama & Ollamazure) or in the cloud (OpenAI & Azure OpenAI) +- [x] Information extraction and Question answering from documents +- [x] Chat to test the AI system +- [x] Efficient async code using asyncio. +- [x] AI Evaluation framework: using Promptfoo, Ragas & more... + +**CI/CD & Maintenance tools:** + +- [x] CI/CD pipelines: ``.github/workflows`` for GitHub and ``.gitlab-ci.yml`` for GitLab +- [x] Local CI/CD pipelines: GitHub Actions using ``github act`` and local GitLab CI using ``gitlab-ci-local`` + +**Documentation tools:** + +- [x] Wiki creation and setup of documentation website using Mkdocs +- [x] GitHub Pages deployment using mkdocs gh-deploy plugin + + +Upcoming features: + +- [ ] optimize caching in CI/CD +- [ ] [Pull requests templates](https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/creating-a-pull-request-template-for-your-repository) +- [ ] Additional MLOps templates: https://github.com/fmind/mlops-python-package +- [ ] Add MLFlow +- [ ] add Langfuse +- [ ] deploy gh pages in actions + +## 1. Getting started +This project contains two parts: + +- The AI app: contains an AI system (local or cloud), a frontend (streamlit), with an optional backend(fastapi). +- (optional)The Evaluation Tool: The evaluation tool is used to evaluate the performance and safety of the AI system. It uses promptfoo & RAGAS, Python 3.11 and NVM are needed, but no need to install them by yourself since the project will handle that for you. + +The following files are used in the contribution pipeline: + +- ``.env.example``: example of the .env file. +- ``.env`` : contains the environment variables used by the app. +- ``Makefile``: contains the commands to run the app locally. +- ``Dockerfile``: the dockerfile used to build the docker image. It uses the Makefile commands to run the app. +- ``.pre-commit-config.yaml``: pre-commit hooks configuration file +- ``pyproject.toml``: contains the pytest, ruff & other configurations. +- ``log_config.py``: logging configuration file for the project. This logger is used in the backend and can be used in + the frontend. +- .github/workflows/**.yml: GitHub actions configuration files. +- .gitlab-ci.yml: Gitlab CI configuration files. + + +### 1.1. Local Prerequisites + +- Ubuntu 22.04 or MacOS +- git clone the repository +- UV & Python 3.11 (will be installed by the Makefile) +- Create a ``.env`` file *(take a look at the ``.env.example`` file)* + + +### ⚙️ Steps for Installation (Users) +#### App (AI, FastAPI, Streamlit) + +1. To install the app, run `make install-prod`. +2. Choose one of the following options: + - Local model: we use Ollama that simulates OpenAI or Azure OpenAI. The model that is used is `phi3:3.8b-mini-4k-instruct-q4_K_M` but can be changed. + - Read about how the app handles different providers and how to emulate OpenAI if you use open source models. + - Update the ``.env`` file *(take a look at the ``.env.example`` file)* + - Install Ollama (for openai) `make install-ollama` or ollamazure (for azure) + - Download the model, run `make download-ollama-model`. It will download the model present in the `OLLAMA_MODEL_NAME` var in the ``.env`` file. + - Run ollama to emulate openai : `make run-ollama` or ollamazure to emulate azure openai : `make run-ollamazure` + - Run `make test-ollama`. You should see an output with a response. + - Or Cloud model: OpenAI or Azure OpenAI: + - Update the ``.env`` file *(take a look at the ``.env.example`` file)* + +3. Run `test-llm-client` to check if your the LLM responds. +4. Run the app: +- To run the app with Streamlit (and without fastapi), run `make run-frontend` +- To run the app with both Streamlit and FastAPI, run `make run-app` + +#### (optional) Evaluation Tool (AI, Promptfoo, RAGAS) + +- To install the evaluation tool, run `make install-promptfoo` (nvm, promptfoo ...), +- We will use the name `LLMAAJ` to refer to LLM_AS_A_JUDGE, which is used to evaluate the AI system in Ragas metrics and other metrics +- Update the ``.env`` file *(take a look at the ``.env.example`` file)* +- To run the app, run `make eval-env-file` + +**How to run the evaluations locally:** + +- You can run promptfoo with ```make eval-env-file``` to run the evaluation reading the env file. take a look at promptfoo section in the ``.env.example`` file, if you want to use similarity metrics, you need to add the embedding model. +- Run `test-llmaaj-client` to check if the LLM as a judge responds. +- If you check the ``make eval-env-file`` command in the makefile, you can see that we do cd src and then set the python path to the current directory so promptfoo can find it. +- You need to be familiar with Promptfoo to use it. I advise you to learn about it before making changes to the evaluations part of this repository. +For now, there are different datasets and configs: +- a simple dataset +- a dataset for information extraction (NER). IE metrics will be also computed on this dataset. +- if you want to change a config, go to the makefile and look at the ``eval-env-file`` command. Change the --config evaluation/promptfooconfig.yaml to other configs + +Steps to evaluate a rag system: + +**retrieval metrics :evaluating retrieved contexts against ground truth**: + +1. dataset : + - Each row of a dataset has a query and an answer. +2. steps and metrics: + - Evaluate the chunking (chunk_size & chunk_overlap): you will need to check the context_recall & precision for example to see if the answer is in the context. + - Evaluate the embeddings: (try different models): same as previous + - try retrieval systems like Azure AI Search: same as previous + +**end-task: evaluating ground truth vs generated answer**: + +1. dataset : + - Depending on the end task and the type of the answer (string, or json...), you will need to build the dataset on that. + - For example, if the answer is a string, the dataset column 'answer' will be a string. + - if you are going to evaluate batch of questions and batch of answers, you will need to build the dataset on that. + +2. steps and metrics: + - For example, if the answer is a string, you will need to use the exact_match & ragas answer_similarity. + - If the answer is a json, you will need to use the json_exact_match, missing_fields... + - Changing the prompt + - Using one question, or multiple questions (batch) at once sent to the LLM. + +3. promptfoo config: + - If you use a config like the one in ``src/evaluation/providers/config_baseline.py``, we use an abstract class to define the global structure. + - Be aware that you need to adapt it if you want to use it for something else. + +## Docker installation (deprecated, will be updated soon) + +[//]: # (todo update docker installation for the app and for promptfoo) +- Set the environment variables (in the system or in a .env file) +- Run docker with the right port bindings. +- Since the app is running in docker and using streamlit, the Internal and External URL addresses won't work. You need to access the app with localhost:forwarded_port + +### ⚙️ Steps for Installation (Contributors and maintainers) +Check the [CONTRIBUTING.md](CONTRIBUTING.md) file for more information. + +## Contributing +Check the [CONTRIBUTING.md](CONTRIBUTING.md) file for more information. diff --git a/assets/icon.svg b/assets/icon.svg new file mode 100644 index 0000000..4b0255e --- /dev/null +++ b/assets/icon.svg @@ -0,0 +1 @@ +
 diff --git a/assets/promptfoo_img.png b/assets/promptfoo_img.png new file mode 100644 index 0000000..74c2f34 Binary files /dev/null and b/assets/promptfoo_img.png differ diff --git a/assets/streamlit_img.png b/assets/streamlit_img.png new file mode 100644 index 0000000..7107830 Binary files /dev/null and b/assets/streamlit_img.png differ diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..55b2cfa --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,36 @@ +services: + promptfoo: + container_name: "promptfoo_container" + image: ghcr.io/promptfoo/promptfoo:main + restart: always + environment: + - PROMPTFOO_SHARE_STORE_TYPE="filesystem" + - PROMPTFOO_SHARE_TTL=2419200 + expose: + - 3000 + ports: + - "3000:3000" + volumes: + - ~/.promptfoo:/home/.promptfoo + + +# mlflow: +# container_name: "mlflow_container" +# image: ghcr.io/mlflow/mlflow:v2.15.1 +# command: "mlflow ui --host 0.0.0.0 -p 5001" +# restart: always +# expose: +# - 5001 +# ports: +# - "5001:5001" +# volumes: +# - mlflow_runs:/mlruns +# - mlflow_artifacts:/mlartifacts + +volumes: + promptfoo_data: + name: "promptfoo_data" +# mlflow_runs: +# name: "mlflow_runs" +# mlflow_artifacts: +# name: "mlflow_artifacts" diff --git a/emulate_openai.ipynb b/emulate_openai.ipynb new file mode 100644 index 0000000..85b8851 --- /dev/null +++ b/emulate_openai.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": " ## Install and run ollama\n", + "id": "ac17cab30ac71a7b" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-22T14:51:04.484409Z", + "start_time": "2024-11-22T14:51:03.382171Z" + } + }, + "cell_type": "code", + "source": [ + "import os\n", + "#you need a variable environment with the model name (check the .env.example)\n", + "os.environ['OLLAMA_MODEL_NAME'] = 'phi3:3.8b-mini-4k-instruct-q4_K_M'\n", + "\n", + "!make install-ollama" + ], + "id": "6dc762b4ab8fdbd8", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[0;33m=========> Installing ollama first...\u001B[0m\r\n", + "Detected macOS. Installing Ollama with Homebrew...\r\n", + "^C\r\n", + "\r\n", + "make: *** [install-ollama] Error 130\r\n" + ] + } + ], + "execution_count": 32 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-22T14:34:44.067469Z", + "start_time": "2024-11-22T14:34:42.690271Z" + } + }, + "cell_type": "code", + "source": [ + "# download a model (default one is in the env variable OLLAMA_MODEL_NAME)\n", + "!make download-ollama-model" + ], + "id": "e3f80472e8f59897", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[0;33mDownloading local model phi3:3.8b-mini-4k-instruct-q4_K_M ...\u001B[0m\r\n", + "\u001B[?25l\u001B[?25l\u001B[?25h\u001B[2K\u001B[1G\u001B[?25h\u001B[?2004h>>> \u001B[38;5;245mSend a message (/? for help)\u001B[28D\u001B[0m\u001B[K\r\n", + "Use Ctrl + d or /bye to exit.\r\n", + ">>> \u001B[38;5;245mSend a message (/? for help)\u001B[28D\u001B[0m\u001B[K\r\n", + ">>> \u001B[38;5;245mSend a message (/? for help)\u001B[28D\u001B[0m" + ] + } + ], + "execution_count": 21 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-22T14:34:45.995237Z", + "start_time": "2024-11-22T14:34:45.795317Z" + } + }, + "cell_type": "code", + "source": [ + "# run ollama server in your terminal (process background doesn't work in jupyter)\n", + "!make run-ollama" + ], + "id": "f2dfebe7d6023848", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[0;33mRunning ollama...\u001B[0m\r\n", + "Error: listen tcp 127.0.0.1:11434: bind: address already in use\r\n", + "make: *** [run-ollama] Error 1\r\n" + ] + } + ], + "execution_count": 22 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 1. Emulating OpenAI with Ollama", + "id": "4c595d1c71e515df" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-22T14:25:09.923625Z", + "start_time": "2024-11-22T14:25:09.717810Z" + } + }, + "cell_type": "code", + "source": [ + "# install ollama python library (you use it without uv)\n", + "!uv pip install ollama" + ], + "id": "164b8a9e108887c1", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[2mAudited \u001B[1m1 package\u001B[0m \u001B[2min 14ms\u001B[0m\u001B[0m\r\n" + ] + } + ], + "execution_count": 10 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-22T23:48:41.898808Z", + "start_time": "2024-11-22T23:48:40.734448Z" + } + }, + "cell_type": "code", + "source": [ + "from openai import OpenAI\n", + "\n", + "client = OpenAI(\n", + " base_url='http://localhost:11434/v1/',\n", + " # required but ignored\n", + " api_key='t',\n", + ")\n", + "\n", + "chat_completion = client.chat.completions.create(\n", + " messages=[\n", + " {\n", + " 'role': 'user',\n", + " 'content': 'Hi',\n", + " }\n", + " ],\n", + " # same model that you downloaded in ollama\n", + " model='phi3:3.8b-mini-4k-instruct-q4_K_M',\n", + ")\n", + "print(chat_completion.choices[0].message.content)" + ], + "id": "4818cad65e45570f", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello! How can I assist you today?\n" + ] + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 2. Emulating AzureOpenAI with Ollama\n", + "id": "530fa68e3828caef" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-22T14:50:52.822075Z", + "start_time": "2024-11-22T14:50:50.035395Z" + } + }, + "cell_type": "code", + "source": [ + "# install ollamazure\n", + "!make install-ollamazure" + ], + "id": "a9f0155e334ad1fb", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "echo \"\\033[0;33m=========> Installing Evaluation app \\033[0m\"\r\n", + "\u001B[0;33m=========> Installing Evaluation app \u001B[0m\r\n", + "\u001B[0;33mNVM is already installed.\u001B[0m\r\n", + "Found '/Users/z736kw/Downloads/git/ai-cloud-project-template/.nvmrc' with version <22.8.0>\r\n", + "Now using node v22.8.0 (npm v10.8.2)\r\n", + "# Activate NVM (makefile runs in a subshell, always use this)\r\n", + "\u001B[0;33mRestart your terminal to use nvm. If you are on MacOS, run nvm ls, if there is no node installed, run nvm install \u001B[0m\r\n", + "\u001B[0;32m-> v22.8.0\u001B[0m\r\n", + "\u001B[0;32mdefault\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;32m22.8.0\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;32mv22.8.0\u001B[0m)\r\n", + "\u001B[0;31miojs\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m \u001B[0;37m(default)\u001B[0m\r\n", + "\u001B[0;31munstable\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m \u001B[0;37m(default)\u001B[0m\r\n", + "\u001B[0;32mnode\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;32mstable\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;32mv22.8.0\u001B[0m) \u001B[0;37m(default)\u001B[0m\r\n", + "\u001B[0;32mstable\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;32m22.8\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;32mv22.8.0\u001B[0m) \u001B[0;37m(default)\u001B[0m\r\n", + "\u001B[1;33mlts/*\u001B[0m \u001B[0;90m->\u001B[0m \u001B[1;33mlts/jod\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "\u001B[1;33mlts/argon\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mv4.9.1\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "\u001B[1;33mlts/boron\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mv6.17.1\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "\u001B[1;33mlts/carbon\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mv8.17.0\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "\u001B[1;33mlts/dubnium\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mv10.24.1\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "\u001B[1;33mlts/erbium\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mv12.22.12\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "\u001B[1;33mlts/fermium\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mv14.21.3\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "\u001B[1;33mlts/gallium\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mv16.20.2\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "\u001B[1;33mlts/hydrogen\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mv18.20.5\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "\u001B[1;33mlts/iron\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mv20.18.0\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "\u001B[1;33mlts/jod\u001B[0m \u001B[0;90m->\u001B[0m \u001B[0;31mv22.11.0\u001B[0m (\u001B[0;90m->\u001B[0m \u001B[0;31mN/A\u001B[0m)\r\n", + "Found '/Users/z736kw/Downloads/git/ai-cloud-project-template/.nvmrc' with version <22.8.0>\r\n", + "Now using node v22.8.0 (npm v10.8.2)\r\n", + "^C\r\n", + "make: *** [install-nvm] Interrupt: 2\r\n" + ] + } + ], + "execution_count": 31 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-22T14:40:14.055582Z", + "start_time": "2024-11-22T14:40:14.046180Z" + } + }, + "cell_type": "code", + "source": [ + "# run ollamazure in your terminal (process background doesn't work in jupyter)\n", + "!make run-ollamazure" + ], + "id": "71e96be57a139984", + "outputs": [ + { + "ename": "OSError", + "evalue": "Background processes not supported.", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mOSError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[26], line 2\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;66;03m# run ollamazure\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m \u001B[43mget_ipython\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msystem\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mmake run-ollamazure &&\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/Downloads/git/ai-cloud-project-template/.venv/lib/python3.11/site-packages/ipykernel/zmqshell.py:641\u001B[0m, in \u001B[0;36mZMQInteractiveShell.system_piped\u001B[0;34m(self, cmd)\u001B[0m\n\u001B[1;32m 634\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m cmd\u001B[38;5;241m.\u001B[39mrstrip()\u001B[38;5;241m.\u001B[39mendswith(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m&\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m 635\u001B[0m \u001B[38;5;66;03m# this is *far* from a rigorous test\u001B[39;00m\n\u001B[1;32m 636\u001B[0m \u001B[38;5;66;03m# We do not support backgrounding processes because we either use\u001B[39;00m\n\u001B[1;32m 637\u001B[0m \u001B[38;5;66;03m# pexpect or pipes to read from. Users can always just call\u001B[39;00m\n\u001B[1;32m 638\u001B[0m \u001B[38;5;66;03m# os.system() or use ip.system=ip.system_raw\u001B[39;00m\n\u001B[1;32m 639\u001B[0m \u001B[38;5;66;03m# if they really want a background process.\u001B[39;00m\n\u001B[1;32m 640\u001B[0m msg \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mBackground processes not supported.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m--> 641\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mOSError\u001B[39;00m(msg)\n\u001B[1;32m 643\u001B[0m \u001B[38;5;66;03m# we explicitly do NOT return the subprocess status code, because\u001B[39;00m\n\u001B[1;32m 644\u001B[0m \u001B[38;5;66;03m# a non-None value would trigger :func:`sys.displayhook` calls.\u001B[39;00m\n\u001B[1;32m 645\u001B[0m \u001B[38;5;66;03m# Instead, we store the exit_code in user_ns.\u001B[39;00m\n\u001B[1;32m 646\u001B[0m \u001B[38;5;66;03m# Also, protect system call from UNC paths on Windows here too\u001B[39;00m\n\u001B[1;32m 647\u001B[0m \u001B[38;5;66;03m# as is done in InteractiveShell.system_raw\u001B[39;00m\n\u001B[1;32m 648\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m sys\u001B[38;5;241m.\u001B[39mplatform \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mwin32\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n", + "\u001B[0;31mOSError\u001B[0m: Background processes not supported." + ] + } + ], + "execution_count": 26 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-22T23:49:02.263654Z", + "start_time": "2024-11-22T23:49:01.038956Z" + } + }, + "cell_type": "code", + "source": [ + "from openai import AzureOpenAI\n", + "\n", + "client = AzureOpenAI(\n", + " api_key='t',\n", + " api_version=\"2024-10-01-preview\",\n", + " azure_endpoint=\"http://localhost:4041\"\n", + ")\n", + "\n", + "chat_completion = client.chat.completions.create(\n", + " messages=[\n", + " {\n", + " 'role': 'user',\n", + " 'content': 'Hi',\n", + " }\n", + " ],\n", + " # same model that you downloaded in ollama\n", + " model='phi3:3.8b-mini-4k-instruct-q4_K_M',\n", + ")\n", + "print(chat_completion.choices[0].message.content)" + ], + "id": "bf836b7b10d551e6", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello! How can I help you today?\n" + ] + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "3fe08eef608b9109" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..f262bbe --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,60 @@ +site_name: Generative AI Project Template +site_description: Documentation +site_author: Amine Djeghri +docs_dir: . +# site_url: #TODO: Fill when deployment CI added +#site_dir: public +#repo_url: +#edit_uri: blob/main/docs/ + + +theme: + + name: "material" # https://squidfunk.github.io/mkdocs-material/getting-started/ + language: en + features: # https://squidfunk.github.io/mkdocs-material/setup/ + - search.suggest + - search.highlight + - search.share + - navigation.instant + - navigation.instant.progress + - navigation.tracking + - navigation.tabs + - navigation.tabs.sticky + # - navigation.sections + - navigation.path + - navigation.indexes + - navigation.top + - toc.follow + - content.code.copy + - content.code.annotate + palette: + # Palette toggle for light mode + - scheme: default + toggle: + icon: material/brightness-7 + name: Switch to dark mode + + # Palette toggle for dark mode + - scheme: slate + toggle: + icon: material/brightness-4 + name: Switch to light mode +plugins: + - mkdocstrings: + default_handler: python + import: + - https://docs.python-requests.org/en/master/objects.inv + load_external_modules: true + handlers: + python: + paths: [., source] + - gen-files: + scripts: + - scripts/gen_doc_stubs.py 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100644 index 0000000..2461a88 --- /dev/null +++ b/package.json @@ -0,0 +1,12 @@ +{ + "engines": { + "npm": ">=10.0.0 <11.0.0", + "node": "=22.8.0" + }, + "dependencies": { + "gitlab-ci-local": "4.55.0", + "langfuse": "^3.31.1", + "ollamazure": "1.4.0", + "promptfoo": "0.99.0" + } +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..ffe7f43 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,78 @@ +[project] +name = "ai-cloud-project-template" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + # AI dependencies + "openai==1.55.0", + "langchain==0.3.7", + "langchain-openai==0.2.9", + "langchain-community==0.3.7", + "giskard[llm]==2.16.0", + "ragas==0.2.6", + "instructor==1.7.0", + "azure-search-documents==11.5.2", + "azure-storage-blob==12.24.0", + # backend & frontend + "python-multipart==0.0.9", + "fastapi[standard]==0.115.5", + "streamlit==1.40.1", + "uvicorn==0.32.1", + # Engineering + "pydantic==2.10.1", + "pydantic-settings>=2.6.1", + "loguru==0.7.2", + "rich==13.9.4" +] + +############### uv configuration +# uses also the depenencies in the [project.dependencies] section +[tool.uv] +managed = true +default-groups = ["dev", "docs"] + +[dependency-groups] +# if you add new dependencies here, make sure to add them to [tool.uv] default-groups up above +dev = [ + "pytest == 8.3.0", + "pytest-asyncio == 0.24.0", + "pre-commit == 4.0.1", + "jupyter==1.1.1", + "ruff==0.8.1" +] +docs = [ + "mkdocs == 1.6.1", + "mkdocs-material>=9.5.41", + "mkdocstrings>=0.26.2", + "mkdocs-mermaid2-plugin>=1.1.1", + "mkdocs-gen-files>=0.5.0", + "mkdocstrings-python", + "mkdocs-same-dir", + "mkdocs-exclude" +] + + +# scripts : inside the root folder, you can run `uv run --directory . hi` or +[project.scripts] +hello = "tmp" # will read from __init__.py. + +# pytest configuration +[tool.pytest.ini_options] +pythonpath = ["src"] + + +# ruff configuration +[tool.ruff] +extend-exclude = ["*.ipynb"] +line-length = 100 + +# Enable all `pydocstyle` rules, limiting to those that adhere to the Google convention via `convention = "google"` +[tool.ruff.lint] +select = ["D", "F401"] +ignore = ["D100", "D101", "D102", "D103", "D104", "D107", "D417"] + +[tool.ruff.lint.pydocstyle] +# Use Google-style docstrings. +convention = "google" diff --git a/scripts/gen_doc_stubs.py b/scripts/gen_doc_stubs.py new file mode 100644 index 0000000..53f7fc6 --- /dev/null +++ b/scripts/gen_doc_stubs.py @@ -0,0 +1,20 @@ +"""This script walks through the source code and generates a markdown file for each python file. + +The goal is then for the markdown files to be used by mkdocs to call the plugin mkdocstring. +""" + +from pathlib import Path + +import mkdocs_gen_files + +src_root = Path("src") +for path in src_root.glob("**/*.py"): + if "__init__" in str(path): + print("Skipping", path) + continue + doc_path = Path("package", path.relative_to(src_root)).with_suffix(".md") + + if "seqly" not in str(path) and "__init__" not in str(path): + with mkdocs_gen_files.open(doc_path, "w") as f: + ident = ".".join(path.with_suffix("").parts) + print("::: " + ident, file=f) diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/api/__init__.py b/src/api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/api/api.py b/src/api/api.py new file mode 100644 index 0000000..257a5fe --- /dev/null +++ b/src/api/api.py @@ -0,0 +1,29 @@ +import os + +# add the parent directory to system path so we can run api_server.py from the src directory +import sys + +sys.path.append(os.path.dirname(os.path.dirname("../"))) + +from fastapi import FastAPI +from fastapi.responses import JSONResponse + +from utils import logger, settings + +from api.api_route import router, TagEnum + +app = FastAPI() + +# ROUTERS +routers = [router] +for router in routers: + app.include_router(router) + + +@app.get("/", tags=[TagEnum.general]) +async def root(): + logger.debug("Server is up and running!") + + logger.debug(f"Settings: {settings}") + + return JSONResponse(content="FastAPI server is up and running!") diff --git a/src/api/api_route.py b/src/api/api_route.py new file mode 100644 index 0000000..5a22954 --- /dev/null +++ b/src/api/api_route.py @@ -0,0 +1,29 @@ +from enum import Enum + +from fastapi import APIRouter +from fastapi.responses import JSONResponse + +from ml.ai import get_rag_response +from utils import logger + + +class TagEnum(str, Enum): + """API tags.""" + + general = "general" + tag_example = "tag_example" + + +router = APIRouter(prefix="/prefix_example", tags=[TagEnum.tag_example]) + + +@router.get("/example/") +async def get_conversation_by_id(conversation_id: str): + return JSONResponse(content="example response : 1234") + + +@router.get("/form/") +async def get_conversation_by_id(question: str): + logger.debug(f"question: {question}") + res = get_rag_response(question) + return JSONResponse(content=res) diff --git a/src/api/log_config.py b/src/api/log_config.py new file mode 100644 index 0000000..c5b8c29 --- /dev/null +++ b/src/api/log_config.py @@ -0,0 +1,56 @@ +from typing import Any + +# This file is used to configure the logging for the application (check the main of api_server.py) +# It is used with the uvicorn/fastapi function to configure the logging +# There is an environment variable named DEV_MODE that is used to configure the logging level + + +LOGGING_CONFIG: dict[str, Any] = { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "()": "uvicorn.logging.DefaultFormatter", + "fmt": "%(asctime)s - %(name)s - %(levelprefix)s - [%(filename)s:%(funcName)s:%(lineno)d] - %(message)s", + "use_colors": None, + }, + "access": { + "()": "uvicorn.logging.AccessFormatter", + "fmt": '%(asctime)s - %(name)s - %(levelprefix)s - %(client_addr)s - "%(request_line)s" %(status_code)s', # noqa: E501 + }, + "access_file": { + "()": "uvicorn.logging.AccessFormatter", + "fmt": '%(asctime)s - %(name)s - %(levelprefix)s - %(client_addr)s - "%(request_line)s" %(status_code)s', # noqa: E501 + "use_colors": False, + }, + }, + "handlers": { + "file_handler": { + "formatter": "access_file", + "class": "logging.handlers.RotatingFileHandler", + "filename": "./app.log", + "mode": "a+", + "maxBytes": 10 * 1024 * 1024, + "backupCount": 0, + }, + "default": { + "formatter": "default", + "class": "logging.StreamHandler", + "stream": "ext://sys.stderr", + }, + "access": { + "formatter": "access", + "class": "logging.StreamHandler", + "stream": "ext://sys.stdout", + }, + }, + "loggers": { + "uvicorn": {"handlers": ["default"], "level": "INFO", "propagate": False}, + "uvicorn.error": {"level": "INFO"}, + "uvicorn.access": { + "handlers": ["access", "file_handler"], + "level": "INFO", + "propagate": False, + }, + }, +} diff --git a/src/evaluation/__init__.py b/src/evaluation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/evaluation/configs/__init__.py b/src/evaluation/configs/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/evaluation/configs/config_baseline.py b/src/evaluation/configs/config_baseline.py new file mode 100644 index 0000000..a91883d --- /dev/null +++ b/src/evaluation/configs/config_baseline.py @@ -0,0 +1,6 @@ +def call_api(query, options, context): + result = { + "output": "output", + } + + return result diff --git a/src/evaluation/configs/config_compare_prompts.yaml b/src/evaluation/configs/config_compare_prompts.yaml new file mode 100644 index 0000000..e6b7af9 --- /dev/null +++ b/src/evaluation/configs/config_compare_prompts.yaml @@ -0,0 +1,29 @@ +# Learn more about building a configuration: https://promptfoo.dev/docs/configuration/guide +description: "My eval" + +prompts: + # the {{query}} and {{context}} are the columns in the dataset (test_simple.csv) + - | + You are an internal corporate chatbot. + Respond to this query: {{query}} + - Here is some context that you can use to write your response: {{context}} + + - | + You are an assistant that gives wrong information. + Respond to this query: {{query}} + - Here is some context that you can use to write your response: {{context}} + +providers: + - id: azureopenai:chat:{{env.AZURE_OPENAI_DEPLOYMENT_NAME}} # env variables are in .env + label: '{{env.AZURE_OPENAI_DEPLOYMENT_NAME}}' + + +defaultTest: + options: + provider: azureopenai:chat:{{env.AZURE_OPENAI_DEPLOYMENT_NAME}} + assert: + - type: select-best + value: choose the right response + +tests: + - file://../data/test_simple.csv diff --git a/src/evaluation/configs/config_json.py b/src/evaluation/configs/config_json.py new file mode 100644 index 0000000..b092185 --- /dev/null +++ b/src/evaluation/configs/config_json.py @@ -0,0 +1,46 @@ +import json + +from evaluation.metrics.utils import safe_eval +from utils import logger + +logger.debug("Loading promptfoo_hooks.py") + + +def get_var(var_name, prompt, other_vars): + """Function used by default by promptfoo call from the column 'context' of the dataset used in config_json.yml (test_json.csv). + + This function returns the context that will be used in the following call_api function + The context can be. For example, the retrieved list of documents + this is an example, and we will return the context that is defined in the csv file + other_vars contains the vars of the csv file. prompt contains the prompt in the config_json.yml + + Args: + prompt (str): The prompt used in the configuration file (prompts section of config_json.yml). + other_vars (dict): A dictionary containing variables from a CSV file. + + """ + context = [ + "The USA Supreme Court ruling on abortion has sparked intense debates and discussions not only within the country but also around the world.", + "Many countries look to the United States as a leader in legal and social issues, so the decision could potentially influence the policies and attitudes towards abortion in other nations.", + "The ruling may impact international organizations and non-governmental groups that work on reproductive rights and women's health issues.", + ] + return {"output": json.dumps(context, ensure_ascii=False)} + + +def call_api(prompt, options, context) -> dict[str, str]: + """Function used by default by promptfoo. Check the config_json.yml. + + Args: + prompt (str): The prompt used in the configuration file (prompts section of config_json.yml). + options: + context (dict): A dictionary containing the other_vars and context return by the previous function get_var + + + """ + query = safe_eval(context["vars"]["query"]) + output = {list(query.keys())[0]: "test"} + result = { + "output": json.dumps(output, ensure_ascii=False), + } + + return result diff --git a/src/evaluation/configs/config_json.yaml b/src/evaluation/configs/config_json.yaml new file mode 100644 index 0000000..139d89e --- /dev/null +++ b/src/evaluation/configs/config_json.yaml @@ -0,0 +1,96 @@ +# Learn more about building a configuration: https://promptfoo.dev/docs/configuration/guide +description: "My eval" + +prompts: +# the {{query}} and {{context}} are the columns in the dataset (test_json.csv) + - | + You are an internal corporate chatbot. + Respond to this query: {{query}} which is a json in form of {field:query} and return a json object in the form of {field:answer} + - Keep the name of fields and just add the answer to it + - Do not include the '''json ''' in your response. Just the json content. + - Here is some context that you can use to write your response: {{context}} +providers: + - id: azureopenai:chat:{{env.AZURE_OPENAI_DEPLOYMENT_NAME}} # env variables are in .env + label: '{{env.AZURE_OPENAI_DEPLOYMENT_NAME}}' + +# - id: openai:chat:phi3:3.8b-mini-4k-instruct-q4_K_M +# config: +# apiHost: localhost:11434/v1/ +# apiKey: ollama +## - id: ollama:chat:phi3:3.8b-mini-4k-instruct-q4_K_M # env variables are in .env +# - id: file://../configs/config_json.py +# label: '{{ env.AZURE_OPENAI_DEPLOYMENT_NAME }}' + + +defaultTest: + assert: + +# retrieval metrics: evaluating retrieved contexts against relevant contexts +# Order unaware retrieval metrics + - type: python + value: file://../metrics/order_unaware/precision_at_k.py + metric: PrecisionK + - type: python + value: file://../metrics/order_unaware/recall_at_k.py + metric: RecallK + - type: python + value: file://../metrics/order_unaware/f1_at_k.py + metric: F1K + # Order aware retrieval metrics + - type: python + value: file://../metrics/order_aware/reciprocal_rank.py + metric: Mean Reciprocal Rank + +# end-task: evaluating ground truth vs generated answer + - type: python + value: file://../metrics/information_extraction/missing_fields.py + metric: Missing Fields + - type: python + value: file://../metrics/information_extraction/exact_match_json.py + metric: Exact Match JSON + - type: python + value: file://../metrics/information_extraction/similarity_json.py + metric: Similarity JSON + - type: equals + value: '{{ground_truth}}' + - type: contains-json + - type: is-json + - type: python + value: file://../metrics/ragas_metrics/ragas_answer_similarity.py + metric: Ragas Answer Similarity +# - type: python +# value: file://../metrics/ragas_metrics/ragas_answer_correctness.py +# metric: Ragas Answer Correctness + + # evaluating answer vs retrieved context: + # - type: python + # value: file://../metrics/ragas_metrics/ragas_answer_relevancy.py + # metric: Ragas Answer Relevancy + + # retrieval metrics: evaluating retrieved contexts against ground truth + - type: python + value: file://../metrics/ragas_metrics/ragas_context_recall.py + metric: Ragas Context Recall + - type: python + value: file://../metrics/ragas_metrics/ragas_context_precision.py + metric: Ragas Context Precision +# - type: python +# value: file://../metrics/ragas_metrics/ragas_context_entity_recall.py +# metric: Ragas Context Entity Recall + # - type: python + # value: file://../metrics/ragas_metrics/ragas_context_utilization.py + # metric: Ragas Context Utilization + + + + ## latency needs cache to be off + # - type: latency + # threshold: 5000 +tests: +# - vars: +# language: [ Spanish, French ] +# input: [ 'Hello world' ] +# - file://data/tests_2.csv + +# - file://./data/tests_2.csv + - file://../data/test_json.csv diff --git a/src/evaluation/configs/config_simple.yaml b/src/evaluation/configs/config_simple.yaml new file mode 100644 index 0000000..816d51a --- /dev/null +++ b/src/evaluation/configs/config_simple.yaml @@ -0,0 +1,71 @@ +# Learn more about building a configuration: https://promptfoo.dev/docs/configuration/guide +description: "My eval" + +prompts: + # the {{query}} and {{context}} are the columns in the dataset (test_simple.csv) + - | + You are an internal corporate chatbot. + Respond to this query: {{query}} + - Here is some context that you can use to write your response: {{context}} + + +providers: + - id: openai:chat:{{env.OPENAI_DEPLOYMENT_NAME}} # env variables are in .env + label: '{{env.OPENAI_DEPLOYMENT_NAME}}' + + + +defaultTest: + assert: +# retrieval metrics: evaluating retrieved contexts against relevant contexts + # Order unaware retrieval metrics + - type: python + value: file://../metrics/order_unaware/precision_at_k.py + metric: PrecisionK + - type: python + value: file://../metrics/order_unaware/recall_at_k.py + metric: RecallK + - type: python + value: file://../metrics/order_unaware/f1_at_k.py + metric: F1K + # Order aware retrieval metrics + - type: python + value: file://../metrics/order_aware/reciprocal_rank.py + metric: Mean Reciprocal Rank + + # end-task: evaluating ground truth vs generated answer + - type: equals + value: '{{ground_truth}}' + # - type: python + # value: file://../metrics/ragas_metrics/ragas_answer_similarity.py + # metric: Ragas Answer Similarity + # - type: python + # value: file://../metrics/ragas_metrics/ragas_answer_correctness.py + # metric: Ragas Answer Correctness + + # evaluating answer vs retrieved context: + # - type: python + # value: file://../metrics/ragas_metrics/ragas_answer_relevancy.py + # metric: Ragas Answer Relevancy + + # retrieval metrics: evaluating retrieved contexts against ground truth + # - type: python + # value: file://../metrics/ragas_metrics/ragas_context_recall.py + # metric: Ragas Context Recall + # - type: python + # value: file://../metrics/ragas_metrics/ragas_context_precision.py + # metric: Ragas Context Precision + # - type: python + # value: file://../metrics/ragas_metrics/ragas_context_entity_recall.py + # metric: Ragas Context Entity Recall + # - type: python + # value: file://../metrics/ragas_metrics/ragas_context_utilization.py + # metric: Ragas Context Utilization + + + + ## latency needs cache to be off + # - type: latency + # threshold: 5000 +tests: + - file://../data/test_simple.csv diff --git a/src/evaluation/configs/redteam_config.yaml b/src/evaluation/configs/redteam_config.yaml new file mode 100644 index 0000000..7948c0c --- /dev/null +++ b/src/evaluation/configs/redteam_config.yaml @@ -0,0 +1,44 @@ +# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json + +# Red teaming configuration + +# Docs: https://promptfoo.dev/docs/red-team/configuration +description: "My first red team" + +# or targets. generates the output +providers: + # Red team targets. To talk directly to your application, use a custom provider. + # See https://promptfoo.dev/docs/red-team/configuration/#providers + - id: azureopenai:chat:{{env.AZURE_OPENAI_DEPLOYMENT_NAME}} + label: '{{env.AZURE_OPENAI_DEPLOYMENT_NAME}}' + + +# Other redteam settings +redteam: + # attacker provider: generates adversial outputs. Some providers such as Anthropic may disable your account for generating harmful test cases. We recommend using the default OpenAI provider. + provider: + id: azureopenai:chat:phi3:3.8b-mini-4k-instruct-q4_K_M + + purpose: "travel test app" + # Default number of inputs to generate for each plugin. + # The total number of tests will be (numTests * plugins.length * (1 + strategies.length)) + numTests: 1 # Each plugin generates 1 adversarial inputs. + + # To control the number of tests for each plugin, use: + # - id: plugin-name + # numTests: 10 + plugins: + - hallucination # Model generating false or misleading information + - harmful:hate # Content that promotes hate or discrimination +# +# +# # Attack methods for applying adversarial inputs +# strategies: +# - jailbreak # Attempts to bypass security measures through iterative prompt refinement +# - prompt-injection # Malicious inputs designed to manipulate the model's behavior + +#defaultTest: +# options: +# # grader/ evaluator : evaluates the generated outputs if llm-rubric metrics are used moderation +# provider: +# id: azureopenai:chat:{{env.AZURE_OPENAI_DEPLOYMENT_NAME}} diff --git a/src/evaluation/context.py b/src/evaluation/context.py new file mode 100644 index 0000000..854451f --- /dev/null +++ b/src/evaluation/context.py @@ -0,0 +1,14 @@ +# Learn more about using dynamic variables: https://promptfoo.dev/docs/configuration/guide/#import-vars-from-separate-files +def get_var(var_name, prompt, other_vars): + # This is where you can fetch documents from a database, call an API, etc. + # ... + + if var_name == "context": + # Return value based on the variable name and test context + return {"output": f"... Documents for {other_vars['inquiry']} in prompt: {prompt} ..."} + + # Default variable value + return {"output": "Document A, Document B, Document C, ..."} + + # Handle potential errors + # return { 'error': 'Error message' } diff --git a/src/evaluation/data/test_json.csv b/src/evaluation/data/test_json.csv new file mode 100644 index 0000000..dd86ea8 --- /dev/null +++ b/src/evaluation/data/test_json.csv @@ -0,0 +1,8 @@ +query,ground_truth,context,relevant_context +"{""global_implications"":""What are the global implications of the USA Supreme Court ruling on abortion?""}","{ ""global_implications"": ""The USA Supreme Court ruling on abortion has sparked intense debates and discussions globally, influencing the discourse on reproductive rights and women's health.""}","file://../configs/config_json.py","[ +'The USA Supreme Court ruling on abortion has sparked intense debates and discussions not only within the country but also around the world.', +'Many countries look to the United States as a leader in legal and social issues, so the decision could potentially influence the policies and attitudes towards abortion in other nations.', +'The ruling may impact international organizations and non-governmental groups that work on reproductive rights and women\'s health issues.', +'Depending on the outcome, there could be shifts in funding, advocacy efforts, and collaborations with American counterparts, leading to ripple effects in the global fight for reproductive justice.', +'Several European countries have announced plans to review their own abortion laws in light of the US ruling, with some considering more restrictive measures.', +'A new international coalition, \'Global Choice\', has been formed by 15 countries to promote and protect abortion rights worldwide in response to the US decision.']" diff --git a/src/evaluation/data/test_simple.csv b/src/evaluation/data/test_simple.csv new file mode 100644 index 0000000..b0fbc1a --- /dev/null +++ b/src/evaluation/data/test_simple.csv @@ -0,0 +1,25 @@ +query,ground_truth,answer,context,relevant_context +What are the global implications of the USA Supreme Court ruling on abortion?,"The global implications of the USA Supreme Court ruling on abortion are significant. The ruling has led to limited or no access to abortion for one in three women and girls of reproductive age in states where abortion access is restricted. These states also have weaker maternal health support, higher maternal death rates, and higher child poverty rates. Additionally, the ruling has had an impact beyond national borders due to the USA's geopolitical and cultural influence globally. Organizations and activists worldwide are concerned that the ruling may inspire anti-abortion legislative and policy attacks in other countries. The ruling has also hindered progressive law reform and the implementation of abortion guidelines in certain African countries. Furthermore, the ruling has created a chilling effect in international policy spaces, empowering anti-abortion actors to undermine human rights protections.","The global implications of the USA Supreme Court ruling on abortion can be significant, as it sets a precedent for other countries and influences the global discourse on reproductive rights. Here are some potential implications: + +1. Influence on other countries: The Supreme Court's ruling can serve as a reference point for other countries grappling with their own abortion laws. It can provide legal arguments and reasoning that advocates for reproductive rights can use to challenge restrictive abortion laws in their respective jurisdictions. + +2. Strengthening of global reproductive rights movements: A favorable ruling by the Supreme Court can energize and empower reproductive rights movements worldwide. It can serve as a rallying point for activists and organizations advocating for women's rights, leading to increased mobilization and advocacy efforts globally. + +3. Counteracting anti-abortion movements: Conversely, a ruling that restricts abortion rights can embolden anti-abortion movements globally. It can provide legitimacy to their arguments and encourage similar restrictive measures in other countries, potentially leading to a rollback of existing reproductive rights. + +4. Impact on international aid and policies: The Supreme Court's ruling can influence international aid and policies related to reproductive health. It can shape the priorities and funding decisions of donor countries and organizations, potentially leading to increased support for reproductive rights initiatives or conversely, restrictions on funding for abortion-related services. + +5. Shaping international human rights standards: The ruling can contribute to the development of international human rights standards regarding reproductive rights. It can influence the interpretation and application of existing human rights treaties and conventions, potentially strengthening the recognition of reproductive rights as fundamental human rights globally. + +6. Global health implications: The Supreme Court's ruling can have implications for global health outcomes, particularly in countries with restrictive abortion laws. It can impact the availability and accessibility of safe and legal abortion services, potentially leading to an increase in unsafe abortions and related health complications. + +It is important to note that the specific implications will depend on the nature of the Supreme Court ruling and the subsequent actions taken by governments, activists, and organizations both within and outside the United States.","[ +'The USA Supreme Court ruling on abortion has sparked intense debates and discussions not only within the country but also around the world.', +'Many countries look to the United States as a leader in legal and social issues, so the decision could potentially influence the policies and attitudes towards abortion in other nations.', +'The ruling may impact international organizations and non-governmental groups that work on reproductive rights and women\'s health issues.']","[ +'The USA Supreme Court ruling on abortion has sparked intense debates and discussions not only within the country but also around the world.', +'Many countries look to the United States as a leader in legal and social issues, so the decision could potentially influence the policies and attitudes towards abortion in other nations.', +'The ruling may impact international organizations and non-governmental groups that work on reproductive rights and women\'s health issues.', +'Depending on the outcome, there could be shifts in funding, advocacy efforts, and collaborations with American counterparts, leading to ripple effects in the global fight for reproductive justice.', +'Several European countries have announced plans to review their own abortion laws in light of the US ruling, with some considering more restrictive measures.', +'A new international coalition, \'Global Choice\', has been formed by 15 countries to promote and protect abortion rights worldwide in response to the US decision.']" diff --git a/src/evaluation/instructions.txt b/src/evaluation/instructions.txt new file mode 100644 index 0000000..b141253 --- /dev/null +++ b/src/evaluation/instructions.txt @@ -0,0 +1 @@ +- Update the Makefile to change the configuration (src/evaluation/configs/..) diff --git a/src/evaluation/metrics/__init__.py b/src/evaluation/metrics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/evaluation/metrics/data_types.py b/src/evaluation/metrics/data_types.py new file mode 100644 index 0000000..67b0aa8 --- /dev/null +++ b/src/evaluation/metrics/data_types.py @@ -0,0 +1,11 @@ +from typing import Optional + +from pydantic import BaseModel + + +class GradingResult(BaseModel): + pass_: bool # 'pass' is a reserved keyword in Python + score: float + reason: str + component_results: Optional[list["GradingResult"]] = None + named_scores: Optional[dict[str, float]] = None # Appear as metrics in the UI diff --git a/src/evaluation/metrics/information_extraction/__init__.py b/src/evaluation/metrics/information_extraction/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/evaluation/metrics/information_extraction/entity_level.py b/src/evaluation/metrics/information_extraction/entity_level.py new file mode 100644 index 0000000..e69de29 diff --git a/src/evaluation/metrics/information_extraction/exact_match_json.py b/src/evaluation/metrics/information_extraction/exact_match_json.py new file mode 100644 index 0000000..9f74dfc --- /dev/null +++ b/src/evaluation/metrics/information_extraction/exact_match_json.py @@ -0,0 +1,39 @@ +from pydantic import ValidationError + +from evaluation.metrics.utils import create_dynamic_model, convert_to_json + + +def get_assert(output: str, context): + """Evaluates the precision at k.""" + threshold = 0.99 + llm_answer, true_answer = convert_to_json(output, context, threshold) + + try: + model_true_answer = create_dynamic_model(true_answer) + true_answer = model_true_answer(**true_answer) + + llm_answer = model_true_answer(**llm_answer) + + if llm_answer == true_answer: + score = 1.0 + reason = f"{score} > {threshold} = {score > threshold}" + else: + dict_a = llm_answer.model_dump() + dict_b = true_answer.model_dump() + differences = [key for key in dict_b.keys() if dict_a.get(key) != dict_b.get(key)] + + score = round(float(1 - (len(differences) / len(llm_answer.model_fields))), 2) + + reason = f"{score} > {threshold} = {score > threshold}. Number of differences: {len(differences)}. Differences: {differences}" + + except ValidationError as e: + total_fields = len(llm_answer.model_fields) + errors_count = len(e.errors()) + score = round(float(1 - (errors_count / total_fields)), 2) + reason = str(e) + + return { + "pass": score > threshold, + "score": score, + "reason": reason, + } diff --git a/src/evaluation/metrics/information_extraction/missing_fields.py b/src/evaluation/metrics/information_extraction/missing_fields.py new file mode 100644 index 0000000..af24107 --- /dev/null +++ b/src/evaluation/metrics/information_extraction/missing_fields.py @@ -0,0 +1,45 @@ +from pydantic import ValidationError + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.utils import create_dynamic_model, convert_to_json + + +def get_assert(output: str, context) -> GradingResult: + """Evaluates the precision at k.""" + threshold = 0.99 + + llm_answer, true_answer = convert_to_json(output, context, threshold) + + try: + model_true_answer = create_dynamic_model(true_answer) + # true_answer = model_true_answer(**true_answer) + + llm_answer = model_true_answer(**llm_answer) + null_fields = [key for key, value in llm_answer.model_dump().items() if value is None] + + score = round(float(1 - (len(null_fields) / len(llm_answer.model_fields))), 2) + + reason = ( + f"{score} > {threshold} = {score > threshold}. Number of null fields: {len(null_fields)}. " + f"null_fields: {null_fields}" + ) + except ValidationError as e: + error = validation_error_message(e) + total_fields = len(llm_answer.model_fields) + errors_count = len(error.errors()) + score = float(1 - (errors_count / total_fields)) + reason = str(error) + + return { + "pass": score > threshold, + "score": score, + "reason": reason, + } + + +def validation_error_message(error: ValidationError) -> ValidationError: + for err in error.errors(): + err.pop("input") + err.pop("url") + + return error diff --git a/src/evaluation/metrics/information_extraction/similarity_json.py b/src/evaluation/metrics/information_extraction/similarity_json.py new file mode 100644 index 0000000..def97a9 --- /dev/null +++ b/src/evaluation/metrics/information_extraction/similarity_json.py @@ -0,0 +1,83 @@ +import numpy as np +from pydantic import ValidationError, BaseModel + +from evaluation.metrics.utils import ( + create_dynamic_model, + convert_to_json, +) +from utils import llmaaj_embedding_client + + +def get_assert(output: str, context): + """Evaluates the precision at k.""" + threshold = 0.99 + llm_answer, true_answer = convert_to_json(output, context, threshold) + + try: + model_true_answer = create_dynamic_model(true_answer) + true_answer = model_true_answer(**true_answer) + + llm_answer = model_true_answer(**llm_answer) + + if llm_answer == true_answer: + score = 1.0 + reason = f"{score} > {threshold} = {score > threshold}" + else: + dict_a = llm_answer.model_dump() + dict_b = true_answer.model_dump() + differences = [key for key in dict_b.keys() if dict_a.get(key) != dict_b.get(key)] + + num_similar_fields = len(llm_answer.model_fields) - len(differences) + + result, similarity = compare_pydantic_objects(llm_answer, true_answer, differences) + score = round( + float((num_similar_fields + similarity) / len(llm_answer.model_fields)), + 2, + ) + + reason = f"{score} > {threshold} = {score > threshold}. Number of differences: {len(differences)}. Differences: {result}" + + except ValidationError as e: + total_fields = len(llm_answer.model_fields) + errors_count = len(e.errors()) + score = round(float(1 - (errors_count / total_fields)), 2) + reason = str(e) + + return { + "pass": score > threshold, + "score": score, + "reason": reason, + } + + +def compare_pydantic_objects( + obj1: BaseModel, obj2: BaseModel, differences: list = None +) -> dict[str, float]: + """Compare two Pydantic objects using cosine similarity.""" + result = {} + total_similarity = 0 + similarity = 0 + if not differences: + differences = obj1.model_fields + + for field in differences: + value1 = getattr(obj1, field) + value2 = getattr(obj2, field) + if value1 != value2: + if value1 and value2: + embedding1 = llmaaj_embedding_client.embed_query(text=str(value1)) + embedding2 = llmaaj_embedding_client.embed_query(text=str(value2)) + similarity = round(cosine_similarity(embedding1, embedding2), 2) + else: + similarity = 0 + else: + similarity = 1 + + result[field] = similarity + total_similarity += similarity + return result, total_similarity + + +def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float: + """Calculate cosine similarity between two vectors.""" + return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) diff --git a/src/evaluation/metrics/order_aware/__init__.py b/src/evaluation/metrics/order_aware/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/evaluation/metrics/order_aware/reciprocal_rank.py b/src/evaluation/metrics/order_aware/reciprocal_rank.py new file mode 100644 index 0000000..e324f73 --- /dev/null +++ b/src/evaluation/metrics/order_aware/reciprocal_rank.py @@ -0,0 +1,34 @@ +import math + +from evaluation.metrics.data_types import GradingResult +from utils import safe_eval + + +# pomptfoo cwd is evaluations + + +# def ragas_context_answer_similarity(input, output, reference, metadata, expected) -> float: +# def get_assert(output: str, context) -> Union[bool, float, Dict[str, Any]]: +def get_assert(output: str, context) -> GradingResult: + """Evaluates the precision at k.""" + retrieved_docs = safe_eval(context["vars"]["context"]) + relevant_docs = safe_eval(context["vars"]["relevant_context"]) + + score = 0 + # compute Reciprocal Rank + try: + score = round(1 / (relevant_docs.index(retrieved_docs[0]) + 1), 2) + except ValueError: + score = -1 + + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } diff --git a/src/evaluation/metrics/order_unaware/__init__.py b/src/evaluation/metrics/order_unaware/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/evaluation/metrics/order_unaware/f1_at_k.py b/src/evaluation/metrics/order_unaware/f1_at_k.py new file mode 100644 index 0000000..2deb88a --- /dev/null +++ b/src/evaluation/metrics/order_unaware/f1_at_k.py @@ -0,0 +1,28 @@ +import math + + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.order_unaware import precision_at_k, recall_at_k + + +def get_assert(output: str, context) -> GradingResult: + """Calculates F1@k.""" + precision = precision_at_k.get_assert(context=context, output=output)["score"] + recall = recall_at_k.get_assert(context=context, output=output)["score"] + + if precision + recall == 0: + score = 0.0 + else: + score = round(float(2 * (precision * recall) / (precision + recall)), 2) + + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } diff --git a/src/evaluation/metrics/order_unaware/precision_at_k.py b/src/evaluation/metrics/order_unaware/precision_at_k.py new file mode 100644 index 0000000..f3c634f --- /dev/null +++ b/src/evaluation/metrics/order_unaware/precision_at_k.py @@ -0,0 +1,27 @@ +import math +import os + +from evaluation.metrics.data_types import GradingResult +from utils import safe_eval + + +def get_assert(output: str, context) -> GradingResult: + """Evaluates the precision at k.""" + retrieved_docs = safe_eval(context["vars"]["context"]) + relevant_docs = safe_eval(context["vars"]["relevant_context"]) + k = os.environ.get("K", 3) + retrieved_docs_at_k = retrieved_docs[:k] + relevant_count = sum([1 for doc in retrieved_docs_at_k if doc in relevant_docs]) + score = float(relevant_count / k) + + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } diff --git a/src/evaluation/metrics/order_unaware/recall_at_k.py b/src/evaluation/metrics/order_unaware/recall_at_k.py new file mode 100644 index 0000000..8f5b04b --- /dev/null +++ b/src/evaluation/metrics/order_unaware/recall_at_k.py @@ -0,0 +1,27 @@ +import math +import os + +from evaluation.metrics.data_types import GradingResult +from utils import safe_eval, time_function + + +@time_function +def get_assert(output: str, context) -> GradingResult: + retrieved_docs = safe_eval(context["vars"]["context"]) + relevant_docs = safe_eval(context["vars"]["relevant_context"]) + k = os.environ.get("K", 3) + retrieved_docs_at_k = retrieved_docs[:k] + relevant_count = sum([1 for doc in retrieved_docs_at_k if doc in relevant_docs]) + score = round(float(relevant_count / len(relevant_docs)), 2) + + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } diff --git a/src/evaluation/metrics/ragas_metrics/__init__.py b/src/evaluation/metrics/ragas_metrics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/evaluation/metrics/ragas_metrics/ragas_answer_correctness.py b/src/evaluation/metrics/ragas_metrics/ragas_answer_correctness.py new file mode 100644 index 0000000..a894473 --- /dev/null +++ b/src/evaluation/metrics/ragas_metrics/ragas_answer_correctness.py @@ -0,0 +1,42 @@ +import math + +from ragas import evaluate, RunConfig +from ragas.metrics import answer_correctness + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.utils import to_dataset +from utils import llmaaj_chat_client, llmaaj_embedding_client + + +# def ragas_context_answer_similarity(input, output, reference, metadata, expected) -> float: +# def get_assert(output: str, context) -> Union[bool, float, Dict[str, Any]]: +def get_assert(output: str, context) -> GradingResult: + eval_dataset = to_dataset(output=output, context=context) + + result = evaluate( + eval_dataset, + metrics=[answer_correctness], + llm=llmaaj_chat_client, + embeddings=llmaaj_embedding_client, + run_config=RunConfig(max_workers=64), + ).to_pandas() + # 'score': result['answer_similarity'], + + score = float(result["answer_correctness"]) + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } + + +if __name__ == "__main__": + x = get_assert("blop", {"vars": {"ground_truth": "blop"}}) + + print("XXXX:", x) diff --git a/src/evaluation/metrics/ragas_metrics/ragas_answer_relevancy.py b/src/evaluation/metrics/ragas_metrics/ragas_answer_relevancy.py new file mode 100644 index 0000000..cfc869f --- /dev/null +++ b/src/evaluation/metrics/ragas_metrics/ragas_answer_relevancy.py @@ -0,0 +1,39 @@ +import math +from ragas import evaluate, RunConfig +from ragas.metrics import answer_relevancy + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.utils import to_dataset +from utils import llmaaj_chat_client, llmaaj_embedding_client + + +def get_assert(output: str, context) -> GradingResult: + eval_dataset = to_dataset(output=output, context=context) + + result = evaluate( + eval_dataset, + metrics=[answer_relevancy], + llm=llmaaj_chat_client, + embeddings=llmaaj_embedding_client, + run_config=RunConfig(max_workers=64), + ).to_pandas() + # 'score': result['answer_similarity'], + + score = float(result["answer_relevancy"]) + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } + + +if __name__ == "__main__": + x = get_assert("blop", {"vars": {"ground_truth": "blop"}}) + + print("XXXX:", x) diff --git a/src/evaluation/metrics/ragas_metrics/ragas_answer_similarity.py b/src/evaluation/metrics/ragas_metrics/ragas_answer_similarity.py new file mode 100644 index 0000000..8f074a8 --- /dev/null +++ b/src/evaluation/metrics/ragas_metrics/ragas_answer_similarity.py @@ -0,0 +1,37 @@ +import math + +from ragas import evaluate, RunConfig +from ragas.metrics import answer_similarity + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.utils import to_dataset +from utils import llmaaj_chat_client, llmaaj_embedding_client +from utils import time_function + + +@time_function +def get_assert(output: str, context) -> GradingResult: + eval_dataset = to_dataset(output=output, context=context) + + result = evaluate( + eval_dataset, + metrics=[ + answer_similarity, + ], + llm=llmaaj_chat_client, + embeddings=llmaaj_embedding_client, + run_config=RunConfig(max_workers=64), + ).to_pandas() + # 'score': result['answer_similarity'], + score = float(result["semantic_similarity"]) + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } diff --git a/src/evaluation/metrics/ragas_metrics/ragas_context_entity_recall.py b/src/evaluation/metrics/ragas_metrics/ragas_context_entity_recall.py new file mode 100644 index 0000000..2d12d7c --- /dev/null +++ b/src/evaluation/metrics/ragas_metrics/ragas_context_entity_recall.py @@ -0,0 +1,40 @@ +import math + +from ragas import evaluate, RunConfig +from ragas.metrics import context_entity_recall + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.utils import to_dataset +from utils import llmaaj_chat_client, llmaaj_embedding_client + + +def get_assert(output: str, context) -> GradingResult: + eval_dataset = to_dataset(output=output, context=context) + + result = evaluate( + eval_dataset, + metrics=[context_entity_recall], + llm=llmaaj_chat_client, + embeddings=llmaaj_embedding_client, + run_config=RunConfig(max_workers=64), + ).to_pandas() + # 'score': result['answer_similarity'], + + score = float(result["context_entity_recall"]) + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } + + +if __name__ == "__main__": + x = get_assert("blop", {"vars": {"ground_truth": "blop"}}) + + print("XXXX:", x) diff --git a/src/evaluation/metrics/ragas_metrics/ragas_context_precision.py b/src/evaluation/metrics/ragas_metrics/ragas_context_precision.py new file mode 100644 index 0000000..0ebf65b --- /dev/null +++ b/src/evaluation/metrics/ragas_metrics/ragas_context_precision.py @@ -0,0 +1,40 @@ +import math + +from ragas import evaluate, RunConfig +from ragas.metrics import context_precision + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.utils import to_dataset +from utils import llmaaj_chat_client, llmaaj_embedding_client + + +def get_assert(output: str, context) -> GradingResult: + eval_dataset = to_dataset(output=output, context=context) + + result = evaluate( + eval_dataset, + metrics=[context_precision], + llm=llmaaj_chat_client, + embeddings=llmaaj_embedding_client, + run_config=RunConfig(max_workers=64), + ).to_pandas() + # 'score': result['answer_similarity'], + + score = float(result["context_precision"]) + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } + + +if __name__ == "__main__": + x = get_assert("blop", {"vars": {"ground_truth": "blop"}}) + + print("XXXX:", x) diff --git a/src/evaluation/metrics/ragas_metrics/ragas_context_recall.py b/src/evaluation/metrics/ragas_metrics/ragas_context_recall.py new file mode 100644 index 0000000..5e97265 --- /dev/null +++ b/src/evaluation/metrics/ragas_metrics/ragas_context_recall.py @@ -0,0 +1,44 @@ +import math +from ragas import evaluate, RunConfig +from ragas.metrics import context_recall + + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.utils import to_dataset +from utils import llmaaj_chat_client, llmaaj_embedding_client +from utils import time_function + + +# def ragas_context_answer_similarity(input, output, reference, metadata, expected) -> float: +# def get_assert(output: str, context) -> Union[bool, float, Dict[str, Any]]: +@time_function +def get_assert(output: str, context) -> GradingResult: + eval_dataset = to_dataset(output=output, context=context) + + result = evaluate( + eval_dataset, + metrics=[context_recall], + llm=llmaaj_chat_client, + embeddings=llmaaj_embedding_client, + run_config=RunConfig(max_workers=64), + ).to_pandas() + # 'score': result['answer_similarity'], + + score = float(result["context_recall"]) + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } + + +if __name__ == "__main__": + x = get_assert("blop", {"vars": {"ground_truth": "blop"}}) + + print("XXXX:", x) diff --git a/src/evaluation/metrics/ragas_metrics/ragas_context_utilization.py b/src/evaluation/metrics/ragas_metrics/ragas_context_utilization.py new file mode 100644 index 0000000..d0f4228 --- /dev/null +++ b/src/evaluation/metrics/ragas_metrics/ragas_context_utilization.py @@ -0,0 +1,42 @@ +import math +from ragas import evaluate, RunConfig +from ragas.metrics import context_utilization + + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.utils import to_dataset +from utils import llmaaj_chat_client, llmaaj_embedding_client + + +# def ragas_context_answer_similarity(input, output, reference, metadata, expected) -> float: +# def get_assert(output: str, context) -> Union[bool, float, Dict[str, Any]]: +def get_assert(output: str, context) -> GradingResult: + eval_dataset = to_dataset(output=output, context=context) + + result = evaluate( + eval_dataset, + metrics=[context_utilization], + llm=llmaaj_chat_client, + embeddings=llmaaj_embedding_client, + run_config=RunConfig(max_workers=64), + ).to_pandas() + # 'score': result['answer_similarity'], + + score = float(result["context_utilization"]) + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } + + +if __name__ == "__main__": + x = get_assert("blop", {"vars": {"ground_truth": "blop"}}) + + print("XXXX:", x) diff --git a/src/evaluation/metrics/ragas_metrics/ragas_faithfulness.py b/src/evaluation/metrics/ragas_metrics/ragas_faithfulness.py new file mode 100644 index 0000000..5b697a8 --- /dev/null +++ b/src/evaluation/metrics/ragas_metrics/ragas_faithfulness.py @@ -0,0 +1,41 @@ +import math +from ragas import evaluate, RunConfig +from ragas.metrics import faithfulness + + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.utils import to_dataset +from utils import llmaaj_chat_client, llmaaj_embedding_client + + +# def ragas_context_answer_similarity(input, output, reference, metadata, expected) -> float: +# def get_assert(output: str, context) -> Union[bool, float, Dict[str, Any]]: +def get_assert(output: str, context) -> GradingResult: + eval_dataset = to_dataset(output=output, context=context) + result = evaluate( + eval_dataset, + metrics=[faithfulness], + llm=llmaaj_chat_client, + embeddings=llmaaj_embedding_client, + run_config=RunConfig(max_workers=64), + ).to_pandas() + # 'score': result['answer_similarity'], + + score = float(result["faithfulness"]) + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } + + +if __name__ == "__main__": + x = get_assert("blop", {"vars": {"ground_truth": "blop"}}) + + print("XXXX:", x) diff --git a/src/evaluation/metrics/ragas_metrics/ragas_harmfulness.py b/src/evaluation/metrics/ragas_metrics/ragas_harmfulness.py new file mode 100644 index 0000000..c73da8c --- /dev/null +++ b/src/evaluation/metrics/ragas_metrics/ragas_harmfulness.py @@ -0,0 +1,40 @@ +import math + +from ragas import evaluate, RunConfig +from ragas.metrics.critique import harmfulness + +from evaluation.metrics.data_types import GradingResult +from evaluation.metrics.utils import to_dataset +from utils import llmaaj_chat_client, llmaaj_embedding_client + + +def get_assert(output: str, context) -> GradingResult: + eval_dataset = to_dataset(output=output, context=context) + + result = evaluate( + eval_dataset, + metrics=[harmfulness], + llm=llmaaj_chat_client, + embeddings=llmaaj_embedding_client, + run_config=RunConfig(max_workers=64), + ).to_pandas() + # 'score': result['answer_similarity'], + + score = float(result["harmfulness"]) + # threshold = context["test"]["metadata"]["threshold_ragas_as"] + threshold = 0 + + if math.isnan(score): + score = 0.0 + + return { + "pass": score > threshold, + "score": score, + "reason": f"{score} > {threshold} = {score > threshold}", + } + + +if __name__ == "__main__": + x = get_assert("blop", {"vars": {"ground_truth": "blop"}}) + + print("XXXX:", x) diff --git a/src/evaluation/metrics/utils.py b/src/evaluation/metrics/utils.py new file mode 100644 index 0000000..6439aff --- /dev/null +++ b/src/evaluation/metrics/utils.py @@ -0,0 +1,229 @@ +import ast +import json +from typing import Optional + +from datasets import Dataset +from pydantic import Field, create_model + +from utils import logger + + +def safe_eval(x): + try: + return ast.literal_eval(x) + except ValueError: + raise Exception(f"Value error in safe") + + +def to_dataset(output, context): + # question, ground truth and output can be dict (json information extraction) or str + # dict: for example '{field:question}' , ground_truth is '{field: ground_truth}', output is '{field: answer}' + # or simply strings + question = context["vars"]["query"] + ground_truth = context["vars"]["ground_truth"] + contexts = context["vars"]["context"] + + # todo: add if is json parameter and also in the promptfoo config to support + # string responses, json responses, + + try: + output = safe_eval(output) + except Exception: + logger.warning(f" safe eval output: {output}") + + try: + question = safe_eval(question) + except Exception: + logger.warning(f" safe eval question: {question}") + + try: + ground_truth = safe_eval(ground_truth) + except Exception: + logger.warning(f" in safe eval ground_truth: {ground_truth}") + + try: + contexts = safe_eval(contexts) + except Exception: + logger.warning(f" in safe eval contexts: {contexts}") + + # context should be a list of strings as input and we transform it to a list of list of str because of ragas + if isinstance(contexts, list): + if isinstance(contexts[0], str): + if isinstance(ground_truth, dict): + # if the output is a json response, we will evaluate each element of the json response to each + # element of the json ground_truth. For each element, we copy the contexts received for the whole json. + contexts = [contexts for _ in range(len(ground_truth))] + else: + contexts = [contexts] + elif isinstance(contexts[0], list) and isinstance(contexts[0][0], str): + pass + else: + raise Exception( + f"Value error in Context should be a list of strings. Context: {contexts}" + ) + else: + raise Exception(f"Value error in Context should be a list of strings. Context: {contexts}") + + # question should be an str and we transform it to a list of string because of ragas + if isinstance(question, dict) and isinstance( + list(question.values())[0], str + ): # format is {field: question} + question = list(question.values()) + elif isinstance(question, str): + question = [question] + elif not isinstance(question, list): + raise Exception(f"Value error in question: {question}") + + # ground_truth should be an str and we transform it to a list of string because of ragas + if isinstance(ground_truth, dict) and isinstance(list(ground_truth.values())[0], str): + ground_truth = list(ground_truth.values()) + elif isinstance(ground_truth, str): + ground_truth = [ground_truth] + elif not isinstance(ground_truth, list): + raise Exception(f"Value error in ground_truth: {ground_truth}") + + # output should be an str and we transform it to a list of string because of ragas + if isinstance(output, dict) and isinstance(list(output.values())[0], str): + output = list(output.values()) + elif isinstance(output, str): + output = [output] + else: + raise Exception(f"Value error in output: {output}") + + # check if all the lists have the same length + lengths = [len(ground_truth), len(contexts), len(question), len(output)] + if len(set(lengths)) != 1: + raise Exception( + f"Output, ground truth, contexts and question should have the same length : " + f"len output {len(output)}, len ground_truth {len(ground_truth)}, len contexts {len(contexts)}, " + f"len question {len(question)}" + ) + + return Dataset.from_dict( + { + "ground_truth": ground_truth, + "answer": output, + "contexts": contexts, + "question": question, + } + ) + + +def to_evaldataset(output, context): + # question, ground truth and output can be dict (json information extraction) or str + # dict: for example '{field:question}' , ground_truth is '{field: ground_truth}', output is '{field: answer}' + # or simply strings + question = context["vars"]["query"] + ground_truth = context["vars"]["ground_truth"] + contexts = context["vars"]["context"] + + # todo: add if is json parameter and also in the promptfoo config to support + # string responses, json responses, + + try: + output = safe_eval(output) + except Exception: + logger.warning(f" safe eval output: {output}") + + try: + question = safe_eval(question) + except Exception: + logger.warning(f" safe eval question: {question}") + + try: + ground_truth = safe_eval(ground_truth) + except Exception: + logger.warning(f" in safe eval ground_truth: {ground_truth}") + + try: + contexts = safe_eval(contexts) + except Exception: + logger.warning(f" in safe eval contexts: {contexts}") + + # context should be a list of strings as input and we transform it to a list of list of str because of ragas + if isinstance(contexts, list): + if isinstance(contexts[0], str): + if isinstance(ground_truth, dict): + # if the output is a json response, we will evaluate each element of the json response to each + # element of the json ground_truth. For each element, we copy the contexts received for the whole json. + contexts = [contexts for _ in range(len(ground_truth))] + else: + contexts = [contexts] + elif isinstance(contexts[0], list) and isinstance(contexts[0][0], str): + pass + else: + raise Exception( + f"Value error in Context should be a list of strings. Context: {contexts}" + ) + else: + raise Exception(f"Value error in Context should be a list of strings. Context: {contexts}") + + # question should be an str and we transform it to a list of string because of ragas + if isinstance(question, dict) and isinstance( + list(question.values())[0], str + ): # format is {field: question} + question = list(question.values()) + elif isinstance(question, str): + question = [question] + elif not isinstance(question, list): + raise Exception(f"Value error in question: {question}") + + # ground_truth should be an str and we transform it to a list of string because of ragas + if isinstance(ground_truth, dict) and isinstance(list(ground_truth.values())[0], str): + ground_truth = list(ground_truth.values()) + elif isinstance(ground_truth, str): + ground_truth = [ground_truth] + elif not isinstance(ground_truth, list): + raise Exception(f"Value error in ground_truth: {ground_truth}") + + # output should be an str and we transform it to a list of string because of ragas + if isinstance(output, dict) and isinstance(list(output.values())[0], str): + output = list(output.values()) + elif isinstance(output, str): + output = [output] + else: + raise Exception(f"Value error in output: {output}") + + # check if all the lists have the same length + lengths = [len(ground_truth), len(contexts), len(question), len(output)] + if len(set(lengths)) != 1: + raise Exception( + f"Output, ground truth, contexts and question should have the same length : " + f"len output {len(output)}, len ground_truth {len(ground_truth)}, len contexts {len(contexts)}, " + f"len question {len(question)}" + ) + + return Dataset.from_dict( + { + "ground_truth": ground_truth, + "answer": output, + "contexts": contexts, + "question": question, + } + ) + + +def create_dynamic_model(input_dict: dict): + fields = { + i: (Optional[str], Field(default=None, description=question)) + for i, question in input_dict.items() + } + + return create_model("DynamicModel", **fields) + + +def convert_to_json(output, context, threshold): + try: + if not isinstance(output, dict): + llm_answer = json.loads(output) + else: + llm_answer = output + true_answer = json.loads(context["vars"]["ground_truth"]) + return llm_answer, true_answer + except Exception: + score = 0 + return { + "pass": score > threshold, + "score": score, + "reason": "answer or ground_truth is not a valid json to be used in this metric", + } diff --git a/src/main_backend.py b/src/main_backend.py new file mode 100644 index 0000000..c9004a0 --- /dev/null +++ b/src/main_backend.py @@ -0,0 +1,22 @@ +import uvicorn + +from api.log_config import LOGGING_CONFIG +from utils import logger, settings + +if __name__ == "__main__": + if settings.DEV_MODE: + logger.info("Running app in DEV mode") + reload = True + LOGGING_CONFIG["loggers"]["uvicorn"]["level"] = "DEBUG" + LOGGING_CONFIG["loggers"]["uvicorn.error"]["level"] = "DEBUG" + LOGGING_CONFIG["loggers"]["uvicorn.access"]["level"] = "DEBUG" + else: + logger.info("Running app in PROD mode") + reload = False + uvicorn.run( + app="api.api:app", + host=settings.FASTAPI_HOST, + port=settings.FASTAPI_PORT, + reload=reload, + log_config=LOGGING_CONFIG, + ) diff --git a/src/main_frontend.py b/src/main_frontend.py new file mode 100644 index 0000000..defdcfe --- /dev/null +++ b/src/main_frontend.py @@ -0,0 +1,15 @@ +import streamlit as st + +st.write("# Home Page") + +st.write( + """This application template showcases the versatility of Streamlit & FastAPI, allowing you to choose between using Streamlit alone or integrating it with FastAPI for enhanced backend capabilities. +There are two pages showcasing the connection with azure : + +- Not using FastAPI: for example, you can directly interact with Azure Blob Storage, manage files, and perform operations like uploading and deleting documents—all. + +- Using FastAPI : leverage the power of FastAPI, this template provides a foundation for building interactive and scalable applications (For example : RAG). the FastAPI backend interacts with Azure Search, allowing you to search for documents and retrieve relevant information. Streamlit then interacts with the FastAPI backend to display the search results. + +- Using only Azure Openai without ani RAG : just a chat. +""" +) diff --git a/src/ml/__init__.py b/src/ml/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/ml/ai.py b/src/ml/ai.py new file mode 100644 index 0000000..5aea517 --- /dev/null +++ b/src/ml/ai.py @@ -0,0 +1,152 @@ +import instructor +import requests +from pydantic import BaseModel + +from utils import logger, settings, search_client, chat_client, chat_model_name + + +def get_completions( + messages: list, + stream: bool = False, + response_model: BaseModel = None, # Use Instructor library + max_tokens: int = 1000, + temperature: int = 0, + top_p: int = 1, + seed: int = 100, + full_response: bool = False, + client=None, +) -> str | BaseModel | None: + """Returns a response from the azure openai model. + + Args: + messages: + stream: + response_model: + monitor: + max_tokens: + temperature: + top_p: + seed: + full_response: + client: + + Returns: + response : str | BaseModel | None : + """ + if not client: + client = chat_client + + input_dict = { + "model": chat_model_name, + "messages": messages, + "max_tokens": max_tokens, + "temperature": temperature, + "top_p": top_p, + "seed": seed, + "stream": stream, + } + if response_model: + # if you use local models instead of openai models, the response_model feature may not work + client = instructor.from_openai(chat_client, mode=instructor.Mode.JSON) + input_dict["response_model"] = response_model + + if stream: + raise NotImplementedError("Stream is not supported right now. Please set stream to False.") + + try: + response = client.chat.completions.create(**input_dict) + except Exception as e: + logger.exception(f"Error in chat GPT: {e}") + logger.error("chat GPT response: None") + return None + + if full_response or response_model: + return response + else: + return response.choices[0].message.content + + +def get_related_document_ai_search(question): + logger.info(f"Azure AI search - find related documents: {question}") + + logger.info("Reformulate QUERY") + system_prompt = "Tu es un modèle qui a pour fonction de convertir des questions utilisateur en phrase affirmative pour faciliter la recherche par similarité dans une base documentaire vectorielle. Modifiez la phrase utilisateur suivante en ce sens et retirez tout ce qui n'est pas pertinent, comment Bonjour, merci etc. Si c'est dans une autre langue que le Français, traduis la question en Français:" + + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": "Convertis cette phrase en affirmative " + question}, + ] + new_question = get_completions( + messages=messages, + ) + logger.debug(f"{question} ==> {new_question}") + content_docs = [] + results = search_client.search( + search_text=new_question, + query_type="semantic", + query_answer="extractive", + semantic_configuration_name=settings.SEMENTIC_CONFIGURATION_NAME, + top=settings.AZURE_SEARCH_TOP_K or 2, + query_answer_count=settings.AZURE_SEARCH_TOP_K or 2, + include_total_count=True, + query_caption="extractive|highlight-true", + ) + for i, result in enumerate(results): + for cap in result["@search.captions"]: + # data = f"Document {i + 1}: {cap.text} \nRéférence: {result['filename']}\n===" + data = f"Numéro document: {i + 1} - nom document:{result['title']} - text:{cap.text} \n===" + content_docs.append(data) + context = "\n".join(content_docs) + return context + + +def get_rag_response(user_input): + """Return the response after running RAG. + + Args: + user_input: + settings: + conversation_id: + + Returns: + response: + + """ + logger.info(f"Running RAG") + + context = get_related_document_ai_search(user_input) + formatted_user_input = f"question :{user_input}, \n\n contexte : \n{context}." + logger.info(f"RAG - final formatted prompt: {formatted_user_input}") + + response = get_completions( + messages=[ + { + "role": "system", + "content": "Tu est un chatbot qui répond aux questions.", + }, + {"role": "user", "content": formatted_user_input}, + ], + ) + return response + + +def run_azure_ai_search_indexer(): + """Run the azure ai search index. + + Returns: + res: response + """ + headers = { + "Content-Type": "application/json", + "api-key": settings.AZURE_SEARCH_API_KEY, + } + params = {"api-version": "2024-07-01"} + url = f"{settings.AZURE_SEARCH_SERVICE_ENDPOINT}/indexers('{settings.AZURE_SEARCH_INDEXER_NAME}')/run" + + res = requests.post(url=url, headers=headers, params=params) + logger.debug(f"run_azure_ai_search_index response: {res.status_code}") + return res + + +if __name__ == "__main__": + print(run_azure_ai_search_indexer()) diff --git a/src/pages/0_azure_chat.py b/src/pages/0_azure_chat.py new file mode 100644 index 0000000..f451a76 --- /dev/null +++ b/src/pages/0_azure_chat.py @@ -0,0 +1,78 @@ +import streamlit as st + +from ml.ai import get_completions +from utils import settings, llmaaj_chat_client, llmaaj_embedding_client + +st.write("# Test your Client Chat") + +st.write(settings.get_active_env_vars()) + +message_response = {"type": None, "message": None} + +st.header("Ask your question", divider="rainbow") +col1, col2 = st.columns([3, 1]) +with col1: + user_query = st.text_input(key="chat", label="Posez votre question") + + +if user_query: + try: + # res = requests.get(f"{backend_url}/prefix_example/form/", params=params).json() + + res = get_completions( + messages=[ + { + "role": "system", + "content": "Tu est un chatbot qui répond aux questions.", + }, + {"role": "user", "content": user_query}, + ], + ) + + st.success(res) + except Exception as e: + res = f"Error: {e}" + st.error(res) + +st.title(" Test your LLMAAJ chat client : llm as a judge") +if not settings.ENABLE_EVALUATION: + st.error("ENABLE_EVALUATION env var is not set to True") +else: + st.write(settings.get_eval_env_vars()) + col1, col2 = st.columns([3, 1]) + with col1: + user_query = st.text_input(key="chat_llmaaj", label="Posez votre question") + + if user_query: + try: + messages = [ + ( + "system", + "You are a helpful assistant", + ), + ("human", user_query), + ] + res = llmaaj_chat_client.invoke(messages) + + st.success(res.content) + except Exception as e: + res = f"Error: {e}" + st.error(res) + + st.title(" Test your LLMAAJ Embeddings") + if st.button("Check LLMAAJ Embeddings"): + try: + from langchain_core.vectorstores import InMemoryVectorStore + + text = "LangChain is the framework for building context-aware reasoning applications" + + vectorstore = InMemoryVectorStore.from_texts( + [text], + embedding=llmaaj_embedding_client, + ) + retriever = vectorstore.as_retriever() + retrieved_documents = retriever.invoke("What is LangChain?") + st.success(retrieved_documents[0].page_content) + except Exception as e: + res = f"Error: {e}" + st.error(res) diff --git a/src/pages/1_azure_rag.py b/src/pages/1_azure_rag.py new file mode 100644 index 0000000..81cc1f0 --- /dev/null +++ b/src/pages/1_azure_rag.py @@ -0,0 +1,78 @@ +import streamlit as st + +from ml.ai import run_azure_ai_search_indexer +from utils import settings, logger + +st.write("# Streamlit Azure RAG without fastapi") + +st.write(settings.get_azure_search_env_vars()) + +if not settings.ENABLE_AZURE_SEARCH: + st.error("ENABLE_AZURE_SEARCH env var is not set to True") + st.stop() + +from azure.storage.blob import BlobServiceClient + +message_response = {"type": None, "message": None} + + +@st.fragment() +def show_upload_documents(): + global message_response + + st.write(f"### Documents disponibles dans le storage {settings.AZURE_CONTAINER_NAME}") + blob_service_client = BlobServiceClient.from_connection_string( + f"DefaultEndpointsProtocol=https;AccountName={settings.AZURE_STORAGE_ACCOUNT_NAME};AccountKey={settings.AZURE_STORAGE_ACCOUNT_KEY}" + ) + container_client = blob_service_client.get_container_client( + container=settings.AZURE_CONTAINER_NAME + ) + + blob_list = container_client.list_blobs() + for i, blob in enumerate(blob_list): + print(f"Name: {blob.name}") + + col1, col2 = st.columns([3, 1]) + with col1: + st.write(f"- {blob.name}") + with col2: + if st.button("Supprimer", key=f"button_{i}"): + container_client.delete_blob(blob.name) + run_azure_ai_search_indexer() + message_response = {"type": "success", "message": "Document supprimé avec succès"} + st.rerun(scope="fragment") + + uploaded_file = st.file_uploader("Transférer vos documents") + if uploaded_file: + blob_service_client = BlobServiceClient.from_connection_string( + f"DefaultEndpointsProtocol=https;AccountName={settings.AZURE_STORAGE_ACCOUNT_NAME};AccountKey={settings.AZURE_STORAGE_ACCOUNT_KEY}" + ) + blob_client = blob_service_client.get_blob_client( + container=settings.AZURE_CONTAINER_NAME, blob=uploaded_file.name + ) + + try: + res = blob_client.upload_blob(uploaded_file) + except Exception as e: + logger.error(f"Error uploading document: {e}") + message_response = {"type": "error", "message": f"Error uploading document: {e}"} + # st.rerun(scope="fragment") + + logger.trace(f"Document {uploaded_file.name} uploaded successfully") + logger.debug(f"Document {uploaded_file.name} uploaded successfully") + res = run_azure_ai_search_indexer() + + if res.status_code != 202: + message_response = {"type": "error", "message": res.text} + else: + message_response = {"type": "success", "message": "Document téléchargé avec succès"} + st.rerun(scope="fragment") + + +show_upload_documents() + +if message_response["message"]: + if message_response["type"] == "success": + st.success(message_response["message"]) + if message_response["type"] == "error": + st.error(message_response["message"]) diff --git a/src/pages/2_fastapi_azure_rag.py b/src/pages/2_fastapi_azure_rag.py new file mode 100644 index 0000000..1bcecf4 --- /dev/null +++ b/src/pages/2_fastapi_azure_rag.py @@ -0,0 +1,96 @@ +import requests +import streamlit as st + +from utils import settings + + +st.write("# RAG using fastapi") + +st.write(get_rag_env_variables()) + + +backend_url = f"http://{settings.FASTAPI_HOST}:{settings.FASTAPI_PORT}/" + + +@st.fragment +def show_app_health(): + try: + res = requests.get(backend_url).json() + st.success(res) + except Exception as e: + st.exception(f"FastAPI server encountered a problem. " f"\n\n Error: {e}") + exit() + + +@st.fragment +def create_form(questions: list, key: str, title: str = "Form"): + st.header(title, divider="rainbow") + + if f"{key}_responses" in st.session_state: + responses = st.session_state[f"{key}_responses"] + successes = st.session_state[f"{key}_success"] + else: + responses = {} + successes = {} + st.session_state[f"{key}_responses"] = responses + st.session_state[f"{key}_success"] = successes + + for i, question in enumerate(questions): + col1, col2 = st.columns([3, 1]) + with col1: + st.write(f"- {question[0]}") + with col2: + if st.button("Envoyer", key=f"button_{i}_{question[0][:10]}"): + try: + params = {"question": question[1]} + res = requests.get(f"{backend_url}/prefix_example/form/", params=params).json() + successes[i] = True + except Exception as e: + res = f"Error: {e}" + successes[i] = False + responses[i] = f"{res}" + + st.session_state[f"{key}_responses"][i] = responses[i] + st.session_state[f"{key}_success"][i] = successes[i] + + if i in responses: + if successes[i]: + st.success(f"Réponse automatique: {responses[i]}") + else: + st.error(f"Erreur {responses[i]}") + + +@st.fragment() +def show_ask_question(): + st.header("Ask your question", divider="rainbow") + col1, col2 = st.columns([3, 1]) + with col1: + q = st.text_input(key="chat", label="Posez votre question") + + params = None + with col2: + if st.button("Envoyer", key="button_chat"): + params = {"question": q} + + if params: + try: + res = requests.get(f"{backend_url}/prefix_example/form/", params=params).json() + st.success(res) + except Exception as e: + res = f"Error: {e}" + st.error(res) + + +# the first element is the question displayed in the UI, the second element is the question detailed to be sent to the LLM. +questions = [ + ( + "Quelle est la date de naissance de la personne ?", + "Quelle est la date de naissance de la personne ?", + ), +] + +show_app_health() + +create_form(questions, key="general") + +show_ask_question() diff --git a/src/pages/3_promptfoo.py b/src/pages/3_promptfoo.py new file mode 100644 index 0000000..e69de29 diff --git a/src/pages/4_giskard.py b/src/pages/4_giskard.py new file mode 100644 index 0000000..e69de29 diff --git a/src/pages/__init__.py b/src/pages/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/settings.py b/src/settings.py new file mode 100644 index 0000000..6910068 --- /dev/null +++ b/src/settings.py @@ -0,0 +1,237 @@ +from enum import Enum +from typing import Optional, Self +from rich.pretty import pretty_repr + +from loguru import logger as loguru_logger +from pydantic import SecretStr, model_validator + +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class ProviderEnum(str, Enum): + openai = "openai" + azure_openai = "azure_openai" + + +class BaseEnvironmentVariables(BaseSettings): + model_config = SettingsConfigDict(env_file="../.env", extra="ignore") + + +class OpenAIChatEnvironmentVariables(BaseEnvironmentVariables): + OPENAI_BASE_URL: Optional[str] = "https://api.openai.com/v1" + OPENAI_API_KEY: Optional[SecretStr] = None + OPENAI_DEPLOYMENT_NAME: Optional[str] = None + + def get_openai_env_vars(self): + return {key: value for key, value in vars(self).items() if key.startswith("OPENAI_")} + + +class AzureOpenAIChatEnvironmentVariables(BaseEnvironmentVariables): + AZURE_OPENAI_DEPLOYMENT_NAME: Optional[str] = None + AZURE_OPENAI_API_KEY: Optional[SecretStr] = None + AZURE_OPENAI_BASE_URL: Optional[str] = None + AZURE_OPENAI_API_VERSION: str = "2024-10-01-preview" + + def get_azure_openai_env_vars(self): + return {key: value for key, value in vars(self).items() if key.startswith("AZURE_OPENAI")} + + +class ChatEnvironmentVariables(OpenAIChatEnvironmentVariables, AzureOpenAIChatEnvironmentVariables): + """Represents environment variables for configuring the chatbot and promptfoo providers.""" + + LLM_PROVIDER: ProviderEnum # openai or azure_openai + # if you want to emulate azure_openai or openai using ollama or ollamazure + OLLAMA_MODEL_NAME: Optional[str] = None # "phi3:3.8b-mini-4k-instruct-q4_K_M" + OLLAMA_EMBEDDING_MODEL_NAME: Optional[str] = None # "all-minilm:l6-v2" + + @model_validator(mode="after") + def check_chat_api_keys(self: Self) -> Self: + """Validate API keys based on the selected provider after model initialization.""" + if self.LLM_PROVIDER == ProviderEnum.openai: + openai_vars = self.get_openai_env_vars() + openai_vars["LLM_PROVIDER"] = self.LLM_PROVIDER + if any(value is None for value in openai_vars.values()): + loguru_logger.error( + "\nOPENAI environment variables must be provided when LLM_PROVIDER is 'openai'." + f"\n{pretty_repr(openai_vars)}" + ) + raise ValueError( + "\nOPENAI environment variables must be provided when LLM_PROVIDER is 'openai'." + f"\n{pretty_repr(openai_vars)}" + ) + + elif self.LLM_PROVIDER == ProviderEnum.azure_openai: + azure_openai_vars = self.get_azure_openai_env_vars() + azure_openai_vars["LLM_PROVIDER"] = self.LLM_PROVIDER + if any(value is None for value in azure_openai_vars.values()): + loguru_logger.error( + "\nAZURE_OPENAI environment variables must be provided when LLM_PROVIDER is 'azure_openai'." + f"\n{pretty_repr(azure_openai_vars)}" + ) + raise ValueError( + "\nAZURE_OPENAI environment variables must be provided when LLM_PROVIDER is 'azure_openai'." + f"\n{pretty_repr(azure_openai_vars)}" + ) + + else: + raise ValueError(f"Unknown LLM_PROVIDER: {self.LLM_PROVIDER}") + + return self + + +class AzureAISearchEnvironmentVariables(BaseEnvironmentVariables): + """Represents environment variables for configuring Azure AI Search and Azure Storage.""" + + ################ Azure Search settings ################ + ENABLE_AZURE_SEARCH: bool = False + AZURE_SEARCH_SERVICE_ENDPOINT: Optional[str] = None + AZURE_SEARCH_INDEX_NAME: Optional[str] = None + AZURE_SEARCH_INDEXER_NAME: Optional[str] = None + AZURE_SEARCH_API_KEY: Optional[str] = None + AZURE_SEARCH_TOP_K: Optional[str] = "2" + SEMENTIC_CONFIGURATION_NAME: Optional[str] = None + # Azure Storage settings + AZURE_STORAGE_ACCOUNT_NAME: Optional[str] = None + AZURE_STORAGE_ACCOUNT_KEY: Optional[str] = None + AZURE_CONTAINER_NAME: Optional[str] = None + + def get_azure_search_env_vars(self): + items_dict = { + "ENABLE_AZURE_SEARCH": self.ENABLE_AZURE_SEARCH, + "SEMENTIC_CONFIGURATION_NAME": self.SEMENTIC_CONFIGURATION_NAME, + "AZURE_STORAGE_ACCOUNT_NAME": self.AZURE_STORAGE_ACCOUNT_NAME, + "AZURE_STORAGE_ACCOUNT_KEY": self.AZURE_STORAGE_ACCOUNT_KEY, + "AZURE_CONTAINER_NAME": self.AZURE_CONTAINER_NAME, + } + + items_dict.update( + {key: value for key, value in vars(self).items() if key.startswith("AZURE_SEARCH")} + ) + return items_dict + + @model_validator(mode="after") + def check_ai_search_keys(self: Self) -> Self: + """Validate API keys based on the selected provider after model initialization.""" + if self.ENABLE_AZURE_SEARCH: + azure_search_vars = self.get_azure_search_env_vars() + if any(value is None for value in azure_search_vars.values()): + loguru_logger.error( + "\nAZURE_SEARCH environment variables must be provided when ENABLE_AZURE_SEARCH is True." + f"\n{pretty_repr(azure_search_vars)}" + ) + raise ValueError( + "\nAZURE_SEARCH environment variables must be provided when ENABLE_AZURE_SEARCH is True." + f"\n{pretty_repr(azure_search_vars)}" + ) + return self + + +class EvaluationEnvironmentVariables(BaseEnvironmentVariables): + """Represents environment variables for configuring Promptfoo and ragas. + + Ragas uses LLM as a judge for its metrics. + + """ + + ENABLE_EVALUATION: bool = False + # LLM as a Judge for RAGAS metrics + LLMAAJ_PROVIDER: Optional[ProviderEnum] = None # openai or azure_openai + + # if judge provider is openai + LLMAAJ_OPENAI_DEPLOYMENT_NAME: Optional[str] = None + LLMAAJ_OPENAI_API_KEY: Optional[SecretStr] = None + LLMAAJ_OPENAI_BASE_URL: Optional[str] = None + LLMAAJ_OPENAI_EMBEDDING_DEPLOYMENT_NAME: Optional[str] = None + + # if judge provider is azure_openai + LLMAAJ_AZURE_OPENAI_DEPLOYMENT_NAME: str = "phi3:3.8b-mini-4k-instruct-q4_K_M" + LLMAAJ_AZURE_OPENAI_API_KEY: SecretStr = "1234" + LLMAAJ_AZURE_OPENAI_BASE_URL: str = "http://localhost:4041" + LLMAAJ_AZURE_OPENAI_API_VERSION: str = "2024-10-01-preview" + LLMAAJ_AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: Optional[str] = "all-minilm:l6-v2" + + def get_eval_env_vars(self): + items_dict = { + "ENABLE_EVALUATION": self.ENABLE_EVALUATION, + "LLMAAJ_PROVIDER": self.LLMAAJ_PROVIDER, + } + if self.LLMAAJ_PROVIDER == ProviderEnum.openai: + items_dict.update( + {key: value for key, value in vars(self).items() if key.startswith("LLMAAJ_OPENAI")} + ) + elif self.LLMAAJ_PROVIDER == ProviderEnum.azure_openai: + items_dict.update( + { + key: value + for key, value in vars(self).items() + if key.startswith("LLMAAJ_AZURE_OPENAI") + } + ) + else: + raise ValueError(f"Unknown LLMAAJ_PROVIDER: {self.LLMAAJ_PROVIDER}") + return items_dict + + @model_validator(mode="after") + def check_eval_api_keys(self: Self) -> Self: + """Validate API keys based on the selected provider after model initialization.""" + if self.ENABLE_EVALUATION: + if not self.LLMAAJ_PROVIDER in [ProviderEnum.openai, ProviderEnum.azure_openai]: + loguru_logger.error( + f"Unsupported env variable LLMAAJ_PROVIDER with value: {self.LLMAAJ_PROVIDER}" + ) + raise ValueError( + f"Unsupported variable LLMAAJ_PROVIDER with value: {self.LLMAAJ_PROVIDER}" + ) + eval_vars = self.get_eval_env_vars() + if any(value is None for value in eval_vars.values()): + loguru_logger.error( + "\nEVALUATION environment variables must be provided when ENABLE_EVALUATION is True." + f"\n{pretty_repr(eval_vars)}" + ) + raise ValueError( + "\nEVALUATION environment variables must be provided when ENABLE_EVALUATION is True." + f"\n{pretty_repr(eval_vars)}" + ) + + return self + + +class Settings( + ChatEnvironmentVariables, AzureAISearchEnvironmentVariables, EvaluationEnvironmentVariables +): + """Settings class for the application. + + This class is automatically initialized with environment variables from the .env file. + It inherits from the following classes and contains additional settings for streamlit and fastapi + - ChatEnvironmentVariables + - AzureAISearchEnvironmentVariables + - EvaluationEnvironmentVariables + + """ + + FASTAPI_HOST: str = "localhost" + FASTAPI_PORT: int = 8080 + STREAMLIT_PORT: int = 8501 + DEV_MODE: bool = True + + def get_active_env_vars(self): + env_vars = { + "DEV_MODE": self.DEV_MODE, + "FASTAPI_PORT": self.FASTAPI_PORT, + "STREAMLIT_PORT": self.STREAMLIT_PORT, + } + + if self.LLM_PROVIDER == ProviderEnum.openai: + env_vars.update(self.get_openai_env_vars()) + elif self.LLM_PROVIDER == ProviderEnum.azure_openai: + env_vars.update(self.get_azure_openai_env_vars()) + else: + raise ValueError(f"Unknown LLM_PROVIDER: {self.LLM_PROVIDER}") + + if self.ENABLE_AZURE_SEARCH: + env_vars.update(self.get_azure_search_env_vars()) + + if self.ENABLE_EVALUATION: + env_vars.update(self.get_eval_env_vars()) + + return env_vars diff --git a/src/utils.py b/src/utils.py new file mode 100644 index 0000000..7b13d14 --- /dev/null +++ b/src/utils.py @@ -0,0 +1,264 @@ +import ast +import os +import sys +import timeit +from pathlib import Path + +from azure.core.credentials import AzureKeyCredential +from azure.search.documents import SearchClient +from langchain_openai import AzureChatOpenAI, AzureOpenAIEmbeddings, OpenAIEmbeddings, ChatOpenAI +from loguru import logger as loguru_logger +from pydantic import ValidationError +from rich.pretty import pretty_repr + +from settings import Settings, ProviderEnum + +# Check if we run the code from the src directory +if Path("src").is_dir(): + loguru_logger.warning("Changing working directory to src") + loguru_logger.warning(f" Current working dir is {Path.cwd()}") + os.chdir("src") +elif Path("ml").is_dir(): + # loguru_logger.warning(f" Current working dir is {Path.cwd()}") + pass +else: + raise Exception("Project should always run from the src directory.") + + +# we will use the name llmaaj instead of llm_as_a_judge + + +def initialize(): + """Initialize the settings, logger, and search client. + + Reads the environment variables from the .env file defined in the Settings class. + + Returns: + settings + loguru_logger + search_client + """ + settings = Settings() + loguru_logger.remove() + + if settings.DEV_MODE: + loguru_logger.add(sys.stderr, level="TRACE") + else: + loguru_logger.add(sys.stderr, level="INFO") + + search_client = None + if settings.ENABLE_AZURE_SEARCH: + search_client = SearchClient( + settings.AZURE_SEARCH_SERVICE_ENDPOINT, + settings.AZURE_SEARCH_INDEX_NAME, + AzureKeyCredential(settings.AZURE_SEARCH_API_KEY), + ) + + return settings, loguru_logger, search_client + + +def safe_eval(x): + try: + return ast.literal_eval(x) + except: + return [] + + +def time_function(func): + def wrapper(*args, **kwargs): + start_time = timeit.default_timer() + result = func(*args, **kwargs) + + end_time = timeit.default_timer() + execution_time = round(end_time - start_time, 2) + if "reason" in result: + result["reason"] = f" Execution time: {execution_time}s | " + result["reason"] + + if "output" in result: + result["output"] = f" Execution time: {execution_time}s | " + result["output"] + logger.debug(f"Function {func.__name__} took {execution_time} seconds to execute.") + + return result + + return wrapper + + +def validation_error_message(error: ValidationError) -> ValidationError: + for err in error.errors(): + del err["input"] + del err["url"] + + return error + + +def get_llm_client() -> tuple[object, str]: + """Initializes and returns a language model client based on the configured provider. + + Depending on the value of `settings.LLM_PROVIDER`, this function will initialize + either an OpenAI or AzureOpenAI client from OpenAI library. It loads the client with the appropriate + settings and logs the model being used. + + Returns: + tuple: A tuple containing the initialized client and the model name. + + Raises: + ValueError: If the configured LLM provider is unsupported. + """ + if settings.LLM_PROVIDER == ProviderEnum.openai: + from openai import OpenAI + + client = OpenAI( + base_url=settings.OPENAI_BASE_URL, + api_key=settings.OPENAI_API_KEY.get_secret_value(), + ) + model_name = settings.OPENAI_DEPLOYMENT_NAME + loguru_logger.info(f"Loaded OpenAI client with model: {model_name}") + + elif settings.LLM_PROVIDER == ProviderEnum.azure_openai: + from openai import AzureOpenAI + + client = AzureOpenAI( + api_key=settings.AZURE_OPENAI_API_KEY.get_secret_value(), + api_version=settings.AZURE_OPENAI_API_VERSION, + azure_endpoint=settings.AZURE_OPENAI_BASE_URL, + ) + model_name = settings.AZURE_OPENAI_DEPLOYMENT_NAME + loguru_logger.info(f"Loaded AzureOpenAI client with model: {model_name}") + + else: + raise ValueError(f"Unsupported LLM provider: {settings.LLM_PROVIDER}") + + return client, model_name + + +def get_llm_as_a_judge_client(): + """Initializes and returns a LLM as a judge client based on the configured provider. + + Depending on the value of `settings.LLMAAJ_PROVIDER`, this function will initialize + either an OpenAI or AzureOpenAI client and embedding client from Langchain OpenAI library. It loads the client with + the appropriate settings and logs the model being used. + + If `settings.ENABLE_EVALUATION` is False, the function will return `(None, None)` and log a warning. + + Returns: + tuple: A tuple containing + - the initialized client and the embedding client (AzureOpenAI and AzureOpenAIEmbeddings or OpenAI and + OpenAIEmbeddings) + - or `(None, None)` if evaluation is disabled. + + Raises: + ValueError: If the configured LLM provider is unsupported. + """ + if settings.ENABLE_EVALUATION: + if settings.LLMAAJ_PROVIDER == ProviderEnum.azure_openai: + client = AzureChatOpenAI( + azure_endpoint=settings.LLMAAJ_AZURE_OPENAI_BASE_URL, + deployment_name=settings.LLMAAJ_AZURE_OPENAI_DEPLOYMENT_NAME, + openai_api_key=settings.LLMAAJ_AZURE_OPENAI_API_KEY, + openai_api_version=settings.LLMAAJ_AZURE_OPENAI_API_VERSION, + ) + embeddings_client = AzureOpenAIEmbeddings( + azure_endpoint=settings.LLMAAJ_AZURE_OPENAI_BASE_URL, + model=settings.LLMAAJ_AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME, + openai_api_key=settings.LLMAAJ_AZURE_OPENAI_API_KEY, + openai_api_version=settings.LLMAAJ_AZURE_OPENAI_API_VERSION, + ) + loguru_logger.info( + f"Loaded LLMAAJ AzureOpenAI client with model: {settings.LLMAAJ_OPENAI_DEPLOYMENT_NAME}" + ) + elif settings.LLMAAJ_PROVIDER == ProviderEnum.openai: + client = ChatOpenAI( + model_name=settings.LLMAAJ_OPENAI_DEPLOYMENT_NAME, + openai_api_base=settings.LLMAAJ_OPENAI_BASE_URL, + openai_api_key=settings.LLMAAJ_OPENAI_API_KEY, + ) + embeddings_client = OpenAIEmbeddings( + model=settings.LLMAAJ_OPENAI_EMBEDDING_DEPLOYMENT_NAME, + openai_api_base=settings.LLMAAJ_OPENAI_BASE_URL, + openai_api_key=settings.LLMAAJ_OPENAI_API_KEY, + ) + loguru_logger.info( + f"Loaded LLMAAJ OpenAI client with model: {settings.LLMAAJ_OPENAI_DEPLOYMENT_NAME}" + ) + + else: + raise ValueError(f"Unsupported LLM provider: {settings.LLMAAJ_PROVIDER}") + + return ( + client, + embeddings_client, + ) + else: + loguru_logger.warning( + "Evaluation is disabled. Set ENABLE_EVALUATION to True to activate it." + ) + return None, None + + +def check_llm_client(): + """Check the LLM client by sending a message to the model. + + Uses OpenAI/AzureOpenAI as the LLM client + """ + client, model_name = get_llm_client() + try: + chat_completion = client.chat.completions.create( + messages=[ + { + "role": "user", + "content": "Hi", + } + ], + model=model_name, + ) + logger.info(chat_completion) + + logger.info( + f"\nActive environment variables are: \n{pretty_repr(settings.get_active_env_vars())}\n" + f"\nmodel response: {chat_completion.choices[0].message.content}" + ) + except Exception as e: + logger.error( + f"Error in check_llm_client:" + f"\nActive environment variables are: {pretty_repr(settings.get_active_env_vars())}" + ) + raise e + + +def check_llm_as_a_judge_client(): + """Check the LLM as a judge client by sending a message to the model. + + Uses Langchain OpenAIChat ou Langchain AzureChatas the LLM client + """ + try: + llmaaj_chat_client, llmaaj_client_embedding = get_llm_as_a_judge_client() + + messages = [ + ( + "system", + "You are a helpful assistant that translates English to French. Translate the user sentence.", + ), + ("human", "I love programming."), + ] + ai_msg = llmaaj_chat_client.invoke(messages) + logger.info( + f"\nEvaluation environment variables are: \n{pretty_repr(settings.get_eval_env_vars())}\n" + f"\nmodel response: {ai_msg.content}" + ) + + except Exception as e: + logger.error( + f"Error in check_llm_as_a_judge_client:" + f"\nevaluation environment variables are: {pretty_repr(settings.get_eval_env_vars())}" + ) + raise e + + +settings, logger, search_client = initialize() +chat_client, chat_model_name = get_llm_client() +# LLMAAJ stands for LLM as a judge +llmaaj_chat_client, llmaaj_embedding_client = get_llm_as_a_judge_client() + +if __name__ == "__main__": + check_llm_client() + # check_llm_as_a_judge_client() diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/readme.txt b/tests/readme.txt new file mode 100644 index 0000000..7d16048 --- /dev/null +++ b/tests/readme.txt @@ -0,0 +1,2 @@ +tests needs to be run from the root of the repo. +You can use make pytest diff --git a/tests/test_chat.py b/tests/test_chat.py new file mode 100644 index 0000000..14c5c3a --- /dev/null +++ b/tests/test_chat.py @@ -0,0 +1,62 @@ +import pytest +from openai.types.chat import ChatCompletion +from pydantic import BaseModel, Field + +from ml.ai import get_completions + +messages = [{"role": "system", "content": "You are a helpful assistant."}] +inputs = { + "messages": messages, +} + + +def test_get_chat_completions(): + response = get_completions( + **inputs, + stream=False, + ) + + assert len(response) > 0 + assert type(response) == str + + +def test_get_chat_completions_model(): + # This function uses instructor, open source models may not pass it. + global messages # overriding global variable + messages = [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "Client number account: 208977"}, + ] + + class UserInfo(BaseModel): + number_account: str = Field(default=None, description="Client number account") + + response = get_completions(**inputs, stream=False, response_model=UserInfo) + assert isinstance(response, UserInfo) + + +def test_get_chat_completions_full_response(): + response = get_completions(**inputs, stream=False, full_response=True) + + assert response is not None + assert type(response) == ChatCompletion + + +def test_get_chat_completions_exception(): + with pytest.raises(NotImplementedError): + get_completions( + **inputs, + stream=True, + ) + + +def test_get_chat_completions_none(): + global inputs + inputs["messages"] = None + + with pytest.raises(NotImplementedError): + response = get_completions( + **inputs, + stream=True, + ) + assert response is None diff --git a/tests/test_llm_endpoint.py b/tests/test_llm_endpoint.py new file mode 100644 index 0000000..fd1aa9e --- /dev/null +++ b/tests/test_llm_endpoint.py @@ -0,0 +1,14 @@ +import pytest + +from utils import check_llm_client, check_llm_as_a_judge_client, settings + + +def test_llm_client(): + """Test the LLM client used to generate answers.""" + check_llm_client() + + +@pytest.mark.skipif(not settings.ENABLE_EVALUATION, reason="requires env ENABLE_EVALUATION=True") +def test_llmaaj_client(): + """Test the LLM as a judge client used in the evaluation.""" + check_llm_as_a_judge_client() diff --git a/tests/test_rag.py b/tests/test_rag.py new file mode 100644 index 0000000..9e128af --- /dev/null +++ b/tests/test_rag.py @@ -0,0 +1,33 @@ +import os + +import pytest + +from ml.ai import get_related_document_ai_search, get_rag_response, run_azure_ai_search_indexer +from utils import logger, settings + +logger.info(f" working directory is {os.getcwd()}") + + +@pytest.mark.skipif( + not settings.ENABLE_AZURE_SEARCH, reason="requires env ENABLE_AZURE_SEARCH=True" +) +def test_get_related_document_ai_search(): + user_input = "What is the capital of France?" + question_context = get_related_document_ai_search(user_input) + + assert type(question_context) == str + + +@pytest.mark.skipif( + not settings.ENABLE_AZURE_SEARCH, reason="requires env ENABLE_AZURE_SEARCH=True" +) +def test_get_rag_response(): + res = get_rag_response("What is the capital of France?") + assert type(res) == str + + +@pytest.mark.skipif( + not settings.ENABLE_AZURE_SEARCH, reason="requires env ENABLE_AZURE_SEARCH=True" +) +def test_run_azure_ai_search_indexer(): + assert run_azure_ai_search_indexer().status_code == 202 diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..58781e4 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4919 @@ +version = 1 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version < '3.12'", + "python_full_version >= '3.12' and python_full_version < '3.12.4'", + "python_full_version >= '3.12.4' and python_full_version < '3.13'", + "python_full_version >= '3.13'", +] + +[[package]] +name = "ai-cloud-project-template" +version = "0.1.0" +source = { virtual = "." } +dependencies = [ + { name = "azure-search-documents" }, + { name = "azure-storage-blob" }, + { name = "fastapi", extra = ["standard"] }, + { name = "giskard", extra = ["llm"] }, + { name = "instructor" }, + { name = "langchain" }, + { name = "langchain-community" }, + { name = "langchain-openai" }, + { name = "loguru" }, + { name = "openai" }, + { name = "pydantic" }, + { name = "pydantic-settings" }, + { name = "python-multipart" }, + { name = "ragas" }, + { name = "rich" }, + { name = "streamlit" }, + { name = "uvicorn" }, +] + +[package.dev-dependencies] +dev = [ + { name = "jupyter" }, + { name = "pre-commit" }, + { name = "pytest" }, + { name = "pytest-asyncio" }, + { name = "ruff" }, +] +docs = [ + { name = "mkdocs" }, + { name = "mkdocs-exclude" }, + { name = "mkdocs-gen-files" }, + { name = "mkdocs-material" }, + { name = "mkdocs-mermaid2-plugin" }, + { name = "mkdocs-same-dir" }, + { name = "mkdocstrings" }, + { name = "mkdocstrings-python" }, +] + 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