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Merge branch 'listing_question' of https://github.com/lambda-feedback/shortTextAnswer into listing_question
2 parents bf99b45 + c1ee593 commit a1a0ebb

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‎.github/workflows/ai-version-deploy.yml‎

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name: Test & Deploy Evaluation Function to AWS Lambda
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on:
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push:
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branches:
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- listing_question
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workflow_dispatch:
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jobs:
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test:
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name: Test
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: [3.11]
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defaults:
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run:
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working-directory: app/
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env:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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python -m pip install flake8 pytest
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python -m pip install -r requirements.txt
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# python -m nltk.downloader wordnet
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# python -m nltk.downloader word2vec_sample
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# python -m nltk.downloader brown
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# python -m nltk.downloader punkt
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# python -m nltk.downloader stopwords
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- name: Lint with flake8
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run: |
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# stop the build if there are Python syntax errors or undefined names
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flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
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# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
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flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
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- name: Test Evaluation Function
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run: |
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pytest -v evaluation_tests.py::TestEvaluationFunction
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deploy-dev:
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name: Deploy Dev
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needs: test
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runs-on: ubuntu-latest
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environment: production
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env:
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ECR_REPOSITORY: lambda-feedback-dev-functions-repository
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set config.json output
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id: set_config_var
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run: |
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content=`cat ./config.json`
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# the following lines are only required for multi line json
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content="${content//'%'/'%25'}"
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content="${content//$'\n'/'%0A'}"
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content="${content//$'\r'/'%0D'}"
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# end of optional handling for multi line json
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echo "::set-output name=configJson::$content"
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- name: set Evaluation Function Name
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id: set_function_name
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run: |
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functionName="${{fromJson(steps.set_config_var.outputs.configJson).EvaluationFunctionName}}"
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[[ -z "$functionName" ]] && { echo "Add EvaluationFunctionName to config.json" ; exit 1; }
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echo "::set-output name=function_name::$functionName"
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- name: Configure AWS credentials
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uses: aws-actions/configure-aws-credentials@v1
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with:
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aws-access-key-id: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_ID }}
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aws-secret-access-key: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_SECRET }}
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aws-region: eu-west-2
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- name: Login to Amazon ECR
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id: login-ecr
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uses: aws-actions/amazon-ecr-login@v1
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- name: Build, tag, and push image to Amazon ECR
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id: build-image
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env:
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ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
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IMAGE_TAG: ${{ steps.set_function_name.outputs.function_name }}
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run: |
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# Build docker image from algorithm, schema and requirements
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docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG app/
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# Push image to ECR
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docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
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echo "::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG"
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- name: deploy evaluation function
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id: deploy-evaluation-function
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env:
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BACKEND_API_URL: https://dev-api.lambdafeedback.com
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API_KEY: ${{ secrets.FUNCTION_ADMIN_API_KEY }}
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ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
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IMAGE_TAG: ${{ steps.set_function_name.outputs.function_name }}
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run: |
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curl --location --request POST "$BACKEND_API_URL/grading-function/ensure" \
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--header 'content-type: application/json' \
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--data-raw "{
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\"apiKey\": \"$API_KEY\",
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\"dockerImageUri\": \"$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG\",
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\"functionName\": \"$IMAGE_TAG\"
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}"

‎app/Dockerfile‎

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# Base image that bundles AWS Lambda Python 3.8 image with some middleware functions
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# FROM base-eval-tmp
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FROM rabidsheep55/python-base-eval-layer
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FROM rabidsheep55/python-base-eval-layer
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# NOTE: this is platform "linux/amd64"
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# FROM ghcr.io/lambda-feedback/evaluation-function-base/python:3.11
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# NOTE: transformers from huggingface requires platform "linux/arm64"
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WORKDIR /app
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RUN mkdir /usr/share/nltk_data
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RUN mkdir -p /usr/share/nltk_data/corpora /usr/share/nltk_data/models /usr/share/nltk_data/tokenizers
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# RUN mkdir /usr/share/nltk_data
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# RUN mkdir -p /usr/share/nltk_data/corpora /usr/share/nltk_data/models /usr/share/nltk_data/tokenizers
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ARG NLTK_DATA=/usr/share/nltk_data
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# ARG NLTK_DATA=/usr/share/nltk_data
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ENV NLTK_DATA=/usr/share/nltk_data
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# ENV NLTK_DATA=/usr/share/nltk_data
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# Copy and install any packages/modules needed for your evaluation script.
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COPY requirements.txt .
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COPY brown_length .
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COPY word_freqs .
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COPY w2v .
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RUN yum install -y wget unzip
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# COPY brown_length .
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# COPY word_freqs .
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# COPY w2v .
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# RUN yum install -y wget unzip
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RUN pip3 install -r requirements.txt
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# # Download NLTK data files
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# RUN rm /usr/share/nltk_data/tokenizers/*.zip
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# Warnings: those commands sometimes download corrupted zips, so it is better to wget each package from the main site
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RUN python -m nltk.downloader wordnet
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RUN python -m nltk.downloader word2vec_sample
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RUN python -m nltk.downloader brown
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RUN python -m nltk.downloader stopwords
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RUN python -m nltk.downloader punkt
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RUN python -m nltk.downloader punkt_tab
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# RUN python -m nltk.downloader wordnet
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# RUN python -m nltk.downloader word2vec_sample
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# RUN python -m nltk.downloader brown
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# RUN python -m nltk.downloader stopwords
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# RUN python -m nltk.downloader punkt
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# RUN python -m nltk.downloader punkt_tab
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# Copy the evaluation and testing scripts
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COPY brown_length ./app/
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COPY word_freqs ./app/
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COPY w2v ./app/
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# COPY brown_length ./app/
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# COPY word_freqs ./app/
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# COPY w2v ./app/
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COPY evaluation.py ./app/
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COPY evaluation_tests.py ./app/
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‎app/evaluation.py‎

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max_tokens=param.max_new_token,
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openai_api_key=param.openai_api_key
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)
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elif param.mode == 'llama3':
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from langchain_huggingface import HuggingFaceEndpoint
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return HuggingFaceEndpoint(
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endpoint_url=param.endpoint_3_1_8B,
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max_new_tokens=param.max_new_token,
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temperature=param.temperature,
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huggingfacehub_api_token=param.huggingfacehub_api_token
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)
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# elif config.mode == 'llama3': # NOTE: langchain_huggingface expected "linux/arm64"
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# from langchain_huggingface import HuggingFaceEndpoint
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# return HuggingFaceEndpoint(
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# endpoint_url=config.endpoint_3_1_8B,
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# max_new_tokens=config.max_new_token,
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# temperature=config.temperature,
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# huggingfacehub_api_token=config.huggingfacehub_api_token
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# )
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def parse_last_boolean(response):

‎app/evaluation_tests.py‎

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import unittest
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from evaluation import evaluation_function, Param
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try:
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from .evaluation import evaluation_function, Config
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except ImportError:
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from evaluation import evaluation_function, Config
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class TestEvaluationFunction(unittest.TestCase):
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"""

‎app/requirements.txt‎

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nltk==3.8.1
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gensim
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matplotlib
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pytest
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# To run on cli: /Applications/Python\ 3.11/Install\ Certificates.command
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# If SSL cert fail on Mac -> command above calls pip install --upgrade certifi -> then calling nltk.download works
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pandas
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langchain
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langchain-openai
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python-dotenv
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python-dotenv
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# langchain_huggingface

‎config.json‎

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{
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"EvaluationFunctionName": "shortTextAnswer"
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}
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"EvaluationFunctionName": "listingQuestion"
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}

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