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Chat with PDF

This is a simple flow that allow you to ask questions about the content of a PDF file and get answers. You can run the flow with a URL to a PDF file and question as argument. Once it's launched it will download the PDF and build an index of the content. Then when you ask a question, it will look up the index to retrieve relevant content and post the question with the relevant content to OpenAI chat model (gpt-3.5-turbo or gpt4) to get an answer.

Learn more on corresponding tutorials.

Tools used in this flow：

  • custom python Tool

Prerequisites

Install promptflow sdk and other dependencies:

pip install -r requirements.txt

Get started

Create connection in this folder

# create connection needed by flow
if pf connection list | grep open_ai_connection; then
    echo "open_ai_connection already exists"
else
    pf connection create --file ../../../connections/azure_openai.yml --name open_ai_connection --set api_key=<your_api_key> api_base=<your_api_base>
fi

CLI Example

Run flow

Note: this sample uses predownloaded PDFs and prebuilt FAISS Index to speed up execution time. You can remove the folders to start a fresh run.

# test with default input value in flow.dag.yaml
pf flow test --flow .

# test with flow inputs
pf flow test --flow . --inputs question="What is the name of the new language representation model introduced in the document?" pdf_url="https://arxiv.org/pdf/1810.04805.pdf"

# (Optional) create a random run name
run_name="web_classification_"$(openssl rand -hex 12)

# run with multiline data, --name is optional
pf run create --file batch_run.yaml --name $run_name

# visualize run output details
pf run visualize --name $run_name

Submit run to cloud

Assume we already have a connection named open_ai_connection in workspace.

# set default workspace
az account set -s <your_subscription_id>
az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name>
# create run
pfazure run create --file batch_run.yaml --name $run_name --runtime example-runtime-ci
# pfazure run create --file batch_run.yaml --name $run_name # automatic runtime

Note: Click portal_url of the run to view the final snapshot.