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177 lines
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# Promptflow examples
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[](https://github.com/psf/black)
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[](../LICENSE)
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## Get started
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**Install dependencies**
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- Bootstrap your python environment.
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- e.g: create a new [conda](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html) environment. `conda create -n pf-examples python=3.9`.
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- install required packages in python environment : `pip install -r requirements.txt`
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- show installed sdk: `pip show promptflow`
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**Quick start**
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| [quickstart.ipynb](tutorials/get-started/quickstart.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_getstarted_quickstart.yml) | A quickstart tutorial to run a flow and evaluate it. |
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| [quickstart-azure.ipynb](tutorials/get-started/quickstart-azure.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_getstarted_quickstartazure.yml) | A quickstart tutorial to run a flow in Azure AI and evaluate it. |
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## CLI examples
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### Tutorials ([tutorials](tutorials))
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| path | status | description |
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| [chat-with-pdf](tutorials/e2e-development/chat-with-pdf.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_e2e_development_chat_with_pdf.yml) | Retrieval Augmented Generation (or RAG) has become a prevalent pattern to build intelligent application with Large Language Models (or LLMs) since it can infuse external knowledge into the model, which is not trained with those up-to-date or proprietary information |
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| [azure-app-service](tutorials/flow-deploy/azure-app-service/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_azure_app_service.yml) | This example demos how to deploy a flow using Azure App Service |
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| [create-service-with-flow](tutorials/flow-deploy/create-service-with-flow/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_create_service_with_flow.yml) | This example shows how to create a simple service with flow |
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| [distribute-flow-as-executable-app](tutorials/flow-deploy/distribute-flow-as-executable-app/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_distribute_flow_as_executable_app.yml) | This example demos how to package flow as a executable app |
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| [docker](tutorials/flow-deploy/docker/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_docker.yml) | This example demos how to deploy flow as a docker app |
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| [kubernetes](tutorials/flow-deploy/kubernetes/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_kubernetes.yml) | This example demos how to deploy flow as a Kubernetes app |
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| [promptflow-quality-improvement](tutorials/flow-fine-tuning-evaluation/promptflow-quality-improvement.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_fine_tuning_evaluation_promptflow_quality_improvement.yml) | This tutorial is designed to enhance your understanding of improving flow quality through prompt tuning and evaluation |
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| [tracing](tutorials/tracing/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_tracing.yml) | Prompt flow provides the tracing feature to capture and visualize the internal execution details for all flows |
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### Prompty ([prompty](prompty))
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| path | status | description |
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| [basic](prompty/basic/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_prompty_basic.yml) | A basic prompt that uses the chat API to answer questions, with connection configured using environment variables |
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| [chat-basic](prompty/chat-basic/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_prompty_chat_basic.yml) | A prompt that uses the chat API to answer questions with chat history, leveraging promptflow connection |
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| [eval-apology](prompty/eval-apology/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_prompty_eval_apology.yml) | A prompt that determines whether a chat conversation contains an apology from the assistant |
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| [eval-basic](prompty/eval-basic/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_prompty_eval_basic.yml) | Basic evaluator prompt for QA scenario |
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| [format-output](prompty/format-output/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_prompty_format_output.yml) | A few examples that demos different prompty response format like text, json_object, and how to enable stream output |
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### Flex Flows ([flex-flows](flex-flows))
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| [basic](flex-flows/basic/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flex_flows_basic.yml) | A basic standard flow define using function entry that calls Azure OpenAI with connection info stored in environment variables |
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| [chat-async-stream](flex-flows/chat-async-stream/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flex_flows_chat_async_stream.yml) | A chat flow defined using async class entry that return output in stream mode |
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| [chat-basic](flex-flows/chat-basic/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flex_flows_chat_basic.yml) | A basic chat flow defined using class entry |
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| [chat-minimal](flex-flows/chat-minimal/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flex_flows_chat_minimal.yml) | A chat flow defined using function with minimal code |
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| [chat-stream](flex-flows/chat-stream/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flex_flows_chat_stream.yml) | A chat flow defined using class entry that return output in stream mode |
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| [chat-with-functions](flex-flows/chat-with-functions/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flex_flows_chat_with_functions.yml) | This flow covers how to use the LLM chat API in combination with external functions to extend the capabilities of GPT models |
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| [eval-checklist](flex-flows/eval-checklist/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flex_flows_eval_checklist.yml) | A example flow defined using class entry which demos how to evaluate the answer pass user specified check list |
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| [eval-code-quality](flex-flows/eval-code-quality/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flex_flows_eval_code_quality.yml) | A example flow defined using class based entry which leverages model config to evaluate the quality of code snippet |
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| [eval-criteria-with-langchain](flex-flows/eval-criteria-with-langchain/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flex_flows_eval_criteria_with_langchain.yml) | A example flow of converting LangChain criteria evaluator application to flex flow |
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### Flows ([flows](flows))
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#### [Standard flows](flows/standard/)
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| [autonomous-agent](flows/standard/autonomous-agent/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_autonomous_agent.yml) | This is a flow showcasing how to construct a AutoGPT agent with promptflow to autonomously figures out how to apply the given functions to solve the goal, which is film trivia that provides accurate and up-to-date information about movies, directors, actors, and more in this sample |
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| [basic](flows/standard/basic/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_basic.yml) | A basic standard flow using custom python tool that calls Azure OpenAI with connection info stored in environment variables |
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| [basic-with-builtin-llm](flows/standard/basic-with-builtin-llm/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_basic_with_builtin_llm.yml) | A basic standard flow that calls Azure OpenAI with builtin llm tool |
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| [basic-with-connection](flows/standard/basic-with-connection/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_basic_with_connection.yml) | A basic standard flow that using custom python tool calls Azure OpenAI with connection info stored in custom connection |
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| [conditional-flow-for-if-else](flows/standard/conditional-flow-for-if-else/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_conditional_flow_for_if_else.yml) | This example is a conditional flow for if-else scenario |
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| [conditional-flow-for-switch](flows/standard/conditional-flow-for-switch/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_conditional_flow_for_switch.yml) | This example is a conditional flow for switch scenario |
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| [customer-intent-extraction](flows/standard/customer-intent-extraction/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_customer_intent_extraction.yml) | This sample is using OpenAI chat model(ChatGPT/GPT4) to identify customer intent from customer's question |
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| [describe-image](flows/standard/describe-image/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_describe_image.yml) | A flow that take image input, flip it horizontally and uses OpenAI GPT-4V tool to describe it |
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| [flow-with-additional-includes](flows/standard/flow-with-additional-includes/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_flow_with_additional_includes.yml) | User sometimes need to reference some common files or folders, this sample demos how to solve the problem using additional_includes |
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| [flow-with-symlinks](flows/standard/flow-with-symlinks/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_flow_with_symlinks.yml) | User sometimes need to reference some common files or folders, this sample demos how to solve the problem using symlinks |
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| [gen-docstring](flows/standard/gen-docstring/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_gen_docstring.yml) | This example can help you automatically generate Python code's docstring and return the modified code |
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| [maths-to-code](flows/standard/maths-to-code/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_maths_to_code.yml) | Math to Code is a project that utilizes the power of the chatGPT model to generate code that models math questions and then executes the generated code to obtain the final numerical answer |
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| [named-entity-recognition](flows/standard/named-entity-recognition/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_named_entity_recognition.yml) | A flow that perform named entity recognition task |
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| [question-simulation](flows/standard/question-simulation/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_question_simulation.yml) | This question simulation flow is used to generate suggestions for the next question based on the previous chat history |
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| [web-classification](flows/standard/web-classification/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_standard_web_classification.yml) | This is a flow demonstrating multi-class classification with LLM |
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#### [Evaluation flows](flows/evaluation/)
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| [eval-basic](flows/evaluation/eval-basic/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_basic.yml) | This example shows how to create a basic evaluation flow |
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| [eval-chat-math](flows/evaluation/eval-chat-math/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_chat_math.yml) | This example shows how to evaluate the answer of math questions, which can compare the output results with the standard answers numerically |
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| [eval-classification-accuracy](flows/evaluation/eval-classification-accuracy/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_classification_accuracy.yml) | This is a flow illustrating how to evaluate the performance of a classification system |
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| [eval-entity-match-rate](flows/evaluation/eval-entity-match-rate/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_entity_match_rate.yml) | This is a flow evaluates: entity match rate |
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| [eval-groundedness](flows/evaluation/eval-groundedness/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_groundedness.yml) | This is a flow leverage llm to eval groundedness: whether answer is stating facts that are all present in the given context |
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| [eval-multi-turn-metrics](flows/evaluation/eval-multi-turn-metrics/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_multi_turn_metrics.yml) | This evaluation flow will evaluate a conversation by using Large Language Models (LLM) to measure the quality of the responses |
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| [eval-perceived-intelligence](flows/evaluation/eval-perceived-intelligence/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_perceived_intelligence.yml) | This is a flow leverage llm to eval perceived intelligence |
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| [eval-qna-non-rag](flows/evaluation/eval-qna-non-rag/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_qna_non_rag.yml) | This is a flow evaluating the Q&A systems by leveraging Large Language Models (LLM) to measure the quality and safety of responses |
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| [eval-qna-rag-metrics](flows/evaluation/eval-qna-rag-metrics/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_qna_rag_metrics.yml) | This is a flow evaluating the Q&A RAG (Retrieval Augmented Generation) systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of responses |
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| [eval-single-turn-metrics](flows/evaluation/eval-single-turn-metrics/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_single_turn_metrics.yml) | This evaluation flow will evaluate a question and answer pair by using Large Language Models (LLM) to measure the quality of the answer |
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| [eval-summarization](flows/evaluation/eval-summarization/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_evaluation_eval_summarization.yml) | This flow implements a reference-free automatic abstractive summarization evaluation across four dimensions: fluency, coherence, consistency, relevance |
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#### [Chat flows](flows/chat/)
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| [chat-basic](flows/chat/chat-basic/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_chat_chat_basic.yml) | This example shows how to create a basic chat flow |
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| [chat-math-variant](flows/chat/chat-math-variant/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_chat_chat_math_variant.yml) | This is a prompt tuning case with 3 prompt variants for math question answering |
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| [chat-with-image](flows/chat/chat-with-image/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_chat_chat_with_image.yml) | This flow demonstrates how to create a chatbot that can take image and text as input |
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| [chat-with-pdf](flows/chat/chat-with-pdf/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_chat_chat_with_pdf.yml) | This is a simple flow that allow you to ask questions about the content of a PDF file and get answers |
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| [chat-with-wikipedia](flows/chat/chat-with-wikipedia/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_chat_chat_with_wikipedia.yml) | This flow demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message |
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| [use_functions_with_chat_models](flows/chat/use_functions_with_chat_models/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_chat_use_functions_with_chat_models.yml) | This flow covers how to use the LLM tool chat API in combination with external functions to extend the capabilities of GPT models |
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### Tool Use Cases ([Tool Use Cases](tools/use-cases))
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| [cascading-inputs-tool-showcase](tools/use-cases/cascading-inputs-tool-showcase/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tools_use_cases_cascading_inputs_tool_showcase.yml) | This is a flow demonstrating the use of a tool with cascading inputs which frequently used in situations where the selection in one input field determines what subsequent inputs should be shown, and it helps in creating a more efficient, user-friendly, and error-free input process |
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| [custom-strong-type-connection-package-tool-showcase](tools/use-cases/custom-strong-type-connection-package-tool-showcase/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tools_use_cases_custom_strong_type_connection_package_tool_showcase.yml) | This is a flow demonstrating the use of a package tool with custom string type connection which provides a secure way to manage credentials for external APIs and data sources, and it offers an improved user-friendly and intellisense experience compared to custom connections |
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| [custom-strong-type-connection-script-tool-showcase](tools/use-cases/custom-strong-type-connection-script-tool-showcase/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tools_use_cases_custom_strong_type_connection_script_tool_showcase.yml) | This is a flow demonstrating the use of a script tool with custom string type connection which provides a secure way to manage credentials for external APIs and data sources, and it offers an improved user-friendly and intellisense experience compared to custom connections |
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| [custom_llm_tool_showcase](tools/use-cases/custom_llm_tool_showcase/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tools_use_cases_custom_llm_tool_showcase.yml) | This is a flow demonstrating how to use a `custom_llm` tool, which enables users to seamlessly connect to a large language model with prompt tuning experience using a `PromptTemplate` |
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| [dynamic-list-input-tool-showcase](tools/use-cases/dynamic-list-input-tool-showcase/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tools_use_cases_dynamic_list_input_tool_showcase.yml) | This is a flow demonstrating how to use a tool with a dynamic list input |
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### Connections ([connections](connections))
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| [connections](connections/README.md) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_connections.yml) | This folder contains example `YAML` files for creating `connection` using `pf` cli |
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## SDK examples
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| [quickstart.ipynb](tutorials/get-started/quickstart.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_getstarted_quickstart.yml) | A quickstart tutorial to run a flow and evaluate it. |
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| [quickstart-azure.ipynb](tutorials/get-started/quickstart-azure.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_getstarted_quickstartazure.yml) | A quickstart tutorial to run a flow in Azure AI and evaluate it. |
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| [flow-as-function.ipynb](tutorials/get-started/flow-as-function.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_getstarted_flowasfunction.yml) | This guide will walk you through the main scenarios of executing flow as a function. |
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| [pipeline.ipynb](tutorials/run-flow-with-pipeline/pipeline.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_runflowwithpipeline_pipeline.yml) | Create pipeline using components to run a distributed job with tensorflow |
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| [cloud-run-management.ipynb](tutorials/run-management/cloud-run-management.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_runmanagement_cloudrunmanagement.yml) | Flow run management in Azure AI |
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| [run-management.ipynb](tutorials/run-management/run-management.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_runmanagement_runmanagement.yml) | Flow run management |
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| [trace-autogen-groupchat.ipynb](tutorials/tracing/autogen-groupchat/trace-autogen-groupchat.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tracing_autogengroupchat_traceautogengroupchat.yml) | Tracing LLM calls in autogen group chat application |
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| [otlp-trace-collector.ipynb](tutorials/tracing/custom-otlp-collector/otlp-trace-collector.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tracing_customotlpcollector_otlptracecollector.yml) | A tutorial on how to levarage custom OTLP collector. |
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| [trace-langchain.ipynb](tutorials/tracing/langchain/trace-langchain.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tracing_langchain_tracelangchain.yml) | Tracing LLM calls in langchain application |
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| [trace-llm.ipynb](tutorials/tracing/llm/trace-llm.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_tracing_llm_tracellm.yml) | Tracing LLM application |
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| [connection.ipynb](connections/connection.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_connections_connection.yml) | Manage various types of connections using sdk |
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| [flex-flow-quickstart-azure.ipynb](flex-flows/basic/flex-flow-quickstart-azure.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flexflows_basic_flexflowquickstartazure.yml) | A quickstart tutorial to run a flex flow and evaluate it in Azure. |
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| [flex-flow-quickstart.ipynb](flex-flows/basic/flex-flow-quickstart.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flexflows_basic_flexflowquickstart.yml) | A quickstart tutorial to run a flex flow and evaluate it. |
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| [chat-stream-with-async-flex-flow.ipynb](flex-flows/chat-async-stream/chat-stream-with-async-flex-flow.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flexflows_chatasyncstream_chatstreamwithasyncflexflow.yml) | A quickstart tutorial to run a class based flex flow in stream mode and evaluate it. |
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| [chat-with-class-based-flow-azure.ipynb](flex-flows/chat-basic/chat-with-class-based-flow-azure.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flexflows_chatbasic_chatwithclassbasedflowazure.yml) | A quickstart tutorial to run a class based flex flow and evaluate it in azure. |
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| [chat-with-class-based-flow.ipynb](flex-flows/chat-basic/chat-with-class-based-flow.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flexflows_chatbasic_chatwithclassbasedflow.yml) | A quickstart tutorial to run a class based flex flow and evaluate it. |
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| [chat-stream-with-flex-flow.ipynb](flex-flows/chat-stream/chat-stream-with-flex-flow.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flexflows_chatstream_chatstreamwithflexflow.yml) | A quickstart tutorial to run a class based flex flow in stream mode and evaluate it. |
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| [langchain-eval.ipynb](flex-flows/eval-criteria-with-langchain/langchain-eval.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flexflows_evalcriteriawithlangchain_langchaineval.yml) | A tutorial to converting LangChain criteria evaluator application to flex flow. |
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| [prompty-quickstart.ipynb](prompty/basic/prompty-quickstart.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_prompty_basic_promptyquickstart.yml) | A quickstart tutorial to run a prompty and evaluate it. |
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| [chat-with-prompty.ipynb](prompty/chat-basic/chat-with-prompty.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_prompty_chatbasic_chatwithprompty.yml) | A quickstart tutorial to run a chat prompty and evaluate it. |
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| [prompty-output-format.ipynb](prompty/format-output/prompty-output-format.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_prompty_formatoutput_promptyoutputformat.yml) | |
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| [chat-with-pdf-azure.ipynb](flows/chat/chat-with-pdf/chat-with-pdf-azure.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_chat_chatwithpdf_chatwithpdfazure.yml) | A tutorial of chat-with-pdf flow that executes in Azure AI |
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| [chat-with-pdf.ipynb](flows/chat/chat-with-pdf/chat-with-pdf.ipynb) | [](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_chat_chatwithpdf_chatwithpdf.yml) | A tutorial of chat-with-pdf flow that allows user ask questions about the content of a PDF file and get answers |
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## Contributing
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We welcome contributions and suggestions! Please see the [contributing guidelines](../CONTRIBUTING.md) for details.
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## Code of Conduct
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). Please see the [code of conduct](../CODE_OF_CONDUCT.md) for details.
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## Reference
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* [Promptflow documentation](https://microsoft.github.io/promptflow/)
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