microsoft--promptflow/migration-guide/PromptFlow-to-MAF/EXAMPLES.md

Migrated Flow Examples

We have generated migrated flows under the examples/flows/ folder for your reference. Each original Prompt Flow (PF) flow has a corresponding Microsoft Agent Framework (MAF) version (suffixed with -maf). The MAF workflows were created by AI using the migration skill.

---

Chat Flows

Example	PF Version	MAF Version	Description
Chat Basic	chat-basic	chat-basic-maf	Chatbot that remembers previous interactions and generates responses using conversation history
Chat Math Variant	chat-math-variant	chat-math-variant-maf	Prompt tuning example with three variants for testing math question answering approaches
Chat with Image	chat-with-image	chat-with-image-maf	Chatbot that accepts both image and text as input using GPT-4V
Chat with PDF	chat-with-pdf	chat-with-pdf-maf	Ask questions about PDF content using retrieval-augmented generation
Chat with Wikipedia	chat-with-wikipedia	chat-with-wikipedia-maf	Chatbot with conversation history that searches Wikipedia for current information
Promptflow Copilot	promptflow-copilot	promptflow-copilot-maf	Chat flow for building a copilot assistant for Promptflow
Use Functions with Chat Models	use_functions_with_chat_models	use_functions_with_chat_models-maf	Extend LLM capabilities with external function specifications for chat models

Standard Flows

Example	PF Version	MAF Version	Description
Autonomous Agent	autonomous-agent	autonomous-agent-maf	AutoGPT agent that autonomously figures out how to apply functions to solve goals
Basic	basic	basic-maf	Basic flow using custom Python tool calling Azure OpenAI with environment variables
Basic with Built-in LLM	basic-with-builtin-llm	basic-with-builtin-llm-maf	Basic flow calling Azure OpenAI using the built-in LLM tool
Basic with Connection	basic-with-connection	basic-with-connection-maf	Basic flow using custom Python tool with connection info stored in custom connection
Conditional Flow (If-Else)	conditional-flow-for-if-else	conditional-flow-for-if-else-maf	Conditional flow that checks content safety and routes accordingly
Conditional Flow (Switch)	conditional-flow-for-switch	conditional-flow-for-switch-maf	Conditional flow that dynamically routes by classifying user intent
Customer Intent Extraction	customer-intent-extraction	customer-intent-extraction-maf	Identify customer intent from customer questions using LLM
Describe Image	describe-image	describe-image-maf	Flow that flips an image horizontally and describes it using GPT-4V
Flow with Additional Includes	flow-with-additional-includes	flow-with-additional-includes-maf	Demonstrates how to reference common files using additional_includes
Flow with Symlinks	flow-with-symlinks	flow-with-symlinks-maf	Flow that uses symbolic links to reference common files across flows
Generate Docstring	gen-docstring	gen-docstring-maf	Automatically generate Python docstrings for code blocks using LLM
Maths to Code	maths-to-code	maths-to-code-maf	Generate executable code from math problems and execute it for answers
Named Entity Recognition	named-entity-recognition	named-entity-recognition-maf	Perform NER task identifying and classifying named entities using GPT-4
Question Simulation	question-simulation	question-simulation-maf	Generate suggestions for next questions based on chat history context
Web Classification	web-classification	web-classification-maf	Multi-class website classification from URLs using few-shot learning

Evaluation Flows

Example	PF Version	MAF Version	Description
Eval Accuracy (Maths to Code)	eval-accuracy-maths-to-code	eval-accuracy-maths-to-code-maf	Evaluates mathematical accuracy by comparing predictions to ground truth
Eval Basic	eval-basic	eval-basic-maf	Basic evaluation flow showing how to calculate point-wise metrics
Eval Chat Math	eval-chat-math	eval-chat-math-maf	Evaluate math question answers by comparing results numerically
Eval Classification Accuracy	eval-classification-accuracy	eval-classification-accuracy-maf	Evaluate classification system performance with accuracy metrics
Eval Entity Match Rate	eval-entity-match-rate	eval-entity-match-rate-maf	Evaluate how well extracted entities match expected entities
Eval Groundedness	eval-groundedness	eval-groundedness-maf	Evaluate if answers are grounded in provided context using LLM
Eval Multi-Turn Metrics	eval-multi-turn-metrics	eval-multi-turn-metrics-maf	Evaluate multi-turn conversations on quality, coherence, and engagement
Eval Perceived Intelligence	eval-perceived-intelligence	eval-perceived-intelligence-maf	Evaluate bot perceived intelligence, creativity, and originality
Eval QnA Non-RAG	eval-qna-non-rag	eval-qna-non-rag-maf	Q&A system evaluation with LLM-assisted coherence, relevance, and similarity metrics
Eval QnA RAG Metrics	eval-qna-rag-metrics	eval-qna-rag-metrics-maf	RAG Q&A system evaluation with retrieval, groundedness, and relevance metrics
Eval Single-Turn Metrics	eval-single-turn-metrics	eval-single-turn-metrics-maf	Single-turn Q&A evaluation on grounding, relevance, and answer quality
Eval Summarization	eval-summarization	eval-summarization-maf	Reference-free abstractive summarization evaluation on fluency, coherence, consistency, relevance