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47 lines
1.5 KiB
Markdown
47 lines
1.5 KiB
Markdown
# Basic Chat — Microsoft Agent Framework
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This is the [Microsoft Agent Framework (MAF)](https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-version-1-0/) version of the [chat-basic](../chat-basic/) Prompt Flow example.
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It implements the same behaviour: a helpful assistant chatbot that remembers conversation history and responds to user questions.
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## Architecture
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```
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[InputExecutor] ──→ [ChatExecutor]
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(question + (Agent with
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chat_history) FoundryChatClient)
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```
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| Prompt Flow concept | MAF equivalent |
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|---|---|
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| `flow.dag.yaml` | `WorkflowBuilder` in `chat_flow.py` |
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| `chat.jinja2` (system prompt) | `Agent(instructions="You are a helpful assistant.")` |
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| LLM node (`api: chat`) | `FoundryChatClient` + `Agent.run()` |
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| `chat_history` input | Message list assembled in `InputExecutor` |
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| `open_ai_connection` | Environment variables (`FOUNDRY_PROJECT_ENDPOINT`, `FOUNDRY_MODEL`) + `DefaultAzureCredential` |
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## Prerequisites
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- Python 3.10+
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- An Azure subscription with a Microsoft Foundry project (or Azure OpenAI resource)
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- `az login` completed
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## Setup
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```bash
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pip install -r requirements.txt
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cp .env.example .env
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# Edit .env with your Foundry project endpoint and model deployment name
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```
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## Run
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```bash
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python chat_flow.py
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```
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This runs two test interactions:
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1. A single-turn question with no history
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2. A follow-up question with one prior turn of chat history
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