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Add Custom Client example to the docs (#435)
* WIP * Add custom client example to docs * Polishing * Minor doc changes * Merge remote-tracking branch 'origin/main' into 424-write-documentation-on-how-to-create-a-custom-client-and-make-it-work-with-the-fastapi-provider
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docs/docs/en/user-guide/adapters/images/custom_chat_output.png
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docs/docs_src/getting_started/fastapi/main_custom_fastapi_client.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,152 @@ | ||
| import os | ||
| from typing import Any | ||
|
|
||
| from autogen.agentchat import ConversableAgent | ||
| from fastapi import FastAPI | ||
| from fastapi.responses import HTMLResponse | ||
|
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| from fastagency import UI | ||
| from fastagency.adapters.fastapi import FastAPIAdapter | ||
| from fastagency.runtimes.autogen import AutoGenWorkflows | ||
|
|
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| html = """ | ||
| <!DOCTYPE html> | ||
| <html> | ||
| <head> | ||
| <title>FastAgency Chat App</title> | ||
| </head> | ||
| <body> | ||
| <h1>FastAgency Chat App</h1> | ||
| <div id="workflows"></div> | ||
| <ul id="messages"></ul> | ||
| <script> | ||
| const API_URL = 'http://0.0.0.0:8008/fastagency'; | ||
| const WS_URL = 'ws://0.0.0.0:8008/fastagency/ws'; // nosemgrep | ||
| let socket; | ||
| async function fetchWorkflows() { | ||
| const response = await fetch(`${API_URL}/discovery`); | ||
| const workflows = await response.json(); | ||
| const container = document.getElementById('workflows'); | ||
| workflows.forEach(workflow => { | ||
| const button = document.createElement('button'); | ||
| button.textContent = workflow.description; | ||
| button.onclick = () => startWorkflow(workflow.name); | ||
| container.appendChild(button); | ||
| }); | ||
| } | ||
| async function startWorkflow(name) { | ||
| const payload = { | ||
| workflow_name: name, | ||
| workflow_uuid: generateUUID(), | ||
| user_id: null, // Set to null for single-user applications; otherwise, provide the appropriate user ID | ||
| params: {} | ||
| }; | ||
| const response = await fetch(`${API_URL}/initiate_workflow`, { | ||
| method: 'POST', | ||
| headers: { 'Content-Type': 'application/json' }, | ||
| body: JSON.stringify(payload) | ||
| }); | ||
| const workflowJson = await response.json(); | ||
| connectWebSocket(workflowJson); | ||
| } | ||
| function connectWebSocket(workflowJson) { | ||
| socket = new WebSocket(WS_URL); | ||
| socket.onopen = () => { | ||
| const initMessage = { | ||
| name: workflowJson.name, | ||
| workflow_uuid: workflowJson.workflow_uuid, | ||
| user_id: workflowJson.user_id, | ||
| params: {} | ||
| }; | ||
| socket.send(JSON.stringify(initMessage)); | ||
| }; | ||
| socket.onmessage = (event) => handleMessage(JSON.parse(event.data)); | ||
| } | ||
| function handleMessage(message) { | ||
| const messagesList = document.getElementById('messages'); | ||
| const li = document.createElement('li'); | ||
| if (message.type === 'text_input') { | ||
| const response = prompt(message.content.prompt); | ||
| socket.send(response); | ||
| li.textContent = `${message.sender} -> ${message.recipient}: ${message.content.prompt}`; | ||
| } else { | ||
| li.textContent = `${message.sender} -> ${message.recipient}: ${message.content?.body || message?.type || JSON.stringify(message)}`; | ||
| } | ||
| messagesList.appendChild(li); | ||
| } | ||
| fetchWorkflows(); | ||
| // Helper function for generating UUID | ||
| function generateUUID() { | ||
| return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) { | ||
| if (c === 'x') { | ||
| return (Math.random() * 16 | 0).toString(16); | ||
| } else { | ||
| return (Math.random() * 16 | 0 & 0x3 | 0x8).toString(16); | ||
| } | ||
| }); | ||
| } | ||
| </script> | ||
| </body> | ||
| </html> | ||
| """ | ||
|
|
||
| llm_config = { | ||
| "config_list": [ | ||
| { | ||
| "model": "gpt-4o-mini", | ||
| "api_key": os.getenv("OPENAI_API_KEY"), | ||
| } | ||
| ], | ||
| "temperature": 0.8, | ||
| } | ||
|
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| wf = AutoGenWorkflows() | ||
|
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| @wf.register(name="simple_learning", description="Student and teacher learning chat") | ||
| def simple_workflow(ui: UI, params: dict[str, Any]) -> str: | ||
| initial_message = ui.text_input( | ||
| sender="Workflow", | ||
| recipient="User", | ||
| prompt="I can help you learn about mathematics. What subject you would like to explore?", | ||
| ) | ||
|
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| student_agent = ConversableAgent( | ||
| name="Student_Agent", | ||
| system_message="You are a student willing to learn.", | ||
| llm_config=llm_config, | ||
| # human_input_mode="ALWAYS", | ||
| ) | ||
| teacher_agent = ConversableAgent( | ||
| name="Teacher_Agent", | ||
| system_message="You are a math teacher.", | ||
| llm_config=llm_config, | ||
| # human_input_mode="ALWAYS", | ||
| ) | ||
|
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| chat_result = student_agent.initiate_chat( | ||
| teacher_agent, | ||
| message=initial_message, | ||
| summary_method="reflection_with_llm", | ||
| max_turns=5, | ||
| ) | ||
|
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| return chat_result.summary # type: ignore[no-any-return] | ||
|
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| adapter = FastAPIAdapter(provider=wf) | ||
|
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| app = FastAPI() | ||
| app.include_router(adapter.router) | ||
|
|
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| @app.get("/") | ||
| async def get() -> HTMLResponse: | ||
| return HTMLResponse(html) | ||
|
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|
|
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| # start the provider with the following command | ||
| # uvicorn main_custom_fastapi_client:app --host 0.0.0.0 --port 8008 --reload |
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