feat: ms-bot-framework with dynamic adaptive cards

This commit is contained in:
nasim 2025-04-03 10:28:32 +02:00
parent 3aa78be3d6
commit f96f4af069
13 changed files with 363 additions and 74 deletions

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@ -0,0 +1,47 @@
from typing import Dict, Any
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
import asyncio
class AdaptiveCards:
def __init__(self):
self.llm = ChatOpenAI(temperature=0)
self.prompt = ChatPromptTemplate.from_template("""
You are a Microsoft Adaptive Card generator. Given a data schema and known values,
generate an Adaptive Card (v1.3) that asks the user only for missing fields.
Use this schema: https://adaptivecards.io/schemas/adaptive-card.json
Respond only with valid Adaptive Card JSON. Do not include explanations.
Always include isRequired, and errorMessage in the schema.
Always include a submit button at the bottom of the card as defined in the schema.
### Schema:
{schema}
### Known values:
{known_values}
""")
self.chain = LLMChain(llm=self.llm, prompt=self.prompt)
async def generate_card(self, schema: Dict[str, Any], known_values: Dict[str, Any]) -> Dict[str, Any]:
loop = asyncio.get_running_loop()
return await loop.run_in_executor(None, self.chain.run, {
"schema": schema,
"known_values": known_values
})
def create_welcome_card(self):
"""Create a welcome card"""
return {
"type": "AdaptiveCard",
"body": [
{
"type": "TextBlock",
"text": "Welcome to the Housing Bot!",
"size": "large"
}
],
"version": "1.0"
}

115
backend/app/bots/dayta.py Normal file
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import json
from typing import Annotated
from fastapi import Depends
from langchain.chat_models import ChatOpenAI
from botbuilder.core import ActivityHandler, TurnContext
from botbuilder.schema import Activity, Attachment, ActivityTypes
import asyncio
from pydantic import ValidationError
from backend.app.bots.adaptive_cards import AdaptiveCards
from backend.app.bots.intent_detector import IntentDetector
from backend.app.bots.slot_filler import SlotFiller
from backend.app.dtos.house.house_features import HouseFeatures
from backend.app.services.house_price_predictor import HousePricePredictor
class Dayta(ActivityHandler):
def __init__(
self,
intent_detector: Annotated[IntentDetector, Depends()],
card_bot: Annotated[AdaptiveCards, Depends()],
slot_filler: Annotated[SlotFiller, Depends()],
price_predictor: Annotated[HousePricePredictor, Depends()],):
self.intent_detector = intent_detector
self.card_bot = card_bot
self.slot_filler = slot_filler
self.price_predictor = price_predictor
self.chat_llm = ChatOpenAI(temperature=0.7)
self.user_sessions = {}
async def on_message_activity(self, turn_context: TurnContext):
user_message = turn_context.activity.text
user_id = turn_context.activity.from_property.id
submitted_values = turn_context.activity.value
known_values = self.user_sessions.get(user_id, {})
schema = HouseFeatures.model_json_schema()
#required_fields = list(HouseFeatures.model_fields.keys())
required_fields = [
name for name, field in HouseFeatures.model_fields.items()
if field.is_required()
]
print(f"required_fields: {required_fields}")
# Update known values
if submitted_values is not None:
known_values.update(submitted_values)
else:
extracted = await self.slot_filler.extract_slots(schema, user_message)
known_values.update(extracted)
self.user_sessions[user_id] = known_values
# Detect intent only if message-based
if not submitted_values:
intent = await self.intent_detector.detect_intent(user_message)
if intent.strip().lower() in ("unknown", ""):
response = await asyncio.get_event_loop().run_in_executor(
None,
lambda: self.chat_llm.predict(f"The user said: '{user_message}'. Respond helpfully.")
)
await turn_context.send_activity(response)
return
# Delegate to common logic
await self._handle_collected_data(turn_context, user_id, known_values, required_fields, schema)
async def _handle_collected_data(
self,
turn_context: TurnContext,
user_id: str,
known_values: dict,
required_fields: list[str],
full_schema: dict
):
missing_fields = [f for f in required_fields if f not in known_values]
print(f"Missing fields: {missing_fields}")
if not missing_fields:
try:
features = HouseFeatures(**known_values)
price = self.price_predictor.predict(features)
await turn_context.send_activity(f"The estimated price of the house is ${price:.2f}")
del self.user_sessions[user_id]
return
except ValidationError as e:
await turn_context.send_activity(f"Validation failed: {e}")
return
# Generate adaptive card for missing fields
filtered_schema = {
**full_schema,
"properties": {
k: v for k, v in full_schema["properties"].items() if k in missing_fields
},
"required": missing_fields
}
card_json = await self.card_bot.generate_card(filtered_schema, known_values)
if isinstance(card_json, str):
card_json = json.loads(card_json)
print(f"card_json: {card_json}")
await turn_context.send_activity(
Activity(
type=ActivityTypes.message,
attachments=[
Attachment(
content_type="application/vnd.microsoft.card.adaptive",
content=card_json
)
]
)
)

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@ -0,0 +1,23 @@
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
import asyncio
class IntentDetector:
def __init__(self, temperature: float = 0.0):
self.llm = ChatOpenAI(temperature=temperature)
self.prompt = ChatPromptTemplate.from_template("""
You are an intent detection bot. Classify the user input into one of the following intents:
- Information about house prices
- unknown
If you're unsure, respond with `unknown`.
User: {message}
Intent:""")
self.chain = LLMChain(llm=self.llm, prompt=self.prompt)
async def detect_intent(self, message: str) -> str:
loop = asyncio.get_running_loop()
return await loop.run_in_executor(None, self.chain.run, {"message": message})

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@ -0,0 +1,31 @@
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
from typing import Dict, Any
import asyncio
class SlotFiller:
def __init__(self):
self.llm = ChatOpenAI(temperature=0)
self.prompt = ChatPromptTemplate.from_template("""
You are a helpful assistant. Given a message and a schema, extract all known values.
Only return a JSON object containing the extracted values and no extra text.
Schema: {schema}
Message: {message}
""")
self.chain = LLMChain(llm=self.llm, prompt=self.prompt)
async def extract_slots(self, schema: Dict[str, Any], message: str) -> Dict[str, Any]:
loop = asyncio.get_event_loop()
result = await loop.run_in_executor(None, self.chain.run, {
"schema": schema,
"message": message
})
import json
try:
return json.loads(result)
except Exception:
return {}

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@ -1,34 +0,0 @@
from pydantic import BaseModel, Field
class HouseCreateRequest(BaseModel):
address: str = Field(
...,
min_length=1,
max_length=255,
description="House address",
examples=["123 Main St"],
)
city: str = Field(
..., description="City where the house is located", examples=["Springfield"]
)
country: str = Field(
..., description="Country where the house is located", examples=["USA"]
)
price: float = Field(..., description="Price of the house", examples=[250000.00])
description: str = Field(
...,
description="Description of the house",
examples=["A beautiful 3-bedroom house"],
)
square_feet: float = Field(
...,
description="Square footage of the house",
examples=[1500.00],
)
bedrooms: int = Field(
..., description="Number of bedrooms in the house", examples=[3]
)
bathrooms: float = Field(
..., description="Number of bathrooms in the house", examples=[2.5]
)

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@ -1,5 +0,0 @@
from pydantic import BaseModel
class HouseCreateResponse(BaseModel):
id: str

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@ -1,14 +0,0 @@
from pydantic import BaseModel
class HouseResponse(BaseModel):
id: str
description: str
address: str
city: str
country: str
price: float
class HousesListResponse(BaseModel):
houses: list[HouseResponse]

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@ -1,7 +0,0 @@
from pydantic import BaseModel
class OwnerDetailResponse(BaseModel):
id: str
user_id: str
email: str

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@ -1,10 +0,0 @@
from pydantic import BaseModel
class OwnerResponse(BaseModel):
id: str
user_id: str
class OwnerListResponse(BaseModel):
owners: list[OwnerResponse]

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from typing import Dict
from backend.app.bots.dayta import Dayta
from backend.app.bots.intent_detector import IntentDetector
from backend.app.bots.slot_filler import SlotFiller
from backend.app.bots.adaptive_cards import AdaptiveCards
from backend.app.services.house_price_predictor import HousePricePredictor
from botbuilder.core import BotFrameworkAdapter, BotFrameworkAdapterSettings
class BotFactory:
def __init__(self):
self._bots: Dict[str, object] = {}
self.adapter_settings = BotFrameworkAdapterSettings(app_id="", app_password="")
self.adapter = BotFrameworkAdapter(self.adapter_settings)
# Shared services
self.intent_detector = IntentDetector()
self.slot_filler = SlotFiller()
self.card_bot = AdaptiveCards()
self.price_predictor = HousePricePredictor()
# Register all bots
self._bots["dayta"] = Dayta(
intent_detector=self.intent_detector,
card_bot=self.card_bot,
slot_filler=self.slot_filler,
price_predictor=self.price_predictor
)
def get_bot(self, name: str):
return self._bots.get(name)
def get_adapter(self):
return self.adapter

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@ -7,14 +7,13 @@ from .middleware.authenticate import authenticate
from .providers.db_provider import create_db_and_tables
from .routers.houses import router as houses_router
from .routers.owners import router as owners_router
from .routers.direct_line import router as direct_line_router
from .routers.bot import router as bot_router
@asynccontextmanager
async def lifespan(_app: FastAPI):
create_db_and_tables()
yield
app = FastAPI(
title="Fair Housing API",
description="Provides access to core functionality for the fair housing platform.",
@ -33,3 +32,5 @@ app.add_middleware(
app.include_router(houses_router, prefix="/houses", tags=["houses"])
app.include_router(owners_router, prefix="/owners", tags=["owners"])
app.include_router(bot_router, tags=["bot"])
app.include_router(direct_line_router, tags=["directline"])

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from fastapi import APIRouter, Request, Depends
from botbuilder.schema import Activity
from botbuilder.core import TurnContext
from backend.app.factories.bot_factory import BotFactory
router = APIRouter()
@router.post("/api/messages", response_model=None)
async def messages(
req: Request,
bot = Depends(lambda: BotFactory().get_bot("dayta")),
adapter = Depends(lambda: BotFactory().get_adapter() )
):
body = await req.json()
activity = Activity().deserialize(body)
async def call_bot_logic(turn_context: TurnContext):
await bot.on_turn(turn_context)
auth_header = req.headers.get("Authorization", "")
await adapter.process_activity(activity, auth_header, call_bot_logic)
return {}

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from fastapi import APIRouter, HTTPException
from typing import Dict, Any
from uuid import uuid4
from botbuilder.core import TurnContext
from botbuilder.schema import Activity, ActivityTypes
from backend.app.factories.bot_factory import BotFactory
router = APIRouter(prefix="/v3/directline")
# In-memory conversation store
conversations: Dict[str, Dict[str, Any]] = {}
# Each conversation will look like:
# { "activities": [ { id, type, text, from } ], "watermark": int }
@router.post("/conversations")
async def start_conversation():
conversation_id = str(uuid4())
conversations[conversation_id] = {
"activities": [],
"watermark": 0
}
return {
"conversationId": conversation_id,
"token": "mock-token", # Optional for dev use
"streamUrl": f"/v3/directline/conversations/{conversation_id}/stream"
}
@router.get("/conversations/{conversation_id}/activities")
async def get_activities(conversation_id: str, watermark: int = 0):
if conversation_id not in conversations:
raise HTTPException(status_code=404, detail="Conversation not found")
activities = conversations[conversation_id]["activities"]
return {
"activities": activities[watermark:],
"watermark": len(activities)
}
@router.post("/conversations/{conversation_id}/activities")
async def post_activity(conversation_id: str, activity: Dict[str, Any]):
if conversation_id not in conversations:
raise HTTPException(status_code=404, detail="Conversation not found")
# Starting with deserializing the activity
act = Activity().deserialize(activity)
# Store my responses in this list please
bot_responses = []
#Patch TurnContext.send_activity to capture output
async def call_bot_logic(turn_context: TurnContext):
async def capture_response(response):
# If it's a string, wrap it into an Activity
if isinstance(response, str):
bot_activity = Activity(
type=ActivityTypes.message,
text=response,
from_property={"id": "bot"}
)
else:
bot_activity = response
bot_responses.append(bot_activity)
turn_context.send_activity = capture_response
await bot.on_turn(turn_context)
# 4. Call the adapter with the activity
adapter = BotFactory().get_adapter()
bot = BotFactory().get_bot("dayta")
auth_header = ""
await adapter.process_activity(act, auth_header, call_bot_logic)
# 5. Store bot responses into conversation memory
for act in bot_responses:
conversations[conversation_id]["activities"].append({
"id": str(uuid4()),
"type": act.type,
"text": act.text,
"from": {"id": "bot"},
"attachments": [a.serialize() for a in (act.attachments or [])]
})
return { "id": str(uuid4()) }