Added functionality to store form answer in mongodb

.gitignore

@@ -0,0 +1,13 @@
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.venv/
+models/
+.cache/
+.env
+*.pdf
+
+# macOS
+.DS_Store
+data/.DS_Store

app/config.py

@@ -0,0 +1,36 @@
+from pymongo.mongo_client import MongoClient
+from pymongo.server_api import ServerApi
+from qdrant_client import QdrantClient
+import os
+from dotenv import load_dotenv
+
+
+load_dotenv()
+
+# MongoDB connection
+MONGODB_CONNECTION_STRING = os.getenv("MONGODB_CONNECTION_STRING")
+DB_NAME = "chatbot_demo"
+
+# Demo chatbot defaults
+DEMO_MAX_MESSAGES = 20
+DEMO_EXPIRY_DAYS = 7
+
+
+# Create a new client and connect to the server
+client = MongoClient(MONGODB_CONNECTION_STRING, server_api=ServerApi('1'))
+
+# Send a ping to confirm a successful connection
+try:
+    client.admin.command('ping')
+    print("Pinged your deployment. You successfully connected to MongoDB!")
+    
+    demo_database = client["Demo"]
+    demo_form_submissions = demo_database["demo_form_submissions"]
+    print("Connected to MongoDB collection successfully!")
+except Exception as e:
+    print(e)
+
+#Qdrant
+QDRANT_URL = os.getenv("QDRANT_URL")
+QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
+client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)

app/ingestion/demo_form_fetch_store.py

@@ -0,0 +1,32 @@
+import uuid
+from app.config import demo_form_submissions
+from ingestion.models import ChatbotIngest
+from pydantic import HttpUrl
+
+def store_demo_chatbot(ingest_data: ChatbotIngest):
+    try:
+        # Convert Pydantic model to dict
+        data_dict = ingest_data.dict()
+
+        # Convert HttpUrl to string
+        if isinstance(data_dict["website_url"], HttpUrl):
+            data_dict["website_url"] = str(data_dict["website_url"])
+
+        # Generate a unique chatbot_id
+        chatbot_id = str(uuid.uuid4())
+        data_dict['chatbot_id'] = chatbot_id
+
+        # Insert into MongoDB
+        demo_form_submissions.insert_one(data_dict)
+        print(f"Chatbot {chatbot_id} ingested successfully for company {ingest_data.company_name}")
+        return chatbot_id
+
+    except Exception as e:
+        print(f"Error storing demo chatbot: {e}")
+        raise
+
+def get_chatbot_config(chatbot_id: str) -> dict:
+    """
+    Fetches a chatbot config from MongoDB by ID.
+    """
+    return demo_form_submissions.find_one({"chatbot_id": chatbot_id})

app/ingestion/models.py

@@ -0,0 +1,16 @@
+from pydantic import BaseModel, EmailStr, HttpUrl
+from typing import List, Optional
+from datetime import datetime, timezone
+
+class ChatbotIngest(BaseModel):
+    company_name: str
+    chatbot_name: Optional[str] = None
+    website_url: HttpUrl
+    chatbot_purpose: List[str] = []
+    additional_content: Optional[List[str]] = []
+    sensitive_topics: Optional[str] = None
+    tone_style: Optional[List[str]] = None
+    contact_email: EmailStr
+    contact_phone: str
+    submitted_at: datetime
+    ingested_at: datetime = datetime.now(timezone.utc)

app/ingestion/rag.py

@@ -0,0 +1,13 @@
+def scrape_website(url: str) -> str:
+    # Fetch website text (BeautifulSoup, requests)
+    return "full text of website"
+
+def chunk_text(text: str) -> list[str]:
+    # Split text into chunks for embeddings
+    return ["chunk1", "chunk2"]
+
+def store_website_chunks(chatbot_id: str, chunks: list, qdrant_client):
+    # Embed chunks and store in Qdrant
+    pass
+
+

app/ingestion/routes.py

@@ -0,0 +1,33 @@
+from fastapi import APIRouter, BackgroundTasks, HTTPException
+from ingestion.models import ChatbotIngest
+from pymongo import MongoClient
+from ingestion.utils import extract_tally_payload, store_demo_chatbot
+from ingestion.workers import build_rag_for_chatbot
+import logging
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+router = APIRouter()
+
+
+@router.post("/ingest")
+async def ingest_chatbot_webhook(payload: dict,):
+    """
+    Adapter endpoint for Tally webhook.
+    Extracts form fields from Tally's nested payload and maps them to ChatbotIngest.
+    """
+    try:
+        ingest_dict = extract_tally_payload(payload)
+        ingest_data = ChatbotIngest(**ingest_dict)
+        print(ingest_data)
+        chatbot_id = store_demo_chatbot(ingest_data)
+
+        return {
+            "chatbot_id": chatbot_id,
+            "demo_url": f"/chatbot/demo/{chatbot_id}",
+            "message": "Demo chatbot ingestion succesfull"
+        }
+    except Exception as e:
+        logger.error(f"Error processing ingestion webhook: {e}")
+        raise HTTPException(status_code=500, detail="Internal Server Error - Failed to ingest chatbot demo")

app/ingestion/utils.py

@@ -0,0 +1,63 @@
+
+from datetime import datetime, timezone
+
+
+def extract_tally_payload(payload: dict):
+    """
+    Extracts key chatbot information from a Tally webhook payload.
+    Handles optional/missing multi-select and dropdown values.
+    """
+    fields = payload.get("data", {}).get("fields", [])
+    
+    # Map key -> field
+    key_map = {f["key"]: f for f in fields}
+
+    # Company info
+    company_name = key_map.get("question_kyp5Dd", {}).get("value")
+    chatbot_name = key_map.get("question_vAp47X", {}).get("value") or None
+    website_url = key_map.get("question_K6gL7z", {}).get("value")
+
+    # Multi-select chatbot purpose
+    purpose_field = key_map.get("question_1lxMZ4", {})
+    option_map = {opt["id"]: opt["text"] for opt in purpose_field.get("options", [])}
+    value_ids = purpose_field.get("value") or []
+    chatbot_purpose = [option_map.get(v, v) for v in value_ids]
+
+    # Multi-select additional content
+    content_field = key_map.get("question_MbgLkE", {})
+    content_option_map = {opt["id"]: opt["text"] for opt in content_field.get("options", [])}
+    value_ids = content_field.get("value") or []
+    additional_content = [content_option_map.get(v, v) for v in value_ids]
+
+    # Sensitive topics
+    sensitive_topics = key_map.get("question_J6gLaz", {}).get("value") or None
+
+    # Tone/style dropdown
+    tone_field = key_map.get("question_N694OQ", {})
+    tone_option_map = {opt["id"]: opt["text"] for opt in tone_field.get("options", [])}
+    value_ids = tone_field.get("value") or []
+    tone_style = [tone_option_map.get(v, v) for v in value_ids] if value_ids else None
+
+    # Contact
+    contact_email = key_map.get("question_qd7YN7", {}).get("value")
+    contact_phone = key_map.get("question_QDaxEp", {}).get("value")
+
+    # Timestamps
+    submitted_at_str = payload.get("data", {}).get("createdAt")
+    submitted_at = datetime.fromisoformat(submitted_at_str.replace("Z", "+00:00"))
+
+    return {
+        "company_name": company_name,
+        "chatbot_name": chatbot_name,
+        "website_url": website_url,
+        "chatbot_purpose": chatbot_purpose,
+        "additional_content": additional_content,
+        "sensitive_topics": sensitive_topics,
+        "tone_style": tone_style,
+        "contact_email": contact_email,
+        "contact_phone": contact_phone,
+        "submitted_at": submitted_at,
+    }
+
+
+

app/ingestion/workers.py

@@ -0,0 +1,11 @@
+from ingestion.utils import get_chatbot_config
+from ingestion.rag import scrape_website, chunk_text, store_website_chunks
+from app.config import qdrant_client
+
+# workers.py
+def build_rag_for_chatbot(chatbot_id: str):
+    # Full RAG pipeline for a single chatbot
+    config = get_chatbot_config(chatbot_id)
+    text = scrape_website(config['website_url'])
+    chunks = chunk_text(text)
+    store_website_chunks(chatbot_id, chunks, qdrant_client)

app/main.py

@@ -0,0 +1,11 @@
+from fastapi import FastAPI
+from ingestion.routes import router as ingestion_router
+
+app = FastAPI(
+    title="Chatbot Platform - Demo Ingestion",
+    description="Handles Tally form ingestion and demo chatbot configuration.",
+    version="1.0.0"
+)
+
+# Mount ingestion routes
+app.include_router(ingestion_router, prefix="/ingestion", tags=["ingestion"])