Why Build Your Chatbot with Bigdata.com? Bigdata.com is designed specifically for business and finance
professionals, delivering real-time, AI-driven insights with precision
and speed.
✅ Unmatched Data Access — Instantly analyze vast, high-quality datasets. Grounding with Bigdata.com — Generated responses reflect the accurate and real-time market conditions.Finance & Business Expertise — Designed for professionals, not generic use. Seamless Integration — Easily power your chatbot with cutting-edge AI.
📐 Overview This tutorial walks you through deploying and integrating the Financial Research Assistant chatbot on your proprietary platform using a sample Python server chat-server.py and connecting it to Bigdata.com’s Chat Service.
Flow Summary :
The Web App sends users’ inputs to the Python-based chat server chat-server.py.
The server processes these inputs and communicates with Bigdata.com’s Chat Service API.
Responses are routed back to the Web App for display.
🧰 Prerequisites
Create a directory for your project and navigate to it.
mkdir chat-server cd chat-server/
Follow the how-to guide Prerequisites to set up your Bigdata.com Python SDK environment.
🧪 Step 1: Set Up chat-server.py
Place the files chat-server.py and requirements.txt in your project directory.
from flask import Flask, request, jsonify from flask_cors import CORS import uuid from bigdata_client import Bigdata from bigdata_client.models.chat import ChatScope from flask import Response, stream_with_context from dotenv import load_dotenv app = Flask( __name__ ) from flask import Response class UnbufferedResponse ( Response ): direct_passthrough = True app.response_class = UnbufferedResponse CORS(app) # Initialize Bigdata instance load_dotenv() db = Bigdata() @app.route ( '/chats' , methods = [ 'POST' , 'OPTIONS' ]) def create_chat (): if request.method == 'OPTIONS' : return _build_cors_preflight_response() data = request.json chat_title = data.get( "chat_title" ) chat = db.chat.new(chat_title) chat_id = chat.id # Extract chat ID from the chat object print ( f "[INFO] Creating chat with ID: { chat_id } " ) print ( f "[INFO] Created chat with ID: { chat_id } , Title: { chat_title } " ) return jsonify({ "chat_id" : chat_id}) @app.route ( '/chats/ask-stream' , methods = [ 'GET' ]) def ask_question_stream (): chat_id = request.args.get( "chat_id" ) question = request.args.get( "question" ) scope = request.args.get( "scope" ) if not chat_id or not question: return jsonify({ "error" : "Missing required parameters" }), 400 try : scope_enum = ChatScope[scope] if scope and scope != "ALL" else None except KeyError : return jsonify({ "error" : "Invalid scope value" }), 400 chat_instance = db.chat.get(chat_id) def generate (): if scope_enum: stream = chat_instance.ask(question, scope = scope_enum, streaming = True ) else : stream = chat_instance.ask(question, streaming = True ) for chunk in stream: # Force flush-friendly formatting (newline-separated, short) yield f "data: { chunk } \n\n " yield "event: done \n data: [DONE] \n\n " return Response(stream_with_context(generate()), mimetype = 'text/event-stream' ) @app.route ( '/chats' , methods = [ 'DELETE' , 'OPTIONS' ]) def delete_chat (): if request.method == 'OPTIONS' : return _build_cors_preflight_response() data = request.json chat_id = data.get( "chat_id" ) db.chat.delete(chat_id) print ( f "[INFO] Deleted chat with ID: { chat_id } " ) return jsonify({ "message" : "Chat deleted successfully" }) def _build_cors_preflight_response (): response = jsonify({}) response.headers.add( "Access-Control-Allow-Origin" , "*" ) response.headers.add( "Access-Control-Allow-Methods" , "GET, POST, OPTIONS, DELETE" ) response.headers.add( "Access-Control-Allow-Headers" , "Content-Type" ) return response if __name__ == "__main__" : app.run( debug = True , port = 5000 )
flask >= 2.0 .0 flask - cors >= 3.0 .10 bigdata - client >= 2.14 .0 Project structure:
chat-server/ ├── bigdata_venv/ ├── .env ├── chat-server.py └── requirements.txt
Install dependencies:
pip install -r requirements.txt
Run the server:
By default, this starts on http://localhost:5000 unless specified otherwise.
🧑💻 Step 2: Connect from Your Web App We built a Sample App that you can use as PoC. By default it communicates to the chat-server.py on http://localhost:5000
Sample App Click here to open it and start chating with Bigdata.com 🥳🚀
