The Bangladesh Election Commission NID chatbot is a conversational AI system that helps citizens with National Identity Card (NID) and voter registration queries. The system operates in two distinct modes:
A production-ready Bengali conversational AI system for National Identity Card (NID) and voter registration queries. The system uses semantic search (RAG) with multi-turn form conversations, interruption handling, and state management.
Core Challenge: Build an intelligent chatbot that understands Bengali queries about NID/voter registration, retrieves relevant answers from a knowledge base, and handles complex multi-turn conversations like form filling.
| import os | |
| import sys | |
| import numpy as np | |
| import psutil | |
| import torch | |
| def get_memory_info(): | |
| """Get current memory usage of the process.""" |
● 🔍 YOUR PROBLEMS (Comprehensive Summary)
Your core issue was that while your Bengali Legal RAG system achieved 94.7% test accuracy using EmbeddingGemma-300M, it suffered from four critical limitations that threatened scalability and real-world usability. First, Bengali linguistic confusion where syntactically similar legal terms like "khatian_correction" vs "khatian_copy" caused 11 classification errors due to EmbeddingGemma's 768D embedding space limitations and under-representation of Bengali legal terminology in multilingual training. Second, rigid threshold problems where your 0.932 FAISS threshold resulted in 0% complex query acceptance, while Linear Head's 0.60 threshold only achieved 14.3% acceptance - both failing to handle compound Bengali queries like "নামজারি করতে কি কি কাগজপত্র লাগে এবং কত টাকা লাগবে?" (documents + fee questions). Third, scalability anxiety about expanding from 14 tags to 200-300+ tags (evidenced by the EC dataset's 221 tags), where current O(n) FAISS complexity wou
| examples/basic/hello_world.py | |
| examples/basic/hello_world_gpt_5.py | |
| examples/basic/hello_world_gpt_oss.py | |
| examples/basic/stream_text.py | |
| examples/basic/stream_items.py | |
| examples/basic/stream_function_call_args.py | |
| examples/basic/dynamic_system_prompt.py | |
| examples/basic/prompt_template.py | |
| examples/basic/agent_lifecycle_example.py | |
| examples/basic/lifecycle_example.py |
| # PyTorch 2.x; Apple Silicon; runs on MPS if available, else CPU. | |
| import math | |
| import torch | |
| if torch.backends.mps.is_available(): # official API to detect MPS | |
| device = torch.device("mps") | |
| else: | |
| device = torch.device("cpu") | |
| # --- Quantization helpers (per-tensor, symmetric) --- |
| import React, { useMemo, useRef, useState, useEffect } from "react"; | |
| // Simple FAISS-like toy: IndexFlatL2 | |
| class IndexFlatL2 { | |
| constructor(d) { | |
| this.d = d; // dimensionality | |
| this.vectors = []; // each item: {id, x: number[], label?: string} | |
| this._nextId = 1; | |
| } | |
| add(vecs) { |
Looking at your GeForce RTX 2050 laptop GPU, here's what I recommend:
| #!/usr/bin/env python3 | |
| """ | |
| Run GPT-OSS using the Metal implementation for Apple Silicon | |
| This requires downloading and converting the model first. | |
| """ | |
| import os | |
| import subprocess | |
| import sys |
| // file: vecadd_test.cu | |
| #include <cstdio> | |
| #include <cstdlib> | |
| #define CUDA_CHECK(call) do { \ | |
| cudaError_t err = call; \ | |
| if (err != cudaSuccess) { \ | |
| fprintf(stderr, "CUDA error %s:%d: %s\n", \ | |
| __FILE__, __LINE__, cudaGetErrorString(err)); \ | |
| exit(EXIT_FAILURE); \ |
