Project: AI-based Chatbot in Hindi/English

Step-by-step project guide to build a production-ready bilingual AI chatbot in Hindi and English: design, data, RAG, models, evaluation, deployment, monitoring and safety.

🗣️ Project: AI-based Chatbot in Hindi and English — End to End Guide

यह प्रोजेक्ट-आधारित गाइड आपको एक production-grade bilingual chatbot बनाने का पूरा रास्ता बताएगा — शुरुआत से requirements और architecture तक, data pipelines, retrieval augmentation, model selection, prompt engineering, evaluation, deployment और production monitoring तक। उद्देश्य यह है कि आप एक ऐसा चैटबोट बना सकें जो हिन्दी और English दोनों में reliable और safe उत्तर दे सके।

1. प्रोजेक्ट का उद्देश्य और स्कोप

पहले प्रोजेक्ट के स्कोप को साफ करें। कुछ सामान्य use-cases:

  • Customer support assistant जो product FAQ और tickets समझे
  • Knowledge assistant जो company documents पर आधारित जवाब दे
  • Education tutor जो हिन्दी और English दोनों में समझा सके

जरूरी design decisions:

  • बॉट bilingual होगा या multilingual
  • कौन से data sources ground करने हैं (docs, FAQs, knowledge base)
  • क्या responses conversational रखेंगे या structured JSON?
  • स्रोतों का authorization और privacy constraints

2. Architecture Overview

एक सामान्य production architecture में ये components होते हैं:

  1. Frontend: chat UI, message composer, language selector
  2. API Layer: authentication, rate limits, routing to services
  3. Retriever: embeddings index and nearest neighbor search
  4. Generator: LLM or instruction-tuned model for response generation
  5. Tooling: calculators, external APIs, knowledge updaters
  6. Safety Filters: toxicity, PII redaction, policy checks
  7. Monitoring: logs, metrics, human review queues

3. Data Strategy and Ingestion

Bilingual chatbot विकसित करते समय data तैयार करना सबसे महत्वपूर्ण चरण है। जरूरी कदम:

  • Source inventory: manuals, product docs, FAQs, transcripts, knowledge graphs
  • Data cleaning: HTML stripping, deduplication, normalize Unicode
  • Chunking: long docs को overlapping chunks में बांटना (500-1000 tokens overlap) ताकि retrieval बेहतर हो
  • Language tagging: हर chunk के metadata में primary language रखें (en / hi / bilingual)
  • Metadata enrichment: title, section id, source url, last updated timestamp
  • PII and privacy: sensitive fields redact या mask करें और ingestion के logs secure रखें

3.1 Example ingestion pseudo-code

# Pseudocode - document chunking and embedding
for doc in documents:
    text = extract_text(doc)
    text = normalize_unicode(text)
    chunks = sliding_window_chunks(text, chunk_size=800, stride=400)
    for i, chunk in enumerate(chunks):
        meta = {"doc_id": doc.id, "chunk_index": i, "lang": detect_language(chunk)}
        emb = embed(chunk)   # use sentence encoder
        index.add_vector(emb, metadata=meta)

4. Embeddings and Vector Store

Retrieval-Augmented Generation के लिए तेज और सटीक embeddings आवश्यक हैं। ध्यान दें:

  • Embeddings model चुनना: multilingual या language-specific, जैसे multilingual sentence transformers
  • Vector index: HNSW, FAISS, or commercial vector DBs; choose by scale and latency needs
  • ANN tuning: efConstruction, M, index memory footprint और query ef parameters
  • Metadata store: retrieved chunks के साथ source attribution और timestamp भी लौटाएँ

5. Retriever design

Retriever responsibilities:

  • Given user query, return top-k candidate chunks with scores
  • Perform language-sensitive retrieval: if user Hindi, prefer hi chunks or bilingual candidates
  • Apply reranking: lightweight cross-encoder or lexical features for final ranking

5.1 Retriever pseudo-code

# Pseudocode: retrieve and rerank
query_emb = embed(query)
candidates = index.search(query_emb, top_k=50)
# optional rerank with cross-encoder
reranked = cross_encoder.rerank(query, [c.text for c in candidates])[:5]
return reranked

6. Generator model choices

Generator के लिए विकल्प:

  • Hosted LLM APIs (OpenAI, Anthropic, Cohere) — तेज rollout पर उपयुक्त
  • Open-source causal LMs (GPT-NeoX, LLaMA variants) — greater control और on-prem possibilities
  • Instruction-tuned models (e.g., models fine-tuned on instruction datasets) for conversational quality
  • Parameter-efficient fine-tunes (LoRA) to adapt base models to domain and bilingual style

Decision factors: latency, cost per token, data residency, support for bilingual generation, and safety controls.

7. Prompt composition and context assembly

Generator को prompt बनाते समय retrieved passages, system instructions, user message और conversation history को संरचित करना जरूरी है. एक robust context composer करता है:

  1. Start with system prompt that defines behavior and refusal policy
  2. Append top retrieved passages with clear separators and attribution
  3. Include short conversation history (prune to keep under token limit)
  4. Finally append user query

7.1 Example prompt template

System: You are a bilingual assistant fluent in Hindi and English. Answer in the language of the user. If the answer is grounded in the documents, cite the source id. If unsure, say "I am not sure" and offer to search the knowledge base.

Retrieved Documents:
[1] Doc A: {doc_text_1}
[2] Doc B: {doc_text_2}

Conversation History:
User: {short_history}

User Query:
{user_query}

Assistant:

8. Handling bilingual queries and language detection

Chatbot को language detect करने के लिए fast langdetect or lightweight classifier use करें. Rules:

  • If user writes primarily in Hindi, respond in Hindi; if English, respond in English
  • For mixed-language messages, prefer code-switching style that user used
  • Keep fallback option: ask user for preferred language if detection confidence low

9. Tools integration and execution

Useful tools to integrate:

  • Search API for web or internal docs
  • Database queries for user-specific data (orders, tickets) via secure connectors
  • Action execution endpoints (create ticket, update record) with authorization
  • Utility tools: calculator, date parser, unit converter

Tools must be called through secure, audited interfaces and never expose raw credentials to generator.

10. Safety layers and PII handling

Safety is critical. Implement layers:

  • Pre-generation filters: detect malicious or disallowed queries and refuse early
  • PII redactors: remove or mask sensitive fields from context and logs
  • Post-generation classifiers: toxicity, hate, legal risk checks before returning
  • Human escalation: queue responses for human review if flagged

11. Response formatting and citations

Good practice: responses must include short answer, followed by evidence citations when grounded. Example structure: 

Answer: {concise_answer}

Evidence:
1) Source: Doc A, paragraph 2
2) Source: Doc B, paragraph 5

If you want more details, say "Expand".

12. Evaluation and metrics

Measurement plan:

  • Accuracy of factual QnA measured via human-verified testset
  • Helpfulness, clarity and fluency rated by humans
  • Grounding rate: fraction of answers with valid citations
  • Hallucination rate: fraction of verified false statements
  • Latency and cost per request
  • User satisfaction and escalation rate

12.1 Automated checks

Automated unit tests can validate response format and presence of citations for certain queries. Use synthetic and adversarial test suites.

13. Deployment blueprint

Deployment considerations:

  • Containerize API services and use autoscaling groups
  • Place vector DB and model servers close to minimize network latency
  • Use GPU-backed inference nodes or managed API for generator
  • Set SLOs for latency and throughput; implement circuit breakers

13.1 Example FastAPI generation endpoint

from fastapi import FastAPI, Request
app = FastAPI()

@app.post("/chat")
async def chat(req: Request):
    data = await req.json()
    user = data.get("user")
    query = data.get("query")
    # 1) detect language
    # 2) retrieve docs
    # 3) compose prompt
    # 4) call generator
    # 5) post filter
    return {"answer": "This is a placeholder response"}

14. Monitoring, logging and feedback loops

Observability is essential:

  • Log inputs, retrieved docs, prompts, model responses and safety flags
  • Store hashed or masked tokens for privacy compliant debugging
  • Track metrics: response time, grounding rate, flag rate
  • Human feedback loop: collect user ratings and corrections to improve models

15. Cost optimization strategies

Ways to reduce cost:

  • Use smaller model for candidate responses and confirm with larger model only for final answer
  • Cache common QnA pairs and template-based responses
  • Compress embeddings and tune index parameters for memory vs accuracy tradeoff
  • Batch inference where possible for throughput

16. User experience and UX patterns

UX serves trust and clarity:

  • Show source citations and time of last update
  • Provide quick follow-ups: "Did that answer your question?"
  • Allow users to switch language explicitly
  • Show when the answer is AI-generated and offer human escalation

17. Example end-to-end flow

1) User asks in Hindi about warranty. 2) Langdetect finds Hindi. 3) Retriever returns warranty policy chunk. 4) Prompt composer builds bilingual prompt with retrieved text. 5) Generator outputs answer in Hindi with citation to policy. 6) Post-filter checks for PII. 7) Answer delivered with option to escalate.

18. Testing and validation plan

Maintain separate test suites:

  • Functional tests for common queries
  • Adversarial tests for prompt injection attempts
  • Language quality tests for both Hindi and English
  • Regression tests to ensure prompt or model changes do not reduce grounding

19. Launch checklist

  1. Define launch scope and rollout plan
  2. Set guardrails and human review for first N days
  3. Monitor metrics and user feedback closely
  4. Plan rollback and hotfix procedures

20. Conclusion and next steps

एक bilingual AI chatbot बनाना सिर्फ मॉडल चुनना नहीं है, यह data engineering, retrieval systems, prompt engineering, safety और production software engineering का संगम है। इस गाइड के साथ आप एक पहला MVP बना सकते हैं और धीरे-धीरे quality, grounding और safety में निवेश करके full production रोलआउट कर सकते हैं।

© Course Content — Keep prompt templates, model versions and dataset provenance under version control for reproducibility and auditability.