Build a Multilingual Public Service Policy Navigator with Bhashini
Translate regional citizen service inquiries using simulated Bhashini translation pipelines to assess mock eligibility rules. Generate localized qualification explanations and step-by-step documentation checklists for community field facilitators.
What you are building
The core problem, expected build, and operating context for this challenge.
Translate regional citizen service inquiries using simulated Bhashini translation pipelines to assess mock eligibility rules. Generate localized qualification explanations and step-by-step documentation checklists for community field facilitators.
Gram panchayat service facilitator
Ingest citizen inquiry text in Indian languages and map ISO-639 language codes via simulated Bhashini pipeline configs.
Normalize inquiry criteria such as landholding size, annual household income, and resident state.
Evaluate parsed criteria against mock state public benefit policy rules without external government calls.
Generate a translated eligibility determination report with specific evidentiary document requirements.
Multi-language web form showing original input, Bhashini translation trace, policy rule outcomes, and checklist.
Create synthetic eligibility policies and synthetic citizen profiles in Hindi, Marathi, and Tamil with English reference translations. Starter examples below are synthetic. They are not downloaded records.
Bhashini documentation Utilizes Bhashini pipeline concepts (Pipeline Config, NMT tasks, ISO-639 language codes) to structure multilingual query flows.
dspy, pydantic, fastapi. DSPy systematically optimizes prompt pipelines to reliably extract structured policy parameters from informal vernacular text. Instruction-tuned multilingual model capable of processing Indic text and generating strict JSON outputs.
Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.
Correctly identifies ISO-639 source language and maps to mock Bhashini translation pipeline.
Accurately extracts numeric income and landholding variables into structured Pydantic models.
Emits clear eligibility outcome and missing prerequisite documents in both English and target language.
```json [ { "input": { "source_lang": "hi", "query_text": "मेरे पास 1.5 एकड़ ज़मीन है और वार्षिक आय 120000 रुपये है, क्या मैं सोलर पंप योजना के पात्र हूँ?", "policy_id": "solar_irrigation_2025" }, "expected_output": { "target_lang": "hi", "normalized_criteria": { "landholding_acres": 1.5, "annual_income_inr": 120000 }, "eligibility_status": "ELIGIBLE", "reasoning": "Landholding is under the 5-acre limit and annual income is below 200,000 INR limit.", "required_documents": [ "Land Ownership Record (Khasra/Khatauni)", "Income Certificate", "Electricity Connection NOC" ] } }, { "input": { "source_lang": "mr", "query_text": "माझे वार्षिक उत्पन्न 450000 रुपये आहे आणि 6 एकर शेती आहे.", "policy_id": "solar_irrigation_2025" }, "expected_output": { "target_lang": "mr", "normalized_criteria": { "landholding_acres": 6, "annual_income_inr": 450000 }, "eligibility_status": "INELIGIBLE", "reasoning": "Landholding exceeds maximum limit of 5 acres and income exceeds 200,000 INR limit.", "required_documents": [] } } ] ```
Mock benefit policies only; no official Aadhaar verification or real administrative determinations are performed. Use mock external writes. Require human review before any real deployment.
Participation guide
Mock benefit policies only; no official Aadhaar verification or real administrative determinations are performed. Use mock external writes and human review.
How work is evaluated
Correctly identifies ISO-639 source language and maps to mock Bhashini translation pipeline. Accurately extracts numeric income and landholding variables into structured Pydantic models. Emits clear eligibility outcome and missing prerequisite documents in both English and target language.
Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Complete workflow
Ingest citizen inquiry text in Indian languages and map ISO-639 language codes via simulated Bhashini pipeline configs. Normalize inquiry criteria such as landholding size, annual household income, and resident state. Evaluate parsed criteria against mock state public benefit policy rules without external government calls. Generate a translated eligibility determination report with specific evidentiary document requirements.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
Correctly identifies ISO-639 source language and maps to mock Bhashini translation pipeline. Accurately extracts numeric income and landholding variables into structured Pydantic models. Emits clear eligibility outcome and missing prerequisite documents in both English and target language.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
Multi-language web form showing original input, Bhashini translation trace, policy rule outcomes, and checklist.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
Mock benefit policies only; no official Aadhaar verification or real administrative determinations are performed.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Ingest citizen inquiry text in Indian languages and map ISO-639 language codes via simulated Bhashini pipeline configs.
Normalize inquiry criteria such as landholding size, annual household income, and resident state.
Evaluate parsed criteria against mock state public benefit policy rules without external government calls.
Generate a translated eligibility determination report with specific evidentiary document requirements.
Reference links and supporting material
Create synthetic eligibility policies and synthetic citizen profiles in Hindi, Marathi, and Tamil with English reference translations. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
DSPy
Prompt and LM program optimization framework.
Evaluated output
No output checks are configured.
- Ingest citizen inquiry text in Indian languages and map ISO-639 language codes via simulated Bhashini pipel...
- Correctly identifies ISO-639 source language and maps to mock Bhashini translation pipeline. Accurately ext...
- Multi-language web form showing original input, Bhashini translation trace, policy rule outcomes, and check...
- 2 public reference cases
- 3 configured tools
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- DSPy · Optional
- DSPy · Optional
- DSPy · Optional
Evaluation contract
- Complete workflow · Weight 40
- Correctness and evidence · Weight 30
- Review interface · Weight 20
- Safe execution · Weight 10
Reference cases
- 2 public reference cases
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
No recipe freeze time is recorded.