Nimma-Guru
Community Mentorship Directory Powered by Google Gemini 2.0 Flash
1. THE PROBLEM
Students in non-metropolitan towns struggle to find verified local mentors for career guidance, technical skills, and academic prep.
2. TECHNICAL APPROACH & DECISIONS
1Google Gemini 2.0 Flash Integration
Integrated Gemini 2.0 Flash API to power natural language mentor matching and voice query intent extraction.
Static SQL tag filters
Conversational query extraction allowed students to search naturally ('someone who can teach me coding in Kannada') rather than selecting rigid UI dropdowns.
CORE ARCHITECTURAL LOGIC
Gemini 2.0 Flash natural language query extraction for Android mentor search.
| 1 | suspend fun findMentorMatches(studentQuery: String): List<MentorProfile> { |
| 2 | val generativeModel = Firebase.ai.generativeModel("gemini-2.0-flash") |
| 3 | val prompt = """ |
| 4 | Extract domain skills, language preference, and intent from query: "$studentQuery". |
| 5 | Respond in JSON schema: {"skills": [], "language": "", "urgency": ""} |
| 6 | """.trimIndent() |
| 7 | val response = generativeModel.generateContent(prompt) |
| 8 | val parsedIntent = jsonDecoder.decodeFromString<SearchIntent>(response.text!!) |
| 9 | return mentorRepository.queryMentorsByIntent(parsedIntent) |
| 10 | } |
3. TRADE-OFFS & HONEST REFLECTION
Cloud AI inference requires active network access, but unlocked rich conversational multi-dialect search capability.
4. CONCRETE OUTCOME & METRICS
Shipped 10+ responsive Material 3 Compose screens connecting students with verified mentors during 3-month MindMatrix internship.