I'm an undergrad at BIT Mesra studying Mathematics and Computing, currently building hybrid search and retrieval systems and contributing to open-source LLM infrastructure. My work sits at the overlap of classical IR (BM25, ranking theory), dense retrieval, and applied ML systems.
- 🌱 Deep in Information Retrieval, RAG systems
- 🛠️ Active contributor to Haystack - deepset's production LLM orchestration framework
- 📐 Math background (Linear Algebra, Graph Theory) directly informs how I approach retrieval and ranking problems.
Contributing to a production Python framework for LLM orchestration and RAG.
| PR | Description |
|---|---|
#11581 |
Added run_async to LLMEvaluator, FaithfulnessEvaluator, and ContextRelevanceEvaluator - enables concurrent evaluation in async apps, with runtime detection of async-capable chat generators and automatic thread-pool fallback for sync-only ones |
#11552 |
Fixed a silent serialization bug in LLMEvaluator.to_dict that caused pipeline reloads to silently reset user-configured parameters |
#3412 |
Extended GoogleGenAITextEmbedder, GoogleGenAIDocumentEmbedder, and GoogleGenAIMultimodalDocumentEmbedder with timeout/retry support, full serialization round-trips, and test coverage |
Python rank-bm25 ChromaDB sentence-transformers NetworkX
A three-signal hybrid retrieval CLI fusing BM25 lexical search, dense vector search, and AST-derived call-graph structural retrieval via Reciprocal Rank Fusion.
- Evaluated on a hand-labeled 30-query benchmark over ~1,000+ indexed functions
- Recall@10: 0.67 (lexical + semantic fusion) vs. 0.57 semantic-alone - a +17% relative gain
- MRR@5: 0.43
- Implemented multi-source BFS call-graph retrieval with hop-decay scoring and a tunable structural-weight flag
- Diagnosed and documented a real node-collision bug (bare-function-name keying causing cross-class collisions, e.g. sklearn's repeated
fit/transform) that degraded structural retrieval - root-caused it down to the graph construction layer - Shipped as a production CLI: OS-native keyring for API keys, SHA-256 multi-codebase index management, mtime-based staleness detection, lazy imports cutting startup latency from ~20s to ~6s
Python FastAPI ChromaDB Gemini API Streamlit
A modular AI backend for embedding-based recommendation over scraped product data.
- Automated scraping pipeline (BeautifulSoup) collecting 370+ product profiles
- Two-stage pipeline: candidate generation → LLM-based reranking, with fault-tolerant fallback across rate limits and provider outages
- Streamlit dashboard for real-time inspection of retrieved recommendations
Languages: 
ML / IR: 
Backend / Infra: 
Tools: 
Birla Institute of Technology, Mesra - B.Sc. (Hons.) Mathematics and Computing
Relevant coursework: Object-Oriented Programming (Java), Data Structures & Algorithms, Discrete Mathematics & Graph Theory, Information Retrieval