AI/ML
LLM-Powered Research Assistant
Created a vector-search-based academic assistant using LangChain, Ollama, Qdrant, and Elasticsearch, allowing users to query papers intelligently.
Senior Software Engineer
2025
completed
Project Overview
Created a vector-search-based academic assistant using LangChain, Ollama, Qdrant, and Elasticsearch, allowing users to query papers intelligently.
Used Python for ingestion pipeline and API development, orchestrated AI tasks using LangChain.
Challenges & Solutions
Challenges
- Handling large-scale academic paper datasets efficiently
- Balancing semantic and keyword-based search precision
- Running local LLMs with low-latency responses
Solutions
- Used Qdrant for high-performance vector storage and similarity search
- Integrated Elasticsearch for full-text indexing and hybrid retrieval
- Built Python ingestion pipelines and LangChain-orchestrated AI tasks
Results & Impact
Intelligent Search
Enabled natural language querying of academic paper collections
Hybrid Retrieval
Combined vector and keyword search for improved result relevance
Modular Architecture
Deployed scalable RAG system supporting dataset ingestion and API access
Technologies Used
PythonLangChainQdrantOllamaElasticsearch
Key Metrics
performanceCombined vector and keyword search for improved retrieval relevance
impactEnabled intelligent querying of academic papers
Project Details
CategoryAI/ML
Year2025
Statuscompleted
RoleSenior Software Engineer