Comparing LanceDB and Elasticsearch for full-text search and vector search performance
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Updated
Feb 8, 2026 - Python
Comparing LanceDB and Elasticsearch for full-text search and vector search performance
Unstract's interface to LLMs, Embeddings and VectorDBs.
The missing developer tool for working with vector databases. A comprehensive desktop app for visualizing, querying, and managing vector data.
A RAG assistant using Ollama (Mistral), Qdrant vector DB, and Streamlit UI. Upload documents, scrape web pages, and interact with your data using real-time, session-isolated chat.
Identify mountain peaks in your photos using AI—zero-shot retrieval, landmark re-ranking, and geospatial priors.
AI- & vector database-powered Quora question search
🚀 An intelligent, LLM-enhanced log parser pipeline that converts multi-format raw logs into structured JSON, learns from missed patterns, and evolves using Drain3 & open-source LLMs.
This project aims to create a comprehensive, searchable knowledge base from the online articles and forum posts of Dr. Ulrich Strunz. The final application will be a Dockerized service that uses a Large Language Model (LLM) accessed via the Meta-Cognitive Prompting (MCP) protocol to provide users with intelligent access to this knowledge.
In this project, I made a resume scoring system. And it not only scores candidates based on Job Description & Resume but it can also have user profiles and their personal hiring preferences. So a particular user can filter Resume in bulk according to their personal preference. It also learns user preferences with time and evolves for each user.
Automation of Prioritization and Categorization of Support Tickets Using LLMs and Vector DBs
A proof-of-concept of retrieval-augmented generation, using Google's PaLM API.
Comparative study evaluating performances of Milvus Vector-based RAG vs Neo4j Graph-based RAG systems for Enterprise Knowledge Retrieval
A personalized AI product search engine using free-tier OpenRouter LLMs, MiniLM semantic search & Weaviate DB. https://medium.com/@rajesh1804/grocerygpt-how-i-built-a-personalized-grocery-search-engine-with-llms-vector-dbs-zero-cloud-fbacddf0feef
A local semantic emoji search engine that uses transformer embeddings and sqlite vector search to find the perfect emoji from natural language queries. Fast, offline, and powered by Streamlit.
Local Retrieval-Augmented Generation (RAG) pipeline using LangChain and ChromaDB to query PDF files with LLMs.
AI-powered medical assistant using Retrieval-Augmented Generation (RAG) with Gemini, Pinecone, LangChain, and Streamlit. Provides grounded answers from a medical knowledge base.
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