#vectordatabase Development Tutorials & Tips | Roast Dev

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Growing the Tree: Multi-Agent LLMs Meet RAG, Vector Search, and Goal-Oriented Thinking - Part 2

Simulating Better Decision-Making in Insurance and Care Management Through RAGIn the Part 1 post, I walked through how I built a CLI that runs multi-agent conversations in a social media (Reddit-inspi...

04.05.2025 0 Read More

Ingest (almost) any non-PDF document in a vector database, effortlessly

One of my areas of focus, recently, has been the development of a universal and zero-effort way of converting text-based documents (and even images) into PDF files, so that they could fit into my RAG ...

25.04.2025 0 Read More

Setting up the Pinecone MCP server in your IDE

Want to prototype and test your Pinecone indexes, right from your IDE?In this blog post, I'll show you how to avoid the IDE-docs-console-app shuffle by adding the Pinecone Developer MCP server to your...

28.04.2025 0 Read More

Semantic Search with Spring Boot & Redis

TL;DR: You’re building a semantic search app using Spring Boot and Redis. Instead of matching exact words, semantic search finds meaning using Vector Similarity Search (VSS). It works by turning ...

29.04.2025 0 Read More

VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval

If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic?Trick question, b/c most can’t. They treat retrieval as nearest-neighbor searc...

29.04.2025 0 Read More

Build Code-RAGent, an agent for your codebase

Introduction Recently, I've been hooked up with automating data ingestion into vector databases, and I came up with ingest-anything, which I talked about in my last post. After Chonkie releas...

29.04.2025 0 Read More

Improve Your Python Search Relevancy with Astra DB Hybrid Search

Astra DB now supports hybrid search, which can increase the accuracy of your search by up to 45%. It does this by performing both vector search and BM25 keyword search and then reranking the results f...

29.04.2025 0 Read More

Vector Databases: their utility and functioning (RAG usage)

A reflection on vector databases and their usage by LLMs, by implementing a rudimentary one! Introduction TLDR;Vector databases have emerged as a critical infrastructure component in the era o...

30.04.2025 0 Read More

Postgres vs. Qdrant: Why Postgres Wins for AI and Vector Workloads

It's Timescale Launch Week and we’re bringing benchmarks: Postgres vs. Qdrant on 50M Embeddings.There’s a belief in the AI infrastructure world that you need to abandon general-purpose databases t...

30.04.2025 0 Read More

Growing the Tree: Multi-Agent LLMs Meet RAG, Vector Search, and Goal-Oriented Thinking - Part 2

Ingest (almost) any non-PDF document in a vector database, effortlessly

Setting up the Pinecone MCP server in your IDE

Semantic Search with Spring Boot & Redis

VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval

Build Code-RAGent, an agent for your codebase

Improve Your Python Search Relevancy with Astra DB Hybrid Search

Vector Databases: their utility and functioning (RAG usage)

Postgres vs. Qdrant: Why Postgres Wins for AI and Vector Workloads

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