VelesDB now has a Haystack connector - build a RAG pipeline with zero infrastructure
VelesDB ships a first-party Haystack 2.x DocumentStore. Build a full RAG pipeline with pip install, no Docker, no API keys.
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VelesDB ships a first-party Haystack 2.x DocumentStore. Build a full RAG pipeline with pip install, no Docker, no API keys.
You have 10,000 photos on your laptop. Some are duplicates. Some are resized. Some are cropped versions of the same scene. Your brain instantly knows they're related. Your computer? It thinks they're
What if your AI assistant worked on an airplane? In a hospital? On a classified network? Most AI stacks fall apart without internet. They depend on OpenAI for inference, Pinecone for vectors, and half a dozen cloud APIs for everything in between. Kil...
Your LLM forgets everything after each session. MCP lets you fix that with 3 tools and zero infrastructure. No database cluster. No Redis. No "memory service" running in the background. Just a Python script and a 3MB embedded engine that persists to ...
LangChain has MultiQueryRetriever. LlamaIndex has SubQuestionQueryEngine. Every serious RAG framework decomposes user questions into multiple search queries before hitting the vector database. Why? Because a single embedding compresses your entire qu...
You know SQL. You have been writing SELECT, WHERE, and JOIN for years. But the moment you need to search by meaning, traverse a knowledge graph, or rank results by relevance, SQL cannot help you. You reach for a proprietary API, a different client, a...
Everyone uses cosine similarity. Tutorials use it. Frameworks default to it. If you ask "which distance metric should I use?", the answer is always "cosine, probably." But here is the thing: your vector database supports other metrics. And those metr...
Most RAG tutorials start with "spin up Docker" and "get your API key." This one starts with pip install. The problem Retrieval-Augmented Generation (RAG) is the standard way to ground LLM answers in your own data. But the typical setup looks like thi...
Most vector database tutorials start the same way: docker pull qdrant/qdrant docker run -p 6333:6333 qdrant/qdrant That's 500MB+ of Docker image, a running server process, a REST API to talk to, and a container to babysit in production. For what? St...