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Fully Local RAG with Qdrant, Ollama, LangChain and LangServe
xAI has just unveiled Grok, LLM inspired by Hitchhiker’s Guide to the Galaxy!
Grok’s unique feature is its ability to access real-time knowledge, a capability powered by Qdrant — open-source vector similarity search engine and vector database written in Rust.
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You can think of Qdrant as a specialized tool for storing, searching, and managing vectors, designed to be both fast and reliable, even under substantial workloads.
You can couple Qdrant with LangChain and Ollama to build applications that can leverage LLMs and semantic search, which you can deploy locally or in the cloud — pretty powerful stuff!
In this article, I’ll guide you through several key processes:
- Handling PDF documents
- Creating and storing embeddings with GPT4AllEmbeddings and storing them with Qdrant
- Serving LLMs such as Llama2 7b Chat locally with Ollama
- Setting up a local Retrieval Augmented Generation (RAG) application using LangChain and LangServe.
Ready to dive? Let’s begin!
