How to integrate Ollama with LangChain for a private RAG pipeline?

All answers to this question.

This is the most common enterprise use case for Ollama right now. You can use the langchain-ollama partner package, which is very stable. For embeddings, you can simply pull nomic-embed-text via Ollama. In your code, you just set the base_url to your server’s IP on port 11434. The beauty of this is that your data never leaves your network. I’ve implemented this for a healthcare client using Ollama running on a Mac Studio, and the retrieval accuracy was indistinguishable from OpenAI’s text-embedding-3-small, but with zero per-token costs.

   Answered 2025-08-20 by Melissa Foster

Does Ollama support concurrent requests if multiple employees are using the RAG bot at once?

   Answered 2025-09-15 by Gregory Scott

• By default, Ollama processes requests sequentially, but you can change this. You need to set the environment variable OLLAMA_NUM_PARALLEL to something like 4 or 8. Just be aware that each parallel request will eat up more VRAM. If you have a beefy GPU, Ollama can serve multiple users quite comfortably. It’s a huge improvement over the earlier versions where a single long generation would block the entire API for everyone else.

     Commented 2025-09-22 by Eric Bennett

I use Ollama with the FAISS vector store. The setup takes less than 10 lines of Python code.

   Answered 2025-10-01 by Laura Bennett

• That simplicity is why Ollama is winning. You spend less time on "plumbing" and more time on the actual retrieval logic and prompt engineering.

     Commented 2025-10-05 by Austin Reynolds