The Ultimate Guide to Local RAG with Claude Code for Semantic Search

So, Why Go Local with RAG?

• Data Privacy is a BIG Deal: This is probably the number one reason for a lot of people. When you're working with proprietary code or sensitive company data, sending it off to a third-party service can be a non-starter. With a local RAG setup, everything—your code, your documents, your queries—stays on your machine. Nothing ever leaves your local network. For businesses concerned about security & compliance, this is HUGE.
• Cost-Effectiveness in the Long Run: While cloud services offer convenience, they come with a price tag. API calls, data storage, & per-user fees can add up quickly, especially for a team of developers. A local setup has an upfront investment in terms of time & maybe some hardware, but after that, it's essentially free to run. No more worrying about token counts or unexpected bills.
• Customization & Control: When you run your own RAG pipeline, you have complete control over every component. You can choose the LLM that works best for your needs, fine-tune the embedding models on your specific data, & configure the vector database to your exact specifications. This level of control just isn't possible with a one-size-fits-all cloud solution.
• Offline Functionality: Ever been stuck on a plane or in a coffee shop with spotty Wi-Fi, trying to get some work done? With a local RAG system, you're not dependent on an internet connection. Your super-powered search works wherever you are, which is a lifesaver for developers on the go.
• Deeper Understanding & Learning: Let's be honest, setting up your own tools is a fantastic way to learn. By building your own local RAG system, you'll gain a much deeper understanding of how these technologies work, which can be incredibly valuable in its own right.

The Building Blocks of a Local RAG System

1. A Local Large Language Model (LLM): This is the "brain" of the operation. The LLM is what will ultimately generate the answers to your queries, based on the information retrieved from your documents. The good news is, the world of open-source LLMs has exploded recently. Tools like Ollama have made it incredibly easy to download & run powerful models like Llama 3, Mistral, & DeepSeek right on your laptop or a local server.
2. An Embedding Model: This is where the "semantic" part of semantic search comes from. An embedding model is a special type of model that takes a piece of text—a line of code, a paragraph from a document, whatever—and converts it into a numerical representation, or "embedding." These embeddings capture the meaning of the text, so that "user authentication" & "verify login credentials" will have similar numerical representations, even though they use different words. There are great open-source options for this too, like Nomic Embed Text or models from the mxbai family.
3. A Vector Database: Once you've created embeddings for all your code & documents, you need a place to store them. That's where a vector database comes in. A vector database is a specialized type of database that's designed to store & search through these numerical embeddings incredibly efficiently. When you make a query, the vector database can quickly find the embeddings that are most similar to your query's embedding, and those are the documents that get passed to the LLM. For local setups, popular choices include Milvus, which is open-source & very powerful, or even simpler options like ChromaDB.

Putting It All Together: A General-Purpose Local RAG Setup

1. Install Ollama & Your LLM: First things first, you'll need to get Ollama up and running. You can download it from their website, and once it's installed, you can pull down an LLM with a simple command like

   1 ollama pull llama3

   .
2. Set Up Your Project & Dependencies: You'll want to create a new Python project for this. You'll need to install a few libraries, most importantly

   1 langchain

   & any libraries for the specific document types you want to work with (like

   1 pypdf

   for PDFs).
3. Load & Chunk Your Documents: The next step is to load your documents into your application. LangChain has a bunch of built-in document loaders that make this easy. Once you've loaded your documents, you'll need to split them into smaller "chunks." This is important because you'll be creating an embedding for each chunk, & you want the chunks to be small enough to be semantically coherent. A good rule of thumb is to split them into chunks of a few hundred to a thousand characters.
4. Create Your Embeddings & Vector Store: Now, you'll use an embedding model to create embeddings for each of your document chunks. LangChain integrates with a lot of embedding models, including those you can run locally via Ollama. As you create the embeddings, you'll store them in your vector database.
5. Build Your RAG Chain: This is where the magic happens. In LangChain, you can create a "chain" that defines the entire RAG process. It will look something like this:
   • The user's query comes in.
   • The query is sent to the retriever, which uses your vector store to find the most relevant document chunks.
   • The query & the retrieved chunks are formatted into a prompt.
   • The prompt is sent to your local LLM (running on Ollama).
   • The LLM generates a response, which is then sent back to you.

Supercharging Claude Code with Local Semantic Search

1. The Architecture: The setup is a bit different from our general-purpose RAG system. The key players are:
   • Claude Code: Our agentic coding assistant.
   • claude-context: A special MCP server that acts as a bridge between Claude Code & our search tool.
   • Milvus: Our open-source vector database, which we'll run locally.
   • Ollama: To run our embedding model locally.
2. Setting Up the Infrastructure: You'll start by getting Milvus & Ollama up and running on your machine. Docker is a great way to manage this. Once they're running, you'll need to index your codebase. This involves a script that goes through all your code files, splits them into manageable chunks, generates embeddings for each chunk using your local embedding model, & then stores those embeddings in Milvus.
3. Configuring Claude Code: The final step is to tell Claude Code about your new semantic search tool. You'll do this by creating a

   1 .mcp.json

   file in your project's root directory. This file tells Claude Code to connect to the

   1 claude-context

   server. You'll also set up an

   1 .envrc

   file to tell

   1 claude-context

   to use your local Ollama instance for embeddings.
4. The Result: Once this is all set up, you can ask Claude Code questions like, "Where in the code do we handle user authentication?" Instead of just looking for the exact words "user authentication," Claude Code will now use your local semantic search engine to find code that is conceptually related to user authentication, even if it uses different terminology. It's a MUCH more powerful & intuitive way to navigate a large & complex codebase.

Local RAG in a Business Context: More Than Just Code Search

Local vs. Cloud-Based RAG: Which One is Right for You?

• Data privacy is your top priority. If you're working with sensitive or proprietary data, local is the way to go.
• You want maximum control & customizability.
• You're on a budget & want to avoid ongoing subscription costs.
• You need to be able to work offline.
• You're willing to put in the time to set it up & maintain it.

• You need to scale to a large number of users.
• You want the convenience of a managed service & don't want to deal with infrastructure.
• You always want to have access to the latest & greatest models without having to manage them yourself.
• Your data isn't particularly sensitive.
• You're building a proof of concept & want to get up and running as quickly as possible.