The conversation around AI in software development has matured beyond the “AI as a chatbot” and into sophisticated AI agents. We’re moving toward building a living blueprint that can reason about your code in its entirety and evolve with it over time.
For developers who want a powerful, all-in-one AI experience, Google’s Gemini Code Assist is a fantastic solution that provides a seamless, out-of-the-box experience, bringing the power of Gemini directly into your workflow.
For those who love to assemble best-in-class technologies from the open ecosystem, this article is for you. We will explore a production-ready stack for those who want a customized and self-hosted solution. This stack combines the Roo Code VS Code extension, powered by Google’s underlying Gemini models, and takes it to the next level with a self-hosted Qdrant vector database on Google Kubernetes Engine.
Solution Components
Roo Code is a VS Code extension that can be thought of as an “AI Dev Team” with modes ranging from Architect to Debug. You can give it a high-level task, like “refactor this module to use the new logging service,” and it will create a plan, identify the necessary code changes, and execute them across multiple files. For a deeper dive, check out the Roo Code documentation.
You can take full advantage of Roo Code’s capabilities with the massive context window available in Gemini models. This allows Roo Code to hold a vast amount of code in its “short-term memory,” enabling it to understand the intricate relationships between files and modules and to generate code that is consistent with the entire project. You can learn more about the Gemini API in the official documentation.
To make the use of this large context window efficient, Roo Code leverages prompt caching, a feature now available in Gemini models. When Roo Code sends the initial instructions and context to the model, Gemini generates an internal representation and returns a cache reference. On subsequent requests, Roo Code can send this cache reference instead of the full prompt, dramatically reducing token usage and improving latency, which is a key feature for making the system both cost-effective and performant.
For codebase indexing, Roo Code supports Gemini’s gemini-embedding-001 state-of-the-art embedding model. This is crucial for the accuracy of the semantic search, and you can find more information on Gemini’s embedding models here.
Using Gemini Models in Roo Code
The connection between Roo Code and a model is what enables its agentic capabilities: planning, executing commands, and writing code across your entire project. You can connect to Gemini’s models through the Gemini API or through Google Cloud’s Vertex AI.
To use the Gemini API, you simply create an API key in Google AI Studio, then in Roo Code’s settings, select the Google Gemini provider, paste your key, and choose a model. For detailed, step-by-step instructions on this process, refer to the Roo Code documentation for the Gemini provider.
For teams and enterprises using Google Cloud, connecting via Vertex AI provides unified billing, IAM permissions, and more. You will create a service account with the “Vertex AI User” role in the Google Cloud Console and download its JSON key file. Within Roo Code’s settings, select the GCP Vertex AI provider, provide the credentials from your JSON key, and enter your Project ID and Region. The Roo Code documentation for Vertex AI provides a complete walkthrough of this setup.
For both connection methods, we recommend starting with gemini-2.5-pro for the best experience. Its powerful reasoning capabilities and large context window are ideal for complex, multi-step tasks. For faster, more cost-effective use, gemini-2.5-flash is an excellent alternative.
With Roo Code’s reasoning engine now powered by Gemini, the next step is to give it a persistent, long-term memory of your code.
Codebase Indexing
Codebase indexing creates a semantic “long-term memory” of your code that the agent can access at any time. This is a multi-stage process that transforms your source code into a searchable knowledge base.
Intelligent Chunking
First, Roo Code uses Tree-sitter to parse your code into an Abstract Syntax Tree (AST). This gives it a deep, structural understanding of your code, just like a compiler does. Instead of arbitrarily splitting a file every few hundred lines, the AST is used to intelligently chunk the code into complete, semantic blocks.
This “semantic chunking” means the pieces of code being indexed are meaningful and self-contained units, such as:
- A complete function or method.
- An entire class or struct definition.
- A specific configuration block.
This ensures that the context isn’t lost by splitting a function in half. For unsupported languages, Roo Code falls back to line-based chunking.
Generating Embeddings
Once the code is broken down into these intelligent chunks, the next step is to capture their semantic meaning in a way a machine can understand. This is where Gemini’s gemini-embedding-001 model comes in.
Each semantic chunk produced by Tree-sitter is fed into the embedding model, which outputs a high-dimensional numerical vector. This vector is the embedding — a mathematical representation of the code’s meaning. The Gemini embedding model captures fine details with 3072 dimensions in every embedding. For a deeper dive into Matryoshka Representation Learning, a technique used to train the model, see this video:
Storing and Searching Embeddings
With the codebase converted into a collection of semantically-rich embeddings, they need a place to be stored and searched efficiently. Roo Code uses Qdrant, a high-performance vector database, for this purpose.
When you ask a question, Roo Code’s search tool follows this process:
- Query: Your natural language query (e.g., “where is our user authentication logic?”) is sent to the Gemini embedding model.
- Vectorize: The model converts your query into an embedding vector.
- Search: Roo Code performs a vector search in the Qdrant database, looking for the code chunk embeddings that are most similar (i.e., closest in vector space) to your query’s embedding.
- Retrieve: The tool then returns the most relevant code snippets, along with their file paths and similarity scores.
Roo Code also provides a user-friendly interface for configuring the codebase indexer. You can easily select your embedding provider, enter your API keys, and specify the Qdrant URL. The advanced configuration options allow you to fine-tune the search behavior by adjusting the Search Score Threshold and Maximum Search Results. You can also specify which files to ignore by adding patterns to a .rooignore file.
From Local to Centralized Indexing
The easiest way to get started is with a local Qdrant instance. As the official Qdrant Quickstart shows, you can be up and running in minutes with a single Docker command:
docker run -p 6333:6333 -v "$(pwd)/qdrant_storage:/qdrant/storage:z" qdrant/qdrant
For an individual developer, this is a fantastic way to get all the benefits of codebase indexing without any external dependencies.
As your team grows, managing dozens of individual Docker instances can become cumbersome. This is where a centralized Qdrant instance provides value — not as a single, conflict-prone shared index, but as a managed, cost-effective platform to host a fleet of personal indexes.
Google Kubernetes Engine, or GKE, is an excellent choice for this, offering high availability and enterprise-grade security. The principle is the same regardless of the platform: provide a robust, central service to host many isolated environments. You can deploy the infrastructure within minutes using the GKE tutorial for deploying Qdrant.
Using the instructions in the tutorial, you can easily access it from your local system using port forwarding:
PROJECT_ID="your-project-id"
REGION="us-central1"
gcloud container clusters get-credentials qdrant-cluster --region "$REGION" --project "$PROJECT_ID"
kubectl port-forward service/qdrant 6333:6333
Roo Code generates a unique Qdrant collection name by hashing the absolute local workspace path. This means that even when using a central Qdrant instance, each developer’s index is completely isolated. To avoid conflicts, each developer needs to ensure they are using a different path:
- Developer A: /Users/alice/projects/my-app
- Developer B: /Users/bob/projects/my-app
Conclusion
The future of AI-assisted development is about choice. Whether you prefer a powerful, all-in-one solution like Google’s Gemini Code Assist for a seamless, integrated experience, or the composable stack detailed in this article, the goal is the same: to create a truly intelligent development environment.
What will you build with Gemini and Roo Code? Feel free to continue the discussion on LinkedIn, X, and Bluesky.
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