Building a Scalable RAG Pipeline on Google Cloud Using Vertex AI and Cloud Run

Organizations generate large volumes of unstructured information, including technical documentation, knowledge-base articles, manuals, policies, and internal procedures. Although cloud storage makes these documents easy to store, finding relevant information across large document collections can be difficult.

Traditional keyword-based search works best when users know the exact terms a document uses. It becomes less effective when a user's query uses different wording than the source material.

Retrieval-Augmented Generation (RAG) addresses this limitation by combining semantic document retrieval with generative AI. Instead of relying solely on a language model's training data, a RAG system retrieves relevant documents and provides them as context for response generation.

Google Cloud offers services such as Cloud Storage, Vertex AI, Cloud Run, and vector-search infrastructure that developers can combine to build scalable RAG applications.

This article provides a high-level overview of how to design a RAG architecture on Google Cloud, from document ingestion and embedding generation to retrieval, response generation, security, and monitoring.

1. Understanding Retrieval-Augmented Generation

1.1 Limitations of Traditional Language Models

Developers train large language models (LLMs) on large datasets, which lets the models generate natural-language responses. However, these models do not automatically have access to an organization's private documents or newly created information.

When users ask questions about internal company information, an LLM may provide incomplete or inaccurate answers if its training data did not include the required information.

RAG addresses this limitation: the system retrieves relevant information from an external knowledge source before generating a response.

1.2 How RAG Works

A typical RAG workflow follows these steps:

1. The system converts the user query into a vector embedding.
2. It searches a vector index for semantically similar document segments.
3. It retrieves the most relevant segments.
4. It provides the retrieved content as context to the language model.
5. The language model generates a response using the retrieved context.

This approach grounds the model's response in organizational documents rather than relying solely on information the model learned during training.

1.3 Typical RAG Workflow

The overall workflow looks like this:

User query → Embedding generation → Vector similarity search → Relevant document retrieval → Context construction → LLM response generation

The quality of the final response depends on factors such as document quality, chunking strategy, retrieval accuracy, and prompt design.

2. High-Level System Architecture

A scalable RAG architecture on Google Cloud typically consists of several components, each handling a specific stage of the workflow.

2.1 Core Components

• Cloud Storage — Stores enterprise documents such as PDFs, Word documents, technical manuals, policies, and training materials.
• Document processing pipeline — Extracts text from documents, divides the content into smaller segments, and prepares it for indexing.
• Vertex AI — Provides embedding models and generative AI capabilities.
• Vector search infrastructure — Stores document embeddings and performs semantic similarity searches.
• Cloud Run — Hosts the backend API that orchestrates the RAG workflow.
• Language model — Generates responses using the retrieved document context.

2.2 Query Processing Flow

When a user submits a question, the system processes it through the following sequence:

1. The user submits a question through a web interface or chatbot.
2. The application sends the query to an API deployed on Cloud Run.
3. The API generates an embedding for the query using Vertex AI.
4. The system searches the vector index for similar document embeddings.
5. It retrieves the most relevant document segments.
6. It adds the retrieved content to the prompt as context.
7. The language model generates a response.
8. The API returns the response to the user.

This separation of ingestion, retrieval, and generation into distinct components lets teams scale and manage each one independently.

3. Preparing Enterprise Documents for Retrieval

Before a RAG system can retrieve information, a pipeline must process and index the enterprise documents.

3.1 Document Ingestion

Organizations can store enterprise documents in a centralized Cloud Storage bucket. These documents may include:

• PDF files
• Word documents
• Technical manuals
• Policy documents
• Training materials

An ingestion pipeline can monitor the storage location and process newly uploaded documents.

3.2 Text Extraction

After the pipeline ingests a document, it extracts the document's textual content.

The extraction method depends on the document type. PDF parsing tools can extract text from structured documents, while Optical Character Recognition (OCR) can recover text stored as scanned images.

3.3 Document Chunking

The pipeline divides large documents into smaller sections, commonly called chunks, before indexing them.

Chunking helps the retrieval system identify the specific portions of a document that are relevant to a user's query.

A typical strategy divides documents into segments of approximately 300–800 tokens. Teams can also use overlapping chunks to preserve context between adjacent segments.

The optimal chunk size depends on the document structure, retrieval requirements, and embedding model.

3.4 Metadata Enrichment

Developers can associate each chunk with metadata such as:

• Document title
• Author
• Department
• Creation date

Metadata lets the system apply additional filters during retrieval and makes the document collection easier to manage.

4. Generating Semantic Embeddings with Vertex AI

4.1 Embedding Models

Embedding models convert text into numerical vectors that represent semantic relationships between pieces of content.

Unlike simple keyword matching, embeddings let the system identify content that is conceptually similar even when it uses different words.

4.2 Document Embedding Generation

During indexing, the pipeline passes each document chunk through an embedding model to generate its vector representation.

The system stores the resulting vector in the vector-search system together with the corresponding text and metadata.

4.3 Query Embedding Generation

When a user submits a query, the application converts the query into a vector using a compatible embedding model.

The system compares the resulting query vector with stored document vectors to identify semantically relevant content.

5. Implementing Vector Search

5.1 Vector Search Options

Vector-search systems efficiently find similar vectors within large collections of embeddings.

Depending on the architecture, developers can use Vertex AI Vector Search or integrate third-party technologies such as Pinecone, Weaviate, or Elasticsearch with vector-search capabilities.

5.2 Similarity Search

Similarity search measures how closely two vectors relate to each other within a high-dimensional space.

Common similarity measures include:

• Cosine similarity
• Euclidean distance

The system uses these measures to identify and rank the document segments that are most similar to the user's query.

5.3 Retrieval Optimization

Teams can improve retrieval quality through several techniques:

• Adjusting chunk size
• Combining keyword and semantic search
• Applying metadata filters
• Ranking results by relevance
• Selecting an appropriate number of retrieved results

Improving retrieval matters because the language model can only use the information the retrieval stage provides.

6. Deploying the Query Service with Cloud Run

6.1 API Service Responsibilities

The backend API orchestrates the RAG workflow. Its responsibilities typically include:

• Receiving user queries
• Generating query embeddings
• Performing vector searches
• Constructing prompts
• Calling the language model
• Returning responses to the user

6.2 Containerized Deployment

Cloud Run can host the containerized API service and automatically scale it according to application traffic.

A container might include:

• A Python or Node.js application
• Vector-search client libraries
• Vertex AI SDK integration
• Application configuration and dependencies

6.3 Automatic Scaling

Cloud Run can provision additional service instances as request volume increases, so the application handles changing traffic without requiring developers to manage individual servers manually.

7. Prompt Engineering for Contextual Responses

Retrieval alone does not guarantee a useful response. The prompt must present the retrieved information to the language model in an appropriate structure.

7.1 Context Construction

A RAG prompt typically contains three main elements:

• Retrieved context
• User question
• Instructions for the model

For example:

Retrieved context:
[Relevant document segments]

User question:
[User's question]

Instructions:
Answer the question using only the provided context.

7.2 Grounded Response Generation

An instruction such as the following can encourage the model to rely on the retrieved information:

Answer the question using only the provided context.

Clear instructions can reduce unsupported claims, although prompt design alone cannot guarantee that every response will be accurate.

8. Security and Governance

Enterprise RAG systems may process confidential or sensitive information, so teams should consider security and access controls throughout the architecture.

8.1 Identity and Access Management

Teams can use Google Cloud Identity and Access Management (IAM) to control access to resources such as storage, AI services, and application infrastructure.

Only authorized users and services should access protected enterprise documents.

8.2 Data Protection

Security measures can include:

• Encrypting stored data
• Securing API communication
• Controlling service-to-service access
• Applying appropriate identity and permission policies

The specific controls required depend on the organization's security and compliance requirements.

8.3 Audit Logging

Teams can use Cloud Logging to record system activity such as application requests, retrieval operations, and other relevant events.

These logs support:

• Debugging
• Compliance monitoring
• Performance analysis
• Incident investigation

9. Monitoring and Observability

Production RAG systems require continuous monitoring to maintain reliability and identify performance issues.

Cloud Monitoring provides visibility into metrics such as:

• Request latency
• Error rates
• Request volume
• Resource utilization

Teams can configure alerts for when selected metrics exceed expected thresholds.

Monitoring also helps teams identify opportunities to improve retrieval quality, system performance, and response behavior.

10. Example Enterprise Use Case: AI Knowledge Assistant

Consider an organization with thousands of internal documents covering policies, procedures, and engineering guidelines.

Employees may spend significant time searching for specific information, which can increase support requests and reduce productivity.

A RAG-based knowledge assistant offers an alternative workflow:

1. An employee submits a question through a chatbot integrated with the company intranet.
2. The system converts the question into an embedding.
3. The retrieval system identifies relevant internal documents.
4. The system provides the retrieved content to the language model.
5. The assistant generates a response based on the retrieved information.
6. The application can provide references or links to the original documents.

This architecture makes organizational knowledge easier to discover while maintaining a connection to the underlying source documents.

11. Conclusion

Retrieval-Augmented Generation combines semantic retrieval with generative AI to build applications that can answer questions using information from external knowledge sources.

A Google Cloud-based RAG architecture can combine Cloud Storage for document storage, Vertex AI for embeddings and generative AI, vector-search infrastructure for retrieval, and Cloud Run for the application layer.

A reliable implementation requires more than connecting a language model to a vector database. Document preparation, chunking, retrieval quality, prompt design, security, and monitoring all contribute to the quality and reliability of the resulting system.

As organizations continue to accumulate large collections of internal information, RAG offers a practical architecture for making that information more accessible through AI-powered knowledge applications.