Generative AI has changed the way businesses build applications, search systems, and knowledge platforms. However, large language models (LLMs) can struggle when they need to answer questions using private, internal, or frequently changing information.

This is where Retrieval Augmented Generation (RAG) becomes useful. A RAG application retrieves relevant information from an external knowledge base and provides that information to an LLM before generating an answer.

In this guide, we will explain how to build a RAG pipeline using Databricks and Vector Search, including document processing, embeddings, semantic search, retrieval, and LLM integration.

What Is Retrieval Augmented Generation (RAG)?

Retrieval Augmented Generation is an AI architecture that combines information retrieval with a large language model. Instead of relying only on the information stored in an LLM’s training data, a RAG system retrieves relevant information from an external data source.

The retrieved information is then added to the prompt sent to the language model. The model uses this context to generate a more relevant and grounded response.

Why Use Databricks for a RAG Pipeline?

Databricks provides a unified platform for data engineering, analytics, machine learning, and generative AI. This makes it a strong choice for organizations that already manage their enterprise data in Databricks.

With Databricks Vector Search, organizations can create searchable vector indexes that allow applications to find information based on semantic similarity.

Some important advantages include:

• Integration with enterprise data pipelines
• Scalable data processing
• Vector Search for semantic retrieval
• Integration with machine learning workflows
• Enterprise data governance
• Support for generative AI applications

RAG Pipeline Architecture

A typical RAG architecture consists of two major workflows: document indexing and user query processing.

Documents
    |
    v
Document Processing
    |
    v
Text Chunking
    |
    v
Embedding Model
    |
    v
Databricks Vector Search
    |
    |
    +--------------------+
                         |
User Question            |
    |                    |
    v                    |
Query Embedding          |
    |                    |
    v                    |
Vector Search -----------+
    |
    v
Relevant Documents
    |
    v
Prompt + Context
    |
    v
Large Language Model
    |
    v
Final Answer
    

The vector search layer is responsible for finding the document chunks that are most relevant to a user’s question.

How to Build a RAG Pipeline Using Databricks

Now let’s look at the major steps involved in building a Databricks RAG pipeline.

Step 1: Prepare Your Documents

The first step is to collect the data that your AI application needs to access. Depending on your use case, this could include PDFs, technical documentation, websites, product information, support articles, database records, or internal company documents.

Before creating embeddings, clean the documents and remove unnecessary content such as duplicate text, navigation elements, and irrelevant formatting.

document_id
title
content
source
category
updated_at
metadata
    

Step 2: Split Documents Into Chunks

Large documents should generally be divided into smaller sections called chunks. Chunking helps the retrieval system return the most relevant portion of a document instead of an entire document.

The ideal chunk size depends on your content, embedding model, and application. You can also use overlapping chunks when preserving context between sections is important.

A basic document processing workflow looks like this:

Document
   ↓
Clean Text
   ↓
Split Into Sections
   ↓
Create Chunks
   ↓
Add Metadata
    

Step 3: Generate Embeddings

Once the documents have been divided into chunks, the next step is to convert those chunks into vector embeddings.

An embedding model converts text into a numerical representation. Texts with similar meanings are represented by vectors that are relatively close to each other in the embedding space.

chunk = "How does the refund policy work?"

embedding = embedding_model.embed(chunk)

print(embedding)
    

Choosing the right embedding model is important for retrieval quality. Consider language support, accuracy, latency, cost, and the type of information contained in your knowledge base.

Step 4: Create a Databricks Vector Search Index

After generating embeddings, store them in a Databricks Vector Search index. The index allows your application to perform semantic similarity searches over the document embeddings.

Each record can contain the original text, its embedding, and useful metadata.

{
  "id": "document_001_chunk_05",
  "text": "Refunds are processed...",
  "embedding": [0.023, -0.115, 0.087],
  "source": "refund-policy.pdf",
  "category": "customer-support"
}
    

Metadata becomes particularly useful when you need filtered retrieval. For example, you might want to search only documents belonging to a specific product, department, region, or document type.

Step 5: Retrieve Relevant Information

When a user asks a question, the application converts the question into an embedding and searches the vector index for similar content.

User Question
      ↓
Generate Query Embedding
      ↓
Search Vector Index
      ↓
Find Similar Chunks
      ↓
Rank Results
      ↓
Return Top Results
    

For example, suppose a customer asks:

The vector search system finds the document chunks that are semantically related to refunds and enterprise subscriptions.

Step 6: Send the Retrieved Context to an LLM

The retrieved information is then added to a prompt and sent to a large language model.

System:
Answer the question using the provided context.

Context:

[Retrieved document 1]

[Retrieved document 2]

[Retrieved document 3]

Question:
What is the refund period?

Answer:
    

The LLM can now use the retrieved information to generate an answer based on your organization’s knowledge base.

Step 7: Generate the Final Answer

After processing the retrieved context, the LLM generates the final response for the user.

For enterprise applications, it is also useful to include source citations with the response. This allows users to verify the information and increases trust in the AI application.

How to Improve RAG Retrieval Quality

Building the basic RAG pipeline is only the beginning. Retrieval quality has a major impact on the final answer generated by the LLM.

Optimize Chunk Size

Very small chunks may lose important context, while very large chunks can contain irrelevant information. Test different chunk sizes using real queries from your application.

Use Metadata Filtering

Metadata filters can improve retrieval accuracy by limiting searches to relevant documents.

Tune Top-K Results

Top-K determines how many results are returned from the vector search. Too few results may miss important information, while too many results can add unnecessary context.

Consider Hybrid Search

Semantic search is useful for understanding meaning, while keyword search can be better for exact terms such as product IDs, error codes, or technical names. Combining both approaches can improve retrieval for certain applications.

Add Reranking

A reranking step can evaluate the initially retrieved documents and reorder them according to their relevance to the user’s query.

RAG Pipeline Best Practices

When moving a RAG application into production, consider the following best practices:

• Keep your source documents clean and structured.
• Store useful metadata with every document chunk.
• Choose an embedding model based on your use case.
• Evaluate retrieval quality separately from LLM performance.
• Monitor unanswered and poorly answered queries.
• Provide source citations whenever possible.
• Implement appropriate data access controls.
• Monitor latency and token consumption.
• Keep the vector index synchronized with source data.
• Continuously test the system using real-world questions.

RAG vs Traditional LLM Applications

Common Challenges When Building a RAG Pipeline

Hallucinations

RAG can reduce hallucinations, but it does not completely eliminate them. Poor retrieval, incomplete data, or weak prompts can still produce inaccurate answers.

Poor Retrieval

If the correct document is not retrieved, the LLM may not have enough information to answer the question correctly.

Stale Knowledge

A RAG application is only as current as its knowledge base. Your ingestion pipeline should have a reliable process for detecting and indexing updated information.

Too Much Context

Sending too many retrieved documents to an LLM can increase cost, latency, and irrelevant information in the prompt.

Conclusion

Building a RAG pipeline using Databricks and Vector Search provides a practical way to create AI applications that can work with private and continuously changing business information.

The basic workflow is straightforward: ingest your documents, clean and chunk the content, generate embeddings, store them in a vector index, retrieve relevant information, and provide that context to an LLM.

However, production-quality RAG requires more than simply connecting a vector database to an LLM. Retrieval quality, chunking strategy, metadata, evaluation, security, monitoring, and prompt design all have an important impact on the final application.

By combining Databricks’ data and AI capabilities with Vector Search, organizations can build scalable and maintainable enterprise RAG applications.

Frequently Asked Questions