How to Build a RAG Pipeline

Key Takeaways

• Collecting, cleaning, and organizing your data sets the foundation for a successful RAG pipeline.
• Storing and indexing embeddings in vector databases enables fast similarity searches, enhancing retrieval accuracy.
• Large language models are deployed to produce coherent responses based on retrieved document chunks, ensuring relevance to user queries.
• Regular assessment of response quality through metrics like relevance and clarity, alongside user feedback, is vital for optimizing the RAG pipeline’s performance.

How to Build a RAG Pipeline

There are ten important steps to building your RAG pipeline:

1. Data preparation
2. Embedded generation
3. Vector database setup
4. Retrieval language implementation
5. Language model generation
6. Prompt engineering
7. Response generation
8. Fine-tuning and optimization
9. Evaluation and iteration
10. Deployment and scaling

Each step ensures your system delivers timely, reliable insights to boost efficiency and accuracy.

Step 1: Data Preparation

Collect all relevant documents (like PDFs, Word files, and web pages) to establish a foundation for your RAG system.

Organize them into a single folder or database to ensure easy access and prevent redundancy. We also recommend performing deduplication and prioritizing the most relevant documents.

Siloed data weakens the effectiveness of the RAG pipeline, so the focus is on bringing all your data together.

Next, extract text from those documents using tools like PyPDF2 for PDFs or BeautifulSoup for web pages. Remove irrelevant elements like headers and footers, but retain important contextual elements, like images or tables.

Once extracted, clean the text by removing unnecessary whitespace, special characters, and irrelevant sections. Standardize the formatting by converting text to lowercase whenever appropriate, correcting errors, and removing boilerplate text.

We also recommend normalizing dates and measurements for consistency and anonymizing sensitive information.

After cleaning, chunk the text into manageable sections, whether by token count, sentence, or paragraph.

Tip: Maintain coherence and context using overlapping chunks when necessary to improve retrieval.

We recommend using Unstructured AI to process complex, unstructured document formats for RAG. It supports a wide range of formats, and it can handle tasks like:

• Converting tables into CSV/Excel formats
• Extracting charts as PNGs with semantic descriptions
• Organizing text semantically while retaining a hierarchical structure
• Generating numerical embeddings and storing them in a vector database

Finally, attach metadata, such as document source, chunk position, or topic, to each chunk. This metadata will aid in tracking and debugging, improving overall system performance.

Data preparation helps ensure the accuracy and efficiency of data retrieval. By following these guidelines, you’ll make your data ready for efficient processing in your RAG pipeline.

Step 2: Embedded Generation

The second step in building your RAG pipeline is implementing embedding models, which transform your cleaned and chunked text into vector representations (embeddings).

Embeddings capture the text’s underlying meaning, which allows retrieving relevant document sections and information based on the semantic content rather than exact keyword matches.

1) Choosing an embedding model

To begin, choose an appropriate embedding model that fits your needs. Some of the most popular embedding models include BERT or OpenAI’s text-embedding-ada-002.

These models are highly effective in capturing the text’s context and meaning, which enables more accurate information retrieval.

2) Creating embeddings

When you select a model, the next task is to generate embeddings for each text chunk in your database. Feed each chunk into the embedding model, which will output dense vector representations.

These vectors represent the semantic relationship within the text and can be compared mathematically to find similarities.

Example

Instead of searching for exact matches to a keyword, the embedding model can understand the broader context.

If a user searches for “mortgage underwriting guidelines for self-employed individuals”, the system can retrieve relevant documents like “home loan policies for freelancers” based on conceptual similarity.

By generating and storing embeddings, your system will be ready to perform fast and accurate similarity searches.

This automatically improves the efficiency of information retrieval upon user query and decision-making.

Step 3: Vector Database Setup

Setting up a vector database is the next crucial step after generating embeddings for your text chunks.

1) Choosing a vector database

A vector database stores and indexes embeddings for fast and efficient similarity searches. The system compares embeddings to retrieve the most relevant information based on semantic content.

To get started, select a vector database that fits your requirements.

We recommend popular options like Pinecone, Faiss, or Weaviate. These databases are optimized for handling large-scale embeddings and performing quick similarity searches.

2) Indexing embeddings

When you choose a database, the next step is to index your generated embeddings.

Load the embeddings into the vector database, ensuring each embedding is linked to its corresponding document chunk. The database will use this index to perform efficient comparisons during search queries.

For example, when a user submits a query, it’s converted into an embedding and compared to the stored embeddings in the vector database.

The system retrieves the chunks with the closest matches. This always provides the user with highly relevant results based on the semantic meaning of the query, not just keywords.

Indexing embeddings in a vector database significantly improves the speed and accuracy of information retrieval.

Step 4: Retrieval System Implementation

With your embeddings indexed in a vector database, the next step is implementing a retrieval system.

The system converts user queries into embeddings and then performs a similarity search to find the most relevant chunks based on semantic content.

1) Implementing a function

Start by implementing a function that converts user queries into embeddings. Use the same embedding model you applied for your document chunks.

When a user submits a query, this function transforms it into a vector representation that captures its meaning, making it ready for comparison with your stored embeddings.

2) Developing a similarity search mechanism

Next, develop a similarity search mechanism to match the query embedding against the embeddings in your vector database.

The goal is to identify the document chunks with the closest semantic relationship to the user’s query. Use the vector database’s built-in similarity search functions to compare the query embedding with indexed document embeddings.

Example

If a user searches for the query “mortgage approval for contractors,” the system will retrieve related chunks like “lending policies for independent workers,” even though these terms aren’t exact matches.

This allows the users to retrieve highly relevant information even when the exact words in the query don’t match those in the documents.

By implementing this retrieval system, your RAG pipeline can deliver accurate and contextually relevant information to improve search efficiency and user satisfaction.

Step 5: Language Model Generation

The next crucial step in your RAG pipeline is generating responses using a large language model (LLM).

This model will leverage the retrieved documents by your retrieval system to generate contextually appropriate responses to user queries.

1) Selecting a language model

Start by choosing a language model that suits your needs.

Some of the most common options include GPT-3, BART, and T5 due to their powerful natural language processing (NLP) capabilities. However, each model has its strengths and weaknesses, so consider your requirements, such as task complexity and query volume.

2) Setting up the language model

Next, set up API access or deploy your LLM locally. If you choose an API, follow the provider’s guidelines to integrate it into your system.

Keep in mind that local deployment requires infrastructure and resources to run the model effectively.

Step 6: Prompt Engineering

‍Prompt engineering involves crafting effective prompts that combine user queries with the relevant information from your vector database to generate the best possible responses.

Your goal is to design effective prompts that seamlessly integrate the user query with the retrieved information.

The prompts should communicate the relevant context and intent behind the query.

Example

User query: “What are the requirements for obtaining a home loan?”

You can design a prompt in the following way: “Based on the following information about mortgage requirements, summarize what a user needs to obtain a home loan: (insert retrieved chunks).

Experiment With Different Prompt Structures

We recommend experimenting with different prompt structures until you determine which ones yield the most accurate and helpful results.

Consider adjusting the prompts' wording, length, and format to see how the language model responds. Also, use variations that emphasize different aspects of the information or change how your questions are phrased.

Example:

For a user query like “What documents do I need for mortgage approval?”, you could test prompts such as:

"List the necessary documents for mortgage approval based on the following guidelines: (insert retrieved chunks)."

Another example would be for a user query like “What are the steps to apply for a business loan?”, you could use test prompts such as:

"Outline the steps involved in applying for a business loan, considering the following financial requirements: (insert retrieved chunks)."

Investing time in prompt engineering enhances the quality and relevance of the responses generated by a RAG pipeline.

Step 7: Response Generation

Response generation refers to using the language model to generate clear, coherent, and contextually relevant responses to user queries.

This step is crucial for delivering a seamless user experience.

• First, send the engineered prompt to your chosen language model.

This prompt should combine the user’s query with the relevant information retrieved and formatted appropriately. Ensure you monitor the API call (if using a cloud model) for response time and errors.

• Next, process and refine the generated responses.

Review the output to ensure clarity and coherence. You may need to edit for grammar, remove redundant information, or restructure sentences to enhance readability.

Additionally, we recommend incorporating contextual information from the retrieved chunks that may not have been fully utilized in the initial response.

Example

If the model generates a response to “What are the requirements for obtaining a home loan?” that states, “You need only a few documents,” refine it to be more informative.

A refined response could look like:

To obtain a home loan, you must provide documentation including your credit score, proof of income, and a list of debts.

Step 8: Fine-Tuning and Optimization

At this step, your RAG pipeline is operational, but not yet completed. The next step is optimizing and fine-tuning your large language model to improve efficiency, accuracy, and relevance.

1) Adjusting retrieval parameters

Adjusting retrieval parameters will help you optimize the document retrieval process.

We recommend tweaking the number of document chunks retrieved for each query or adjusting the similarity threshold to ensure only highly relevant chunks are returned.

Tip: Test various configurations to find the balance between precision and coverage.

2) Fine-tuning the language model

If your LLM isn’t performing as expected or your industry requires specialized knowledge, consider fine-tuning the language model.

This involves training the model on domain-specific data, such as banking regulations or insurance policies, to make it more adept at handling specialized queries.

We also recommend implementing caching mechanisms for frequently asked questions. This step can speed up responses for common queries, as the system can skip retrieval and language model interference for previously answered questions.

Storing responses to FAQs allows you to instantly serve them for similar queries.

Example

If users often ask about “mortgage application steps”, cache the generated response so that similar queries, like “how do I apply for a home loan,” trigger the cached response rather than querying the entire pipeline.

Step 9: Evaluation and Iteration

Continuous evaluation and interaction ensure that the RAG pipeline consistently delivers high-quality responses and adapts to evolving user needs.

Start by developing metrics to assess the quality of the generated responses.

Our recommended metrics include:

• Relevance - Does the response accurately answer the user’s query?
• Clarity - Is the response easy to understand and free of ambiguity?
• Accuracy - Does the information provided reflect the correct details?
• Response time - How quickly does the system generate and return responses?

These are only a few metrics you can use to systematically evaluate the effectiveness of your RAG pipeline.

Additionally, a feedback loop can help you continuously gather user feedback that will help you further improve your RAG pipeline.

Encourage users to rate the helpfulness of responses or flag inaccurate ones.

For example, if users frequently report irrelevant or confusing responses, you might need to adjust the retrieval parameters or refine your prompt engineering approach.

Step 10: Deployment and Scaling

The final step is deploying and scaling your RAG pipeline in a way that helps it run smoothly in production and efficiently handle growth.

1) Setting up infrastructure

Set up the necessary infrastructure for deployment. Depending on your use case, this might involve deploying the pipeline on cloud platforms like AWS, Google Cloud, or Azure or using on-premises infrastructure.

Ensure the infrastructure supports the computational requirements of your RAG pipeline, especially if you’re using LLMs and vector databases.

2) Monitoring and logging

Monitoring and logging help track the pipeline’s performance in real-time.

This includes monitoring response time, query loads, and error rates.

Logging key activities helps identify issues quickly and allows you to track system health, ensuring that your pipeline remains reliable and scalable.

3) Scaling

As a part of scaling, optimize the system for performance and cost-efficiency.

This might involve autoscaling your cloud infrastructure based on demand, caching frequently retrieved results, and adjusting retrieval parameters to reduce unnecessary computations.

Additionally, consider using optimized hardware such as GPUs for inference or leveraging serverless functions to manage costs.

Focusing on the right infrastructure, monitoring tools, and cost-effective scaling strategies ensures that your RAG pipeline can meet user demands while maintaining high performance and staying within budget.

Need Help Building Your RAG Pipeline?

Need help with preparing and cleaning your data or more? Please schedule a 30-minute call with our experts. We can show you how Unstructured AI works live and how it can help you get the most out of your data.