In-Depth Step-By-Step Guide for Building a RAG Pipeline

Building a RAG pipeline doesn't have to be complicated. Let this guide simplify the process and help you achieve your pipeline goals.

May 6, 2024

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In-Depth Step-By-Step Guide for Building a RAG Pipeline

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What Is a RAG Pipeline?

RAG, otherwise known as retrieval augmented generation is an architectural approach that improves the performance of large language models (LLMs) by providing them with relevant external data as context. LLMs are the most efficient and powerful NLP models to this date. We have seen the potential of LLMs in translation, essay writing, and general question-answering. But when it comes to domain-specific question-answering, they suffer from hallucinations. Besides, in a domain-specific QA app, only a few documents contain relevant context per query. So, we need a unified system that streamlines document extraction to answer generation and all the processes between them. This process is called Retrieval Augmented Generation.

How does RAG Pipeline Combine Retrieval and Generation Models?

Prompting for answers from text documents is effective, but these documents are often much larger than the context windows of Large Language Models (LLMs), posing a challenge. Retrieval Augmented Generation (RAG) pipelines address this by processing, storing, and retrieving relevant document sections, allowing LLMs to answer queries efficiently.

What are the Common Applications of RAG Pipelines?

A RAG-based application can be helpful in many real-life use cases. For instance, in Academic Research, researchers often deal with numerous research papers and articles in PDF format. A RAG pipeline could help them extract relevant information, create bibliographies, and organize their references efficiently. In Law Firms, a RAG-enabled Q&A chatbot can streamline the document retrieval process, saving a lot of time. Additionally, Educational Institutions can use RAG pipelines to extract content from educational resources to create customized learning materials or to prepare course content. RAG-enabled Q&A chatbots can also be employed in Administration to streamline document retrieval processes for government and private administrative departments. In Customer Care, a RAG-enabled Q&A chatbot with an existing knowledge base can be utilized to answer customer queries.

How Does Rag Work

Rag Llm

Rag Rating

Rag Workflow

7 Benefits of RAG Pipeline

1. Easy Understanding with RAG Pipelines

RAG pipelines and RAG with LlamaIndex simplify complex information by using colors like red, amber, and green to represent status updates. Red denotes a problem, amber indicates a moderate risk, and green signifies a favorable status. This color-coding system makes it easy to understand the current state of affairs at a glance

2. Spotting Problems Early with RAG Pipelines

RAG pipelines and RAG with LlamaIndex enable early detection of issues. When a task or project is labeled red or amber, it alerts us to address the problem promptly before it escalates.

3. Managing Risks with RAG Pipelines

RAG pipelines and RAG with LlamaIndex categorize risks based on severity: red for high risks and amber or green for lesser risks. By prioritizing and addressing high-risk items first, teams can effectively manage risks.

4. Keeping Everyone on the Same Page with RAG Pipelines

RAG pipelines and RAG with LlamaIndex facilitate clear communication by providing a common language to discuss performance and challenges. This ensures that all team members are well-informed and aligned on the progress of tasks and projects.

5. Encouraging Responsibility with RAG Pipelines

RAG pipelines and RAG with LlamaIndex assign clear responsibilities to individuals or teams. This fosters accountability and empowers team members to take ownership of their tasks and projects.

6. Enhancing Reports with RAG Pipelines

RAG pipelines and RAG with LlamaIndex can be integrated into reports to visually represent progress and risks. This visual approach enhances the readability of reports, enabling stakeholders to quickly grasp the key information.

7. Assisting Decision-Making with RAG Pipelines

In situations with multiple tasks or projects, RAG pipelines and RAG with LlamaIndex help prioritize by highlighting the importance of items. Tasks marked in red or amber may need immediate attention, while green items are progressing well, aiding in decision-making processes.

Optimizing Internal Operations with ChatBees

ChatBees optimizes RAG for internal operations like customer support, employee support, etc., with the most accurate response and easily integrating into their workflows in a low-code, no-code manner. ChatBees' agentic framework automatically chooses the best strategy to improve the quality of responses for these use cases. This improves predictability/accuracy enabling these operations teams to handle higher volume of queries.

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Onboarding: Quickly access onboarding materials and resources be it for customers or internal employees like support, sales, and research team.

Sales enablement: Easily find product information and customer data, Customer support: Respond to customer inquiries promptly and accurately

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5 Crucial Components of a RAG Pipeline

1. Text Splitter

The Text Splitter plays a critical role in the RAG pipeline, as it is responsible for dividing documents into sections to match the context windows of Large Language Models (LLMs). By splitting the documents effectively, the Text Splitter ensures that the LLMs can process the text in a manner that optimizes the accuracy of the generated answers.

2. Embedding Model

The Embedding Model is a deep learning model that is employed to generate embeddings of the documents. These embeddings are essential for the processing and retrieval of information from the stored documents. By using advanced deep learning techniques, the Embedding Model can accurately represent the content of the documents in a format that is easily interpretable by other components of the RAG pipeline.

3. Vector Stores

Vector Stores serve as the databases where document embeddings and their associated metadata are stored. This component is crucial for the efficient querying of the document database. By storing the embeddings in vector stores, the RAG pipeline can quickly access and retrieve the necessary information to generate responses to user queries. Vector stores are also essential for maintaining the integrity and speed of the querying process within the RAG pipeline.

4. LLM

The Large Language Model (LLM) is the core component responsible for generating accurate responses to user queries. By leveraging state-of-the-art language processing techniques, the LLM can analyze the content of the documents and find the most suitable answers to user questions. Integrating the LLM within the RAG pipeline ensures that the answers generated are contextually appropriate and accurate.

5. Utility Functions

Utility Functions are additional tools within the RAG pipeline that provide support for data retrieval and preprocessing. These functions include Webretrivers and document parsers that aid in fetching and preparing files for processing within the RAG pipeline. By leveraging Utility Functions, the RAG pipeline can enhance the efficiency and accuracy of the data retrieval and processing stages, leading to more robust and reliable answers to user queries.

What Is RAG LLM

RAG LLM Meaning

Rag Model Llm

Rag Use Case

Llm Tech Stack

Rag Fine Tuning

Rag Nlp

Rag Api

Rag Stack

Rag Systems

Rag Evaluation

Rag Service

Rag Use Cases

Rag Software

RAG Architecture LLM

LLM Rag Meaning

RAG LLM Example

In-Depth Step-By-Step Guide for Building a RAG Pipeline

The first step in building a RAG pipeline is to read the external text file and split it into chunks. By chunking the text, it's easier to process and understand each part individually.

An embedding model needs to be initialized. This model will help to generate embeddings for each chunk of text and the query.

Once the embedding model is in place, the embeddings for each chunk can be generated using the text data. These embeddings will be used later to compare with the query embedding.

The RAG pipeline also requires generating an embedding for the query. The query embedding will be compared with each chunk embedding to find relevant information. Calculating the similarity score between the query embedding and each of the chunk embeddings is essential. This score helps in identifying the most relevant chunks of information.

Generating Responses with Prompted Information

By extracting the top K chunks based on the similarity score calculated in the previous step, the RAG pipeline can provide the most appropriate information to answer the query. Creating a prompt that includes the query and the top-K chunks enables the pipeline to generate a response effectively. The prompt sets the context for the model to generate a meaningful answer.

Processing Queries with Large Language Models

Prompting a Large Language Model (LLM) with the framed prompt from the previous step is the final stage in building an RAG pipeline. The LLM processes the prompt and generates an answer to the query using the relevant information gathered from the chunks.

In the world of hyperparameters, various factors play a critical role in determining the efficiency of an RAG pipeline:

1. The ideal chunk size is crucial for optimal performance in a given use case.

2. Choosing the right embedding models is essential to generate accurate embeddings for chunks and queries.

3. Determining the right value of K, the number of chunks to extract based on similarity scores, is crucial for obtaining relevant information.

4. Storing chunk embeddings effectively supports quick retrieval and comparison during the pipeline process.

5. Ensuring that the specific LLM used in the RAG pipeline fits the use case and generates accurate responses.

6. Reframing prompts when necessary can enhance the relevance and accuracy of the generated responses based on the query and chunks selected.

By fine-tuning these parameters and understanding the specifics of the use case, an ML/AI Engineer can create an efficient RAG pipeline for information retrieval and generation. The RAG pipeline's success depends on systematically analyzing these factors to achieve optimal performance and accurate responses.

3 Ways to Optimize the RAG Pipeline

1. Limited Explainability

To address this limitation, a possible solution is to enhance the explainability of the RAG pipeline by incorporating interpretable methods and visualization tools. These mechanisms can help provide insights into why certain passages were retrieved and how they influenced the final response. Developing a clear, traceable path from the input query to the generated response can improve transparency and build trust with users and stakeholders.

2. Potential for Bias

Curating high-quality datasets and implementing bias mitigation strategies are essential for reducing the likelihood of biased output in RAG pipelines. Leveraging diverse datasets and performing thorough data preprocessing, including debiasing techniques, can help counteract biases that may exist in the retrieved passages. Additionally, constant monitoring and evaluation of the system for bias can aid in identifying and rectifying biased outcomes promptly.

3. Computational Cost

To address the computational cost associated with RAG pipelines, adopting optimization techniques can significantly enhance operational efficiency. Employing strategies such as data pruning for irrelevant information, parallel processing, and resource-efficient algorithms can help streamline the computational workload. Additionally, leveraging distributed computing frameworks and cloud-based services can help scale the system's processing capabilities without incurring excessive operational costs.

Optimizing the RAG Pipeline

Fine-tuning Retrieval Models

Fine-tuning retrieval models on specific tasks or domains can significantly enhance their performance in identifying relevant information. By training these models on task-specific data, they can better discern pertinent passages, leading to more accurate and precise responses generated by the language model.

Query Reformulation

Reformulating user queries to increase precision and specificity can improve the relevance of retrieved passages. By refining the search query to capture the core intent of the user's information needs, the retrieval process can yield more relevant and contextually appropriate information for the subsequent response generation.

Re-ranking

Applying re-ranking techniques after the initial retrieval phase can further enhance the quality of the generated responses. By prioritizing the most relevant passages through a secondary ranking process, the language model can leverage the most informative content to create accurate and coherent responses.

Rag Platform

Rag Apps

Langserve

Rag Llama

Rag Scale

Openai Rag

Langchain Alternatives

Bedrock Knowledge Base

Credal Ai

Langchain Rag

Databricks Rag

Nuclia

Aws Rag

Azure Rag

Use ChatBees’ Serverless LLM to 10x Internal Operations

ChatBees, a cutting-edge platform that leverages RAG for optimizing internal operations such as customer support and employee assistance. Our agentic framework automatically selects the best strategy to enhance the quality of responses in these scenarios, boosting predictability and accuracy for operations teams. This can be a game-changer for companies looking to improve their operational efficiency in various facets, including sales enablement, onboarding processes, customer support, and product development.

Reach out today to try out ChatBees.