Retrieval-Augmented Generation (RAG) pipelines, powered by large language models (LLMs), are becoming a cornerstone for developing sophisticated question-answering systems. Frameworks such as LlamaIndex and Haystack have made strides in simplifying the implementation of RAG pipelines. However, while these frameworks offer remarkable ease of use, they might obscure the intricate mechanisms that operate behind the scenes.

Quick Start

If you are eager to delve into the workings of RAG pipelines, follow these commands to run the application:

pip install -r requirements.txt
echo OPENAI_API_KEY=yourkey .env
python complex_qa.py

RAG Overview

RAG is an innovative AI paradigm designed for LLM-based question answering. Typically, a RAG pipeline includes:

• Data Warehouse: A collection of data sources like documents or tables, housing information relevant to question answering.
• Vector Retrieval: This component identifies the top K most similar data chunks to a given question using a vector store (e.g., Faiss).
• Response Generation: It generates a response using a large language model (e.g., GPT-4) based on the retrieved data chunks.

RAG offers significant advantages over traditional LLM approaches:

• Up-to-date information: The data warehouse can be updated in real-time, ensuring always-accessible accuracy.
• Source tracking: It provides users with clear traceability, essential for verifying information accuracy and reducing LLM hallucinations.

Building Advanced RAG Pipelines

Recent frameworks such as LlamaIndex have introduced advanced abstractions like the Sub-question Query Engine to tackle more intricate questions. In this application, we will demystify RAG pipelines using the Sub-question Query Engine as a focal point, clarifying its core components and associated challenges.

The Setup

In this example, our data warehouse comprises multiple Wikipedia articles covering various popular cities. Each article serves as a distinct data source. Our objective is to construct a system capable of addressing questions such as:

• What is the population of Chicago?
• Give me a summary of the positive aspects of Atlanta.
• Which city has the highest population?

The questions can range from straightforward factual inquiries to more complex summarization tasks involving multiple data sources.

The Secret Sauce

At the core of an advanced RAG pipeline is the power of a single LLM call. The entire pipeline operates as a sequence of LLM calls guided by meticulously crafted prompt templates, our secret sauce for enabling advanced functionalities.

Here is a universal input pattern exemplifying this:

LLM input = Prompt Template + Context + Question

Where:

• Prompt Template: A well-defined prompt tailored for a specific task (e.g., sub-question generation).
• Context: Relevant data chunks used during the task.
• Question: The initial question being addressed.

Task Breakdown

To illustrate the mechanics of the Sub-question Query Engine, we explore three vital tasks:

1. Sub-question Generation: This involves deconstructing a complex user question into manageable sub-questions while identifying relevant functions and data sources.
2. Vector/Summary Retrieval: Utilizing the selected retrieval function (either vector or summary) on the appropriate data source to gather information.
3. Response Aggregation: Compiling the results from each sub-question to form a comprehensive response.

Challenges in RAG Pipelines

Despite their capabilities, RAG pipelines face various challenges:

• Question Sensitivity: LLMs can be highly sensitive, leading to unexpected failures when handling certain user inquiries.
• Cost Dynamics: The overall expense can vary dramatically based on the number of generated sub-questions and selected retrieval methods.

Troubleshooting Tips

If you encounter issues while interacting with advanced RAG pipelines, consider the following troubleshooting ideas:

• Ensure that your API keys are correctly set up in the environment.
• Double-check the context length limits, especially when dealing with larger documents.
• Experiment with different prompt structures to see how the responses are affected.

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Conclusion

Advanced RAG pipelines represent a significant leap forward in the domain of question answering. While they provide tremendous capabilities, it is crucial to understand their internal workings and the intricate design behind them. By demystifying these pipelines, we lay the groundwork for more robust and effective AI solutions. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.