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Build a RAG pipeline in LlamaIndex (simple)

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Learn to build a powerful Retrieval-Augmented Generation (RAG) pipeline using LlamaIndex to enhance large language models with external knowledge. This tutorial explains the core concepts of RAG, from document processing and indexing to context retrieval and response generation…

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What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is a technique that transforms how Large Language Models (LLMs) work by connecting them to external data sources. This process grounds the model's responses in factual, up-to-date knowledge, enhancing both the accuracy and relevance of its outputs.

The RAG Pipeline Workflow

A RAG pipeline follows a structured, multi-step process to generate informed responses:

Document Processing: Source documents from knowledge bases are pre-processed, split into smaller chunks, and converted into numerical representations (vectors) using an embedding model.
Indexing: The document vectors are stored and indexed in a vector database for efficient searching.
User Query Processing: When a user submits a query, it's converted into a vector using the same embedding model.
Context Retrieval: The system performs a semantic search, comparing the query vector against the document vectors to find the most relevant chunks of text.
Prompt Augmentation: The retrieved text chunks (the "context") are appended to the original user prompt.
Response Generation: This final, augmented prompt is sent to the LLM, which generates a response that is now informed by the provided context.

Note
The goal is to combine the broad capabilities of LLMs with the precision of retrieving specific, and often proprietary, information that was not part of the model's original training data.

Simple RAG Pipeline with LlamaIndex

You can build a basic RAG pipeline with just a few lines of code using LlamaIndex.

Key Tools: SimpleDirectoryReader for loading documents and VectorStoreIndex for creating a searchable index.
Steps:
1. Load Data: Use SimpleDirectoryReader to read documents from a local directory (e.g., text files, PDFs).
2. Create Index: Use VectorStoreIndex.from_documents() to automatically process and index the loaded documents.
3. Query: Create a query engine from the index (index.as_query_engine()) and ask complex, natural language questions.

Advanced RAG: The Router Query Engine

For more complex applications, a Router Query Engine can enhance your RAG pipeline by intelligently routing queries to different indexes based on the nature of the question.

Purpose: To direct a query to the most appropriate index or processing method. This is useful when dealing with diverse data types (e.g., summaries vs. technical specifications).
Implementation Example:
- Create multiple, specialized indexes, such as a SummaryIndex for high-level questions and a VectorStoreIndex for detailed semantic search.
- Create a query engine for each index.
- Use the RouterQueryEngine to define logic that decides which engine to use. For example, a short query might be routed to the summary index, while a longer, more detailed query goes to the vector index.

Key Advantages of Advanced RAG

Implementing advanced techniques like the Router Query Engine offers significant benefits for enterprise-grade applications.

Improved Accuracy: By directing queries to targeted indexes (e.g., sending "What was our Q3 2004 revenue growth?" to a financial index), you get more precise answers.
Enhanced Efficiency: Specialized indexes handle queries faster. A query like "Summarize our product lineup" is answered quickly by a summary index without searching through detailed documents.
Versatility: The system can efficiently handle a mix of broad summary questions and highly specific, detailed queries from a single interface.

Important
These advanced RAG techniques empower developers to build sophisticated services that give users access to both high-level policies and specific, granular details, improving decision-making across an organization.

Introduction to Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation, or RAG, is transforming how we interact with large language models. Hi, I'm Stuart, and I'm going to show you the basic and advanced RAG pipelines using LlamaIndex.

RAG combines external data sources with language models to generate informed responses. This approach grounds AI outputs in factual knowledge, enhancing accuracy and also relevance.

Let's take a look at the following diagram. This is going to illustrate our workflow through the demos that we use today. We can dive deeper into how RAG works. Here, we've got document processing. We start here by pre-processing documents from configured knowledge bases. These documents are split into chunks and are embedded into vectors using a specific embedding model.

Following this, we have indexing. These document vectors are then indexed in a vector database for efficient retrieval. Next, we have user query processing. This is when a user submits a query. The same embedding model converts this query text into a vector representation.

After we've done this, we go into context retrieval. The system here compares the query vector against indexed document vectors. What this comparison does is it identifies the most semantically similar chunks from the knowledge base.

After this, we move on to prompt augmentation. The retrieved chunks here, which are provided with additional contexts, relate to the user's query and are appended to the original user prompt. The next step in the process is the response generation. This augmented prompt is then passed to the foundational model, or FM if you like, to generate a response. By incorporating the relevant data from the knowledge base, the model's output is informed by the organization's proprietary information.

The RAG Workflow and Its Benefits

Next, we have an optional system. This is the agent orchestration. In more advanced setups, this RAG process can be orchestrated by agents. These agents use the FM to determine whether to query with the knowledge base and how to incorporate retrieved context into the workflow.

The way this works is the workflow allows the RAG system to ground its responses in specific, up-to-date information from the organization's knowledge base. It combines the broad capabilities of large language models with the precision of retrieving relevant, often proprietary, information. By augmenting the user's original prompt with the text from the knowledge base, it then enables the model to provide responses which are both relevant to the user's query and accurate based on the organization's latest information.

This is a very powerful model for handling queries about things like recent events, company-specific data, or any information that wouldn't be part of the model's general training.

Building a Simple RAG Pipeline with LlamaIndex

Let's break down a simplified RAG pipeline using LlamaIndex. We're going to use this code as a piece of example. So first, we import a SimpleDirectoryReader and VectorStoreIndex from LlamaIndex. These are key tools for loading data and creating a searchable index.

After we've done this, we then use the SimpleDirectoryReader to load documents from a data/ directory. This could contain various file types like texts or PDFs. Next, we create a VectorStoreIndex from these documents. This is a crucial step as it converts our documents into a vector representation for efficient semantic searching.

We can now query our index with a simple question like, "Analyze the philosophical implications of artificial intelligence's potential to surpass human intelligence." The index finds the most relevant document to answer this query. And now finally, we print the response, which is the answer generated by the language model based on the retrieval of the context.

By using a complex query here, we showcase the LLM's ability to process and understand nuanced information from a collection of documents which we provided. The VectorStoreIndex becomes more valuable in this scenario as it can effectively retrieve relevant information from the documents to answer the complex query. This simple RAG pipeline showcases RAG's core concepts by using relevant information from a knowledge base to inform the language model's response.

Advanced RAG: The Router Query Engine

While the simple RAG pipeline is powerful, we can enhance it further with additional, um, advanced techniques. One such technique is the router query. A router query engine allows you to route a query to different indexes or query engines based on the nature of the query. This is particularly useful when dealing with diverse types of information or when different queries require different processing approaches. So let's look at an example of how to implement a router query.

Looking at this code, firstly, we're going to import additional classes from LlamaIndex, including ListSummaryIndex and RouterQueryEngine. Now we create two different types of indexes: a SummaryIndex and a VectorStoreIndex, each is suited for different types of queries. We can create a query engine from each index. Now these engines know how to search their respective index types.

Next up, we create a RouterQueryEngine which will decide which query engine to use based on the nature of the query. So for this example, I've pre-built a little piece of code here for this. And here we could use the router query function to take the input query as a string and return an integer indicating which query engine to use. The routing logic is fairly simple. If the query is shorter than 10 characters, use the summary_query_engine; otherwise use the vector_query_engine.

Now, back to our main piece of code. Finally, in the router query engine setup, we define the logic for how queries should be routed. This could be based on keywords, query length, or even more complex criteria. The router query approach allows us to handle a wide range of query types efficiently.

Advantages and Use Cases of Advanced RAG

For example, we could route general questions to a summary index for a quick, high-level answer, while sending detailed or specific queries to the vector index for a more precise information retrieval. This advanced technique enhances the flexibility and performance of our RAG pipeline, allowing it to adapt to different query types and information needs.

By implementing advanced RAG techniques like router query, this offers several key advantages. And so we can do the following: we can have improved accuracy through targeted indexing. For example, we can ask questions like, "What was our Q3 2004 revenue growth?" The router directs the query to a financial report index for a precise answer. Enhanced efficiency with specialized indexes; for example, we could use something like, "Summarize our product lineup." This is quickly answered using a summary index, avoiding detailed product specifics. We can have versatility in handling for diverse queries as well. So here, for example, we could have the system efficiently manage both. So we could have something like, "Explain our returns policy step-by-step" and or "Overview of our sustainability initiatives," using appropriate indexes. This nuanced response to complex questions really makes it simple.

Another example we could use is we could have, "Compare our Q4 performances to competitors and suggest improvements." This would utilize multiple indexes for a comprehensive answer. These improvements allow our RAG system to handle real-world, enterprise-grade tasks. It really does empower our developers and our customers to help build services to both access broad policies and specific details, while researchers can obtain both overviews and detailed results, enhancing decision-making across various organizational needs.

Conclusion and Next Steps

So try this implementation of RAG in your pipeline and your projects. You can find the examples in a notebook which is in the description to try out all these steps on your own and look at some of the examples we've talked about in the description here. You can experiment with different document types and queries to see how it enhances your AI applications.

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