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Building A RAG Ebook “Librarian” Using LlamaIndex

Authored by: Jonathan Jin


This notebook demonstrates how to quickly build a RAG-based “librarian” for your local ebook library.

Think about the last time you visited a library and took advantage of the expertise of the knowledgeable staff there to help you find what you need out of the troves of textbooks, novels, and other resources at the library. Our RAG “librarian” will do the same for us, except for our own local collection of ebooks.


We’d like our librarian to be lightweight and run locally as much as possible with minimal dependencies. This means that we will leverage open-source to the fullest extent possible, as well as bias towards models that can be executed locally on typical hardware, e.g. M1 Macbooks.


Our solution will consist of the following components:


First let’s install our dependencies.

%pip install -q \
    llama-index \
    EbookLib \
    html2text \
    llama-index-embeddings-huggingface \
!brew install ollama

Test Library Setup

Next, let’s create our test “library.”

For simplicity’s sake, let’s say that our “library” is simply a nested directory of .epub files. We can easily see this solution generalizing to, say, a Calibre library with a metadata.db database file. We’ll leave that extension as an exercise for the reader. 😇

Let’s pull two .epub files from Project Gutenberg for our library.

!mkdir -p ".test/library/jane-austen"
!mkdir -p ".test/library/victor-hugo"
!wget -O ".test/library/jane-austen/pride-and-prejudice.epub"
!wget -O ".test/library/victor-hugo/les-miserables.epub"

RAG with LlamaIndex

RAG with LlamaIndex, at its core, consists of the following broad phases:

  1. Loading, in which you tell LlamaIndex where your data lives and how to load it;
  2. Indexing, in which you augment your loaded data to facilitate querying, e.g. with vector embeddings;
  3. Querying, in which you configure an LLM to act as the query interface for your indexed data.

This explanation only scratches at the surface of what’s possible with LlamaIndex. For more in-depth details, I highly recommend reading the “High-Level Concepts” page of the LlamaIndex documentation.


Naturally, let’s start with the loading phase.

I mentioned before that LlamaIndex is designed specifically for RAG. This immediately becomes obvious from its SimpleDirectoryReader construct, which ✨ magically ✨ supports a whole host of multi-model file types for free. Conveniently for us, .epub is in the supported set.

from llama_index.core import SimpleDirectoryReader

loader = SimpleDirectoryReader(

documents = loader.load_data()

SimpleDirectoryReader.load_data() converts our ebooks into a set of Documents for LlamaIndex to work with.

One important thing to note here is that the documents have not been chunked at this stage — that will happen during indexing. Read on…


Next up after loading the data is to index it. This will allow our RAG pipeline to look up the relevant context for our query to pass to our LLM to augment their generated response. This is also where document chunking will take place.

VectorStoreIndex is a “default” entrypoint for indexing in LlamaIndex. By default, VectorStoreIndex uses a simple, in-memory dictionary to store the indices, but LlamaIndex also supports a wide variety of vector storage solutions for you to graduate to as you scale.

By default, LlamaIndex uses a chunk size of 1024 and a chunk overlap of 20. For more details, see the [LlamaIndex documentation](

Like mentioned before, we’ll use the BAAI/bge-small-en-v1.5 to generate our embeddings. By default, LlamaIndex uses OpenAI (specifically gpt-3.5-turbo), which we’d like to avoid given our desire for a lightweight, locally-runnable end-to-end solution.

Thankfully, LlamaIndex supports retrieving embedding models from Hugging Face through the convenient HuggingFaceEmbedding class, so we’ll use that here.

from llama_index.embeddings.huggingface import HuggingFaceEmbedding

embedding_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

We’ll pass that in to VectorStoreIndex as our embedding model to circumvent the OpenAI default behavior.

from llama_index.core import VectorStoreIndex

index = VectorStoreIndex.from_documents(


Now for the final piece of the RAG puzzle — wiring up the query layer.

We’ll use Llama 2 for the purposes of this recipe, but I encourage readers to play around with different models to see which produces the “best” responses here.

First let’s start up the Ollama server. Unfortunately, there is no support in the Ollama Python client for actually starting and stopping the server itself, so we’ll have to pop out of Python land for this.

In a separate terminal, run: ollama serve. Remember to terminate this after we’re done here!

Now let’s hook Llama 2 up to LlamaIndex and use it as the basis of our query engine.

from llama_index.llms.ollama import Ollama

llama = Ollama(

query_engine = index.as_query_engine(llm=llama)

Final Result

With that, our basic RAG librarian is set up and we can start asking questions about our library. For example:

>>> print(
...     query_engine.query(
...         "What are the titles of all the books available? Show me the context used to derive your answer."
...     )
... )
Based on the context provided, there are two books available:

1. "Pride and Prejudice" by Jane Austen
2. "Les Misérables" by Victor Hugo

The context used to derive this answer includes:

* The file path for each book, which provides information about the location of the book files on the computer.
* The titles of the books, which are mentioned in the context as being available for reading.
* A list of words associated with each book, such as "epub" and "notebooks", which provide additional information about the format and storage location of each book.
>>> print(query_engine.query("Who is the main character of 'Pride and Prejudice'?"))
The main character of 'Pride and Prejudice' is Elizabeth Bennet.

Conclusion and Future Improvements

We’ve demonstrated how to build a basic RAG-based “librarian” that runs entirely locally, even on Apple silicon Macs. In doing so, we’ve also carried out a “grand tour” of LlamaIndex and how it streamlines the process of setting up RAG-based applications.

That said, we’ve really only scratched the surface of what’s possible here. Here are some ideas of how to refine and build upon this foundation.

Forcing Citations

To guard against the risk of our librarian hallucinating, how might we require that it provide citations for everything that it says?

Using Extended Metadata

Ebook library management solutions like Calibre create additional metadata for ebooks in a library. This can provide information such as publisher or edition that might not be readily available in the text of the book itself. How could we extend our RAG pipeline to account for additional sources of information that aren’t .epub files?

Efficient Indexing

If we were to collect everything we built here into a script/executable, the resulting script would re-index our library on each invocation. For our tiny test library of two files, this is “fine,” but for any library of non-trivial size this will very quickly become annoying for users. How could we persist the embedding indices and only update them when the contents of the library have meaningfully changed, e.g. new books have been added?