What are components in LlamaIndex?

Components are building blocks for agentic workflows. LlamaIndex has many components but instead of going over each of the components one by one, we will take a look at the components that are used to create a QueryEngine. We will focus on those components because they are most relevant for building agentic workflows in LlamaIndex.

Many of the components rely on integrations with other libraries. So, before using them, we first need to learn how to install these dependencies.

Integrations

Installation

Most frameworks add their installation guide to their main documentation but LlamaIndex keep a well structured overview in their GitHub repository. This might be a bit overwhelming at first, but the installation commands generally follow an easy to remember format:

pip install llama-index-{component-type}-{framework-name}

Let’s try install the depencies for an LLM and embedding component using Hugging Face inference API as framework.

pip install llama-index-llms-huggingface-api llama-index-embeddings-huggingface-api

Usage

Once installed, we can use the component in our workflow. The usage patterns have been outlined in the documentation but framework specific versions are also shown in the GitHub repository. Underneath, we can see an example of the usage of the Hugging Face inference API for an LLM component.

from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI

llm = HuggingFaceInferenceAPI(
    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
    temperature=0.7,
    max_tokens=100,
    token="<your-token>",  # Optional
)

llm.complete("Hello, how are you?")
# I am good, how can I help you today?

Now, let’s dive a bit deeper into the components and see how you can use them to create a QueryEngine.

Loading and embedding documents

As mentioned before, LlamaIndex can work on top of your own data, however, before accessing data, we need to load it. There are three main ways to do to load data into LlamaIndex:

  1. SimpleDirectoryReader: A built-in loader for various file types from a local directory.
  2. LlamaParse: LlamaParse, LlamaIndex’s official tool for PDF parsing, available as a managed API.
  3. LlamaHub: A registry of hundreds of data loading libraries to ingest data from any source.

Get familiar with LlamaHub loaders and LlamaParse parser for more complex data sources.

The easiest way to load data is with SimpleDirectoryReader. It can load different types of files from a folder and turn them into Document objects that LlamaIndex can work with.

from llama_index.core import SimpleDirectoryReader

reader = SimpleDirectoryReader(input_dir="path/to/directory")
documents = reader.load_data()

After loading our documents, we need to break them into smaller pieces called Node objects. A Node is just a chunk of text from the original document that’s easier for the AI to work with, while it still has reteain references to the original Document object.

To create these nodes, we use the IngestionPipeline with two simple simple transformations:

  1. SentenceSplitter: Break the document into smaller pieces of 25 sentences each
  2. HuggingFaceInferenceAPIEmbedding: Turn each piece into numbers (embeddings) that the AI can understand better

This process helps us organize our documents in a way that’s more useful for searching and analysis.

from llama_index.core import Document
from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.ingestion import IngestionPipeline, IngestionCache

# create the pipeline with transformations
pipeline = IngestionPipeline(
    transformations=[
        SentenceSplitter(chunk_size=25, chunk_overlap=0),
        HuggingFaceInferenceAPIEmbedding("BAAI/bge-small-en-v1.5"),
    ]
)

# run the pipeline
nodes = pipeline.run(documents=[Document.example()])

To save time and computer power, LlamaIndex caches the results of the ingestion pipeline so you don’t need to load and embed the same documents twice.

Storing and indexing documents

After creating our Node objects we need to index them to make them searchable but before we can do that, we need a place to store our data.

Within LlamaIndex, we can use a StorageContext to handle all the storage. It supports various stores for different purposes:

We can set up a StorageContext ourselves, or let LlamaIndex create one for us when creating a search index. When we save the StorageContext, it creates files that store all the important information about our data.

Now, let’s see how to create a VectorStoreIndex and save it to your computer. We also need to provide an embedding model which should be the same as the one used during ingestion.

from llama_index.core import VectorStoreIndex
from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding

embed_model = HuggingFaceInferenceAPIEmbedding("BAAI/bge-small-en-v1.5")
index = VectorStoreIndex.from_documents(nodes, embed_model=embed_model)
index.storage_context.persist("path/to/vector/store")

We can load our index again using files that were created when saving the StorageContext.

from llama_index.core import StorageContext, load_index_from_storage
from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding

embed_model = HuggingFaceInferenceAPIEmbedding("BAAI/bge-small-en-v1.5")
storage_context = StorageContext.from_defaults(persist_dir="path/to/vector/store")
index = load_index_from_storage(storage_context, embed_model=embed_model)

Great! Now that we can save and load our index easily, let’s explore how to query it in different ways.

Querying a VectorStoreIndex with prompts and LLMs

Before querying our index, we need to convert it to a query interface. The most common options are:

We’ll focus on the query engine since it is more common for agent-like interactions. We also pass in an LLM to the query engine to use for the response.

from llama_index.llms.huggingface_api import HuggingFaceInferenceAPILM

llm = HuggingFaceInferenceAPILM(model_name="meta-llama/Meta-Llama-3-8B-Instruct")
query_engine = index.as_query_engine(llm=llm)
query_engine.query("What is the meaning of life?")
# the meaning of life is 42

Under the hood, the query engine doesn’t only use the LLM to answer the question, but also uses a ResponseSynthesizer as strategy to process the response. Once again, this is fully customisable but there are three main strategies that work well out of the box:

Take fine-grained control of your query workflows with the low-level composition API. This API lets you customize and fine-tune every step of the query process to match your exact needs.

We have seen how to use components to create a QueryEngine. Now, let’s see how we can use that same QueryEngine as a tool for an agent!.

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