When working with the wonderful world of data and generative AI, knowing how to save your work efficiently is a BIG deal. With LlamaIndex, storing your indexes properly can save you tons of time & even future headaches. Think about it: you’ve spent all that time LOADING, INDEXING, and readying your data for some smooth queries. You wouldn’t wanna start from scratch again, right? So, let’s dive into how you can save indexes in LlamaIndex with a detailed guide that’ll make the process easier for you!
1. Understanding Why You Need to Save Indexes
At its core, LlamaIndex allows you to create and work with indexes which are super helpful when you want to query data efficiently. The default behavior is to store this indexed data in-memory, which is neat for quick tests or small datasets. However, if you’re dealing with large datasets or you just want to avoid the time cost of having to re-index all over again, you need to store that data on disk. It’s all about avoiding re-indexing. Trust me, it’s a GAME CHANGER!
2. Persisting to Disk
Persisting your index to disk is pretty straightforward in LlamaIndex. The built-in
1
.persist()
method allows you to save your indexed data to a specified location. Here’s a simple code snippet to get you started:
Once you’ve saved your index, you wanna load it up when you need it! To avoid unnecessary loading & re-indexing, you can load your previously persisted index using this approach:
1
2
3
4
5
6
7
from llama_index.core import StorageContext, load_index_from_storage
# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir="<persist_dir>")
# load index
index = load_index_from_storage(storage_context)
Tip: If you have initialized your index with any custom transformations, embedding models, etc., you'll need to pass options during the loading process. Make sure you manage global settings to help with that!
3. Using Vector Stores
When discussing indexes, one common type you’ll encounter is the VectorStoreIndex. Creating embeddings through API calls can be pretty expensive in terms of time & computational resources. Hence, it's important to save these embeddings when using LlamaIndex to avoid the repeated overhead of re-indexing.
LlamaIndex supports a bunch of vector stores. Let’s take Chroma, an open-source vector store, as an example. First, you’ll need to install it:
1
pip install chromadb
Once that’s done, you can use Chroma to store your embeddings! Here’s a sneak peek of what the code looks like:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
import chromadb
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
# load documents
documents = SimpleDirectoryReader("./data").load_data()
# initialize client, setting path save data
db = chromadb.PersistentClient(path="./chroma_db")
# create collection
chroma_collection = db.get_or_create_collection("quickstart")
# assign chroma vector_store to context
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
# create index
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
The query engine can be created like this:
1
2
3
query_engine = index.as_query_engine()
response = query_engine.query("What is the meaning of life?")
print(response)
3.1. Loading Stored Embeddings
If you already have stored embeddings, you don’t need to load documents again or create a new VectorStoreIndex. Instead, load the stored vectors directly:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
import chromadb
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
# initialize client
db = chromadb.PersistentClient(path="./chroma_db")
# collection
chroma_collection = db.get_or_create_collection("quickstart")
# assign chroma vector_store to context
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
# load index from stored vectors
index = VectorStoreIndex.from_vector_store(vector_store, storage_context=storage_context)
# create query engine
query_engine = index.as_query_engine()
response = query_engine.query("What does llama2 mean?")
print(response)
Tip: Check out more comprehensive examples using Chroma if you wanna dive deeper into this!
4. Inserting New Documents/Nodess
So, you’ve created your index & stored it. But what if you want to add any new documents to the index? It's simple to do so with the
1
insert
method.
Here's how you can insert new documents:
1
2
3
4
5
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex([]) # create an empty index
documents = ["new_doc1", "new_doc2"] # your new documents
for doc in documents:
index.insert(doc)
Make sure to check out the document management how-to for more examples on managing these documents!
5. Best Practices When Saving Indexes
Alright, you've got the basics. But if you really want to master saving your indexes, here are some best practices you can follow:
Be Consistent: Always save your indexes after significant updates. Don’t wait until the last minute!
Organize Directories: Keep your persist directories organized to make future loads cleaner.
Monitor Changes: Keep an eye on how your new data affects existing indexes. This can save you future troubles.
Optimize Performance: Use different vector stores according to the needs of your project.
6. Conclusion
Saving indexes in LlamaIndex is crucial for efficient data querying and managing your generative AI applications. Whether you’re using regular indices or vector store indices, knowing the right steps to persist your data can drastically optimize your workflow.
If you're looking for ways to elevate your DATA MANAGEMENT game, you should check out Arsturn. With Arsturn, you can create an AI chatbot tailored to your brand, making interactions with your audience more engaging & efficient. Why start from scratch when you could leverage existing powerful models designed to BOOST your audience connection? Whether you need quick responses to FAQs or more intricate data interactions, Arsturn can provide insights & customization tailored to your needs.
So don’t miss the chance! Get started with Arsturn today, it’s user-friendly, and you don’t even need a credit card to join. Connect with your audience better than ever before!