Integrate LlamaIndex with Astra DB Serverless

query_builder 15 min

LlamaIndex can use Astra DB Serverless to store and retrieve vectors for ML applications.

Prerequisites

The code samples on this page assume the following:

You have an active Astra account.
You have created a Serverless (Vector) database.
You have created an application token with the Database Administrator role.
You have created an OpenAI API key.
You have installed Python 3.7+ and pip 23.0+.

You have installed the required dependencies:

Local installation
Google Colab

pip install llama-index llama-index-vector-stores-astra-db python-dotenv

!pip install llama-index llama-index-vector-stores-astra-db python-dotenv

Connect to your Serverless (Vector) database

Import libraries and connect to the database.
- Local installation
- Google Colab
Create a .env file in the root of your program. Populate the file with the Astra token and endpoint values from the Database Details section of your database’s Overview tab, and your OpenAI API key.
.env

ASTRA_DB_APPLICATION_TOKEN="TOKEN" ASTRA_DB_API_ENDPOINT="API_ENDPOINT" OPENAI_API_KEY="API_KEY"
import os from getpass import getpass os.environ["ASTRA_DB_APPLICATION_TOKEN"] = getpass("ASTRA_DB_APPLICATION_TOKEN = ") os.environ["ASTRA_DB_API_ENDPOINT"] = input("ASTRA_DB_API_ENDPOINT = ") os.environ["OPENAI_API_KEY"] = getpass("OPENAI_API_KEY = ")
The endpoint format is https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com.

Import your dependencies.

Local installation
Google Colab

integrate.py

import os

from llama_index.vector_stores.astra_db import AstraDBVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.llama_dataset import download_llama_dataset
from dotenv import load_dotenv

import os

from llama_index.vector_stores.astra_db import AstraDBVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.llama_dataset import download_llama_dataset

Load your environment variables.

Local installation
Google Colab

load_dotenv()

ASTRA_DB_APPLICATION_TOKEN = os.environ.get("ASTRA_DB_APPLICATION_TOKEN")
ASTRA_DB_API_ENDPOINT = os.environ.get("ASTRA_DB_API_ENDPOINT")
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")

ASTRA_DB_APPLICATION_TOKEN = os.environ.get("ASTRA_DB_APPLICATION_TOKEN")
ASTRA_DB_API_ENDPOINT = os.environ.get("ASTRA_DB_API_ENDPOINT")
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")

See Advanced configuration for Azure OpenAI values.

Don’t name the file llamaindex.py to avoid a namespace collision.

Create embeddings from text

Download a sample dataset from LlamaHub and load it as a Document object.

integrate.py

rag_dataset, documents = download_llama_dataset(
  "PaulGrahamEssayDataset", "./data"
)

print(f"Number of loaded documents: {len(documents)}")
print(f"First document, id: {documents[0].doc_id}")
print(f"First document, hash: {documents[0].hash}")
print(
    "First document, text"
    f" ({len(documents[0].text)} characters):\n"
    f"{'=' * 20}\n"
    f"{documents[0].text[:360]} ..."
)

Optional: Chunk the documents using the default splitter. This is optional because the documents are split automatically when ingested by the vector store.

integrate.py

# This step is optional because splitting happens automatically during ingestion
from llama_index.core.node_parser import SentenceSplitter
default_splitter = SentenceSplitter()
split_nodes = default_splitter(documents)
print(f"Number of split nodes: {len(split_nodes)}")
print(f"Third split node, document reference ID: {split_nodes[2].ref_doc_id}")
print(f"Third split node, node ID: {split_nodes[2].node_id}")
print(f"Third split node, hash: {split_nodes[2].hash}")
print(
    "Third split node, text"
    f" ({len(split_nodes[2].text)} characters):\n"
    f"{'=' * 20}\n"
    f"{split_nodes[2].text[:360]} ..."
)

Create an AstraDB vector store.

integrate.py

astra_db_store = AstraDBVectorStore(
    token=ASTRA_DB_APPLICATION_TOKEN,
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    collection_name="llama_index_rag_test",
    embedding_dimension=1536,
)

Build the index for your documents. The StorageContext.from_defaults method tells LlamaIndex to use the AstraDBVectorStore you created. The from_documents method splits your Documents into Nodes and creates embeddings from the text of every Node.
integrate.py
```
storage_context = StorageContext.from_defaults(vector_store=astra_db_store)

index = VectorStoreIndex.from_documents(
    documents=documents, storage_context=storage_context
)
```

Verify the integration

Ask a question about the stored text and verify the response is relevant.

integrate.py

query_engine = index.as_query_engine()
query_string_1 = "Why did the author choose to work on AI?"
response = query_engine.query(query_string_1)

print("\n\n" + query_string_1)
print(response.response)

Add an additional query using Max Marginal Relevance (MMR). MMR selects the Nodes that are relevant to the query while also selecting the most different from each other. The query results are printed with scores, so you can see where the relevant Nodes rank and where the LLM’s results came from.

integrate.py

retriever = index.as_retriever(
    vector_store_query_mode="mmr",
    similarity_top_k=3,
    vector_store_kwargs={"mmr_prefetch_factor": 4},
)

query_string_2 = "Why did the author choose to work on AI?"
nodes_with_scores = retriever.retrieve(query_string_2)

print("\n\n" + query_string_2 + " (question asked with MMR)")
print(f"Found {len(nodes_with_scores)} nodes.")
for idx, node_with_score in enumerate(nodes_with_scores):
    print(f"    [{idx}] score = {node_with_score.score}")
    print(f"        id    = {node_with_score.node.node_id}")
    print(f"        text  = {node_with_score.node.text[:90]} ...")

Run the code

Run the code.

python integrate.py

Advanced configuration

If you’re using Azure OpenAI, include these additional environment variables:

OPENAI_API_TYPE="azure"
OPENAI_API_VERSION="2023-05-15"
OPENAI_API_BASE="https://RESOURCE_NAME.openai.azure.com"
OPENAI_API_KEY="API_KEY"

Next steps

Build a chatbot with LangChain auto_stories Tutorial

Integrate LlamaIndex with Astra DB Serverless

Prerequisites

Connect to your Serverless (Vector) database

Create embeddings from text

Verify the integration

Run the code

Advanced configuration

Next steps

Was this helpful?

Give Feedback