Using Managed Indices

LlamaIndex offers multiple integration points with Managed Indices. A managed index is a special type of index that is not managed locally as part of LlamaIndex but instead is managed via an API, such as Vectara.

Using a Managed Index

Similar to any other index within LlamaIndex (tree, keyword table, list), any ManagedIndex can be constructed with a collection of documents. Once constructed, the index can be used for querying.

If the Index has been previously populated with documents - it can also be used directly for querying.

VectaraIndex is currently the only supported managed index, although we expect more to be available soon. Below we show how to use it.

Vectara Index Construction/Querying

First, sign up and use the Vectara Console to create a corpus (aka Index), and add an API key for access. Then put the customer id, corpus id, and API key in your environment.

Then construct the Vectara Index and query it as follows:

from llama_index import ManagedIndex, SimpleDirectoryReade
from llama_index.indices import VectaraIndex

# Load documents and build index
vectara_customer_id = os.environ.get("VECTARA_CUSTOMER_ID")
vectara_corpus_id = os.environ.get("VECTARA_CORPUS_ID")
vectara_api_key = os.environ.get("VECTARA_API_KEY")
documents = SimpleDirectoryReader("../paul_graham_essay/data").load_data()
index = VectaraIndex.from_documents(
    documents,
    vectara_customer_id=vectara_customer_id,
    vectara_corpus_id=vectara_corpus_id,
    vectara_api_key=vectara_api_key,
)

# Query index
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")

Note that if the environment variables VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY are in the environment already, you do not have to explicitly specifying them in your call and the VectaraIndex class will read them from the environment. For example this should be equivalent to the above, if these variables are in the environment already:

from llama_index import ManagedIndex, SimpleDirectoryReade
from llama_index.indices import VectaraIndex

# Load documents and build index
documents = SimpleDirectoryReader("../paul_graham_essay/data").load_data()
index = VectaraIndex.from_documents(documents)

# Query index
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")

Examples

• Vectara Managed Index