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.

Google Generative Language Semantic Retriever.#

Google’s Semantic Retrieve provides both querying and retrieval capabilities. Create a managed index, insert documents, and use a query engine or retriever anywhere in LlamaIndex!

from llama_index.core import SimpleDirectoryReader
from llama_index.indices.managed.google import GoogleIndex

# Create a corpus
index = GoogleIndex.create_corpus(display_name="My first corpus!")
print(f"Newly created corpus ID is {index.corpus_id}.")

# Ingestion
documents = SimpleDirectoryReader("data").load_data()
index.insert_documents(documents)

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

# Retrieving
retriever = index.as_retriever()
source_nodes = retriever.retrieve("What did the author do growing up?")

See the notebook guide for full details.

Examples

Vectara#

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.core import ManagedIndex, SimpleDirectoryReade
from llama_index.indices.managed.vectara 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.core import ManagedIndex, SimpleDirectoryReade
from llama_index.indices.managed.vectara 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?")

If you already have documents in your corpus you can just access them directly by constructing the VectaraIndex as follows:

index = VectaraIndex()

And the index will connect to the existing corpus without loading any new documents.

Examples

Zilliz#

First, set up your Zilliz Cloud account and create a free serverless cluster. Then copy the Project ID, Cluster ID and API Key from your account.

Now you can construct ZillizCloudPipelineIndex to index docs and query as follows:

import os

from llama_index.core import ManagedIndex
from llama_index.indices.managed.zilliz import ZillizCloudPipelineIndex

# Load documents from url and build document index
zcp_index = ZillizCloudPipelineIndex.from_document_url(
    url="https://publicdataset.zillizcloud.com/milvus_doc.md",
    project_id="<YOUR_ZILLIZ_PROJECT_ID>",
    cluster_id="<YOUR_ZILLIZ_CLUSTER_ID>",
    token="<YOUR_ZILLIZ_API_KEY>",
    metadata={"version": "2.3"},  # optional
)

# Insert more docs into index, eg. a Milvus v2.2 document
zcp_index.insert_doc_url(
    url="https://publicdataset.zillizcloud.com/milvus_doc_22.md",
    metadata={"version": "2.2"},
)

# Query index
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

query_engine_milvus23 = zcp_index.as_query_engine(
    search_top_k=3,
    filters=MetadataFilters(
        filters=[
            ExactMatchFilter(key="version", value="2.3")
        ]  # version == "2.3"
    ),
    output_metadata=["version"],
)

question = "Can users delete entities by complex boolean expressions?"
# Retrieving
retrieval_result = query_engine_with_filters.retrieve(question)
# Querying
answer = query_engine_with_filters.query(question)

Examples