| name | Retrieve relevant information through RAG |
| description | Leverage Retrieval Augmented Generation to retrieve relevant information from a a LlamaCloud Index. Requires the llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable. |
Information Retrieval
Quick start
You can create an index on LlamaCloud using the following code. By default, new indexes use managed embeddings (OpenAI text-embedding-3-small, 1536 dimensions, 1 credit/page):
import os
from llama_index.core import SimpleDirectoryReader
from llama_cloud_services import LlamaCloudIndex
# create a new index (uses managed embeddings by default)
index = LlamaCloudIndex.from_documents(
documents,
"my_first_index",
project_name="default",
api_key="llx-...",
verbose=True,
)
# connect to an existing index
index = LlamaCloudIndex("my_first_index", project_name="default")
You can also configure a retriever for managed retrieval:
# from the existing index
index.as_retriever()
# from scratch
from llama_cloud_services import LlamaCloudRetriever
retriever = LlamaCloudRetriever("my_first_index", project_name="default")
# perform retrieval
result = retriever.retrieve("What is the capital of France?")
And of course, you can use other index shortcuts to get use out of your new managed index:
query_engine = index.as_query_engine(llm=llm)
# perform retrieval and generation
result = query_engine.query("What is the capital of France?")
Retriever Settings
A full list of retriever settings/kwargs is below:
dense_similarity_top_k: Optional[int] -- If greater than 0, retrieveknodes using dense retrievalsparse_similarity_top_k: Optional[int] -- If greater than 0, retrieveknodes using sparse retrievalenable_reranking: Optional[bool] -- Whether to enable reranking or not. Sacrifices some speed for accuracyrerank_top_n: Optional[int] -- The number of nodes to return after reranking initial retrieval resultsalphaOptional[float] -- The weighting between dense and sparse retrieval. 1 = Full dense retrieval, 0 = Full sparse retrieval.
Requirements
The llama_cloud_services and llama-index-core packages must be installed in your environment:
pip install llama-index-core llama_cloud_services
And the LLAMA_CLOUD_API_KEY must be available as an environment variable:
export LLAMA_CLOUD_API_KEY="..."