---
title: "LlamaIndex"
sidebarTitle: "LlamaIndex"
---
import {
PyFrameworksLlamaindexAddReranker,
PyFrameworksLlamaindexFiltering,
PyFrameworksLlamaindexHybridSearch,
PyFrameworksLlamaindexQuickStart,
} from '/snippets/integrations.mdx';
## Quickstart {#quickstart}
LlamaIndex is a well-known framework for building LLM-powered agents over your data with LLMs and workflows.
You can build your LlamaIndex pipeline and persist your metadata and embeddings in LanceDB via the `LanceDBVectorStore` class.
First, install the LlamaIndex-LanceDB integration.
pip install llama-index-vector-stores-LanceDB
Run the below script as an example.
{PyFrameworksLlamaindexQuickStart}
The vector store connector will open an existing LanceDB directory or create the directory if it does not exist.
### Filtering {#filtering}
For metadata filtering, you can use a Lance SQL-like string filter as demonstrated in the example above. Additionally, you can also filter using the `MetadataFilters` class from LlamaIndex:
{PyFrameworksLlamaindexFiltering}
### Hybrid Search {#hybrid-search}
For complete documentation, refer [here](https://docs.lancedb.com/search/hybrid-search). This example uses the `colbert` reranker. Make sure to install necessary dependencies for the reranker you choose.
{PyFrameworksLlamaindexHybridSearch}
In the snippet above, you can change/specify `query_type` when creating the engine/retriever
to use different search strategies, such as vector search or FTS.
## API reference {#api-reference}
See the official LlamaIndex Vector Stores API reference for more details.