--- 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.