> ## Documentation Index
> Fetch the complete documentation index at: https://actianvectorai-docs-feedback-implementation.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Connect VectorAI DB with embedding providers and AI frameworks to build semantic search, RAG pipelines, and AI-powered applications.

VectorAI DB integrates with popular AI frameworks. Use any supported integration to generate embeddings, store vectors, and run similarity searches with minimal setup.

## Frameworks

<CardGroup cols={2}>
  <Card title="LangChain" icon="link" href="/docs/integrations/langchain">
    Use VectorAI DB as a vector store in LangChain for RAG pipelines, similarity search, and retriever-based chains. Supports sync and async operations.
  </Card>

  <Card title="LlamaIndex" icon="book" href="/docs/integrations/llama-index">
    Build RAG applications and query engines with VectorAI DB as the storage backend in LlamaIndex.
  </Card>
</CardGroup>

## How integrations work

All integrations follow the same pattern:

1. **Generate embeddings** — Use an embedding provider (such as OpenAI or Cohere) to convert your data into vectors.
2. **Store in VectorAI DB** — Insert vectors into a collection with optional metadata payloads.
3. **Search** — Query with a vector to find semantically similar results, with optional metadata filtering.

## Quick reference

| Integration                                  | Type      | Use case                                                                      |
| -------------------------------------------- | --------- | ----------------------------------------------------------------------------- |
| [LangChain](/docs/integrations/langchain)    | Framework | RAG pipelines, retriever chains, similarity search with document abstractions |
| [LlamaIndex](/docs/integrations/llama-index) | Framework | Query engines, data agents, and RAG applications                              |