> ## Documentation Index
> Fetch the complete documentation index at: https://docs.orq.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LlamaIndex framework integration

> Connect LlamaIndex to Orq.ai's AI Gateway for complete observability, built-in reliability, and access to 300+ LLMs across 20+ providers.

## AI Gateway

### Overview

LlamaIndex is a powerful framework for building RAG (Retrieval-Augmented Generation) applications with LLMs. Connecting LlamaIndex to Orq.ai's AI Gateway transforms experimental RAG applications into production-ready systems with enterprise-grade capabilities.

### Key Benefits

Orq.ai's AI Gateway enhances LlamaIndex applications with:

<CardGroup cols={2}>
  <Card title="Complete Observability" icon="chart-line">
    Track document indexing, retrieval performance, and query processing with detailed traces
  </Card>

  <Card title="Built-in Reliability" icon="shield-check">
    Automatic fallbacks, retries, and load balancing for production resilience
  </Card>

  <Card title="Cost Optimization" icon="chart-pie">
    Real-time cost tracking and spend management across all AI operations
  </Card>

  <Card title="Multi-Provider Access" icon="cubes">
    Access 300+ LLMs and 20+ providers through a single, unified integration
  </Card>
</CardGroup>

### Prerequisites

Before integrating LlamaIndex with **Orq.ai**, ensure the following are in place:

* An Orq.ai account and [API Key](/docs/ai-gateway/configuration/api-keys)
* Python 3.8 or higher
* LlamaIndex installed in your project

<Info>
  To set up an API key, see [API keys & Endpoints](/docs/ai-gateway/configuration/api-keys).
</Info>

### Installation

Install LlamaIndex and required dependencies:

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
pip install llama-index llama-index-llms-openai-like
```

### Configuration

Configure LlamaIndex to use Orq.ai's AI Gateway with the `OpenAILike` class:

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from llama_index.llms.openai_like import OpenAILike
import os

# Configure OpenAI-compatible LLM with Orq.ai AI Gateway
llm = OpenAILike(
    model="gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    api_base="https://api.orq.ai/v3/router",
    is_chat_model=True,
)
```

> **api\_base**: `https://api.orq.ai/v3/router`

### Basic RAG Example

Here's a complete example of building a RAG application with LlamaIndex through Orq.ai:

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai_like import OpenAILike
from llama_index.embeddings.openai import OpenAIEmbedding
import os

# Configure LLM with Orq.ai AI Gateway
llm = OpenAILike(
    model="gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    api_base="https://api.orq.ai/v3/router",
    is_chat_model=True,
)

# Configure embeddings through Orq.ai (required - LlamaIndex defaults to OpenAI)
embed_model = OpenAIEmbedding(
    model="text-embedding-3-small",
    api_key=os.getenv("ORQ_API_KEY"),
    api_base="https://api.orq.ai/v3/router",
)

# Set as global defaults
Settings.llm = llm
Settings.embed_model = embed_model

# Load documents and create index
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)

# Query the index
query_engine = index.as_query_engine()
response = query_engine.query("What is the main topic of these documents?")
print(response)
```

### Model Selection

Orq.ai supports any model from 20+ providers:

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from llama_index.llms.openai_like import OpenAILike
import os

# Use Claude
claude_llm = OpenAILike(
    model="anthropic/claude-sonnet-4-6",
    api_key=os.getenv("ORQ_API_KEY"),
    api_base="https://api.orq.ai/v3/router",
    is_chat_model=True,
)

# Use Gemini
gemini_llm = OpenAILike(
    model="google-ai/gemini-2.5-flash",
    api_key=os.getenv("ORQ_API_KEY"),
    api_base="https://api.orq.ai/v3/router",
    is_chat_model=True,
)

# Use any other model
groq_llm = OpenAILike(
    model="groq/llama-3.3-70b-versatile",
    api_key=os.getenv("ORQ_API_KEY"),
    api_base="https://api.orq.ai/v3/router",
    is_chat_model=True,
)
```

### Streaming Responses

LlamaIndex supports streaming with Orq.ai:

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai_like import OpenAILike
from llama_index.embeddings.openai import OpenAIEmbedding
import os

# Configure LLM
llm = OpenAILike(
    model="gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    api_base="https://api.orq.ai/v3/router",
    is_chat_model=True,
)

# Configure embeddings
embed_model = OpenAIEmbedding(
    model="text-embedding-3-small",
    api_key=os.getenv("ORQ_API_KEY"),
    api_base="https://api.orq.ai/v3/router",
)

Settings.llm = llm
Settings.embed_model = embed_model

# Create index and query engine
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(streaming=True)

# Stream response
streaming_response = query_engine.query("Explain the main concepts")
for text in streaming_response.response_gen:
    print(text, end="", flush=True)
print()
```