> ## 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 Router for complete observability, built-in reliability, and access to 300+ LLMs across 20+ providers.

<CardGroup cols={2}>
  <Card title="AI Router" icon="arrow-right-arrow-left" href="#ai-router">
    Route your LLM calls through the AI Router with a single base URL change. Zero vendor lock-in: always run on the best model at the lowest cost for your use case.
  </Card>

  <Card title="Observability" icon="chart-line" href="#observability">
    Instrument your code with OpenTelemetry to capture traces, logs, and metrics for every LLM call, agent step, and tool use.
  </Card>
</CardGroup>

## AI Router

### Overview

LlamaIndex is a powerful framework for building RAG (Retrieval-Augmented Generation) applications with LLMs. By connecting LlamaIndex to Orq.ai's AI Router, you transform experimental RAG applications into production-ready systems with enterprise-grade capabilities.

### Key Benefits

Orq.ai's AI Router enhances your 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 your 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 you have:

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

<Info>
  To setup your API key, see [API keys & Endpoints](/docs/administer/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 Router 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 Router
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 Router
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

With Orq.ai, you can use any supported 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="claude-sonnet-4-5-20250929",
    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="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="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()
```

## Observability

### Getting Started

Integrate LlamaIndex with Orq.ai's observability to gain comprehensive insights into document indexing, retrieval performance, query processing, and LLM interactions using OpenTelemetry.

### Prerequisites

Before you begin, ensure you have:

* An Orq.ai account and [API Key](/docs/administer/api-keys)
* LlamaIndex installed in your project
* Python 3.8+
* OpenAI API key (or other LLM provider credentials)

### Install Dependencies

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
# Core LlamaIndex and OpenInference packages
pip install llama-index openinference-instrumentation-llama-index

# OpenTelemetry packages
pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http

# LLM providers
pip install openai anthropic

# Optional: For advanced vector stores and embeddings
pip install llama-index-vector-stores-chroma llama-index-embeddings-openai
```

### Configure Orq.ai

Set up your environment variables to connect to Orq.ai's OpenTelemetry collector:

**Unix/Linux/macOS:**

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.orq.ai/v2/otel"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer <ORQ_API_KEY>"
export OTEL_RESOURCE_ATTRIBUTES="service.name=llamaindex-app,service.version=1.0.0"
export OPENAI_API_KEY="<YOUR_OPENAI_API_KEY>"
```

**Windows (PowerShell):**

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
$env:OTEL_EXPORTER_OTLP_ENDPOINT = "https://api.orq.ai/v2/otel"
$env:OTEL_EXPORTER_OTLP_HEADERS = "Authorization=Bearer <ORQ_API_KEY>"
$env:OTEL_RESOURCE_ATTRIBUTES = "service.name=llamaindex-app,service.version=1.0.0"
$env:OPENAI_API_KEY = "<YOUR_OPENAI_API_KEY>"
```

**Using .env file:**

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
OTEL_EXPORTER_OTLP_ENDPOINT=https://api.orq.ai/v2/otel
OTEL_EXPORTER_OTLP_HEADERS=Authorization=Bearer <ORQ_API_KEY>
OTEL_RESOURCE_ATTRIBUTES=service.name=llamaindex-app,service.version=1.0.0
OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
```

### Integration Example

<Info>
  We'll be using OpenInference as TracerProvider with LlamaIndex
</Info>

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Initialize OpenTelemetry
tracer_provider = trace_sdk.TracerProvider()
tracer_provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter(
    endpoint="https://api.orq.ai/v2/otel/v1/traces",
    headers={"Authorization": "Bearer <ORQ_API_KEY>"}
)))
trace.set_tracer_provider(tracer_provider)

# Instrument LlamaIndex
LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)

# Your LlamaIndex code is automatically traced
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)

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

### View Traces

View your traces in the [AI Studio](/docs/observability/overview) in the **Traces** tab.

<Frame caption="Traces from your LlamaIndex execution will be visible within the Traces menu in your AI Studio.">
  <img src="https://mintcdn.com/orqai/EqUGDI2og-dnTmDI/images/docs/779f47487a2811d05b67d019e49c9b8b69c00b517a572d583b045c765976965e-Screenshot_2025-09-26_at_12.09.15.png?fit=max&auto=format&n=EqUGDI2og-dnTmDI&q=85&s=3a305403abe65cef5daf1b3cb75f67cc" alt="Traces from your LlamaIndex execution will be visible within the Traces menu in your AI Studio." width="1571" height="1074" data-path="images/docs/779f47487a2811d05b67d019e49c9b8b69c00b517a572d583b045c765976965e-Screenshot_2025-09-26_at_12.09.15.png" />
</Frame>

<Info>
  Visit your [AI Studio](https://my.orq.ai) to view real-time analytics and traces.
</Info>

## Evaluations & Experiments

Once your agents are running, use **Evaluatorq** to score outputs across a dataset and **Experiments** to compare configurations side-by-side.

<CardGroup cols={2}>
  <Card title="Run Evaluations with Evaluatorq" icon="flask" href="/docs/evaluators/build#evaluatorq">
    Run parallel evaluations across your agents and compare results.
  </Card>

  <Card title="Run Experiments via the API" icon="flask-vial" href="/docs/experiments/api">
    Compare agent configurations and view results in the AI Studio.
  </Card>
</CardGroup>