> For the complete documentation index, see [llms.txt](https://docs.tensorx.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tensorx.ai/developer-sdks/llamaindex.md).

# LlamaIndex

[LlamaIndex](https://www.llamaindex.ai/) is a data framework for LLM applications. Connect it to TensorX using the OpenAI-compatible configuration.

## Prerequisites

* Python 3.8+
* LlamaIndex installed
* TensorX API key from [app.tensorx.ai](https://app.tensorx.ai)

## Installation

```bash
pip install llama-index llama-index-llms-openai
```

## Configuration

### Basic Usage

```python
import os
from llama_index.llms.openai import OpenAI

# Set environment variables
os.environ["OPENAI_API_KEY"] = "your-tensorx-api-key"
os.environ["OPENAI_API_BASE"] = "https://api.tensorx.ai/v1"

# Create LLM instance
llm = OpenAI(
    model="claude-sonnet-4-20250514",
    api_key="your-tensorx-api-key",
    api_base="https://api.tensorx.ai/v1"
)

# Simple completion
response = llm.complete("Explain quantum computing in simple terms")
print(response)
```

### Chat Interface

```python
from llama_index.core.llms import ChatMessage
from llama_index.llms.openai import OpenAI

llm = OpenAI(
    model="claude-sonnet-4-20250514",
    api_key="your-tensorx-api-key",
    api_base="https://api.tensorx.ai/v1"
)

messages = [
    ChatMessage(role="system", content="You are a helpful coding assistant"),
    ChatMessage(role="user", content="Write a Python function to calculate fibonacci numbers")
]

response = llm.chat(messages)
print(response)
```

### Streaming Responses

```python
from llama_index.llms.openai import OpenAI

llm = OpenAI(
    model="gpt-4o",
    api_key="your-tensorx-api-key",
    api_base="https://api.tensorx.ai/v1"
)

# Streaming completion
response = llm.stream_complete("Write a short story about AI")
for chunk in response:
    print(chunk.delta, end="")
```

## RAG Applications

Build retrieval-augmented generation (RAG) systems with TensorX:

```python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# Configure TensorX LLM
Settings.llm = OpenAI(
    model="claude-sonnet-4-20250514",
    api_key="your-tensorx-api-key",
    api_base="https://api.tensorx.ai/v1"
)

# Configure embeddings (if using TensorX embeddings)
Settings.embed_model = OpenAIEmbedding(
    model="text-embedding-3-small",
    api_key="your-tensorx-api-key",
    api_base="https://api.tensorx.ai/v1"
)

# Load documents
documents = SimpleDirectoryReader("./data").load_data()

# Create index
index = VectorStoreIndex.from_documents(documents)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What are the key points in these documents?")
print(response)
```

## Agent Applications

Create agents with TensorX:

```python
from llama_index.core.agent import ReActAgent
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI

# Define tools
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

def add(a: int, b: int) -> int:
    """Add two numbers."""
    return a + b

multiply_tool = FunctionTool.from_defaults(fn=multiply)
add_tool = FunctionTool.from_defaults(fn=add)

# Create agent
llm = OpenAI(
    model="gpt-4o",
    api_key="your-tensorx-api-key",
    api_base="https://api.tensorx.ai/v1"
)

agent = ReActAgent.from_tools(
    [multiply_tool, add_tool],
    llm=llm,
    verbose=True
)

response = agent.chat("What is 20 plus 30, then multiplied by 2?")
print(response)
```

## Configuration with Settings

For global configuration across your application:

```python
from llama_index.core import Settings
from llama_index.llms.openai import OpenAI

# Set global LLM
Settings.llm = OpenAI(
    model="claude-sonnet-4-20250514",
    api_key="your-tensorx-api-key",
    api_base="https://api.tensorx.ai/v1",
    temperature=0.7,
    max_tokens=4000
)
```

## Available Models

See [TensorX Models](/api-reference/models.md) for all available models.

| Model                        | Best For                 |
| ---------------------------- | ------------------------ |
| `claude-sonnet-4-20250514`   | Complex RAG, agents      |
| `claude-3-5-sonnet-20241022` | General LLM tasks        |
| `gpt-4o`                     | Multi-modal applications |
| `gpt-4o-mini`                | Cost-effective inference |

## Troubleshooting

### Connection errors

Ensure `api_base` ends with `/v1`:

```python
api_base="https://api.tensorx.ai/v1"  # Correct
api_base="https://api.tensorx.ai"     # Wrong
```

### Token limits

Adjust `max_tokens` based on your needs:

```python
llm = OpenAI(
    model="claude-sonnet-4-20250514",
    api_key="your-tensorx-api-key",
    api_base="https://api.tensorx.ai/v1",
    max_tokens=8000  # Increase if needed
)
```

## Resources

* [LlamaIndex Documentation](https://docs.llamaindex.ai/)
* [LlamaIndex GitHub](https://github.com/run-llama/llama_index)
* [TensorX API Reference](/api-reference/overview.md)


---

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Perform an HTTP GET request on the current page URL with the `ask` query parameter:

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