LlamaIndex

LlamaIndex 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

Installation

pip install llama-index llama-index-llms-openai

Configuration

Basic Usage

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

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

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:

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:

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:

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 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:

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

Token limits

Adjust max_tokens based on your needs:

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
• LlamaIndex GitHub
• TensorX API Reference