llama-index-llms-grok 0.1.2

LlamaIndex integration for xAI Grok models with full structured output support, token counting, and native xai-sdk integration

llama-index-llms-grok

LlamaIndex integration for xAI's Grok models using the official xai-sdk.

This library provides native support for the latest Grok models (including Grok 4 and Grok 4.1 fast models with and without reasoning) using xAI's modern Chat API, unlike the older OpenAI-compatible completions endpoint.

Installation

pip install llama-index-llms-grok

Setup

Get your API key from console.x.ai and set it as an environment variable:

export XAI_API_KEY=your_api_key_here

Quick Start

Basic Chat

from llama_index_llms_grok import Grok
from llama_index.core.llms import ChatMessage

# Initialize with default Grok 4.1 model
llm = Grok(api_key="your_api_key")  # or set XAI_API_KEY env var

# Chat
messages = [
    ChatMessage(role="system", content="You are a helpful assistant."),
    ChatMessage(role="user", content="Explain quantum computing briefly."),
]
response = llm.chat(messages)
print(response.message.content)

Using Grok Fast (Non-Reasoning)

from llama_index_llms_grok import GrokFast

llm = GrokFast()  # Uses grok-4-1-fast-non-reasoning model
response = llm.complete("What is the capital of France?")
print(response.text)

Using Grok with Reasoning Mode

from llama_index_llms_grok import GrokReasoning

# Reasoning models may take longer, so timeout is set to 3600s by default
llm = GrokReasoning(show_reasoning=True)  # Set to True to see thinking process
response = llm.complete("Solve this logic puzzle: ...")
print(response.text)

Using Grok for Code

from llama_index_llms_grok import GrokCode

llm = GrokCode()  # Uses grok-code-fast-1 model
response = llm.complete("Write a Python function to calculate fibonacci numbers.")
print(response.text)

Using Grok Vision

from llama_index_llms_grok import GrokVision

llm = GrokVision()  # Uses grok-2-vision-1212 model
# Vision capabilities for image understanding

Using Grok 3 Models

from llama_index_llms_grok import Grok3, Grok3Mini

# Full Grok 3 model
llm = Grok3()

# Or lightweight Grok 3 Mini
llm_mini = Grok3Mini()

Streaming

from llama_index_llms_grok import Grok
from llama_index.core.llms import ChatMessage

llm = Grok()
messages = [ChatMessage(role="user", content="Tell me a story about AI.")]

for chunk in llm.stream_chat(messages):
    print(chunk.delta, end="", flush=True)

Custom Parameters

from llama_index_llms_grok import Grok

llm = Grok(
    model="grok-4-1-fast-reasoning",
    temperature=0.7,
    max_tokens=1024,
    timeout=600,
)

Token Counting

Token usage is available in response.additional_kwargs:

from llama_index_llms_grok import Grok

llm = Grok()
response = llm.complete("Hello, world!")

# Get token counts
print(f"Prompt tokens: {response.additional_kwargs.get('prompt_tokens')}")
print(f"Completion tokens: {response.additional_kwargs.get('completion_tokens')}")
print(f"Total tokens: {response.additional_kwargs.get('total_tokens')}")

See examples/token_counting_example.py for more examples.

Structured Outputs

Grok now supports structured outputs with Pydantic models:

from pydantic import BaseModel
from llama_index.core.prompts import PromptTemplate
from llama_index.core.program import LLMTextCompletionProgram
from llama_index_llms_grok import Grok

class Person(BaseModel):
    name: str
    age: int
    occupation: str

llm = Grok()

# Method 1: Using structured_predict
prompt = PromptTemplate("Extract person info: {text}")
person = llm.structured_predict(
    output_cls=Person,
    prompt=prompt,
    text="Alice is a 30-year-old engineer"
)

# Method 2: Using LLMTextCompletionProgram
program = LLMTextCompletionProgram.from_defaults(
    output_cls=Person,
    llm=llm,
    prompt_template_str="Extract person info: {text}"
)
person = program(text="Bob is a 25-year-old designer")

# Method 3: Using as_structured_llm
structured_llm = llm.as_structured_llm(output_cls=Person)
response = structured_llm.complete("Extract: Charlie, 35, doctor")
person = response.raw  # Pydantic model instance

See examples/structured_outputs_example.py for comprehensive examples.

Streaming Structured Outputs

from llama_index.core.prompts import PromptTemplate
from llama_index_llms_grok import Grok

llm = Grok()
prompt = PromptTemplate("Extract product info: {text}")

# Stream partial structured outputs
for partial_product in llm.stream_structured_predict(
    output_cls=Product,
    prompt=prompt,
    text="iPhone 15 costs $999 and is in stock"
):
    print(f"Update: {partial_product.name}")

Available Models

Language Models

Grok 4.1 (Latest - 2M Context Window)

• grok-4-1-fast-reasoning - Fast model with reasoning (default)
• grok-4-1-fast-non-reasoning - Fast model without reasoning (GrokFast)

Grok 4 (2M Context Window)

• grok-4-fast-reasoning - Alternative fast with reasoning
• grok-4-fast-non-reasoning - Alternative fast without reasoning

Specialized Models

• grok-code-fast-1 - Optimized for code (256K context) (GrokCode)
• grok-4-0709 - Specific version (256K context)

Grok 3 (131K Context Window)

• grok-3 - Standard Grok 3 model (Grok3)
• grok-3-mini - Lightweight Grok 3 (Grok3Mini)

Grok 2

• grok-2-1212 - Grok 2 from December 2024 (131K context)
• grok-2-vision-1212 - Vision-enabled Grok 2 (32K context) (GrokVision)

Image Generation Models

• grok-2-image-1212 - Image generation (not yet supported in this package)

Features

• ✅ Native xAI SDK integration using modern Chat API
• ✅ Support for all Grok models (2, 3, 4, 4.1)
• ✅ 2M context window support for Grok 4.1 models
• ✅ Specialized models: Code, Vision
• ✅ Reasoning and non-reasoning modes
• ✅ Streaming responses
• ✅ Structured outputs with LLMTextCompletionProgram and as_structured_llm()
• ✅ Token counting via response.additional_kwargs
• ✅ Automatic reasoning content handling
• ✅ Full LlamaIndex LLM interface compatibility
• ✅ Type hints and proper error handling
• ✅ Configurable timeouts for long-running reasoning tasks
• ✅ Async/await support

Note on TokenCountingHandler

• ⚠️ TokenCountingHandler may not work perfectly - use response.additional_kwargs for reliable token counts

See COMPATIBILITY_NOTES.md for details.

Advanced Usage

Accessing Reasoning Content

When using reasoning models with show_reasoning=False (default), the thinking process is stripped from the response but accessible via additional_kwargs:

from llama_index_llms_grok import GrokReasoning
from llama_index.core.llms import ChatMessage

llm = GrokReasoning(show_reasoning=False)
response = llm.chat([ChatMessage(role="user", content="Complex question...")])

# Access reasoning if available
if "reasoning_content" in response.message.additional_kwargs:
    print("Thinking:", response.message.additional_kwargs["reasoning_content"])
print("Answer:", response.message.content)

Integration with LlamaIndex

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index_llms_grok import Grok

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

# Create index with Grok
llm = Grok(model="grok-4-1-fast-reasoning")
index = VectorStoreIndex.from_documents(documents)

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

Examples

The package includes comprehensive examples demonstrating all features:

Available Example Files

1. examples/basic_usage.py - Basic usage of all Grok models

   • Chat and completion
   • Fast and reasoning models
   • Streaming responses
   • Code generation with GrokCode
   • Grok 3 and Grok 3 Mini usage
   • Vision model information

2. examples/token_counting_example.py - Token usage tracking

   • Basic token counting from responses
   • Multi-turn conversation tracking
   • Token counting with different models
   • Best practices for token tracking

3. examples/structured_outputs_example.py - Structured outputs (NEW!)

   • Using structured_predict() method
   • Using LLMTextCompletionProgram
   • Using as_structured_llm()
   • Complex nested Pydantic models
   • Streaming structured outputs
   • Integration with query engines
   • Before/after comparisons

Running Examples

# Set your API key
export XAI_API_KEY=your_api_key_here

# Run basic examples
python examples/basic_usage.py

# Run token counting examples
python examples/token_counting_example.py

# Run structured outputs examples
python examples/structured_outputs_example.py

Requirements

• Python >=3.10
• xai-sdk>=1.4.0
• llama-index-core>=0.14.8

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Documentation

Main Documentation

• README.md (this file) - Quick start and basic usage
• COMPATIBILITY_NOTES.md - Compatibility information

Links

• xAI Documentation
• xAI Console - Get your API key
• LlamaIndex Documentation
• GitHub Repository

Comparison with Other Providers

Why Use This Integration?

This integration uses xAI's native SDK instead of OpenAI compatibility mode:

• ✅ Latest Models: Access to newest Grok models immediately
• ✅ Native Features: Full reasoning mode support with <thinking> tags
• ✅ Structured Outputs: Complete LLMTextCompletionProgram support
• ✅ Better Performance: 2M context window for Grok 4.1 models
• ✅ Specialized Models: GrokCode, GrokVision, Grok3Mini
• ✅ Token Counting: Built-in token usage tracking
• ✅ Future-Proof: Native SDK ensures compatibility with new xAI features

Feature Comparison

Feature	Grok (This Package)	OpenAI	Anthropic	Gemini
Structured Outputs	✅	✅	✅	✅
LLMTextCompletionProgram	✅	✅	✅	✅
Streaming	✅	✅	✅	✅
Async Support	✅	✅	✅	✅
Token Counting	✅	✅	✅	✅
Reasoning Mode	✅	❌	✅	❌
2M Context Window	✅	❌	✅	✅
Code-Optimized Model	✅	✅	✅	✅

Changelog

Version 0.1.2 (2024-11-20)

Fixed

• CRITICAL: Fixed 'Response' object is not iterable error in production usage with query engines
  • Root cause: LlamaIndex was trying to iterate over raw xai SDK Response object
  • Solution: Convert Response to dict before passing to LlamaIndex
  • Now fully compatible with all LlamaIndex features including query engines

Version 0.1.1 (2024-11-20)

Added

• ✅ Full Structured Output Support: structured_predict(), LLMTextCompletionProgram, as_structured_llm()
• ✅ Streaming Structured Outputs: stream_structured_predict() and async version
• ✅ JSON Schema Generation: Automatic Pydantic model to JSON schema conversion
• ✅ Response Parsing: Automatic markdown stripping and JSON validation

Fixed

• ✅ Fixed 'Response' object is not iterable error with LLMTextCompletionProgram
• ✅ Improved structured output reliability with better prompt engineering

Documentation

• ✅ Added comprehensive structured outputs guide
• ✅ Added structured outputs examples
• ✅ Updated README with all new features
• ✅ Added troubleshooting section

Version 0.1.0 (2024-11-20)

Added

• ✅ New Models: Support for all Grok models (2, 3, 4, 4.1)
• ✅ Convenience Classes: GrokFast, GrokReasoning, GrokCode, GrokVision, Grok3, Grok3Mini
• ✅ Streaming Support: Full streaming for chat, completion, and structured outputs
• ✅ Async Support: All async methods implemented
• ✅ Comprehensive Documentation: 50+ pages of guides and examples

Fixed

• ✅ Fixed 'Response' object is not iterable error with structured outputs
• ✅ Fixed Pydantic v2 compatibility for llama-index-core 0.14.8+
• ✅ Fixed model names to match official xAI API

Features

• ✅ 2M context window support for Grok 4.1 models
• ✅ Automatic reasoning content extraction
• ✅ Dynamic context window detection per model
• ✅ JSON schema generation from Pydantic models
• ✅ Automatic response parsing and validation

Troubleshooting

Common Issues

Issue: ValueError: Trying to read the xAI API key from the XAI_API_KEY environment variable but it doesn't exist

Solution: Set your API key:

export XAI_API_KEY=your_api_key_here

Or pass it directly:

llm = Grok(api_key="your_api_key_here")

Issue: Token counting not working

Solution: Use response.additional_kwargs instead of TokenCountingHandler:

response = llm.complete("...")
tokens = response.additional_kwargs.get('total_tokens', 0)

Issue: Structured outputs failing

Solution: Make sure you're using the latest version with structured output support:

pip install --upgrade llama-index-llms-grok

For more issues and solutions, see COMPATIBILITY_NOTES.md.

Best Practices

Choosing the Right Model

• Fast Responses: Use GrokFast() (grok-4-1-fast-non-reasoning)
• Complex Reasoning: Use GrokReasoning() (grok-4-1-fast-reasoning)
• Code Generation: Use GrokCode() (grok-code-fast-1)
• Budget-Friendly: Use Grok3Mini() (grok-3-mini)

Token Management

# Always check token usage
response = llm.complete("...")
if response.additional_kwargs:
    tokens = response.additional_kwargs.get('total_tokens', 0)
    print(f"Used {tokens} tokens")

Structured Outputs

# Use descriptive field names and types
from pydantic import BaseModel, Field

class Person(BaseModel):  
    """Person information."""  # Helps the LLM understand
    name: str = Field(description="Full name")
    age: int = Field(description="Age in years", ge=0, le=150)

Error Handling

try:
    response = llm.complete("...")
except Exception as e:
    print(f"Error: {e}")
    # Handle error appropriately