LLMs and Embeddings¶

UiPath provides pre-configured LLM and embedding classes that handle authentication, routing, and configuration automatically, allowing you to focus on building your agents. You do not need to add API keys from OpenAI, AWS, or Google, usage of these models will consume Agent Units on your account.

UiPathOpenAI¶

The UiPathOpenAI class is a pre-configured Azure OpenAI client that routes requests through UiPath.

Available Models¶

The following OpenAI models are available through the OpenAIModel enum:

GPT_4_1_2025_04_14
GPT_4_1_MINI_2025_04_14
GPT_4_1_NANO_2025_04_14
GPT_4O_2024_05_13
GPT_4O_2024_08_06
GPT_4O_2024_11_20
GPT_4O_MINI_2024_07_18 (default)
O3_MINI_2025_01_31
TEXT_DAVINCI_003

Basic Usage¶

from uipath_llamaindex.llms import UiPathOpenAI
from llama_index.core.llms import ChatMessage

# Create an LLM instance with default settings
llm = UiPathOpenAI()

# Create chat messages
messages = [
    ChatMessage(
        role="system", content="You are a pirate with colorful personality."
    ),
    ChatMessage(role="user", content="Hello"),
]

# Generate a response
response = llm.chat(messages)
print(response)

Custom Model Configuration¶

from uipath_llamaindex.llms import UiPathOpenAI, OpenAIModel

# Use a specific model
llm = UiPathOpenAI(model=OpenAIModel.GPT_4_1_2025_04_14)

# Or use a model string directly
llm = UiPathOpenAI(model="gpt-4.1-2025-04-14")

UiPathOpenAIEmbedding¶

The UiPathOpenAIEmbedding class provides text embedding capabilities using OpenAI's embedding models through UiPath.

Available Embedding Models¶

The following embedding models are available through the OpenAIEmbeddingModel enum:

TEXT_EMBEDDING_ADA_002 (default)
TEXT_EMBEDDING_3_LARGE

Basic Usage¶

from uipath_llamaindex.embeddings import UiPathOpenAIEmbedding

# Create an embedding model instance
embed_model = UiPathOpenAIEmbedding()

# Get embeddings for a single text
result = embed_model.get_text_embedding("the quick brown fox jumps over the lazy dog")
print(f"Embedding dimension: {len(result)}")

Batch Embeddings¶

from uipath_llamaindex.embeddings import UiPathOpenAIEmbedding

embed_model = UiPathOpenAIEmbedding()

# Get embeddings for multiple texts
texts = [
    "Hello world",
    "How are you?",
    "This is a test"
]

embeddings = embed_model.get_text_embedding_batch(texts)
print(f"Number of embeddings: {len(embeddings)}")

UiPathChatBedrock and UiPathChatBedrockConverse¶

UiPathChatBedrock and UiPathChatBedrockConverse provide access to AWS Bedrock models through UiPath using the Invoke API and Converse API respectively.

Installation¶

These classes require additional dependencies. Install them with:

pip install uipath-llamaindex[bedrock]
# or using uv:
uv add 'uipath-llamaindex[bedrock]'

Example Usage¶

from uipath_llamaindex.llms.bedrock import UiPathChatBedrockConverse
from uipath_llamaindex.llms import BedrockModel
from llama_index.core.llms import ChatMessage

# Create an LLM instance with default settings
llm = UiPathChatBedrockConverse()

# Or use a specific model
llm = UiPathChatBedrockConverse(model=BedrockModel.anthropic_claude_sonnet_4_5)

# Create chat messages
messages = [
    ChatMessage(role="user", content="Hello"),
]

# Generate a response
response = llm.chat(messages)
print(response)

Similarly, UiPathChatBedrock can be used with the Invoke API:

from uipath_llamaindex.llms.bedrock import UiPathChatBedrock
from uipath_llamaindex.llms import BedrockModel

llm = UiPathChatBedrock(model=BedrockModel.anthropic_claude_sonnet_4)

Currently, the following models can be used (this list can be updated in the future):

anthropic.claude-3-7-sonnet-20250219-v1:0, anthropic.claude-sonnet-4-20250514-v1:0, anthropic.claude-sonnet-4-5-20250929-v1:0, anthropic.claude-haiku-4-5-20251001-v1:0

UiPathVertex¶

UiPathVertex provides access to Google Vertex AI (Gemini) models through UiPath.

Installation¶

This class requires additional dependencies. Install them with:

pip install uipath-llamaindex[vertex]
# or using uv:
uv add 'uipath-llamaindex[vertex]'

Example Usage¶

from uipath_llamaindex.llms.vertex import UiPathVertex
from uipath_llamaindex.llms import GeminiModel
from llama_index.core.llms import ChatMessage

# Create an LLM instance with default settings
llm = UiPathVertex()

# Or use a specific model
llm = UiPathVertex(model=GeminiModel.gemini_2_5_pro)

# Create chat messages
messages = [
    ChatMessage(role="user", content="Hello"),
]

# Generate a response
response = llm.chat(messages)
print(response)

Currently, the following models can be used (this list can be updated in the future):

gemini-2.0-flash-001, gemini-2.5-flash, gemini-2.5-pro

Integration with LlamaIndex¶

These classes integrate seamlessly with LlamaIndex components:

Using with Agents¶

import asyncio
from llama_index.core.agent.workflow import ReActAgent
from uipath_llamaindex.llms import UiPathOpenAI, OpenAIModel

def multiply(a: int, b: int) -> int:
    """Multiply two integers and returns the result integer"""
    return a * b


def add(a: int, b: int) -> int:
    """Add two integers and returns the result integer"""
    return a + b

# Create agent with UiPath LLM
agent = ReActAgent(
    tools=[multiply, add],
    llm=UiPathOpenAI(model=OpenAIModel.GPT_4_1_2025_04_14))

async def main():
    handler = agent.run("What is 2+(2*4)?")
    response = await handler

asyncio.run(main())

Using with VectorStoreIndex¶

from llama_index.core import VectorStoreIndex, Document
from uipath_llamaindex.llms import UiPathOpenAI
from uipath_llamaindex.embeddings import UiPathOpenAIEmbedding

# Create documents
documents = [
    Document(text="This is a sample document about artificial intelligence."),
    Document(text="Machine learning is a subset of AI that focuses on algorithms."),
]

# Create index with UiPath models
index = VectorStoreIndex.from_documents(
    documents,
    embed_model=UiPathOpenAIEmbedding()
)

# Create query engine with UiPath LLM
query_engine = index.as_query_engine(
    llm=UiPathOpenAI(model=OpenAIModel.GPT_4_1_2025_04_14)
)

response = query_engine.query("What is machine learning?")

Warning

Please note that you may get errors related to data residency, as some models are not available on all regions.

Example: [Enforced Region] No model configuration found for product uipath-python-sdk in EU.