UiPath LangChain Client
LangChain-compatible chat models and embeddings for accessing LLMs through UiPath's infrastructure.
Installation
# Base installation (normalized API only)
pip install uipath-langchain-client
# With specific provider extras for passthrough mode
pip install "uipath-langchain-client[openai]" # OpenAI/Azure models
pip install "uipath-langchain-client[google]" # Google Gemini models
pip install "uipath-langchain-client[anthropic]" # Anthropic Claude models
pip install "uipath-langchain-client[azure]" # Azure AI models
pip install "uipath-langchain-client[bedrock]" # AWS Bedrock models
pip install "uipath-langchain-client[vertexai]" # Google VertexAI models
pip install "uipath-langchain-client[fireworks]" # Fireworks AI models
pip install "uipath-langchain-client[all]" # All providers
Quick Start
Using Factory Functions (Recommended)
The factory functions automatically detect the model vendor and return the appropriate client:
from uipath_langchain_client import get_chat_model, get_embedding_model
from uipath_langchain_client.settings import get_default_client_settings
# Get default settings (uses UIPATH_LLM_SERVICE env var or defaults to AgentHub)
settings = get_default_client_settings()
# Chat model - vendor auto-detected from model name
chat_model = get_chat_model(
model_name="gpt-4o-2024-11-20",
client_settings=settings,
)
response = chat_model.invoke("Hello, how are you?")
print(response.content)
# Embeddings model
embeddings = get_embedding_model(
model_name="text-embedding-3-large",
client_settings=settings,
)
vectors = embeddings.embed_documents(["Hello world"])
print(f"Embedding dimension: {len(vectors[0])}")
Using Direct Client Classes
For more control, instantiate provider-specific classes directly:
from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI
from uipath_langchain_client.clients.google.chat_models import UiPathChatGoogleGenerativeAI
from uipath_langchain_client.clients.anthropic.chat_models import UiPathChatAnthropic
from uipath_langchain_client.clients.normalized.chat_models import UiPathChat
from uipath_langchain_client.settings import get_default_client_settings
settings = get_default_client_settings()
# OpenAI/Azure
openai_chat = UiPathAzureChatOpenAI(model="gpt-4o-2024-11-20", settings=settings)
# Google Gemini
gemini_chat = UiPathChatGoogleGenerativeAI(model="gemini-2.5-flash", settings=settings)
# Anthropic Claude (via AWS Bedrock)
claude_chat = UiPathChatAnthropic(
model="anthropic.claude-sonnet-4-5-20250929-v1:0",
settings=settings,
vendor_type="awsbedrock",
)
# Normalized (provider-agnostic)
normalized_chat = UiPathChat(model="gpt-4o-2024-11-20", settings=settings)
Available Client Types
Passthrough Mode (Default)
Uses vendor-specific APIs through UiPath's gateway. Full feature parity with native SDKs.
Chat Models:
| Class | Provider | Extra | Models |
|---|---|---|---|
UiPathAzureChatOpenAI |
OpenAI/Azure (UiPath-owned) | [openai] |
GPT-4o, GPT-4, o1, o3, etc. |
UiPathChatOpenAI |
OpenAI (BYO) | [openai] |
GPT-4o, GPT-4, etc. |
UiPathChatGoogleGenerativeAI |
[google] |
Gemini 2.5, 2.0, 1.5 | |
UiPathChatAnthropic |
Anthropic (via Bedrock) | [anthropic] |
Claude Sonnet 4.5, Opus, etc. |
UiPathChatAnthropicVertex |
Anthropic (via VertexAI) | [vertexai] |
Claude models |
UiPathChatBedrock |
AWS Bedrock (invoke API) | [bedrock] |
Bedrock-hosted models |
UiPathChatBedrockConverse |
AWS Bedrock (Converse API) | [bedrock] |
Bedrock-hosted models |
UiPathChatFireworks |
Fireworks AI | [fireworks] |
Various open-source models |
UiPathAzureAIChatCompletionsModel |
Azure AI | [azure] |
Various Azure AI models |
Embeddings:
| Class | Provider | Extra | Models |
|---|---|---|---|
UiPathAzureOpenAIEmbeddings |
OpenAI/Azure (UiPath-owned) | [openai] |
text-embedding-3-large/small |
UiPathOpenAIEmbeddings |
OpenAI (BYO) | [openai] |
text-embedding-3-large/small |
UiPathGoogleGenerativeAIEmbeddings |
[google] |
text-embedding-004 | |
UiPathBedrockEmbeddings |
AWS Bedrock | [bedrock] |
Titan Embeddings, etc. |
UiPathFireworksEmbeddings |
Fireworks AI | [fireworks] |
Various |
UiPathAzureAIEmbeddingsModel |
Azure AI | [azure] |
Various Azure AI models |
Normalized Mode
Uses UiPath's normalized API for a consistent interface across all providers. No extra dependencies required.
| Class | Type | Description |
|---|---|---|
UiPathChat |
Chat | Provider-agnostic chat completions |
UiPathEmbeddings |
Embeddings | Provider-agnostic embeddings |
Features
Streaming
from uipath_langchain_client import get_chat_model
from uipath_langchain_client.settings import get_default_client_settings
settings = get_default_client_settings()
chat_model = get_chat_model(model_name="gpt-4o-2024-11-20", client_settings=settings)
# Sync streaming
for chunk in chat_model.stream("Write a haiku about Python"):
print(chunk.content, end="", flush=True)
# Async streaming
async for chunk in chat_model.astream("Write a haiku about Python"):
print(chunk.content, end="", flush=True)
Tool Calling
from langchain_core.tools import tool
@tool
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
return f"Sunny, 72°F in {city}"
chat_model = get_chat_model(model_name="gpt-4o-2024-11-20", client_settings=settings)
model_with_tools = chat_model.bind_tools([get_weather])
response = model_with_tools.invoke("What's the weather in Tokyo?")
print(response.tool_calls)
LangGraph Agents
from langgraph.prebuilt import create_react_agent
from langchain_core.tools import tool
@tool
def search(query: str) -> str:
"""Search the web."""
return f"Results for: {query}"
chat_model = get_chat_model(model_name="gpt-4o-2024-11-20", client_settings=settings)
agent = create_react_agent(chat_model, [search])
result = agent.invoke({"messages": [("user", "Search for UiPath documentation")]})
Extended Thinking (Model-Specific)
# OpenAI o1/o3 reasoning
chat_model = get_chat_model(
model_name="o3-mini",
client_settings=settings,
client_type="normalized",
reasoning_effort="medium", # "low", "medium", "high"
)
# Anthropic Claude thinking
chat_model = get_chat_model(
model_name="claude-sonnet-4-5",
client_settings=settings,
client_type="normalized",
thinking={"type": "enabled", "budget_tokens": 10000},
)
# Gemini thinking
chat_model = get_chat_model(
model_name="gemini-2.5-pro",
client_settings=settings,
client_type="normalized",
thinking_level="medium",
include_thoughts=True,
)
Configuration
Retry Configuration
# RetryConfig is a TypedDict - all fields are optional with sensible defaults
retry_config = {
"initial_delay": 2.0, # Initial delay before first retry
"max_delay": 60.0, # Maximum delay between retries
"exp_base": 2.0, # Exponential backoff base
"jitter": 1.0, # Random jitter to add
}
chat_model = get_chat_model(
model_name="gpt-4o-2024-11-20",
client_settings=settings,
max_retries=3,
retry_config=retry_config,
)
Request Timeout
chat_model = get_chat_model(
model_name="gpt-4o-2024-11-20",
client_settings=settings,
request_timeout=120, # Client-side timeout in seconds
)
API Reference
get_chat_model()
Factory function to create a chat model. Automatically detects the model vendor by querying UiPath's discovery endpoint and returns the appropriate LangChain model class.
Parameters:
model_name(str): Name of the model (e.g., "gpt-4o-2024-11-20")byo_connection_id(str | None): Optional BYO connection ID for custom-enrolled models (default: None)client_settings(UiPathBaseSettings | None): Client settings for authentication (default: auto-detected)client_type(Literal["passthrough", "normalized"]): API mode (default: "passthrough")**model_kwargs: Additional arguments passed to the model constructor (e.g.,max_retries,retry_config,request_timeout)
Returns: UiPathBaseChatModel - A LangChain-compatible chat model
Raises: ValueError - If the model is not found in available models or vendor is not supported
get_embedding_model()
Factory function to create an embeddings model. Automatically detects the model vendor by querying UiPath's discovery endpoint and returns the appropriate LangChain embeddings class.
Parameters:
model_name(str): Name of the embeddings model (e.g., "text-embedding-3-large")byo_connection_id(str | None): Optional BYO connection ID for custom-enrolled models (default: None)client_settings(UiPathBaseSettings | None): Client settings for authentication (default: auto-detected)client_type(Literal["passthrough", "normalized"]): API mode (default: "passthrough")**model_kwargs: Additional arguments passed to the embeddings constructor (e.g.,max_retries,retry_config,request_timeout)
Returns: UiPathBaseEmbeddings - A LangChain-compatible embeddings model
Raises: ValueError - If the model is not found or the vendor is not supported
UiPathChat Parameter Reference
The normalized UiPathChat model supports the following parameters:
Standard Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
model (alias: model_name) |
str |
Required | Model identifier (e.g., "gpt-4o-2024-11-20", "gemini-2.5-flash") |
max_tokens |
int | None |
None |
Maximum number of tokens in the response |
temperature |
float | None |
None |
Sampling temperature (0.0 to 2.0) |
stop (alias: stop_sequences) |
list[str] | str | None |
None |
Stop sequences to end generation |
n |
int | None |
None |
Number of completions to generate |
top_p |
float | None |
None |
Nucleus sampling probability mass |
presence_penalty |
float | None |
None |
Penalty for repeated tokens (-2.0 to 2.0) |
frequency_penalty |
float | None |
None |
Frequency-based repetition penalty (-2.0 to 2.0) |
verbosity |
str | None |
None |
Response verbosity: "low", "medium", or "high" |
model_kwargs |
dict[str, Any] |
{} |
Additional model-specific parameters |
disabled_params |
dict[str, Any] | None |
None |
Parameters to exclude from requests |
Extended Thinking Parameters
| Parameter | Provider | Type | Description |
|---|---|---|---|
reasoning |
OpenAI (o1/o3) | dict[str, Any] | None |
Reasoning config, e.g., {"effort": "medium", "summary": "auto"} |
reasoning_effort |
OpenAI (o1/o3) | str | None |
Shorthand: "minimal", "low", "medium", or "high" |
thinking |
Anthropic Claude | dict[str, Any] | None |
Thinking config, e.g., {"type": "enabled", "budget_tokens": 10000} |
thinking_level |
Google Gemini | str | None |
Thinking depth level |
thinking_budget |
Google Gemini | int | None |
Token budget for thinking |
include_thoughts |
Google Gemini | bool | None |
Whether to include thinking in responses |
Base Client Parameters (All Models)
All LangChain model classes (UiPathChat, UiPathAzureChatOpenAI, etc.) inherit these from UiPathBaseLLMClient:
| Parameter | Type | Default | Description |
|---|---|---|---|
model (alias: model_name) |
str |
Required | Model identifier |
settings (alias: client_settings) |
UiPathBaseSettings |
Auto-detected | Client settings for auth and routing |
byo_connection_id |
str | None |
None |
BYO connection ID for custom-enrolled models |
request_timeout (aliases: timeout, default_request_timeout) |
int | None |
None |
Client-side request timeout in seconds |
max_retries |
int |
0 |
Maximum number of retries for failed requests |
retry_config |
RetryConfig | None |
None |
Retry configuration for failed requests |
logger |
logging.Logger | None |
None |
Logger instance for request/response logging |
default_headers |
Mapping[str, str] | None |
See note | Additional request headers (see Default Headers) |
Low-Level Methods
UiPathBaseLLMClient also exposes these methods for advanced use cases:
| Method | Description |
|---|---|
uipath_request(method, url, *, request_body, **kwargs) |
Synchronous HTTP request, returns httpx.Response |
uipath_arequest(method, url, *, request_body, **kwargs) |
Asynchronous HTTP request, returns httpx.Response |
uipath_stream(method, url, *, request_body, stream_type, **kwargs) |
Synchronous streaming, yields str | bytes |
uipath_astream(method, url, *, request_body, stream_type, **kwargs) |
Asynchronous streaming, yields str | bytes |
The stream_type parameter controls iteration: "lines" (default, best for SSE), "text", "bytes", or "raw".
See Also
- Main README - Overview and core client documentation
- UiPath LLM Client - Low-level HTTP client
Metadata
Release files for uipath-langchain-client 1.18.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
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Total release size: 95.0 kB
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