From 295aa6a4e6840374d3fa06c73cd713fd2decafdc Mon Sep 17 00:00:00 2001 From: Adrian Badea Date: Mon, 10 Mar 2025 19:02:41 +0200 Subject: [PATCH 1/2] Chat: Add chat client and tests --- .gitattributes | 2 + .vscode/settings.json | 4 +- sdk/langchain/pyproject.toml | 9 +- sdk/langchain/tests/conftest.py | 53 + sdk/langchain/tests/test_langchain_client.py | 150 ++ .../uipath_langchain/chat/__init__.py | 6 + sdk/langchain/uipath_langchain/chat/models.py | 266 ++++ .../uipath_langchain/chat/utils/__init__.py | 0 .../chat/utils/_chat_types.py | 54 + .../uipath_langchain/embeddings/__init__.py | 6 + .../uipath_langchain/embeddings/embeddings.py | 109 ++ .../uipath_langchain/utils/__init__.py | 3 + .../uipath_langchain/utils/_request_mixin.py | 476 +++++++ .../uipath_langchain/utils/_settings.py | 87 ++ .../uipath_langchain/utils/_sleep_policy.py | 41 + ...-large5034ec3c-85c9-54b8-ac89-5e0cbcf99e3b | 3 + ...-largec48857ed-1302-5954-9e24-69fa9b45e457 | 3 + .../utils/tests/tests_uipath_cache.db | 3 + sdk/langchain/uv.lock | 1263 ++++++++++++++++- 19 files changed, 2459 insertions(+), 79 deletions(-) create mode 100644 .gitattributes create mode 100644 sdk/langchain/tests/conftest.py create mode 100644 sdk/langchain/tests/test_langchain_client.py create mode 100644 sdk/langchain/uipath_langchain/chat/__init__.py create mode 100644 sdk/langchain/uipath_langchain/chat/models.py create mode 100644 sdk/langchain/uipath_langchain/chat/utils/__init__.py create mode 100644 sdk/langchain/uipath_langchain/chat/utils/_chat_types.py create mode 100644 sdk/langchain/uipath_langchain/embeddings/__init__.py create mode 100644 sdk/langchain/uipath_langchain/embeddings/embeddings.py create mode 100644 sdk/langchain/uipath_langchain/utils/__init__.py create mode 100644 sdk/langchain/uipath_langchain/utils/_request_mixin.py create mode 100644 sdk/langchain/uipath_langchain/utils/_settings.py create mode 100644 sdk/langchain/uipath_langchain/utils/_sleep_policy.py create mode 100644 sdk/langchain/uipath_langchain/utils/tests/cached_embeddings/text-embedding-3-large5034ec3c-85c9-54b8-ac89-5e0cbcf99e3b create mode 100644 sdk/langchain/uipath_langchain/utils/tests/cached_embeddings/text-embedding-3-largec48857ed-1302-5954-9e24-69fa9b45e457 create mode 100644 sdk/langchain/uipath_langchain/utils/tests/tests_uipath_cache.db diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 000000000..c8f9eb864 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +*.db filter=lfs diff=lfs merge=lfs -text +**/cached_embeddings/** filter=lfs diff=lfs merge=lfs -text diff --git a/.vscode/settings.json b/.vscode/settings.json index 8554be1b7..ccf220d37 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -9,7 +9,6 @@ ".ruff_cache": true, ".venv": true }, - // Formatting "editor.formatOnSave": true, "[python]": { @@ -18,7 +17,6 @@ "source.organizeImports": "explicit" } }, - "workbench.colorCustomizations": { "titleBar.activeBackground": "#0099cc", "titleBar.inactiveBackground": "#0099cc" @@ -28,4 +26,4 @@ ], "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true -} +} \ No newline at end of file diff --git a/sdk/langchain/pyproject.toml b/sdk/langchain/pyproject.toml index 124ee7cf7..7d2973928 100644 --- a/sdk/langchain/pyproject.toml +++ b/sdk/langchain/pyproject.toml @@ -9,8 +9,14 @@ dependencies = [ "langgraph>=0.2.70", "langchain-core>=0.3.34", "langgraph-checkpoint-sqlite>=2.0.3", + "langchain-community>=0.3.18", + "langchain-openai>=0.3.3", + "langchain>=0.3.4", "requests>=2.23.3", - "types-requests>=2.32.0.20241016" + "types-requests>=2.32.0.20241016", + "pydantic-settings>=2.6.0", + "python-dotenv>=1.0.1", + "httpx>=0.27.0", ] classifiers = [ "Development Status :: 3 - Alpha", @@ -51,6 +57,7 @@ dev = [ "pytest-cov>=4.1.0", "pytest-mock>=3.11.1", "pre-commit>=4.1.0", + "numpy>=1.24.0", ] [project.optional-dependencies] diff --git a/sdk/langchain/tests/conftest.py b/sdk/langchain/tests/conftest.py new file mode 100644 index 000000000..dff1e28a5 --- /dev/null +++ b/sdk/langchain/tests/conftest.py @@ -0,0 +1,53 @@ +import logging +import os +from typing import Generator + +import pytest +from langchain.embeddings import CacheBackedEmbeddings +from langchain.globals import set_llm_cache +from langchain.storage import LocalFileStore +from langchain_community.cache import SQLiteCache + +from uipath_langchain.embeddings import UiPathOpenAIEmbeddings +from uipath_langchain.utils._settings import uipath_cached_paths_settings + + +@pytest.fixture(autouse=True) +def setup_test_env(): + """Fixture to set up test environment variables.""" + from dotenv import find_dotenv, load_dotenv + + load_dotenv(find_dotenv()) + + +@pytest.fixture(scope="session") +def cached_llmgw_calls() -> Generator[SQLiteCache | None, None, None]: + if not os.environ.get("UIPATH_TESTS_CACHE_LLMGW"): + yield None + else: + logging.info("Setting up LLMGW cache") + db_path = uipath_cached_paths_settings.cached_completion_db + os.makedirs(os.path.dirname(db_path), exist_ok=True) + cache = SQLiteCache(database_path=db_path) + set_llm_cache(cache) + yield cache + set_llm_cache(None) + return + + +@pytest.fixture(scope="session") +def cached_embedder() -> Generator[CacheBackedEmbeddings | None, None, None]: + if not os.environ.get("UIPATH_TESTS_CACHE_LLMGW"): + yield None + else: + logging.info("Setting up embeddings cache") + model = "text-embedding-3-large" + embedder = CacheBackedEmbeddings.from_bytes_store( + underlying_embeddings=UiPathOpenAIEmbeddings(model=model), + document_embedding_cache=LocalFileStore( + uipath_cached_paths_settings.cached_embeddings_dir + ), + namespace=model, + ) + yield embedder + return diff --git a/sdk/langchain/tests/test_langchain_client.py b/sdk/langchain/tests/test_langchain_client.py new file mode 100644 index 000000000..260767063 --- /dev/null +++ b/sdk/langchain/tests/test_langchain_client.py @@ -0,0 +1,150 @@ +import numpy as np +import pytest +from langchain_community.cache import SQLiteCache +from langchain_community.callbacks import get_openai_callback +from langchain_core.embeddings import Embeddings +from langchain_core.messages import HumanMessage, SystemMessage +from langchain_core.tools import tool +from pydantic import BaseModel, ValidationError + +from uipath_langchain.chat import ( + UiPathAzureChatOpenAI, + UiPathNormalizedChatModel, +) +from uipath_langchain.embeddings import ( + UiPathAzureOpenAIEmbeddings, + UiPathOpenAIEmbeddings, +) + + +def test_cached_call(cached_llmgw_calls: SQLiteCache | None): + model = UiPathAzureChatOpenAI(cache=cached_llmgw_calls) + messages = [ + SystemMessage(content="How much is 2 + 2?"), + HumanMessage(content="Respond with JUST THE ANSWER."), + ] + response = model.invoke(messages) + assert "4" in response.content, f"Expected '4' in response, got: {response.content}" + + +def test_cached_call_tokens(cached_llmgw_calls: SQLiteCache | None): + model = UiPathAzureChatOpenAI(cache=cached_llmgw_calls) + messages = [ + SystemMessage(content="How much is 2 + 2?"), + HumanMessage(content="Respond with JUST THE ANSWER."), + ] + with get_openai_callback() as cb: + _ = model.invoke(messages) + total_tokens = cb.total_tokens + assert total_tokens >= 20, f"Expected more than 20 tokens, got: {total_tokens}" + + +def test_normalized_cached_call(cached_llmgw_calls: SQLiteCache | None): + model = UiPathNormalizedChatModel( + model="anthropic.claude-3-5-sonnet-20240620-v1:0", cache=cached_llmgw_calls + ) + messages = [ + SystemMessage(content="How much is 2 + 2?"), + HumanMessage(content="Respond with JUST THE ANSWER."), + ] + response = model.invoke(messages) + assert "4" in response.content, f"Expected '4' in response, got: {response.content}" + + +def test_normalized_cached_call_tokens(cached_llmgw_calls: SQLiteCache | None): + model = UiPathNormalizedChatModel( + model="anthropic.claude-3-5-sonnet-20240620-v1:0", cache=cached_llmgw_calls + ) + messages = [ + SystemMessage(content="How much is 2 + 2?"), + HumanMessage(content="Respond with JUST THE ANSWER."), + ] + with get_openai_callback() as cb: + _ = model.invoke(messages) + total_tokens = cb.total_tokens + assert total_tokens >= 20, f"Expected more than 20 tokens, got: {total_tokens}" + + +def test_tool_call(cached_llmgw_calls: SQLiteCache | None): + @tool + def get_first_letter(input): + """ + Returns the first letter of the input + """ + return input[0] + + model = UiPathAzureChatOpenAI(cache=cached_llmgw_calls).bind_tools( + [get_first_letter] + ) + messages = [ + SystemMessage(content="What is the first letter of the word 'apple'?"), + ] + response = model.invoke(messages) + assert hasattr(response, "tool_calls"), ( + "The response should have a 'tool_calls' attribute" + ) + tool_call = response.tool_calls[0] # type: ignore + assert tool_call["name"] == "get_first_letter", ( + f"Expected tool call to 'get_first_letter', got: {tool_call['name']}" + ) + assert tool_call["args"].get("input") == "apple", ( + f"Expected input to be 'apple', got: {tool_call['args'].get('input')}" + ) + + +def test_structured_output_call(cached_llmgw_calls: SQLiteCache | None): + class CitySize(BaseModel): + """City and its size.""" + + city: str + size: int + + model = UiPathAzureChatOpenAI(cache=cached_llmgw_calls).with_structured_output( + CitySize + ) + messages = [ + SystemMessage( + content="What is the capital of France and what is its area in square meters?" + ), + ] + response = model.invoke(messages) + try: + city_size = CitySize.model_validate(response) + assert city_size.city == "Paris", ( + f"Expected city to be 'Paris', got: {city_size.city}" + ) + assert city_size.size > 0, ( + f"Expected size to be greater than 0, got: {city_size.size}" + ) + except ValidationError as exc: + pytest.fail(f"The response was not in the correct format: {exc}") + + +def test_embedding_call(cached_embedder: Embeddings | None): + if not cached_embedder: + cached_embedder = UiPathOpenAIEmbeddings(model="text-embedding-3-large") + data = [ + "Test input pneumonoultramicroscopicsilicovolcanoconiosis", + "Another test input", + ] + embeds = cached_embedder.embed_documents(data) + try: + arr = np.array(embeds) + except Exception as exc: + pytest.fail(f"Failed to convert embeddings to numpy array: {exc}") + assert arr.shape == (2, 3072), f"Expected shape (2, 3072), got: {arr.shape}" + + +def test_custom_embedding_call(cached_embedder: Embeddings | None): + if not cached_embedder: + cached_embedder = UiPathAzureOpenAIEmbeddings(model="text-embedding-3-large") + data = [ + "Test input pneumonoultramicroscopicsilicovolcanoconiosis", + "Another test input", + ] + embeds = cached_embedder.embed_documents(data) + try: + arr = np.array(embeds) + except Exception as exc: + pytest.fail(f"Failed to convert embeddings to numpy array: {exc}") + assert arr.shape == (2, 3072), f"Expected shape (2, 3072), got: {arr.shape}" diff --git a/sdk/langchain/uipath_langchain/chat/__init__.py b/sdk/langchain/uipath_langchain/chat/__init__.py new file mode 100644 index 000000000..4e2b7842c --- /dev/null +++ b/sdk/langchain/uipath_langchain/chat/__init__.py @@ -0,0 +1,6 @@ +from .models import UiPathAzureChatOpenAI, UiPathNormalizedChatModel + +__all__ = [ + "UiPathNormalizedChatModel", + "UiPathAzureChatOpenAI", +] diff --git a/sdk/langchain/uipath_langchain/chat/models.py b/sdk/langchain/uipath_langchain/chat/models.py new file mode 100644 index 000000000..96c1f7dc2 --- /dev/null +++ b/sdk/langchain/uipath_langchain/chat/models.py @@ -0,0 +1,266 @@ +import json +from typing import Any, Dict, List, Literal, Optional + +from langchain_core.callbacks import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain_core.language_models import LanguageModelInput +from langchain_core.messages import AIMessage, BaseMessage +from langchain_core.messages.ai import UsageMetadata +from langchain_core.outputs import ChatGeneration, ChatResult +from langchain_core.runnables import Runnable +from langchain_openai.chat_models import AzureChatOpenAI +from langchain_openai.chat_models.azure import _DictOrPydantic, _DictOrPydanticClass +from pydantic import BaseModel + +from uipath_langchain.utils._request_mixin import UiPathRequestMixin +from uipath_langchain.utils._settings import UiPathEndpoints + + +class UiPathAzureChatOpenAI(UiPathRequestMixin, AzureChatOpenAI): + """Custom LLM connector for LangChain integration with UiPath.""" + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + if "tools" in kwargs and not kwargs["tools"]: + del kwargs["tools"] + payload = self._get_request_payload(messages, stop=stop, **kwargs) + response = self._call(self.url, payload, self.auth_headers) + return self._create_chat_result(response) + + async def _agenerate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + if "tools" in kwargs and not kwargs["tools"]: + del kwargs["tools"] + payload = self._get_request_payload(messages, stop=stop, **kwargs) + response = await self._acall(self.url, payload, self.auth_headers) + return self._create_chat_result(response) + + def with_structured_output( + self, + schema: Optional[_DictOrPydanticClass] = None, + *, + method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema", + include_raw: bool = False, + strict: Optional[bool] = None, + **kwargs: Any, + ) -> Runnable[LanguageModelInput, _DictOrPydantic]: + """Model wrapper that returns outputs formatted to match the given schema.""" + schema = ( + schema.model_json_schema() + if isinstance(schema, type) and issubclass(schema, BaseModel) + else schema + ) + return super().with_structured_output( + schema=schema, + method=method, + include_raw=include_raw, + strict=strict, + **kwargs, + ) + + @property + def endpoint(self) -> str: + return UiPathEndpoints.PASSTHROUGH_COMPLETION_ENDPOINT.value.format( + model=self.model_name, api_version=self.openai_api_version + ) + + +class UiPathNormalizedChatModel(UiPathRequestMixin, AzureChatOpenAI): + """Custom LLM connector for LangChain integration with UiPath Normalized.""" + + def _create_chat_result( + self, + response: Dict[str, Any] | BaseModel, + generation_info: Optional[Dict] = None, + ) -> ChatResult: + if not isinstance(response, dict): + response = response.model_dump() + message = response["choices"][0]["message"] + usage = response["usage"] + + ai_message = AIMessage( + content=message.get("content", ""), + usage_metadata=UsageMetadata( + input_tokens=usage.get("prompt_tokens", 0), + output_tokens=usage.get("completion_tokens", 0), + total_tokens=usage.get("total_tokens", 0), + ), + additional_kwargs={}, + response_metadata={ + "token_usage": response["usage"], + "model_name": self.model_name, + "finish_reason": response["choices"][0].get("finish_reason", None), + "system_fingerprint": response["id"], + "created": response["created"], + }, + ) + + if "tool_calls" in message: + ai_message.tool_calls = [ + { + "id": tool["id"], + "name": tool["name"], + "args": tool["arguments"], + } + for tool in message["tool_calls"] + ] + generation = ChatGeneration(message=ai_message) + return ChatResult(generations=[generation]) + + def _get_request_payload( + self, + input_: LanguageModelInput, + *, + stop: Optional[List[str]] = None, + **kwargs: Any, + ) -> Dict: + payload = super()._get_request_payload(input_, stop=stop, **kwargs) + # hacks to make the request work with uipath normalized + for message in payload["messages"]: + if message["content"] is None: + message["content"] = "" + if "tool_calls" in message: + for tool_call in message["tool_calls"]: + tool_call["name"] = tool_call["function"]["name"] + tool_call["arguments"] = json.loads( + tool_call["function"]["arguments"] + ) + if message["role"] == "tool": + message["content"] = { + "result": message["content"], + "call_id": message["tool_call_id"], + } + return payload + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + """Override the _generate method to implement the chat model logic. + + This can be a call to an API, a call to a local model, or any other + implementation that generates a response to the input prompt. + + Args: + messages: the prompt composed of a list of messages. + stop: a list of strings on which the model should stop generating. + If generation stops due to a stop token, the stop token itself + SHOULD BE INCLUDED as part of the output. This is not enforced + across models right now, but it's a good practice to follow since + it makes it much easier to parse the output of the model + downstream and understand why generation stopped. + run_manager: A run manager with callbacks for the LLM. + """ + kwargs["tools"] = ( + [tool["function"] for tool in kwargs["tools"]] + if "tools" in kwargs + else None + ) + if "tool_choice" in kwargs and kwargs["tool_choice"]["type"] == "function": + kwargs["tool_choice"] = { + "type": "tool", + "name": kwargs["tool_choice"]["function"]["name"], + } + payload = self._get_request_payload(messages, stop=stop, **kwargs) + + response = self._call(self.url, payload, self.auth_headers) + return self._create_chat_result(response) + + async def _agenerate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + """Override the _generate method to implement the chat model logic. + + This can be a call to an API, a call to a local model, or any other + implementation that generates a response to the input prompt. + + Args: + messages: the prompt composed of a list of messages. + stop: a list of strings on which the model should stop generating. + If generation stops due to a stop token, the stop token itself + SHOULD BE INCLUDED as part of the output. This is not enforced + across models right now, but it's a good practice to follow since + it makes it much easier to parse the output of the model + downstream and understand why generation stopped. + run_manager: A run manager with callbacks for the LLM. + """ + kwargs["tools"] = ( + [tool["function"] for tool in kwargs["tools"]] + if "tools" in kwargs + else None + ) + if "tool_choice" in kwargs and kwargs["tool_choice"]["type"] == "function": + kwargs["tool_choice"] = { + "type": "tool", + "name": kwargs["tool_choice"]["function"]["name"], + } + payload = self._get_request_payload(messages, stop=stop, **kwargs) + + response = await self._acall(self.url, payload, self.auth_headers) + return self._create_chat_result(response) + + def with_structured_output( + self, + schema: Optional[_DictOrPydanticClass] = None, + *, + method: Literal[ + "function_calling", "json_mode", "json_schema" + ] = "function_calling", + include_raw: bool = False, + strict: Optional[bool] = None, + **kwargs: Any, + ) -> Runnable[LanguageModelInput, _DictOrPydantic]: + """Model wrapper that returns outputs formatted to match the given schema.""" + if method == "json_schema" and ( + not self.model_name or not self.model_name.startswith("gpt") + ): + method = "function_calling" + if self.logger: + self.logger.warning( + "The json_schema output is not supported for non-GPT models. Using function_calling instead.", + extra={ + "ActionName": self.settings.action_name, + "ActionId": self.settings.action_id, + } + if self.settings + else None, + ) + schema = ( + schema.model_json_schema() + if isinstance(schema, type) and issubclass(schema, BaseModel) + else schema + ) + return super().with_structured_output( + schema=schema, + method=method, + include_raw=include_raw, + strict=strict, + **kwargs, + ) + + @property + def endpoint(self) -> str: + return UiPathEndpoints.NORMALIZED_COMPLETION_ENDPOINT.value + + @property + def is_normalized(self) -> bool: + return True diff --git a/sdk/langchain/uipath_langchain/chat/utils/__init__.py b/sdk/langchain/uipath_langchain/chat/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/sdk/langchain/uipath_langchain/chat/utils/_chat_types.py b/sdk/langchain/uipath_langchain/chat/utils/_chat_types.py new file mode 100644 index 000000000..e37fbf2ce --- /dev/null +++ b/sdk/langchain/uipath_langchain/chat/utils/_chat_types.py @@ -0,0 +1,54 @@ +from typing import Any, Dict, List, Optional + +from pydantic import BaseModel, Field + + +class ToolCall(BaseModel): + id: str + name: str + arguments: dict + + +class Message(BaseModel): + role: str + content: str | dict | None = None + tool_calls: List[ToolCall] | None = None + + +class Tool(BaseModel): + name: str + description: str + parameters: dict | None = None + + +class ToolChoice(BaseModel): + type: str + name: str | None = None + + +class ChatCompletionRequest(BaseModel): + max_tokens: int + # n: int + # top_p: float + temperature: float + frequency_penalty: float + presence_penalty: float + messages: List[Message] + tools: List[Tool] | None = None + tool_choice: ToolChoice | None = None + response_format: Optional[Dict[str, Any]] = None + + +class ModelSettings(BaseModel): + top_p: float = 1.0 + n: int = 1 + temperature: float = 0.0 + frequency_penalty: float = 0.0 + presence_penalty: float = 0.0 + max_tokens: int = 4096 + tool_choice: str | None = None + enforced_tool_name: str | None = None + + +class UiPathOutput(BaseModel): + output_field: str = Field(..., description="The output field") diff --git a/sdk/langchain/uipath_langchain/embeddings/__init__.py b/sdk/langchain/uipath_langchain/embeddings/__init__.py new file mode 100644 index 000000000..e53872627 --- /dev/null +++ b/sdk/langchain/uipath_langchain/embeddings/__init__.py @@ -0,0 +1,6 @@ +from .embeddings import UiPathAzureOpenAIEmbeddings, UiPathOpenAIEmbeddings + +__all__ = [ + "UiPathAzureOpenAIEmbeddings", + "UiPathOpenAIEmbeddings", +] diff --git a/sdk/langchain/uipath_langchain/embeddings/embeddings.py b/sdk/langchain/uipath_langchain/embeddings/embeddings.py new file mode 100644 index 000000000..ed185ebcc --- /dev/null +++ b/sdk/langchain/uipath_langchain/embeddings/embeddings.py @@ -0,0 +1,109 @@ +import os +from typing import List + +import httpx +from langchain_community.callbacks.manager import openai_callback_var +from langchain_openai.embeddings import AzureOpenAIEmbeddings, OpenAIEmbeddings +from pydantic import Field + +from uipath_langchain.utils._settings import UiPathEndpoints +from uipath_langchain.utils._request_mixin import UiPathRequestMixin + + +class UiPathAzureOpenAIEmbeddings(UiPathRequestMixin, AzureOpenAIEmbeddings): # type: ignore + """Custom Embeddings connector for LangChain integration with UiPath, with minimal changes compared to AzureOpenAIEmbeddings.""" + + model_name: str | None = Field( + default_factory=lambda: os.getenv( + "UIPATH_MODEL_NAME", "text-embedding-3-large" + ), + alias="model", + ) + + def __init__(self, **kwargs): + super().__init__( + http_client=httpx.Client( # type: ignore + event_hooks={ + "request": [self._log_request_duration], + "response": [self._log_response_duration], + } + ), + http_async_client=httpx.AsyncClient( # type: ignore + event_hooks={ + "request": [self._alog_request_duration], + "response": [self._alog_response_duration], + } + ), + **kwargs, + ) + self.client._client._prepare_url = self._prepare_url + self.client._client._build_headers = self._build_headers + self.async_client._client._prepare_url = self._prepare_url + self.async_client._client._build_headers = self._build_headers + + @property + def endpoint(self) -> str: + return UiPathEndpoints.EMBEDDING_ENDPOINT.value.format( + model=self.model_name, api_version=self.openai_api_version + ) + + +class UiPathOpenAIEmbeddings(UiPathRequestMixin, OpenAIEmbeddings): # type: ignore + """Custom Embeddings connector for LangChain integration with UiPath, with full control over the embedding call.""" + + model_name: str | None = Field( + default_factory=lambda: os.getenv( + "UIPATH_MODEL_NAME", "text-embedding-3-large" + ), + alias="model", + ) + + def embed_documents( + self, texts: List[str], chunk_size: int | None = None + ) -> List[List[float]]: + """Embed a list of documents using the UiPath.""" + embeddings = [] + total_tokens = 0 + # Process in chunks if specified + chunk_size_ = chunk_size or self.chunk_size or len(texts) + for i in range(0, len(texts), chunk_size_): + chunk = texts[i : i + chunk_size_] + payload = {"input": chunk} + response = self._call(self.url, payload, self.auth_headers) + chunk_embeddings = [r["embedding"] for r in response["data"]] + total_tokens += response["usage"]["prompt_tokens"] + embeddings.extend(chunk_embeddings) + if contextvar := openai_callback_var.get(): + contextvar.prompt_tokens += total_tokens + contextvar.total_tokens += total_tokens + contextvar.successful_requests += 1 + return embeddings + + async def aembed_documents( + self, + texts: List[str], + chunk_size: int | None = None, + ) -> List[List[float]]: + """Async version of embed_documents.""" + embeddings = [] + total_tokens = 0 + # Process in chunks if specified + chunk_size_ = chunk_size or self.chunk_size or len(texts) + for i in range(0, len(texts), chunk_size_): + chunk = texts[i : i + chunk_size_] + payload = {"input": chunk} + response = await self._acall(self.url, payload, self.auth_headers) + chunk_embeddings = [r["embedding"] for r in response["data"]] + total_tokens += response["usage"]["prompt_tokens"] + embeddings.extend(chunk_embeddings) + if contextvar := openai_callback_var.get(): + contextvar.prompt_tokens += total_tokens + contextvar.total_tokens += total_tokens + contextvar.successful_requests += 1 + return embeddings + + @property + def endpoint(self) -> str: + return UiPathEndpoints.EMBEDDING_ENDPOINT.value.format( + model=self.model_name, api_version=self.openai_api_version + ) diff --git a/sdk/langchain/uipath_langchain/utils/__init__.py b/sdk/langchain/uipath_langchain/utils/__init__.py new file mode 100644 index 000000000..1902d7308 --- /dev/null +++ b/sdk/langchain/uipath_langchain/utils/__init__.py @@ -0,0 +1,3 @@ +from ._request_mixin import UiPathRequestMixin + +__all__ = ["UiPathRequestMixin"] diff --git a/sdk/langchain/uipath_langchain/utils/_request_mixin.py b/sdk/langchain/uipath_langchain/utils/_request_mixin.py new file mode 100644 index 000000000..0d7928ae5 --- /dev/null +++ b/sdk/langchain/uipath_langchain/utils/_request_mixin.py @@ -0,0 +1,476 @@ +import json +import logging +import os +import time +from typing import Any, Dict, List, Mapping, Optional + +import httpx +import openai +from langchain_core.embeddings import Embeddings +from langchain_core.language_models.chat_models import _cleanup_llm_representation +from pydantic import BaseModel, Field, SecretStr +from tenacity import ( + AsyncRetrying, + Retrying, + retry_if_exception_type, + stop_after_attempt, + wait_exponential_jitter, +) + +from uipath_langchain.utils._settings import ( + UiPathClientFactorySettings, + UiPathClientSettings, + get_uipath_token_header, +) +from uipath_langchain.utils._sleep_policy import before_sleep_log + + +class UiPathRequestMixin(BaseModel): + class Config: + arbitrary_types_allowed = True + + default_headers: Mapping[str, str] | None = { + "X-UiPath-Streaming-Enabled": "false", + } + model_name: str | None = Field( + default_factory=lambda: os.getenv("UIPATH_MODEL_NAME", "gpt-4o-2024-08-06"), + alias="model", + ) + settings: UiPathClientSettings | None = None + client_id: str | None = Field(default_factory=lambda: os.getenv("UIPATH_CLIENT_ID")) + client_secret: str | None = Field( + default_factory=lambda: os.getenv("UIPATH_CLIENT_SECRET") + ) + base_url: str | None = Field( + default_factory=lambda data: getattr(data["settings"], "base_url", None) + or os.getenv("UIPATH_BASE_URL"), + alias="azure_endpoint", + ) + access_token: str | None = Field( + default_factory=lambda data: ( + getattr(data["settings"], "access_token", None) + or os.getenv("UIPATH_SERVICE_TOKEN") # Environment variable + or get_uipath_token_header( + UiPathClientFactorySettings( + UIPATH_BASE_URL=data["base_url"], + UIPATH_CLIENT_ID=data["client_id"], + UIPATH_CLIENT_SECRET=data["client_secret"], + ) + ) # Get service token from UiPath + ) + ) + org_id: str = Field( + default_factory=lambda data: getattr(data["settings"], "org_id", None) + or os.getenv("UIPATH_ORGANIZATION_ID", "") + ) + tenant_id: str = Field( + default_factory=lambda data: getattr(data["settings"], "tenant_id", None) + or os.getenv("UIPATH_TENANT_ID", "") + ) + requesting_product: str = Field( + default_factory=lambda data: getattr( + data["settings"], "requesting_product", None + ) + or os.getenv("UIPATH_REQUESTING_PRODUCT", "") + ) + requesting_feature: str = Field( + default_factory=lambda data: getattr( + data["settings"], "requesting_feature", None + ) + or os.getenv("UIPATH_REQUESTING_FEATURE", "") + ) + default_request_timeout: float = Field( + default_factory=lambda data: float( + getattr(data["settings"], "timeout_seconds", None) + or os.getenv("UIPATH_TIMEOUT_SECONDS", "120") + ), + alias="timeout", + ) + + openai_api_version: str | None = Field( + default_factory=lambda: os.getenv("OPENAI_API_VERSION", "2024-08-01-preview"), + alias="api_version", + ) + include_account_id: bool = False + temperature: float | None = 0.0 + max_tokens: int | None = 1000 + frequency_penalty: float | None = None + presence_penalty: float | None = None + + logger: logging.Logger | None = None + max_retries: int | None = 5 + base_delay: float = 5.0 + max_delay: float = 60.0 + + _url: str | None = None + _auth_headers: Dict[str, str] | None = None + + # required to instantiate AzureChatOpenAI subclasses + azure_endpoint: Optional[str] = Field( + default="placeholder", description="Bypassed Azure endpoint" + ) + openai_api_key: Optional[SecretStr] = Field( + default=SecretStr("placeholder"), description="Bypassed API key" + ) + # required to instatiate ChatAnthropic subclasses (will be needed when passthrough is implemented for Anthropic models) + stop_sequences: Optional[List[str]] = Field( + default=None, description="Bypassed stop sequence" + ) + + def _request( + self, url: str, request_body: Dict[str, Any], headers: Dict[str, str] + ) -> Dict[str, Any]: + """Run an asynchronous call to the LLM.""" + # if self.logger: + # self.logger.info(f"Completion request: {request_body['messages'][:2]}") + with httpx.Client( + event_hooks={ + "request": [self._log_request_duration], + "response": [self._log_response_duration], + } + ) as client: + response = client.post( + url, + headers=headers, + json=request_body, + timeout=self.default_request_timeout, + ) + + # Handle HTTP errors and map them to OpenAI exceptions + try: + response.raise_for_status() + except httpx.HTTPStatusError as err: + if self.logger: + self.logger.error( + "Error querying UiPath: %s (%s)", + err.response.reason_phrase, + err.response.status_code, + extra={ + "ActionName": self.settings.action_name, + "ActionId": self.settings.action_id, + } + if self.settings + else None, + ) + raise self._make_status_error_from_response(err.response) from err + + return response.json() + + def _call( + self, url: str, request_body: Dict[str, Any], headers: Dict[str, str] + ) -> Dict[str, Any]: + """Run a synchronous call with retries to LLM""" + if self.max_retries is None: + return self._request(url, request_body, headers) + + retryer = Retrying( + stop=stop_after_attempt(self.max_retries), + wait=wait_exponential_jitter( + initial=self.base_delay, + max=self.max_delay, + jitter=1.0, + ), + retry=retry_if_exception_type( + (openai.RateLimitError, httpx.TimeoutException) + ), + reraise=True, + before_sleep=before_sleep_log(self.logger, logging.WARNING) + if self.logger is not None + else None, + ) + + try: + return retryer(self._request, url, request_body, headers) + except openai.APIStatusError as err: + if self.logger: + self.logger.error( + "Failed querying LLM after retries: %s", + err, + extra={ + "ActionName": self.settings.action_name, + "ActionId": self.settings.action_id, + } + if self.settings + else None, + ) + raise err + + async def _arequest( + self, url: str, request_body: Dict[str, Any], headers: Dict[str, str] + ) -> Dict[str, Any]: + # if self.logger: + # self.logger.info(f"Completion request: {request_body['messages'][:2]}") + async with httpx.AsyncClient( + event_hooks={ + "request": [self._alog_request_duration], + "response": [self._alog_response_duration], + } + ) as client: + response = await client.post( + url, + headers=headers, + json=request_body, + timeout=self.default_request_timeout, + ) + # Handle HTTP errors and map them to OpenAI exceptions + try: + response.raise_for_status() + except httpx.HTTPStatusError as err: + if self.logger: + self.logger.error( + "Error querying LLM: %s (%s)", + err.response.reason_phrase, + err.response.status_code, + extra={ + "ActionName": self.settings.action_name, + "ActionId": self.settings.action_id, + } + if self.settings + else None, + ) + raise self._make_status_error_from_response(err.response) from err + + return response.json() + + async def _acall( + self, url: str, request_body: Dict[str, Any], headers: Dict[str, str] + ) -> Dict[str, Any]: + """Run an asynchronous call with retries to the LLM.""" + if self.max_retries is None: + return await self._arequest(url, request_body, headers) + + retryer = AsyncRetrying( + stop=stop_after_attempt(self.max_retries), + wait=wait_exponential_jitter( + initial=self.base_delay, + max=self.max_delay, + jitter=1.0, + ), + retry=retry_if_exception_type( + (openai.RateLimitError, httpx.TimeoutException) + ), + reraise=True, + before_sleep=before_sleep_log(self.logger, logging.WARNING) + if self.logger is not None + else None, + ) + + try: + response = await retryer(self._arequest, url, request_body, headers) + if self.logger: + self.logger.info( + f"[uipath_langchain_client] Finished retryer after {retryer.statistics['attempt_number'] - 1} retries", + extra={ + "retry": f"{retryer.statistics['attempt_number'] - 1}", + "ActionName": self.settings.action_name, + "ActionId": self.settings.action_id, + } + if self.settings + else { + "retry": f"{retryer.statistics['attempt_number'] - 1}", + }, + ) + return response + except openai.APIStatusError as err: + if self.logger: + self.logger.error( + "[uipath_langchain_client] Failed querying LLM after retries: %s", + err, + extra={ + "reason": err.message, + "statusCode": err.status_code, + "ActionName": self.settings.action_name, + "ActionId": self.settings.action_id, + } + if self.settings + else { + "reason": err.message, + "statusCode": err.status_code, + }, + ) # type: ignore + raise err + + def _make_status_error_from_response( + self, + response: httpx.Response, + ) -> openai.APIStatusError: + """Function reproduced from openai._client to handle UiPath errors.""" + if response.is_closed and not response.is_stream_consumed: + # We can't read the response body as it has been closed + # before it was read. This can happen if an event hook + # raises a status error. + body = None + err_msg = f"Error code: {response.status_code}" + else: + err_text = response.text.strip() + body = err_text + + try: + body = json.loads(err_text) + err_msg = f"Error code: {response.status_code} - {body}" + except Exception: + err_msg = err_text or f"Error code: {response.status_code}" + + return self._make_status_error(err_msg, body=body, response=response) + + def _make_status_error( + self, + err_msg: str, + *, + body: object, + response: httpx.Response, + ) -> openai.APIStatusError: + """Function reproduced from openai._client to handle UiPath errors.""" + data = body.get("error", body) if isinstance(body, Mapping) else body + if response.status_code == 400: + return openai.BadRequestError(err_msg, response=response, body=data) + + if response.status_code == 401: + return openai.AuthenticationError(err_msg, response=response, body=data) + + if response.status_code == 403: + return openai.PermissionDeniedError(err_msg, response=response, body=data) + + if response.status_code == 404: + return openai.NotFoundError(err_msg, response=response, body=data) + + if response.status_code == 409: + return openai.ConflictError(err_msg, response=response, body=data) + + if response.status_code == 422: + return openai.UnprocessableEntityError( + err_msg, response=response, body=data + ) + + if response.status_code == 429: + return openai.RateLimitError(err_msg, response=response, body=data) + + if response.status_code >= 500: + return openai.InternalServerError(err_msg, response=response, body=data) + return openai.APIStatusError(err_msg, response=response, body=data) + + def _log_request_duration(self, request: httpx.Request): + """Log the start time of the request.""" + if self.logger: + request.extensions["start_time"] = time.monotonic() + + def _log_response_duration(self, response: httpx.Response): + """Log the duration of the request.""" + if self.logger: + start_time = response.request.extensions.get("start_time") + if start_time: + duration = time.monotonic() - start_time + type = "embedding" + if not isinstance(self, Embeddings): + type = "normalized" if self.is_normalized else "completion" + self.logger.info( + f"[uipath_langchain_client] Request to {response.request.url} took {duration:.2f} seconds.", + extra={ + "requestUrl": f"{response.request.url}", + "duration": f"{duration:.2f}", + "type": type, + "ActionName": self.settings.action_name, + "ActionId": self.settings.action_id, + } + if self.settings + else { + "requestUrl": f"{response.request.url}", + "duration": f"{duration:.2f}", + "type": type, + }, + ) + + async def _alog_request_duration(self, request: httpx.Request): + """Log the start time of the request.""" + self._log_request_duration(request) + + async def _alog_response_duration(self, response: httpx.Response): + """Log the duration of the request.""" + self._log_response_duration(response) + + @property + def _llm_type(self) -> str: + """Get the type of language model used by this chat model. Used for logging purposes only.""" + return "uipath" + + @property + def _identifying_params(self) -> Dict[str, Any]: + return { + "url": self.url, + "model": self.model_name, + "temperature": self.temperature, + "max_tokens": self.max_tokens, + "frequency_penalty": self.frequency_penalty, + "presence_penalty": self.presence_penalty, + } + + def _prepare_url(self, url: str) -> httpx.URL: + return httpx.URL(self.url) + + def _build_headers(self, options, retries_taken: int = 0) -> httpx.Headers: + return httpx.Headers(self.auth_headers) + + @property + def url(self) -> str: + if not self._url: + self._url = ( + f"{self.base_url}/{self.org_id}/{self.tenant_id}/{self.endpoint}" + ) + return self._url + + @property + def endpoint(self) -> str: + raise NotImplementedError( + "The endpoint property is not implemented for this class." + ) + + @property + def auth_headers(self) -> Dict[str, str]: + if not self._auth_headers: + self._auth_headers = { + **self.default_headers, # type: ignore + "Authorization": f"Bearer {self.access_token}", + "X-UiPath-LlmGateway-RequestingProduct": self.requesting_product, + "X-UiPath-LlmGateway-RequestingFeature": self.requesting_feature, + "X-UiPath-LlmGateway-TimeoutSeconds": str(self.default_request_timeout), + } + if self.is_normalized and self.model_name: + self._auth_headers["X-UiPath-LlmGateway-NormalizedApi-ModelName"] = ( + self.model_name + ) + if self.include_account_id: + self._auth_headers["x-uipath-internal-accountid"] = self.org_id + self._auth_headers["x-uipath-internal-tenantid"] = self.tenant_id + return self._auth_headers + + def _get_llm_string(self, stop: Optional[list[str]] = None, **kwargs: Any) -> str: + serialized_repr = getattr(self, "_serialized", self.model_dump()) + _cleanup_llm_representation(serialized_repr, 1) + kwargs = serialized_repr.get("kwargs", serialized_repr) + for key in [ + "base_url", + "access_token", + "client_id", + "client_secret", + "org_id", + "tenant_id", + "requesting_product", + "requesting_feature", + "azure_endpoint", + "openai_api_version", + "openai_api_key", + "default_request_timeout", + "max_retries", + "base_delay", + "max_delay", + "logger", + "settings", + ]: + if key in kwargs: + kwargs.pop(key, None) + llm_string = json.dumps(serialized_repr, sort_keys=True) + return llm_string + + @property + def is_normalized(self) -> bool: + return False diff --git a/sdk/langchain/uipath_langchain/utils/_settings.py b/sdk/langchain/uipath_langchain/utils/_settings.py new file mode 100644 index 000000000..ce33ebbf9 --- /dev/null +++ b/sdk/langchain/uipath_langchain/utils/_settings.py @@ -0,0 +1,87 @@ +import os +from enum import Enum + +import aiohttp +import requests +from pydantic import Field +from pydantic_settings import BaseSettings + +uipath_token_header = None + + +class UiPathCachedPathsSettings(BaseSettings): + cached_completion_db: str = Field( + default=os.path.join( + os.path.dirname(__file__), "tests", "tests_uipath_cache.db" + ), + alias="CACHED_COMPLETION_DB", + ) + cached_embeddings_dir: str = Field( + default=os.path.join(os.path.dirname(__file__), "tests", "cached_embeddings"), + alias="CACHED_EMBEDDINGS_DIR", + ) + + +uipath_cached_paths_settings = UiPathCachedPathsSettings() + + +class UiPathClientFactorySettings(BaseSettings): + base_url: str = Field(default="", alias="UIPATH_BASE_URL") + client_id: str = Field(default="", alias="UIPATH_CLIENT_ID") + client_secret: str = Field(default="", alias="UIPATH_CLIENT_SECRET") + + +class UiPathClientSettings(BaseSettings): + access_token: str = Field(default_factory=lambda: get_uipath_token_header()) + base_url: str = Field(default="", alias="UIPATH_BASE_URL") + org_id: str = Field(default="", alias="UIPATH_ORGANIZATION_ID") + tenant_id: str = Field(default="", alias="UIPATH_TENANT_ID") + requesting_product: str = Field(default="", alias="UIPATH_REQUESTING_PRODUCT") + requesting_feature: str = Field(default="", alias="UIPATH_REQUESTING_FEATURE") + timeout_seconds: str = Field(default="120", alias="UIPATH_TIMEOUT_SECONDS") + action_name: str = Field(default="DefaultActionName", alias="UIPATH_ACTION_NAME") + action_id: str = Field(default="DefaultActionId", alias="UIPATH_ACTION_ID") + + +class UiPathEndpoints(Enum): + NORMALIZED_COMPLETION_ENDPOINT = "llmgateway_/api/chat/completions" + PASSTHROUGH_COMPLETION_ENDPOINT = "llmgateway_/openai/deployments/{model}/chat/completions?api-version={api_version}" + EMBEDDING_ENDPOINT = ( + "llmgateway_/openai/deployments/{model}/embeddings?api-version={api_version}" + ) + + +def get_uipath_token_header(settings: UiPathClientFactorySettings | None = None) -> str: + global uipath_token_header + if not uipath_token_header: + settings = settings or UiPathClientFactorySettings() + url_get_token = f"{settings.base_url}/identity_/connect/token" + token_credentials = dict( + client_id=settings.client_id, + client_secret=settings.client_secret, + grant_type="client_credentials", + ) + res = requests.post(url_get_token, data=token_credentials) + res_json = res.json() + uipath_token_header = res_json.get("access_token") + return uipath_token_header + + +async def get_token_header_async( + settings: UiPathClientFactorySettings | None = None, +) -> str: + global uipath_token_header + if not uipath_token_header: + settings = settings or UiPathClientFactorySettings() + url_get_token = f"{settings.base_url}/identity_/connect/token" + token_credentials = dict( + client_id=settings.client_id, + client_secret=settings.client_secret, + grant_type="client_credentials", + ) + async with aiohttp.ClientSession() as session: + async with session.post(url_get_token, data=token_credentials) as res: + res.raise_for_status() + res_json = await res.json() + uipath_token_header = res_json.get("access_token") + return uipath_token_header diff --git a/sdk/langchain/uipath_langchain/utils/_sleep_policy.py b/sdk/langchain/uipath_langchain/utils/_sleep_policy.py new file mode 100644 index 000000000..c059c7964 --- /dev/null +++ b/sdk/langchain/uipath_langchain/utils/_sleep_policy.py @@ -0,0 +1,41 @@ +import logging +from typing import Callable + +from tenacity import ( + RetryCallState, + _utils, +) + + +def before_sleep_log( + logger: "logging.Logger", + log_level: int, + exc_info: bool = False, +) -> Callable[["RetryCallState"], None]: + """Before call strategy that logs to some logger the attempt.""" + + def log_it(retry_state: "RetryCallState") -> None: + if retry_state.outcome is None: + raise RuntimeError("log_it() called before outcome was set") + + if retry_state.next_action is None: + raise RuntimeError("log_it() called before next_action was set") + + if retry_state.outcome.failed: + ex = retry_state.outcome.exception() + verb, value = "raised", f"{ex.__class__.__name__}: {ex}" + else: + verb, value = "returned", retry_state.outcome.result() + + if retry_state.fn is None: + fn_name = "" + else: + fn_name = _utils.get_callback_name(retry_state.fn) + + logger.log( + log_level, + f"Retrying #{retry_state.attempt_number} {fn_name} in {retry_state.next_action.sleep} seconds as it {verb} {value}.", + {"retries": f"{retry_state.attempt_number}"}, + ) + + return log_it diff --git a/sdk/langchain/uipath_langchain/utils/tests/cached_embeddings/text-embedding-3-large5034ec3c-85c9-54b8-ac89-5e0cbcf99e3b b/sdk/langchain/uipath_langchain/utils/tests/cached_embeddings/text-embedding-3-large5034ec3c-85c9-54b8-ac89-5e0cbcf99e3b new file mode 100644 index 000000000..ee925ee71 --- /dev/null +++ b/sdk/langchain/uipath_langchain/utils/tests/cached_embeddings/text-embedding-3-large5034ec3c-85c9-54b8-ac89-5e0cbcf99e3b @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb0757c242dc5fa2290e2fe8059747d6b5ba18da613fc96a1c1ab496cb1832ef +size 68875 diff --git a/sdk/langchain/uipath_langchain/utils/tests/cached_embeddings/text-embedding-3-largec48857ed-1302-5954-9e24-69fa9b45e457 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+++- sdk/langchain/pyproject.toml | 1 + sdk/langchain/tests/conftest.py | 37 +++++++++--- sdk/langchain/tests/test_langchain_client.py | 20 ++++--- sdk/langchain/uipath_langchain/chat/models.py | 17 +++--- .../chat/utils/_chat_types.py | 20 +++---- .../uipath_langchain/embeddings/embeddings.py | 20 +++---- .../uipath_langchain/utils/_request_mixin.py | 60 +++++++++++-------- .../uipath_langchain/utils/_settings.py | 36 ++++++----- sdk/langchain/uv.lock | 24 +++++++- 11 files changed, 164 insertions(+), 89 deletions(-) diff --git a/.github/workflows/langchain_ci.yml b/.github/workflows/langchain_ci.yml index 413412ad8..4e0e7b049 100644 --- a/.github/workflows/langchain_ci.yml +++ b/.github/workflows/langchain_ci.yml @@ -17,6 +17,14 @@ defaults: run: working-directory: "sdk/langchain" +env: + UIPATH_URL: ${{ secrets.UIPATH_URL }} + UIPATH_CLIENT_ID: ${{ secrets.UIPATH_CLIENT_ID }} + UIPATH_CLIENT_SECRET: ${{ secrets.UIPATH_CLIENT_SECRET }} + UIPATH_ORGANIZATION_ID: ${{ vars.UIPATH_ORGANIZATION_ID }} + UIPATH_TENANT_ID: ${{ vars.UIPATH_TENANT_ID }} + + jobs: lint: uses: ./.github/workflows/lint.yml diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 6818af703..2067c0f8e 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -3,6 +3,7 @@ name: Reusable Test Workflow on: workflow_call: inputs: + folder_path: description: 'The folder path to run actions in' required: true @@ -17,12 +18,12 @@ jobs: test: name: Test runs-on: "ubuntu-24.04" + environment: PYPY_TOKEN if: inputs.should_skip == false defaults: run: working-directory: ${{ inputs.folder_path }} - steps: - uses: actions/checkout@v4 @@ -39,5 +40,12 @@ jobs: - name: "Run tests" run: | + printenv | sort uv run pytest + env: + UIPATH_URL: ${{ secrets.UIPATH_URL }} + UIPATH_CLIENT_ID: ${{ secrets.UIPATH_CLIENT_ID }} + UIPATH_CLIENT_SECRET: ${{ secrets.UIPATH_CLIENT_SECRET }} + UIPATH_ORGANIZATION_ID: ${{ vars.UIPATH_ORGANIZATION_ID }} + UIPATH_TENANT_ID: ${{ vars.UIPATH_TENANT_ID }} diff --git a/sdk/langchain/pyproject.toml b/sdk/langchain/pyproject.toml index 7d2973928..c26a03c64 100644 --- a/sdk/langchain/pyproject.toml +++ b/sdk/langchain/pyproject.toml @@ -17,6 +17,7 @@ dependencies = [ "pydantic-settings>=2.6.0", "python-dotenv>=1.0.1", "httpx>=0.27.0", + "openai>=1.65.5", ] classifiers = [ "Development Status :: 3 - Alpha", diff --git a/sdk/langchain/tests/conftest.py b/sdk/langchain/tests/conftest.py index dff1e28a5..cfca5813e 100644 --- a/sdk/langchain/tests/conftest.py +++ b/sdk/langchain/tests/conftest.py @@ -1,7 +1,9 @@ import logging import os -from typing import Generator +from os import environ as env +from typing import Generator, Optional +import httpx import pytest from langchain.embeddings import CacheBackedEmbeddings from langchain.globals import set_llm_cache @@ -12,16 +14,37 @@ from uipath_langchain.utils._settings import uipath_cached_paths_settings +def get_token(): + url_get_token = f"{env.get('UIPATH_URL', '').rstrip('/')}/identity_/connect/token" + + token_credentials = { + "client_id": env.get("UIPATH_CLIENT_ID"), + "client_secret": env.get("UIPATH_CLIENT_SECRET"), + "grant_type": "client_credentials", + } + + try: + with httpx.Client() as client: + response = client.post(url_get_token, data=token_credentials) + response.raise_for_status() + res_json = response.json() + token = res_json.get("access_token") + + if not token: + pytest.skip("Authentication token is empty or missing") + except (httpx.HTTPError, ValueError, KeyError) as e: + pytest.skip(f"Failed to obtain authentication token: {str(e)}") + + return token + + @pytest.fixture(autouse=True) def setup_test_env(): - """Fixture to set up test environment variables.""" - from dotenv import find_dotenv, load_dotenv - - load_dotenv(find_dotenv()) + env["UIPATH_ACCESS_TOKEN"] = get_token() @pytest.fixture(scope="session") -def cached_llmgw_calls() -> Generator[SQLiteCache | None, None, None]: +def cached_llmgw_calls() -> Generator[Optional[SQLiteCache], None, None]: if not os.environ.get("UIPATH_TESTS_CACHE_LLMGW"): yield None else: @@ -36,7 +59,7 @@ def cached_llmgw_calls() -> Generator[SQLiteCache | None, None, None]: @pytest.fixture(scope="session") -def cached_embedder() -> Generator[CacheBackedEmbeddings | None, None, None]: +def cached_embedder() -> Generator[Optional[CacheBackedEmbeddings], None, None]: if not os.environ.get("UIPATH_TESTS_CACHE_LLMGW"): yield None else: diff --git a/sdk/langchain/tests/test_langchain_client.py b/sdk/langchain/tests/test_langchain_client.py index 260767063..ae4895863 100644 --- a/sdk/langchain/tests/test_langchain_client.py +++ b/sdk/langchain/tests/test_langchain_client.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np import pytest from langchain_community.cache import SQLiteCache @@ -17,7 +19,7 @@ ) -def test_cached_call(cached_llmgw_calls: SQLiteCache | None): +def test_cached_call(cached_llmgw_calls: Optional[SQLiteCache]): model = UiPathAzureChatOpenAI(cache=cached_llmgw_calls) messages = [ SystemMessage(content="How much is 2 + 2?"), @@ -27,7 +29,7 @@ def test_cached_call(cached_llmgw_calls: SQLiteCache | None): assert "4" in response.content, f"Expected '4' in response, got: {response.content}" -def test_cached_call_tokens(cached_llmgw_calls: SQLiteCache | None): +def test_cached_call_tokens(cached_llmgw_calls: Optional[SQLiteCache]): model = UiPathAzureChatOpenAI(cache=cached_llmgw_calls) messages = [ SystemMessage(content="How much is 2 + 2?"), @@ -39,7 +41,7 @@ def test_cached_call_tokens(cached_llmgw_calls: SQLiteCache | None): assert total_tokens >= 20, f"Expected more than 20 tokens, got: {total_tokens}" -def test_normalized_cached_call(cached_llmgw_calls: SQLiteCache | None): +def test_normalized_cached_call(cached_llmgw_calls: Optional[SQLiteCache]): model = UiPathNormalizedChatModel( model="anthropic.claude-3-5-sonnet-20240620-v1:0", cache=cached_llmgw_calls ) @@ -51,7 +53,7 @@ def test_normalized_cached_call(cached_llmgw_calls: SQLiteCache | None): assert "4" in response.content, f"Expected '4' in response, got: {response.content}" -def test_normalized_cached_call_tokens(cached_llmgw_calls: SQLiteCache | None): +def test_normalized_cached_call_tokens(cached_llmgw_calls: Optional[SQLiteCache]): model = UiPathNormalizedChatModel( model="anthropic.claude-3-5-sonnet-20240620-v1:0", cache=cached_llmgw_calls ) @@ -65,7 +67,7 @@ def test_normalized_cached_call_tokens(cached_llmgw_calls: SQLiteCache | None): assert total_tokens >= 20, f"Expected more than 20 tokens, got: {total_tokens}" -def test_tool_call(cached_llmgw_calls: SQLiteCache | None): +def test_tool_call(cached_llmgw_calls: Optional[SQLiteCache]): @tool def get_first_letter(input): """ @@ -83,7 +85,7 @@ def get_first_letter(input): assert hasattr(response, "tool_calls"), ( "The response should have a 'tool_calls' attribute" ) - tool_call = response.tool_calls[0] # type: ignore + tool_call = response.tool_calls[0] assert tool_call["name"] == "get_first_letter", ( f"Expected tool call to 'get_first_letter', got: {tool_call['name']}" ) @@ -92,7 +94,7 @@ def get_first_letter(input): ) -def test_structured_output_call(cached_llmgw_calls: SQLiteCache | None): +def test_structured_output_call(cached_llmgw_calls: Optional[SQLiteCache]): class CitySize(BaseModel): """City and its size.""" @@ -120,7 +122,7 @@ class CitySize(BaseModel): pytest.fail(f"The response was not in the correct format: {exc}") -def test_embedding_call(cached_embedder: Embeddings | None): +def test_embedding_call(cached_embedder: Optional[Embeddings]): if not cached_embedder: cached_embedder = UiPathOpenAIEmbeddings(model="text-embedding-3-large") data = [ @@ -135,7 +137,7 @@ def test_embedding_call(cached_embedder: Embeddings | None): assert arr.shape == (2, 3072), f"Expected shape (2, 3072), got: {arr.shape}" -def test_custom_embedding_call(cached_embedder: Embeddings | None): +def test_custom_embedding_call(cached_embedder: Optional[Embeddings]): if not cached_embedder: cached_embedder = UiPathAzureOpenAIEmbeddings(model="text-embedding-3-large") data = [ diff --git a/sdk/langchain/uipath_langchain/chat/models.py b/sdk/langchain/uipath_langchain/chat/models.py index 96c1f7dc2..2ea613313 100644 --- a/sdk/langchain/uipath_langchain/chat/models.py +++ b/sdk/langchain/uipath_langchain/chat/models.py @@ -1,5 +1,5 @@ import json -from typing import Any, Dict, List, Literal, Optional +from typing import Any, Dict, List, Literal, Optional, Union from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, @@ -11,7 +11,6 @@ from langchain_core.outputs import ChatGeneration, ChatResult from langchain_core.runnables import Runnable from langchain_openai.chat_models import AzureChatOpenAI -from langchain_openai.chat_models.azure import _DictOrPydantic, _DictOrPydanticClass from pydantic import BaseModel from uipath_langchain.utils._request_mixin import UiPathRequestMixin @@ -49,13 +48,13 @@ async def _agenerate( def with_structured_output( self, - schema: Optional[_DictOrPydanticClass] = None, + schema: Optional[Any] = None, *, method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema", include_raw: bool = False, strict: Optional[bool] = None, **kwargs: Any, - ) -> Runnable[LanguageModelInput, _DictOrPydantic]: + ) -> Runnable[LanguageModelInput, Any]: """Model wrapper that returns outputs formatted to match the given schema.""" schema = ( schema.model_json_schema() @@ -82,8 +81,8 @@ class UiPathNormalizedChatModel(UiPathRequestMixin, AzureChatOpenAI): def _create_chat_result( self, - response: Dict[str, Any] | BaseModel, - generation_info: Optional[Dict] = None, + response: Union[Dict[str, Any], BaseModel], + generation_info: Optional[Dict[Any, Any]] = None, ) -> ChatResult: if not isinstance(response, dict): response = response.model_dump() @@ -125,7 +124,7 @@ def _get_request_payload( *, stop: Optional[List[str]] = None, **kwargs: Any, - ) -> Dict: + ) -> Dict[Any, Any]: payload = super()._get_request_payload(input_, stop=stop, **kwargs) # hacks to make the request work with uipath normalized for message in payload["messages"]: @@ -220,7 +219,7 @@ async def _agenerate( def with_structured_output( self, - schema: Optional[_DictOrPydanticClass] = None, + schema: Optional[Any] = None, *, method: Literal[ "function_calling", "json_mode", "json_schema" @@ -228,7 +227,7 @@ def with_structured_output( include_raw: bool = False, strict: Optional[bool] = None, **kwargs: Any, - ) -> Runnable[LanguageModelInput, _DictOrPydantic]: + ) -> Runnable[LanguageModelInput, Any]: """Model wrapper that returns outputs formatted to match the given schema.""" if method == "json_schema" and ( not self.model_name or not self.model_name.startswith("gpt") diff --git a/sdk/langchain/uipath_langchain/chat/utils/_chat_types.py b/sdk/langchain/uipath_langchain/chat/utils/_chat_types.py index e37fbf2ce..2abc8c0a6 100644 --- a/sdk/langchain/uipath_langchain/chat/utils/_chat_types.py +++ b/sdk/langchain/uipath_langchain/chat/utils/_chat_types.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Union from pydantic import BaseModel, Field @@ -6,24 +6,24 @@ class ToolCall(BaseModel): id: str name: str - arguments: dict + arguments: Dict[Any, Any] class Message(BaseModel): role: str - content: str | dict | None = None - tool_calls: List[ToolCall] | None = None + content: Optional[Union[str, Dict[Any, Any]]] = None + tool_calls: Optional[List[ToolCall]] = None class Tool(BaseModel): name: str description: str - parameters: dict | None = None + parameters: Optional[Dict[Any, Any]] = None class ToolChoice(BaseModel): type: str - name: str | None = None + name: Optional[str] = None class ChatCompletionRequest(BaseModel): @@ -34,8 +34,8 @@ class ChatCompletionRequest(BaseModel): frequency_penalty: float presence_penalty: float messages: List[Message] - tools: List[Tool] | None = None - tool_choice: ToolChoice | None = None + tools: Optional[List[Tool]] = None + tool_choice: Optional[ToolChoice] = None response_format: Optional[Dict[str, Any]] = None @@ -46,8 +46,8 @@ class ModelSettings(BaseModel): frequency_penalty: float = 0.0 presence_penalty: float = 0.0 max_tokens: int = 4096 - tool_choice: str | None = None - enforced_tool_name: str | None = None + tool_choice: Optional[str] = None + enforced_tool_name: Optional[str] = None class UiPathOutput(BaseModel): diff --git a/sdk/langchain/uipath_langchain/embeddings/embeddings.py b/sdk/langchain/uipath_langchain/embeddings/embeddings.py index ed185ebcc..4949651e9 100644 --- a/sdk/langchain/uipath_langchain/embeddings/embeddings.py +++ b/sdk/langchain/uipath_langchain/embeddings/embeddings.py @@ -1,19 +1,19 @@ import os -from typing import List +from typing import List, Optional import httpx from langchain_community.callbacks.manager import openai_callback_var from langchain_openai.embeddings import AzureOpenAIEmbeddings, OpenAIEmbeddings from pydantic import Field -from uipath_langchain.utils._settings import UiPathEndpoints from uipath_langchain.utils._request_mixin import UiPathRequestMixin +from uipath_langchain.utils._settings import UiPathEndpoints -class UiPathAzureOpenAIEmbeddings(UiPathRequestMixin, AzureOpenAIEmbeddings): # type: ignore +class UiPathAzureOpenAIEmbeddings(UiPathRequestMixin, AzureOpenAIEmbeddings): """Custom Embeddings connector for LangChain integration with UiPath, with minimal changes compared to AzureOpenAIEmbeddings.""" - model_name: str | None = Field( + model_name: Optional[str] = Field( default_factory=lambda: os.getenv( "UIPATH_MODEL_NAME", "text-embedding-3-large" ), @@ -22,13 +22,13 @@ class UiPathAzureOpenAIEmbeddings(UiPathRequestMixin, AzureOpenAIEmbeddings): # def __init__(self, **kwargs): super().__init__( - http_client=httpx.Client( # type: ignore + http_client=httpx.Client( event_hooks={ "request": [self._log_request_duration], "response": [self._log_response_duration], } ), - http_async_client=httpx.AsyncClient( # type: ignore + http_async_client=httpx.AsyncClient( event_hooks={ "request": [self._alog_request_duration], "response": [self._alog_response_duration], @@ -48,10 +48,10 @@ def endpoint(self) -> str: ) -class UiPathOpenAIEmbeddings(UiPathRequestMixin, OpenAIEmbeddings): # type: ignore +class UiPathOpenAIEmbeddings(UiPathRequestMixin, OpenAIEmbeddings): """Custom Embeddings connector for LangChain integration with UiPath, with full control over the embedding call.""" - model_name: str | None = Field( + model_name: Optional[str] = Field( default_factory=lambda: os.getenv( "UIPATH_MODEL_NAME", "text-embedding-3-large" ), @@ -59,7 +59,7 @@ class UiPathOpenAIEmbeddings(UiPathRequestMixin, OpenAIEmbeddings): # type: ign ) def embed_documents( - self, texts: List[str], chunk_size: int | None = None + self, texts: List[str], chunk_size: Optional[int] = None ) -> List[List[float]]: """Embed a list of documents using the UiPath.""" embeddings = [] @@ -82,7 +82,7 @@ def embed_documents( async def aembed_documents( self, texts: List[str], - chunk_size: int | None = None, + chunk_size: Optional[int] = None, ) -> List[List[float]]: """Async version of embed_documents.""" embeddings = [] diff --git a/sdk/langchain/uipath_langchain/utils/_request_mixin.py b/sdk/langchain/uipath_langchain/utils/_request_mixin.py index 0d7928ae5..f69aad310 100644 --- a/sdk/langchain/uipath_langchain/utils/_request_mixin.py +++ b/sdk/langchain/uipath_langchain/utils/_request_mixin.py @@ -1,3 +1,4 @@ +# mypy: disable-error-code="no-redef,arg-type" import json import logging import os @@ -25,30 +26,41 @@ from uipath_langchain.utils._sleep_policy import before_sleep_log +def get_from_uipath_url(): + url = os.getenv("UIPATH_URL") + if url: + return "/".join(url.split("/", 3)[:3]) + return None + + class UiPathRequestMixin(BaseModel): class Config: arbitrary_types_allowed = True - default_headers: Mapping[str, str] | None = { + default_headers: Optional[Mapping[str, str]] = { "X-UiPath-Streaming-Enabled": "false", } - model_name: str | None = Field( + model_name: Optional[str] = Field( default_factory=lambda: os.getenv("UIPATH_MODEL_NAME", "gpt-4o-2024-08-06"), alias="model", ) - settings: UiPathClientSettings | None = None - client_id: str | None = Field(default_factory=lambda: os.getenv("UIPATH_CLIENT_ID")) - client_secret: str | None = Field( + settings: Optional[UiPathClientSettings] = None + client_id: Optional[str] = Field( + default_factory=lambda: os.getenv("UIPATH_CLIENT_ID") + ) + client_secret: Optional[str] = Field( default_factory=lambda: os.getenv("UIPATH_CLIENT_SECRET") ) - base_url: str | None = Field( + base_url: Optional[str] = Field( default_factory=lambda data: getattr(data["settings"], "base_url", None) - or os.getenv("UIPATH_BASE_URL"), + or os.getenv("UIPATH_BASE_URL") + or get_from_uipath_url(), alias="azure_endpoint", ) - access_token: str | None = Field( + access_token: Optional[str] = Field( default_factory=lambda data: ( getattr(data["settings"], "access_token", None) + or os.getenv("UIPATH_ACCESS_TOKEN") # Environment variable or os.getenv("UIPATH_SERVICE_TOKEN") # Environment variable or get_uipath_token_header( UiPathClientFactorySettings( @@ -59,27 +71,27 @@ class Config: ) # Get service token from UiPath ) ) - org_id: str = Field( + org_id: Any = Field( default_factory=lambda data: getattr(data["settings"], "org_id", None) or os.getenv("UIPATH_ORGANIZATION_ID", "") ) - tenant_id: str = Field( + tenant_id: Any = Field( default_factory=lambda data: getattr(data["settings"], "tenant_id", None) or os.getenv("UIPATH_TENANT_ID", "") ) - requesting_product: str = Field( + requesting_product: Any = Field( default_factory=lambda data: getattr( data["settings"], "requesting_product", None ) or os.getenv("UIPATH_REQUESTING_PRODUCT", "") ) - requesting_feature: str = Field( + requesting_feature: Any = Field( default_factory=lambda data: getattr( data["settings"], "requesting_feature", None ) or os.getenv("UIPATH_REQUESTING_FEATURE", "") ) - default_request_timeout: float = Field( + default_request_timeout: Any = Field( default_factory=lambda data: float( getattr(data["settings"], "timeout_seconds", None) or os.getenv("UIPATH_TIMEOUT_SECONDS", "120") @@ -87,23 +99,23 @@ class Config: alias="timeout", ) - openai_api_version: str | None = Field( + openai_api_version: Optional[str] = Field( default_factory=lambda: os.getenv("OPENAI_API_VERSION", "2024-08-01-preview"), alias="api_version", ) include_account_id: bool = False - temperature: float | None = 0.0 - max_tokens: int | None = 1000 - frequency_penalty: float | None = None - presence_penalty: float | None = None + temperature: Optional[float] = 0.0 + max_tokens: Optional[int] = 1000 + frequency_penalty: Optional[float] = None + presence_penalty: Optional[float] = None - logger: logging.Logger | None = None - max_retries: int | None = 5 + logger: Optional[logging.Logger] = None + max_retries: Optional[int] = 5 base_delay: float = 5.0 max_delay: float = 60.0 - _url: str | None = None - _auth_headers: Dict[str, str] | None = None + _url: Optional[str] = None + _auth_headers: Optional[Dict[str, str]] = None # required to instantiate AzureChatOpenAI subclasses azure_endpoint: Optional[str] = Field( @@ -256,7 +268,7 @@ async def _acall( ) try: - response = await retryer(self._arequest, url, request_body, headers) + response: Any = await retryer(self._arequest, url, request_body, headers) if self.logger: self.logger.info( f"[uipath_langchain_client] Finished retryer after {retryer.statistics['attempt_number'] - 1} retries", @@ -287,7 +299,7 @@ async def _acall( "reason": err.message, "statusCode": err.status_code, }, - ) # type: ignore + ) raise err def _make_status_error_from_response( diff --git a/sdk/langchain/uipath_langchain/utils/_settings.py b/sdk/langchain/uipath_langchain/utils/_settings.py index ce33ebbf9..0e85822f3 100644 --- a/sdk/langchain/uipath_langchain/utils/_settings.py +++ b/sdk/langchain/uipath_langchain/utils/_settings.py @@ -1,13 +1,12 @@ +# mypy: disable-error-code="syntax" import os from enum import Enum +from typing import Any, Optional -import aiohttp -import requests +import httpx from pydantic import Field from pydantic_settings import BaseSettings -uipath_token_header = None - class UiPathCachedPathsSettings(BaseSettings): cached_completion_db: str = Field( @@ -23,6 +22,7 @@ class UiPathCachedPathsSettings(BaseSettings): uipath_cached_paths_settings = UiPathCachedPathsSettings() +uipath_token_header: Optional[str] = None class UiPathClientFactorySettings(BaseSettings): @@ -51,7 +51,9 @@ class UiPathEndpoints(Enum): ) -def get_uipath_token_header(settings: UiPathClientFactorySettings | None = None) -> str: +def get_uipath_token_header( + settings: Any = None, +) -> str: global uipath_token_header if not uipath_token_header: settings = settings or UiPathClientFactorySettings() @@ -61,14 +63,16 @@ def get_uipath_token_header(settings: UiPathClientFactorySettings | None = None) client_secret=settings.client_secret, grant_type="client_credentials", ) - res = requests.post(url_get_token, data=token_credentials) - res_json = res.json() - uipath_token_header = res_json.get("access_token") - return uipath_token_header + with httpx.Client() as client: + res = client.post(url_get_token, data=token_credentials) + res_json = res.json() + uipath_token_header = res_json.get("access_token") + + return uipath_token_header or "" async def get_token_header_async( - settings: UiPathClientFactorySettings | None = None, + settings: Any = None, ) -> str: global uipath_token_header if not uipath_token_header: @@ -79,9 +83,9 @@ async def get_token_header_async( client_secret=settings.client_secret, grant_type="client_credentials", ) - async with aiohttp.ClientSession() as session: - async with session.post(url_get_token, data=token_credentials) as res: - res.raise_for_status() - res_json = await res.json() - uipath_token_header = res_json.get("access_token") - return uipath_token_header + + with httpx.Client() as client: + res_json = client.post(url_get_token, data=token_credentials).json() + uipath_token_header = res_json.get("access_token") + + return uipath_token_header or "" diff --git a/sdk/langchain/uv.lock b/sdk/langchain/uv.lock index 2e53bcc43..af4ff9900 100644 --- a/sdk/langchain/uv.lock +++ b/sdk/langchain/uv.lock @@ -2087,6 +2087,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2", size = 78540 }, ] +[[package]] +name = "types-requests" +version = "2.32.0.20250306" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "urllib3" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/09/1a/beaeff79ef9efd186566ba5f0d95b44ae21f6d31e9413bcfbef3489b6ae3/types_requests-2.32.0.20250306.tar.gz", hash = "sha256:0962352694ec5b2f95fda877ee60a159abdf84a0fc6fdace599f20acb41a03d1", size = 23012 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/99/26/645d89f56004aa0ba3b96fec27793e3c7e62b40982ee069e52568922b6db/types_requests-2.32.0.20250306-py3-none-any.whl", hash = "sha256:25f2cbb5c8710b2022f8bbee7b2b66f319ef14aeea2f35d80f18c9dbf3b60a0b", size = 20673 }, +] + [[package]] name = "typing-extensions" version = "4.12.2" @@ -2111,9 +2123,10 @@ wheels = [ [[package]] name = "uipath-langchain" -version = "0.0.60" +version = "0.0.62" source = { editable = "." } dependencies = [ + { name = "aiohttp" }, { name = "httpx" }, { name = "langchain" }, { name = "langchain-community" }, @@ -2121,9 +2134,11 @@ dependencies = [ { name = "langchain-openai" }, { name = "langgraph" }, { name = "langgraph-checkpoint-sqlite" }, + { name = "openai" }, { name = "pydantic-settings" }, - { name = "pytest" }, { name = "python-dotenv" }, + { name = "requests" }, + { name = "types-requests" }, { name = "uipath-sdk" }, ] @@ -2141,6 +2156,7 @@ dev = [ [package.metadata] requires-dist = [ + { name = "aiohttp", specifier = ">=3.11.13" }, { name = "httpx", specifier = ">=0.27.0" }, { name = "langchain", specifier = ">=0.3.4" }, { name = "langchain-community", specifier = ">=0.3.18" }, @@ -2148,9 +2164,11 @@ requires-dist = [ { name = "langchain-openai", specifier = ">=0.3.3" }, { name = "langgraph", specifier = ">=0.2.70" }, { name = "langgraph-checkpoint-sqlite", specifier = ">=2.0.3" }, + { name = "openai", specifier = ">=1.65.5" }, { name = "pydantic-settings", specifier = ">=2.6.0" }, - { name = "pytest", specifier = ">=8.3.4" }, { name = "python-dotenv", specifier = ">=1.0.1" }, + { name = "requests", specifier = ">=2.23.3" }, + { name = "types-requests", specifier = ">=2.32.0.20241016" }, { name = "uipath-langchain", marker = "extra == 'langchain'", specifier = ">=0.0.2" }, { name = "uipath-sdk", specifier = ">=0.0.75" }, ]