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chore: drop UP045 (#33362)

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Python 3.9 EOL
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Mason Daugherty authored and GitHub committed 2025-10-08 21:17:53 -04:00
1 parent 0039b3b046
commit 31eeb50ce0
119 files changed
+1423 -1504
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AGENTS.md
+1 -1
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@@ -149,7 +149,7 @@ def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
Args:
to: The email address of the recipient.
msg: The message body to send.
priority: Email priority level (``'low'``, ``'normal'``, ``'high'``).
priority: Email priority level (`'low'`, ``'normal'``, `'high'`).
Returns:
True if email was sent successfully, False otherwise.
CLAUDE.md
+1 -1
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@@ -149,7 +149,7 @@ def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
Args:
to: The email address of the recipient.
msg: The message body to send.
priority: Email priority level (``'low'``, ``'normal'``, ``'high'``).
priority: Email priority level (`'low'`, ``'normal'``, `'high'`).
Returns:
True if email was sent successfully, False otherwise.
libs/core/langchain_core/prompt_values.py
+2 -2
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@@ -113,8 +113,8 @@ class ImageURL(TypedDict, total=False):
"""Image URL."""
detail: Literal["auto", "low", "high"]
"""Specifies the detail level of the image. Defaults to ``'auto'``.
Can be ``'auto'``, ``'low'``, or ``'high'``.
"""Specifies the detail level of the image. Defaults to `'auto'`.
Can be `'auto'`, `'low'`, or `'high'`.
This follows OpenAI's Chat Completion API's image URL format.
libs/langchain/langchain_classic/chains/moderation.py
+1 -1
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@@ -16,7 +16,7 @@ from langchain_classic.chains.base import Chain
class OpenAIModerationChain(Chain):
"""Pass input through a moderation endpoint.
To use, you should have the ``openai`` python package installed, and the
To use, you should have the `openai` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
libs/langchain/langchain_classic/chat_models/base.py
+31 -31
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@@ -91,41 +91,41 @@ def init_chat_model(
above). Supported model_provider values and the corresponding integration
package are:
- ``openai`` -> `langchain-openai`
- ``anthropic`` -> `langchain-anthropic`
- ``azure_openai`` -> `langchain-openai`
- ``azure_ai`` -> ``langchain-azure-ai``
- ``google_vertexai`` -> ``langchain-google-vertexai``
- ``google_genai`` -> ``langchain-google-genai``
- ``bedrock`` -> ``langchain-aws``
- ``bedrock_converse`` -> ``langchain-aws``
- ``cohere`` -> ``langchain-cohere``
- ``fireworks`` -> ``langchain-fireworks``
- ``together`` -> ``langchain-together``
- ``mistralai`` -> ``langchain-mistralai``
- ``huggingface`` -> ``langchain-huggingface``
- ``groq`` -> ``langchain-groq``
- ``ollama`` -> ``langchain-ollama``
- ``google_anthropic_vertex`` -> ``langchain-google-vertexai``
- ``deepseek`` -> ``langchain-deepseek``
- ``ibm`` -> ``langchain-ibm``
- ``nvidia`` -> ``langchain-nvidia-ai-endpoints``
- ``xai`` -> ``langchain-xai``
- ``perplexity`` -> ``langchain-perplexity``
- `openai` -> `langchain-openai`
- `anthropic` -> `langchain-anthropic`
- `azure_openai` -> `langchain-openai`
- `azure_ai` -> `langchain-azure-ai`
- `google_vertexai` -> `langchain-google-vertexai`
- `google_genai` -> `langchain-google-genai`
- `bedrock` -> `langchain-aws`
- `bedrock_converse` -> `langchain-aws`
- `cohere` -> `langchain-cohere`
- `fireworks` -> `langchain-fireworks`
- `together` -> `langchain-together`
- `mistralai` -> `langchain-mistralai`
- `huggingface` -> `langchain-huggingface`
- `groq` -> `langchain-groq`
- `ollama` -> `langchain-ollama`
- `google_anthropic_vertex` -> `langchain-google-vertexai`
- `deepseek` -> `langchain-deepseek`
- `ibm` -> `langchain-ibm`
- `nvidia` -> `langchain-nvidia-ai-endpoints`
- `xai` -> `langchain-xai`
- `perplexity` -> `langchain-perplexity`
Will attempt to infer model_provider from model if not specified. The
following providers will be inferred based on these model prefixes:
- ``gpt-...`` | ``o1...`` | ``o3...`` -> ``openai``
- ``claude...`` -> ``anthropic``
- ``amazon...`` -> ``bedrock``
- ``gemini...`` -> ``google_vertexai``
- ``command...`` -> ``cohere``
- ``accounts/fireworks...`` -> ``fireworks``
- ``mistral...`` -> ``mistralai``
- ``deepseek...`` -> ``deepseek``
- ``grok...`` -> ``xai``
- ``sonar...`` -> ``perplexity``
- `gpt-...` | `o1...` | `o3...` -> `openai`
- `claude...` -> `anthropic`
- `amazon...` -> `bedrock`
- `gemini...` -> `google_vertexai`
- `command...` -> `cohere`
- `accounts/fireworks...` -> `fireworks`
- `mistral...` -> `mistralai`
- `deepseek...` -> `deepseek`
- `grok...` -> `xai`
- `sonar...` -> `perplexity`
configurable_fields: Which model parameters are configurable:
- None: No configurable fields.
libs/partners/anthropic/langchain_anthropic/_client_utils.py
+5 -5
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@@ -11,7 +11,7 @@ from __future__ import annotations
import asyncio
import os
from functools import lru_cache
from typing import Any, Optional
from typing import Any
import anthropic
@@ -48,9 +48,9 @@ class _AsyncHttpxClientWrapper(anthropic.DefaultAsyncHttpxClient):
@lru_cache
def _get_default_httpx_client(
*,
base_url: Optional[str],
base_url: str | None,
timeout: Any = _NOT_GIVEN,
anthropic_proxy: Optional[str] = None,
anthropic_proxy: str | None = None,
) -> _SyncHttpxClientWrapper:
kwargs: dict[str, Any] = {
"base_url": base_url
@@ -67,9 +67,9 @@ def _get_default_httpx_client(
@lru_cache
def _get_default_async_httpx_client(
*,
base_url: Optional[str],
base_url: str | None,
timeout: Any = _NOT_GIVEN,
anthropic_proxy: Optional[str] = None,
anthropic_proxy: str | None = None,
) -> _AsyncHttpxClientWrapper:
kwargs: dict[str, Any] = {
"base_url": base_url
libs/partners/anthropic/langchain_anthropic/_compat.py
+2 -2
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@@ -1,7 +1,7 @@
from __future__ import annotations
import json
from typing import Any, Optional, cast
from typing import Any, cast
from langchain_core.messages import content as types
@@ -97,7 +97,7 @@ def _convert_annotation_from_v1(annotation: types.Annotation) -> dict[str, Any]:
def _convert_from_v1_to_anthropic(
content: list[types.ContentBlock],
tool_calls: list[types.ToolCall],
model_provider: Optional[str],
model_provider: str | None,
) -> list[dict[str, Any]]:
new_content: list = []
for block in content:
libs/partners/anthropic/langchain_anthropic/chat_models.py
+45 -50
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@@ -9,7 +9,7 @@ import warnings
from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
from functools import cached_property
from operator import itemgetter
from typing import Any, Final, Literal, Optional, Union, cast
from typing import Any, Final, Literal, cast
import anthropic
from langchain_core.callbacks import (
@@ -170,7 +170,7 @@ def _format_image(url: str) -> dict:
def _merge_messages(
messages: Sequence[BaseMessage],
) -> list[Union[SystemMessage, AIMessage, HumanMessage]]:
) -> list[SystemMessage | AIMessage | HumanMessage]:
"""Merge runs of human/tool messages into single human messages with content blocks.""" # noqa: E501
merged: list = []
for curr in messages:
@@ -344,9 +344,9 @@ def _format_data_content_block(block: dict) -> dict:
def _format_messages(
messages: Sequence[BaseMessage],
) -> tuple[Union[str, list[dict], None], list[dict]]:
) -> tuple[str | list[dict] | None, list[dict]]:
"""Format messages for Anthropic's API."""
system: Union[str, list[dict], None] = None
system: str | list[dict] | None = None
formatted_messages: list[dict] = []
merged_messages = _merge_messages(messages)
for _i, message in enumerate(merged_messages):
@@ -368,7 +368,7 @@ def _format_messages(
continue
role = _message_type_lookups[message.type]
content: Union[str, list]
content: str | list
if not isinstance(message.content, str):
# parse as dict
@@ -1419,29 +1419,29 @@ class ChatAnthropic(BaseChatModel):
model: str = Field(alias="model_name")
"""Model name to use."""
max_tokens: Optional[int] = Field(default=None, alias="max_tokens_to_sample")
max_tokens: int | None = Field(default=None, alias="max_tokens_to_sample")
"""Denotes the number of tokens to predict per generation."""
temperature: Optional[float] = None
temperature: float | None = None
"""A non-negative float that tunes the degree of randomness in generation."""
top_k: Optional[int] = None
top_k: int | None = None
"""Number of most likely tokens to consider at each step."""
top_p: Optional[float] = None
top_p: float | None = None
"""Total probability mass of tokens to consider at each step."""
default_request_timeout: Optional[float] = Field(None, alias="timeout")
default_request_timeout: float | None = Field(None, alias="timeout")
"""Timeout for requests to Anthropic Completion API."""
# sdk default = 2: https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#retries
max_retries: int = 2
"""Number of retries allowed for requests sent to the Anthropic Completion API."""
stop_sequences: Optional[list[str]] = Field(None, alias="stop")
stop_sequences: list[str] | None = Field(None, alias="stop")
"""Default stop sequences."""
anthropic_api_url: Optional[str] = Field(
anthropic_api_url: str | None = Field(
alias="base_url",
default_factory=from_env(
["ANTHROPIC_API_URL", "ANTHROPIC_BASE_URL"],
@@ -1462,7 +1462,7 @@ class ChatAnthropic(BaseChatModel):
)
"""Automatically read from env var ``ANTHROPIC_API_KEY`` if not provided."""
anthropic_proxy: Optional[str] = Field(
anthropic_proxy: str | None = Field(
default_factory=from_env("ANTHROPIC_PROXY", default=None)
)
"""Proxy to use for the Anthropic clients, will be used for every API call.
@@ -1470,10 +1470,10 @@ class ChatAnthropic(BaseChatModel):
If not provided, will attempt to read from the ``ANTHROPIC_PROXY`` environment
variable."""
default_headers: Optional[Mapping[str, str]] = None
default_headers: Mapping[str, str] | None = None
"""Headers to pass to the Anthropic clients, will be used for every API call."""
betas: Optional[list[str]] = None
betas: list[str] | None = None
"""List of beta features to enable. If specified, invocations will be routed
through client.beta.messages.create.
@@ -1490,18 +1490,18 @@ class ChatAnthropic(BaseChatModel):
message chunks will be generated during the stream including usage metadata.
"""
thinking: Optional[dict[str, Any]] = Field(default=None)
thinking: dict[str, Any] | None = Field(default=None)
"""Parameters for Claude reasoning,
e.g., ``{"type": "enabled", "budget_tokens": 10_000}``"""
mcp_servers: Optional[list[dict[str, Any]]] = None
mcp_servers: list[dict[str, Any]] | None = None
"""List of MCP servers to use for the request.
Example: ``mcp_servers=[{"type": "url", "url": "https://mcp.example.com/mcp",
"name": "example-mcp"}]``
"""
context_management: Optional[dict[str, Any]] = None
context_management: dict[str, Any] | None = None
"""Configuration for
`context management <https://docs.claude.com/en/docs/build-with-claude/context-editing>`__.
"""
@@ -1547,7 +1547,7 @@ class ChatAnthropic(BaseChatModel):
def _get_ls_params(
self,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> LangSmithParams:
"""Get standard params for tracing."""
@@ -1630,7 +1630,7 @@ class ChatAnthropic(BaseChatModel):
self,
input_: LanguageModelInput,
*,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: dict,
) -> dict:
"""Get the request payload for the Anthropic API."""
@@ -1717,10 +1717,10 @@ class ChatAnthropic(BaseChatModel):
def _stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
*,
stream_usage: Optional[bool] = None,
stream_usage: bool | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
if stream_usage is None:
@@ -1753,10 +1753,10 @@ class ChatAnthropic(BaseChatModel):
async def _astream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
*,
stream_usage: Optional[bool] = None,
stream_usage: bool | None = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
if stream_usage is None:
@@ -1839,8 +1839,8 @@ class ChatAnthropic(BaseChatModel):
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
payload = self._get_request_payload(messages, stop=stop, **kwargs)
@@ -1853,8 +1853,8 @@ class ChatAnthropic(BaseChatModel):
async def _agenerate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
payload = self._get_request_payload(messages, stop=stop, **kwargs)
@@ -1866,7 +1866,7 @@ class ChatAnthropic(BaseChatModel):
def _get_llm_for_structured_output_when_thinking_is_enabled(
self,
schema: Union[dict, type],
schema: dict | type,
formatted_tool: AnthropicTool,
) -> Runnable[LanguageModelInput, BaseMessage]:
thinking_admonition = (
@@ -1894,12 +1894,10 @@ class ChatAnthropic(BaseChatModel):
def bind_tools(
self,
tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
tools: Sequence[dict[str, Any] | type | Callable | BaseTool],
*,
tool_choice: Optional[
Union[dict[str, str], Literal["any", "auto"], str] # noqa: PYI051
] = None,
parallel_tool_calls: Optional[bool] = None,
tool_choice: dict[str, str] | str | None = None,
parallel_tool_calls: bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]:
r"""Bind tool-like objects to this chat model.
@@ -1910,15 +1908,14 @@ class ChatAnthropic(BaseChatModel):
by `langchain_core.utils.function_calling.convert_to_openai_tool`.
tool_choice: Which tool to require the model to call. Options are:
- name of the tool as a string or as dict ``{"type": "tool", "name": "<<tool_name>>"}``: calls corresponding tool;
- ``'auto'``, ``{"type: "auto"}``, or `None`: automatically selects a tool (including no tool);
- `'any'` or ``{"type: "any"}``: force at least one tool to be called;
- name of the tool as a string or as dict `{"type": "tool", "name": "<<tool_name>>"}`: calls corresponding tool;
- `'auto'`, `{"type: "auto"}`, or `None`: automatically selects a tool (including no tool);
- `'any'` or `{"type: "any"}`: force at least one tool to be called;
parallel_tool_calls: Set to `False` to disable parallel tool use.
Defaults to `None` (no specification, which allows parallel tool use).
!!! version-added "Added in version 0.3.2"
kwargs: Any additional parameters are passed directly to
`langchain_anthropic.chat_models.ChatAnthropic.bind`.
kwargs: Any additional parameters are passed directly to `bind`.
Example:
@@ -2170,11 +2167,11 @@ class ChatAnthropic(BaseChatModel):
def with_structured_output(
self,
schema: Union[dict, type],
schema: dict | type,
*,
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
) -> Runnable[LanguageModelInput, dict | BaseModel]:
"""Model wrapper that returns outputs formatted to match the given schema.
Args:
@@ -2340,9 +2337,7 @@ class ChatAnthropic(BaseChatModel):
def get_num_tokens_from_messages(
self,
messages: list[BaseMessage],
tools: Optional[
Sequence[Union[dict[str, Any], type, Callable, BaseTool]]
] = None,
tools: Sequence[dict[str, Any] | type | Callable | BaseTool] | None = None,
**kwargs: Any,
) -> int:
"""Count tokens in a sequence of input messages.
@@ -2430,7 +2425,7 @@ class ChatAnthropic(BaseChatModel):
def convert_to_anthropic_tool(
tool: Union[dict[str, Any], type, Callable, BaseTool],
tool: dict[str, Any] | type | Callable | BaseTool,
) -> AnthropicTool:
"""Convert a tool-like object to an Anthropic tool definition."""
# already in Anthropic tool format
@@ -2500,8 +2495,8 @@ def _make_message_chunk_from_anthropic_event(
*,
stream_usage: bool = True,
coerce_content_to_string: bool,
block_start_event: Optional[anthropic.types.RawMessageStreamEvent] = None,
) -> tuple[Optional[AIMessageChunk], Optional[anthropic.types.RawMessageStreamEvent]]:
block_start_event: anthropic.types.RawMessageStreamEvent | None = None,
) -> tuple[AIMessageChunk | None, anthropic.types.RawMessageStreamEvent | None]:
"""Convert Anthropic streaming event to `AIMessageChunk`.
Args:
@@ -2526,7 +2521,7 @@ def _make_message_chunk_from_anthropic_event(
updating the `block_start_event` for context tracking.
"""
message_chunk: Optional[AIMessageChunk] = None
message_chunk: AIMessageChunk | None = None
# Reference: Anthropic SDK streaming implementation
# https://github.com/anthropics/anthropic-sdk-python/blob/main/src/anthropic/lib/streaming/_messages.py # noqa: E501
libs/partners/anthropic/langchain_anthropic/experimental.py
+1 -2
View File
@@ -5,7 +5,6 @@ from __future__ import annotations
import json
from typing import (
Any,
Union,
)
SYSTEM_PROMPT_FORMAT = """In this environment you have access to a set of tools you can use to answer the user's question.
@@ -83,7 +82,7 @@ def get_system_message(tools: list[dict]) -> str:
return SYSTEM_PROMPT_FORMAT.format(formatted_tools=tools_formatted)
def _xml_to_dict(t: Any) -> Union[str, dict[str, Any]]:
def _xml_to_dict(t: Any) -> str | dict[str, Any]:
# Base case: If the element has no children, return its text or an empty string.
if len(t) == 0:
return t.text or ""
libs/partners/anthropic/langchain_anthropic/llms.py
+19 -19
View File
@@ -5,7 +5,7 @@ from __future__ import annotations
import re
import warnings
from collections.abc import AsyncIterator, Callable, Iterator, Mapping
from typing import Any, Optional
from typing import Any
import anthropic
from langchain_core.callbacks import (
@@ -31,25 +31,25 @@ class _AnthropicCommon(BaseLanguageModel):
max_tokens: int = Field(default=1024, alias="max_tokens_to_sample")
"""Denotes the number of tokens to predict per generation."""
temperature: Optional[float] = None
temperature: float | None = None
"""A non-negative float that tunes the degree of randomness in generation."""
top_k: Optional[int] = None
top_k: int | None = None
"""Number of most likely tokens to consider at each step."""
top_p: Optional[float] = None
top_p: float | None = None
"""Total probability mass of tokens to consider at each step."""
streaming: bool = False
"""Whether to stream the results."""
default_request_timeout: Optional[float] = None
default_request_timeout: float | None = None
"""Timeout for requests to Anthropic Completion API. Default is 600 seconds."""
max_retries: int = 2
"""Number of retries allowed for requests sent to the Anthropic Completion API."""
anthropic_api_url: Optional[str] = Field(
anthropic_api_url: str | None = Field(
alias="base_url",
default_factory=from_env(
"ANTHROPIC_API_URL",
@@ -69,9 +69,9 @@ class _AnthropicCommon(BaseLanguageModel):
)
"""Automatically read from env var ``ANTHROPIC_API_KEY`` if not provided."""
HUMAN_PROMPT: Optional[str] = None
AI_PROMPT: Optional[str] = None
count_tokens: Optional[Callable[[str], int]] = None
HUMAN_PROMPT: str | None = None
AI_PROMPT: str | None = None
count_tokens: Callable[[str], int] | None = None
model_kwargs: dict[str, Any] = Field(default_factory=dict)
@model_validator(mode="before")
@@ -120,7 +120,7 @@ class _AnthropicCommon(BaseLanguageModel):
"""Get the identifying parameters."""
return {**self._default_params}
def _get_anthropic_stop(self, stop: Optional[list[str]] = None) -> list[str]:
def _get_anthropic_stop(self, stop: list[str] | None = None) -> list[str]:
if stop is None:
stop = []
return stop
@@ -190,7 +190,7 @@ class AnthropicLLM(LLM, _AnthropicCommon):
def _get_ls_params(
self,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> LangSmithParams:
"""Get standard params for tracing."""
@@ -245,8 +245,8 @@ class AnthropicLLM(LLM, _AnthropicCommon):
def _call(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
r"""Call out to Anthropic's completion endpoint.
@@ -299,8 +299,8 @@ class AnthropicLLM(LLM, _AnthropicCommon):
async def _acall(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
"""Call out to Anthropic's completion endpoint asynchronously."""
@@ -331,8 +331,8 @@ class AnthropicLLM(LLM, _AnthropicCommon):
def _stream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
r"""Call Anthropic completion_stream and return the resulting generator.
@@ -378,8 +378,8 @@ class AnthropicLLM(LLM, _AnthropicCommon):
async def _astream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
r"""Call Anthropic completion_stream and return the resulting generator.
libs/partners/anthropic/langchain_anthropic/output_parsers.py
+3 -3
View File
@@ -2,7 +2,7 @@
from __future__ import annotations
from typing import Any, Optional, Union, cast
from typing import Any, cast
from langchain_core.messages import AIMessage, ToolCall
from langchain_core.messages.tool import tool_call
@@ -18,7 +18,7 @@ class ToolsOutputParser(BaseGenerationOutputParser):
"""Whether to return only the first tool call."""
args_only: bool = False
"""Whether to return only the arguments of the tool calls."""
pydantic_schemas: Optional[list[type[BaseModel]]] = None
pydantic_schemas: list[type[BaseModel]] | None = None
"""Pydantic schemas to parse tool calls into."""
model_config = ConfigDict(
@@ -77,7 +77,7 @@ def _extract_tool_calls_from_message(message: AIMessage) -> list[ToolCall]:
return extract_tool_calls(message.content)
def extract_tool_calls(content: Union[str, list[Union[str, dict]]]) -> list[ToolCall]:
def extract_tool_calls(content: str | list[str | dict]) -> list[ToolCall]:
"""Extract tool calls from a list of content blocks."""
if isinstance(content, list):
tool_calls = []
libs/partners/anthropic/pyproject.toml
-2
View File
@@ -69,8 +69,6 @@ ignore = [
"COM812", # Messes with the formatter
"ISC001", # Messes with the formatter
"PERF203", # Rarely useful
"UP007", # non-pep604-annotation-union
"UP045", # non-pep604-annotation-optional
"SIM105", # Rarely useful
"FIX", # TODOs
"TD", # TODOs
libs/partners/anthropic/tests/integration_tests/test_chat_models.py
+13 -13
View File
@@ -6,7 +6,7 @@ import asyncio
import json
import os
from base64 import b64encode
from typing import Literal, Optional, cast
from typing import Literal, cast
import httpx
import pytest
@@ -40,7 +40,7 @@ def test_stream() -> None:
"""Test streaming tokens from Anthropic."""
llm = ChatAnthropic(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks_with_input_token_counts = 0
chunks_with_output_token_counts = 0
chunks_with_model_name = 0
@@ -86,7 +86,7 @@ async def test_astream() -> None:
"""Test streaming tokens from Anthropic."""
llm = ChatAnthropic(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks_with_input_token_counts = 0
chunks_with_output_token_counts = 0
async for token in llm.astream("I'm Pickle Rick"):
@@ -810,7 +810,7 @@ def test_citations(output_version: Literal["v0", "v1"]) -> None:
assert any("citations" in block for block in response.content)
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream(messages):
full = cast("BaseMessageChunk", chunk) if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
@@ -850,7 +850,7 @@ def test_thinking() -> None:
assert isinstance(block["signature"], str)
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream([input_message]):
full = cast("BaseMessageChunk", chunk) if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
@@ -894,7 +894,7 @@ def test_thinking_v1() -> None:
assert isinstance(signature, str)
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream([input_message]):
full = cast(BaseMessageChunk, chunk) if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
@@ -948,7 +948,7 @@ def test_redacted_thinking(output_version: Literal["v0", "v1"]) -> None:
assert value is not None
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream([input_message]):
full = cast("BaseMessageChunk", chunk) if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
@@ -1108,7 +1108,7 @@ def test_web_search(output_version: Literal["v0", "v1"]) -> None:
assert block_types == {"text", "server_tool_call", "server_tool_result"}
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm_with_tools.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -1173,7 +1173,7 @@ def test_web_fetch_v1(output_version: Literal["v0", "v1"]) -> None:
assert block_types == {"text", call_key, result_key}
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm_with_tools.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -1394,7 +1394,7 @@ def test_web_fetch() -> None:
)
# Streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm_with_tools.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -1493,7 +1493,7 @@ def test_code_execution(output_version: Literal["v0", "v1"]) -> None:
assert block_types == {"text", "server_tool_call", "server_tool_result"}
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm_with_tools.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -1562,7 +1562,7 @@ def test_remote_mcp(output_version: Literal["v0", "v1"]) -> None:
assert block_types == {"text", "server_tool_call", "server_tool_result"}
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -1798,7 +1798,7 @@ def test_context_management() -> None:
assert response.response_metadata.get("context_management")
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm_with_tools.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
libs/partners/anthropic/tests/unit_tests/_utils.py
+2 -2
View File
@@ -2,7 +2,7 @@
from __future__ import annotations
from typing import Any, Union
from typing import Any
from langchain_core.callbacks import BaseCallbackHandler
from pydantic import BaseModel
@@ -22,7 +22,7 @@ class BaseFakeCallbackHandler(BaseModel):
ignore_chat_model_: bool = False
# to allow for similar callback handlers that are not technically equal
fake_id: Union[str, None] = None
fake_id: str | None = None
# add finer-grained counters for easier debugging of failing tests
chain_starts: int = 0
libs/partners/anthropic/tests/unit_tests/test_chat_models.py
+5 -5
View File
@@ -4,7 +4,7 @@ from __future__ import annotations
import os
from collections.abc import Callable
from typing import Any, Literal, Optional, cast
from typing import Any, Literal, cast
from unittest.mock import MagicMock, patch
import anthropic
@@ -1324,10 +1324,10 @@ def test_usage_metadata_standardization() -> None:
# Null input and output tokens
class UsageModelNulls(BaseModel):
input_tokens: Optional[int] = None
output_tokens: Optional[int] = None
cache_read_input_tokens: Optional[int] = None
cache_creation_input_tokens: Optional[int] = None
input_tokens: int | None = None
output_tokens: int | None = None
cache_read_input_tokens: int | None = None
cache_creation_input_tokens: int | None = None
usage_nulls = UsageModelNulls()
result = _create_usage_metadata(usage_nulls)
libs/partners/anthropic/uv.lock
Generated
+2 -2
View File
@@ -1,5 +1,5 @@
version = 1
revision = 2
revision = 3
requires-python = ">=3.10.0, <4.0.0"
resolution-markers = [
"python_full_version >= '3.13' and platform_python_implementation == 'PyPy'",
@@ -496,7 +496,7 @@ typing = [
[[package]]
name = "langchain-core"
version = "1.0.0a7"
version = "1.0.0a8"
source = { editable = "../../core" }
dependencies = [
{ name = "jsonpatch" },
libs/partners/chroma/langchain_chroma/__init__.py
+2
View File
@@ -1,3 +1,5 @@
"""LangChain integration for Chroma vector database."""
from langchain_chroma.vectorstores import Chroma
__all__ = [
libs/partners/chroma/langchain_chroma/vectorstores.py
+80 -83
View File
@@ -9,11 +9,10 @@ import base64
import logging
import uuid
from collections.abc import Callable, Iterable, Sequence
from pathlib import Path
from typing import (
TYPE_CHECKING,
Any,
Optional,
Union,
)
import chromadb
@@ -67,10 +66,10 @@ def _results_to_docs_and_vectors(results: Any) -> list[tuple[Document, np.ndarra
]
Matrix = Union[list[list[float]], list[np.ndarray], np.ndarray]
Matrix = list[list[float]], list[np.ndarray] | np.ndarray
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: # type: ignore[valid-type]
"""Row-wise cosine similarity between two equal-width matrices.
Raises:
@@ -301,20 +300,20 @@ class Chroma(VectorStore):
def __init__(
self,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
embedding_function: Optional[Embeddings] = None,
persist_directory: Optional[str] = None,
host: Optional[str] = None,
port: Optional[int] = None,
headers: Optional[dict[str, str]] = None,
chroma_cloud_api_key: Optional[str] = None,
tenant: Optional[str] = None,
database: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
collection_metadata: Optional[dict] = None,
collection_configuration: Optional[CreateCollectionConfiguration] = None,
client: Optional[chromadb.ClientAPI] = None,
relevance_score_fn: Optional[Callable[[float], float]] = None,
create_collection_if_not_exists: Optional[bool] = True, # noqa: FBT001, FBT002
embedding_function: Embeddings | None = None,
persist_directory: str | None = None,
host: str | None = None,
port: int | None = None,
headers: dict[str, str] | None = None,
chroma_cloud_api_key: str | None = None,
tenant: str | None = None,
database: str | None = None,
client_settings: chromadb.config.Settings | None = None,
collection_metadata: dict | None = None,
collection_configuration: CreateCollectionConfiguration | None = None,
client: chromadb.ClientAPI | None = None,
relevance_score_fn: Callable[[float], float] | None = None,
create_collection_if_not_exists: bool | None = True, # noqa: FBT001, FBT002
*,
ssl: bool = False,
) -> None:
@@ -408,7 +407,7 @@ class Chroma(VectorStore):
self._client = chromadb.Client(settings=_settings)
self._embedding_function = embedding_function
self._chroma_collection: Optional[chromadb.Collection] = None
self._chroma_collection: chromadb.Collection | None = None
self._collection_name = collection_name
self._collection_metadata = collection_metadata
self._collection_configuration = collection_configuration
@@ -441,20 +440,20 @@ class Chroma(VectorStore):
return self._chroma_collection
@property
def embeddings(self) -> Optional[Embeddings]:
def embeddings(self) -> Embeddings | None:
"""Access the query embedding object."""
return self._embedding_function
@xor_args(("query_texts", "query_embeddings"))
def __query_collection(
self,
query_texts: Optional[list[str]] = None,
query_embeddings: Optional[list[list[float]]] = None,
query_texts: list[str] | None = None,
query_embeddings: list[list[float]] | None = None,
n_results: int = 4,
where: Optional[dict[str, str]] = None,
where_document: Optional[dict[str, str]] = None,
where: dict[str, str] | None = None,
where_document: dict[str, str] | None = None,
**kwargs: Any,
) -> Union[list[Document], chromadb.QueryResult]:
) -> list[Document] | chromadb.QueryResult:
"""Query the chroma collection.
Args:
@@ -485,7 +484,7 @@ class Chroma(VectorStore):
@staticmethod
def encode_image(uri: str) -> str:
"""Get base64 string from image URI."""
with open(uri, "rb") as image_file:
with Path(uri).open("rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
def fork(self, new_name: str) -> Chroma:
@@ -508,8 +507,8 @@ class Chroma(VectorStore):
def add_images(
self,
uris: list[str],
metadatas: Optional[list[dict]] = None,
ids: Optional[list[str]] = None,
metadatas: list[dict] | None = None,
ids: list[str] | None = None,
) -> list[str]:
"""Run more images through the embeddings and add to the vectorstore.
@@ -596,8 +595,8 @@ class Chroma(VectorStore):
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[list[dict]] = None,
ids: Optional[list[str]] = None,
metadatas: list[dict] | None = None,
ids: list[str] | None = None,
**kwargs: Any,
) -> list[str]:
"""Run more texts through the embeddings and add to the vectorstore.
@@ -684,7 +683,7 @@ class Chroma(VectorStore):
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[dict[str, str]] = None, # noqa: A002
filter: dict[str, str] | None = None, # noqa: A002
**kwargs: Any,
) -> list[Document]:
"""Run similarity search with Chroma.
@@ -710,8 +709,8 @@ class Chroma(VectorStore):
self,
embedding: list[float],
k: int = DEFAULT_K,
filter: Optional[dict[str, str]] = None, # noqa: A002
where_document: Optional[dict[str, str]] = None,
filter: dict[str, str] | None = None, # noqa: A002
where_document: dict[str, str] | None = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs most similar to embedding vector.
@@ -740,8 +739,8 @@ class Chroma(VectorStore):
self,
embedding: list[float],
k: int = DEFAULT_K,
filter: Optional[dict[str, str]] = None, # noqa: A002
where_document: Optional[dict[str, str]] = None,
filter: dict[str, str] | None = None, # noqa: A002
where_document: dict[str, str] | None = None,
**kwargs: Any,
) -> list[tuple[Document, float]]:
"""Return docs most similar to embedding vector and similarity score.
@@ -771,8 +770,8 @@ class Chroma(VectorStore):
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[dict[str, str]] = None, # noqa: A002
where_document: Optional[dict[str, str]] = None,
filter: dict[str, str] | None = None, # noqa: A002
where_document: dict[str, str] | None = None,
**kwargs: Any,
) -> list[tuple[Document, float]]:
"""Run similarity search with Chroma with distance.
@@ -813,8 +812,8 @@ class Chroma(VectorStore):
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[dict[str, str]] = None, # noqa: A002
where_document: Optional[dict[str, str]] = None,
filter: dict[str, str] | None = None, # noqa: A002
where_document: dict[str, str] | None = None,
**kwargs: Any,
) -> list[tuple[Document, np.ndarray]]:
"""Run similarity search with Chroma with vectors.
@@ -873,12 +872,10 @@ class Chroma(VectorStore):
return self.override_relevance_score_fn
hnsw_config = self._collection.configuration.get("hnsw")
hnsw_distance: Optional[str] = hnsw_config.get("space") if hnsw_config else None
hnsw_distance: str | None = hnsw_config.get("space") if hnsw_config else None
spann_config = self._collection.configuration.get("spann")
spann_distance: Optional[str] = (
spann_config.get("space") if spann_config else None
)
spann_distance: str | None = spann_config.get("space") if spann_config else None
distance = hnsw_distance or spann_distance
@@ -901,7 +898,7 @@ class Chroma(VectorStore):
self,
uri: str,
k: int = DEFAULT_K,
filter: Optional[dict[str, str]] = None, # noqa: A002
filter: dict[str, str] | None = None, # noqa: A002
**kwargs: Any,
) -> list[Document]:
"""Search for similar images based on the given image URI.
@@ -943,7 +940,7 @@ class Chroma(VectorStore):
self,
uri: str,
k: int = DEFAULT_K,
filter: Optional[dict[str, str]] = None, # noqa: A002
filter: dict[str, str] | None = None, # noqa: A002
**kwargs: Any,
) -> list[tuple[Document, float]]:
"""Search for similar images based on the given image URI.
@@ -987,8 +984,8 @@ class Chroma(VectorStore):
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict[str, str]] = None, # noqa: A002
where_document: Optional[dict[str, str]] = None,
filter: dict[str, str] | None = None, # noqa: A002
where_document: dict[str, str] | None = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance.
@@ -1038,8 +1035,8 @@ class Chroma(VectorStore):
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict[str, str]] = None, # noqa: A002
where_document: Optional[dict[str, str]] = None,
filter: dict[str, str] | None = None, # noqa: A002
where_document: dict[str, str] | None = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance.
@@ -1097,12 +1094,12 @@ class Chroma(VectorStore):
def get(
self,
ids: Optional[Union[str, list[str]]] = None,
where: Optional[Where] = None,
limit: Optional[int] = None,
offset: Optional[int] = None,
where_document: Optional[WhereDocument] = None,
include: Optional[list[str]] = None,
ids: str | list[str] | None = None,
where: Where | None = None,
limit: int | None = None,
offset: int | None = None,
where_document: WhereDocument | None = None,
include: list[str] | None = None,
) -> dict[str, Any]:
"""Gets the collection.
@@ -1233,21 +1230,21 @@ class Chroma(VectorStore):
def from_texts(
cls: type[Chroma],
texts: list[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[list[dict]] = None,
ids: Optional[list[str]] = None,
embedding: Embeddings | None = None,
metadatas: list[dict] | None = None,
ids: list[str] | None = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
persist_directory: Optional[str] = None,
host: Optional[str] = None,
port: Optional[int] = None,
headers: Optional[dict[str, str]] = None,
chroma_cloud_api_key: Optional[str] = None,
tenant: Optional[str] = None,
database: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.ClientAPI] = None,
collection_metadata: Optional[dict] = None,
collection_configuration: Optional[CreateCollectionConfiguration] = None,
persist_directory: str | None = None,
host: str | None = None,
port: int | None = None,
headers: dict[str, str] | None = None,
chroma_cloud_api_key: str | None = None,
tenant: str | None = None,
database: str | None = None,
client_settings: chromadb.config.Settings | None = None,
client: chromadb.ClientAPI | None = None,
collection_metadata: dict | None = None,
collection_configuration: CreateCollectionConfiguration | None = None,
*,
ssl: bool = False,
**kwargs: Any,
@@ -1335,20 +1332,20 @@ class Chroma(VectorStore):
def from_documents(
cls: type[Chroma],
documents: list[Document],
embedding: Optional[Embeddings] = None,
ids: Optional[list[str]] = None,
embedding: Embeddings | None = None,
ids: list[str] | None = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
persist_directory: Optional[str] = None,
host: Optional[str] = None,
port: Optional[int] = None,
headers: Optional[dict[str, str]] = None,
chroma_cloud_api_key: Optional[str] = None,
tenant: Optional[str] = None,
database: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.ClientAPI] = None, # Add this line
collection_metadata: Optional[dict] = None,
collection_configuration: Optional[CreateCollectionConfiguration] = None,
persist_directory: str | None = None,
host: str | None = None,
port: int | None = None,
headers: dict[str, str] | None = None,
chroma_cloud_api_key: str | None = None,
tenant: str | None = None,
database: str | None = None,
client_settings: chromadb.config.Settings | None = None,
client: chromadb.ClientAPI | None = None, # Add this line
collection_metadata: dict | None = None,
collection_configuration: CreateCollectionConfiguration | None = None,
*,
ssl: bool = False,
**kwargs: Any,
@@ -1410,7 +1407,7 @@ class Chroma(VectorStore):
**kwargs,
)
def delete(self, ids: Optional[list[str]] = None, **kwargs: Any) -> None:
def delete(self, ids: list[str] | None = None, **kwargs: Any) -> None:
"""Delete by vector IDs.
Args:
libs/partners/chroma/pyproject.toml
+28 -48
View File
@@ -66,53 +66,27 @@ disallow_untyped_defs = true
docstring-code-format = true
[tool.ruff.lint]
select = [
"A", # flake8-builtins
"B", # flake8-bugbear
"ASYNC", # flake8-async
"C4", # flake8-comprehensions
"COM", # flake8-commas
"D", # pydocstyle
"E", # pycodestyle error
"EM", # flake8-errmsg
"F", # pyflakes
"FA", # flake8-future-annotations
"FBT", # flake8-boolean-trap
"FLY", # flake8-flynt
"I", # isort
"ICN", # flake8-import-conventions
"INT", # flake8-gettext
"ISC", # isort-comprehensions
"PGH", # pygrep-hooks
"PIE", # flake8-pie
"PERF", # flake8-perf
"PYI", # flake8-pyi
"Q", # flake8-quotes
"RET", # flake8-return
"RSE", # flake8-rst-docstrings
"RUF", # ruff
"S", # flake8-bandit
"SLF", # flake8-self
"SLOT", # flake8-slots
"SIM", # flake8-simplify
"T10", # flake8-debugger
"T20", # flake8-print
"TID", # flake8-tidy-imports
"UP", # pyupgrade
"W", # pycodestyle warning
"YTT", # flake8-2020
]
select = [ "ALL" ]
ignore = [
"D100", # pydocstyle: Missing docstring in public module
"D101", # pydocstyle: Missing docstring in public class
"D102", # pydocstyle: Missing docstring in public method
"D103", # pydocstyle: Missing docstring in public function
"D104", # pydocstyle: Missing docstring in public package
"D105", # pydocstyle: Missing docstring in magic method
"D107", # pydocstyle: Missing docstring in __init__
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
"COM812", # Messes with the formatter
"PLC0415", # Import top of file
"FIX002", # TODO
"TD002", # TODO
"TD003", # TODO
"PLR0912", # Too many branches
"PLR0913", # Too many arguments
"C901", # Too complex
# TODO
"ANN204",
"ANN401",
"TRY201",
"ARG002",
"N803",
"TC002",
"TC003",
"TRY300",
"N806",
]
unfixable = ["B028"] # People should intentionally tune the stacklevel
@@ -136,7 +110,13 @@ ignore-var-parameters = true # ignore missing documentation for *args and **kwa
[tool.ruff.lint.extend-per-file-ignores]
"tests/**/*.py" = [
"S101", # Tests need assertions
"S311", # Standard pseudo-random generators are not suitable for cryptographic purposes
"SLF001", # Private member access in tests
"S101", # Tests need assertions
"S311", # Standard pseudo-random generators are not suitable for cryptographic purposes
"SLF001", # Private member access in tests
"PLR2004", # Comparison to magic number
"PT011", # Exception too broad
"BLE001", # Blind except
]
"scripts/*.py" = [
"INP001", # Not a package
]
libs/partners/chroma/scripts/check_imports.py
+1 -1
View File
@@ -10,7 +10,7 @@ if __name__ == "__main__":
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception: # noqa: PERF203
except Exception: # noqa: PERF203, BLE001
has_failure = True
print(file) # noqa: T201
traceback.print_exc()
libs/partners/chroma/tests/integration_tests/test_vectorstores.py
+7 -9
View File
@@ -1,9 +1,8 @@
"""Test Chroma functionality."""
import os.path
import tempfile
import uuid
from collections.abc import Generator
from pathlib import Path
from typing import (
cast,
)
@@ -29,15 +28,14 @@ class MyEmbeddingFunction:
self.fak = fak
def __call__(self, input_: Embeddable) -> list[list[float]]:
texts = cast(list[str], input_)
texts = cast("list[str]", input_)
return self.fak.embed_documents(texts=texts)
@pytest.fixture()
def client() -> Generator[chromadb.ClientAPI, None, None]:
@pytest.fixture
def client() -> chromadb.ClientAPI:
SharedSystemClient.clear_system_cache()
client = chromadb.Client(chromadb.config.Settings())
yield client
return chromadb.Client(chromadb.config.Settings())
def test_chroma() -> None:
@@ -297,7 +295,7 @@ def test_chroma_with_persistence() -> None:
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", id="id_0")]
assert os.path.exists(chroma_persist_dir)
assert Path(chroma_persist_dir).exists()
# Get a new VectorStore from the persisted directory
docsearch = Chroma(
@@ -344,7 +342,7 @@ def test_chroma_with_persistence_with_client_settings() -> None:
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", id="id_0")]
assert os.path.exists(chroma_persist_dir)
assert Path(chroma_persist_dir).exists()
# Get a new VectorStore from the persisted directory
docsearch = Chroma(
libs/partners/chroma/tests/unit_tests/test_standard.py
+1 -1
View File
@@ -9,7 +9,7 @@ from langchain_chroma import Chroma
class TestChromaStandard(VectorStoreIntegrationTests):
@pytest.fixture()
@pytest.fixture
def vectorstore(self) -> Generator[VectorStore, None, None]: # type: ignore[override]
"""Get an empty vectorstore for unit tests."""
store = Chroma(embedding_function=self.get_embeddings())
libs/partners/chroma/uv.lock
Generated
+1 -1
View File
@@ -839,7 +839,7 @@ typing = [
[[package]]
name = "langchain-core"
version = "1.0.0a6"
version = "1.0.0a8"
source = { editable = "../../core" }
dependencies = [
{ name = "jsonpatch" },
libs/partners/deepseek/langchain_deepseek/__init__.py
+2
View File
@@ -1,3 +1,5 @@
"""LangChain DeepSeek integration."""
from importlib import metadata
from langchain_deepseek.chat_models import ChatDeepSeek
libs/partners/deepseek/langchain_deepseek/chat_models.py
+29 -18
View File
@@ -5,7 +5,7 @@ from __future__ import annotations
import json
from collections.abc import Iterator
from json import JSONDecodeError
from typing import Any, Literal, Optional, TypeVar, Union
from typing import Any, Literal, TypeAlias
import openai
from langchain_core.callbacks import (
@@ -22,16 +22,15 @@ from typing_extensions import Self
DEFAULT_API_BASE = "https://api.deepseek.com/v1"
_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[dict[str, Any], type[_BM], type]
_DictOrPydantic = Union[dict, _BM]
_DictOrPydanticClass: TypeAlias = dict[str, Any] | type[BaseModel]
_DictOrPydantic: TypeAlias = dict[str, Any] | BaseModel
class ChatDeepSeek(BaseChatOpenAI):
"""DeepSeek chat model integration to access models hosted in DeepSeek's API.
Setup:
Install ``langchain-deepseek`` and set environment variable ``DEEPSEEK_API_KEY``.
Install `langchain-deepseek` and set environment variable ``DEEPSEEK_API_KEY``.
.. code-block:: bash
@@ -170,7 +169,7 @@ class ChatDeepSeek(BaseChatOpenAI):
model_name: str = Field(alias="model")
"""The name of the model"""
api_key: Optional[SecretStr] = Field(
api_key: SecretStr | None = Field(
default_factory=secret_from_env("DEEPSEEK_API_KEY", default=None),
)
"""DeepSeek API key"""
@@ -193,7 +192,7 @@ class ChatDeepSeek(BaseChatOpenAI):
def _get_ls_params(
self,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> LangSmithParams:
ls_params = super()._get_ls_params(stop=stop, **kwargs)
@@ -202,6 +201,7 @@ class ChatDeepSeek(BaseChatOpenAI):
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate necessary environment vars and client params."""
if self.api_base == DEFAULT_API_BASE and not (
self.api_key and self.api_key.get_secret_value()
):
@@ -237,19 +237,30 @@ class ChatDeepSeek(BaseChatOpenAI):
self,
input_: LanguageModelInput,
*,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict:
payload = super()._get_request_payload(input_, stop=stop, **kwargs)
for message in payload["messages"]:
if message["role"] == "tool" and isinstance(message["content"], list):
message["content"] = json.dumps(message["content"])
elif message["role"] == "assistant" and isinstance(
message["content"], list
):
# DeepSeek API expects assistant content to be a string, not a list.
# Extract text blocks and join them, or use empty string if none exist.
text_parts = [
block.get("text", "")
for block in message["content"]
if isinstance(block, dict) and block.get("type") == "text"
]
message["content"] = "".join(text_parts) if text_parts else ""
return payload
def _create_chat_result(
self,
response: Union[dict, openai.BaseModel],
generation_info: Optional[dict] = None,
response: dict | openai.BaseModel,
generation_info: dict | None = None,
) -> ChatResult:
rtn = super()._create_chat_result(response, generation_info)
@@ -277,8 +288,8 @@ class ChatDeepSeek(BaseChatOpenAI):
self,
chunk: dict,
default_chunk_class: type,
base_generation_info: Optional[dict],
) -> Optional[ChatGenerationChunk]:
base_generation_info: dict | None,
) -> ChatGenerationChunk | None:
generation_chunk = super()._convert_chunk_to_generation_chunk(
chunk,
default_chunk_class,
@@ -304,8 +315,8 @@ class ChatDeepSeek(BaseChatOpenAI):
def _stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
try:
@@ -329,8 +340,8 @@ class ChatDeepSeek(BaseChatOpenAI):
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
try:
@@ -353,7 +364,7 @@ class ChatDeepSeek(BaseChatOpenAI):
def with_structured_output(
self,
schema: Optional[_DictOrPydanticClass] = None,
schema: _DictOrPydanticClass | None = None,
*,
method: Literal[
"function_calling",
@@ -361,7 +372,7 @@ class ChatDeepSeek(BaseChatOpenAI):
"json_schema",
] = "function_calling",
include_raw: bool = False,
strict: Optional[bool] = None,
strict: bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, _DictOrPydantic]:
"""Model wrapper that returns outputs formatted to match the given schema.
libs/partners/deepseek/pyproject.toml
+14 -44
View File
@@ -53,52 +53,16 @@ docstring-code-format = true
docstring-code-line-length = 100
[tool.ruff.lint]
select = [
"A", # flake8-builtins
"ASYNC", # flake8-async
"C4", # flake8-comprehensions
"COM", # flake8-commas
"D", # pydocstyle
"E", # pycodestyle error
"EM", # flake8-errmsg
"F", # pyflakes
"FA", # flake8-future-annotations
"FBT", # flake8-boolean-trap
"FLY", # flake8-flynt
"I", # isort
"ICN", # flake8-import-conventions
"INT", # flake8-gettext
"ISC", # isort-comprehensions
"PGH", # pygrep-hooks
"PIE", # flake8-pie
"PERF", # flake8-perf
"PYI", # flake8-pyi
"Q", # flake8-quotes
"RET", # flake8-return
"RSE", # flake8-rst-docstrings
"RUF", # ruff
"S", # flake8-bandit
"SLF", # flake8-self
"SLOT", # flake8-slots
"SIM", # flake8-simplify
"T10", # flake8-debugger
"T20", # flake8-print
"TID", # flake8-tidy-imports
"UP", # pyupgrade
"W", # pycodestyle warning
"YTT", # flake8-2020
]
select = [ "ALL" ]
ignore = [
"COM812", # Conflicts with formatter
"D100", # Missing docstring in public module
"D101", # Missing docstring in public class
"D102", # Missing docstring in public method
"D103", # Missing docstring in public function
"D104", # Missing docstring in public package
"D105", # Missing docstring in magic method
"D107", # Missing docstring in __init__
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
"PLR0913", # Too many arguments
# TODO
"ANN401",
"TC002",
"TC003",
"ANN401",
]
unfixable = ["B028"] # People should intentionally tune the stacklevel
@@ -121,4 +85,10 @@ ignore-var-parameters = true # ignore missing documentation for *args and **kwa
"S101", # Tests need assertions
"S311", # Standard pseudo-random generators are not suitable for cryptographic purposes
"SLF001", # Private member access
# TODO
"ARG002", # Unused method argument:
]
"scripts/*.py" = [
"INP001", # Not a package
]
libs/partners/deepseek/scripts/check_imports.py
+3 -1
View File
@@ -1,3 +1,5 @@
"""Script to check imports of given Python files."""
import sys
import traceback
from importlib.machinery import SourceFileLoader
@@ -8,7 +10,7 @@ if __name__ == "__main__":
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception: # noqa: PERF203
except Exception: # noqa: PERF203, BLE001
has_failure = True
print(file) # noqa: T201
traceback.print_exc()
libs/partners/deepseek/tests/__init__.py
+1
View File
@@ -0,0 +1 @@
"""Tests for `langchain_deepseek` package."""
libs/partners/deepseek/tests/integration_tests/__init__.py
+1
View File
@@ -0,0 +1 @@
"""Integration tests for `langchain_deepseek` package."""
libs/partners/deepseek/tests/integration_tests/test_chat_models.py
+7 -4
View File
@@ -2,8 +2,6 @@
from __future__ import annotations
from typing import Optional
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessageChunk, BaseMessageChunk
@@ -14,13 +12,16 @@ from langchain_deepseek.chat_models import ChatDeepSeek
class TestChatDeepSeek(ChatModelIntegrationTests):
"""Test `ChatDeepSeek` chat model."""
@property
def chat_model_class(self) -> type[ChatDeepSeek]:
"""Return class of chat model being tested."""
return ChatDeepSeek
@property
def chat_model_params(self) -> dict:
# These should be parameters used to initialize your integration for testing
"""Parameters to create chat model instance for testing."""
return {
"model": "deepseek-chat",
"temperature": 0,
@@ -37,6 +38,7 @@ class TestChatDeepSeek(ChatModelIntegrationTests):
model: BaseChatModel,
my_adder_tool: BaseTool,
) -> None:
"""Override test for tool message histories with list content."""
super().test_tool_message_histories_list_content(model, my_adder_tool)
@@ -52,8 +54,9 @@ def test_reasoning_content() -> None:
@pytest.mark.xfail(reason="Takes > 30s to run.")
def test_reasoning_content_streaming() -> None:
"""Test reasoning content with streaming."""
chat_model = ChatDeepSeek(model="deepseek-reasoner")
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in chat_model.stream("What is 3^3?"):
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
libs/partners/deepseek/tests/integration_tests/test_compile.py
+2
View File
@@ -1,3 +1,5 @@
"""Test compilation of integration tests."""
import pytest
libs/partners/deepseek/tests/unit_tests/__init__.py
+1
View File
@@ -0,0 +1 @@
"""Unit tests for `langchain_deepseek` package."""
libs/partners/deepseek/tests/unit_tests/test_chat_models.py
+12 -5
View File
@@ -2,7 +2,7 @@
from __future__ import annotations
from typing import Any, Literal, Union
from typing import Any, Literal
from unittest.mock import MagicMock
from langchain_core.messages import AIMessageChunk, ToolMessage
@@ -15,13 +15,15 @@ from langchain_deepseek.chat_models import ChatDeepSeek
class MockOpenAIResponse(BaseModel):
"""Mock OpenAI response model."""
choices: list
error: None = None
def model_dump( # type: ignore[override]
self,
*,
mode: Union[Literal["json", "python"], str] = "python", # noqa: PYI051
mode: Literal["json", "python"] | str = "python", # noqa: PYI051
include: Any = None,
exclude: Any = None,
by_alias: bool = False,
@@ -29,10 +31,11 @@ class MockOpenAIResponse(BaseModel):
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: Union[Literal["none", "warn", "error"], bool] = True,
context: Union[dict[str, Any], None] = None,
warnings: Literal["none", "warn", "error"] | bool = True,
context: dict[str, Any] | None = None,
serialize_as_any: bool = False,
) -> dict[str, Any]:
"""Convert to dictionary, ensuring reasoning_content is included."""
choices_list = []
for choice in self.choices:
if isinstance(choice.message, ChatCompletionMessage):
@@ -58,12 +61,16 @@ class MockOpenAIResponse(BaseModel):
class TestChatDeepSeekUnit(ChatModelUnitTests):
"""Unit tests for `ChatDeepSeek` chat model."""
@property
def chat_model_class(self) -> type[ChatDeepSeek]:
"""Chat model class being tested."""
return ChatDeepSeek
@property
def init_from_env_params(self) -> tuple[dict, dict, dict]:
"""Parameters to initialize from environment variables."""
return (
{
"DEEPSEEK_API_KEY": "api_key",
@@ -80,7 +87,7 @@ class TestChatDeepSeekUnit(ChatModelUnitTests):
@property
def chat_model_params(self) -> dict:
# These should be parameters used to initialize your integration for testing
"""Parameters to create chat model instance for testing."""
return {
"model": "deepseek-chat",
"api_key": "api_key",
libs/partners/deepseek/uv.lock
Generated
+3 -3
View File
@@ -212,7 +212,7 @@ name = "exceptiongroup"
version = "1.3.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
{ name = "typing-extensions", marker = "python_full_version < '3.11'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" }
wheels = [
@@ -370,7 +370,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.0.0a6"
version = "1.0.0a8"
source = { editable = "../../core" }
dependencies = [
{ name = "jsonpatch" },
@@ -475,7 +475,7 @@ typing = [{ name = "mypy", specifier = ">=1.10.0,<2.0.0" }]
[[package]]
name = "langchain-openai"
version = "1.0.0a3"
version = "1.0.0a4"
source = { editable = "../openai" }
dependencies = [
{ name = "langchain-core" },
libs/partners/exa/langchain_exa/__init__.py
+2
View File
@@ -1,3 +1,5 @@
"""LangChain integration for Exa."""
from exa_py.api import (
HighlightsContentsOptions,
TextContentsOptions,
libs/partners/exa/langchain_exa/retrievers.py
+15 -15
View File
@@ -1,6 +1,8 @@
"""Retriever using Exa Search API."""
from __future__ import annotations
from typing import Any, Literal, Optional, Union
from typing import Any, Literal
from exa_py import Exa # type: ignore[untyped-import]
from exa_py.api import (
@@ -39,39 +41,37 @@ class ExaSearchRetriever(BaseRetriever):
k: int = 10 # num_results
"""The number of search results to return (1 to 100)."""
include_domains: Optional[list[str]] = None
include_domains: list[str] | None = None
"""A list of domains to include in the search."""
exclude_domains: Optional[list[str]] = None
exclude_domains: list[str] | None = None
"""A list of domains to exclude from the search."""
start_crawl_date: Optional[str] = None
start_crawl_date: str | None = None
"""The start date for the crawl (in YYYY-MM-DD format)."""
end_crawl_date: Optional[str] = None
end_crawl_date: str | None = None
"""The end date for the crawl (in YYYY-MM-DD format)."""
start_published_date: Optional[str] = None
start_published_date: str | None = None
"""The start date for when the document was published (in YYYY-MM-DD format)."""
end_published_date: Optional[str] = None
end_published_date: str | None = None
"""The end date for when the document was published (in YYYY-MM-DD format)."""
use_autoprompt: Optional[bool] = None
use_autoprompt: bool | None = None
"""Whether to use autoprompt for the search."""
type: str = "neural"
"""The type of search, 'keyword', 'neural', or 'auto'. Default: neural"""
highlights: Optional[Union[HighlightsContentsOptions, bool]] = None
highlights: HighlightsContentsOptions | bool | None = None
"""Whether to set the page content to the highlights of the results."""
text_contents_options: Union[TextContentsOptions, dict[str, Any], Literal[True]] = (
True
)
text_contents_options: TextContentsOptions | dict[str, Any] | Literal[True] = True
"""How to set the page content of the results. Can be True or a dict with options
like max_characters."""
livecrawl: Optional[Literal["always", "fallback", "never"]] = None
livecrawl: Literal["always", "fallback", "never"] | None = None
"""Option to crawl live webpages if content is not in the index. Options: "always",
"fallback", "never"."""
summary: Optional[Union[bool, dict[str, str]]] = None
summary: bool | dict[str, str] | None = None
"""Whether to include a summary of the content. Can be a boolean or a dict with a
custom query."""
client: Exa = Field(default=None)
exa_api_key: SecretStr = Field(default=None)
exa_base_url: Optional[str] = None
exa_base_url: str | None = None
@model_validator(mode="before")
@classmethod
libs/partners/exa/langchain_exa/tools.py
+36 -34
View File
@@ -2,7 +2,7 @@
from __future__ import annotations
from typing import Any, Literal, Optional, Union
from typing import Any, Literal
from exa_py import Exa # type: ignore[untyped-import]
from exa_py.api import (
@@ -103,22 +103,23 @@ class ExaSearchResults(BaseTool): # type: ignore[override]
self,
query: str,
num_results: int = 10,
text_contents_options: Optional[ # noqa: FBT001
Union[TextContentsOptions, dict[str, Any], bool]
] = None,
highlights: Optional[Union[HighlightsContentsOptions, bool]] = None, # noqa: FBT001
include_domains: Optional[list[str]] = None,
exclude_domains: Optional[list[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
use_autoprompt: Optional[bool] = None, # noqa: FBT001
livecrawl: Optional[Literal["always", "fallback", "never"]] = None,
summary: Optional[Union[bool, dict[str, str]]] = None, # noqa: FBT001
type: Optional[Literal["neural", "keyword", "auto"]] = None, # noqa: A002
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> Union[list[dict], str]:
text_contents_options: TextContentsOptions # noqa: FBT001
| dict[str, Any]
| bool
| None = None,
highlights: HighlightsContentsOptions | bool | None = None, # noqa: FBT001
include_domains: list[str] | None = None,
exclude_domains: list[str] | None = None,
start_crawl_date: str | None = None,
end_crawl_date: str | None = None,
start_published_date: str | None = None,
end_published_date: str | None = None,
use_autoprompt: bool | None = None, # noqa: FBT001
livecrawl: Literal["always", "fallback", "never"] | None = None,
summary: bool | dict[str, str] | None = None, # noqa: FBT001
type: Literal["neural", "keyword", "auto"] | None = None, # noqa: A002
run_manager: CallbackManagerForToolRun | None = None,
) -> list[dict] | str:
# TODO: rename `type` to something else, as it is a reserved keyword
"""Use the tool.
@@ -172,7 +173,7 @@ class ExaFindSimilarResults(BaseTool): # type: ignore[override]
)
client: Exa = Field(default=None)
exa_api_key: SecretStr = Field(default=None)
exa_base_url: Optional[str] = None
exa_base_url: str | None = None
@model_validator(mode="before")
@classmethod
@@ -184,22 +185,23 @@ class ExaFindSimilarResults(BaseTool): # type: ignore[override]
self,
url: str,
num_results: int = 10,
text_contents_options: Optional[ # noqa: FBT001
Union[TextContentsOptions, dict[str, Any], bool]
] = None,
highlights: Optional[Union[HighlightsContentsOptions, bool]] = None, # noqa: FBT001
include_domains: Optional[list[str]] = None,
exclude_domains: Optional[list[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
exclude_source_domain: Optional[bool] = None, # noqa: FBT001
category: Optional[str] = None,
livecrawl: Optional[Literal["always", "fallback", "never"]] = None,
summary: Optional[Union[bool, dict[str, str]]] = None, # noqa: FBT001
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> Union[list[dict], str]:
text_contents_options: TextContentsOptions # noqa: FBT001
| dict[str, Any]
| bool
| None = None,
highlights: HighlightsContentsOptions | bool | None = None, # noqa: FBT001
include_domains: list[str] | None = None,
exclude_domains: list[str] | None = None,
start_crawl_date: str | None = None,
end_crawl_date: str | None = None,
start_published_date: str | None = None,
end_published_date: str | None = None,
exclude_source_domain: bool | None = None, # noqa: FBT001
category: str | None = None,
livecrawl: Literal["always", "fallback", "never"] | None = None,
summary: bool | dict[str, str] | None = None, # noqa: FBT001
run_manager: CallbackManagerForToolRun | None = None,
) -> list[dict] | str:
"""Use the tool.
Args:
libs/partners/exa/pyproject.toml
+3 -9
View File
@@ -93,21 +93,12 @@ select = [
"YTT", # flake8-2020
]
ignore = [
"D100", # Missing docstring in public module
"D101", # Missing docstring in public class
"D102", # Missing docstring in public method
"D103", # Missing docstring in public function
"D104", # Missing docstring in public package
"D105", # Missing docstring in magic method
"D107", # Missing docstring in __init__
"COM812", # Messes with the formatter
"ISC001", # Messes with the formatter
"PERF203", # Rarely useful
"S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
]
unfixable = ["B028"] # People should intentionally tune the stacklevel
@@ -131,3 +122,6 @@ ignore-var-parameters = true # ignore missing documentation for *args and **kwa
"S101", # Tests need assertions
"S311", # Standard pseudo-random generators are not suitable for cryptographic purposes
]
"scripts/*.py" = [
"INP001", # Not a package
]
libs/partners/exa/scripts/check_imports.py
+2
View File
@@ -1,3 +1,5 @@
"""Check that the given files can be imported."""
import sys
import traceback
from importlib.machinery import SourceFileLoader
libs/partners/exa/tests/__init__.py
+1
View File
@@ -0,0 +1 @@
"""Exa tests."""
libs/partners/exa/tests/integration_tests/__init__.py
+1
View File
@@ -0,0 +1 @@
"""Exa integration tests."""
libs/partners/exa/tests/integration_tests/test_compile.py
+2
View File
@@ -1,3 +1,5 @@
"""Test that the integration tests compile."""
import pytest # type: ignore[import-not-found, import-not-found]
libs/partners/exa/tests/integration_tests/test_find_similar_tool.py
+3
View File
@@ -1,9 +1,12 @@
"""Integration tests for Exa find similar tool."""
from langchain_exa import (
ExaFindSimilarResults, # type: ignore[import-not-found, import-not-found]
)
def test_similarity_tool() -> None:
"""Test that the Exa find similar tool works."""
tool = ExaFindSimilarResults()
res = tool.invoke(
{
libs/partners/exa/tests/integration_tests/test_retriever.py
+5
View File
@@ -1,3 +1,5 @@
"""Integration tests for `ExaSearchRetriever`."""
from langchain_core.documents import (
Document, # type: ignore[import-not-found, import-not-found]
)
@@ -6,6 +8,7 @@ from langchain_exa import ExaSearchRetriever
def test_exa_retriever() -> None:
"""Test basic functionality of the `ExaSearchRetriever`."""
retriever = ExaSearchRetriever()
res = retriever.invoke("best time to visit japan")
print(res) # noqa: T201
@@ -15,6 +18,7 @@ def test_exa_retriever() -> None:
def test_exa_retriever_highlights() -> None:
"""Test highlights feature of the `ExaSearchRetriever`."""
retriever = ExaSearchRetriever(highlights=True)
res = retriever.invoke("best time to visit japan")
print(res) # noqa: T201
@@ -29,6 +33,7 @@ def test_exa_retriever_highlights() -> None:
def test_exa_retriever_advanced_features() -> None:
"""Test advanced features of the `ExaSearchRetriever`."""
retriever = ExaSearchRetriever(
k=3, text_contents_options={"max_characters": 1000}, summary=True, type="auto"
)
libs/partners/exa/tests/integration_tests/test_search_tool.py
+4
View File
@@ -1,9 +1,12 @@
"""Integration tests for Exa search tool."""
from langchain_exa import (
ExaSearchResults, # type: ignore[import-not-found, import-not-found]
)
def test_search_tool() -> None:
"""Test that the Exa search tool works."""
tool = ExaSearchResults()
res = tool.invoke({"query": "best time to visit japan", "num_results": 5})
print(res) # noqa: T201
@@ -11,6 +14,7 @@ def test_search_tool() -> None:
def test_search_tool_advanced_features() -> None:
"""Test advanced features of the Exa search tool."""
tool = ExaSearchResults()
res = tool.invoke(
{
libs/partners/exa/tests/unit_tests/__init__.py
+1
View File
@@ -0,0 +1 @@
"""Unit tests for `langchain_exa` package."""
libs/partners/exa/tests/unit_tests/test_imports.py
+3
View File
@@ -1,3 +1,5 @@
"""Unit tests for imports in `langchain_exa`."""
from langchain_exa import __all__ # type: ignore[import-not-found, import-not-found]
EXPECTED_ALL = [
@@ -10,4 +12,5 @@ EXPECTED_ALL = [
def test_all_imports() -> None:
"""Test that all expected imports are in `__all__`."""
assert sorted(EXPECTED_ALL) == sorted(__all__)
libs/partners/exa/tests/unit_tests/test_standard.py
+2
View File
@@ -1,3 +1,5 @@
"""Standard unit tests for ExaSearchRetriever."""
import pytest
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped]
libs/partners/fireworks/langchain_fireworks/chat_models.py
+27 -31
View File
@@ -10,8 +10,6 @@ from operator import itemgetter
from typing import (
Any,
Literal,
Optional,
Union,
cast,
)
@@ -303,9 +301,9 @@ class ChatFireworks(BaseChatModel):
async_client: Any = Field(default=None, exclude=True) #: :meta private:
model_name: str = Field(alias="model")
"""Model name to use."""
temperature: Optional[float] = None
temperature: float | None = None
"""What sampling temperature to use."""
stop: Optional[Union[str, list[str]]] = Field(default=None, alias="stop_sequences")
stop: str | list[str] | None = Field(default=None, alias="stop_sequences")
"""Default stop sequences."""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
@@ -325,12 +323,12 @@ class ChatFireworks(BaseChatModel):
Automatically read from env variable ``FIREWORKS_API_KEY`` if not provided.
"""
fireworks_api_base: Optional[str] = Field(
fireworks_api_base: str | None = Field(
alias="base_url", default_factory=from_env("FIREWORKS_API_BASE", default=None)
)
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
request_timeout: Union[float, tuple[float, float], Any, None] = Field(
request_timeout: float | tuple[float, float] | Any | None = Field(
default=None, alias="timeout"
)
"""Timeout for requests to Fireworks completion API. Can be ``float``,
@@ -339,9 +337,9 @@ class ChatFireworks(BaseChatModel):
"""Whether to stream the results or not."""
n: int = 1
"""Number of chat completions to generate for each prompt."""
max_tokens: Optional[int] = None
max_tokens: int | None = None
"""Maximum number of tokens to generate."""
max_retries: Optional[int] = None
max_retries: int | None = None
"""Maximum number of retries to make when generating."""
model_config = ConfigDict(
@@ -401,7 +399,7 @@ class ChatFireworks(BaseChatModel):
return params
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self, stop: list[str] | None = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
@@ -417,7 +415,7 @@ class ChatFireworks(BaseChatModel):
ls_params["ls_stop"] = ls_stop
return ls_params
def _combine_llm_outputs(self, llm_outputs: list[Optional[dict]]) -> dict:
def _combine_llm_outputs(self, llm_outputs: list[dict | None]) -> dict:
overall_token_usage: dict = {}
system_fingerprint = None
for output in llm_outputs:
@@ -441,8 +439,8 @@ class ChatFireworks(BaseChatModel):
def _stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
@@ -476,9 +474,9 @@ class ChatFireworks(BaseChatModel):
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None, # noqa: FBT001
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
stream: bool | None = None, # noqa: FBT001
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
@@ -497,7 +495,7 @@ class ChatFireworks(BaseChatModel):
return self._create_chat_result(response)
def _create_message_dicts(
self, messages: list[BaseMessage], stop: Optional[list[str]]
self, messages: list[BaseMessage], stop: list[str] | None
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
params = self._default_params
if stop is not None:
@@ -505,7 +503,7 @@ class ChatFireworks(BaseChatModel):
message_dicts = [_convert_message_to_dict(m) for m in messages]
return message_dicts, params
def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
def _create_chat_result(self, response: dict | BaseModel) -> ChatResult:
generations = []
if not isinstance(response, dict):
response = response.model_dump()
@@ -536,8 +534,8 @@ class ChatFireworks(BaseChatModel):
async def _astream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
@@ -573,9 +571,9 @@ class ChatFireworks(BaseChatModel):
async def _agenerate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None, # noqa: FBT001
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
stream: bool | None = None, # noqa: FBT001
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
@@ -600,7 +598,7 @@ class ChatFireworks(BaseChatModel):
return {"model_name": self.model_name, **self._default_params}
def _get_invocation_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self, stop: list[str] | None = None, **kwargs: Any
) -> dict[str, Any]:
"""Get the parameters used to invoke the model."""
return {
@@ -617,11 +615,9 @@ class ChatFireworks(BaseChatModel):
def bind_tools(
self,
tools: Sequence[Union[dict[str, Any], type[BaseModel], Callable, BaseTool]],
tools: Sequence[dict[str, Any] | type[BaseModel] | Callable | BaseTool],
*,
tool_choice: Optional[
Union[dict, str, Literal["auto", "any", "none"], bool] # noqa: PYI051
] = None,
tool_choice: dict | str | bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]:
"""Bind tool-like objects to this chat model.
@@ -634,10 +630,10 @@ class ChatFireworks(BaseChatModel):
`langchain_core.utils.function_calling.convert_to_openai_tool`.
tool_choice: Which tool to require the model to call.
Must be the name of the single provided function,
``'auto'`` to automatically determine which function to call
`'auto'` to automatically determine which function to call
with the option to not call any function, `'any'` to enforce that some
function is called, or a dict of the form:
``{"type": "function", "function": {"name": <<tool_name>>}}``.
`{"type": "function", "function": {"name": <<tool_name>>}}`.
**kwargs: Any additional parameters to pass to
`langchain_fireworks.chat_models.ChatFireworks.bind`
@@ -666,14 +662,14 @@ class ChatFireworks(BaseChatModel):
def with_structured_output(
self,
schema: Optional[Union[dict, type[BaseModel]]] = None,
schema: dict | type[BaseModel] | None = None,
*,
method: Literal[
"function_calling", "json_mode", "json_schema"
] = "function_calling",
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
) -> Runnable[LanguageModelInput, dict | BaseModel]:
"""Model wrapper that returns outputs formatted to match the given schema.
Args:
libs/partners/fireworks/langchain_fireworks/llms.py
+15 -15
View File
@@ -3,7 +3,7 @@
from __future__ import annotations
import logging
from typing import Any, Optional
from typing import Any
import requests
from aiohttp import ClientSession, ClientTimeout
@@ -56,35 +56,35 @@ class Fireworks(LLM):
"""
model: str
"""Model name. `(Available models) <https://readme.fireworks.ai/>`__"""
temperature: Optional[float] = None
temperature: float | None = None
"""Model temperature."""
top_p: Optional[float] = None
top_p: float | None = None
"""Used to dynamically adjust the number of choices for each predicted token based
on the cumulative probabilities. A value of ``1`` will always yield the same output.
A temperature less than ``1`` favors more correctness and is appropriate for
question answering or summarization. A value greater than ``1`` introduces more
on the cumulative probabilities. A value of `1` will always yield the same output.
A temperature less than `1` favors more correctness and is appropriate for
question answering or summarization. A value greater than `1` introduces more
randomness in the output.
"""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for ``create`` call not explicitly specified."""
top_k: Optional[int] = None
top_k: int | None = None
"""Used to limit the number of choices for the next predicted word or token. It
specifies the maximum number of tokens to consider at each step, based on their
probability of occurrence. This technique helps to speed up the generation process
and can improve the quality of the generated text by focusing on the most likely
options.
"""
max_tokens: Optional[int] = None
max_tokens: int | None = None
"""The maximum number of tokens to generate."""
repetition_penalty: Optional[float] = None
repetition_penalty: float | None = None
"""A number that controls the diversity of generated text by reducing the likelihood
of repeated sequences. Higher values decrease repetition.
"""
logprobs: Optional[int] = None
logprobs: int | None = None
"""An integer that specifies how many top token log probabilities are included in
the response for each token generation step.
"""
timeout: Optional[int] = 30
timeout: int | None = 30
"""Timeout in seconds for requests to the Fireworks API."""
model_config = ConfigDict(
@@ -125,8 +125,8 @@ class Fireworks(LLM):
def _call(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
"""Call out to Fireworks's text generation endpoint.
@@ -178,8 +178,8 @@ class Fireworks(LLM):
async def _acall(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
"""Call Fireworks model to get predictions based on the prompt.
libs/partners/fireworks/pyproject.toml
-2
View File
@@ -108,8 +108,6 @@ ignore = [
"S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
]
unfixable = ["B028"] # People should intentionally tune the stacklevel
libs/partners/fireworks/tests/integration_tests/test_chat_models.py
+2 -2
View File
@@ -6,7 +6,7 @@ You will need FIREWORKS_API_KEY set in your environment to run these tests.
from __future__ import annotations
import json
from typing import Annotated, Any, Literal, Optional
from typing import Annotated, Any, Literal
import pytest
from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessageChunk
@@ -48,7 +48,7 @@ async def test_astream() -> None:
"""Test streaming tokens from ChatFireworks."""
llm = ChatFireworks(model=_MODEL)
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks_with_token_counts = 0
chunks_with_response_metadata = 0
async for token in llm.astream("I'm Pickle Rick"):
libs/partners/groq/langchain_groq/chat_models.py
+34 -38
View File
@@ -6,7 +6,7 @@ import json
import warnings
from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
from operator import itemgetter
from typing import Any, Literal, Optional, Union, cast
from typing import Any, Literal, cast
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
@@ -308,9 +308,9 @@ class ChatGroq(BaseChatModel):
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
stop: Optional[Union[list[str], str]] = Field(default=None, alias="stop_sequences")
stop: list[str] | str | None = Field(default=None, alias="stop_sequences")
"""Default stop sequences."""
reasoning_format: Optional[Literal["parsed", "raw", "hidden"]] = Field(default=None)
reasoning_format: Literal["parsed", "raw", "hidden"] | None = Field(default=None)
"""The format for reasoning output. Groq will default to raw if left undefined.
- ``'parsed'``: Separates reasoning into a dedicated field while keeping the
@@ -325,7 +325,7 @@ class ChatGroq(BaseChatModel):
See the `Groq documentation <https://console.groq.com/docs/reasoning#reasoning>`__
for more details and a list of supported models.
"""
reasoning_effort: Optional[str] = Field(default=None)
reasoning_effort: str | None = Field(default=None)
"""The level of effort the model will put into reasoning. Groq will default to
enabling reasoning if left undefined.
@@ -335,20 +335,18 @@ class ChatGroq(BaseChatModel):
"""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
groq_api_key: Optional[SecretStr] = Field(
groq_api_key: SecretStr | None = Field(
alias="api_key", default_factory=secret_from_env("GROQ_API_KEY", default=None)
)
"""Automatically inferred from env var ``GROQ_API_KEY`` if not provided."""
groq_api_base: Optional[str] = Field(
groq_api_base: str | None = Field(
alias="base_url", default_factory=from_env("GROQ_API_BASE", default=None)
)
"""Base URL path for API requests. Leave blank if not using a proxy or service
emulator."""
# to support explicit proxy for Groq
groq_proxy: Optional[str] = Field(
default_factory=from_env("GROQ_PROXY", default=None)
)
request_timeout: Union[float, tuple[float, float], Any, None] = Field(
groq_proxy: str | None = Field(default_factory=from_env("GROQ_PROXY", default=None))
request_timeout: float | tuple[float, float] | Any | None = Field(
default=None, alias="timeout"
)
"""Timeout for requests to Groq completion API. Can be float, ``httpx.Timeout`` or
@@ -359,7 +357,7 @@ class ChatGroq(BaseChatModel):
"""Whether to stream the results or not."""
n: int = 1
"""Number of chat completions to generate for each prompt."""
max_tokens: Optional[int] = None
max_tokens: int | None = None
"""Maximum number of tokens to generate."""
service_tier: Literal["on_demand", "flex", "auto"] = Field(default="on_demand")
"""Optional parameter that you can include to specify the service tier you'd like to
@@ -369,20 +367,20 @@ class ChatGroq(BaseChatModel):
- ``'flex'``: On-demand processing when capacity is available, with rapid timeouts
if resources are constrained. Provides balance between performance and reliability
for workloads that don't require guaranteed processing.
- ``'auto'``: Uses on-demand rate limits, then falls back to ``'flex'`` if those
- `'auto'`: Uses on-demand rate limits, then falls back to ``'flex'`` if those
limits are exceeded
See the `Groq documentation
<https://console.groq.com/docs/flex-processing>`__ for more details and a list of
service tiers and descriptions.
"""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
default_headers: Mapping[str, str] | None = None
default_query: Mapping[str, object] | None = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
http_client: Union[Any, None] = None
http_client: Any | None = None
"""Optional ``httpx.Client``."""
http_async_client: Union[Any, None] = None
http_async_client: Any | None = None
"""Optional ``httpx.AsyncClient``. Only used for async invocations. Must specify
``http_client`` as well if you'd like a custom client for sync invocations."""
@@ -490,7 +488,7 @@ class ChatGroq(BaseChatModel):
return "groq-chat"
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self, stop: list[str] | None = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
@@ -510,9 +508,9 @@ class ChatGroq(BaseChatModel):
self,
*,
async_api: bool,
run_manager: Optional[
Union[CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun]
] = None,
run_manager: CallbackManagerForLLMRun
| AsyncCallbackManagerForLLMRun
| None = None,
**kwargs: Any,
) -> bool:
"""Determine if a given model call should hit the streaming API."""
@@ -532,8 +530,8 @@ class ChatGroq(BaseChatModel):
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
@@ -552,8 +550,8 @@ class ChatGroq(BaseChatModel):
async def _agenerate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
@@ -573,8 +571,8 @@ class ChatGroq(BaseChatModel):
def _stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
@@ -625,8 +623,8 @@ class ChatGroq(BaseChatModel):
async def _astream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
@@ -738,7 +736,7 @@ class ChatGroq(BaseChatModel):
return ChatResult(generations=generations, llm_output=llm_output)
def _create_message_dicts(
self, messages: list[BaseMessage], stop: Optional[list[str]]
self, messages: list[BaseMessage], stop: list[str] | None
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
params = self._default_params
if stop is not None:
@@ -746,7 +744,7 @@ class ChatGroq(BaseChatModel):
message_dicts = [_convert_message_to_dict(m) for m in messages]
return message_dicts, params
def _combine_llm_outputs(self, llm_outputs: list[Optional[dict]]) -> dict:
def _combine_llm_outputs(self, llm_outputs: list[dict | None]) -> dict:
overall_token_usage: dict = {}
system_fingerprint = None
for output in llm_outputs:
@@ -771,11 +769,9 @@ class ChatGroq(BaseChatModel):
def bind_tools(
self,
tools: Sequence[Union[dict[str, Any], type[BaseModel], Callable, BaseTool]],
tools: Sequence[dict[str, Any] | type[BaseModel] | Callable | BaseTool],
*,
tool_choice: Optional[
Union[dict, str, Literal["auto", "any", "none"], bool] # noqa: PYI051
] = None,
tool_choice: dict | str | bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]:
"""Bind tool-like objects to this chat model.
@@ -786,10 +782,10 @@ class ChatGroq(BaseChatModel):
`langchain_core.utils.function_calling.convert_to_openai_tool`.
tool_choice: Which tool to require the model to call.
Must be the name of the single provided function,
"auto" to automatically determine which function to call
with the option to not call any function, "any" to enforce that some
`'auto'` to automatically determine which function to call
with the option to not call any function, `'any'` to enforce that some
function is called, or a dict of the form:
``{"type": "function", "function": {"name": <<tool_name>>}}``.
`{"type": "function", "function": {"name": <<tool_name>>}}`.
**kwargs: Any additional parameters to pass to the
`langchain.runnable.Runnable` constructor.
@@ -820,7 +816,7 @@ class ChatGroq(BaseChatModel):
def with_structured_output(
self,
schema: Optional[Union[dict, type[BaseModel]]] = None,
schema: dict | type[BaseModel] | None = None,
*,
method: Literal[
"function_calling", "json_mode", "json_schema"
libs/partners/groq/pyproject.toml
-2
View File
@@ -62,8 +62,6 @@ ignore = [
"S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
"PLR0911",
"PLR0912",
"C901",
libs/partners/groq/tests/integration_tests/test_chat_models.py
+3 -3
View File
@@ -3,7 +3,7 @@
from __future__ import annotations
import json
from typing import Any, Optional, cast
from typing import Any, cast
import pytest
from groq import BadRequestError
@@ -102,7 +102,7 @@ async def test_astream() -> None:
"""Test streaming tokens from Groq."""
chat = ChatGroq(model=DEFAULT_MODEL_NAME, max_tokens=10)
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks_with_token_counts = 0
chunks_with_response_metadata = 0
async for token in chat.astream("Welcome to the Groqetship!"):
@@ -254,7 +254,7 @@ def test_reasoning_output_stream() -> None:
HumanMessage(content="I love programming."),
]
full_response: Optional[AIMessageChunk] = None
full_response: AIMessageChunk | None = None
for token in chat.stream(message):
assert isinstance(token, AIMessageChunk)
libs/partners/groq/tests/unit_tests/fake/callbacks.py
+3 -3
View File
@@ -3,7 +3,7 @@
from __future__ import annotations
from itertools import chain
from typing import Any, Optional, Union
from typing import Any
from uuid import UUID
from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
@@ -26,7 +26,7 @@ class BaseFakeCallbackHandler(BaseModel):
ignore_chat_model_: bool = False
# to allow for similar callback handlers that are not technically equal
fake_id: Union[str, None] = None
fake_id: str | None = None
# add finer-grained counters for easier debugging of failing tests
chain_starts: int = 0
@@ -270,7 +270,7 @@ class FakeCallbackHandlerWithChatStart(FakeCallbackHandler):
messages: list[list[BaseMessage]],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
parent_run_id: UUID | None = None,
**kwargs: Any,
) -> Any:
assert all(isinstance(m, BaseMessage) for m in chain(*messages))
libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py
+34 -40
View File
@@ -7,7 +7,7 @@ import json
from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
from dataclasses import dataclass
from operator import itemgetter
from typing import Any, Literal, Optional, Union, cast
from typing import Any, Literal, cast
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
@@ -316,7 +316,7 @@ class ChatHuggingFace(BaseChatModel):
the HuggingFace Hub.
Setup:
Install ``langchain-huggingface`` and ensure your Hugging Face token
Install `langchain-huggingface` and ensure your Hugging Face token
is saved.
.. code-block:: bash
@@ -478,33 +478,33 @@ class ChatHuggingFace(BaseChatModel):
HuggingFaceHub, or HuggingFacePipeline."""
tokenizer: Any = None
"""Tokenizer for the model. Only used for HuggingFacePipeline."""
model_id: Optional[str] = None
model_id: str | None = None
"""Model ID for the model. Only used for HuggingFaceEndpoint."""
temperature: Optional[float] = None
temperature: float | None = None
"""What sampling temperature to use."""
stop: Optional[Union[str, list[str]]] = Field(default=None, alias="stop_sequences")
stop: str | list[str] | None = Field(default=None, alias="stop_sequences")
"""Default stop sequences."""
presence_penalty: Optional[float] = None
presence_penalty: float | None = None
"""Penalizes repeated tokens."""
frequency_penalty: Optional[float] = None
frequency_penalty: float | None = None
"""Penalizes repeated tokens according to frequency."""
seed: Optional[int] = None
seed: int | None = None
"""Seed for generation"""
logprobs: Optional[bool] = None
logprobs: bool | None = None
"""Whether to return logprobs."""
top_logprobs: Optional[int] = None
top_logprobs: int | None = None
"""Number of most likely tokens to return at each token position, each with
an associated log probability. `logprobs` must be set to true
if this parameter is used."""
logit_bias: Optional[dict[int, int]] = None
logit_bias: dict[int, int] | None = None
"""Modify the likelihood of specified tokens appearing in the completion."""
streaming: bool = False
"""Whether to stream the results or not."""
n: Optional[int] = None
n: int | None = None
"""Number of chat completions to generate for each prompt."""
top_p: Optional[float] = None
top_p: float | None = None
"""Total probability mass of tokens to consider at each step."""
max_tokens: Optional[int] = None
max_tokens: int | None = None
"""Maximum number of tokens to generate."""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
@@ -558,9 +558,9 @@ class ChatHuggingFace(BaseChatModel):
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None, # noqa: FBT001
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
stream: bool | None = None, # noqa: FBT001
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
@@ -599,9 +599,9 @@ class ChatHuggingFace(BaseChatModel):
async def _agenerate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None, # noqa: FBT001
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
stream: bool | None = None, # noqa: FBT001
**kwargs: Any,
) -> ChatResult:
if _is_huggingface_textgen_inference(self.llm):
@@ -638,8 +638,8 @@ class ChatHuggingFace(BaseChatModel):
def _stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
if _is_huggingface_endpoint(self.llm):
@@ -687,8 +687,8 @@ class ChatHuggingFace(BaseChatModel):
async def _astream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
@@ -779,7 +779,7 @@ class ChatHuggingFace(BaseChatModel):
self.model_id = self.llm.repo_id
return
if _is_huggingface_textgen_inference(self.llm):
endpoint_url: Optional[str] = self.llm.inference_server_url
endpoint_url: str | None = self.llm.inference_server_url
if _is_huggingface_pipeline(self.llm):
from transformers import AutoTokenizer # type: ignore[import]
@@ -809,11 +809,9 @@ class ChatHuggingFace(BaseChatModel):
def bind_tools(
self,
tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
tools: Sequence[dict[str, Any] | type | Callable | BaseTool],
*,
tool_choice: Optional[
Union[dict, str, Literal["auto", "none", "required"], bool] # noqa: PYI051
] = None,
tool_choice: dict | str | bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]:
"""Bind tool-like objects to this chat model.
@@ -826,7 +824,7 @@ class ChatHuggingFace(BaseChatModel):
`langchain_core.utils.function_calling.convert_to_openai_tool`.
tool_choice: Which tool to require the model to call.
Must be the name of the single provided function or
``'auto'`` to automatically determine which function to call
`'auto'` to automatically determine which function to call
(if any), or a dict of the form:
{"type": "function", "function": {"name": <<tool_name>>}}.
**kwargs: Any additional parameters to pass to the
@@ -870,14 +868,14 @@ class ChatHuggingFace(BaseChatModel):
def with_structured_output(
self,
schema: Optional[Union[dict, type[BaseModel]]] = None,
schema: dict | type[BaseModel] | None = None,
*,
method: Literal[
"function_calling", "json_mode", "json_schema"
] = "function_calling",
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
) -> Runnable[LanguageModelInput, dict | BaseModel]:
"""Model wrapper that returns outputs formatted to match the given schema.
Args:
@@ -948,7 +946,7 @@ class ChatHuggingFace(BaseChatModel):
if is_pydantic_schema:
msg = "Pydantic schema is not supported for function calling"
raise NotImplementedError(msg)
output_parser: Union[JsonOutputKeyToolsParser, JsonOutputParser] = (
output_parser: JsonOutputKeyToolsParser | JsonOutputParser = (
JsonOutputKeyToolsParser(key_name=tool_name, first_tool_only=True)
)
elif method == "json_schema":
@@ -966,9 +964,7 @@ class ChatHuggingFace(BaseChatModel):
"schema": schema,
},
)
output_parser: Union[ # type: ignore[no-redef]
JsonOutputKeyToolsParser, JsonOutputParser
] = JsonOutputParser() # type: ignore[arg-type]
output_parser = JsonOutputParser() # type: ignore[arg-type]
elif method == "json_mode":
llm = self.bind(
response_format={"type": "json_object"},
@@ -977,9 +973,7 @@ class ChatHuggingFace(BaseChatModel):
"schema": schema,
},
)
output_parser: Union[ # type: ignore[no-redef]
JsonOutputKeyToolsParser, JsonOutputParser
] = JsonOutputParser() # type: ignore[arg-type]
output_parser = JsonOutputParser() # type: ignore[arg-type]
else:
msg = (
f"Unrecognized method argument. Expected one of 'function_calling' or "
@@ -999,7 +993,7 @@ class ChatHuggingFace(BaseChatModel):
return llm | output_parser
def _create_message_dicts(
self, messages: list[BaseMessage], stop: Optional[list[str]]
self, messages: list[BaseMessage], stop: list[str] | None
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
params = self._default_params
if stop is not None:
libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py
+2 -2
View File
@@ -1,6 +1,6 @@
from __future__ import annotations
from typing import Any, Optional
from typing import Any
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, Field
@@ -40,7 +40,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
model_name: str = Field(default=DEFAULT_MODEL_NAME, alias="model")
"""Model name to use."""
cache_folder: Optional[str] = None
cache_folder: str | None = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py
+7 -7
View File
@@ -1,7 +1,7 @@
from __future__ import annotations
import os
from typing import Any, Optional
from typing import Any
from langchain_core.embeddings import Embeddings
from langchain_core.utils import from_env
@@ -35,20 +35,20 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
client: Any = None #: :meta private:
async_client: Any = None #: :meta private:
model: Optional[str] = None
model: str | None = None
"""Model name to use."""
provider: Optional[str] = None
provider: str | None = None
"""Name of the provider to use for inference with the model specified in
``repo_id``. e.g. "sambanova". if not specified, defaults to HF Inference API.
available providers can be found in the [huggingface_hub documentation](https://huggingface.co/docs/huggingface_hub/guides/inference#supported-providers-and-tasks)."""
repo_id: Optional[str] = None
repo_id: str | None = None
"""Huggingfacehub repository id, for backward compatibility."""
task: Optional[str] = "feature-extraction"
task: str | None = "feature-extraction"
"""Task to call the model with."""
model_kwargs: Optional[dict] = None
model_kwargs: dict | None = None
"""Keyword arguments to pass to the model."""
huggingfacehub_api_token: Optional[str] = Field(
huggingfacehub_api_token: str | None = Field(
default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None)
)
libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py
+26 -26
View File
@@ -4,7 +4,7 @@ import inspect
import logging
import os
from collections.abc import AsyncIterator, Iterator, Mapping
from typing import Any, Optional
from typing import Any
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
@@ -79,42 +79,42 @@ class HuggingFaceEndpoint(LLM):
""" # noqa: E501
endpoint_url: Optional[str] = None
endpoint_url: str | None = None
"""Endpoint URL to use. If repo_id is not specified then this needs to given or
should be pass as env variable in `HF_INFERENCE_ENDPOINT`"""
repo_id: Optional[str] = None
repo_id: str | None = None
"""Repo to use. If endpoint_url is not specified then this needs to given"""
provider: Optional[str] = None
provider: str | None = None
"""Name of the provider to use for inference with the model specified in `repo_id`.
e.g. "cerebras". if not specified, Defaults to "auto" i.e. the first of the
providers available for the model, sorted by the user's order in https://hf.co/settings/inference-providers.
available providers can be found in the [huggingface_hub documentation](https://huggingface.co/docs/huggingface_hub/guides/inference#supported-providers-and-tasks)."""
huggingfacehub_api_token: Optional[str] = Field(
huggingfacehub_api_token: str | None = Field(
default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None)
)
max_new_tokens: int = 512
"""Maximum number of generated tokens"""
top_k: Optional[int] = None
top_k: int | None = None
"""The number of highest probability vocabulary tokens to keep for
top-k-filtering."""
top_p: Optional[float] = 0.95
top_p: float | None = 0.95
"""If set to < 1, only the smallest set of most probable tokens with probabilities
that add up to `top_p` or higher are kept for generation."""
typical_p: Optional[float] = 0.95
typical_p: float | None = 0.95
"""Typical Decoding mass. See [Typical Decoding for Natural Language
Generation](https://arxiv.org/abs/2202.00666) for more information."""
temperature: Optional[float] = 0.8
temperature: float | None = 0.8
"""The value used to module the logits distribution."""
repetition_penalty: Optional[float] = None
repetition_penalty: float | None = None
"""The parameter for repetition penalty. 1.0 means no penalty.
See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details."""
return_full_text: bool = False
"""Whether to prepend the prompt to the generated text"""
truncate: Optional[int] = None
truncate: int | None = None
"""Truncate inputs tokens to the given size"""
stop_sequences: list[str] = Field(default_factory=list)
"""Stop generating tokens if a member of `stop_sequences` is generated"""
seed: Optional[int] = None
seed: int | None = None
"""Random sampling seed"""
inference_server_url: str = ""
"""text-generation-inference instance base url"""
@@ -134,7 +134,7 @@ class HuggingFaceEndpoint(LLM):
model: str
client: Any = None #: :meta private:
async_client: Any = None #: :meta private:
task: Optional[str] = None
task: str | None = None
"""Task to call the model with. Should be a task that returns `generated_text`."""
model_config = ConfigDict(
@@ -292,7 +292,7 @@ class HuggingFaceEndpoint(LLM):
return "huggingface_endpoint"
def _invocation_params(
self, runtime_stop: Optional[list[str]], **kwargs: Any
self, runtime_stop: list[str] | None, **kwargs: Any
) -> dict[str, Any]:
params = {**self._default_params, **kwargs}
params["stop"] = params["stop"] + (runtime_stop or [])
@@ -301,8 +301,8 @@ class HuggingFaceEndpoint(LLM):
def _call(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
"""Call out to HuggingFace Hub's inference endpoint."""
@@ -331,8 +331,8 @@ class HuggingFaceEndpoint(LLM):
async def _acall(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
invocation_params = self._invocation_params(stop, **kwargs)
@@ -361,8 +361,8 @@ class HuggingFaceEndpoint(LLM):
def _stream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
invocation_params = self._invocation_params(stop, **kwargs)
@@ -371,13 +371,13 @@ class HuggingFaceEndpoint(LLM):
prompt, **invocation_params, stream=True
):
# identify stop sequence in generated text, if any
stop_seq_found: Optional[str] = None
stop_seq_found: str | None = None
for stop_seq in invocation_params["stop"]:
if stop_seq in response:
stop_seq_found = stop_seq
# identify text to yield
text: Optional[str] = None
text: str | None = None
if stop_seq_found:
text = response[: response.index(stop_seq_found)]
else:
@@ -398,8 +398,8 @@ class HuggingFaceEndpoint(LLM):
async def _astream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
invocation_params = self._invocation_params(stop, **kwargs)
@@ -407,13 +407,13 @@ class HuggingFaceEndpoint(LLM):
prompt, **invocation_params, stream=True
):
# identify stop sequence in generated text, if any
stop_seq_found: Optional[str] = None
stop_seq_found: str | None = None
for stop_seq in invocation_params["stop"]:
if stop_seq in response:
stop_seq_found = stop_seq
# identify text to yield
text: Optional[str] = None
text: str | None = None
if stop_seq_found:
text = response[: response.index(stop_seq_found)]
else:
libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py
+12 -12
View File
@@ -3,7 +3,7 @@ from __future__ import annotations # type: ignore[import-not-found]
import importlib.util
import logging
from collections.abc import Iterator, Mapping
from typing import Any, Optional
from typing import Any
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import BaseLLM
@@ -71,13 +71,13 @@ class HuggingFacePipeline(BaseLLM):
"""
pipeline: Any = None #: :meta private:
model_id: Optional[str] = None
model_id: str | None = None
"""The model name. If not set explicitly by the user,
it will be inferred from the provided pipeline (if available).
If neither is provided, the DEFAULT_MODEL_ID will be used."""
model_kwargs: Optional[dict] = None
model_kwargs: dict | None = None
"""Keyword arguments passed to the model."""
pipeline_kwargs: Optional[dict] = None
pipeline_kwargs: dict | None = None
"""Keyword arguments passed to the pipeline."""
batch_size: int = DEFAULT_BATCH_SIZE
"""Batch size to use when passing multiple documents to generate."""
@@ -103,10 +103,10 @@ class HuggingFacePipeline(BaseLLM):
model_id: str,
task: str,
backend: str = "default",
device: Optional[int] = None,
device_map: Optional[str] = None,
model_kwargs: Optional[dict] = None,
pipeline_kwargs: Optional[dict] = None,
device: int | None = None,
device_map: str | None = None,
model_kwargs: dict | None = None,
pipeline_kwargs: dict | None = None,
batch_size: int = DEFAULT_BATCH_SIZE,
**kwargs: Any,
) -> HuggingFacePipeline:
@@ -311,8 +311,8 @@ class HuggingFacePipeline(BaseLLM):
def _generate(
self,
prompts: list[str],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> LLMResult:
# List to hold all results
@@ -363,8 +363,8 @@ class HuggingFacePipeline(BaseLLM):
def _stream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
from threading import Thread
libs/partners/huggingface/langchain_huggingface/utils/import_utils.py
+1 -2
View File
@@ -3,7 +3,6 @@ from __future__ import annotations
import importlib.metadata
import importlib.util
import operator as op
from typing import Union
from packaging import version
@@ -44,7 +43,7 @@ _openvino_available = importlib.util.find_spec("openvino") is not None
# This function was copied from: https://github.com/huggingface/accelerate/blob/874c4967d94badd24f893064cc3bef45f57cadf7/src/accelerate/utils/versions.py#L319
def compare_versions(
library_or_version: Union[str, version.Version],
library_or_version: str | version.Version,
operation: str,
requirement_version: str,
) -> bool:
libs/partners/huggingface/pyproject.toml
-2
View File
@@ -119,8 +119,6 @@ ignore = [
"S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
]
unfixable = ["B028"] # People should intentionally tune the stacklevel
libs/partners/mistralai/langchain_mistralai/chat_models.py
+28 -32
View File
@@ -13,8 +13,6 @@ from typing import (
TYPE_CHECKING,
Any,
Literal,
Optional,
Union,
cast,
)
@@ -93,9 +91,7 @@ global_ssl_context = ssl.create_default_context(cafile=certifi.where())
def _create_retry_decorator(
llm: ChatMistralAI,
run_manager: Optional[
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
] = None,
run_manager: AsyncCallbackManagerForLLMRun | CallbackManagerForLLMRun | None = None,
) -> Callable[[Any], Any]:
"""Return a tenacity retry decorator, preconfigured to handle exceptions."""
errors = [httpx.RequestError, httpx.StreamError]
@@ -211,7 +207,7 @@ async def _aiter_sse(
async def acompletion_with_retry(
llm: ChatMistralAI,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the async completion call."""
@@ -397,23 +393,23 @@ class ChatMistralAI(BaseChatModel):
async_client: httpx.AsyncClient = Field( # type: ignore[assignment] # : meta private:
default=None, exclude=True
) #: :meta private:
mistral_api_key: Optional[SecretStr] = Field(
mistral_api_key: SecretStr | None = Field(
alias="api_key",
default_factory=secret_from_env("MISTRAL_API_KEY", default=None),
)
endpoint: Optional[str] = Field(default=None, alias="base_url")
endpoint: str | None = Field(default=None, alias="base_url")
max_retries: int = 5
timeout: int = 120
max_concurrent_requests: int = 64
model: str = Field(default="mistral-small", alias="model_name")
temperature: float = 0.7
max_tokens: Optional[int] = None
max_tokens: int | None = None
top_p: float = 1
"""Decode using nucleus sampling: consider the smallest set of tokens whose
probability sum is at least ``top_p``. Must be in the closed interval
``[0.0, 1.0]``."""
random_seed: Optional[int] = None
safe_mode: Optional[bool] = None
random_seed: int | None = None
safe_mode: bool | None = None
streaming: bool = False
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Holds any invocation parameters not explicitly specified."""
@@ -445,7 +441,7 @@ class ChatMistralAI(BaseChatModel):
return {k: v for k, v in defaults.items() if v is not None}
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self, stop: list[str] | None = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
@@ -467,7 +463,7 @@ class ChatMistralAI(BaseChatModel):
return self._default_params
def completion_with_retry(
self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
self, run_manager: CallbackManagerForLLMRun | None = None, **kwargs: Any
) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
@@ -496,7 +492,7 @@ class ChatMistralAI(BaseChatModel):
return _completion_with_retry(**kwargs)
def _combine_llm_outputs(self, llm_outputs: list[Optional[dict]]) -> dict:
def _combine_llm_outputs(self, llm_outputs: list[dict | None]) -> dict:
overall_token_usage: dict = {}
for output in llm_outputs:
if output is None:
@@ -515,7 +511,7 @@ class ChatMistralAI(BaseChatModel):
def validate_environment(self) -> Self:
"""Validate api key, python package exists, temperature, and top_p."""
if isinstance(self.mistral_api_key, SecretStr):
api_key_str: Optional[str] = self.mistral_api_key.get_secret_value()
api_key_str: str | None = self.mistral_api_key.get_secret_value()
else:
api_key_str = self.mistral_api_key
@@ -563,9 +559,9 @@ class ChatMistralAI(BaseChatModel):
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None, # noqa: FBT001
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
stream: bool | None = None, # noqa: FBT001
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
@@ -608,7 +604,7 @@ class ChatMistralAI(BaseChatModel):
return ChatResult(generations=generations, llm_output=llm_output)
def _create_message_dicts(
self, messages: list[BaseMessage], stop: Optional[list[str]]
self, messages: list[BaseMessage], stop: list[str] | None
) -> tuple[list[dict], dict[str, Any]]:
params = self._client_params
if stop is not None or "stop" in params:
@@ -623,8 +619,8 @@ class ChatMistralAI(BaseChatModel):
def _stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
@@ -649,8 +645,8 @@ class ChatMistralAI(BaseChatModel):
async def _astream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
@@ -675,9 +671,9 @@ class ChatMistralAI(BaseChatModel):
async def _agenerate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None, # noqa: FBT001
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
stream: bool | None = None, # noqa: FBT001
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
@@ -696,8 +692,8 @@ class ChatMistralAI(BaseChatModel):
def bind_tools(
self,
tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
tool_choice: Optional[Union[dict, str, Literal["auto", "any"]]] = None, # noqa: PYI051
tools: Sequence[dict[str, Any] | type | Callable | BaseTool],
tool_choice: dict | str | Literal["auto", "any"] | None = None, # noqa: PYI051
**kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]:
"""Bind tool-like objects to this chat model.
@@ -710,7 +706,7 @@ class ChatMistralAI(BaseChatModel):
`langchain_core.utils.function_calling.convert_to_openai_tool`.
tool_choice: Which tool to require the model to call.
Must be the name of the single provided function or
``'auto'`` to automatically determine which function to call
`'auto'` to automatically determine which function to call
(if any), or a dict of the form:
{"type": "function", "function": {"name": <<tool_name>>}}.
kwargs: Any additional parameters are passed directly to
@@ -738,14 +734,14 @@ class ChatMistralAI(BaseChatModel):
def with_structured_output(
self,
schema: Optional[Union[dict, type]] = None,
schema: dict | type | None = None,
*,
method: Literal[
"function_calling", "json_mode", "json_schema"
] = "function_calling",
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
) -> Runnable[LanguageModelInput, dict | BaseModel]:
r"""Model wrapper that returns outputs formatted to match the given schema.
Args:
@@ -1085,7 +1081,7 @@ class ChatMistralAI(BaseChatModel):
def _convert_to_openai_response_format(
schema: Union[dict[str, Any], type], *, strict: Optional[bool] = None
schema: dict[str, Any] | type, *, strict: bool | None = None
) -> dict:
"""Perform same op as in ChatOpenAI, but do not pass through Pydantic BaseModels."""
if (
libs/partners/mistralai/pyproject.toml
-2
View File
@@ -62,8 +62,6 @@ ignore = [
"S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
"TD",
"PLR0912",
"C901",
libs/partners/mistralai/tests/integration_tests/test_chat_models.py
+2 -2
View File
@@ -5,7 +5,7 @@ from __future__ import annotations
import json
import logging
import time
from typing import Any, Optional
from typing import Any
import pytest
from httpx import ReadTimeout
@@ -33,7 +33,7 @@ async def test_astream() -> None:
"""Test streaming tokens from ChatMistralAI."""
llm = ChatMistralAI()
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks_with_token_counts = 0
chunks_with_response_metadata = 0
async for token in llm.astream("Hello"):
libs/partners/nomic/langchain_nomic/embeddings.py
+13 -13
View File
@@ -3,7 +3,7 @@
from __future__ import annotations
import os
from typing import Literal, Optional, overload
from typing import Literal, overload
import nomic # type: ignore[import]
from langchain_core.embeddings import Embeddings
@@ -28,8 +28,8 @@ class NomicEmbeddings(Embeddings):
self,
*,
model: str,
nomic_api_key: Optional[str] = ...,
dimensionality: Optional[int] = ...,
nomic_api_key: str | None = ...,
dimensionality: int | None = ...,
inference_mode: Literal["remote"] = ...,
) -> None: ...
@@ -38,10 +38,10 @@ class NomicEmbeddings(Embeddings):
self,
*,
model: str,
nomic_api_key: Optional[str] = ...,
dimensionality: Optional[int] = ...,
nomic_api_key: str | None = ...,
dimensionality: int | None = ...,
inference_mode: Literal["local", "dynamic"],
device: Optional[str] = ...,
device: str | None = ...,
) -> None: ...
@overload
@@ -49,21 +49,21 @@ class NomicEmbeddings(Embeddings):
self,
*,
model: str,
nomic_api_key: Optional[str] = ...,
dimensionality: Optional[int] = ...,
nomic_api_key: str | None = ...,
dimensionality: int | None = ...,
inference_mode: str,
device: Optional[str] = ...,
device: str | None = ...,
) -> None: ...
def __init__(
self,
*,
model: str,
nomic_api_key: Optional[str] = None,
dimensionality: Optional[int] = None,
nomic_api_key: str | None = None,
dimensionality: int | None = None,
inference_mode: str = "remote",
device: Optional[str] = None,
vision_model: Optional[str] = None,
device: str | None = None,
vision_model: str | None = None,
):
"""Initialize NomicEmbeddings model.
libs/partners/nomic/pyproject.toml
-2
View File
@@ -61,8 +61,6 @@ ignore = [
"S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
# TODO
"PLR0913",
libs/partners/ollama/langchain_ollama/_utils.py
+3 -4
View File
@@ -3,7 +3,6 @@
from __future__ import annotations
import base64
from typing import Optional
from urllib.parse import unquote, urlparse
from httpx import ConnectError
@@ -49,8 +48,8 @@ def validate_model(client: Client, model_name: str) -> None:
def parse_url_with_auth(
url: Optional[str],
) -> tuple[Optional[str], Optional[dict[str, str]]]:
url: str | None,
) -> tuple[str | None, dict[str, str] | None]:
"""Parse URL and extract `userinfo` credentials for headers.
Handles URLs of the form: `https://user:password@host:port/path`
@@ -101,7 +100,7 @@ def parse_url_with_auth(
def merge_auth_headers(
client_kwargs: dict,
auth_headers: Optional[dict[str, str]],
auth_headers: dict[str, str] | None,
) -> None:
"""Merge authentication headers into client kwargs in-place.
libs/partners/ollama/langchain_ollama/chat_models.py
+59 -59
View File
@@ -46,7 +46,7 @@ import json
import logging
from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
from operator import itemgetter
from typing import Any, Literal, Optional, Union, cast
from typing import Any, Literal, cast
from uuid import uuid4
from langchain_core.callbacks import CallbackManagerForLLMRun
@@ -96,13 +96,13 @@ log = logging.getLogger(__name__)
def _get_usage_metadata_from_generation_info(
generation_info: Optional[Mapping[str, Any]],
) -> Optional[UsageMetadata]:
generation_info: Mapping[str, Any] | None,
) -> UsageMetadata | None:
"""Get usage metadata from Ollama generation info mapping."""
if generation_info is None:
return None
input_tokens: Optional[int] = generation_info.get("prompt_eval_count")
output_tokens: Optional[int] = generation_info.get("eval_count")
input_tokens: int | None = generation_info.get("prompt_eval_count")
output_tokens: int | None = generation_info.get("eval_count")
if input_tokens is not None and output_tokens is not None:
return UsageMetadata(
input_tokens=input_tokens,
@@ -166,7 +166,7 @@ def _parse_json_string(
def _parse_arguments_from_tool_call(
raw_tool_call: dict[str, Any],
) -> Optional[dict[str, Any]]:
) -> dict[str, Any] | None:
"""Parse arguments by trying to parse any shallowly nested string-encoded JSON.
Band-aid fix for issue in Ollama with inconsistent tool call argument structure.
@@ -259,7 +259,7 @@ class ChatOllama(BaseChatModel):
???+ note "Setup"
Install ``langchain-ollama`` and download any models you want to use from ollama.
Install `langchain-ollama` and download any models you want to use from ollama.
.. code-block:: bash
@@ -523,7 +523,7 @@ class ChatOllama(BaseChatModel):
model: str
"""Model name to use."""
reasoning: Optional[Union[bool, str]] = None
reasoning: bool | str | None = None
"""Controls the reasoning/thinking mode for `supported models <https://ollama.com/search?c=thinking>`__.
- `True`: Enables reasoning mode. The model's reasoning process will be
@@ -536,7 +536,7 @@ class ChatOllama(BaseChatModel):
however, if the model's default behavior *is* to perform reasoning, think tags
()``<think>`` and ``</think>``) will be present within the main response content
unless you set ``reasoning`` to `True`.
- `str`: e.g. ``'low'``, ``'medium'``, ``'high'``. Enables reasoning with a custom
- `str`: e.g. `'low'`, ``'medium'``, `'high'`. Enables reasoning with a custom
intensity level. Currently, this is only supported ``gpt-oss``. See the
`Ollama docs <https://github.com/ollama/ollama-python/blob/da79e987f0ac0a4986bf396f043b36ef840370bc/ollama/_types.py#L210>`__
for more information.
@@ -548,13 +548,13 @@ class ChatOllama(BaseChatModel):
!!! version-added "Added in version 0.3.4"
"""
mirostat: Optional[int] = None
mirostat: int | None = None
"""Enable Mirostat sampling for controlling perplexity.
(Default: `0`, `0` = disabled, ``1`` = Mirostat, ``2`` = Mirostat 2.0)
(Default: `0`, `0` = disabled, `1` = Mirostat, `2` = Mirostat 2.0)
"""
mirostat_eta: Optional[float] = None
mirostat_eta: float | None = None
"""Influences how quickly the algorithm responds to feedback from generated text.
A lower learning rate will result in slower adjustments, while a higher learning
@@ -563,7 +563,7 @@ class ChatOllama(BaseChatModel):
(Default: ``0.1``)
"""
mirostat_tau: Optional[float] = None
mirostat_tau: float | None = None
"""Controls the balance between coherence and diversity of the output.
A lower value will result in more focused and coherent text.
@@ -571,19 +571,19 @@ class ChatOllama(BaseChatModel):
(Default: ``5.0``)
"""
num_ctx: Optional[int] = None
num_ctx: int | None = None
"""Sets the size of the context window used to generate the next token.
(Default: ``2048``)
"""
num_gpu: Optional[int] = None
num_gpu: int | None = None
"""The number of GPUs to use.
On macOS it defaults to ``1`` to enable metal support, `0` to disable.
On macOS it defaults to `1` to enable metal support, `0` to disable.
"""
num_thread: Optional[int] = None
num_thread: int | None = None
"""Sets the number of threads to use during computation.
By default, Ollama will detect this for optimal performance. It is recommended to
@@ -591,26 +591,26 @@ class ChatOllama(BaseChatModel):
the logical number of cores).
"""
num_predict: Optional[int] = None
num_predict: int | None = None
"""Maximum number of tokens to predict when generating text.
(Default: ``128``, ``-1`` = infinite generation, ``-2`` = fill context)
"""
repeat_last_n: Optional[int] = None
repeat_last_n: int | None = None
"""Sets how far back for the model to look back to prevent repetition.
(Default: ``64``, `0` = disabled, ``-1`` = ``num_ctx``)
"""
repeat_penalty: Optional[float] = None
repeat_penalty: float | None = None
"""Sets how strongly to penalize repetitions.
A higher value (e.g., ``1.5``) will penalize repetitions more strongly, while a
lower value (e.g., ``0.9``) will be more lenient. (Default: ``1.1``)
"""
temperature: Optional[float] = None
temperature: float | None = None
"""The temperature of the model.
Increasing the temperature will make the model answer more creatively.
@@ -618,17 +618,17 @@ class ChatOllama(BaseChatModel):
(Default: ``0.8``)
"""
seed: Optional[int] = None
seed: int | None = None
"""Sets the random number seed to use for generation.
Setting this to a specific number will make the model generate the same text for the
same prompt.
"""
stop: Optional[list[str]] = None
stop: list[str] | None = None
"""Sets the stop tokens to use."""
tfs_z: Optional[float] = None
tfs_z: float | None = None
"""Tail free sampling.
Used to reduce the impact of less probable tokens from the output.
@@ -636,10 +636,10 @@ class ChatOllama(BaseChatModel):
A higher value (e.g., ``2.0``) will reduce the impact more, while a value of ``1.0``
disables this setting.
(Default: ``1``)
(Default: `1`)
"""
top_k: Optional[int] = None
top_k: int | None = None
"""Reduces the probability of generating nonsense.
A higher value (e.g. ``100``) will give more diverse answers, while a lower value
@@ -648,7 +648,7 @@ class ChatOllama(BaseChatModel):
(Default: ``40``)
"""
top_p: Optional[float] = None
top_p: float | None = None
"""Works together with top-k.
A higher value (e.g., ``0.95``) will lead to more diverse text, while a lower value
@@ -657,13 +657,13 @@ class ChatOllama(BaseChatModel):
(Default: ``0.9``)
"""
format: Optional[Union[Literal["", "json"], JsonSchemaValue]] = None
format: Literal["", "json"] | JsonSchemaValue | None = None
"""Specify the format of the output (options: ``'json'``, JSON schema)."""
keep_alive: Optional[Union[int, str]] = None
keep_alive: int | str | None = None
"""How long the model will stay loaded into memory."""
base_url: Optional[str] = None
base_url: str | None = None
"""Base url the model is hosted under.
If none, defaults to the Ollama client default.
@@ -685,7 +685,7 @@ class ChatOllama(BaseChatModel):
"""
client_kwargs: Optional[dict] = {}
client_kwargs: dict | None = {}
"""Additional kwargs to pass to the httpx clients. Pass headers in here.
These arguments are passed to both synchronous and async clients.
@@ -694,7 +694,7 @@ class ChatOllama(BaseChatModel):
to synchronous and asynchronous clients.
"""
async_client_kwargs: Optional[dict] = {}
async_client_kwargs: dict | None = {}
"""Additional kwargs to merge with `client_kwargs` before passing to httpx client.
These are clients unique to the async client; for shared args use `client_kwargs`.
@@ -702,7 +702,7 @@ class ChatOllama(BaseChatModel):
For a full list of the params, see the `httpx documentation <https://www.python-httpx.org/api/#asyncclient>`__.
"""
sync_client_kwargs: Optional[dict] = {}
sync_client_kwargs: dict | None = {}
"""Additional kwargs to merge with `client_kwargs` before passing to httpx client.
These are clients unique to the sync client; for shared args use `client_kwargs`.
@@ -719,7 +719,7 @@ class ChatOllama(BaseChatModel):
def _chat_params(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict[str, Any]:
"""Assemble the parameters for a chat completion request.
@@ -834,8 +834,8 @@ class ChatOllama(BaseChatModel):
ollama_messages: list = []
for message in messages:
role: str
tool_call_id: Optional[str] = None
tool_calls: Optional[list[dict[str, Any]]] = None
tool_call_id: str | None = None
tool_calls: list[dict[str, Any]] | None = None
if isinstance(message, HumanMessage):
role = "user"
elif isinstance(message, AIMessage):
@@ -925,9 +925,9 @@ class ChatOllama(BaseChatModel):
async def _acreate_chat_stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> AsyncIterator[Union[Mapping[str, Any], str]]:
) -> AsyncIterator[Mapping[str, Any] | str]:
chat_params = self._chat_params(messages, stop, **kwargs)
if chat_params["stream"]:
@@ -939,9 +939,9 @@ class ChatOllama(BaseChatModel):
def _create_chat_stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> Iterator[Union[Mapping[str, Any], str]]:
) -> Iterator[Mapping[str, Any] | str]:
chat_params = self._chat_params(messages, stop, **kwargs)
if chat_params["stream"]:
@@ -953,8 +953,8 @@ class ChatOllama(BaseChatModel):
def _chat_stream_with_aggregation(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
verbose: bool = False, # noqa: FBT002
**kwargs: Any,
) -> ChatGenerationChunk:
@@ -979,8 +979,8 @@ class ChatOllama(BaseChatModel):
async def _achat_stream_with_aggregation(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
verbose: bool = False, # noqa: FBT002
**kwargs: Any,
) -> ChatGenerationChunk:
@@ -1003,7 +1003,7 @@ class ChatOllama(BaseChatModel):
return final_chunk
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self, stop: list[str] | None = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
@@ -1020,8 +1020,8 @@ class ChatOllama(BaseChatModel):
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
final_chunk = self._chat_stream_with_aggregation(
@@ -1044,7 +1044,7 @@ class ChatOllama(BaseChatModel):
def _iterate_over_stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
reasoning = kwargs.get("reasoning", self.reasoning)
@@ -1106,8 +1106,8 @@ class ChatOllama(BaseChatModel):
def _stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
for chunk in self._iterate_over_stream(messages, stop, **kwargs):
@@ -1121,7 +1121,7 @@ class ChatOllama(BaseChatModel):
async def _aiterate_over_stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
reasoning = kwargs.get("reasoning", self.reasoning)
@@ -1183,8 +1183,8 @@ class ChatOllama(BaseChatModel):
async def _astream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
async for chunk in self._aiterate_over_stream(messages, stop, **kwargs):
@@ -1198,8 +1198,8 @@ class ChatOllama(BaseChatModel):
async def _agenerate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
final_chunk = await self._achat_stream_with_aggregation(
@@ -1226,9 +1226,9 @@ class ChatOllama(BaseChatModel):
def bind_tools(
self,
tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
tools: Sequence[dict[str, Any] | type | Callable | BaseTool],
*,
tool_choice: Optional[Union[dict, str, Literal["auto", "any"], bool]] = None, # noqa: PYI051, ARG002
tool_choice: dict | str | Literal["auto", "any"] | bool | None = None, # noqa: PYI051, ARG002
**kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]:
"""Bind tool-like objects to this chat model.
@@ -1249,12 +1249,12 @@ class ChatOllama(BaseChatModel):
def with_structured_output(
self,
schema: Union[dict, type],
schema: dict | type,
*,
method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema",
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
) -> Runnable[LanguageModelInput, dict | BaseModel]:
r"""Model wrapper that returns outputs formatted to match the given schema.
Args:
libs/partners/ollama/langchain_ollama/embeddings.py
+24 -24
View File
@@ -2,7 +2,7 @@
from __future__ import annotations
from typing import Any, Optional
from typing import Any
from langchain_core.embeddings import Embeddings
from ollama import AsyncClient, Client
@@ -128,7 +128,7 @@ class OllamaEmbeddings(BaseModel, Embeddings):
"""
base_url: Optional[str] = None
base_url: str | None = None
"""Base url the model is hosted under.
If none, defaults to the Ollama client default.
@@ -150,7 +150,7 @@ class OllamaEmbeddings(BaseModel, Embeddings):
"""
client_kwargs: Optional[dict] = {}
client_kwargs: dict | None = {}
"""Additional kwargs to pass to the httpx clients. Pass headers in here.
These arguments are passed to both synchronous and async clients.
@@ -159,7 +159,7 @@ class OllamaEmbeddings(BaseModel, Embeddings):
to synchronous and asynchronous clients.
"""
async_client_kwargs: Optional[dict] = {}
async_client_kwargs: dict | None = {}
"""Additional kwargs to merge with `client_kwargs` before passing to httpx client.
These are clients unique to the async client; for shared args use `client_kwargs`.
@@ -167,7 +167,7 @@ class OllamaEmbeddings(BaseModel, Embeddings):
For a full list of the params, see the `httpx documentation <https://www.python-httpx.org/api/#asyncclient>`__.
"""
sync_client_kwargs: Optional[dict] = {}
sync_client_kwargs: dict | None = {}
"""Additional kwargs to merge with `client_kwargs` before passing to httpx client.
These are clients unique to the sync client; for shared args use `client_kwargs`.
@@ -175,73 +175,73 @@ class OllamaEmbeddings(BaseModel, Embeddings):
For a full list of the params, see the `httpx documentation <https://www.python-httpx.org/api/#client>`__.
"""
_client: Optional[Client] = PrivateAttr(default=None)
_client: Client | None = PrivateAttr(default=None)
"""The client to use for making requests."""
_async_client: Optional[AsyncClient] = PrivateAttr(default=None)
_async_client: AsyncClient | None = PrivateAttr(default=None)
"""The async client to use for making requests."""
mirostat: Optional[int] = None
mirostat: int | None = None
"""Enable Mirostat sampling for controlling perplexity.
(default: `0`, `0` = disabled, ``1`` = Mirostat, ``2`` = Mirostat 2.0)"""
(default: `0`, `0` = disabled, `1` = Mirostat, `2` = Mirostat 2.0)"""
mirostat_eta: Optional[float] = None
mirostat_eta: float | None = None
"""Influences how quickly the algorithm responds to feedback
from the generated text. A lower learning rate will result in
slower adjustments, while a higher learning rate will make
the algorithm more responsive. (Default: ``0.1``)"""
mirostat_tau: Optional[float] = None
mirostat_tau: float | None = None
"""Controls the balance between coherence and diversity
of the output. A lower value will result in more focused and
coherent text. (Default: ``5.0``)"""
num_ctx: Optional[int] = None
num_ctx: int | None = None
"""Sets the size of the context window used to generate the
next token. (Default: ``2048``) """
num_gpu: Optional[int] = None
"""The number of GPUs to use. On macOS it defaults to ``1`` to
num_gpu: int | None = None
"""The number of GPUs to use. On macOS it defaults to `1` to
enable metal support, `0` to disable."""
keep_alive: Optional[int] = None
keep_alive: int | None = None
"""Controls how long the model will stay loaded into memory
following the request (default: ``5m``)
"""
num_thread: Optional[int] = None
num_thread: int | None = None
"""Sets the number of threads to use during computation.
By default, Ollama will detect this for optimal performance.
It is recommended to set this value to the number of physical
CPU cores your system has (as opposed to the logical number of cores)."""
repeat_last_n: Optional[int] = None
repeat_last_n: int | None = None
"""Sets how far back for the model to look back to prevent
repetition. (Default: ``64``, `0` = disabled, ``-1`` = ``num_ctx``)"""
repeat_penalty: Optional[float] = None
repeat_penalty: float | None = None
"""Sets how strongly to penalize repetitions. A higher value (e.g., ``1.5``)
will penalize repetitions more strongly, while a lower value (e.g., ``0.9``)
will be more lenient. (Default: ``1.1``)"""
temperature: Optional[float] = None
temperature: float | None = None
"""The temperature of the model. Increasing the temperature will
make the model answer more creatively. (Default: ``0.8``)"""
stop: Optional[list[str]] = None
stop: list[str] | None = None
"""Sets the stop tokens to use."""
tfs_z: Optional[float] = None
tfs_z: float | None = None
"""Tail free sampling is used to reduce the impact of less probable
tokens from the output. A higher value (e.g., ``2.0``) will reduce the
impact more, while a value of ``1.0`` disables this setting. (default: ``1``)"""
impact more, while a value of ``1.0`` disables this setting. (default: `1`)"""
top_k: Optional[int] = None
top_k: int | None = None
"""Reduces the probability of generating nonsense. A higher value (e.g. ``100``)
will give more diverse answers, while a lower value (e.g. ``10``)
will be more conservative. (Default: ``40``)"""
top_p: Optional[float] = None
top_p: float | None = None
"""Works together with top-k. A higher value (e.g., ``0.95``) will lead
to more diverse text, while a lower value (e.g., ``0.5``) will
generate more focused and conservative text. (Default: ``0.9``)"""
libs/partners/ollama/langchain_ollama/llms.py
+46 -46
View File
@@ -3,7 +3,7 @@
from __future__ import annotations
from collections.abc import AsyncIterator, Iterator, Mapping
from typing import Any, Literal, Optional, Union
from typing import Any, Literal
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
@@ -22,7 +22,7 @@ class OllamaLLM(BaseLLM):
"""Ollama large language models.
Setup:
Install ``langchain-ollama`` and install/run the Ollama server locally:
Install `langchain-ollama` and install/run the Ollama server locally:
.. code-block:: bash
@@ -112,7 +112,7 @@ class OllamaLLM(BaseLLM):
model: str
"""Model name to use."""
reasoning: Optional[bool] = None
reasoning: bool | None = None
"""Controls the reasoning/thinking mode for
`supported models <https://ollama.com/search?c=thinking>`__.
@@ -132,71 +132,71 @@ class OllamaLLM(BaseLLM):
!!! version-added "Added in version 0.3.4"
"""
mirostat: Optional[int] = None
mirostat: int | None = None
"""Enable Mirostat sampling for controlling perplexity.
(default: `0`, `0` = disabled, ``1`` = Mirostat, ``2`` = Mirostat 2.0)"""
(default: `0`, `0` = disabled, `1` = Mirostat, `2` = Mirostat 2.0)"""
mirostat_eta: Optional[float] = None
mirostat_eta: float | None = None
"""Influences how quickly the algorithm responds to feedback
from the generated text. A lower learning rate will result in
slower adjustments, while a higher learning rate will make
the algorithm more responsive. (Default: ``0.1``)"""
mirostat_tau: Optional[float] = None
mirostat_tau: float | None = None
"""Controls the balance between coherence and diversity
of the output. A lower value will result in more focused and
coherent text. (Default: ``5.0``)"""
num_ctx: Optional[int] = None
num_ctx: int | None = None
"""Sets the size of the context window used to generate the
next token. (Default: ``2048``)"""
num_gpu: Optional[int] = None
"""The number of GPUs to use. On macOS it defaults to ``1`` to
num_gpu: int | None = None
"""The number of GPUs to use. On macOS it defaults to `1` to
enable metal support, `0` to disable."""
num_thread: Optional[int] = None
num_thread: int | None = None
"""Sets the number of threads to use during computation.
By default, Ollama will detect this for optimal performance.
It is recommended to set this value to the number of physical
CPU cores your system has (as opposed to the logical number of cores)."""
num_predict: Optional[int] = None
num_predict: int | None = None
"""Maximum number of tokens to predict when generating text.
(Default: ``128``, ``-1`` = infinite generation, ``-2`` = fill context)"""
repeat_last_n: Optional[int] = None
repeat_last_n: int | None = None
"""Sets how far back for the model to look back to prevent
repetition. (Default: ``64``, `0` = disabled, ``-1`` = ``num_ctx``)"""
repeat_penalty: Optional[float] = None
repeat_penalty: float | None = None
"""Sets how strongly to penalize repetitions. A higher value (e.g., ``1.5``)
will penalize repetitions more strongly, while a lower value (e.g., ``0.9``)
will be more lenient. (Default: ``1.1``)"""
temperature: Optional[float] = None
temperature: float | None = None
"""The temperature of the model. Increasing the temperature will
make the model answer more creatively. (Default: ``0.8``)"""
seed: Optional[int] = None
seed: int | None = None
"""Sets the random number seed to use for generation. Setting this
to a specific number will make the model generate the same text for
the same prompt."""
stop: Optional[list[str]] = None
stop: list[str] | None = None
"""Sets the stop tokens to use."""
tfs_z: Optional[float] = None
tfs_z: float | None = None
"""Tail free sampling is used to reduce the impact of less probable
tokens from the output. A higher value (e.g., ``2.0``) will reduce the
impact more, while a value of 1.0 disables this setting. (default: ``1``)"""
impact more, while a value of 1.0 disables this setting. (default: `1`)"""
top_k: Optional[int] = None
top_k: int | None = None
"""Reduces the probability of generating nonsense. A higher value (e.g. ``100``)
will give more diverse answers, while a lower value (e.g. ``10``)
will be more conservative. (Default: ``40``)"""
top_p: Optional[float] = None
top_p: float | None = None
"""Works together with top-k. A higher value (e.g., ``0.95``) will lead
to more diverse text, while a lower value (e.g., ``0.5``) will
generate more focused and conservative text. (Default: ``0.9``)"""
@@ -204,10 +204,10 @@ class OllamaLLM(BaseLLM):
format: Literal["", "json"] = ""
"""Specify the format of the output (options: ``'json'``)"""
keep_alive: Optional[Union[int, str]] = None
keep_alive: int | str | None = None
"""How long the model will stay loaded into memory."""
base_url: Optional[str] = None
base_url: str | None = None
"""Base url the model is hosted under.
If none, defaults to the Ollama client default.
@@ -229,7 +229,7 @@ class OllamaLLM(BaseLLM):
"""
client_kwargs: Optional[dict] = {}
client_kwargs: dict | None = {}
"""Additional kwargs to pass to the httpx clients. Pass headers in here.
These arguments are passed to both synchronous and async clients.
@@ -238,7 +238,7 @@ class OllamaLLM(BaseLLM):
to synchronous and asynchronous clients.
"""
async_client_kwargs: Optional[dict] = {}
async_client_kwargs: dict | None = {}
"""Additional kwargs to merge with `client_kwargs` before passing to httpx client.
These are clients unique to the async client; for shared args use `client_kwargs`.
@@ -246,7 +246,7 @@ class OllamaLLM(BaseLLM):
For a full list of the params, see the `httpx documentation <https://www.python-httpx.org/api/#asyncclient>`__.
"""
sync_client_kwargs: Optional[dict] = {}
sync_client_kwargs: dict | None = {}
"""Additional kwargs to merge with `client_kwargs` before passing to httpx client.
These are clients unique to the sync client; for shared args use `client_kwargs`.
@@ -254,16 +254,16 @@ class OllamaLLM(BaseLLM):
For a full list of the params, see the `httpx documentation <https://www.python-httpx.org/api/#client>`__.
"""
_client: Optional[Client] = PrivateAttr(default=None)
_client: Client | None = PrivateAttr(default=None)
"""The client to use for making requests."""
_async_client: Optional[AsyncClient] = PrivateAttr(default=None)
_async_client: AsyncClient | None = PrivateAttr(default=None)
"""The async client to use for making requests."""
def _generate_params(
self,
prompt: str,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict[str, Any]:
if self.stop is not None and stop is not None:
@@ -310,7 +310,7 @@ class OllamaLLM(BaseLLM):
return "ollama-llm"
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self, stop: list[str] | None = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = super()._get_ls_params(stop=stop, **kwargs)
@@ -343,9 +343,9 @@ class OllamaLLM(BaseLLM):
async def _acreate_generate_stream(
self,
prompt: str,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> AsyncIterator[Union[Mapping[str, Any], str]]:
) -> AsyncIterator[Mapping[str, Any] | str]:
if self._async_client:
async for part in await self._async_client.generate(
**self._generate_params(prompt, stop=stop, **kwargs)
@@ -355,9 +355,9 @@ class OllamaLLM(BaseLLM):
def _create_generate_stream(
self,
prompt: str,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> Iterator[Union[Mapping[str, Any], str]]:
) -> Iterator[Mapping[str, Any] | str]:
if self._client:
yield from self._client.generate(
**self._generate_params(prompt, stop=stop, **kwargs)
@@ -366,8 +366,8 @@ class OllamaLLM(BaseLLM):
async def _astream_with_aggregation(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
verbose: bool = False, # noqa: FBT002
**kwargs: Any,
) -> GenerationChunk:
@@ -408,8 +408,8 @@ class OllamaLLM(BaseLLM):
def _stream_with_aggregation(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
verbose: bool = False, # noqa: FBT002
**kwargs: Any,
) -> GenerationChunk:
@@ -450,8 +450,8 @@ class OllamaLLM(BaseLLM):
def _generate(
self,
prompts: list[str],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> LLMResult:
generations = []
@@ -469,8 +469,8 @@ class OllamaLLM(BaseLLM):
async def _agenerate(
self,
prompts: list[str],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> LLMResult:
generations = []
@@ -488,8 +488,8 @@ class OllamaLLM(BaseLLM):
def _stream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
reasoning = kwargs.get("reasoning", self.reasoning)
@@ -519,8 +519,8 @@ class OllamaLLM(BaseLLM):
async def _astream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
reasoning = kwargs.get("reasoning", self.reasoning)
libs/partners/ollama/pyproject.toml
-2
View File
@@ -62,8 +62,6 @@ ignore = [
"S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
"FIX002", # TODOs
"TD002", # TODO authors
"TD003", # TODO missing url
libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models.py
+5 -5
View File
@@ -2,7 +2,7 @@
from __future__ import annotations
from typing import Annotated, Optional
from typing import Annotated
from unittest.mock import MagicMock, patch
import pytest
@@ -120,11 +120,11 @@ def test_structured_output_deeply_nested(model: str) -> None:
class Person(BaseModel):
"""Information about a person."""
name: Optional[str] = Field(default=None, description="The name of the person")
hair_color: Optional[str] = Field(
name: str | None = Field(default=None, description="The name of the person")
hair_color: str | None = Field(
default=None, description="The color of the person's hair if known"
)
height_in_meters: Optional[str] = Field(
height_in_meters: str | None = Field(
default=None, description="Height measured in meters"
)
@@ -243,7 +243,7 @@ async def test_tool_astreaming(model: str) -> None:
("model", "output_version"),
[(REASONING_MODEL_NAME, None), (REASONING_MODEL_NAME, "v1")],
)
def test_agent_loop(model: str, output_version: Optional[str]) -> None:
def test_agent_loop(model: str, output_version: str | None) -> None:
"""Test agent loop with tool calling and message passing."""
@tool
libs/partners/openai/langchain_openai/chat_models/_client_utils.py
+7 -7
View File
@@ -11,7 +11,7 @@ from __future__ import annotations
import asyncio
import os
from functools import lru_cache
from typing import Any, Optional
from typing import Any
import openai
@@ -44,7 +44,7 @@ class _AsyncHttpxClientWrapper(openai.DefaultAsyncHttpxClient):
def _build_sync_httpx_client(
base_url: Optional[str], timeout: Any
base_url: str | None, timeout: Any
) -> _SyncHttpxClientWrapper:
return _SyncHttpxClientWrapper(
base_url=base_url
@@ -55,7 +55,7 @@ def _build_sync_httpx_client(
def _build_async_httpx_client(
base_url: Optional[str], timeout: Any
base_url: str | None, timeout: Any
) -> _AsyncHttpxClientWrapper:
return _AsyncHttpxClientWrapper(
base_url=base_url
@@ -67,20 +67,20 @@ def _build_async_httpx_client(
@lru_cache
def _cached_sync_httpx_client(
base_url: Optional[str], timeout: Any
base_url: str | None, timeout: Any
) -> _SyncHttpxClientWrapper:
return _build_sync_httpx_client(base_url, timeout)
@lru_cache
def _cached_async_httpx_client(
base_url: Optional[str], timeout: Any
base_url: str | None, timeout: Any
) -> _AsyncHttpxClientWrapper:
return _build_async_httpx_client(base_url, timeout)
def _get_default_httpx_client(
base_url: Optional[str], timeout: Any
base_url: str | None, timeout: Any
) -> _SyncHttpxClientWrapper:
"""Get default httpx client.
@@ -95,7 +95,7 @@ def _get_default_httpx_client(
def _get_default_async_httpx_client(
base_url: Optional[str], timeout: Any
base_url: str | None, timeout: Any
) -> _AsyncHttpxClientWrapper:
"""Get default httpx client.
libs/partners/openai/langchain_openai/chat_models/_compat.py
+2 -2
View File
@@ -69,7 +69,7 @@ from __future__ import annotations
import json
from collections.abc import Iterable, Iterator
from typing import Any, Union, cast
from typing import Any, cast
from langchain_core.messages import AIMessage, is_data_content_block
from langchain_core.messages import content as types
@@ -83,7 +83,7 @@ def _convert_to_v03_ai_message(
) -> AIMessage:
"""Mutate an `AIMessage` to the old-style v0.3 format."""
if isinstance(message.content, list):
new_content: list[Union[dict, str]] = []
new_content: list[dict | str] = []
for block in message.content:
if isinstance(block, dict):
if block.get("type") == "reasoning":
libs/partners/openai/langchain_openai/chat_models/azure.py
+20 -20
View File
@@ -5,7 +5,7 @@ from __future__ import annotations
import logging
import os
from collections.abc import AsyncIterator, Awaitable, Callable, Iterator
from typing import Any, Literal, Optional, TypeVar, Union
from typing import Any, Literal, TypeAlias, TypeVar
import openai
from langchain_core.language_models import LanguageModelInput
@@ -23,8 +23,8 @@ logger = logging.getLogger(__name__)
_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[dict[str, Any], type[_BM]]
_DictOrPydantic = Union[dict, _BM]
_DictOrPydanticClass: TypeAlias = dict[str, Any] | type[_BM] | type
_DictOrPydantic: TypeAlias = dict | _BM
def _is_pydantic_class(obj: Any) -> bool:
@@ -462,7 +462,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
""" # noqa: E501
azure_endpoint: Optional[str] = Field(
azure_endpoint: str | None = Field(
default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
)
"""Your Azure endpoint, including the resource.
@@ -471,7 +471,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
Example: ``https://example-resource.azure.openai.com/``
"""
deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
deployment_name: str | None = Field(default=None, alias="azure_deployment")
"""A model deployment.
If given sets the base client URL to include ``/deployments/{azure_deployment}``
@@ -479,7 +479,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
!!! note
This means you won't be able to use non-deployment endpoints.
"""
openai_api_version: Optional[str] = Field(
openai_api_version: str | None = Field(
alias="api_version",
default_factory=from_env("OPENAI_API_VERSION", default=None),
)
@@ -487,14 +487,14 @@ class AzureChatOpenAI(BaseChatOpenAI):
# Check OPENAI_API_KEY for backwards compatibility.
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
# other forms of azure credentials.
openai_api_key: Optional[SecretStr] = Field(
openai_api_key: SecretStr | None = Field(
alias="api_key",
default_factory=secret_from_env(
["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
),
)
"""Automatically inferred from env var ``AZURE_OPENAI_API_KEY`` if not provided."""
azure_ad_token: Optional[SecretStr] = Field(
azure_ad_token: SecretStr | None = Field(
default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
)
"""Your Azure Active Directory token.
@@ -503,14 +503,14 @@ class AzureChatOpenAI(BaseChatOpenAI):
For more, see `this page <https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id>`__.
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
azure_ad_token_provider: Callable[[], str] | None = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every sync request. For async requests,
will be invoked if ``azure_ad_async_token_provider`` is not provided.
"""
azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
azure_ad_async_token_provider: Callable[[], Awaitable[str]] | None = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every async request.
@@ -529,7 +529,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
correct cost.
"""
openai_api_type: Optional[str] = Field(
openai_api_type: str | None = Field(
default_factory=from_env("OPENAI_API_TYPE", default="azure")
)
"""Legacy, for ``openai<1.0.0`` support."""
@@ -539,7 +539,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
``base_url`` or ``azure_endpoint`` and update client params accordingly.
"""
model_name: Optional[str] = Field(default=None, alias="model") # type: ignore[assignment]
model_name: str | None = Field(default=None, alias="model") # type: ignore[assignment]
"""Name of the deployed OpenAI model, e.g. ``'gpt-4o'``, ``'gpt-35-turbo'``, etc.
Distinct from the Azure deployment name, which is set by the Azure user.
@@ -549,7 +549,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
Does NOT affect completion.
"""
disabled_params: Optional[dict[str, Any]] = Field(default=None)
disabled_params: dict[str, Any] | None = Field(default=None)
"""Parameters of the OpenAI client or chat.completions endpoint that should be
disabled for the given model.
@@ -571,7 +571,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
``'parallel_tools_calls'`` will be disabled.
"""
max_tokens: Optional[int] = Field(default=None, alias="max_completion_tokens") # type: ignore[assignment]
max_tokens: int | None = Field(default=None, alias="max_completion_tokens") # type: ignore[assignment]
"""Maximum number of tokens to generate."""
@classmethod
@@ -731,7 +731,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
return params
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self, stop: list[str] | None = None, **kwargs: Any
) -> LangSmithParams:
"""Get the parameters used to invoke the model."""
params = super()._get_ls_params(stop=stop, **kwargs)
@@ -749,8 +749,8 @@ class AzureChatOpenAI(BaseChatOpenAI):
def _create_chat_result(
self,
response: Union[dict, openai.BaseModel],
generation_info: Optional[dict] = None,
response: dict | openai.BaseModel,
generation_info: dict | None = None,
) -> ChatResult:
chat_result = super()._create_chat_result(response, generation_info)
@@ -790,7 +790,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
self,
input_: LanguageModelInput,
*,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict:
"""Get the request payload, using deployment name for Azure Responses API."""
@@ -825,11 +825,11 @@ class AzureChatOpenAI(BaseChatOpenAI):
def with_structured_output(
self,
schema: Optional[_DictOrPydanticClass] = None,
schema: _DictOrPydanticClass | None = None,
*,
method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema",
include_raw: bool = False,
strict: Optional[bool] = None,
strict: bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, _DictOrPydantic]:
r"""Model wrapper that returns outputs formatted to match the given schema.
libs/partners/openai/langchain_openai/chat_models/base.py
+95 -100
View File
@@ -20,9 +20,8 @@ from typing import (
TYPE_CHECKING,
Any,
Literal,
Optional,
TypeAlias,
TypeVar,
Union,
cast,
)
from urllib.parse import urlparse
@@ -207,7 +206,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
def _format_message_content(
content: Any,
api: Literal["chat/completions", "responses"] = "chat/completions",
role: Optional[str] = None,
role: str | None = None,
) -> Any:
"""Format message content."""
if content and isinstance(content, list):
@@ -301,7 +300,7 @@ def _convert_message_to_dict(
if "function_call" in message_dict or "tool_calls" in message_dict:
message_dict["content"] = message_dict["content"] or None
audio: Optional[dict[str, Any]] = None
audio: dict[str, Any] | None = None
for block in message.content:
if (
isinstance(block, dict)
@@ -396,8 +395,8 @@ def _convert_delta_to_message_chunk(
def _update_token_usage(
overall_token_usage: Union[int, dict], new_usage: Union[int, dict]
) -> Union[int, dict]:
overall_token_usage: int | dict, new_usage: int | dict
) -> int | dict:
# Token usage is either ints or dictionaries
# `reasoning_tokens` is nested inside `completion_tokens_details`
if isinstance(new_usage, int):
@@ -449,8 +448,8 @@ def _handle_openai_bad_request(e: openai.BadRequestError) -> None:
_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[dict[str, Any], type[_BM], type]
_DictOrPydantic = Union[dict, _BM]
_DictOrPydanticClass: TypeAlias = dict[str, Any] | type[_BM] | type
_DictOrPydantic: TypeAlias = dict | _BM
class BaseChatOpenAI(BaseChatModel):
@@ -462,27 +461,27 @@ class BaseChatOpenAI(BaseChatModel):
root_async_client: Any = Field(default=None, exclude=True) #: :meta private:
model_name: str = Field(default="gpt-3.5-turbo", alias="model")
"""Model name to use."""
temperature: Optional[float] = None
temperature: float | None = None
"""What sampling temperature to use."""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[SecretStr] = Field(
openai_api_key: SecretStr | None = Field(
alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None)
)
openai_api_base: Optional[str] = Field(default=None, alias="base_url")
openai_api_base: str | None = Field(default=None, alias="base_url")
"""Base URL path for API requests, leave blank if not using a proxy or service emulator.""" # noqa: E501
openai_organization: Optional[str] = Field(default=None, alias="organization")
openai_organization: str | None = Field(default=None, alias="organization")
"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = Field(
openai_proxy: str | None = Field(
default_factory=from_env("OPENAI_PROXY", default=None)
)
request_timeout: Union[float, tuple[float, float], Any, None] = Field(
request_timeout: float | tuple[float, float] | Any | None = Field(
default=None, alias="timeout"
)
"""Timeout for requests to OpenAI completion API. Can be float, `httpx.Timeout` or
`None`."""
stream_usage: Optional[bool] = None
stream_usage: bool | None = None
"""Whether to include usage metadata in streaming output. If enabled, an additional
message chunk will be generated during the stream including usage metadata.
@@ -495,31 +494,31 @@ class BaseChatOpenAI(BaseChatModel):
!!! warning "Behavior changed in 0.3.35"
Enabled for default base URL and client.
"""
max_retries: Optional[int] = None
max_retries: int | None = None
"""Maximum number of retries to make when generating."""
presence_penalty: Optional[float] = None
presence_penalty: float | None = None
"""Penalizes repeated tokens."""
frequency_penalty: Optional[float] = None
frequency_penalty: float | None = None
"""Penalizes repeated tokens according to frequency."""
seed: Optional[int] = None
seed: int | None = None
"""Seed for generation"""
logprobs: Optional[bool] = None
logprobs: bool | None = None
"""Whether to return logprobs."""
top_logprobs: Optional[int] = None
top_logprobs: int | None = None
"""Number of most likely tokens to return at each token position, each with an
associated log probability. `logprobs` must be set to true if this parameter is
used."""
logit_bias: Optional[dict[int, int]] = None
logit_bias: dict[int, int] | None = None
"""Modify the likelihood of specified tokens appearing in the completion."""
streaming: bool = False
"""Whether to stream the results or not."""
n: Optional[int] = None
n: int | None = None
"""Number of chat completions to generate for each prompt."""
top_p: Optional[float] = None
top_p: float | None = None
"""Total probability mass of tokens to consider at each step."""
max_tokens: Optional[int] = Field(default=None)
max_tokens: int | None = Field(default=None)
"""Maximum number of tokens to generate."""
reasoning_effort: Optional[str] = None
reasoning_effort: str | None = None
"""Constrains effort on reasoning for reasoning models. For use with the Chat
Completions API.
@@ -529,7 +528,7 @@ class BaseChatOpenAI(BaseChatModel):
`'high'`. Reducing reasoning effort can result in faster responses and fewer
tokens used on reasoning in a response.
"""
reasoning: Optional[dict[str, Any]] = None
reasoning: dict[str, Any] | None = None
"""Reasoning parameters for reasoning models. For use with the Responses API.
```python
@@ -541,7 +540,7 @@ class BaseChatOpenAI(BaseChatModel):
!!! version-added "Added in version 0.3.24"
"""
verbosity: Optional[str] = None
verbosity: str | None = None
"""Controls the verbosity level of responses for reasoning models. For use with the
Responses API.
@@ -549,7 +548,7 @@ class BaseChatOpenAI(BaseChatModel):
!!! version-added "Added in version 0.3.28"
"""
tiktoken_model_name: Optional[str] = None
tiktoken_model_name: str | None = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
@@ -559,20 +558,20 @@ class BaseChatOpenAI(BaseChatModel):
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here."""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
default_headers: Mapping[str, str] | None = None
default_query: Mapping[str, object] | None = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
http_client: Union[Any, None] = Field(default=None, exclude=True)
http_client: Any | None = Field(default=None, exclude=True)
"""Optional `httpx.Client`. Only used for sync invocations. Must specify
`http_async_client` as well if you'd like a custom client for async invocations.
"""
http_async_client: Union[Any, None] = Field(default=None, exclude=True)
http_async_client: Any | None = Field(default=None, exclude=True)
"""Optional `httpx.AsyncClient`. Only used for async invocations. Must specify
`http_client` as well if you'd like a custom client for sync invocations."""
stop: Optional[Union[list[str], str]] = Field(default=None, alias="stop_sequences")
stop: list[str] | str | None = Field(default=None, alias="stop_sequences")
"""Default stop sequences."""
extra_body: Optional[Mapping[str, Any]] = None
extra_body: Mapping[str, Any] | None = None
"""Optional additional JSON properties to include in the request parameters when
making requests to OpenAI compatible APIs, such as vLLM, LM Studio, or other
providers.
@@ -594,7 +593,7 @@ class BaseChatOpenAI(BaseChatModel):
include_response_headers: bool = False
"""Whether to include response headers in the output message `response_metadata`."""
disabled_params: Optional[dict[str, Any]] = Field(default=None)
disabled_params: dict[str, Any] | None = Field(default=None)
"""Parameters of the OpenAI client or `chat.completions` endpoint that should be
disabled for the given model.
@@ -611,7 +610,7 @@ class BaseChatOpenAI(BaseChatModel):
passed in the parameter during invocation.
"""
include: Optional[list[str]] = None
include: list[str] | None = None
"""Additional fields to include in generations from Responses API.
Supported values:
@@ -625,19 +624,19 @@ class BaseChatOpenAI(BaseChatModel):
!!! version-added "Added in version 0.3.24"
"""
service_tier: Optional[str] = None
service_tier: str | None = None
"""Latency tier for request. Options are `'auto'`, `'default'`, or `'flex'`.
Relevant for users of OpenAI's scale tier service.
"""
store: Optional[bool] = None
store: bool | None = None
"""If `True`, OpenAI may store response data for future use. Defaults to `True`
for the Responses API and `False` for the Chat Completions API.
!!! version-added "Added in version 0.3.24"
"""
truncation: Optional[str] = None
truncation: str | None = None
"""Truncation strategy (Responses API). Can be `'auto'` or `'disabled'` (default).
If `'auto'`, model may drop input items from the middle of the message sequence to
fit the context window.
@@ -676,7 +675,7 @@ class BaseChatOpenAI(BaseChatModel):
!!! version-added "Added in version 0.3.26"
"""
use_responses_api: Optional[bool] = None
use_responses_api: bool | None = None
"""Whether to use the Responses API instead of the Chat API.
If not specified then will be inferred based on invocation params.
@@ -684,7 +683,7 @@ class BaseChatOpenAI(BaseChatModel):
!!! version-added "Added in version 0.3.9"
"""
output_version: Optional[str] = Field(
output_version: str | None = Field(
default_factory=from_env("LC_OUTPUT_VERSION", default=None)
)
"""Version of `AIMessage` output format to use.
@@ -876,7 +875,7 @@ class BaseChatOpenAI(BaseChatModel):
**self.model_kwargs,
}
def _combine_llm_outputs(self, llm_outputs: list[Optional[dict]]) -> dict:
def _combine_llm_outputs(self, llm_outputs: list[dict | None]) -> dict:
overall_token_usage: dict = {}
system_fingerprint = None
for output in llm_outputs:
@@ -905,8 +904,8 @@ class BaseChatOpenAI(BaseChatModel):
self,
chunk: dict,
default_chunk_class: type,
base_generation_info: Optional[dict],
) -> Optional[ChatGenerationChunk]:
base_generation_info: dict | None,
) -> ChatGenerationChunk | None:
if chunk.get("type") == "content.delta": # From beta.chat.completions.stream
return None
token_usage = chunk.get("usage")
@@ -916,7 +915,7 @@ class BaseChatOpenAI(BaseChatModel):
or chunk.get("chunk", {}).get("choices", [])
)
usage_metadata: Optional[UsageMetadata] = (
usage_metadata: UsageMetadata | None = (
_create_usage_metadata(token_usage) if token_usage else None
)
if len(choices) == 0:
@@ -966,8 +965,8 @@ class BaseChatOpenAI(BaseChatModel):
def _stream_responses(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
kwargs["stream"] = True
@@ -1019,8 +1018,8 @@ class BaseChatOpenAI(BaseChatModel):
async def _astream_responses(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
kwargs["stream"] = True
@@ -1072,7 +1071,7 @@ class BaseChatOpenAI(BaseChatModel):
yield generation_chunk
def _should_stream_usage(
self, stream_usage: Optional[bool] = None, **kwargs: Any
self, stream_usage: bool | None = None, **kwargs: Any
) -> bool:
"""Determine whether to include usage metadata in streaming output.
@@ -1093,10 +1092,10 @@ class BaseChatOpenAI(BaseChatModel):
def _stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
*,
stream_usage: Optional[bool] = None,
stream_usage: bool | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
kwargs["stream"] = True
@@ -1163,8 +1162,8 @@ class BaseChatOpenAI(BaseChatModel):
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
payload = self._get_request_payload(messages, stop=stop, **kwargs)
@@ -1233,7 +1232,7 @@ class BaseChatOpenAI(BaseChatModel):
self,
input_: LanguageModelInput,
*,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict:
messages = self._convert_input(input_).to_messages()
@@ -1262,8 +1261,8 @@ class BaseChatOpenAI(BaseChatModel):
def _create_chat_result(
self,
response: Union[dict, openai.BaseModel],
generation_info: Optional[dict] = None,
response: dict | openai.BaseModel,
generation_info: dict | None = None,
) -> ChatResult:
generations = []
@@ -1329,10 +1328,10 @@ class BaseChatOpenAI(BaseChatModel):
async def _astream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
*,
stream_usage: Optional[bool] = None,
stream_usage: bool | None = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
kwargs["stream"] = True
@@ -1403,8 +1402,8 @@ class BaseChatOpenAI(BaseChatModel):
async def _agenerate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
payload = self._get_request_payload(messages, stop=stop, **kwargs)
@@ -1468,7 +1467,7 @@ class BaseChatOpenAI(BaseChatModel):
return {"model_name": self.model_name, **self._default_params}
def _get_invocation_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self, stop: list[str] | None = None, **kwargs: Any
) -> dict[str, Any]:
"""Get the parameters used to invoke the model."""
params = {
@@ -1489,7 +1488,7 @@ class BaseChatOpenAI(BaseChatModel):
return params
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self, stop: list[str] | None = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
@@ -1546,9 +1545,7 @@ class BaseChatOpenAI(BaseChatModel):
def get_num_tokens_from_messages(
self,
messages: Sequence[BaseMessage],
tools: Optional[
Sequence[Union[dict[str, Any], type, Callable, BaseTool]]
] = None,
tools: Sequence[dict[str, Any] | type | Callable | BaseTool] | None = None,
) -> int:
"""Calculate num tokens for `gpt-3.5-turbo` and `gpt-4` with `tiktoken` package.
@@ -1642,13 +1639,11 @@ class BaseChatOpenAI(BaseChatModel):
def bind_tools(
self,
tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
tools: Sequence[dict[str, Any] | type | Callable | BaseTool],
*,
tool_choice: Optional[
Union[dict, str, Literal["auto", "none", "required", "any"], bool] # noqa: PYI051
] = None,
strict: Optional[bool] = None,
parallel_tool_calls: Optional[bool] = None,
tool_choice: dict | str | bool | None = None,
strict: bool | None = None,
parallel_tool_calls: bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]:
"""Bind tool-like objects to this chat model.
@@ -1723,14 +1718,14 @@ class BaseChatOpenAI(BaseChatModel):
def with_structured_output(
self,
schema: Optional[_DictOrPydanticClass] = None,
schema: _DictOrPydanticClass | None = None,
*,
method: Literal[
"function_calling", "json_mode", "json_schema"
] = "function_calling",
include_raw: bool = False,
strict: Optional[bool] = None,
tools: Optional[list] = None,
strict: bool | None = None,
tools: list | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, _DictOrPydantic]:
"""Model wrapper that returns outputs formatted to match the given schema.
@@ -2740,7 +2735,7 @@ class ChatOpenAI(BaseChatOpenAI): # type: ignore[override]
cached tokens.
""" # noqa: E501
max_tokens: Optional[int] = Field(default=None, alias="max_completion_tokens")
max_tokens: int | None = Field(default=None, alias="max_completion_tokens")
"""Maximum number of tokens to generate."""
@property
@@ -2787,7 +2782,7 @@ class ChatOpenAI(BaseChatOpenAI): # type: ignore[override]
self,
input_: LanguageModelInput,
*,
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict:
payload = super()._get_request_payload(input_, stop=stop, **kwargs)
@@ -2822,11 +2817,11 @@ class ChatOpenAI(BaseChatOpenAI): # type: ignore[override]
def with_structured_output(
self,
schema: Optional[_DictOrPydanticClass] = None,
schema: _DictOrPydanticClass | None = None,
*,
method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema",
include_raw: bool = False,
strict: Optional[bool] = None,
strict: bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, _DictOrPydantic]:
r"""Model wrapper that returns outputs formatted to match the given schema.
@@ -3239,7 +3234,7 @@ def _lc_invalid_tool_call_to_openai_tool_call(
}
def _url_to_size(image_source: str) -> Optional[tuple[int, int]]:
def _url_to_size(image_source: str) -> tuple[int, int] | None:
try:
from PIL import Image # type: ignore[import]
except ImportError:
@@ -3311,8 +3306,8 @@ def _resize(width: int, height: int) -> tuple[int, int]:
def _convert_to_openai_response_format(
schema: Union[dict[str, Any], type], *, strict: Optional[bool] = None
) -> Union[dict, TypeBaseModel]:
schema: dict[str, Any] | type, *, strict: bool | None = None
) -> dict | TypeBaseModel:
if isinstance(schema, type) and is_basemodel_subclass(schema):
return schema
@@ -3351,7 +3346,7 @@ def _convert_to_openai_response_format(
def _oai_structured_outputs_parser(
ai_msg: AIMessage, schema: type[_BM]
) -> Optional[PydanticBaseModel]:
) -> PydanticBaseModel | None:
if parsed := ai_msg.additional_kwargs.get("parsed"):
if isinstance(parsed, dict):
return schema(**parsed)
@@ -3471,7 +3466,7 @@ def _use_responses_api(payload: dict) -> bool:
def _get_last_messages(
messages: Sequence[BaseMessage],
) -> tuple[Sequence[BaseMessage], Optional[str]]:
) -> tuple[Sequence[BaseMessage], str | None]:
"""Get the last part of the conversation after the last `AIMessage` with an `id`.
Return:
@@ -3620,7 +3615,7 @@ def _convert_chat_completions_blocks_to_responses(
return block
def _ensure_valid_tool_message_content(tool_output: Any) -> Union[str, list[dict]]:
def _ensure_valid_tool_message_content(tool_output: Any) -> str | list[dict]:
if isinstance(tool_output, str):
return tool_output
if isinstance(tool_output, list) and all(
@@ -3645,8 +3640,8 @@ def _ensure_valid_tool_message_content(tool_output: Any) -> Union[str, list[dict
def _make_computer_call_output_from_message(
message: ToolMessage,
) -> Optional[dict[str, Any]]:
computer_call_output: Optional[dict[str, Any]] = None
) -> dict[str, Any] | None:
computer_call_output: dict[str, Any] | None = None
if isinstance(message.content, list):
for block in message.content:
if (
@@ -3685,7 +3680,7 @@ def _make_computer_call_output_from_message(
return computer_call_output
def _make_custom_tool_output_from_message(message: ToolMessage) -> Optional[dict]:
def _make_custom_tool_output_from_message(message: ToolMessage) -> dict | None:
custom_tool_output = None
for block in message.content:
if isinstance(block, dict) and block.get("type") == "custom_tool_call_output":
@@ -3902,9 +3897,9 @@ def _get_output_text(response: Response) -> str:
def _construct_lc_result_from_responses_api(
response: Response,
schema: Optional[type[_BM]] = None,
metadata: Optional[dict] = None,
output_version: Optional[str] = None,
schema: type[_BM] | None = None,
metadata: dict | None = None,
output_version: str | None = None,
) -> ChatResult:
"""Construct `ChatResponse` from OpenAI Response API response."""
if response.error:
@@ -4072,12 +4067,12 @@ def _convert_responses_chunk_to_generation_chunk(
current_index: int, # index in content
current_output_index: int, # index in Response output
current_sub_index: int, # index of content block in output item
schema: Optional[type[_BM]] = None,
metadata: Optional[dict] = None,
schema: type[_BM] | None = None,
metadata: dict | None = None,
has_reasoning: bool = False,
output_version: Optional[str] = None,
) -> tuple[int, int, int, Optional[ChatGenerationChunk]]:
def _advance(output_idx: int, sub_idx: Optional[int] = None) -> None:
output_version: str | None = None,
) -> tuple[int, int, int, ChatGenerationChunk | None]:
def _advance(output_idx: int, sub_idx: int | None = None) -> None:
"""Advance indexes tracked during streaming.
Example: we stream a response item of the form:
@@ -4133,7 +4128,7 @@ def _convert_responses_chunk_to_generation_chunk(
response_metadata = metadata or {}
response_metadata["model_provider"] = "openai"
usage_metadata = None
chunk_position: Optional[Literal["last"]] = None
chunk_position: Literal["last"] | None = None
id = None
if chunk.type == "response.output_text.delta":
_advance(chunk.output_index, chunk.content_index)
libs/partners/openai/langchain_openai/embeddings/azure.py
+9 -9
View File
@@ -3,7 +3,7 @@
from __future__ import annotations
from collections.abc import Awaitable, Callable
from typing import Optional, Union, cast
from typing import cast
import openai
from langchain_core.utils import from_env, secret_from_env
@@ -102,7 +102,7 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override]
""" # noqa: E501
azure_endpoint: Optional[str] = Field(
azure_endpoint: str | None = Field(
default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
)
"""Your Azure endpoint, including the resource.
@@ -111,7 +111,7 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override]
Example: `https://example-resource.azure.openai.com/`
"""
deployment: Optional[str] = Field(default=None, alias="azure_deployment")
deployment: str | None = Field(default=None, alias="azure_deployment")
"""A model deployment.
If given sets the base client URL to include `/deployments/{azure_deployment}`.
@@ -123,14 +123,14 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override]
# Check OPENAI_KEY for backwards compatibility.
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
# other forms of azure credentials.
openai_api_key: Optional[SecretStr] = Field(
openai_api_key: SecretStr | None = Field(
alias="api_key",
default_factory=secret_from_env(
["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
),
)
"""Automatically inferred from env var ``AZURE_OPENAI_API_KEY`` if not provided."""
openai_api_version: Optional[str] = Field(
openai_api_version: str | None = Field(
default_factory=from_env("OPENAI_API_VERSION", default="2023-05-15"),
alias="api_version",
)
@@ -139,7 +139,7 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override]
Set to ``'2023-05-15'`` by default if env variable ``OPENAI_API_VERSION`` is not
set.
"""
azure_ad_token: Optional[SecretStr] = Field(
azure_ad_token: SecretStr | None = Field(
default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
)
"""Your Azure Active Directory token.
@@ -148,18 +148,18 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override]
`For more, see this page. <https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id>`__
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
azure_ad_token_provider: Callable[[], str] | None = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every sync request. For async requests,
will be invoked if ``azure_ad_async_token_provider`` is not provided.
"""
azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
azure_ad_async_token_provider: Callable[[], Awaitable[str]] | None = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every async request.
"""
openai_api_type: Optional[str] = Field(
openai_api_type: str | None = Field(
default_factory=from_env("OPENAI_API_TYPE", default="azure")
)
validate_base_url: bool = True
libs/partners/openai/langchain_openai/embeddings/base.py
+28 -28
View File
@@ -5,7 +5,7 @@ from __future__ import annotations
import logging
import warnings
from collections.abc import Iterable, Mapping, Sequence
from typing import Any, Literal, Optional, Union, cast
from typing import Any, Literal, cast
import openai
import tiktoken
@@ -20,11 +20,11 @@ logger = logging.getLogger(__name__)
def _process_batched_chunked_embeddings(
num_texts: int,
tokens: list[Union[list[int], str]],
tokens: list[list[int] | str],
batched_embeddings: list[list[float]],
indices: list[int],
skip_empty: bool,
) -> list[Optional[list[float]]]:
) -> list[list[float] | None]:
# for each text, this is the list of embeddings (list of list of floats)
# corresponding to the chunks of the text
results: list[list[list[float]]] = [[] for _ in range(num_texts)]
@@ -41,7 +41,7 @@ def _process_batched_chunked_embeddings(
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
# for each text, this is the final embedding
embeddings: list[Optional[list[float]]] = []
embeddings: list[list[float] | None] = []
for i in range(num_texts):
# an embedding for each chunk
_result: list[list[float]] = results[i]
@@ -164,53 +164,53 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
client: Any = Field(default=None, exclude=True) #: :meta private:
async_client: Any = Field(default=None, exclude=True) #: :meta private:
model: str = "text-embedding-ada-002"
dimensions: Optional[int] = None
dimensions: int | None = None
"""The number of dimensions the resulting output embeddings should have.
Only supported in `text-embedding-3` and later models.
"""
# to support Azure OpenAI Service custom deployment names
deployment: Optional[str] = model
deployment: str | None = model
# TODO: Move to AzureOpenAIEmbeddings.
openai_api_version: Optional[str] = Field(
openai_api_version: str | None = Field(
default_factory=from_env("OPENAI_API_VERSION", default=None),
alias="api_version",
)
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
# to support Azure OpenAI Service custom endpoints
openai_api_base: Optional[str] = Field(
openai_api_base: str | None = Field(
alias="base_url", default_factory=from_env("OPENAI_API_BASE", default=None)
)
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
# to support Azure OpenAI Service custom endpoints
openai_api_type: Optional[str] = Field(
openai_api_type: str | None = Field(
default_factory=from_env("OPENAI_API_TYPE", default=None)
)
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = Field(
openai_proxy: str | None = Field(
default_factory=from_env("OPENAI_PROXY", default=None)
)
embedding_ctx_length: int = 8191
"""The maximum number of tokens to embed at once."""
openai_api_key: Optional[SecretStr] = Field(
openai_api_key: SecretStr | None = Field(
alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None)
)
"""Automatically inferred from env var ``OPENAI_API_KEY`` if not provided."""
openai_organization: Optional[str] = Field(
openai_organization: str | None = Field(
alias="organization",
default_factory=from_env(
["OPENAI_ORG_ID", "OPENAI_ORGANIZATION"], default=None
),
)
"""Automatically inferred from env var ``OPENAI_ORG_ID`` if not provided."""
allowed_special: Union[Literal["all"], set[str], None] = None
disallowed_special: Union[Literal["all"], set[str], Sequence[str], None] = None
allowed_special: Literal["all"] | set[str] | None = None
disallowed_special: Literal["all"] | set[str] | Sequence[str] | None = None
chunk_size: int = 1000
"""Maximum number of texts to embed in each batch"""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
request_timeout: Optional[Union[float, tuple[float, float], Any]] = Field(
request_timeout: float | tuple[float, float] | Any | None = Field(
default=None, alias="timeout"
)
"""Timeout for requests to OpenAI completion API. Can be float, ``httpx.Timeout`` or
@@ -219,7 +219,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
tiktoken_enabled: bool = True
"""Set this to False for non-OpenAI implementations of the embeddings API, e.g.
the ``--extensions openai`` extension for ``text-generation-webui``"""
tiktoken_model_name: Optional[str] = None
tiktoken_model_name: str | None = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
@@ -236,20 +236,20 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
skip_empty: bool = False
"""Whether to skip empty strings when embedding or raise an error.
Defaults to not skipping."""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
default_headers: Mapping[str, str] | None = None
default_query: Mapping[str, object] | None = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
retry_min_seconds: int = 4
"""Min number of seconds to wait between retries"""
retry_max_seconds: int = 20
"""Max number of seconds to wait between retries"""
http_client: Union[Any, None] = None
http_client: Any | None = None
"""Optional ``httpx.Client``. Only used for sync invocations. Must specify
``http_async_client`` as well if you'd like a custom client for async
invocations.
"""
http_async_client: Union[Any, None] = None
http_async_client: Any | None = None
"""Optional ``httpx.AsyncClient``. Only used for async invocations. Must specify
``http_client`` as well if you'd like a custom client for sync invocations."""
check_embedding_ctx_length: bool = True
@@ -359,7 +359,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
def _tokenize(
self, texts: list[str], chunk_size: int
) -> tuple[Iterable[int], list[Union[list[int], str]], list[int]]:
) -> tuple[Iterable[int], list[list[int] | str], list[int]]:
"""Take the input `texts` and `chunk_size` and return 3 iterables as a tuple.
We have `batches`, where batches are sets of individual texts
@@ -379,7 +379,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
indices: An iterable of the same length as `tokens` that maps each token-array
to the index of the original text in `texts`.
"""
tokens: list[Union[list[int], str]] = []
tokens: list[list[int] | str] = []
indices: list[int] = []
model_name = self.tiktoken_model_name or self.model
@@ -460,7 +460,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
texts: list[str],
*,
engine: str,
chunk_size: Optional[int] = None,
chunk_size: int | None = None,
**kwargs: Any,
) -> list[list[float]]:
"""Generate length-safe embeddings for a list of texts.
@@ -492,7 +492,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
embeddings = _process_batched_chunked_embeddings(
len(texts), tokens, batched_embeddings, indices, self.skip_empty
)
_cached_empty_embedding: Optional[list[float]] = None
_cached_empty_embedding: list[float] | None = None
def empty_embedding() -> list[float]:
nonlocal _cached_empty_embedding
@@ -512,7 +512,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
texts: list[str],
*,
engine: str,
chunk_size: Optional[int] = None,
chunk_size: int | None = None,
**kwargs: Any,
) -> list[list[float]]:
"""Asynchronously generate length-safe embeddings for a list of texts.
@@ -547,7 +547,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
embeddings = _process_batched_chunked_embeddings(
len(texts), tokens, batched_embeddings, indices, self.skip_empty
)
_cached_empty_embedding: Optional[list[float]] = None
_cached_empty_embedding: list[float] | None = None
async def empty_embedding() -> list[float]:
nonlocal _cached_empty_embedding
@@ -563,7 +563,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
return [e if e is not None else await empty_embedding() for e in embeddings]
def embed_documents(
self, texts: list[str], chunk_size: Optional[int] = None, **kwargs: Any
self, texts: list[str], chunk_size: int | None = None, **kwargs: Any
) -> list[list[float]]:
"""Call out to OpenAI's embedding endpoint for embedding search docs.
@@ -597,7 +597,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
)
async def aembed_documents(
self, texts: list[str], chunk_size: Optional[int] = None, **kwargs: Any
self, texts: list[str], chunk_size: int | None = None, **kwargs: Any
) -> list[list[float]]:
"""Call out to OpenAI's embedding endpoint async for embedding search docs.
libs/partners/openai/langchain_openai/llms/azure.py
+11 -11
View File
@@ -4,7 +4,7 @@ from __future__ import annotations
import logging
from collections.abc import Awaitable, Callable, Mapping
from typing import Any, Optional, Union, cast
from typing import Any, cast
import openai
from langchain_core.language_models import LangSmithParams
@@ -20,7 +20,7 @@ logger = logging.getLogger(__name__)
class AzureOpenAI(BaseOpenAI):
"""Azure-specific OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
To use, you should have the `openai` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
@@ -35,7 +35,7 @@ class AzureOpenAI(BaseOpenAI):
"""
azure_endpoint: Optional[str] = Field(
azure_endpoint: str | None = Field(
default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
)
"""Your Azure endpoint, including the resource.
@@ -44,7 +44,7 @@ class AzureOpenAI(BaseOpenAI):
Example: ``'https://example-resource.azure.openai.com/'``
"""
deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
deployment_name: str | None = Field(default=None, alias="azure_deployment")
"""A model deployment.
If given sets the base client URL to include `/deployments/{azure_deployment}`.
@@ -53,7 +53,7 @@ class AzureOpenAI(BaseOpenAI):
This means you won't be able to use non-deployment endpoints.
"""
openai_api_version: Optional[str] = Field(
openai_api_version: str | None = Field(
alias="api_version",
default_factory=from_env("OPENAI_API_VERSION", default=None),
)
@@ -61,13 +61,13 @@ class AzureOpenAI(BaseOpenAI):
# Check OPENAI_KEY for backwards compatibility.
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
# other forms of azure credentials.
openai_api_key: Optional[SecretStr] = Field(
openai_api_key: SecretStr | None = Field(
alias="api_key",
default_factory=secret_from_env(
["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
),
)
azure_ad_token: Optional[SecretStr] = Field(
azure_ad_token: SecretStr | None = Field(
default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
)
"""Your Azure Active Directory token.
@@ -76,18 +76,18 @@ class AzureOpenAI(BaseOpenAI):
`For more, see this page <https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id>.`__
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
azure_ad_token_provider: Callable[[], str] | None = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every sync request. For async requests,
will be invoked if ``azure_ad_async_token_provider`` is not provided.
"""
azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
azure_ad_async_token_provider: Callable[[], Awaitable[str]] | None = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every async request.
"""
openai_api_type: Optional[str] = Field(
openai_api_type: str | None = Field(
default_factory=from_env("OPENAI_API_TYPE", default="azure")
)
"""Legacy, for ``openai<1.0.0`` support."""
@@ -205,7 +205,7 @@ class AzureOpenAI(BaseOpenAI):
return {**openai_params, **super()._invocation_params}
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self, stop: list[str] | None = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = super()._get_ls_params(stop=stop, **kwargs)
libs/partners/openai/langchain_openai/llms/base.py
+31 -31
View File
@@ -5,7 +5,7 @@ from __future__ import annotations
import logging
import sys
from collections.abc import AsyncIterator, Collection, Iterator, Mapping
from typing import Any, Literal, Optional, Union
from typing import Any, Literal
import openai
import tiktoken
@@ -188,16 +188,16 @@ class BaseOpenAI(BaseLLM):
"""Generates best_of completions server-side and returns the "best"."""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[SecretStr] = Field(
openai_api_key: SecretStr | None = Field(
alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None)
)
"""Automatically inferred from env var ``OPENAI_API_KEY`` if not provided."""
openai_api_base: Optional[str] = Field(
openai_api_base: str | None = Field(
alias="base_url", default_factory=from_env("OPENAI_API_BASE", default=None)
)
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
openai_organization: Optional[str] = Field(
openai_organization: str | None = Field(
alias="organization",
default_factory=from_env(
["OPENAI_ORG_ID", "OPENAI_ORGANIZATION"], default=None
@@ -205,32 +205,32 @@ class BaseOpenAI(BaseLLM):
)
"""Automatically inferred from env var ``OPENAI_ORG_ID`` if not provided."""
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = Field(
openai_proxy: str | None = Field(
default_factory=from_env("OPENAI_PROXY", default=None)
)
batch_size: int = 20
"""Batch size to use when passing multiple documents to generate."""
request_timeout: Union[float, tuple[float, float], Any, None] = Field(
request_timeout: float | tuple[float, float] | Any | None = Field(
default=None, alias="timeout"
)
"""Timeout for requests to OpenAI completion API. Can be float, ``httpx.Timeout`` or
None."""
logit_bias: Optional[dict[str, float]] = None
logit_bias: dict[str, float] | None = None
"""Adjust the probability of specific tokens being generated."""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
seed: Optional[int] = None
seed: int | None = None
"""Seed for generation"""
logprobs: Optional[int] = None
logprobs: int | None = None
"""Include the log probabilities on the logprobs most likely output tokens,
as well the chosen tokens."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], set[str]] = set()
allowed_special: Literal["all"] | set[str] = set()
"""Set of special tokens that are allowed。"""
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
disallowed_special: Literal["all"] | Collection[str] = "all"
"""Set of special tokens that are not allowed。"""
tiktoken_model_name: Optional[str] = None
tiktoken_model_name: str | None = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
@@ -240,19 +240,19 @@ class BaseOpenAI(BaseLLM):
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here."""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
default_headers: Mapping[str, str] | None = None
default_query: Mapping[str, object] | None = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
http_client: Union[Any, None] = None
http_client: Any | None = None
"""Optional ``httpx.Client``. Only used for sync invocations. Must specify
``http_async_client`` as well if you'd like a custom client for async
invocations.
"""
http_async_client: Union[Any, None] = None
http_async_client: Any | None = None
"""Optional ``httpx.AsyncClient``. Only used for async invocations. Must specify
``http_client`` as well if you'd like a custom client for sync invocations."""
extra_body: Optional[Mapping[str, Any]] = None
extra_body: Mapping[str, Any] | None = None
"""Optional additional JSON properties to include in the request parameters when
making requests to OpenAI compatible APIs, such as vLLM."""
@@ -333,8 +333,8 @@ class BaseOpenAI(BaseLLM):
def _stream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
params = {**self._invocation_params, **kwargs, "stream": True}
@@ -360,8 +360,8 @@ class BaseOpenAI(BaseLLM):
async def _astream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
params = {**self._invocation_params, **kwargs, "stream": True}
@@ -389,8 +389,8 @@ class BaseOpenAI(BaseLLM):
def _generate(
self,
prompts: list[str],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint with k unique prompts.
@@ -418,14 +418,14 @@ class BaseOpenAI(BaseLLM):
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
system_fingerprint: Optional[str] = None
system_fingerprint: str | None = None
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
msg = "Cannot stream results with multiple prompts."
raise ValueError(msg)
generation: Optional[GenerationChunk] = None
generation: GenerationChunk | None = None
for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs):
if generation is None:
generation = chunk
@@ -474,8 +474,8 @@ class BaseOpenAI(BaseLLM):
async def _agenerate(
self,
prompts: list[str],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint async with k unique prompts."""
@@ -487,14 +487,14 @@ class BaseOpenAI(BaseLLM):
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
system_fingerprint: Optional[str] = None
system_fingerprint: str | None = None
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
msg = "Cannot stream results with multiple prompts."
raise ValueError(msg)
generation: Optional[GenerationChunk] = None
generation: GenerationChunk | None = None
async for chunk in self._astream(
_prompts[0], stop, run_manager, **kwargs
):
@@ -534,7 +534,7 @@ class BaseOpenAI(BaseLLM):
self,
params: dict[str, Any],
prompts: list[str],
stop: Optional[list[str]] = None,
stop: list[str] | None = None,
) -> list[list[str]]:
"""Get the sub prompts for llm call."""
if stop is not None:
@@ -556,7 +556,7 @@ class BaseOpenAI(BaseLLM):
params: dict[str, Any],
token_usage: dict[str, int],
*,
system_fingerprint: Optional[str] = None,
system_fingerprint: str | None = None,
) -> LLMResult:
"""Create the LLMResult from the choices and prompts."""
generations = []
libs/partners/openai/pyproject.toml
-2
View File
@@ -76,8 +76,6 @@ ignore = [
"COM812", # Messes with the formatter
"ISC001", # Messes with the formatter
"PERF203", # Rarely useful
"UP007", # non-pep604-annotation-union
"UP045", # non-pep604-annotation-optional
"SIM105", # Rarely useful
"FIX", # TODOs
"TD", # TODOs
libs/partners/openai/tests/integration_tests/chat_models/test_azure.py
+7 -7
View File
@@ -4,7 +4,7 @@ from __future__ import annotations
import json
import os
from typing import Any, Optional
from typing import Any
import pytest
from langchain_core.callbacks import CallbackManager
@@ -164,7 +164,7 @@ async def test_async_chat_openai_streaming() -> None:
@pytest.mark.scheduled
def test_openai_streaming(llm: AzureChatOpenAI) -> None:
"""Test streaming tokens from OpenAI."""
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream("I'm Pickle Rick"):
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
@@ -176,7 +176,7 @@ def test_openai_streaming(llm: AzureChatOpenAI) -> None:
async def test_openai_astream(llm: AzureChatOpenAI) -> None:
"""Test streaming tokens from OpenAI."""
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
async for chunk in llm.astream("I'm Pickle Rick"):
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
@@ -239,7 +239,7 @@ def test_json_mode(llm: AzureChatOpenAI) -> None:
assert json.loads(response.content) == {"a": 1}
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream(
"Return this as json: {'a': 1}", response_format={"type": "json_object"}
):
@@ -257,7 +257,7 @@ async def test_json_mode_async(llm: AzureChatOpenAI) -> None:
assert json.loads(response.content) == {"a": 1}
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
async for chunk in llm.astream(
"Return this as json: {'a': 1}", response_format={"type": "json_object"}
):
@@ -272,7 +272,7 @@ class Foo(BaseModel):
def test_stream_response_format(llm: AzureChatOpenAI) -> None:
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks = []
for chunk in llm.stream("how are ya", response_format=Foo):
chunks.append(chunk)
@@ -287,7 +287,7 @@ def test_stream_response_format(llm: AzureChatOpenAI) -> None:
async def test_astream_response_format(llm: AzureChatOpenAI) -> None:
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks = []
async for chunk in llm.astream("how are ya", response_format=Foo):
chunks.append(chunk)
libs/partners/openai/tests/integration_tests/chat_models/test_base.py
+13 -13
View File
@@ -5,7 +5,7 @@ import json
from collections.abc import AsyncIterator
from pathlib import Path
from textwrap import dedent
from typing import Any, Literal, Optional, cast
from typing import Any, Literal, cast
import httpx
import openai
@@ -202,7 +202,7 @@ def test_stream() -> None:
"""Test streaming tokens from OpenAI."""
llm = ChatOpenAI(model="gpt-4.1-mini")
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream("I'm Pickle Rick"):
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
@@ -211,7 +211,7 @@ def test_stream() -> None:
assert full.response_metadata.get("model_name") is not None
# check token usage
aggregate: Optional[BaseMessageChunk] = None
aggregate: BaseMessageChunk | None = None
chunks_with_token_counts = 0
chunks_with_response_metadata = 0
for chunk in llm.stream("Hello"):
@@ -242,7 +242,7 @@ async def test_astream() -> None:
"""Test streaming tokens from OpenAI."""
async def _test_stream(stream: AsyncIterator, expect_usage: bool) -> None:
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks_with_token_counts = 0
chunks_with_response_metadata = 0
async for chunk in stream:
@@ -357,7 +357,7 @@ async def test_async_response_metadata() -> None:
def test_response_metadata_streaming() -> None:
llm = ChatOpenAI()
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream("I'm Pickle Rick", logprobs=True):
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
@@ -370,7 +370,7 @@ def test_response_metadata_streaming() -> None:
async def test_async_response_metadata_streaming() -> None:
llm = ChatOpenAI()
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
async for chunk in llm.astream("I'm Pickle Rick", logprobs=True):
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
@@ -564,7 +564,7 @@ def test_openai_response_headers(use_responses_api: bool) -> None:
assert "content-type" in headers
# Stream
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in chat_openai.stream(query, max_tokens=MAX_TOKEN_COUNT): # type: ignore[call-arg]
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessage)
@@ -588,7 +588,7 @@ async def test_openai_response_headers_async(use_responses_api: bool) -> None:
assert "content-type" in headers
# Stream
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
async for chunk in chat_openai.astream(query, max_tokens=MAX_TOKEN_COUNT): # type: ignore[call-arg]
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessage)
@@ -672,7 +672,7 @@ def test_tool_calling_strict(use_responses_api: bool) -> None:
class magic_function_notrequired_arg(BaseModel): # noqa: N801
"""Applies a magic function to an input."""
input: Optional[int] = Field(default=None)
input: int | None = Field(default=None)
model = ChatOpenAI(
model="gpt-5-nano", temperature=0, use_responses_api=use_responses_api
@@ -694,7 +694,7 @@ def test_tool_calling_strict(use_responses_api: bool) -> None:
model_with_invalid_tool_schema.invoke(query)
# Test stream
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in model_with_tools.stream(query):
full = chunk if full is None else full + chunk # type: ignore
assert isinstance(full, AIMessage)
@@ -960,7 +960,7 @@ class Foo(BaseModel):
def test_stream_response_format() -> None:
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks = []
for chunk in ChatOpenAI(model="gpt-5-nano").stream(
"how are ya", response_format=Foo
@@ -977,7 +977,7 @@ def test_stream_response_format() -> None:
async def test_astream_response_format() -> None:
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks = []
async for chunk in ChatOpenAI(model="gpt-5-nano").astream(
"how are ya", response_format=Foo
@@ -1048,7 +1048,7 @@ def test_structured_output_and_tools() -> None:
assert isinstance(response.additional_kwargs["parsed"], ResponseFormat)
# Test streaming tool calls
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream(
"Generate a user name for Alice, black hair. Use the tool."
):
libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py
+17 -17
View File
@@ -2,7 +2,7 @@
import json
import os
from typing import Annotated, Any, Literal, Optional, cast
from typing import Annotated, Any, Literal, cast
import openai
import pytest
@@ -22,7 +22,7 @@ from langchain_openai import ChatOpenAI, custom_tool
MODEL_NAME = "gpt-4o-mini"
def _check_response(response: Optional[BaseMessage]) -> None:
def _check_response(response: BaseMessage | None) -> None:
assert isinstance(response, AIMessage)
assert isinstance(response.content, list)
for block in response.content:
@@ -69,7 +69,7 @@ def test_web_search(output_version: Literal["responses/v1", "v1"]) -> None:
_check_response(first_response)
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream(
"What was a positive news story from today?",
tools=[{"type": "web_search_preview"}],
@@ -123,7 +123,7 @@ async def test_web_search_async() -> None:
assert response.response_metadata["status"]
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
async for chunk in llm.astream(
"What was a positive news story from today?",
tools=[{"type": "web_search_preview"}],
@@ -194,7 +194,7 @@ def test_parsed_pydantic_schema(
assert parsed.response
# Test stream
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream("how are ya", response_format=Foo):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -212,7 +212,7 @@ async def test_parsed_pydantic_schema_async() -> None:
assert parsed.response
# Test stream
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
async for chunk in llm.astream("how are ya", response_format=Foo):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -233,7 +233,7 @@ def test_parsed_dict_schema(schema: Any) -> None:
assert isinstance(parsed["response"], str)
# Test stream
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream("how are ya", response_format=schema):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -280,7 +280,7 @@ async def test_parsed_dict_schema_async(schema: Any) -> None:
assert isinstance(parsed["response"], str)
# Test stream
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
async for chunk in llm.astream("how are ya", response_format=schema):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -325,7 +325,7 @@ def test_reasoning(output_version: Literal["v0", "responses/v1", "v1"]) -> None:
llm = ChatOpenAI(
model="o4-mini", reasoning={"effort": "low"}, output_version=output_version
)
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream("Hello"):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -386,7 +386,7 @@ def test_file_search() -> None:
response = llm.invoke([input_message], tools=[tool])
_check_response(response)
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream([input_message], tools=[tool]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -413,7 +413,7 @@ def test_stream_reasoning_summary(
"role": "user",
"content": "What was the third tallest buliding in the year 2000?",
}
response_1: Optional[BaseMessageChunk] = None
response_1: BaseMessageChunk | None = None
for chunk in llm.stream([message_1]):
assert isinstance(chunk, AIMessageChunk)
response_1 = chunk if response_1 is None else response_1 + chunk
@@ -520,7 +520,7 @@ def test_code_interpreter(output_version: Literal["v0", "responses/v1", "v1"]) -
[{"type": "code_interpreter", "container": container_id}]
)
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm_with_tools.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -624,7 +624,7 @@ def test_mcp_builtin_zdr() -> None:
"spec (modelcontextprotocol/modelcontextprotocol) support?"
),
}
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm_with_tools.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -678,7 +678,7 @@ def test_mcp_builtin_zdr_v1() -> None:
"spec (modelcontextprotocol/modelcontextprotocol) support?"
),
}
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm_with_tools.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -756,7 +756,7 @@ def test_image_generation_streaming(
"type",
}
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream("Draw a random short word in green font.", tools=[tool]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -801,7 +801,7 @@ def test_image_generation_streaming_v1() -> None:
"status",
}
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream("Draw a random short word in green font.", tools=[tool]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
@@ -1029,7 +1029,7 @@ def test_custom_tool(output_version: Literal["responses/v1", "v1"]) -> None:
assert isinstance(response, AIMessage)
# Test streaming
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
for chunk in llm.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
libs/partners/openai/tests/unit_tests/chat_models/test_base.py
+17 -17
View File
@@ -5,7 +5,7 @@ from __future__ import annotations
import json
from functools import partial
from types import TracebackType
from typing import Any, Literal, Optional, Union, cast
from typing import Any, Literal, cast
from unittest.mock import AsyncMock, MagicMock, patch
import httpx
@@ -291,9 +291,9 @@ class MockAsyncContextManager:
async def __aexit__(
self,
exc_type: Optional[type[BaseException]],
exc: Optional[BaseException],
tb: Optional[TracebackType],
exc_type: type[BaseException] | None,
exc: BaseException | None,
tb: TracebackType | None,
) -> None:
pass
@@ -319,9 +319,9 @@ class MockSyncContextManager:
def __exit__(
self,
exc_type: Optional[type[BaseException]],
exc: Optional[BaseException],
tb: Optional[TracebackType],
exc_type: type[BaseException] | None,
exc: BaseException | None,
tb: TracebackType | None,
) -> None:
pass
@@ -370,7 +370,7 @@ async def test_glm4_astream(mock_glm4_completion: list) -> None:
mock_client.create = mock_create
usage_chunk = mock_glm4_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
usage_metadata: UsageMetadata | None = None
with patch.object(llm, "async_client", mock_client):
async for chunk in llm.astream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
@@ -395,7 +395,7 @@ def test_glm4_stream(mock_glm4_completion: list) -> None:
mock_client.create = mock_create
usage_chunk = mock_glm4_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
usage_metadata: UsageMetadata | None = None
with patch.object(llm, "client", mock_client):
for chunk in llm.stream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
@@ -450,7 +450,7 @@ async def test_deepseek_astream(mock_deepseek_completion: list) -> None:
mock_client.create = mock_create
usage_chunk = mock_deepseek_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
usage_metadata: UsageMetadata | None = None
with patch.object(llm, "async_client", mock_client):
async for chunk in llm.astream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
@@ -474,7 +474,7 @@ def test_deepseek_stream(mock_deepseek_completion: list) -> None:
mock_client.create = mock_create
usage_chunk = mock_deepseek_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
usage_metadata: UsageMetadata | None = None
with patch.object(llm, "client", mock_client):
for chunk in llm.stream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
@@ -519,7 +519,7 @@ async def test_openai_astream(mock_openai_completion: list) -> None:
mock_client.create = mock_create
usage_chunk = mock_openai_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
usage_metadata: UsageMetadata | None = None
with patch.object(llm, "async_client", mock_client):
async for chunk in llm.astream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
@@ -547,7 +547,7 @@ def test_openai_stream(mock_openai_completion: list) -> None:
mock_client.create = mock_create
usage_chunk = mock_openai_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
usage_metadata: UsageMetadata | None = None
with patch.object(llm, "client", mock_client):
for chunk in llm.stream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
@@ -873,7 +873,7 @@ class MakeASandwich(BaseModel):
],
)
@pytest.mark.parametrize("strict", [True, False, None])
def test_bind_tools_tool_choice(tool_choice: Any, strict: Optional[bool]) -> None:
def test_bind_tools_tool_choice(tool_choice: Any, strict: bool | None) -> None:
"""Test passing in manually construct tool call message."""
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
llm.bind_tools(
@@ -888,10 +888,10 @@ def test_bind_tools_tool_choice(tool_choice: Any, strict: Optional[bool]) -> Non
@pytest.mark.parametrize("include_raw", [True, False])
@pytest.mark.parametrize("strict", [True, False, None])
def test_with_structured_output(
schema: Union[type, dict[str, Any], None],
schema: type | dict[str, Any] | None,
method: Literal["function_calling", "json_mode", "json_schema"],
include_raw: bool,
strict: Optional[bool],
strict: bool | None,
) -> None:
"""Test passing in manually construct tool call message."""
if method == "json_mode":
@@ -1088,7 +1088,7 @@ def test__convert_to_openai_response_format() -> None:
@pytest.mark.parametrize("method", ["function_calling", "json_schema"])
@pytest.mark.parametrize("strict", [True, None])
def test_structured_output_strict(
method: Literal["function_calling", "json_schema"], strict: Optional[bool]
method: Literal["function_calling", "json_schema"], strict: bool | None
) -> None:
"""Test to verify structured output with strict=True."""
libs/partners/openai/tests/unit_tests/chat_models/test_responses_stream.py
+2 -2
View File
@@ -1,6 +1,6 @@
from __future__ import annotations
from typing import Any, Optional
from typing import Any
from unittest.mock import MagicMock, patch
import pytest
@@ -728,7 +728,7 @@ def test_responses_stream(output_version: str, expected_content: list[dict]) ->
mock_client.responses.create = mock_create
full: Optional[BaseMessageChunk] = None
full: BaseMessageChunk | None = None
chunks = []
with patch.object(llm, "root_client", mock_client):
for chunk in llm.stream("test"):
libs/partners/openai/tests/unit_tests/fake/callbacks.py
+3 -3
View File
@@ -3,7 +3,7 @@
from __future__ import annotations
from itertools import chain
from typing import Any, Optional, Union
from typing import Any
from uuid import UUID
from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
@@ -26,7 +26,7 @@ class BaseFakeCallbackHandler(BaseModel):
ignore_chat_model_: bool = False
# to allow for similar callback handlers that are not technically equal
fake_id: Union[str, None] = None
fake_id: str | None = None
# add finer-grained counters for easier debugging of failing tests
chain_starts: int = 0
@@ -201,7 +201,7 @@ class FakeCallbackHandlerWithChatStart(FakeCallbackHandler):
messages: list[list[BaseMessage]],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
parent_run_id: UUID | None = None,
**kwargs: Any,
) -> Any:
assert all(isinstance(m, BaseMessage) for m in chain(*messages))
libs/partners/perplexity/langchain_perplexity/chat_models.py
+15 -18
View File
@@ -5,7 +5,7 @@ from __future__ import annotations
import logging
from collections.abc import Iterator, Mapping
from operator import itemgetter
from typing import Any, Literal, Optional, TypeVar, Union
from typing import Any, Literal, TypeAlias
import openai
from langchain_core.callbacks import CallbackManagerForLLMRun
@@ -38,9 +38,8 @@ from langchain_core.utils.pydantic import is_basemodel_subclass
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self
_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[dict[str, Any], type[_BM], type]
_DictOrPydantic = Union[dict, _BM]
_DictOrPydanticClass: TypeAlias = dict[str, Any] | type[BaseModel]
_DictOrPydantic: TypeAlias = dict | BaseModel
logger = logging.getLogger(__name__)
@@ -151,20 +150,18 @@ class ChatPerplexity(BaseChatModel):
"""What sampling temperature to use."""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
pplx_api_key: Optional[SecretStr] = Field(
pplx_api_key: SecretStr | None = Field(
default_factory=secret_from_env("PPLX_API_KEY", default=None), alias="api_key"
)
"""Base URL path for API requests,
leave blank if not using a proxy or service emulator."""
request_timeout: Optional[Union[float, tuple[float, float]]] = Field(
None, alias="timeout"
)
request_timeout: float | tuple[float, float] | None = Field(None, alias="timeout")
"""Timeout for requests to PerplexityChat completion API. Default is None."""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
streaming: bool = False
"""Whether to stream the results or not."""
max_tokens: Optional[int] = None
max_tokens: int | None = None
"""Maximum number of tokens to generate."""
model_config = ConfigDict(populate_by_name=True)
@@ -242,7 +239,7 @@ class ChatPerplexity(BaseChatModel):
return message_dict
def _create_message_dicts(
self, messages: list[BaseMessage], stop: Optional[list[str]]
self, messages: list[BaseMessage], stop: list[str] | None
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
params = dict(self._invocation_params)
if stop is not None:
@@ -284,8 +281,8 @@ class ChatPerplexity(BaseChatModel):
def _stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
@@ -298,7 +295,7 @@ class ChatPerplexity(BaseChatModel):
messages=message_dicts, stream=True, **params
)
first_chunk = True
prev_total_usage: Optional[UsageMetadata] = None
prev_total_usage: UsageMetadata | None = None
added_model_name: bool = False
for chunk in stream_resp:
@@ -308,7 +305,7 @@ class ChatPerplexity(BaseChatModel):
if total_usage := chunk.get("usage"):
lc_total_usage = _create_usage_metadata(total_usage)
if prev_total_usage:
usage_metadata: Optional[UsageMetadata] = subtract_usage(
usage_metadata: UsageMetadata | None = subtract_usage(
lc_total_usage, prev_total_usage
)
else:
@@ -355,8 +352,8 @@ class ChatPerplexity(BaseChatModel):
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
@@ -399,11 +396,11 @@ class ChatPerplexity(BaseChatModel):
def with_structured_output(
self,
schema: Optional[_DictOrPydanticClass] = None,
schema: _DictOrPydanticClass | None = None,
*,
method: Literal["json_schema"] = "json_schema",
include_raw: bool = False,
strict: Optional[bool] = None,
strict: bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, _DictOrPydantic]:
"""Model wrapper that returns outputs formatted to match the given schema for Preplexity.
libs/partners/perplexity/pyproject.toml
-1
View File
@@ -67,7 +67,6 @@ docstring-code-format = true
[tool.ruff.lint]
select = ["E", "F", "I", "T201", "UP", "S"]
ignore = [ "UP007", "UP045"]
[tool.ruff.lint.pydocstyle]
convention = "google"
libs/partners/perplexity/tests/unit_tests/test_chat_models.py
+5 -5
View File
@@ -1,4 +1,4 @@
from typing import Any, Optional, cast
from typing import Any, cast
from unittest.mock import MagicMock
from langchain_core.messages import AIMessageChunk, BaseMessage
@@ -61,7 +61,7 @@ def test_perplexity_stream_includes_citations(mocker: MockerFixture) -> None:
llm.client.chat.completions, "create", return_value=mock_stream
)
stream = llm.stream("Hello langchain")
full: Optional[BaseMessage] = None
full: BaseMessage | None = None
chunks_list = list(stream)
# BaseChatModel.stream() adds an extra chunk after the final chunk from _stream
assert len(chunks_list) == 4
@@ -124,7 +124,7 @@ def test_perplexity_stream_includes_citations_and_images(mocker: MockerFixture)
llm.client.chat.completions, "create", return_value=mock_stream
)
stream = llm.stream("Hello langchain")
full: Optional[BaseMessage] = None
full: BaseMessage | None = None
chunks_list = list(stream)
# BaseChatModel.stream() adds an extra chunk after the final chunk from _stream
assert len(chunks_list) == 4
@@ -194,7 +194,7 @@ def test_perplexity_stream_includes_citations_and_related_questions(
llm.client.chat.completions, "create", return_value=mock_stream
)
stream = llm.stream("Hello langchain")
full: Optional[BaseMessage] = None
full: BaseMessage | None = None
chunks_list = list(stream)
# BaseChatModel.stream() adds an extra chunk after the final chunk from _stream
assert len(chunks_list) == 4
@@ -258,7 +258,7 @@ def test_perplexity_stream_includes_citations_and_search_results(
llm.client.chat.completions, "create", return_value=mock_stream
)
stream = llm.stream("Hello langchain")
full: Optional[BaseMessage] = None
full: BaseMessage | None = None
chunks_list = list(stream)
# BaseChatModel.stream() adds an extra chunk after the final chunk from _stream
assert len(chunks_list) == 4
libs/partners/prompty/langchain_prompty/core.py
+3 -3
View File
@@ -5,7 +5,7 @@ import json
import os
import re
from pathlib import Path
from typing import Any, Generic, Literal, Optional, TypeVar, Union
from typing import Any, Generic, Literal, TypeVar
import yaml
from pydantic import BaseModel, ConfigDict, Field, FilePath
@@ -24,7 +24,7 @@ class PropertySettings(BaseModel):
model_config = ConfigDict(arbitrary_types_allowed=True)
type: Literal["string", "number", "array", "object", "boolean"]
default: Union[str, int, float, list, dict, bool, None] = Field(default=None)
default: str | int | float | list | dict | bool | None = Field(default=None)
description: str = Field(default="")
@@ -62,7 +62,7 @@ class Prompty(BaseModel):
tags: list[str] = Field(default=[])
version: str = Field(default="")
base: str = Field(default="")
basePrompty: Optional[Prompty] = Field(default=None)
basePrompty: Prompty | None = Field(default=None)
# Model
model: ModelSettings = Field(default_factory=ModelSettings)
libs/partners/prompty/langchain_prompty/parsers.py
+1 -2
View File
@@ -1,6 +1,5 @@
import base64
import re
from typing import Union
from langchain_core.messages import (
AIMessage,
@@ -60,7 +59,7 @@ class PromptyChatParser(Invoker):
"and .jpg / .jpeg are supported."
)
def parse_content(self, content: str) -> Union[str, list]:
def parse_content(self, content: str) -> str | list:
"""for parsing inline images"""
# regular expression to parse markdown images
image = r"(?P<alt>!\[[^\]]*\])\((?P<filename>.*?)(?=\"|\))\)"
libs/partners/prompty/langchain_prompty/utils.py
+3 -3
View File
@@ -1,6 +1,6 @@
import traceback
from pathlib import Path
from typing import Any, Union
from typing import Any
from .core import (
Frontmatter,
@@ -166,7 +166,7 @@ def prepare(
def run(
prompt: Prompty,
content: Union[dict, list, str],
content: dict | list | str,
configuration: dict[str, Any] = {},
parameters: dict[str, Any] = {},
raw: bool = False,
@@ -218,7 +218,7 @@ def run(
def execute(
prompt: Union[str, Prompty],
prompt: str | Prompty,
configuration: dict[str, Any] = {},
parameters: dict[str, Any] = {},
inputs: dict[str, Any] = {},
libs/partners/prompty/pyproject.toml
-1
View File
@@ -65,7 +65,6 @@ docstring-code-format = true
[tool.ruff.lint]
select = ["E", "F", "I", "T201", "UP", "S"]
ignore = [ "UP007", "UP045" ]
[tool.ruff.lint.pydocstyle]
convention = "google"
libs/partners/prompty/tests/unit_tests/fake_callback_handler.py
+3 -3
View File
@@ -1,7 +1,7 @@
"""A fake callback handler for testing purposes."""
from itertools import chain
from typing import Any, Optional, Union
from typing import Any
from uuid import UUID
from langchain_core.callbacks import AsyncCallbackHandler, BaseCallbackHandler
@@ -23,7 +23,7 @@ class BaseFakeCallbackHandler(BaseModel):
ignore_chat_model_: bool = False
# to allow for similar callback handlers that are not technically equal
fake_id: Union[str, None] = None
fake_id: str | None = None
# add finer-grained counters for easier debugging of failing tests
chain_starts: int = 0
@@ -271,7 +271,7 @@ class FakeCallbackHandlerWithChatStart(FakeCallbackHandler):
messages: list[list[BaseMessage]],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
parent_run_id: UUID | None = None,
**kwargs: Any,
) -> Any:
assert all(isinstance(m, BaseMessage) for m in chain(*messages))
libs/partners/prompty/tests/unit_tests/fake_chat_model.py
+5 -5
View File
@@ -1,7 +1,7 @@
"""Fake Chat Model wrapper for testing purposes."""
import json
from typing import Any, Optional
from typing import Any
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
@@ -18,8 +18,8 @@ class FakeEchoPromptChatModel(SimpleChatModel):
def _call(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
return json.dumps([message.model_dump() for message in messages])
@@ -27,8 +27,8 @@ class FakeEchoPromptChatModel(SimpleChatModel):
async def _agenerate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
output_str = "fake response 2"
libs/partners/prompty/tests/unit_tests/fake_output_parser.py
+2 -4
View File
@@ -1,10 +1,8 @@
from typing import Optional, Union
from langchain_classic.agents import AgentOutputParser
from langchain_core.agents import AgentAction, AgentFinish
def extract_action_details(text: str) -> tuple[Optional[str], Optional[str]]:
def extract_action_details(text: str) -> tuple[str | None, str | None]:
# Split the text into lines and strip whitespace
lines = [line.strip() for line in text.strip().split("\n")]
@@ -23,7 +21,7 @@ def extract_action_details(text: str) -> tuple[Optional[str], Optional[str]]:
class FakeOutputParser(AgentOutputParser):
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
def parse(self, text: str) -> AgentAction | AgentFinish:
action, input = extract_action_details(text)
if action:
libs/partners/qdrant/langchain_qdrant/_utils.py
+4 -4
View File
@@ -1,8 +1,8 @@
from typing import Union
from typing import TypeAlias
import numpy as np
Matrix = Union[list[list[float]], list[np.ndarray], np.ndarray]
Matrix: TypeAlias = list[list[float]] | list[np.ndarray] | np.ndarray
def maximal_marginal_relevance(
@@ -44,8 +44,8 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: # noqa: N803
if len(X) == 0 or len(Y) == 0:
return np.array([])
x = np.array(X)
y = np.array(Y)
x: np.ndarray = np.array(X)
y: np.ndarray = np.array(Y)
if x.shape[1] != y.shape[1]:
msg = (
f"Number of columns in X and Y must be the same. X has shape {x.shape} "
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