mirror of https://github.com/hwchase17/langchain
openai[minor]: implement langchain-openai package (#15503)
Todo - [x] copy over integration tests - [x] update docs with new instructions in #15513 - [x] add linear ticket to bump core -> community, community->langchain, and core->openai deps - [ ] (optional): add `pip install langchain-openai` command to each notebook using it - [x] Update docstrings to not need `openai` install - [x] Add serialization - [x] deprecate old models Contributor steps: - [x] Add secret names to manual integrations workflow in .github/workflows/_integration_test.yml - [x] Add secrets to release workflow (for pre-release testing) in .github/workflows/_release.yml Maintainer steps (Contributors should not do these): - [x] set up pypi and test pypi projects - [x] add credential secrets to Github Actions - [ ] add package to conda-forge Functional changes to existing classes: - now relies on openai client v1 (1.6.1) via concrete dep in langchain-openai package Codebase organization - some function calling stuff moved to `langchain_core.utils.function_calling` in order to be used in both community and langchain-openaipull/15611/head
Erick Friis
6 months ago
committed by
GitHub
parent
a7d023aaf0
commit
ebc75c5ca7
@ -1,51 +1,15 @@
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from typing import Literal, Optional, Type, TypedDict
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.utils.json_schema import dereference_refs
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class FunctionDescription(TypedDict):
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"""Representation of a callable function to the OpenAI API."""
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name: str
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"""The name of the function."""
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description: str
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"""A description of the function."""
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parameters: dict
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"""The parameters of the function."""
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class ToolDescription(TypedDict):
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"""Representation of a callable function to the OpenAI API."""
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type: Literal["function"]
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function: FunctionDescription
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def convert_pydantic_to_openai_function(
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model: Type[BaseModel],
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*,
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name: Optional[str] = None,
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description: Optional[str] = None,
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) -> FunctionDescription:
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"""Converts a Pydantic model to a function description for the OpenAI API."""
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schema = dereference_refs(model.schema())
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schema.pop("definitions", None)
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return {
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"name": name or schema["title"],
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"description": description or schema["description"],
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"parameters": schema,
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}
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def convert_pydantic_to_openai_tool(
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model: Type[BaseModel],
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*,
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name: Optional[str] = None,
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description: Optional[str] = None,
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) -> ToolDescription:
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"""Converts a Pydantic model to a function description for the OpenAI API."""
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function = convert_pydantic_to_openai_function(
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model, name=name, description=description
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)
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return {"type": "function", "function": function}
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# these stubs are just for backwards compatibility
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from langchain_core.utils.function_calling import (
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FunctionDescription,
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ToolDescription,
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convert_pydantic_to_openai_function,
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convert_pydantic_to_openai_tool,
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)
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__all__ = [
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"FunctionDescription",
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"ToolDescription",
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"convert_pydantic_to_openai_function",
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"convert_pydantic_to_openai_tool",
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]
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@ -0,0 +1,202 @@
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"""Methods for creating function specs in the style of OpenAI Functions"""
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import inspect
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from typing import (
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Any,
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Callable,
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Dict,
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List,
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Literal,
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Optional,
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Tuple,
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Type,
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Union,
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cast,
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)
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from typing_extensions import TypedDict
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.utils.json_schema import dereference_refs
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PYTHON_TO_JSON_TYPES = {
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"str": "string",
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"int": "number",
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"float": "number",
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"bool": "boolean",
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}
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class FunctionDescription(TypedDict):
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"""Representation of a callable function to the OpenAI API."""
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name: str
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"""The name of the function."""
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description: str
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"""A description of the function."""
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parameters: dict
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"""The parameters of the function."""
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class ToolDescription(TypedDict):
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"""Representation of a callable function to the OpenAI API."""
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type: Literal["function"]
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function: FunctionDescription
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def convert_pydantic_to_openai_function(
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model: Type[BaseModel],
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*,
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name: Optional[str] = None,
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description: Optional[str] = None,
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) -> FunctionDescription:
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"""Converts a Pydantic model to a function description for the OpenAI API."""
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schema = dereference_refs(model.schema())
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schema.pop("definitions", None)
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return {
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"name": name or schema["title"],
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"description": description or schema["description"],
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"parameters": schema,
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}
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def convert_pydantic_to_openai_tool(
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model: Type[BaseModel],
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*,
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name: Optional[str] = None,
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description: Optional[str] = None,
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) -> ToolDescription:
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"""Converts a Pydantic model to a function description for the OpenAI API."""
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function = convert_pydantic_to_openai_function(
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model, name=name, description=description
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)
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return {"type": "function", "function": function}
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def _get_python_function_name(function: Callable) -> str:
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"""Get the name of a Python function."""
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return function.__name__
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def _parse_python_function_docstring(function: Callable) -> Tuple[str, dict]:
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"""Parse the function and argument descriptions from the docstring of a function.
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Assumes the function docstring follows Google Python style guide.
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"""
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docstring = inspect.getdoc(function)
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if docstring:
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docstring_blocks = docstring.split("\n\n")
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descriptors = []
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args_block = None
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past_descriptors = False
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for block in docstring_blocks:
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if block.startswith("Args:"):
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args_block = block
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break
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elif block.startswith("Returns:") or block.startswith("Example:"):
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# Don't break in case Args come after
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past_descriptors = True
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elif not past_descriptors:
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descriptors.append(block)
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else:
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continue
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description = " ".join(descriptors)
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else:
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description = ""
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args_block = None
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arg_descriptions = {}
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if args_block:
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arg = None
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for line in args_block.split("\n")[1:]:
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if ":" in line:
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arg, desc = line.split(":", maxsplit=1)
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arg_descriptions[arg.strip()] = desc.strip()
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elif arg:
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arg_descriptions[arg.strip()] += " " + line.strip()
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return description, arg_descriptions
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def _get_python_function_arguments(function: Callable, arg_descriptions: dict) -> dict:
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"""Get JsonSchema describing a Python functions arguments.
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Assumes all function arguments are of primitive types (int, float, str, bool) or
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are subclasses of pydantic.BaseModel.
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"""
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properties = {}
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annotations = inspect.getfullargspec(function).annotations
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for arg, arg_type in annotations.items():
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if arg == "return":
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continue
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if isinstance(arg_type, type) and issubclass(arg_type, BaseModel):
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# Mypy error:
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# "type" has no attribute "schema"
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properties[arg] = arg_type.schema() # type: ignore[attr-defined]
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elif arg_type.__name__ in PYTHON_TO_JSON_TYPES:
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properties[arg] = {"type": PYTHON_TO_JSON_TYPES[arg_type.__name__]}
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if arg in arg_descriptions:
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if arg not in properties:
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properties[arg] = {}
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properties[arg]["description"] = arg_descriptions[arg]
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return properties
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def _get_python_function_required_args(function: Callable) -> List[str]:
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"""Get the required arguments for a Python function."""
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spec = inspect.getfullargspec(function)
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required = spec.args[: -len(spec.defaults)] if spec.defaults else spec.args
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required += [k for k in spec.kwonlyargs if k not in (spec.kwonlydefaults or {})]
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is_class = type(function) is type
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if is_class and required[0] == "self":
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required = required[1:]
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return required
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def convert_python_function_to_openai_function(
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function: Callable,
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) -> Dict[str, Any]:
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"""Convert a Python function to an OpenAI function-calling API compatible dict.
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Assumes the Python function has type hints and a docstring with a description. If
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the docstring has Google Python style argument descriptions, these will be
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included as well.
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"""
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description, arg_descriptions = _parse_python_function_docstring(function)
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return {
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"name": _get_python_function_name(function),
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"description": description,
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"parameters": {
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"type": "object",
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"properties": _get_python_function_arguments(function, arg_descriptions),
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"required": _get_python_function_required_args(function),
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},
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}
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def convert_to_openai_function(
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function: Union[Dict[str, Any], Type[BaseModel], Callable],
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) -> Dict[str, Any]:
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"""Convert a raw function/class to an OpenAI function.
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Args:
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function: Either a dictionary, a pydantic.BaseModel class, or a Python function.
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If a dictionary is passed in, it is assumed to already be a valid OpenAI
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function.
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Returns:
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A dict version of the passed in function which is compatible with the
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OpenAI function-calling API.
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"""
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if isinstance(function, dict):
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return function
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elif isinstance(function, type) and issubclass(function, BaseModel):
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return cast(Dict, convert_pydantic_to_openai_function(function))
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elif callable(function):
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return convert_python_function_to_openai_function(function)
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else:
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raise ValueError(
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f"Unsupported function type {type(function)}. Functions must be passed in"
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f" as Dict, pydantic.BaseModel, or Callable."
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)
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@ -0,0 +1 @@
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__pycache__
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@ -0,0 +1,21 @@
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MIT License
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Copyright (c) 2023 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
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|
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The above copyright notice and this permission notice shall be included in all
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||||
copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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@ -0,0 +1,59 @@
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.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
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# Default target executed when no arguments are given to make.
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all: help
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# Define a variable for the test file path.
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TEST_FILE ?= tests/unit_tests/
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test:
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poetry run pytest $(TEST_FILE)
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tests:
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poetry run pytest $(TEST_FILE)
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######################
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# LINTING AND FORMATTING
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######################
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# Define a variable for Python and notebook files.
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PYTHON_FILES=.
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MYPY_CACHE=.mypy_cache
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lint format: PYTHON_FILES=.
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lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/openai --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
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lint_package: PYTHON_FILES=langchain_openai
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lint_tests: PYTHON_FILES=tests
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lint_tests: MYPY_CACHE=.mypy_cache_test
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lint lint_diff lint_package lint_tests:
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poetry run ruff .
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poetry run ruff format $(PYTHON_FILES) --diff
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poetry run ruff --select I $(PYTHON_FILES)
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mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
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format format_diff:
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poetry run ruff format $(PYTHON_FILES)
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poetry run ruff --select I --fix $(PYTHON_FILES)
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spell_check:
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poetry run codespell --toml pyproject.toml
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spell_fix:
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poetry run codespell --toml pyproject.toml -w
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check_imports: $(shell find langchain_openai -name '*.py')
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poetry run python ./scripts/check_imports.py $^
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######################
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# HELP
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######################
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help:
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@echo '----'
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@echo 'check_imports - check imports'
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@echo 'format - run code formatters'
|
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@echo 'lint - run linters'
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@echo 'test - run unit tests'
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@echo 'tests - run unit tests'
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@echo 'test TEST_FILE=<test_file> - run all tests in file'
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@ -0,0 +1 @@
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# langchain-openai
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@ -0,0 +1,18 @@
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from langchain_openai.chat_models import (
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AzureChatOpenAI,
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ChatOpenAI,
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)
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from langchain_openai.embeddings import (
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AzureOpenAIEmbeddings,
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OpenAIEmbeddings,
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)
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from langchain_openai.llms import AzureOpenAI, OpenAI
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__all__ = [
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"OpenAI",
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"ChatOpenAI",
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"OpenAIEmbeddings",
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"AzureOpenAI",
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"AzureChatOpenAI",
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"AzureOpenAIEmbeddings",
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]
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@ -0,0 +1,7 @@
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from langchain_openai.chat_models.azure import AzureChatOpenAI
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from langchain_openai.chat_models.base import ChatOpenAI
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__all__ = [
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"ChatOpenAI",
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"AzureChatOpenAI",
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]
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@ -0,0 +1,218 @@
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"""Azure OpenAI chat wrapper."""
|
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from __future__ import annotations
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|
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import logging
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import os
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from typing import Any, Callable, Dict, List, Union
|
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import openai
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from langchain_core.outputs import ChatResult
|
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from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
|
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from langchain_core.utils import get_from_dict_or_env
|
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|
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from langchain_openai.chat_models.base import ChatOpenAI
|
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|
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logger = logging.getLogger(__name__)
|
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|
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|
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class AzureChatOpenAI(ChatOpenAI):
|
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"""`Azure OpenAI` Chat Completion API.
|
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|
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To use this class you
|
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must have a deployed model on Azure OpenAI. Use `deployment_name` in the
|
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constructor to refer to the "Model deployment name" in the Azure portal.
|
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|
||||
In addition, you should have the
|
||||
following environment variables set or passed in constructor in lower case:
|
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- ``AZURE_OPENAI_API_KEY``
|
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- ``AZURE_OPENAI_ENDPOINT``
|
||||
- ``AZURE_OPENAI_AD_TOKEN``
|
||||
- ``OPENAI_API_VERSION``
|
||||
- ``OPENAI_PROXY``
|
||||
|
||||
For example, if you have `gpt-3.5-turbo` deployed, with the deployment name
|
||||
`35-turbo-dev`, the constructor should look like:
|
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|
||||
.. code-block:: python
|
||||
|
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AzureChatOpenAI(
|
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azure_deployment="35-turbo-dev",
|
||||
openai_api_version="2023-05-15",
|
||||
)
|
||||
|
||||
Be aware the API version may change.
|
||||
|
||||
You can also specify the version of the model using ``model_version`` constructor
|
||||
parameter, as Azure OpenAI doesn't return model version with the response.
|
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|
||||
Default is empty. When you specify the version, it will be appended to the
|
||||
model name in the response. Setting correct version will help you to calculate the
|
||||
cost properly. Model version is not validated, so make sure you set it correctly
|
||||
to get the correct cost.
|
||||
|
||||
Any parameters that are valid to be passed to the openai.create call can be passed
|
||||
in, even if not explicitly saved on this class.
|
||||
"""
|
||||
|
||||
azure_endpoint: Union[str, None] = None
|
||||
"""Your Azure endpoint, including the resource.
|
||||
|
||||
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
|
||||
|
||||
Example: `https://example-resource.azure.openai.com/`
|
||||
"""
|
||||
deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
|
||||
"""A model deployment.
|
||||
|
||||
If given sets the base client URL to include `/deployments/{azure_deployment}`.
|
||||
Note: this means you won't be able to use non-deployment endpoints.
|
||||
"""
|
||||
openai_api_version: str = Field(default="", alias="api_version")
|
||||
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
|
||||
openai_api_key: Union[str, None] = Field(default=None, alias="api_key")
|
||||
"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
|
||||
azure_ad_token: Union[str, None] = None
|
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"""Your Azure Active Directory token.
|
||||
|
||||
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
|
||||
|
||||
For more:
|
||||
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
|
||||
""" # noqa: E501
|
||||
azure_ad_token_provider: Union[Callable[[], str], None] = None
|
||||
"""A function that returns an Azure Active Directory token.
|
||||
|
||||
Will be invoked on every request.
|
||||
"""
|
||||
model_version: str = ""
|
||||
"""Legacy, for openai<1.0.0 support."""
|
||||
openai_api_type: str = ""
|
||||
"""Legacy, for openai<1.0.0 support."""
|
||||
validate_base_url: bool = True
|
||||
"""For backwards compatibility. If legacy val openai_api_base is passed in, try to
|
||||
infer if it is a base_url or azure_endpoint and update accordingly.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def get_lc_namespace(cls) -> List[str]:
|
||||
"""Get the namespace of the langchain object."""
|
||||
return ["langchain", "chat_models", "azure_openai"]
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
if values["n"] < 1:
|
||||
raise ValueError("n must be at least 1.")
|
||||
if values["n"] > 1 and values["streaming"]:
|
||||
raise ValueError("n must be 1 when streaming.")
|
||||
|
||||
# Check OPENAI_KEY for backwards compatibility.
|
||||
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
|
||||
# other forms of azure credentials.
|
||||
values["openai_api_key"] = (
|
||||
values["openai_api_key"]
|
||||
or os.getenv("AZURE_OPENAI_API_KEY")
|
||||
or os.getenv("OPENAI_API_KEY")
|
||||
)
|
||||
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
|
||||
"OPENAI_API_BASE"
|
||||
)
|
||||
values["openai_api_version"] = values["openai_api_version"] or os.getenv(
|
||||
"OPENAI_API_VERSION"
|
||||
)
|
||||
# Check OPENAI_ORGANIZATION for backwards compatibility.
|
||||
values["openai_organization"] = (
|
||||
values["openai_organization"]
|
||||
or os.getenv("OPENAI_ORG_ID")
|
||||
or os.getenv("OPENAI_ORGANIZATION")
|
||||
)
|
||||
values["azure_endpoint"] = values["azure_endpoint"] or os.getenv(
|
||||
"AZURE_OPENAI_ENDPOINT"
|
||||
)
|
||||
values["azure_ad_token"] = values["azure_ad_token"] or os.getenv(
|
||||
"AZURE_OPENAI_AD_TOKEN"
|
||||
)
|
||||
|
||||
values["openai_api_type"] = get_from_dict_or_env(
|
||||
values, "openai_api_type", "OPENAI_API_TYPE", default="azure"
|
||||
)
|
||||
values["openai_proxy"] = get_from_dict_or_env(
|
||||
values, "openai_proxy", "OPENAI_PROXY", default=""
|
||||
)
|
||||
# For backwards compatibility. Before openai v1, no distinction was made
|
||||
# between azure_endpoint and base_url (openai_api_base).
|
||||
openai_api_base = values["openai_api_base"]
|
||||
if openai_api_base and values["validate_base_url"]:
|
||||
if "/openai" not in openai_api_base:
|
||||
raise ValueError(
|
||||
"As of openai>=1.0.0, Azure endpoints should be specified via "
|
||||
"the `azure_endpoint` param not `openai_api_base` "
|
||||
"(or alias `base_url`)."
|
||||
)
|
||||
if values["deployment_name"]:
|
||||
raise ValueError(
|
||||
"As of openai>=1.0.0, if `deployment_name` (or alias "
|
||||
"`azure_deployment`) is specified then "
|
||||
"`openai_api_base` (or alias `base_url`) should not be. "
|
||||
"Instead use `deployment_name` (or alias `azure_deployment`) "
|
||||
"and `azure_endpoint`."
|
||||
)
|
||||
client_params = {
|
||||
"api_version": values["openai_api_version"],
|
||||
"azure_endpoint": values["azure_endpoint"],
|
||||
"azure_deployment": values["deployment_name"],
|
||||
"api_key": values["openai_api_key"],
|
||||
"azure_ad_token": values["azure_ad_token"],
|
||||
"azure_ad_token_provider": values["azure_ad_token_provider"],
|
||||
"organization": values["openai_organization"],
|
||||
"base_url": values["openai_api_base"],
|
||||
"timeout": values["request_timeout"],
|
||||
"max_retries": values["max_retries"],
|
||||
"default_headers": values["default_headers"],
|
||||
"default_query": values["default_query"],
|
||||
"http_client": values["http_client"],
|
||||
}
|
||||
values["client"] = openai.AzureOpenAI(**client_params).chat.completions
|
||||
values["async_client"] = openai.AsyncAzureOpenAI(
|
||||
**client_params
|
||||
).chat.completions
|
||||
return values
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Dict[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {**self._default_params}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "azure-openai-chat"
|
||||
|
||||
@property
|
||||
def lc_attributes(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"openai_api_type": self.openai_api_type,
|
||||
"openai_api_version": self.openai_api_version,
|
||||
}
|
||||
|
||||
def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
|
||||
if not isinstance(response, dict):
|
||||
response = response.dict()
|
||||
for res in response["choices"]:
|
||||
if res.get("finish_reason", None) == "content_filter":
|
||||
raise ValueError(
|
||||
"Azure has not provided the response due to a content filter "
|
||||
"being triggered"
|
||||
)
|
||||
chat_result = super()._create_chat_result(response)
|
||||
|
||||
if "model" in response:
|
||||
model = response["model"]
|
||||
if self.model_version:
|
||||
model = f"{model}-{self.model_version}"
|
||||
|
||||
if chat_result.llm_output is not None and isinstance(
|
||||
chat_result.llm_output, dict
|
||||
):
|
||||
chat_result.llm_output["model_name"] = model
|
||||
|
||||
return chat_result
|
||||
@ -0,0 +1,655 @@
|
||||
"""OpenAI chat wrapper."""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import warnings
|
||||
from typing import (
|
||||
Any,
|
||||
AsyncIterator,
|
||||
Callable,
|
||||
Dict,
|
||||
Iterator,
|
||||
List,
|
||||
Mapping,
|
||||
Optional,
|
||||
Sequence,
|
||||
Tuple,
|
||||
Type,
|
||||
Union,
|
||||
cast,
|
||||
)
|
||||
|
||||
import openai
|
||||
import tiktoken
|
||||
from langchain_core.callbacks import (
|
||||
AsyncCallbackManagerForLLMRun,
|
||||
CallbackManagerForLLMRun,
|
||||
)
|
||||
from langchain_core.language_models import LanguageModelInput
|
||||
from langchain_core.language_models.chat_models import (
|
||||
BaseChatModel,
|
||||
agenerate_from_stream,
|
||||
generate_from_stream,
|
||||
)
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
AIMessageChunk,
|
||||
BaseMessage,
|
||||
BaseMessageChunk,
|
||||
ChatMessage,
|
||||
ChatMessageChunk,
|
||||
FunctionMessage,
|
||||
FunctionMessageChunk,
|
||||
HumanMessage,
|
||||
HumanMessageChunk,
|
||||
SystemMessage,
|
||||
SystemMessageChunk,
|
||||
ToolMessage,
|
||||
ToolMessageChunk,
|
||||
)
|
||||
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
|
||||
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
|
||||
from langchain_core.runnables import Runnable
|
||||
from langchain_core.utils import (
|
||||
get_from_dict_or_env,
|
||||
get_pydantic_field_names,
|
||||
)
|
||||
from langchain_core.utils.function_calling import convert_to_openai_function
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
|
||||
"""Convert a dictionary to a LangChain message.
|
||||
|
||||
Args:
|
||||
_dict: The dictionary.
|
||||
|
||||
Returns:
|
||||
The LangChain message.
|
||||
"""
|
||||
role = _dict.get("role")
|
||||
if role == "user":
|
||||
return HumanMessage(content=_dict.get("content", ""))
|
||||
elif role == "assistant":
|
||||
# Fix for azure
|
||||
# Also OpenAI returns None for tool invocations
|
||||
content = _dict.get("content", "") or ""
|
||||
additional_kwargs: Dict = {}
|
||||
if function_call := _dict.get("function_call"):
|
||||
additional_kwargs["function_call"] = dict(function_call)
|
||||
if tool_calls := _dict.get("tool_calls"):
|
||||
additional_kwargs["tool_calls"] = tool_calls
|
||||
return AIMessage(content=content, additional_kwargs=additional_kwargs)
|
||||
elif role == "system":
|
||||
return SystemMessage(content=_dict.get("content", ""))
|
||||
elif role == "function":
|
||||
return FunctionMessage(content=_dict.get("content", ""), name=_dict.get("name"))
|
||||
elif role == "tool":
|
||||
additional_kwargs = {}
|
||||
if "name" in _dict:
|
||||
additional_kwargs["name"] = _dict["name"]
|
||||
return ToolMessage(
|
||||
content=_dict.get("content", ""),
|
||||
tool_call_id=_dict.get("tool_call_id"),
|
||||
additional_kwargs=additional_kwargs,
|
||||
)
|
||||
else:
|
||||
return ChatMessage(content=_dict.get("content", ""), role=role)
|
||||
|
||||
|
||||
def _convert_message_to_dict(message: BaseMessage) -> dict:
|
||||
"""Convert a LangChain message to a dictionary.
|
||||
|
||||
Args:
|
||||
message: The LangChain message.
|
||||
|
||||
Returns:
|
||||
The dictionary.
|
||||
"""
|
||||
message_dict: Dict[str, Any]
|
||||
if isinstance(message, ChatMessage):
|
||||
message_dict = {"role": message.role, "content": message.content}
|
||||
elif isinstance(message, HumanMessage):
|
||||
message_dict = {"role": "user", "content": message.content}
|
||||
elif isinstance(message, AIMessage):
|
||||
message_dict = {"role": "assistant", "content": message.content}
|
||||
if "function_call" in message.additional_kwargs:
|
||||
message_dict["function_call"] = message.additional_kwargs["function_call"]
|
||||
# If function call only, content is None not empty string
|
||||
if message_dict["content"] == "":
|
||||
message_dict["content"] = None
|
||||
if "tool_calls" in message.additional_kwargs:
|
||||
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
|
||||
# If tool calls only, content is None not empty string
|
||||
if message_dict["content"] == "":
|
||||
message_dict["content"] = None
|
||||
elif isinstance(message, SystemMessage):
|
||||
message_dict = {"role": "system", "content": message.content}
|
||||
elif isinstance(message, FunctionMessage):
|
||||
message_dict = {
|
||||
"role": "function",
|
||||
"content": message.content,
|
||||
"name": message.name,
|
||||
}
|
||||
elif isinstance(message, ToolMessage):
|
||||
message_dict = {
|
||||
"role": "tool",
|
||||
"content": message.content,
|
||||
"tool_call_id": message.tool_call_id,
|
||||
}
|
||||
else:
|
||||
raise TypeError(f"Got unknown type {message}")
|
||||
if "name" in message.additional_kwargs:
|
||||
message_dict["name"] = message.additional_kwargs["name"]
|
||||
return message_dict
|
||||
|
||||
|
||||
def _convert_delta_to_message_chunk(
|
||||
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
|
||||
) -> BaseMessageChunk:
|
||||
role = cast(str, _dict.get("role"))
|
||||
content = cast(str, _dict.get("content") or "")
|
||||
additional_kwargs: Dict = {}
|
||||
if _dict.get("function_call"):
|
||||
function_call = dict(_dict["function_call"])
|
||||
if "name" in function_call and function_call["name"] is None:
|
||||
function_call["name"] = ""
|
||||
additional_kwargs["function_call"] = function_call
|
||||
if _dict.get("tool_calls"):
|
||||
additional_kwargs["tool_calls"] = _dict["tool_calls"]
|
||||
|
||||
if role == "user" or default_class == HumanMessageChunk:
|
||||
return HumanMessageChunk(content=content)
|
||||
elif role == "assistant" or default_class == AIMessageChunk:
|
||||
return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
|
||||
elif role == "system" or default_class == SystemMessageChunk:
|
||||
return SystemMessageChunk(content=content)
|
||||
elif role == "function" or default_class == FunctionMessageChunk:
|
||||
return FunctionMessageChunk(content=content, name=_dict["name"])
|
||||
elif role == "tool" or default_class == ToolMessageChunk:
|
||||
return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
|
||||
elif role or default_class == ChatMessageChunk:
|
||||
return ChatMessageChunk(content=content, role=role)
|
||||
else:
|
||||
return default_class(content=content) # type: ignore
|
||||
|
||||
|
||||
class ChatOpenAI(BaseChatModel):
|
||||
"""`OpenAI` Chat large language models API.
|
||||
|
||||
To use, you should have 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
|
||||
in, even if not explicitly saved on this class.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.chat_models import ChatOpenAI
|
||||
openai = ChatOpenAI(model_name="gpt-3.5-turbo")
|
||||
"""
|
||||
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {"openai_api_key": "OPENAI_API_KEY"}
|
||||
|
||||
@classmethod
|
||||
def get_lc_namespace(cls) -> List[str]:
|
||||
"""Get the namespace of the langchain object."""
|
||||
return ["langchain", "chat_models", "openai"]
|
||||
|
||||
@property
|
||||
def lc_attributes(self) -> Dict[str, Any]:
|
||||
attributes: Dict[str, Any] = {}
|
||||
|
||||
if self.openai_organization:
|
||||
attributes["openai_organization"] = self.openai_organization
|
||||
|
||||
if self.openai_api_base:
|
||||
attributes["openai_api_base"] = self.openai_api_base
|
||||
|
||||
if self.openai_proxy:
|
||||
attributes["openai_proxy"] = self.openai_proxy
|
||||
|
||||
return attributes
|
||||
|
||||
@classmethod
|
||||
def is_lc_serializable(cls) -> bool:
|
||||
"""Return whether this model can be serialized by Langchain."""
|
||||
return True
|
||||
|
||||
client: Any = Field(default=None, exclude=True) #: :meta private:
|
||||
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: float = 0.7
|
||||
"""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."""
|
||||
# When updating this to use a SecretStr
|
||||
# Check for classes that derive from this class (as some of them
|
||||
# may assume openai_api_key is a str)
|
||||
openai_api_key: Optional[str] = Field(default=None, alias="api_key")
|
||||
"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
|
||||
openai_api_base: Optional[str] = Field(default=None, alias="base_url")
|
||||
"""Base URL path for API requests, leave blank if not using a proxy or service
|
||||
emulator."""
|
||||
openai_organization: Optional[str] = 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] = None
|
||||
request_timeout: Union[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."""
|
||||
max_retries: int = 2
|
||||
"""Maximum number of retries to make when generating."""
|
||||
streaming: bool = False
|
||||
"""Whether to stream the results or not."""
|
||||
n: int = 1
|
||||
"""Number of chat completions to generate for each prompt."""
|
||||
max_tokens: Optional[int] = None
|
||||
"""Maximum number of tokens to generate."""
|
||||
tiktoken_model_name: Optional[str] = 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
|
||||
be the same as the embedding model name. However, there are some cases
|
||||
where you may want to use this Embedding class with a model name not
|
||||
supported by tiktoken. This can include when using Azure embeddings or
|
||||
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
|
||||
# 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
|
||||
"""Optional httpx.Client."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
allow_population_by_field_name = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Build extra kwargs from additional params that were passed in."""
|
||||
all_required_field_names = get_pydantic_field_names(cls)
|
||||
extra = values.get("model_kwargs", {})
|
||||
for field_name in list(values):
|
||||
if field_name in extra:
|
||||
raise ValueError(f"Found {field_name} supplied twice.")
|
||||
if field_name not in all_required_field_names:
|
||||
warnings.warn(
|
||||
f"""WARNING! {field_name} is not default parameter.
|
||||
{field_name} was transferred to model_kwargs.
|
||||
Please confirm that {field_name} is what you intended."""
|
||||
)
|
||||
extra[field_name] = values.pop(field_name)
|
||||
|
||||
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
|
||||
if invalid_model_kwargs:
|
||||
raise ValueError(
|
||||
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
|
||||
f"Instead they were passed in as part of `model_kwargs` parameter."
|
||||
)
|
||||
|
||||
values["model_kwargs"] = extra
|
||||
return values
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
if values["n"] < 1:
|
||||
raise ValueError("n must be at least 1.")
|
||||
if values["n"] > 1 and values["streaming"]:
|
||||
raise ValueError("n must be 1 when streaming.")
|
||||
|
||||
values["openai_api_key"] = get_from_dict_or_env(
|
||||
values, "openai_api_key", "OPENAI_API_KEY"
|
||||
)
|
||||
# Check OPENAI_ORGANIZATION for backwards compatibility.
|
||||
values["openai_organization"] = (
|
||||
values["openai_organization"]
|
||||
or os.getenv("OPENAI_ORG_ID")
|
||||
or os.getenv("OPENAI_ORGANIZATION")
|
||||
)
|
||||
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
|
||||
"OPENAI_API_BASE"
|
||||
)
|
||||
values["openai_proxy"] = get_from_dict_or_env(
|
||||
values,
|
||||
"openai_proxy",
|
||||
"OPENAI_PROXY",
|
||||
default="",
|
||||
)
|
||||
|
||||
client_params = {
|
||||
"api_key": values["openai_api_key"],
|
||||
"organization": values["openai_organization"],
|
||||
"base_url": values["openai_api_base"],
|
||||
"timeout": values["request_timeout"],
|
||||
"max_retries": values["max_retries"],
|
||||
"default_headers": values["default_headers"],
|
||||
"default_query": values["default_query"],
|
||||
"http_client": values["http_client"],
|
||||
}
|
||||
|
||||
if not values.get("client"):
|
||||
values["client"] = openai.OpenAI(**client_params).chat.completions
|
||||
if not values.get("async_client"):
|
||||
values["async_client"] = openai.AsyncOpenAI(
|
||||
**client_params
|
||||
).chat.completions
|
||||
return values
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling OpenAI API."""
|
||||
params = {
|
||||
"model": self.model_name,
|
||||
"stream": self.streaming,
|
||||
"n": self.n,
|
||||
"temperature": self.temperature,
|
||||
**self.model_kwargs,
|
||||
}
|
||||
if self.max_tokens is not None:
|
||||
params["max_tokens"] = self.max_tokens
|
||||
return params
|
||||
|
||||
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
|
||||
overall_token_usage: dict = {}
|
||||
system_fingerprint = None
|
||||
for output in llm_outputs:
|
||||
if output is None:
|
||||
# Happens in streaming
|
||||
continue
|
||||
token_usage = output["token_usage"]
|
||||
if token_usage is not None:
|
||||
for k, v in token_usage.items():
|
||||
if k in overall_token_usage:
|
||||
overall_token_usage[k] += v
|
||||
else:
|
||||
overall_token_usage[k] = v
|
||||
if system_fingerprint is None:
|
||||
system_fingerprint = output.get("system_fingerprint")
|
||||
combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
|
||||
if system_fingerprint:
|
||||
combined["system_fingerprint"] = system_fingerprint
|
||||
return combined
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
||||
for chunk in self.client.create(messages=message_dicts, **params):
|
||||
if not isinstance(chunk, dict):
|
||||
chunk = chunk.dict()
|
||||
if len(chunk["choices"]) == 0:
|
||||
continue
|
||||
choice = chunk["choices"][0]
|
||||
chunk = _convert_delta_to_message_chunk(
|
||||
choice["delta"], default_chunk_class
|
||||
)
|
||||
finish_reason = choice.get("finish_reason")
|
||||
generation_info = (
|
||||
dict(finish_reason=finish_reason) if finish_reason is not None else None
|
||||
)
|
||||
default_chunk_class = chunk.__class__
|
||||
chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
should_stream = stream if stream is not None else self.streaming
|
||||
if should_stream:
|
||||
stream_iter = self._stream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return generate_from_stream(stream_iter)
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {
|
||||
**params,
|
||||
**({"stream": stream} if stream is not None else {}),
|
||||
**kwargs,
|
||||
}
|
||||
response = self.client.create(messages=message_dicts, **params)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
def _create_message_dicts(
|
||||
self, messages: List[BaseMessage], stop: Optional[List[str]]
|
||||
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
|
||||
params = self._default_params
|
||||
if stop is not None:
|
||||
if "stop" in params:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
params["stop"] = stop
|
||||
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:
|
||||
generations = []
|
||||
if not isinstance(response, dict):
|
||||
response = response.dict()
|
||||
for res in response["choices"]:
|
||||
message = _convert_dict_to_message(res["message"])
|
||||
generation_info = dict(finish_reason=res.get("finish_reason"))
|
||||
if "logprobs" in res:
|
||||
generation_info["logprobs"] = res["logprobs"]
|
||||
gen = ChatGeneration(
|
||||
message=message,
|
||||
generation_info=generation_info,
|
||||
)
|
||||
generations.append(gen)
|
||||
token_usage = response.get("usage", {})
|
||||
llm_output = {
|
||||
"token_usage": token_usage,
|
||||
"model_name": self.model_name,
|
||||
"system_fingerprint": response.get("system_fingerprint", ""),
|
||||
}
|
||||
return ChatResult(generations=generations, llm_output=llm_output)
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
||||
async for chunk in await self.async_client.create(
|
||||
messages=message_dicts, **params
|
||||
):
|
||||
if not isinstance(chunk, dict):
|
||||
chunk = chunk.dict()
|
||||
if len(chunk["choices"]) == 0:
|
||||
continue
|
||||
choice = chunk["choices"][0]
|
||||
chunk = _convert_delta_to_message_chunk(
|
||||
choice["delta"], default_chunk_class
|
||||
)
|
||||
finish_reason = choice.get("finish_reason")
|
||||
generation_info = (
|
||||
dict(finish_reason=finish_reason) if finish_reason is not None else None
|
||||
)
|
||||
default_chunk_class = chunk.__class__
|
||||
chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(token=chunk.text, chunk=chunk)
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
should_stream = stream if stream is not None else self.streaming
|
||||
if should_stream:
|
||||
stream_iter = self._astream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return await agenerate_from_stream(stream_iter)
|
||||
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {
|
||||
**params,
|
||||
**({"stream": stream} if stream is not None else {}),
|
||||
**kwargs,
|
||||
}
|
||||
response = await self.async_client.create(messages=message_dicts, **params)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Dict[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {"model_name": self.model_name, **self._default_params}
|
||||
|
||||
def _get_invocation_params(
|
||||
self, stop: Optional[List[str]] = None, **kwargs: Any
|
||||
) -> Dict[str, Any]:
|
||||
"""Get the parameters used to invoke the model."""
|
||||
return {
|
||||
"model": self.model_name,
|
||||
**super()._get_invocation_params(stop=stop),
|
||||
**self._default_params,
|
||||
**kwargs,
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of chat model."""
|
||||
return "openai-chat"
|
||||
|
||||
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
|
||||
if self.tiktoken_model_name is not None:
|
||||
model = self.tiktoken_model_name
|
||||
else:
|
||||
model = self.model_name
|
||||
if model == "gpt-3.5-turbo":
|
||||
# gpt-3.5-turbo may change over time.
|
||||
# Returning num tokens assuming gpt-3.5-turbo-0301.
|
||||
model = "gpt-3.5-turbo-0301"
|
||||
elif model == "gpt-4":
|
||||
# gpt-4 may change over time.
|
||||
# Returning num tokens assuming gpt-4-0314.
|
||||
model = "gpt-4-0314"
|
||||
# Returns the number of tokens used by a list of messages.
|
||||
try:
|
||||
encoding = tiktoken.encoding_for_model(model)
|
||||
except KeyError:
|
||||
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
||||
model = "cl100k_base"
|
||||
encoding = tiktoken.get_encoding(model)
|
||||
return model, encoding
|
||||
|
||||
def get_token_ids(self, text: str) -> List[int]:
|
||||
"""Get the tokens present in the text with tiktoken package."""
|
||||
# tiktoken NOT supported for Python 3.7 or below
|
||||
if sys.version_info[1] <= 7:
|
||||
return super().get_token_ids(text)
|
||||
_, encoding_model = self._get_encoding_model()
|
||||
return encoding_model.encode(text)
|
||||
|
||||
def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
|
||||
"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
|
||||
|
||||
Official documentation: https://github.com/openai/openai-cookbook/blob/
|
||||
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
|
||||
if sys.version_info[1] <= 7:
|
||||
return super().get_num_tokens_from_messages(messages)
|
||||
model, encoding = self._get_encoding_model()
|
||||
if model.startswith("gpt-3.5-turbo-0301"):
|
||||
# every message follows <im_start>{role/name}\n{content}<im_end>\n
|
||||
tokens_per_message = 4
|
||||
# if there's a name, the role is omitted
|
||||
tokens_per_name = -1
|
||||
elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4"):
|
||||
tokens_per_message = 3
|
||||
tokens_per_name = 1
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f"get_num_tokens_from_messages() is not presently implemented "
|
||||
f"for model {model}. See "
|
||||
"https://platform.openai.com/docs/guides/text-generation/managing-tokens"
|
||||
" for information on how messages are converted to tokens."
|
||||
)
|
||||
num_tokens = 0
|
||||
messages_dict = [_convert_message_to_dict(m) for m in messages]
|
||||
for message in messages_dict:
|
||||
num_tokens += tokens_per_message
|
||||
for key, value in message.items():
|
||||
# Cast str(value) in case the message value is not a string
|
||||
# This occurs with function messages
|
||||
num_tokens += len(encoding.encode(str(value)))
|
||||
if key == "name":
|
||||
num_tokens += tokens_per_name
|
||||
# every reply is primed with <im_start>assistant
|
||||
num_tokens += 3
|
||||
return num_tokens
|
||||
|
||||
def bind_functions(
|
||||
self,
|
||||
functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
|
||||
function_call: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> Runnable[LanguageModelInput, BaseMessage]:
|
||||
"""Bind functions (and other objects) to this chat model.
|
||||
|
||||
Args:
|
||||
functions: A list of function definitions to bind to this chat model.
|
||||
Can be a dictionary, pydantic model, or callable. Pydantic
|
||||
models and callables will be automatically converted to
|
||||
their schema dictionary representation.
|
||||
function_call: Which function to require the model to call.
|
||||
Must be the name of the single provided function or
|
||||
"auto" to automatically determine which function to call
|
||||
(if any).
|
||||
kwargs: Any additional parameters to pass to the
|
||||
:class:`~langchain.runnable.Runnable` constructor.
|
||||
"""
|
||||
|
||||
formatted_functions = [convert_to_openai_function(fn) for fn in functions]
|
||||
if function_call is not None:
|
||||
if len(formatted_functions) != 1:
|
||||
raise ValueError(
|
||||
"When specifying `function_call`, you must provide exactly one "
|
||||
"function."
|
||||
)
|
||||
if formatted_functions[0]["name"] != function_call:
|
||||
raise ValueError(
|
||||
f"Function call {function_call} was specified, but the only "
|
||||
f"provided function was {formatted_functions[0]['name']}."
|
||||
)
|
||||
function_call_ = {"name": function_call}
|
||||
kwargs = {**kwargs, "function_call": function_call_}
|
||||
return super().bind(
|
||||
functions=formatted_functions,
|
||||
**kwargs,
|
||||
)
|
||||
@ -0,0 +1,7 @@
|
||||
from langchain_openai.embeddings.azure import AzureOpenAIEmbeddings
|
||||
from langchain_openai.embeddings.base import OpenAIEmbeddings
|
||||
|
||||
__all__ = [
|
||||
"OpenAIEmbeddings",
|
||||
"AzureOpenAIEmbeddings",
|
||||
]
|
||||
@ -0,0 +1,130 @@
|
||||
"""Azure OpenAI embeddings wrapper."""
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import Callable, Dict, Optional, Union
|
||||
|
||||
import openai
|
||||
from langchain_core.pydantic_v1 import Field, root_validator
|
||||
from langchain_core.utils import get_from_dict_or_env
|
||||
|
||||
from langchain_openai.embeddings.base import OpenAIEmbeddings
|
||||
|
||||
|
||||
class AzureOpenAIEmbeddings(OpenAIEmbeddings):
|
||||
"""`Azure OpenAI` Embeddings API."""
|
||||
|
||||
azure_endpoint: Union[str, None] = None
|
||||
"""Your Azure endpoint, including the resource.
|
||||
|
||||
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
|
||||
|
||||
Example: `https://example-resource.azure.openai.com/`
|
||||
"""
|
||||
deployment: Optional[str] = Field(default=None, alias="azure_deployment")
|
||||
"""A model deployment.
|
||||
|
||||
If given sets the base client URL to include `/deployments/{azure_deployment}`.
|
||||
Note: this means you won't be able to use non-deployment endpoints.
|
||||
"""
|
||||
openai_api_key: Union[str, None] = Field(default=None, alias="api_key")
|
||||
"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
|
||||
azure_ad_token: Union[str, None] = None
|
||||
"""Your Azure Active Directory token.
|
||||
|
||||
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
|
||||
|
||||
For more:
|
||||
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
|
||||
""" # noqa: E501
|
||||
azure_ad_token_provider: Union[Callable[[], str], None] = None
|
||||
"""A function that returns an Azure Active Directory token.
|
||||
|
||||
Will be invoked on every request.
|
||||
"""
|
||||
openai_api_version: Optional[str] = Field(default=None, alias="api_version")
|
||||
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
|
||||
validate_base_url: bool = True
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
# Check OPENAI_KEY for backwards compatibility.
|
||||
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
|
||||
# other forms of azure credentials.
|
||||
values["openai_api_key"] = (
|
||||
values["openai_api_key"]
|
||||
or os.getenv("AZURE_OPENAI_API_KEY")
|
||||
or os.getenv("OPENAI_API_KEY")
|
||||
)
|
||||
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
|
||||
"OPENAI_API_BASE"
|
||||
)
|
||||
values["openai_api_version"] = values["openai_api_version"] or os.getenv(
|
||||
"OPENAI_API_VERSION", default="2023-05-15"
|
||||
)
|
||||
values["openai_api_type"] = get_from_dict_or_env(
|
||||
values, "openai_api_type", "OPENAI_API_TYPE", default="azure"
|
||||
)
|
||||
values["openai_organization"] = (
|
||||
values["openai_organization"]
|
||||
or os.getenv("OPENAI_ORG_ID")
|
||||
or os.getenv("OPENAI_ORGANIZATION")
|
||||
)
|
||||
values["openai_proxy"] = get_from_dict_or_env(
|
||||
values,
|
||||
"openai_proxy",
|
||||
"OPENAI_PROXY",
|
||||
default="",
|
||||
)
|
||||
values["azure_endpoint"] = values["azure_endpoint"] or os.getenv(
|
||||
"AZURE_OPENAI_ENDPOINT"
|
||||
)
|
||||
values["azure_ad_token"] = values["azure_ad_token"] or os.getenv(
|
||||
"AZURE_OPENAI_AD_TOKEN"
|
||||
)
|
||||
# Azure OpenAI embedding models allow a maximum of 16 texts
|
||||
# at a time in each batch
|
||||
# See: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings
|
||||
values["chunk_size"] = min(values["chunk_size"], 16)
|
||||
# For backwards compatibility. Before openai v1, no distinction was made
|
||||
# between azure_endpoint and base_url (openai_api_base).
|
||||
openai_api_base = values["openai_api_base"]
|
||||
if openai_api_base and values["validate_base_url"]:
|
||||
if "/openai" not in openai_api_base:
|
||||
values["openai_api_base"] += "/openai"
|
||||
raise ValueError(
|
||||
"As of openai>=1.0.0, Azure endpoints should be specified via "
|
||||
"the `azure_endpoint` param not `openai_api_base` "
|
||||
"(or alias `base_url`). "
|
||||
)
|
||||
if values["deployment"]:
|
||||
raise ValueError(
|
||||
"As of openai>=1.0.0, if `deployment` (or alias "
|
||||
"`azure_deployment`) is specified then "
|
||||
"`openai_api_base` (or alias `base_url`) should not be. "
|
||||
"Instead use `deployment` (or alias `azure_deployment`) "
|
||||
"and `azure_endpoint`."
|
||||
)
|
||||
client_params = {
|
||||
"api_version": values["openai_api_version"],
|
||||
"azure_endpoint": values["azure_endpoint"],
|
||||
"azure_deployment": values["deployment"],
|
||||
"api_key": values["openai_api_key"],
|
||||
"azure_ad_token": values["azure_ad_token"],
|
||||
"azure_ad_token_provider": values["azure_ad_token_provider"],
|
||||
"organization": values["openai_organization"],
|
||||
"base_url": values["openai_api_base"],
|
||||
"timeout": values["request_timeout"],
|
||||
"max_retries": values["max_retries"],
|
||||
"default_headers": values["default_headers"],
|
||||
"default_query": values["default_query"],
|
||||
"http_client": values["http_client"],
|
||||
}
|
||||
values["client"] = openai.AzureOpenAI(**client_params).embeddings
|
||||
values["async_client"] = openai.AsyncAzureOpenAI(**client_params).embeddings
|
||||
return values
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "azure-openai-chat"
|
||||
@ -0,0 +1,523 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
import warnings
|
||||
from typing import (
|
||||
Any,
|
||||
Dict,
|
||||
Iterable,
|
||||
List,
|
||||
Literal,
|
||||
Mapping,
|
||||
Optional,
|
||||
Sequence,
|
||||
Set,
|
||||
Tuple,
|
||||
Union,
|
||||
cast,
|
||||
)
|
||||
|
||||
import numpy as np
|
||||
import openai
|
||||
import tiktoken
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
|
||||
from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class OpenAIEmbeddings(BaseModel, Embeddings):
|
||||
"""OpenAI embedding models.
|
||||
|
||||
To use, you should have the
|
||||
environment variable ``OPENAI_API_KEY`` set with your API key or pass it
|
||||
as a named parameter to the constructor.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.embeddings import OpenAIEmbeddings
|
||||
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
|
||||
|
||||
In order to use the library with Microsoft Azure endpoints, you need to set
|
||||
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION.
|
||||
The OPENAI_API_TYPE must be set to 'azure' and the others correspond to
|
||||
the properties of your endpoint.
|
||||
In addition, the deployment name must be passed as the model parameter.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
import os
|
||||
|
||||
os.environ["OPENAI_API_TYPE"] = "azure"
|
||||
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
|
||||
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
|
||||
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
|
||||
os.environ["OPENAI_PROXY"] = "http://your-corporate-proxy:8080"
|
||||
|
||||
from langchain_community.embeddings.openai import OpenAIEmbeddings
|
||||
embeddings = OpenAIEmbeddings(
|
||||
deployment="your-embeddings-deployment-name",
|
||||
model="your-embeddings-model-name",
|
||||
openai_api_base="https://your-endpoint.openai.azure.com/",
|
||||
openai_api_type="azure",
|
||||
)
|
||||
text = "This is a test query."
|
||||
query_result = embeddings.embed_query(text)
|
||||
|
||||
"""
|
||||
|
||||
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"
|
||||
# to support Azure OpenAI Service custom deployment names
|
||||
deployment: Optional[str] = model
|
||||
# TODO: Move to AzureOpenAIEmbeddings.
|
||||
openai_api_version: Optional[str] = Field(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(default=None, alias="base_url")
|
||||
"""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] = None
|
||||
# to support explicit proxy for OpenAI
|
||||
openai_proxy: Optional[str] = None
|
||||
embedding_ctx_length: int = 8191
|
||||
"""The maximum number of tokens to embed at once."""
|
||||
openai_api_key: Optional[str] = Field(default=None, alias="api_key")
|
||||
"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
|
||||
openai_organization: Optional[str] = Field(default=None, alias="organization")
|
||||
"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
|
||||
allowed_special: Union[Literal["all"], Set[str]] = set()
|
||||
disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
|
||||
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(
|
||||
default=None, alias="timeout"
|
||||
)
|
||||
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
|
||||
None."""
|
||||
headers: Any = None
|
||||
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
|
||||
"""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
|
||||
be the same as the embedding model name. However, there are some cases
|
||||
where you may want to use this Embedding class with a model name not
|
||||
supported by tiktoken. This can include when using Azure embeddings or
|
||||
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."""
|
||||
show_progress_bar: bool = False
|
||||
"""Whether to show a progress bar when embedding."""
|
||||
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||
"""Holds any model parameters valid for `create` call not explicitly specified."""
|
||||
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
|
||||
# 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
|
||||
"""Optional httpx.Client."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
allow_population_by_field_name = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Build extra kwargs from additional params that were passed in."""
|
||||
all_required_field_names = get_pydantic_field_names(cls)
|
||||
extra = values.get("model_kwargs", {})
|
||||
for field_name in list(values):
|
||||
if field_name in extra:
|
||||
raise ValueError(f"Found {field_name} supplied twice.")
|
||||
if field_name not in all_required_field_names:
|
||||
warnings.warn(
|
||||
f"""WARNING! {field_name} is not default parameter.
|
||||
{field_name} was transferred to model_kwargs.
|
||||
Please confirm that {field_name} is what you intended."""
|
||||
)
|
||||
extra[field_name] = values.pop(field_name)
|
||||
|
||||
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
|
||||
if invalid_model_kwargs:
|
||||
raise ValueError(
|
||||
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
|
||||
f"Instead they were passed in as part of `model_kwargs` parameter."
|
||||
)
|
||||
|
||||
values["model_kwargs"] = extra
|
||||
return values
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
values["openai_api_key"] = get_from_dict_or_env(
|
||||
values, "openai_api_key", "OPENAI_API_KEY"
|
||||
)
|
||||
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
|
||||
"OPENAI_API_BASE"
|
||||
)
|
||||
values["openai_api_type"] = get_from_dict_or_env(
|
||||
values,
|
||||
"openai_api_type",
|
||||
"OPENAI_API_TYPE",
|
||||
default="",
|
||||
)
|
||||
values["openai_proxy"] = get_from_dict_or_env(
|
||||
values,
|
||||
"openai_proxy",
|
||||
"OPENAI_PROXY",
|
||||
default="",
|
||||
)
|
||||
if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
|
||||
default_api_version = "2023-05-15"
|
||||
# Azure OpenAI embedding models allow a maximum of 16 texts
|
||||
# at a time in each batch
|
||||
# See: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings
|
||||
values["chunk_size"] = min(values["chunk_size"], 16)
|
||||
else:
|
||||
default_api_version = ""
|
||||
values["openai_api_version"] = get_from_dict_or_env(
|
||||
values,
|
||||
"openai_api_version",
|
||||
"OPENAI_API_VERSION",
|
||||
default=default_api_version,
|
||||
)
|
||||
# Check OPENAI_ORGANIZATION for backwards compatibility.
|
||||
values["openai_organization"] = (
|
||||
values["openai_organization"]
|
||||
or os.getenv("OPENAI_ORG_ID")
|
||||
or os.getenv("OPENAI_ORGANIZATION")
|
||||
)
|
||||
if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
|
||||
raise ValueError(
|
||||
"If you are using Azure, "
|
||||
"please use the `AzureOpenAIEmbeddings` class."
|
||||
)
|
||||
client_params = {
|
||||
"api_key": values["openai_api_key"],
|
||||
"organization": values["openai_organization"],
|
||||
"base_url": values["openai_api_base"],
|
||||
"timeout": values["request_timeout"],
|
||||
"max_retries": values["max_retries"],
|
||||
"default_headers": values["default_headers"],
|
||||
"default_query": values["default_query"],
|
||||
"http_client": values["http_client"],
|
||||
}
|
||||
if not values.get("client"):
|
||||
values["client"] = openai.OpenAI(**client_params).embeddings
|
||||
if not values.get("async_client"):
|
||||
values["async_client"] = openai.AsyncOpenAI(**client_params).embeddings
|
||||
return values
|
||||
|
||||
@property
|
||||
def _invocation_params(self) -> Dict[str, Any]:
|
||||
return {"model": self.model, **self.model_kwargs}
|
||||
|
||||
# please refer to
|
||||
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
|
||||
def _get_len_safe_embeddings(
|
||||
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
|
||||
) -> List[List[float]]:
|
||||
"""
|
||||
Generate length-safe embeddings for a list of texts.
|
||||
|
||||
This method handles tokenization and embedding generation, respecting the
|
||||
set embedding context length and chunk size. It supports both tiktoken
|
||||
and HuggingFace tokenizer based on the tiktoken_enabled flag.
|
||||
|
||||
Args:
|
||||
texts (List[str]): A list of texts to embed.
|
||||
engine (str): The engine or model to use for embeddings.
|
||||
chunk_size (Optional[int]): The size of chunks for processing embeddings.
|
||||
|
||||
Returns:
|
||||
List[List[float]]: A list of embeddings for each input text.
|
||||
"""
|
||||
|
||||
tokens = []
|
||||
indices = []
|
||||
model_name = self.tiktoken_model_name or self.model
|
||||
_chunk_size = chunk_size or self.chunk_size
|
||||
|
||||
# If tiktoken flag set to False
|
||||
if not self.tiktoken_enabled:
|
||||
try:
|
||||
from transformers import AutoTokenizer # noqa: F401
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import transformers python package. "
|
||||
"This is needed in order to for OpenAIEmbeddings without "
|
||||
"`tiktoken`. Please install it with `pip install transformers`. "
|
||||
)
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
pretrained_model_name_or_path=model_name
|
||||
)
|
||||
for i, text in enumerate(texts):
|
||||
# Tokenize the text using HuggingFace transformers
|
||||
tokenized = tokenizer.encode(text, add_special_tokens=False)
|
||||
|
||||
# Split tokens into chunks respecting the embedding_ctx_length
|
||||
for j in range(0, len(tokenized), self.embedding_ctx_length):
|
||||
token_chunk = tokenized[j : j + self.embedding_ctx_length]
|
||||
|
||||
# Convert token IDs back to a string
|
||||
chunk_text = tokenizer.decode(token_chunk)
|
||||
tokens.append(chunk_text)
|
||||
indices.append(i)
|
||||
else:
|
||||
try:
|
||||
encoding = tiktoken.encoding_for_model(model_name)
|
||||
except KeyError:
|
||||
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
||||
model = "cl100k_base"
|
||||
encoding = tiktoken.get_encoding(model)
|
||||
for i, text in enumerate(texts):
|
||||
if self.model.endswith("001"):
|
||||
# See: https://github.com/openai/openai-python/
|
||||
# issues/418#issuecomment-1525939500
|
||||
# replace newlines, which can negatively affect performance.
|
||||
text = text.replace("\n", " ")
|
||||
|
||||
token = encoding.encode(
|
||||
text=text,
|
||||
allowed_special=self.allowed_special,
|
||||
disallowed_special=self.disallowed_special,
|
||||
)
|
||||
|
||||
# Split tokens into chunks respecting the embedding_ctx_length
|
||||
for j in range(0, len(token), self.embedding_ctx_length):
|
||||
tokens.append(token[j : j + self.embedding_ctx_length])
|
||||
indices.append(i)
|
||||
|
||||
if self.show_progress_bar:
|
||||
try:
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
_iter: Iterable = tqdm(range(0, len(tokens), _chunk_size))
|
||||
except ImportError:
|
||||
_iter = range(0, len(tokens), _chunk_size)
|
||||
else:
|
||||
_iter = range(0, len(tokens), _chunk_size)
|
||||
|
||||
batched_embeddings: List[List[float]] = []
|
||||
for i in _iter:
|
||||
response = self.client.create(
|
||||
input=tokens[i : i + _chunk_size], **self._invocation_params
|
||||
)
|
||||
if not isinstance(response, dict):
|
||||
response = response.dict()
|
||||
batched_embeddings.extend(r["embedding"] for r in response["data"])
|
||||
|
||||
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
|
||||
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
|
||||
for i in range(len(indices)):
|
||||
if self.skip_empty and len(batched_embeddings[i]) == 1:
|
||||
continue
|
||||
results[indices[i]].append(batched_embeddings[i])
|
||||
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
|
||||
|
||||
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
|
||||
for i in range(len(texts)):
|
||||
_result = results[i]
|
||||
if len(_result) == 0:
|
||||
average_embedded = self.client.create(
|
||||
input="", **self._invocation_params
|
||||
)
|
||||
if not isinstance(average_embedded, dict):
|
||||
average_embedded = average_embedded.dict()
|
||||
average = average_embedded["data"][0]["embedding"]
|
||||
else:
|
||||
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
|
||||
embeddings[i] = (average / np.linalg.norm(average)).tolist()
|
||||
|
||||
return embeddings
|
||||
|
||||
# please refer to
|
||||
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
|
||||
async def _aget_len_safe_embeddings(
|
||||
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
|
||||
) -> List[List[float]]:
|
||||
"""
|
||||
Asynchronously generate length-safe embeddings for a list of texts.
|
||||
|
||||
This method handles tokenization and asynchronous embedding generation,
|
||||
respecting the set embedding context length and chunk size. It supports both
|
||||
`tiktoken` and HuggingFace `tokenizer` based on the tiktoken_enabled flag.
|
||||
|
||||
Args:
|
||||
texts (List[str]): A list of texts to embed.
|
||||
engine (str): The engine or model to use for embeddings.
|
||||
chunk_size (Optional[int]): The size of chunks for processing embeddings.
|
||||
|
||||
Returns:
|
||||
List[List[float]]: A list of embeddings for each input text.
|
||||
"""
|
||||
|
||||
tokens = []
|
||||
indices = []
|
||||
model_name = self.tiktoken_model_name or self.model
|
||||
_chunk_size = chunk_size or self.chunk_size
|
||||
|
||||
# If tiktoken flag set to False
|
||||
if not self.tiktoken_enabled:
|
||||
try:
|
||||
from transformers import AutoTokenizer
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import transformers python package. "
|
||||
"This is needed in order to for OpenAIEmbeddings without "
|
||||
" `tiktoken`. Please install it with `pip install transformers`."
|
||||
)
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
pretrained_model_name_or_path=model_name
|
||||
)
|
||||
for i, text in enumerate(texts):
|
||||
# Tokenize the text using HuggingFace transformers
|
||||
tokenized = tokenizer.encode(text, add_special_tokens=False)
|
||||
|
||||
# Split tokens into chunks respecting the embedding_ctx_length
|
||||
for j in range(0, len(tokenized), self.embedding_ctx_length):
|
||||
token_chunk = tokenized[j : j + self.embedding_ctx_length]
|
||||
|
||||
# Convert token IDs back to a string
|
||||
chunk_text = tokenizer.decode(token_chunk)
|
||||
tokens.append(chunk_text)
|
||||
indices.append(i)
|
||||
else:
|
||||
try:
|
||||
encoding = tiktoken.encoding_for_model(model_name)
|
||||
except KeyError:
|
||||
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
||||
model = "cl100k_base"
|
||||
encoding = tiktoken.get_encoding(model)
|
||||
for i, text in enumerate(texts):
|
||||
if self.model.endswith("001"):
|
||||
# See: https://github.com/openai/openai-python/
|
||||
# issues/418#issuecomment-1525939500
|
||||
# replace newlines, which can negatively affect performance.
|
||||
text = text.replace("\n", " ")
|
||||
|
||||
token = encoding.encode(
|
||||
text=text,
|
||||
allowed_special=self.allowed_special,
|
||||
disallowed_special=self.disallowed_special,
|
||||
)
|
||||
|
||||
# Split tokens into chunks respecting the embedding_ctx_length
|
||||
for j in range(0, len(token), self.embedding_ctx_length):
|
||||
tokens.append(token[j : j + self.embedding_ctx_length])
|
||||
indices.append(i)
|
||||
|
||||
batched_embeddings: List[List[float]] = []
|
||||
_chunk_size = chunk_size or self.chunk_size
|
||||
for i in range(0, len(tokens), _chunk_size):
|
||||
response = await self.async_client.create(
|
||||
input=tokens[i : i + _chunk_size], **self._invocation_params
|
||||
)
|
||||
|
||||
if not isinstance(response, dict):
|
||||
response = response.dict()
|
||||
batched_embeddings.extend(r["embedding"] for r in response["data"])
|
||||
|
||||
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
|
||||
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
|
||||
for i in range(len(indices)):
|
||||
results[indices[i]].append(batched_embeddings[i])
|
||||
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
|
||||
|
||||
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
|
||||
for i in range(len(texts)):
|
||||
_result = results[i]
|
||||
if len(_result) == 0:
|
||||
average_embedded = await self.async_client.create(
|
||||
input="", **self._invocation_params
|
||||
)
|
||||
if not isinstance(average_embedded, dict):
|
||||
average_embedded = average_embedded.dict()
|
||||
average = average_embedded["data"][0]["embedding"]
|
||||
else:
|
||||
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
|
||||
embeddings[i] = (average / np.linalg.norm(average)).tolist()
|
||||
|
||||
return embeddings
|
||||
|
||||
def embed_documents(
|
||||
self, texts: List[str], chunk_size: Optional[int] = 0
|
||||
) -> List[List[float]]:
|
||||
"""Call out to OpenAI's embedding endpoint for embedding search docs.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.
|
||||
chunk_size: The chunk size of embeddings. If None, will use the chunk size
|
||||
specified by the class.
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
# NOTE: to keep things simple, we assume the list may contain texts longer
|
||||
# than the maximum context and use length-safe embedding function.
|
||||
engine = cast(str, self.deployment)
|
||||
return self._get_len_safe_embeddings(texts, engine=engine)
|
||||
|
||||
async def aembed_documents(
|
||||
self, texts: List[str], chunk_size: Optional[int] = 0
|
||||
) -> List[List[float]]:
|
||||
"""Call out to OpenAI's embedding endpoint async for embedding search docs.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.
|
||||
chunk_size: The chunk size of embeddings. If None, will use the chunk size
|
||||
specified by the class.
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
# NOTE: to keep things simple, we assume the list may contain texts longer
|
||||
# than the maximum context and use length-safe embedding function.
|
||||
engine = cast(str, self.deployment)
|
||||
return await self._aget_len_safe_embeddings(texts, engine=engine)
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Call out to OpenAI's embedding endpoint for embedding query text.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embedding for the text.
|
||||
"""
|
||||
return self.embed_documents([text])[0]
|
||||
|
||||
async def aembed_query(self, text: str) -> List[float]:
|
||||
"""Call out to OpenAI's embedding endpoint async for embedding query text.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embedding for the text.
|
||||
"""
|
||||
embeddings = await self.aembed_documents([text])
|
||||
return embeddings[0]
|
||||
@ -0,0 +1,7 @@
|
||||
from langchain_openai.llms.azure import AzureOpenAI
|
||||
from langchain_openai.llms.base import OpenAI
|
||||
|
||||
__all__ = [
|
||||
"OpenAI",
|
||||
"AzureOpenAI",
|
||||
]
|
||||
@ -0,0 +1,190 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
from typing import (
|
||||
Any,
|
||||
Callable,
|
||||
Dict,
|
||||
List,
|
||||
Mapping,
|
||||
Union,
|
||||
)
|
||||
|
||||
import openai
|
||||
from langchain_core.pydantic_v1 import Field, root_validator
|
||||
from langchain_core.utils import get_from_dict_or_env
|
||||
|
||||
from langchain_openai.llms.base import BaseOpenAI
|
||||
|
||||
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
|
||||
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
|
||||
in, even if not explicitly saved on this class.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.llms import AzureOpenAI
|
||||
openai = AzureOpenAI(model_name="gpt-3.5-turbo-instruct")
|
||||
"""
|
||||
|
||||
azure_endpoint: Union[str, None] = None
|
||||
"""Your Azure endpoint, including the resource.
|
||||
|
||||
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
|
||||
|
||||
Example: `https://example-resource.azure.openai.com/`
|
||||
"""
|
||||
deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
|
||||
"""A model deployment.
|
||||
|
||||
If given sets the base client URL to include `/deployments/{azure_deployment}`.
|
||||
Note: this means you won't be able to use non-deployment endpoints.
|
||||
"""
|
||||
openai_api_version: str = Field(default="", alias="api_version")
|
||||
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
|
||||
openai_api_key: Union[str, None] = Field(default=None, alias="api_key")
|
||||
"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
|
||||
azure_ad_token: Union[str, None] = None
|
||||
"""Your Azure Active Directory token.
|
||||
|
||||
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
|
||||
|
||||
For more:
|
||||
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
|
||||
""" # noqa: E501
|
||||
azure_ad_token_provider: Union[Callable[[], str], None] = None
|
||||
"""A function that returns an Azure Active Directory token.
|
||||
|
||||
Will be invoked on every request.
|
||||
"""
|
||||
openai_api_type: str = ""
|
||||
"""Legacy, for openai<1.0.0 support."""
|
||||
validate_base_url: bool = True
|
||||
"""For backwards compatibility. If legacy val openai_api_base is passed in, try to
|
||||
infer if it is a base_url or azure_endpoint and update accordingly.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def get_lc_namespace(cls) -> List[str]:
|
||||
"""Get the namespace of the langchain object."""
|
||||
return ["langchain", "llms", "openai"]
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
if values["n"] < 1:
|
||||
raise ValueError("n must be at least 1.")
|
||||
if values["streaming"] and values["n"] > 1:
|
||||
raise ValueError("Cannot stream results when n > 1.")
|
||||
if values["streaming"] and values["best_of"] > 1:
|
||||
raise ValueError("Cannot stream results when best_of > 1.")
|
||||
|
||||
# Check OPENAI_KEY for backwards compatibility.
|
||||
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
|
||||
# other forms of azure credentials.
|
||||
values["openai_api_key"] = (
|
||||
values["openai_api_key"]
|
||||
or os.getenv("AZURE_OPENAI_API_KEY")
|
||||
or os.getenv("OPENAI_API_KEY")
|
||||
)
|
||||
|
||||
values["azure_endpoint"] = values["azure_endpoint"] or os.getenv(
|
||||
"AZURE_OPENAI_ENDPOINT"
|
||||
)
|
||||
values["azure_ad_token"] = values["azure_ad_token"] or os.getenv(
|
||||
"AZURE_OPENAI_AD_TOKEN"
|
||||
)
|
||||
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
|
||||
"OPENAI_API_BASE"
|
||||
)
|
||||
values["openai_proxy"] = get_from_dict_or_env(
|
||||
values,
|
||||
"openai_proxy",
|
||||
"OPENAI_PROXY",
|
||||
default="",
|
||||
)
|
||||
values["openai_organization"] = (
|
||||
values["openai_organization"]
|
||||
or os.getenv("OPENAI_ORG_ID")
|
||||
or os.getenv("OPENAI_ORGANIZATION")
|
||||
)
|
||||
values["openai_api_version"] = values["openai_api_version"] or os.getenv(
|
||||
"OPENAI_API_VERSION"
|
||||
)
|
||||
values["openai_api_type"] = get_from_dict_or_env(
|
||||
values, "openai_api_type", "OPENAI_API_TYPE", default="azure"
|
||||
)
|
||||
# For backwards compatibility. Before openai v1, no distinction was made
|
||||
# between azure_endpoint and base_url (openai_api_base).
|
||||
openai_api_base = values["openai_api_base"]
|
||||
if openai_api_base and values["validate_base_url"]:
|
||||
if "/openai" not in openai_api_base:
|
||||
values["openai_api_base"] = (
|
||||
values["openai_api_base"].rstrip("/") + "/openai"
|
||||
)
|
||||
raise ValueError(
|
||||
"As of openai>=1.0.0, Azure endpoints should be specified via "
|
||||
"the `azure_endpoint` param not `openai_api_base` "
|
||||
"(or alias `base_url`)."
|
||||
)
|
||||
if values["deployment_name"]:
|
||||
raise ValueError(
|
||||
"As of openai>=1.0.0, if `deployment_name` (or alias "
|
||||
"`azure_deployment`) is specified then "
|
||||
"`openai_api_base` (or alias `base_url`) should not be. "
|
||||
"Instead use `deployment_name` (or alias `azure_deployment`) "
|
||||
"and `azure_endpoint`."
|
||||
)
|
||||
values["deployment_name"] = None
|
||||
client_params = {
|
||||
"api_version": values["openai_api_version"],
|
||||
"azure_endpoint": values["azure_endpoint"],
|
||||
"azure_deployment": values["deployment_name"],
|
||||
"api_key": values["openai_api_key"],
|
||||
"azure_ad_token": values["azure_ad_token"],
|
||||
"azure_ad_token_provider": values["azure_ad_token_provider"],
|
||||
"organization": values["openai_organization"],
|
||||
"base_url": values["openai_api_base"],
|
||||
"timeout": values["request_timeout"],
|
||||
"max_retries": values["max_retries"],
|
||||
"default_headers": values["default_headers"],
|
||||
"default_query": values["default_query"],
|
||||
"http_client": values["http_client"],
|
||||
}
|
||||
values["client"] = openai.AzureOpenAI(**client_params).completions
|
||||
values["async_client"] = openai.AsyncAzureOpenAI(**client_params).completions
|
||||
|
||||
return values
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
return {
|
||||
**{"deployment_name": self.deployment_name},
|
||||
**super()._identifying_params,
|
||||
}
|
||||
|
||||
@property
|
||||
def _invocation_params(self) -> Dict[str, Any]:
|
||||
openai_params = {"model": self.deployment_name}
|
||||
return {**openai_params, **super()._invocation_params}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "azure"
|
||||
|
||||
@property
|
||||
def lc_attributes(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"openai_api_type": self.openai_api_type,
|
||||
"openai_api_version": self.openai_api_version,
|
||||
}
|
||||
@ -0,0 +1,611 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from typing import (
|
||||
AbstractSet,
|
||||
Any,
|
||||
AsyncIterator,
|
||||
Collection,
|
||||
Dict,
|
||||
Iterator,
|
||||
List,
|
||||
Literal,
|
||||
Mapping,
|
||||
Optional,
|
||||
Set,
|
||||
Tuple,
|
||||
Union,
|
||||
)
|
||||
|
||||
import openai
|
||||
import tiktoken
|
||||
from langchain_core.callbacks import (
|
||||
AsyncCallbackManagerForLLMRun,
|
||||
CallbackManagerForLLMRun,
|
||||
)
|
||||
from langchain_core.language_models.llms import BaseLLM
|
||||
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
|
||||
from langchain_core.pydantic_v1 import Field, root_validator
|
||||
from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names
|
||||
from langchain_core.utils.utils import build_extra_kwargs
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _update_token_usage(
|
||||
keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
|
||||
) -> None:
|
||||
"""Update token usage."""
|
||||
_keys_to_use = keys.intersection(response["usage"])
|
||||
for _key in _keys_to_use:
|
||||
if _key not in token_usage:
|
||||
token_usage[_key] = response["usage"][_key]
|
||||
else:
|
||||
token_usage[_key] += response["usage"][_key]
|
||||
|
||||
|
||||
def _stream_response_to_generation_chunk(
|
||||
stream_response: Dict[str, Any],
|
||||
) -> GenerationChunk:
|
||||
"""Convert a stream response to a generation chunk."""
|
||||
if not stream_response["choices"]:
|
||||
return GenerationChunk(text="")
|
||||
return GenerationChunk(
|
||||
text=stream_response["choices"][0]["text"],
|
||||
generation_info=dict(
|
||||
finish_reason=stream_response["choices"][0].get("finish_reason", None),
|
||||
logprobs=stream_response["choices"][0].get("logprobs", None),
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class BaseOpenAI(BaseLLM):
|
||||
"""Base OpenAI large language model class."""
|
||||
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {"openai_api_key": "OPENAI_API_KEY"}
|
||||
|
||||
@property
|
||||
def lc_attributes(self) -> Dict[str, Any]:
|
||||
attributes: Dict[str, Any] = {}
|
||||
if self.openai_api_base:
|
||||
attributes["openai_api_base"] = self.openai_api_base
|
||||
|
||||
if self.openai_organization:
|
||||
attributes["openai_organization"] = self.openai_organization
|
||||
|
||||
if self.openai_proxy:
|
||||
attributes["openai_proxy"] = self.openai_proxy
|
||||
|
||||
return attributes
|
||||
|
||||
client: Any = Field(default=None, exclude=True) #: :meta private:
|
||||
async_client: Any = Field(default=None, exclude=True) #: :meta private:
|
||||
model_name: str = Field(default="gpt-3.5-turbo-instruct", alias="model")
|
||||
"""Model name to use."""
|
||||
temperature: float = 0.7
|
||||
"""What sampling temperature to use."""
|
||||
max_tokens: int = 256
|
||||
"""The maximum number of tokens to generate in the completion.
|
||||
-1 returns as many tokens as possible given the prompt and
|
||||
the models maximal context size."""
|
||||
top_p: float = 1
|
||||
"""Total probability mass of tokens to consider at each step."""
|
||||
frequency_penalty: float = 0
|
||||
"""Penalizes repeated tokens according to frequency."""
|
||||
presence_penalty: float = 0
|
||||
"""Penalizes repeated tokens."""
|
||||
n: int = 1
|
||||
"""How many completions to generate for each prompt."""
|
||||
best_of: int = 1
|
||||
"""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."""
|
||||
# When updating this to use a SecretStr
|
||||
# Check for classes that derive from this class (as some of them
|
||||
# may assume openai_api_key is a str)
|
||||
openai_api_key: Optional[str] = Field(default=None, alias="api_key")
|
||||
"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
|
||||
openai_api_base: Optional[str] = Field(default=None, alias="base_url")
|
||||
"""Base URL path for API requests, leave blank if not using a proxy or service
|
||||
emulator."""
|
||||
openai_organization: Optional[str] = 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] = 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(
|
||||
default=None, alias="timeout"
|
||||
)
|
||||
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
|
||||
None."""
|
||||
logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
|
||||
"""Adjust the probability of specific tokens being generated."""
|
||||
max_retries: int = 2
|
||||
"""Maximum number of retries to make when generating."""
|
||||
streaming: bool = False
|
||||
"""Whether to stream the results or not."""
|
||||
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
|
||||
"""Set of special tokens that are allowed。"""
|
||||
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
|
||||
"""Set of special tokens that are not allowed。"""
|
||||
tiktoken_model_name: Optional[str] = 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
|
||||
be the same as the embedding model name. However, there are some cases
|
||||
where you may want to use this Embedding class with a model name not
|
||||
supported by tiktoken. This can include when using Azure embeddings or
|
||||
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
|
||||
# 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
|
||||
"""Optional httpx.Client."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
allow_population_by_field_name = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Build extra kwargs from additional params that were passed in."""
|
||||
all_required_field_names = get_pydantic_field_names(cls)
|
||||
extra = values.get("model_kwargs", {})
|
||||
values["model_kwargs"] = build_extra_kwargs(
|
||||
extra, values, all_required_field_names
|
||||
)
|
||||
return values
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
if values["n"] < 1:
|
||||
raise ValueError("n must be at least 1.")
|
||||
if values["streaming"] and values["n"] > 1:
|
||||
raise ValueError("Cannot stream results when n > 1.")
|
||||
if values["streaming"] and values["best_of"] > 1:
|
||||
raise ValueError("Cannot stream results when best_of > 1.")
|
||||
|
||||
values["openai_api_key"] = get_from_dict_or_env(
|
||||
values, "openai_api_key", "OPENAI_API_KEY"
|
||||
)
|
||||
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
|
||||
"OPENAI_API_BASE"
|
||||
)
|
||||
values["openai_proxy"] = get_from_dict_or_env(
|
||||
values,
|
||||
"openai_proxy",
|
||||
"OPENAI_PROXY",
|
||||
default="",
|
||||
)
|
||||
values["openai_organization"] = (
|
||||
values["openai_organization"]
|
||||
or os.getenv("OPENAI_ORG_ID")
|
||||
or os.getenv("OPENAI_ORGANIZATION")
|
||||
)
|
||||
|
||||
client_params = {
|
||||
"api_key": values["openai_api_key"],
|
||||
"organization": values["openai_organization"],
|
||||
"base_url": values["openai_api_base"],
|
||||
"timeout": values["request_timeout"],
|
||||
"max_retries": values["max_retries"],
|
||||
"default_headers": values["default_headers"],
|
||||
"default_query": values["default_query"],
|
||||
"http_client": values["http_client"],
|
||||
}
|
||||
if not values.get("client"):
|
||||
values["client"] = openai.OpenAI(**client_params).completions
|
||||
if not values.get("async_client"):
|
||||
values["async_client"] = openai.AsyncOpenAI(**client_params).completions
|
||||
|
||||
return values
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling OpenAI API."""
|
||||
normal_params: Dict[str, Any] = {
|
||||
"temperature": self.temperature,
|
||||
"top_p": self.top_p,
|
||||
"frequency_penalty": self.frequency_penalty,
|
||||
"presence_penalty": self.presence_penalty,
|
||||
"n": self.n,
|
||||
"logit_bias": self.logit_bias,
|
||||
}
|
||||
|
||||
if self.max_tokens is not None:
|
||||
normal_params["max_tokens"] = self.max_tokens
|
||||
|
||||
# Azure gpt-35-turbo doesn't support best_of
|
||||
# don't specify best_of if it is 1
|
||||
if self.best_of > 1:
|
||||
normal_params["best_of"] = self.best_of
|
||||
|
||||
return {**normal_params, **self.model_kwargs}
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[GenerationChunk]:
|
||||
params = {**self._invocation_params, **kwargs, "stream": True}
|
||||
self.get_sub_prompts(params, [prompt], stop) # this mutates params
|
||||
for stream_resp in self.client.create(prompt=prompt, **params):
|
||||
if not isinstance(stream_resp, dict):
|
||||
stream_resp = stream_resp.dict()
|
||||
chunk = _stream_response_to_generation_chunk(stream_resp)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=self.verbose,
|
||||
logprobs=chunk.generation_info["logprobs"]
|
||||
if chunk.generation_info
|
||||
else None,
|
||||
)
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[GenerationChunk]:
|
||||
params = {**self._invocation_params, **kwargs, "stream": True}
|
||||
self.get_sub_prompts(params, [prompt], stop) # this mutates params
|
||||
async for stream_resp in await self.async_client.create(
|
||||
prompt=prompt, **params
|
||||
):
|
||||
if not isinstance(stream_resp, dict):
|
||||
stream_resp = stream_resp.dict()
|
||||
chunk = _stream_response_to_generation_chunk(stream_resp)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=self.verbose,
|
||||
logprobs=chunk.generation_info["logprobs"]
|
||||
if chunk.generation_info
|
||||
else None,
|
||||
)
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
prompts: List[str],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> LLMResult:
|
||||
"""Call out to OpenAI's endpoint with k unique prompts.
|
||||
|
||||
Args:
|
||||
prompts: The prompts to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
|
||||
Returns:
|
||||
The full LLM output.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
response = openai.generate(["Tell me a joke."])
|
||||
"""
|
||||
# TODO: write a unit test for this
|
||||
params = self._invocation_params
|
||||
params = {**params, **kwargs}
|
||||
sub_prompts = self.get_sub_prompts(params, prompts, stop)
|
||||
choices = []
|
||||
token_usage: Dict[str, int] = {}
|
||||
# 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
|
||||
for _prompts in sub_prompts:
|
||||
if self.streaming:
|
||||
if len(_prompts) > 1:
|
||||
raise ValueError("Cannot stream results with multiple prompts.")
|
||||
|
||||
generation: Optional[GenerationChunk] = None
|
||||
for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs):
|
||||
if generation is None:
|
||||
generation = chunk
|
||||
else:
|
||||
generation += chunk
|
||||
assert generation is not None
|
||||
choices.append(
|
||||
{
|
||||
"text": generation.text,
|
||||
"finish_reason": generation.generation_info.get("finish_reason")
|
||||
if generation.generation_info
|
||||
else None,
|
||||
"logprobs": generation.generation_info.get("logprobs")
|
||||
if generation.generation_info
|
||||
else None,
|
||||
}
|
||||
)
|
||||
else:
|
||||
response = self.client.create(prompt=_prompts, **params)
|
||||
if not isinstance(response, dict):
|
||||
# V1 client returns the response in an PyDantic object instead of
|
||||
# dict. For the transition period, we deep convert it to dict.
|
||||
response = response.dict()
|
||||
|
||||
choices.extend(response["choices"])
|
||||
_update_token_usage(_keys, response, token_usage)
|
||||
if not system_fingerprint:
|
||||
system_fingerprint = response.get("system_fingerprint")
|
||||
return self.create_llm_result(
|
||||
choices,
|
||||
prompts,
|
||||
params,
|
||||
token_usage,
|
||||
system_fingerprint=system_fingerprint,
|
||||
)
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
prompts: List[str],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> LLMResult:
|
||||
"""Call out to OpenAI's endpoint async with k unique prompts."""
|
||||
params = self._invocation_params
|
||||
params = {**params, **kwargs}
|
||||
sub_prompts = self.get_sub_prompts(params, prompts, stop)
|
||||
choices = []
|
||||
token_usage: Dict[str, int] = {}
|
||||
# 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
|
||||
for _prompts in sub_prompts:
|
||||
if self.streaming:
|
||||
if len(_prompts) > 1:
|
||||
raise ValueError("Cannot stream results with multiple prompts.")
|
||||
|
||||
generation: Optional[GenerationChunk] = None
|
||||
async for chunk in self._astream(
|
||||
_prompts[0], stop, run_manager, **kwargs
|
||||
):
|
||||
if generation is None:
|
||||
generation = chunk
|
||||
else:
|
||||
generation += chunk
|
||||
assert generation is not None
|
||||
choices.append(
|
||||
{
|
||||
"text": generation.text,
|
||||
"finish_reason": generation.generation_info.get("finish_reason")
|
||||
if generation.generation_info
|
||||
else None,
|
||||
"logprobs": generation.generation_info.get("logprobs")
|
||||
if generation.generation_info
|
||||
else None,
|
||||
}
|
||||
)
|
||||
else:
|
||||
response = await self.async_client.create(prompt=_prompts, **params)
|
||||
if not isinstance(response, dict):
|
||||
response = response.dict()
|
||||
choices.extend(response["choices"])
|
||||
_update_token_usage(_keys, response, token_usage)
|
||||
return self.create_llm_result(
|
||||
choices,
|
||||
prompts,
|
||||
params,
|
||||
token_usage,
|
||||
system_fingerprint=system_fingerprint,
|
||||
)
|
||||
|
||||
def get_sub_prompts(
|
||||
self,
|
||||
params: Dict[str, Any],
|
||||
prompts: List[str],
|
||||
stop: Optional[List[str]] = None,
|
||||
) -> List[List[str]]:
|
||||
"""Get the sub prompts for llm call."""
|
||||
if stop is not None:
|
||||
if "stop" in params:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
params["stop"] = stop
|
||||
if params["max_tokens"] == -1:
|
||||
if len(prompts) != 1:
|
||||
raise ValueError(
|
||||
"max_tokens set to -1 not supported for multiple inputs."
|
||||
)
|
||||
params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
|
||||
sub_prompts = [
|
||||
prompts[i : i + self.batch_size]
|
||||
for i in range(0, len(prompts), self.batch_size)
|
||||
]
|
||||
return sub_prompts
|
||||
|
||||
def create_llm_result(
|
||||
self,
|
||||
choices: Any,
|
||||
prompts: List[str],
|
||||
params: Dict[str, Any],
|
||||
token_usage: Dict[str, int],
|
||||
*,
|
||||
system_fingerprint: Optional[str] = None,
|
||||
) -> LLMResult:
|
||||
"""Create the LLMResult from the choices and prompts."""
|
||||
generations = []
|
||||
n = params.get("n", self.n)
|
||||
for i, _ in enumerate(prompts):
|
||||
sub_choices = choices[i * n : (i + 1) * n]
|
||||
generations.append(
|
||||
[
|
||||
Generation(
|
||||
text=choice["text"],
|
||||
generation_info=dict(
|
||||
finish_reason=choice.get("finish_reason"),
|
||||
logprobs=choice.get("logprobs"),
|
||||
),
|
||||
)
|
||||
for choice in sub_choices
|
||||
]
|
||||
)
|
||||
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
|
||||
if system_fingerprint:
|
||||
llm_output["system_fingerprint"] = system_fingerprint
|
||||
return LLMResult(generations=generations, llm_output=llm_output)
|
||||
|
||||
@property
|
||||
def _invocation_params(self) -> Dict[str, Any]:
|
||||
"""Get the parameters used to invoke the model."""
|
||||
return self._default_params
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {**{"model_name": self.model_name}, **self._default_params}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "openai"
|
||||
|
||||
def get_token_ids(self, text: str) -> List[int]:
|
||||
"""Get the token IDs using the tiktoken package."""
|
||||
# tiktoken NOT supported for Python < 3.8
|
||||
if sys.version_info[1] < 8:
|
||||
return super().get_num_tokens(text)
|
||||
|
||||
model_name = self.tiktoken_model_name or self.model_name
|
||||
try:
|
||||
enc = tiktoken.encoding_for_model(model_name)
|
||||
except KeyError:
|
||||
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
||||
model = "cl100k_base"
|
||||
enc = tiktoken.get_encoding(model)
|
||||
|
||||
return enc.encode(
|
||||
text,
|
||||
allowed_special=self.allowed_special,
|
||||
disallowed_special=self.disallowed_special,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def modelname_to_contextsize(modelname: str) -> int:
|
||||
"""Calculate the maximum number of tokens possible to generate for a model.
|
||||
|
||||
Args:
|
||||
modelname: The modelname we want to know the context size for.
|
||||
|
||||
Returns:
|
||||
The maximum context size
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
max_tokens = openai.modelname_to_contextsize("gpt-3.5-turbo-instruct")
|
||||
"""
|
||||
model_token_mapping = {
|
||||
"gpt-4": 8192,
|
||||
"gpt-4-0314": 8192,
|
||||
"gpt-4-0613": 8192,
|
||||
"gpt-4-32k": 32768,
|
||||
"gpt-4-32k-0314": 32768,
|
||||
"gpt-4-32k-0613": 32768,
|
||||
"gpt-3.5-turbo": 4096,
|
||||
"gpt-3.5-turbo-0301": 4096,
|
||||
"gpt-3.5-turbo-0613": 4096,
|
||||
"gpt-3.5-turbo-16k": 16385,
|
||||
"gpt-3.5-turbo-16k-0613": 16385,
|
||||
"gpt-3.5-turbo-instruct": 4096,
|
||||
"text-ada-001": 2049,
|
||||
"ada": 2049,
|
||||
"text-babbage-001": 2040,
|
||||
"babbage": 2049,
|
||||
"text-curie-001": 2049,
|
||||
"curie": 2049,
|
||||
"davinci": 2049,
|
||||
"text-davinci-003": 4097,
|
||||
"text-davinci-002": 4097,
|
||||
"code-davinci-002": 8001,
|
||||
"code-davinci-001": 8001,
|
||||
"code-cushman-002": 2048,
|
||||
"code-cushman-001": 2048,
|
||||
}
|
||||
|
||||
# handling finetuned models
|
||||
if "ft-" in modelname:
|
||||
modelname = modelname.split(":")[0]
|
||||
|
||||
context_size = model_token_mapping.get(modelname, None)
|
||||
|
||||
if context_size is None:
|
||||
raise ValueError(
|
||||
f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
|
||||
"Known models are: " + ", ".join(model_token_mapping.keys())
|
||||
)
|
||||
|
||||
return context_size
|
||||
|
||||
@property
|
||||
def max_context_size(self) -> int:
|
||||
"""Get max context size for this model."""
|
||||
return self.modelname_to_contextsize(self.model_name)
|
||||
|
||||
def max_tokens_for_prompt(self, prompt: str) -> int:
|
||||
"""Calculate the maximum number of tokens possible to generate for a prompt.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
|
||||
Returns:
|
||||
The maximum number of tokens to generate for a prompt.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
|
||||
"""
|
||||
num_tokens = self.get_num_tokens(prompt)
|
||||
return self.max_context_size - num_tokens
|
||||
|
||||
|
||||
class OpenAI(BaseOpenAI):
|
||||
"""OpenAI large language models.
|
||||
|
||||
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
|
||||
in, even if not explicitly saved on this class.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.llms import OpenAI
|
||||
openai = OpenAI(model_name="gpt-3.5-turbo-instruct")
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def get_lc_namespace(cls) -> List[str]:
|
||||
"""Get the namespace of the langchain object."""
|
||||
return ["langchain", "llms", "openai"]
|
||||
|
||||
@classmethod
|
||||
def is_lc_serializable(cls) -> bool:
|
||||
"""Return whether this model can be serialized by Langchain."""
|
||||
return True
|
||||
|
||||
@property
|
||||
def _invocation_params(self) -> Dict[str, Any]:
|
||||
return {**{"model": self.model_name}, **super()._invocation_params}
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,97 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-openai"
|
||||
version = "0.0.1"
|
||||
description = "An integration package connecting OpenAI and LangChain"
|
||||
authors = []
|
||||
readme = "README.md"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.8.1,<4.0"
|
||||
langchain-core = ">=0.0.12"
|
||||
openai = "^1.6.1"
|
||||
numpy = "^1"
|
||||
tiktoken = "^0.5.2"
|
||||
|
||||
[tool.poetry.group.test]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^7.3.0"
|
||||
freezegun = "^1.2.2"
|
||||
pytest-mock = "^3.10.0"
|
||||
syrupy = "^4.0.2"
|
||||
pytest-watcher = "^0.3.4"
|
||||
pytest-asyncio = "^0.21.1"
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.poetry.group.codespell]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.codespell.dependencies]
|
||||
codespell = "^2.2.0"
|
||||
|
||||
[tool.poetry.group.test_integration]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test_integration.dependencies]
|
||||
|
||||
[tool.poetry.group.lint]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.lint.dependencies]
|
||||
ruff = "^0.1.5"
|
||||
|
||||
[tool.poetry.group.typing.dependencies]
|
||||
mypy = "^0.991"
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
types-tqdm = "^4.66.0.5"
|
||||
|
||||
[tool.poetry.group.dev]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.ruff]
|
||||
select = [
|
||||
"E", # pycodestyle
|
||||
"F", # pyflakes
|
||||
"I", # isort
|
||||
]
|
||||
|
||||
[tool.mypy]
|
||||
disallow_untyped_defs = "True"
|
||||
|
||||
[[tool.mypy.overrides]]
|
||||
module = "transformers"
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.coverage.run]
|
||||
omit = [
|
||||
"tests/*",
|
||||
]
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=1.0.0"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
# --strict-markers will raise errors on unknown marks.
|
||||
# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
|
||||
#
|
||||
# https://docs.pytest.org/en/7.1.x/reference/reference.html
|
||||
# --strict-config any warnings encountered while parsing the `pytest`
|
||||
# section of the configuration file raise errors.
|
||||
#
|
||||
# https://github.com/tophat/syrupy
|
||||
# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite.
|
||||
addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
|
||||
# Registering custom markers.
|
||||
# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
|
||||
markers = [
|
||||
"requires: mark tests as requiring a specific library",
|
||||
"asyncio: mark tests as requiring asyncio",
|
||||
"compile: mark placeholder test used to compile integration tests without running them",
|
||||
"scheduled: mark tests to run in scheduled testing",
|
||||
]
|
||||
asyncio_mode = "auto"
|
||||
@ -0,0 +1,17 @@
|
||||
import sys
|
||||
import traceback
|
||||
from importlib.machinery import SourceFileLoader
|
||||
|
||||
if __name__ == "__main__":
|
||||
files = sys.argv[1:]
|
||||
has_failure = False
|
||||
for file in files:
|
||||
try:
|
||||
SourceFileLoader("x", file).load_module()
|
||||
except Exception:
|
||||
has_faillure = True
|
||||
print(file)
|
||||
traceback.print_exc()
|
||||
print()
|
||||
|
||||
sys.exit(1 if has_failure else 0)
|
||||
@ -0,0 +1,27 @@
|
||||
#!/bin/bash
|
||||
#
|
||||
# This script searches for lines starting with "import pydantic" or "from pydantic"
|
||||
# in tracked files within a Git repository.
|
||||
#
|
||||
# Usage: ./scripts/check_pydantic.sh /path/to/repository
|
||||
|
||||
# Check if a path argument is provided
|
||||
if [ $# -ne 1 ]; then
|
||||
echo "Usage: $0 /path/to/repository"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
repository_path="$1"
|
||||
|
||||
# Search for lines matching the pattern within the specified repository
|
||||
result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
|
||||
|
||||
# Check if any matching lines were found
|
||||
if [ -n "$result" ]; then
|
||||
echo "ERROR: The following lines need to be updated:"
|
||||
echo "$result"
|
||||
echo "Please replace the code with an import from langchain_core.pydantic_v1."
|
||||
echo "For example, replace 'from pydantic import BaseModel'"
|
||||
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
|
||||
exit 1
|
||||
fi
|
||||
@ -0,0 +1,17 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -eu
|
||||
|
||||
# Initialize a variable to keep track of errors
|
||||
errors=0
|
||||
|
||||
# make sure not importing from langchain or langchain_experimental
|
||||
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
|
||||
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
|
||||
|
||||
# Decide on an exit status based on the errors
|
||||
if [ "$errors" -gt 0 ]; then
|
||||
exit 1
|
||||
else
|
||||
exit 0
|
||||
fi
|
||||
@ -0,0 +1,221 @@
|
||||
"""Test AzureChatOpenAI wrapper."""
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
from langchain_core.callbacks import CallbackManager
|
||||
from langchain_core.messages import BaseMessage, HumanMessage
|
||||
from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
|
||||
|
||||
from langchain_openai import AzureChatOpenAI
|
||||
from tests.unit_tests.fake.callbacks import FakeCallbackHandler
|
||||
|
||||
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
|
||||
OPENAI_API_BASE = os.environ.get("AZURE_OPENAI_API_BASE", "")
|
||||
OPENAI_API_KEY = os.environ.get("AZURE_OPENAI_API_KEY", "")
|
||||
DEPLOYMENT_NAME = os.environ.get(
|
||||
"AZURE_OPENAI_DEPLOYMENT_NAME",
|
||||
os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", ""),
|
||||
)
|
||||
|
||||
|
||||
def _get_llm(**kwargs: Any) -> AzureChatOpenAI:
|
||||
return AzureChatOpenAI(
|
||||
deployment_name=DEPLOYMENT_NAME,
|
||||
openai_api_version=OPENAI_API_VERSION,
|
||||
azure_endpoint=OPENAI_API_BASE,
|
||||
openai_api_key=OPENAI_API_KEY,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
@pytest.fixture
|
||||
def llm() -> AzureChatOpenAI:
|
||||
return _get_llm(
|
||||
max_tokens=10,
|
||||
)
|
||||
|
||||
|
||||
def test_chat_openai(llm: AzureChatOpenAI) -> None:
|
||||
"""Test AzureChatOpenAI wrapper."""
|
||||
message = HumanMessage(content="Hello")
|
||||
response = llm([message])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_chat_openai_generate() -> None:
|
||||
"""Test AzureChatOpenAI wrapper with generate."""
|
||||
chat = _get_llm(max_tokens=10, n=2)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat.generate([[message], [message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
for generations in response.generations:
|
||||
assert len(generations) == 2
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_chat_openai_multiple_completions() -> None:
|
||||
"""Test AzureChatOpenAI wrapper with multiple completions."""
|
||||
chat = _get_llm(max_tokens=10, n=5)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat._generate([message])
|
||||
assert isinstance(response, ChatResult)
|
||||
assert len(response.generations) == 5
|
||||
for generation in response.generations:
|
||||
assert isinstance(generation.message, BaseMessage)
|
||||
assert isinstance(generation.message.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_chat_openai_streaming() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
chat = _get_llm(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, BaseMessage)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_chat_openai_streaming_generation_info() -> None:
|
||||
"""Test that generation info is preserved when streaming."""
|
||||
|
||||
class _FakeCallback(FakeCallbackHandler):
|
||||
saved_things: dict = {}
|
||||
|
||||
def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
# Save the generation
|
||||
self.saved_things["generation"] = args[0]
|
||||
|
||||
callback = _FakeCallback()
|
||||
callback_manager = CallbackManager([callback])
|
||||
chat = _get_llm(
|
||||
max_tokens=2,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
)
|
||||
list(chat.stream("hi"))
|
||||
generation = callback.saved_things["generation"]
|
||||
# `Hello!` is two tokens, assert that that is what is returned
|
||||
assert generation.generations[0][0].text == "Hello!"
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_async_chat_openai() -> None:
|
||||
"""Test async generation."""
|
||||
chat = _get_llm(max_tokens=10, n=2)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = await chat.agenerate([[message], [message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
for generations in response.generations:
|
||||
assert len(generations) == 2
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_async_chat_openai_streaming() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
chat = _get_llm(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = await chat.agenerate([[message], [message]])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
for generations in response.generations:
|
||||
assert len(generations) == 1
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_streaming(llm: AzureChatOpenAI) -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
|
||||
for token in llm.stream("I'm Pickle Rick"):
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_astream(llm: AzureChatOpenAI) -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
async for token in llm.astream("I'm Pickle Rick"):
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_abatch(llm: AzureChatOpenAI) -> None:
|
||||
"""Test streaming tokens from AzureChatOpenAI."""
|
||||
|
||||
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_abatch_tags(llm: AzureChatOpenAI) -> None:
|
||||
"""Test batch tokens from AzureChatOpenAI."""
|
||||
|
||||
result = await llm.abatch(
|
||||
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
|
||||
)
|
||||
for token in result:
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_batch(llm: AzureChatOpenAI) -> None:
|
||||
"""Test batch tokens from AzureChatOpenAI."""
|
||||
|
||||
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_ainvoke(llm: AzureChatOpenAI) -> None:
|
||||
"""Test invoke tokens from AzureChatOpenAI."""
|
||||
|
||||
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
||||
assert isinstance(result.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_invoke(llm: AzureChatOpenAI) -> None:
|
||||
"""Test invoke tokens from AzureChatOpenAI."""
|
||||
|
||||
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
||||
assert isinstance(result.content, str)
|
||||
@ -0,0 +1,393 @@
|
||||
"""Test ChatOpenAI chat model."""
|
||||
from typing import Any, Optional
|
||||
|
||||
import pytest
|
||||
from langchain_core.callbacks import CallbackManager
|
||||
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
|
||||
from langchain_core.outputs import (
|
||||
ChatGeneration,
|
||||
ChatResult,
|
||||
LLMResult,
|
||||
)
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
from langchain_core.pydantic_v1 import BaseModel, Field
|
||||
|
||||
from langchain_openai import ChatOpenAI
|
||||
from tests.unit_tests.fake.callbacks import FakeCallbackHandler
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_chat_openai() -> None:
|
||||
"""Test ChatOpenAI wrapper."""
|
||||
chat = ChatOpenAI(
|
||||
temperature=0.7,
|
||||
base_url=None,
|
||||
organization=None,
|
||||
openai_proxy=None,
|
||||
timeout=10.0,
|
||||
max_retries=3,
|
||||
http_client=None,
|
||||
n=1,
|
||||
max_tokens=10,
|
||||
default_headers=None,
|
||||
default_query=None,
|
||||
)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_chat_openai_model() -> None:
|
||||
"""Test ChatOpenAI wrapper handles model_name."""
|
||||
chat = ChatOpenAI(model="foo")
|
||||
assert chat.model_name == "foo"
|
||||
chat = ChatOpenAI(model_name="bar")
|
||||
assert chat.model_name == "bar"
|
||||
|
||||
|
||||
def test_chat_openai_system_message() -> None:
|
||||
"""Test ChatOpenAI wrapper with system message."""
|
||||
chat = ChatOpenAI(max_tokens=10)
|
||||
system_message = SystemMessage(content="You are to chat with the user.")
|
||||
human_message = HumanMessage(content="Hello")
|
||||
response = chat([system_message, human_message])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_chat_openai_generate() -> None:
|
||||
"""Test ChatOpenAI wrapper with generate."""
|
||||
chat = ChatOpenAI(max_tokens=10, n=2)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat.generate([[message], [message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
assert response.llm_output
|
||||
for generations in response.generations:
|
||||
assert len(generations) == 2
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_chat_openai_multiple_completions() -> None:
|
||||
"""Test ChatOpenAI wrapper with multiple completions."""
|
||||
chat = ChatOpenAI(max_tokens=10, n=5)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat._generate([message])
|
||||
assert isinstance(response, ChatResult)
|
||||
assert len(response.generations) == 5
|
||||
for generation in response.generations:
|
||||
assert isinstance(generation.message, BaseMessage)
|
||||
assert isinstance(generation.message.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_chat_openai_streaming() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
chat = ChatOpenAI(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, BaseMessage)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_chat_openai_streaming_generation_info() -> None:
|
||||
"""Test that generation info is preserved when streaming."""
|
||||
|
||||
class _FakeCallback(FakeCallbackHandler):
|
||||
saved_things: dict = {}
|
||||
|
||||
def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
# Save the generation
|
||||
self.saved_things["generation"] = args[0]
|
||||
|
||||
callback = _FakeCallback()
|
||||
callback_manager = CallbackManager([callback])
|
||||
chat = ChatOpenAI(
|
||||
max_tokens=2,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
)
|
||||
list(chat.stream("hi"))
|
||||
generation = callback.saved_things["generation"]
|
||||
# `Hello!` is two tokens, assert that that is what is returned
|
||||
assert generation.generations[0][0].text == "Hello!"
|
||||
|
||||
|
||||
def test_chat_openai_llm_output_contains_model_name() -> None:
|
||||
"""Test llm_output contains model_name."""
|
||||
chat = ChatOpenAI(max_tokens=10)
|
||||
message = HumanMessage(content="Hello")
|
||||
llm_result = chat.generate([[message]])
|
||||
assert llm_result.llm_output is not None
|
||||
assert llm_result.llm_output["model_name"] == chat.model_name
|
||||
|
||||
|
||||
def test_chat_openai_streaming_llm_output_contains_model_name() -> None:
|
||||
"""Test llm_output contains model_name."""
|
||||
chat = ChatOpenAI(max_tokens=10, streaming=True)
|
||||
message = HumanMessage(content="Hello")
|
||||
llm_result = chat.generate([[message]])
|
||||
assert llm_result.llm_output is not None
|
||||
assert llm_result.llm_output["model_name"] == chat.model_name
|
||||
|
||||
|
||||
def test_chat_openai_invalid_streaming_params() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
with pytest.raises(ValueError):
|
||||
ChatOpenAI(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
n=5,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_async_chat_openai() -> None:
|
||||
"""Test async generation."""
|
||||
chat = ChatOpenAI(max_tokens=10, n=2)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = await chat.agenerate([[message], [message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
assert response.llm_output
|
||||
for generations in response.generations:
|
||||
assert len(generations) == 2
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_async_chat_openai_streaming() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
chat = ChatOpenAI(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = await chat.agenerate([[message], [message]])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
for generations in response.generations:
|
||||
assert len(generations) == 1
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_async_chat_openai_bind_functions() -> None:
|
||||
"""Test ChatOpenAI wrapper with multiple completions."""
|
||||
|
||||
class Person(BaseModel):
|
||||
"""Identifying information about a person."""
|
||||
|
||||
name: str = Field(..., title="Name", description="The person's name")
|
||||
age: int = Field(..., title="Age", description="The person's age")
|
||||
fav_food: Optional[str] = Field(
|
||||
default=None, title="Fav Food", description="The person's favorite food"
|
||||
)
|
||||
|
||||
chat = ChatOpenAI(
|
||||
max_tokens=30,
|
||||
n=1,
|
||||
streaming=True,
|
||||
).bind_functions(functions=[Person], function_call="Person")
|
||||
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
("system", "Use the provided Person function"),
|
||||
("user", "{input}"),
|
||||
]
|
||||
)
|
||||
|
||||
chain = prompt | chat
|
||||
|
||||
message = HumanMessage(content="Sally is 13 years old")
|
||||
response = await chain.abatch([{"input": message}])
|
||||
|
||||
assert isinstance(response, list)
|
||||
assert len(response) == 1
|
||||
for generation in response:
|
||||
assert isinstance(generation, AIMessage)
|
||||
|
||||
|
||||
def test_chat_openai_extra_kwargs() -> None:
|
||||
"""Test extra kwargs to chat openai."""
|
||||
# Check that foo is saved in extra_kwargs.
|
||||
llm = ChatOpenAI(foo=3, max_tokens=10)
|
||||
assert llm.max_tokens == 10
|
||||
assert llm.model_kwargs == {"foo": 3}
|
||||
|
||||
# Test that if extra_kwargs are provided, they are added to it.
|
||||
llm = ChatOpenAI(foo=3, model_kwargs={"bar": 2})
|
||||
assert llm.model_kwargs == {"foo": 3, "bar": 2}
|
||||
|
||||
# Test that if provided twice it errors
|
||||
with pytest.raises(ValueError):
|
||||
ChatOpenAI(foo=3, model_kwargs={"foo": 2})
|
||||
|
||||
# Test that if explicit param is specified in kwargs it errors
|
||||
with pytest.raises(ValueError):
|
||||
ChatOpenAI(model_kwargs={"temperature": 0.2})
|
||||
|
||||
# Test that "model" cannot be specified in kwargs
|
||||
with pytest.raises(ValueError):
|
||||
ChatOpenAI(model_kwargs={"model": "gpt-3.5-turbo-instruct"})
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_streaming() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = ChatOpenAI(max_tokens=10)
|
||||
|
||||
for token in llm.stream("I'm Pickle Rick"):
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_astream() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = ChatOpenAI(max_tokens=10)
|
||||
|
||||
async for token in llm.astream("I'm Pickle Rick"):
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_abatch() -> None:
|
||||
"""Test streaming tokens from ChatOpenAI."""
|
||||
llm = ChatOpenAI(max_tokens=10)
|
||||
|
||||
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_abatch_tags() -> None:
|
||||
"""Test batch tokens from ChatOpenAI."""
|
||||
llm = ChatOpenAI(max_tokens=10)
|
||||
|
||||
result = await llm.abatch(
|
||||
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
|
||||
)
|
||||
for token in result:
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_batch() -> None:
|
||||
"""Test batch tokens from ChatOpenAI."""
|
||||
llm = ChatOpenAI(max_tokens=10)
|
||||
|
||||
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_ainvoke() -> None:
|
||||
"""Test invoke tokens from ChatOpenAI."""
|
||||
llm = ChatOpenAI(max_tokens=10)
|
||||
|
||||
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
||||
assert isinstance(result.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_invoke() -> None:
|
||||
"""Test invoke tokens from ChatOpenAI."""
|
||||
llm = ChatOpenAI(max_tokens=10)
|
||||
|
||||
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
||||
assert isinstance(result.content, str)
|
||||
|
||||
|
||||
def test_stream() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = ChatOpenAI()
|
||||
|
||||
for token in llm.stream("I'm Pickle Rick"):
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
async def test_astream() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = ChatOpenAI()
|
||||
|
||||
async for token in llm.astream("I'm Pickle Rick"):
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
async def test_abatch() -> None:
|
||||
"""Test streaming tokens from ChatOpenAI."""
|
||||
llm = ChatOpenAI()
|
||||
|
||||
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
async def test_abatch_tags() -> None:
|
||||
"""Test batch tokens from ChatOpenAI."""
|
||||
llm = ChatOpenAI()
|
||||
|
||||
result = await llm.abatch(
|
||||
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
|
||||
)
|
||||
for token in result:
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
def test_batch() -> None:
|
||||
"""Test batch tokens from ChatOpenAI."""
|
||||
llm = ChatOpenAI()
|
||||
|
||||
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
async def test_ainvoke() -> None:
|
||||
"""Test invoke tokens from ChatOpenAI."""
|
||||
llm = ChatOpenAI()
|
||||
|
||||
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
||||
assert isinstance(result.content, str)
|
||||
|
||||
|
||||
def test_invoke() -> None:
|
||||
"""Test invoke tokens from ChatOpenAI."""
|
||||
llm = ChatOpenAI()
|
||||
|
||||
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
||||
assert isinstance(result.content, str)
|
||||
@ -0,0 +1,132 @@
|
||||
"""Test azure openai embeddings."""
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import openai
|
||||
import pytest
|
||||
|
||||
from langchain_openai import AzureOpenAIEmbeddings
|
||||
|
||||
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
|
||||
OPENAI_API_BASE = os.environ.get("AZURE_OPENAI_API_BASE", "")
|
||||
OPENAI_API_KEY = os.environ.get("AZURE_OPENAI_API_KEY", "")
|
||||
DEPLOYMENT_NAME = os.environ.get(
|
||||
"AZURE_OPENAI_DEPLOYMENT_NAME",
|
||||
os.environ.get("AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME", ""),
|
||||
)
|
||||
print
|
||||
|
||||
|
||||
def _get_embeddings(**kwargs: Any) -> AzureOpenAIEmbeddings:
|
||||
return AzureOpenAIEmbeddings(
|
||||
azure_deployment=DEPLOYMENT_NAME,
|
||||
api_version=OPENAI_API_VERSION,
|
||||
openai_api_base=OPENAI_API_BASE,
|
||||
openai_api_key=OPENAI_API_KEY,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_azure_openai_embedding_documents() -> None:
|
||||
"""Test openai embeddings."""
|
||||
documents = ["foo bar"]
|
||||
embedding = _get_embeddings()
|
||||
output = embedding.embed_documents(documents)
|
||||
assert len(output) == 1
|
||||
assert len(output[0]) == 1536
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_azure_openai_embedding_documents_multiple() -> None:
|
||||
"""Test openai embeddings."""
|
||||
documents = ["foo bar", "bar foo", "foo"]
|
||||
embedding = _get_embeddings(chunk_size=2)
|
||||
embedding.embedding_ctx_length = 8191
|
||||
output = embedding.embed_documents(documents)
|
||||
assert embedding.chunk_size == 2
|
||||
assert len(output) == 3
|
||||
assert len(output[0]) == 1536
|
||||
assert len(output[1]) == 1536
|
||||
assert len(output[2]) == 1536
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_azure_openai_embedding_documents_chunk_size() -> None:
|
||||
"""Test openai embeddings."""
|
||||
documents = ["foo bar"] * 20
|
||||
embedding = _get_embeddings()
|
||||
embedding.embedding_ctx_length = 8191
|
||||
output = embedding.embed_documents(documents)
|
||||
# Max 16 chunks per batch on Azure OpenAI embeddings
|
||||
assert embedding.chunk_size == 16
|
||||
assert len(output) == 20
|
||||
assert all([len(out) == 1536 for out in output])
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_azure_openai_embedding_documents_async_multiple() -> None:
|
||||
"""Test openai embeddings."""
|
||||
documents = ["foo bar", "bar foo", "foo"]
|
||||
embedding = _get_embeddings(chunk_size=2)
|
||||
embedding.embedding_ctx_length = 8191
|
||||
output = await embedding.aembed_documents(documents)
|
||||
assert len(output) == 3
|
||||
assert len(output[0]) == 1536
|
||||
assert len(output[1]) == 1536
|
||||
assert len(output[2]) == 1536
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_azure_openai_embedding_query() -> None:
|
||||
"""Test openai embeddings."""
|
||||
document = "foo bar"
|
||||
embedding = _get_embeddings()
|
||||
output = embedding.embed_query(document)
|
||||
assert len(output) == 1536
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_azure_openai_embedding_async_query() -> None:
|
||||
"""Test openai embeddings."""
|
||||
document = "foo bar"
|
||||
embedding = _get_embeddings()
|
||||
output = await embedding.aembed_query(document)
|
||||
assert len(output) == 1536
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_azure_openai_embedding_with_empty_string() -> None:
|
||||
"""Test openai embeddings with empty string."""
|
||||
|
||||
document = ["", "abc"]
|
||||
embedding = _get_embeddings()
|
||||
output = embedding.embed_documents(document)
|
||||
assert len(output) == 2
|
||||
assert len(output[0]) == 1536
|
||||
expected_output = (
|
||||
openai.AzureOpenAI(
|
||||
api_version=OPENAI_API_VERSION,
|
||||
api_key=OPENAI_API_KEY,
|
||||
base_url=embedding.openai_api_base,
|
||||
azure_deployment=DEPLOYMENT_NAME,
|
||||
) # type: ignore
|
||||
.embeddings.create(input="", model="text-embedding-ada-002")
|
||||
.data[0]
|
||||
.embedding
|
||||
)
|
||||
assert np.allclose(output[0], expected_output)
|
||||
assert len(output[1]) == 1536
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_embed_documents_normalized() -> None:
|
||||
output = _get_embeddings().embed_documents(["foo walked to the market"])
|
||||
assert np.isclose(np.linalg.norm(output[0]), 1.0)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_embed_query_normalized() -> None:
|
||||
output = _get_embeddings().embed_query("foo walked to the market")
|
||||
assert np.isclose(np.linalg.norm(output), 1.0)
|
||||
@ -0,0 +1,19 @@
|
||||
"""Test OpenAI embeddings."""
|
||||
from langchain_openai.embeddings.base import OpenAIEmbeddings
|
||||
|
||||
|
||||
def test_langchain_openai_embedding_documents() -> None:
|
||||
"""Test cohere embeddings."""
|
||||
documents = ["foo bar"]
|
||||
embedding = OpenAIEmbeddings()
|
||||
output = embedding.embed_documents(documents)
|
||||
assert len(output) == 1
|
||||
assert len(output[0]) > 0
|
||||
|
||||
|
||||
def test_langchain_openai_embedding_query() -> None:
|
||||
"""Test cohere embeddings."""
|
||||
document = "foo bar"
|
||||
embedding = OpenAIEmbeddings()
|
||||
output = embedding.embed_query(document)
|
||||
assert len(output) > 0
|
||||
@ -0,0 +1,176 @@
|
||||
"""Test AzureOpenAI wrapper."""
|
||||
import os
|
||||
from typing import Any, Generator
|
||||
|
||||
import pytest
|
||||
from langchain_core.callbacks import CallbackManager
|
||||
from langchain_core.outputs import LLMResult
|
||||
|
||||
from langchain_openai import AzureOpenAI
|
||||
from tests.unit_tests.fake.callbacks import FakeCallbackHandler
|
||||
|
||||
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
|
||||
OPENAI_API_BASE = os.environ.get("AZURE_OPENAI_API_BASE", "")
|
||||
OPENAI_API_KEY = os.environ.get("AZURE_OPENAI_API_KEY", "")
|
||||
DEPLOYMENT_NAME = os.environ.get(
|
||||
"AZURE_OPENAI_DEPLOYMENT_NAME",
|
||||
os.environ.get("AZURE_OPENAI_LLM_DEPLOYMENT_NAME", ""),
|
||||
)
|
||||
|
||||
|
||||
def _get_llm(**kwargs: Any) -> AzureOpenAI:
|
||||
return AzureOpenAI(
|
||||
deployment_name=DEPLOYMENT_NAME,
|
||||
openai_api_version=OPENAI_API_VERSION,
|
||||
openai_api_base=OPENAI_API_BASE,
|
||||
openai_api_key=OPENAI_API_KEY,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def llm() -> AzureOpenAI:
|
||||
return _get_llm(
|
||||
max_tokens=10,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_call(llm: AzureOpenAI) -> None:
|
||||
"""Test valid call to openai."""
|
||||
output = llm("Say something nice:")
|
||||
assert isinstance(output, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_streaming(llm: AzureOpenAI) -> None:
|
||||
"""Test streaming tokens from AzureOpenAI."""
|
||||
generator = llm.stream("I'm Pickle Rick")
|
||||
|
||||
assert isinstance(generator, Generator)
|
||||
|
||||
full_response = ""
|
||||
for token in generator:
|
||||
assert isinstance(token, str)
|
||||
full_response += token
|
||||
assert full_response
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_astream(llm: AzureOpenAI) -> None:
|
||||
"""Test streaming tokens from AzureOpenAI."""
|
||||
async for token in llm.astream("I'm Pickle Rick"):
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_abatch(llm: AzureOpenAI) -> None:
|
||||
"""Test streaming tokens from AzureOpenAI."""
|
||||
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_openai_abatch_tags(llm: AzureOpenAI) -> None:
|
||||
"""Test streaming tokens from AzureOpenAI."""
|
||||
result = await llm.abatch(
|
||||
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
|
||||
)
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_batch(llm: AzureOpenAI) -> None:
|
||||
"""Test streaming tokens from AzureOpenAI."""
|
||||
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_ainvoke(llm: AzureOpenAI) -> None:
|
||||
"""Test streaming tokens from AzureOpenAI."""
|
||||
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
||||
assert isinstance(result, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_invoke(llm: AzureOpenAI) -> None:
|
||||
"""Test streaming tokens from AzureOpenAI."""
|
||||
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
||||
assert isinstance(result, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_multiple_prompts(llm: AzureOpenAI) -> None:
|
||||
"""Test completion with multiple prompts."""
|
||||
output = llm.generate(["I'm Pickle Rick", "I'm Pickle Rick"])
|
||||
assert isinstance(output, LLMResult)
|
||||
assert isinstance(output.generations, list)
|
||||
assert len(output.generations) == 2
|
||||
|
||||
|
||||
def test_openai_streaming_best_of_error() -> None:
|
||||
"""Test validation for streaming fails if best_of is not 1."""
|
||||
with pytest.raises(ValueError):
|
||||
_get_llm(best_of=2, streaming=True)
|
||||
|
||||
|
||||
def test_openai_streaming_n_error() -> None:
|
||||
"""Test validation for streaming fails if n is not 1."""
|
||||
with pytest.raises(ValueError):
|
||||
_get_llm(n=2, streaming=True)
|
||||
|
||||
|
||||
def test_openai_streaming_multiple_prompts_error() -> None:
|
||||
"""Test validation for streaming fails if multiple prompts are given."""
|
||||
with pytest.raises(ValueError):
|
||||
_get_llm(streaming=True).generate(["I'm Pickle Rick", "I'm Pickle Rick"])
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_streaming_call() -> None:
|
||||
"""Test valid call to openai."""
|
||||
llm = _get_llm(max_tokens=10, streaming=True)
|
||||
output = llm("Say foo:")
|
||||
assert isinstance(output, str)
|
||||
|
||||
|
||||
def test_openai_streaming_callback() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
llm = _get_llm(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
llm("Write me a sentence with 100 words.")
|
||||
assert callback_handler.llm_streams == 11
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_async_generate() -> None:
|
||||
"""Test async generation."""
|
||||
llm = _get_llm(max_tokens=10)
|
||||
output = await llm.agenerate(["Hello, how are you?"])
|
||||
assert isinstance(output, LLMResult)
|
||||
|
||||
|
||||
async def test_openai_async_streaming_callback() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
llm = _get_llm(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
result = await llm.agenerate(["Write me a sentence with 100 words."])
|
||||
assert callback_handler.llm_streams == 11
|
||||
assert isinstance(result, LLMResult)
|
||||
@ -0,0 +1,280 @@
|
||||
"""Test OpenAI llm."""
|
||||
from typing import Generator
|
||||
|
||||
import pytest
|
||||
from langchain_core.callbacks import CallbackManager
|
||||
from langchain_core.outputs import LLMResult
|
||||
|
||||
from langchain_openai import OpenAI
|
||||
from tests.unit_tests.fake.callbacks import (
|
||||
FakeCallbackHandler,
|
||||
)
|
||||
|
||||
|
||||
def test_stream() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OpenAI()
|
||||
|
||||
for token in llm.stream("I'm Pickle Rick"):
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_astream() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OpenAI()
|
||||
|
||||
async for token in llm.astream("I'm Pickle Rick"):
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_abatch() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OpenAI()
|
||||
|
||||
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_abatch_tags() -> None:
|
||||
"""Test batch tokens from OpenAI."""
|
||||
llm = OpenAI()
|
||||
|
||||
result = await llm.abatch(
|
||||
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
|
||||
)
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
def test_batch() -> None:
|
||||
"""Test batch tokens from OpenAI."""
|
||||
llm = OpenAI()
|
||||
|
||||
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_ainvoke() -> None:
|
||||
"""Test invoke tokens from OpenAI."""
|
||||
llm = OpenAI()
|
||||
|
||||
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
||||
assert isinstance(result, str)
|
||||
|
||||
|
||||
def test_invoke() -> None:
|
||||
"""Test invoke tokens from OpenAI."""
|
||||
llm = OpenAI()
|
||||
|
||||
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
||||
assert isinstance(result, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_call() -> None:
|
||||
"""Test valid call to openai."""
|
||||
llm = OpenAI()
|
||||
output = llm("Say something nice:")
|
||||
assert isinstance(output, str)
|
||||
|
||||
|
||||
def test_openai_llm_output_contains_model_name() -> None:
|
||||
"""Test llm_output contains model_name."""
|
||||
llm = OpenAI(max_tokens=10)
|
||||
llm_result = llm.generate(["Hello, how are you?"])
|
||||
assert llm_result.llm_output is not None
|
||||
assert llm_result.llm_output["model_name"] == llm.model_name
|
||||
|
||||
|
||||
def test_openai_stop_valid() -> None:
|
||||
"""Test openai stop logic on valid configuration."""
|
||||
query = "write an ordered list of five items"
|
||||
first_llm = OpenAI(stop="3", temperature=0)
|
||||
first_output = first_llm(query)
|
||||
second_llm = OpenAI(temperature=0)
|
||||
second_output = second_llm(query, stop=["3"])
|
||||
# Because it stops on new lines, shouldn't return anything
|
||||
assert first_output == second_output
|
||||
|
||||
|
||||
def test_openai_stop_error() -> None:
|
||||
"""Test openai stop logic on bad configuration."""
|
||||
llm = OpenAI(stop="3", temperature=0)
|
||||
with pytest.raises(ValueError):
|
||||
llm("write an ordered list of five items", stop=["\n"])
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_streaming() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OpenAI(max_tokens=10)
|
||||
generator = llm.stream("I'm Pickle Rick")
|
||||
|
||||
assert isinstance(generator, Generator)
|
||||
|
||||
for token in generator:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_astream() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OpenAI(max_tokens=10)
|
||||
|
||||
async for token in llm.astream("I'm Pickle Rick"):
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_abatch() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OpenAI(max_tokens=10)
|
||||
|
||||
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_openai_abatch_tags() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OpenAI(max_tokens=10)
|
||||
|
||||
result = await llm.abatch(
|
||||
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
|
||||
)
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_batch() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OpenAI(max_tokens=10)
|
||||
|
||||
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_ainvoke() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OpenAI(max_tokens=10)
|
||||
|
||||
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
||||
assert isinstance(result, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_invoke() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OpenAI(max_tokens=10)
|
||||
|
||||
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
||||
assert isinstance(result, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_multiple_prompts() -> None:
|
||||
"""Test completion with multiple prompts."""
|
||||
llm = OpenAI(max_tokens=10)
|
||||
output = llm.generate(["I'm Pickle Rick", "I'm Pickle Rick"])
|
||||
assert isinstance(output, LLMResult)
|
||||
assert isinstance(output.generations, list)
|
||||
assert len(output.generations) == 2
|
||||
|
||||
|
||||
def test_openai_streaming_best_of_error() -> None:
|
||||
"""Test validation for streaming fails if best_of is not 1."""
|
||||
with pytest.raises(ValueError):
|
||||
OpenAI(best_of=2, streaming=True)
|
||||
|
||||
|
||||
def test_openai_streaming_n_error() -> None:
|
||||
"""Test validation for streaming fails if n is not 1."""
|
||||
with pytest.raises(ValueError):
|
||||
OpenAI(n=2, streaming=True)
|
||||
|
||||
|
||||
def test_openai_streaming_multiple_prompts_error() -> None:
|
||||
"""Test validation for streaming fails if multiple prompts are given."""
|
||||
with pytest.raises(ValueError):
|
||||
OpenAI(streaming=True).generate(["I'm Pickle Rick", "I'm Pickle Rick"])
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_openai_streaming_call() -> None:
|
||||
"""Test valid call to openai."""
|
||||
llm = OpenAI(max_tokens=10, streaming=True)
|
||||
output = llm("Say foo:")
|
||||
assert isinstance(output, str)
|
||||
|
||||
|
||||
def test_openai_streaming_callback() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
llm = OpenAI(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
llm("Write me a sentence with 100 words.")
|
||||
|
||||
# new client sometimes passes 2 tokens at once
|
||||
assert callback_handler.llm_streams >= 5
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_openai_async_generate() -> None:
|
||||
"""Test async generation."""
|
||||
llm = OpenAI(max_tokens=10)
|
||||
output = await llm.agenerate(["Hello, how are you?"])
|
||||
assert isinstance(output, LLMResult)
|
||||
|
||||
|
||||
async def test_openai_async_streaming_callback() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
llm = OpenAI(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
result = await llm.agenerate(["Write me a sentence with 100 words."])
|
||||
|
||||
# new client sometimes passes 2 tokens at once
|
||||
assert callback_handler.llm_streams >= 5
|
||||
assert isinstance(result, LLMResult)
|
||||
|
||||
|
||||
def test_openai_modelname_to_contextsize_valid() -> None:
|
||||
"""Test model name to context size on a valid model."""
|
||||
assert OpenAI().modelname_to_contextsize("davinci") == 2049
|
||||
|
||||
|
||||
def test_openai_modelname_to_contextsize_invalid() -> None:
|
||||
"""Test model name to context size on an invalid model."""
|
||||
with pytest.raises(ValueError):
|
||||
OpenAI().modelname_to_contextsize("foobar")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_completion() -> dict:
|
||||
return {
|
||||
"id": "cmpl-3evkmQda5Hu7fcZavknQda3SQ",
|
||||
"object": "text_completion",
|
||||
"created": 1689989000,
|
||||
"model": "gpt-3.5-turbo-instruct",
|
||||
"choices": [
|
||||
{"text": "Bar Baz", "index": 0, "logprobs": None, "finish_reason": "length"}
|
||||
],
|
||||
"usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
|
||||
}
|
||||
@ -0,0 +1,7 @@
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.mark.compile
|
||||
def test_placeholder() -> None:
|
||||
"""Used for compiling integration tests without running any real tests."""
|
||||
pass
|
||||
@ -0,0 +1,120 @@
|
||||
"""Test OpenAI Chat API wrapper."""
|
||||
import json
|
||||
from typing import Any
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
FunctionMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langchain_openai.chat_models.base import _convert_dict_to_message
|
||||
|
||||
|
||||
def test_openai_model_param() -> None:
|
||||
llm = ChatOpenAI(model="foo")
|
||||
assert llm.model_name == "foo"
|
||||
llm = ChatOpenAI(model_name="foo")
|
||||
assert llm.model_name == "foo"
|
||||
|
||||
|
||||
def test_function_message_dict_to_function_message() -> None:
|
||||
content = json.dumps({"result": "Example #1"})
|
||||
name = "test_function"
|
||||
result = _convert_dict_to_message(
|
||||
{
|
||||
"role": "function",
|
||||
"name": name,
|
||||
"content": content,
|
||||
}
|
||||
)
|
||||
assert isinstance(result, FunctionMessage)
|
||||
assert result.name == name
|
||||
assert result.content == content
|
||||
|
||||
|
||||
def test__convert_dict_to_message_human() -> None:
|
||||
message = {"role": "user", "content": "foo"}
|
||||
result = _convert_dict_to_message(message)
|
||||
expected_output = HumanMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_dict_to_message_ai() -> None:
|
||||
message = {"role": "assistant", "content": "foo"}
|
||||
result = _convert_dict_to_message(message)
|
||||
expected_output = AIMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_dict_to_message_system() -> None:
|
||||
message = {"role": "system", "content": "foo"}
|
||||
result = _convert_dict_to_message(message)
|
||||
expected_output = SystemMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_completion() -> dict:
|
||||
return {
|
||||
"id": "chatcmpl-7fcZavknQda3SQ",
|
||||
"object": "chat.completion",
|
||||
"created": 1689989000,
|
||||
"model": "gpt-3.5-turbo-0613",
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": "Bar Baz",
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
}
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def test_openai_predict(mock_completion: dict) -> None:
|
||||
llm = ChatOpenAI()
|
||||
mock_client = MagicMock()
|
||||
completed = False
|
||||
|
||||
def mock_create(*args: Any, **kwargs: Any) -> Any:
|
||||
nonlocal completed
|
||||
completed = True
|
||||
return mock_completion
|
||||
|
||||
mock_client.create = mock_create
|
||||
with patch.object(
|
||||
llm,
|
||||
"client",
|
||||
mock_client,
|
||||
):
|
||||
res = llm.predict("bar")
|
||||
assert res == "Bar Baz"
|
||||
assert completed
|
||||
|
||||
|
||||
async def test_openai_apredict(mock_completion: dict) -> None:
|
||||
llm = ChatOpenAI()
|
||||
mock_client = MagicMock()
|
||||
completed = False
|
||||
|
||||
def mock_create(*args: Any, **kwargs: Any) -> Any:
|
||||
nonlocal completed
|
||||
completed = True
|
||||
return mock_completion
|
||||
|
||||
mock_client.create = mock_create
|
||||
with patch.object(
|
||||
llm,
|
||||
"client",
|
||||
mock_client,
|
||||
):
|
||||
res = llm.predict("bar")
|
||||
assert res == "Bar Baz"
|
||||
assert completed
|
||||
@ -0,0 +1,7 @@
|
||||
from langchain_openai.chat_models import __all__
|
||||
|
||||
EXPECTED_ALL = ["ChatOpenAI", "AzureChatOpenAI"]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
||||
@ -0,0 +1,18 @@
|
||||
import os
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain_openai import OpenAIEmbeddings
|
||||
|
||||
os.environ["OPENAI_API_KEY"] = "foo"
|
||||
|
||||
|
||||
def test_openai_invalid_model_kwargs() -> None:
|
||||
with pytest.raises(ValueError):
|
||||
OpenAIEmbeddings(model_kwargs={"model": "foo"})
|
||||
|
||||
|
||||
def test_openai_incorrect_field() -> None:
|
||||
with pytest.warns(match="not default parameter"):
|
||||
llm = OpenAIEmbeddings(foo="bar")
|
||||
assert llm.model_kwargs == {"foo": "bar"}
|
||||
@ -0,0 +1,7 @@
|
||||
from langchain_openai.embeddings import __all__
|
||||
|
||||
EXPECTED_ALL = ["OpenAIEmbeddings", "AzureOpenAIEmbeddings"]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
||||
@ -0,0 +1,393 @@
|
||||
"""A fake callback handler for testing purposes."""
|
||||
from itertools import chain
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
from uuid import UUID
|
||||
|
||||
from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
|
||||
from langchain_core.messages import BaseMessage
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
|
||||
|
||||
class BaseFakeCallbackHandler(BaseModel):
|
||||
"""Base fake callback handler for testing."""
|
||||
|
||||
starts: int = 0
|
||||
ends: int = 0
|
||||
errors: int = 0
|
||||
errors_args: List[Any] = []
|
||||
text: int = 0
|
||||
ignore_llm_: bool = False
|
||||
ignore_chain_: bool = False
|
||||
ignore_agent_: bool = False
|
||||
ignore_retriever_: bool = False
|
||||
ignore_chat_model_: bool = False
|
||||
|
||||
# to allow for similar callback handlers that are not technicall equal
|
||||
fake_id: Union[str, None] = None
|
||||
|
||||
# add finer-grained counters for easier debugging of failing tests
|
||||
chain_starts: int = 0
|
||||
chain_ends: int = 0
|
||||
llm_starts: int = 0
|
||||
llm_ends: int = 0
|
||||
llm_streams: int = 0
|
||||
tool_starts: int = 0
|
||||
tool_ends: int = 0
|
||||
agent_actions: int = 0
|
||||
agent_ends: int = 0
|
||||
chat_model_starts: int = 0
|
||||
retriever_starts: int = 0
|
||||
retriever_ends: int = 0
|
||||
retriever_errors: int = 0
|
||||
retries: int = 0
|
||||
|
||||
|
||||
class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
|
||||
"""Base fake callback handler mixin for testing."""
|
||||
|
||||
def on_llm_start_common(self) -> None:
|
||||
self.llm_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_llm_end_common(self) -> None:
|
||||
self.llm_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_llm_error_common(self, *args: Any, **kwargs: Any) -> None:
|
||||
self.errors += 1
|
||||
self.errors_args.append({"args": args, "kwargs": kwargs})
|
||||
|
||||
def on_llm_new_token_common(self) -> None:
|
||||
self.llm_streams += 1
|
||||
|
||||
def on_retry_common(self) -> None:
|
||||
self.retries += 1
|
||||
|
||||
def on_chain_start_common(self) -> None:
|
||||
self.chain_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_chain_end_common(self) -> None:
|
||||
self.chain_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_chain_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_tool_start_common(self) -> None:
|
||||
self.tool_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_tool_end_common(self) -> None:
|
||||
self.tool_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_tool_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_agent_action_common(self) -> None:
|
||||
self.agent_actions += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_agent_finish_common(self) -> None:
|
||||
self.agent_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_chat_model_start_common(self) -> None:
|
||||
self.chat_model_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_text_common(self) -> None:
|
||||
self.text += 1
|
||||
|
||||
def on_retriever_start_common(self) -> None:
|
||||
self.starts += 1
|
||||
self.retriever_starts += 1
|
||||
|
||||
def on_retriever_end_common(self) -> None:
|
||||
self.ends += 1
|
||||
self.retriever_ends += 1
|
||||
|
||||
def on_retriever_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
self.retriever_errors += 1
|
||||
|
||||
|
||||
class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
|
||||
"""Fake callback handler for testing."""
|
||||
|
||||
@property
|
||||
def ignore_llm(self) -> bool:
|
||||
"""Whether to ignore LLM callbacks."""
|
||||
return self.ignore_llm_
|
||||
|
||||
@property
|
||||
def ignore_chain(self) -> bool:
|
||||
"""Whether to ignore chain callbacks."""
|
||||
return self.ignore_chain_
|
||||
|
||||
@property
|
||||
def ignore_agent(self) -> bool:
|
||||
"""Whether to ignore agent callbacks."""
|
||||
return self.ignore_agent_
|
||||
|
||||
@property
|
||||
def ignore_retriever(self) -> bool:
|
||||
"""Whether to ignore retriever callbacks."""
|
||||
return self.ignore_retriever_
|
||||
|
||||
def on_llm_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_start_common()
|
||||
|
||||
def on_llm_new_token(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_new_token_common()
|
||||
|
||||
def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_end_common()
|
||||
|
||||
def on_llm_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_error_common(*args, **kwargs)
|
||||
|
||||
def on_retry(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retry_common()
|
||||
|
||||
def on_chain_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_start_common()
|
||||
|
||||
def on_chain_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_end_common()
|
||||
|
||||
def on_chain_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_error_common()
|
||||
|
||||
def on_tool_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_start_common()
|
||||
|
||||
def on_tool_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_end_common()
|
||||
|
||||
def on_tool_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_error_common()
|
||||
|
||||
def on_agent_action(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_agent_action_common()
|
||||
|
||||
def on_agent_finish(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_agent_finish_common()
|
||||
|
||||
def on_text(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_text_common()
|
||||
|
||||
def on_retriever_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_start_common()
|
||||
|
||||
def on_retriever_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_end_common()
|
||||
|
||||
def on_retriever_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_error_common()
|
||||
|
||||
def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler":
|
||||
return self
|
||||
|
||||
|
||||
class FakeCallbackHandlerWithChatStart(FakeCallbackHandler):
|
||||
def on_chat_model_start(
|
||||
self,
|
||||
serialized: Dict[str, Any],
|
||||
messages: List[List[BaseMessage]],
|
||||
*,
|
||||
run_id: UUID,
|
||||
parent_run_id: Optional[UUID] = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
assert all(isinstance(m, BaseMessage) for m in chain(*messages))
|
||||
self.on_chat_model_start_common()
|
||||
|
||||
|
||||
class FakeAsyncCallbackHandler(AsyncCallbackHandler, BaseFakeCallbackHandlerMixin):
|
||||
"""Fake async callback handler for testing."""
|
||||
|
||||
@property
|
||||
def ignore_llm(self) -> bool:
|
||||
"""Whether to ignore LLM callbacks."""
|
||||
return self.ignore_llm_
|
||||
|
||||
@property
|
||||
def ignore_chain(self) -> bool:
|
||||
"""Whether to ignore chain callbacks."""
|
||||
return self.ignore_chain_
|
||||
|
||||
@property
|
||||
def ignore_agent(self) -> bool:
|
||||
"""Whether to ignore agent callbacks."""
|
||||
return self.ignore_agent_
|
||||
|
||||
async def on_retry(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retry_common()
|
||||
|
||||
async def on_llm_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_start_common()
|
||||
|
||||
async def on_llm_new_token(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_new_token_common()
|
||||
|
||||
async def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_end_common()
|
||||
|
||||
async def on_llm_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_error_common(*args, **kwargs)
|
||||
|
||||
async def on_chain_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_chain_start_common()
|
||||
|
||||
async def on_chain_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_chain_end_common()
|
||||
|
||||
async def on_chain_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_chain_error_common()
|
||||
|
||||
async def on_tool_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_tool_start_common()
|
||||
|
||||
async def on_tool_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_tool_end_common()
|
||||
|
||||
async def on_tool_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_tool_error_common()
|
||||
|
||||
async def on_agent_action(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_agent_action_common()
|
||||
|
||||
async def on_agent_finish(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_agent_finish_common()
|
||||
|
||||
async def on_text(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_text_common()
|
||||
|
||||
def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler":
|
||||
return self
|
||||
@ -0,0 +1,48 @@
|
||||
import os
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain_openai import OpenAI
|
||||
|
||||
os.environ["OPENAI_API_KEY"] = "foo"
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_openai_model_param() -> None:
|
||||
llm = OpenAI(model="foo")
|
||||
assert llm.model_name == "foo"
|
||||
llm = OpenAI(model_name="foo")
|
||||
assert llm.model_name == "foo"
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_openai_model_kwargs() -> None:
|
||||
llm = OpenAI(model_kwargs={"foo": "bar"})
|
||||
assert llm.model_kwargs == {"foo": "bar"}
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_openai_invalid_model_kwargs() -> None:
|
||||
with pytest.raises(ValueError):
|
||||
OpenAI(model_kwargs={"model_name": "foo"})
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_openai_incorrect_field() -> None:
|
||||
with pytest.warns(match="not default parameter"):
|
||||
llm = OpenAI(foo="bar")
|
||||
assert llm.model_kwargs == {"foo": "bar"}
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_completion() -> dict:
|
||||
return {
|
||||
"id": "cmpl-3evkmQda5Hu7fcZavknQda3SQ",
|
||||
"object": "text_completion",
|
||||
"created": 1689989000,
|
||||
"model": "text-davinci-003",
|
||||
"choices": [
|
||||
{"text": "Bar Baz", "index": 0, "logprobs": None, "finish_reason": "length"}
|
||||
],
|
||||
"usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
|
||||
}
|
||||
@ -0,0 +1,7 @@
|
||||
from langchain_openai.llms import __all__
|
||||
|
||||
EXPECTED_ALL = ["OpenAI", "AzureOpenAI"]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
||||
@ -0,0 +1,14 @@
|
||||
from langchain_openai import __all__
|
||||
|
||||
EXPECTED_ALL = [
|
||||
"OpenAI",
|
||||
"ChatOpenAI",
|
||||
"OpenAIEmbeddings",
|
||||
"AzureOpenAI",
|
||||
"AzureChatOpenAI",
|
||||
"AzureOpenAIEmbeddings",
|
||||
]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
||||
@ -0,0 +1,39 @@
|
||||
import pytest
|
||||
|
||||
from langchain_openai import ChatOpenAI, OpenAI
|
||||
|
||||
_EXPECTED_NUM_TOKENS = {
|
||||
"ada": 17,
|
||||
"babbage": 17,
|
||||
"curie": 17,
|
||||
"davinci": 17,
|
||||
"gpt-4": 12,
|
||||
"gpt-4-32k": 12,
|
||||
"gpt-3.5-turbo": 12,
|
||||
}
|
||||
|
||||
_MODELS = models = [
|
||||
"ada",
|
||||
"babbage",
|
||||
"curie",
|
||||
"davinci",
|
||||
]
|
||||
_CHAT_MODELS = [
|
||||
"gpt-4",
|
||||
"gpt-4-32k",
|
||||
"gpt-3.5-turbo",
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model", _MODELS)
|
||||
def test_openai_get_num_tokens(model: str) -> None:
|
||||
"""Test get_tokens."""
|
||||
llm = OpenAI(model=model)
|
||||
assert llm.get_num_tokens("表情符号是\n🦜🔗") == _EXPECTED_NUM_TOKENS[model]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model", _CHAT_MODELS)
|
||||
def test_chat_openai_get_num_tokens(model: str) -> None:
|
||||
"""Test get_tokens."""
|
||||
llm = ChatOpenAI(model=model)
|
||||
assert llm.get_num_tokens("表情符号是\n🦜🔗") == _EXPECTED_NUM_TOKENS[model]
|
||||
Loading…
Reference in New Issue