mirror of
https://github.com/microsoft/graphrag.git
synced 2025-06-26 23:19:58 +00:00
53950f8442
* Check available models for type validation * Semver * Fix ruff and pyright * Apply feedback
120 lines
3.4 KiB
Python
120 lines
3.4 KiB
Python
# Copyright (c) 2025 Microsoft Corporation.
|
|
# Licensed under the MIT License
|
|
|
|
"""A module containing mock model provider definitions."""
|
|
|
|
from collections.abc import AsyncGenerator, Generator
|
|
from typing import Any
|
|
|
|
from pydantic import BaseModel
|
|
|
|
from graphrag.config.models.language_model_config import LanguageModelConfig
|
|
from graphrag.language_model.response.base import (
|
|
BaseModelOutput,
|
|
BaseModelResponse,
|
|
ModelResponse,
|
|
)
|
|
|
|
|
|
class MockChatLLM:
|
|
"""A mock chat LLM provider."""
|
|
|
|
def __init__(
|
|
self,
|
|
responses: list[str | BaseModel] | None = None,
|
|
config: LanguageModelConfig | None = None,
|
|
json: bool = False,
|
|
**kwargs: Any,
|
|
):
|
|
self.responses = config.responses if config and config.responses else responses
|
|
self.response_index = 0
|
|
|
|
async def achat(
|
|
self,
|
|
prompt: str,
|
|
history: list | None = None,
|
|
**kwargs,
|
|
) -> ModelResponse:
|
|
"""Return the next response in the list."""
|
|
return self.chat(prompt, history, **kwargs)
|
|
|
|
async def achat_stream(
|
|
self,
|
|
prompt: str,
|
|
history: list | None = None,
|
|
**kwargs,
|
|
) -> AsyncGenerator[str, None]:
|
|
"""Return the next response in the list."""
|
|
if not self.responses:
|
|
return
|
|
|
|
for response in self.responses:
|
|
response = (
|
|
response.model_dump_json()
|
|
if isinstance(response, BaseModel)
|
|
else response
|
|
)
|
|
|
|
yield response
|
|
|
|
def chat(
|
|
self,
|
|
prompt: str,
|
|
history: list | None = None,
|
|
**kwargs,
|
|
) -> ModelResponse:
|
|
"""Return the next response in the list."""
|
|
if not self.responses:
|
|
return BaseModelResponse(output=BaseModelOutput(content=""))
|
|
|
|
response = self.responses[self.response_index % len(self.responses)]
|
|
self.response_index += 1
|
|
|
|
parsed_json = response if isinstance(response, BaseModel) else None
|
|
response = (
|
|
response.model_dump_json() if isinstance(response, BaseModel) else response
|
|
)
|
|
|
|
return BaseModelResponse(
|
|
output=BaseModelOutput(content=response),
|
|
parsed_response=parsed_json,
|
|
)
|
|
|
|
def chat_stream(
|
|
self,
|
|
prompt: str,
|
|
history: list | None = None,
|
|
**kwargs,
|
|
) -> Generator[str, None]:
|
|
"""Return the next response in the list."""
|
|
raise NotImplementedError
|
|
|
|
|
|
class MockEmbeddingLLM:
|
|
"""A mock embedding LLM provider."""
|
|
|
|
def __init__(self, **kwargs: Any):
|
|
pass
|
|
|
|
def embed_batch(self, text_list: list[str], **kwargs: Any) -> list[list[float]]:
|
|
"""Generate an embedding for the input text."""
|
|
if isinstance(text_list, str):
|
|
return [[1.0, 1.0, 1.0]]
|
|
return [[1.0, 1.0, 1.0] for _ in text_list]
|
|
|
|
def embed(self, text: str, **kwargs: Any) -> list[float]:
|
|
"""Generate an embedding for the input text."""
|
|
return [1.0, 1.0, 1.0]
|
|
|
|
async def aembed(self, text: str, **kwargs: Any) -> list[float]:
|
|
"""Generate an embedding for the input text."""
|
|
return [1.0, 1.0, 1.0]
|
|
|
|
async def aembed_batch(
|
|
self, text_list: list[str], **kwargs: Any
|
|
) -> list[list[float]]:
|
|
"""Generate an embedding for the input text."""
|
|
if isinstance(text_list, str):
|
|
return [[1.0, 1.0, 1.0]]
|
|
return [[1.0, 1.0, 1.0] for _ in text_list]
|