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65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
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# Copyright (c) 2025 Microsoft Corporation.
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# Licensed under the MIT License
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"""A module containing mock model provider definitions."""
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from typing import Any
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from pydantic import BaseModel
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from graphrag.config.models.language_model_config import LanguageModelConfig
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from graphrag.language_model.response.base import (
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BaseModelOutput,
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BaseModelResponse,
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ModelResponse,
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)
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class MockChatLLM:
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"""A mock chat LLM provider."""
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def __init__(
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self,
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responses: list[str | BaseModel] | None = None,
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config: LanguageModelConfig | None = None,
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json: bool = False,
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**kwargs: Any,
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):
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self.responses = config.responses if config and config.responses else responses
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self.response_index = 0
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async def chat(
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self,
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prompt: str,
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**kwargs,
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) -> ModelResponse:
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"""Return the next response in the list."""
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if not self.responses:
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return BaseModelResponse(output=BaseModelOutput(content=""))
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response = self.responses[self.response_index % len(self.responses)]
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self.response_index += 1
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parsed_json = response if isinstance(response, BaseModel) else None
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response = (
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response.model_dump_json() if isinstance(response, BaseModel) else response
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)
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return BaseModelResponse(
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output=BaseModelOutput(content=response),
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parsed_response=parsed_json,
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)
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class MockEmbeddingLLM:
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"""A mock embedding LLM provider."""
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def __init__(self, **kwargs: Any):
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pass
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async def embed(self, text: str | list[str], **kwargs: Any) -> list[list[float]]:
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"""Generate an embedding for the input text."""
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if isinstance(text, str):
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return [[1.0, 1.0, 1.0]]
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return [[1.0, 1.0, 1.0] for _ in text]
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