mirror of
https://github.com/microsoft/graphrag.git
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ad4cdd685f
* Update tiktoken * Add max_completion_tokens to model config * Update/remove outdated comments * Remove max_tokens from report generation * Remove max_tokens from entity summarization * Remove logit_bias from graph extraction * Remove logit_bias from claim extraction * Swap params if reasoning model * Add reasoning model support to basic search * Add reasoning model support for local and global search * Support reasoning models with dynamic community selection * Support reasoning models in DRIFT search * Remove unused num_threads entry * Semver * Update openai * Add reasoning_effort param
126 lines
3.7 KiB
Python
126 lines
3.7 KiB
Python
# 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 collections.abc import AsyncGenerator, Generator
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from typing import Any
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from pydantic import BaseModel
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from graphrag.config.enums import ModelType
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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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self.config = config or LanguageModelConfig(
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type=ModelType.MockChat, model="gpt-4o", api_key="mock"
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)
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async def achat(
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self,
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prompt: str,
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history: list | None = None,
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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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return self.chat(prompt, history, **kwargs)
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async def achat_stream(
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self,
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prompt: str,
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history: list | None = None,
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**kwargs,
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) -> AsyncGenerator[str, None]:
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"""Return the next response in the list."""
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if not self.responses:
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return
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for response in self.responses:
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response = (
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response.model_dump_json()
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if isinstance(response, BaseModel)
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else response
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)
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yield response
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def chat(
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self,
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prompt: str,
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history: list | None = None,
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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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def chat_stream(
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self,
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prompt: str,
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history: list | None = None,
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**kwargs,
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) -> Generator[str, None]:
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"""Return the next response in the list."""
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raise NotImplementedError
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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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self.config = LanguageModelConfig(
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type=ModelType.MockEmbedding, model="text-embedding-ada-002", api_key="mock"
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)
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def embed_batch(self, text_list: 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_list, 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_list]
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def embed(self, text: str, **kwargs: Any) -> list[float]:
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"""Generate an embedding for the input text."""
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return [1.0, 1.0, 1.0]
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async def aembed(self, text: str, **kwargs: Any) -> list[float]:
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"""Generate an embedding for the input text."""
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return [1.0, 1.0, 1.0]
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async def aembed_batch(
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self, text_list: list[str], **kwargs: Any
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) -> list[list[float]]:
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"""Generate an embedding for the input text."""
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if isinstance(text_list, 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_list]
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