forked from Archives/langchain
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
"""Fake LLM wrapper for testing purposes."""
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from typing import Any, List, Mapping, Optional, cast
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from pydantic import validator
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from langchain.llms.base import LLM
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class FakeLLM(LLM):
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"""Fake LLM wrapper for testing purposes."""
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queries: Optional[Mapping] = None
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sequential_responses: Optional[bool] = False
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response_index: int = 0
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@validator("queries", always=True)
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def check_queries_required(
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cls, queries: Optional[Mapping], values: Mapping[str, Any]
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) -> Optional[Mapping]:
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if values.get("sequential_response") and not queries:
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raise ValueError(
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"queries is required when sequential_response is set to True"
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)
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return queries
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "fake"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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if self.sequential_responses:
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return self._get_next_response_in_sequence
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if self.queries is not None:
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return self.queries[prompt]
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if stop is None:
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return "foo"
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else:
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return "bar"
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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return {}
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@property
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def _get_next_response_in_sequence(self) -> str:
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queries = cast(Mapping, self.queries)
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response = queries[list(queries.keys())[self.response_index]]
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self.response_index = self.response_index + 1
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return response
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