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"""Fake LLM wrapper for testing purposes."""
from typing import Any, Dict, List, Mapping, Optional, cast
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models import LLM
from langchain_experimental.pydantic_v1 import validator
class FakeLLM(LLM):
"""Fake LLM wrapper for testing purposes."""
queries: Optional[Mapping] = None
sequential_responses: Optional[bool] = False
response_index: int = 0
@validator("queries", always=True)
def check_queries_required(
cls, queries: Optional[Mapping], values: Mapping[str, Any]
) -> Optional[Mapping]:
if values.get("sequential_response") and not queries:
raise ValueError(
"queries is required when sequential_response is set to True"
)
return queries
def get_num_tokens(self, text: str) -> int:
"""Return number of tokens."""
return len(text.split())
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "fake"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
if self.sequential_responses:
return self._get_next_response_in_sequence
if self.queries is not None:
return self.queries[prompt]
if stop is None:
return "foo"
else:
return "bar"
@property
def _identifying_params(self) -> Dict[str, Any]:
return {}
@property
def _get_next_response_in_sequence(self) -> str:
queries = cast(Mapping, self.queries)
response = queries[list(queries.keys())[self.response_index]]
self.response_index = self.response_index + 1
return response
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