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# [WIP] Tree of Thought introducing a new ToTChain. This PR adds a new chain called ToTChain that implements the ["Large Language Model Guided Tree-of-Though"](https://arxiv.org/pdf/2305.08291.pdf) paper. There's a notebook example `docs/modules/chains/examples/tot.ipynb` that shows how to use it. Implements #4975 ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: - @hwchase17 - @vowelparrot --------- Co-authored-by: Vadim Gubergrits <vgubergrits@outbox.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
"""Fake LLM wrapper for testing purposes."""
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from typing import Any, Dict, List, Mapping, Optional, cast
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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from pydantic import validator
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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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def get_num_tokens(self, text: str) -> int:
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"""Return number of tokens."""
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return len(text.split())
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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(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> 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) -> Dict[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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