a dg @sdZddlmZddlmZmZmZmZddlm Z ddl m Z ddl m Z ddlmZmZGdd d e ZGd d d eZGd d d eZdS)a We provide two strategies for generating thoughts in the Tree of Thoughts (ToT) framework to avoid repetition: These strategies ensure that the language model generates diverse and non-repeating thoughts, which are crucial for problem-solving tasks that require exploration. )abstractmethod)AnyDictListTuple)LLMChain)BasePromptTemplate)Field)get_cot_promptget_propose_promptc@s@eZdZUdZdZeed<ed ee edfe edddZ d S) BaseThoughtGenerationStrategyz7 Base class for a thought generation strategy. c.problem_description thoughts_pathkwargsreturncKsdS)zt Generate the next thought given the problem description and the thoughts generated so far. Nr)selfrrrrr{/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_experimental/tot/thought_generation.py next_thoughtsz*BaseThoughtGenerationStrategy.next_thoughtN)r) __name__ __module__ __qualname____doc__rint__annotations__rstrrrrrrrrr s   r c@sBeZdZUdZeedZeed<d e e e dfe e dddZ d S) SampleCoTStrategya Sample strategy from a Chain-of-Thought (CoT) prompt. This strategy works better when the thought space is rich, such as when each thought is a paragraph. Independent and identically distributed samples lead to diversity, which helps to avoid repetition. default_factorypromptr.rcKs(|jf||d|}t|tr$|SdS)N)rthoughts)predict_and_parse isinstancer)rrrrZ response_textrrrr4s zSampleCoTStrategy.next_thoughtN)r) rrrrr r r"rrrrrrrrrrr)s  rc@sheZdZUdZeedZeed<ee dZ e e e dfee fed<d e e e dfee ddd Zd S) ProposePromptStrategya  Strategy that is sequentially using a "propose prompt". This strategy works better when the thought space is more constrained, such as when each thought is just a word or a line. Proposing different thoughts in the same prompt completion helps to avoid duplication. r r". tot_memoryrrcKsh||jvs|j|sZ|jf|||jd|}|s6dSt|trV|ddd|j|<ndS|j|S)N)rr#nr$)r(r%rr&listpop)rrrrZ new_thoughtsrrrrLs z"ProposePromptStrategy.next_thoughtN)r)rrrrr r r"rrdictr(rrrrrrrrrrr'@s & r'N)rabcrtypingrrrrZlangchain.chains.llmrZlangchain_core.prompts.baserZpydanticr Z"langchain_experimental.tot.promptsr r r rr'rrrrs