a dg4@sdZddlmZmZmZmZmZmZddlm Z ddl m Z ddl m Z ddlmZmZddlmZddlmZdd lmZmZmZmZdd lmZmZGd d d e Zd S)z=Chain for applying self-critique using the SmartGPT workflow.)AnyDictListOptionalTupleType)BaseLanguageModel)Chain)get_colored_text) LLMResult PromptValue)CallbackManagerForChainRun)BasePromptTemplate)AIMessagePromptTemplateBaseMessagePromptTemplateChatPromptTemplateHumanMessagePromptTemplate) ConfigDictmodel_validatorc@seZdZUdZGdddZeed<dZeed<dZ e e ed<dZ e e ed <dZ e e ed <dZe e ed <d Zeed <dZeed<eZeed<eddZeddeeeefedddZeeedddZeeedddZd6eeefe ee e!e eefdddZ"d7eeefe eeeefd d!d"Z#e$eed#d$d%Z%eee e&e'efd&d'd(Z(e)dd)d*Z*e)dd+d,Z+e)dd-d.Z,d8e eee eeed/d0d1Z-d9e eee eed/d2d3Z.d:e eee eed/d4d5Z/dS); SmartLLMChainaChain for applying self-critique using the SmartGPT workflow. See details at https://youtu.be/wVzuvf9D9BU A SmartLLMChain is an LLMChain that instead of simply passing the prompt to the LLM performs these 3 steps: 1. Ideate: Pass the user prompt to an ideation LLM n_ideas times, each result is an "idea" 2. Critique: Pass the ideas to a critique LLM which looks for flaws in the ideas & picks the best one 3. Resolve: Pass the critique to a resolver LLM which improves upon the best idea & outputs only the (improved version of) the best output In total, SmartLLMChain pass will use n_ideas+2 LLM calls Note that SmartLLMChain will only improve results (compared to a basic LLMChain), when the underlying models have the capability for reflection, which smaller models often don't. Finally, a SmartLLMChain assumes that each underlying LLM outputs exactly 1 result. c@seZdZUdZeed<gZeeed<dZeed<e e dddZ e ee fddd Ze ee fdd d Ze ee fdd d ZdS)z"SmartLLMChain.SmartLLMChainHistoryquestionideascritiquereturncCs t|jS)N)lenrselfrs/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_experimental/smart_llm/base.pyn_ideas0sz*SmartLLMChain.SmartLLMChainHistory.n_ideascCs d|jiS)Nr)rrrrr ideation_prompt_inputs4sz9SmartLLMChain.SmartLLMChainHistory.ideation_prompt_inputscCsd|jiddt|jDS)NrcSs i|]\}}d|d|qSZidea_r.0iidearrr :zMSmartLLMChain.SmartLLMChainHistory.critique_prompt_inputs..)r enumeraterrrrr critique_prompt_inputs7sz9SmartLLMChain.SmartLLMChainHistory.critique_prompt_inputscCs(d|jiddt|jDd|jiS)NrcSs i|]\}}d|d|qSr#rr%rrr r)@r*zLSmartLLMChain.SmartLLMChainHistory.resolve_prompt_inputs..r)rr+rrrrrr resolve_prompt_inputs=s z8SmartLLMChain.SmartLLMChainHistory.resolve_prompt_inputsN)__name__ __module__ __qualname__rstr__annotations__rrrpropertyintr!rrr"r,r-rrrr SmartLLMChainHistory+s   r5prompt resolution output_keyN ideation_llm critique_llm resolver_llmllmr!Freturn_intermediate_stepshistoryZforbid)extrabefore)mode)valuesrcCst|d}|d}|d}|d}|s8|s8td|sH|sHtd|sX|sXtd|rp|rp|rp|rptd|S) z$Ensure we have an LLM for each step.r<r9r:r;zEither ideation_llm or llm needs to be given. Pass llm, if you want to use the same llm for all steps, or pass ideation_llm, critique_llm and resolver_llm if you want to use different llms for each step.zEither critique_llm or llm needs to be given. Pass llm, if you want to use the same llm for all steps, or pass ideation_llm, critique_llm and resolver_llm if you want to use different llms for each step.zEither resolve_llm or llm needs to be given. Pass llm, if you want to use the same llm for all steps, or pass ideation_llm, critique_llm and resolver_llm if you want to use different llms for each step.zLLMs are given for each step (ideation_llm, critique_llm, resolver_llm), but backup LLM (llm) is also given, which would not be used.)get ValueError)clsrCr<r9r:r;rrr validate_inputsYs*    zSmartLLMChain.validate_inputsrcCs|jjS)zDefines the input keys.)r6input_variablesrrrr input_keysszSmartLLMChain.input_keyscCs|jrdd|jgS|jgS)zDefines the output keys.rr)r>r8rrrr output_keyss zSmartLLMChain.output_keys)inputs run_managerrcsd}dvrd}fdd|jjD}|jjfi|}t|d}d|}|rh|j|d|jddvrd|krtd ||fS) zPrepare prompts from inputs.Nstopcsi|]}||qSrr)r&krKrr r)r*z.SmartLLMChain.prep_prompts..greenzPrompt after formatting:  endverbosez=If `stop` is present in any inputs, should be present in all.)r6rH format_promptr to_stringon_textrTrE)rrKrLrMZselected_inputsr6 _colored_text_textrrOr prep_promptsszSmartLLMChain.prep_prompts) input_listrLrcCst|j||d\}}||j_|||}||j_|||}||j_|||}|j rjd|d||j |iS|j |iS)N)rLrr) rZrVr?r_ideater _critiquer_resolver>r8)rr[rLr6rMrrr7rrr _calls    zSmartLLMChain._call)resultsteprcCsPt|jdkrtd|dt|jddkr@td|d|jddjS)zBetween steps, only the LLM result text is passed, not the LLMResult object. This function extracts the text from an LLMResult.r$z#In SmartLLM the LLM result in step z3 is not exactly 1 element. This should never happenrzIn SmartLLM the LLM in step zW returned more than 1 output. SmartLLM only works with LLMs returning exactly 1 output.)rZ generationsrEtext)rr`rarrr _get_text_from_llm_results  z'SmartLLMChain._get_text_from_llm_result)stagercCsg}|tdf|dkr|S|gddt|jDtd|jdf|dkrZ|S|tdftd |jd fg|d kr|Std |d dS)NzkQuestion: {question} Answer: Let's work this out in a step by step way to be sure we have the right answer:ideationcSs4g|],}tdt|ddt|ddfqS)Idea r$z: {idea_})rr1)r&r'rrr s"z4SmartLLMChain.get_prompt_strings..z3You are a researcher tasked with investigating the z response options provided. List the flaws and faulty logic of each answer option. Let's work this out in a step by step way to be sure we have all the errors:rzCritique: {critique}z7You are a resolver tasked with 1) finding which of the z answer options the researcher thought was best, 2) improving that answer and 3) printing the answer in full. Don't output anything for step 1 or 2, only the full answer in 3. Let's work this out in a step by step way to be sure we have the right answer:resolvezGstage should be either 'ideation', 'critique' or 'resolve', but it is 'z'. This should never happen.)appendrextendranger!rrE)rrdZ role_stringsrrr get_prompt_stringssN z SmartLLMChain.get_prompt_stringscCst|dS)NrerZ from_stringsrmrrrr ideation_promptszSmartLLMChain.ideation_promptcCst|dS)Nrrnrrrr critique_promptszSmartLLMChain.critique_promptcCst|dS)Nrirnrrrr resolve_promptszSmartLLMChain.resolve_prompt)rMrLrcsjr jnjjfij|r8|ndrfddtjD}t |D]<\}}t |d}d|dd|}|rh|j |dj d qh|St d dS) z2Generate n_ideas ideas as response to user prompt.Ncs&g|]}jgddqS)Zideatera)rcgenerate_prompt)r&_ callbacksr<r6rrMrr rh s z)SmartLLMChain._ideate..bluerfr$z: rQrR&llm is none, which should never happen)r9r<rorUr?r"Z get_childrlr!r+r rWrTrE)rrMrLrr'r(rXrYrrur r\s   zSmartLLMChain._ideatec Cs|jr |jn|j}|jfi|j}|r6|jnd}|r|j||g||dd}t |d}d|}|r|j |d|j d|St ddS) zACritique each of the ideas from ideation stage & select best one.Nrrryellowz Critique: rQrRrx) r:r<rprUr?r,handlersrcrsr rWrTrE) rrMrLr<r6rvrrXrYrrr r]s  zSmartLLMChain._critiquec Cs|jr |jn|j}|jfi|j}|r6|jnd}|r|j||g||dd}t |d}d|}|r|j |d|j d|St ddS) zBImprove upon the best idea as chosen in critique step & return it.NrirrrPz Resolution: rQrRrx) r;r<rqrUr?r-rzrcrsr rWrTrE) rrMrLr<r6rvr7rXrYrrr r^2s  zSmartLLMChain._resolve)N)N)NN)NN)NN)0r.r/r0__doc__r5rr2r8r1r9rrr:r;r<r!r4r>boolr?rZ model_configr classmethodrrrGr3rrIrJr rr rZr_r rcrrrmrrorprqr\r]r^rrrr rsz    $      6     rN)r{typingrrrrrrZlangchain.base_languagerZlangchain.chains.baser Zlangchain.inputr Zlangchain.schemar r Z langchain_core.callbacks.managerr Zlangchain_core.prompts.baserZlangchain_core.prompts.chatrrrrZpydanticrrrrrrr s