m iddlmZddlmZddlmZGddeeeefZ dZ ee gdZ d Z ee gd Z d S) )BaseOutputParser)PromptTemplate)overridecTeZdZUdZdZeed< ededeee ffdZ dS)FinishedOutputParserz4Output parser that checks if the output is finished.FINISHEDfinished_valuetextreturncv|}|j|v}||jd|fS)N)stripr replace)selfr cleanedfinisheds C:\Users\Dell Inspiron 16\Desktop\tws\AgrotaPowerBi\back-agrota-powerbi\mcp-client-agrota\venv\Lib\site-packages\langchain_classic/chains/flare/prompts.pyparsezFinishedOutputParser.parse s8**,,&'1t2B77AAN) __name__ __module__ __qualname____doc__r str__annotations__rtupleboolrrrrrsl>>$NC$$$6 B#B%T "2BBBXBBBrrzRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED. >>> CONTEXT: {context} >>> USER INPUT: {user_input} >>> RESPONSE: {response}) user_inputcontextresponse)templateinput_variablesa&Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase: >>> USER INPUT: {user_input} >>> EXISTING PARTIAL RESPONSE: {current_response} The question to which the answer is the term/entity/phrase "{uncertain_span}" is:)rcurrent_responseuncertain_spanN)langchain_core.output_parsersrlangchain_core.promptsrtyping_extensionsrrrrrPROMPT_TEMPLATEPROMPT"QUESTION_GENERATOR_PROMPT_TEMPLATEQUESTION_GENERATOR_PROMPTrrrr-s::::::111111&&&&&& B B B B B+E#t),<= B B B  999    &U"+N /HHHr