jp'dZddlmZddlZddlmZmZmZmZddl m Z ddl m Z ddl mZddlmZdd lmZdd lmZdd lmZmZmZdd lmZGd de Zy)z Question answering over a graph.) annotationsN)AnyDictListOptional)ChainLLMChain)CallbackManagerForChainRun)BaseLanguageModel)BasePromptTemplate)Field)AQL_FIX_PROMPTAQL_GENERATION_PROMPT AQL_QA_PROMPT) ArangoGraphcPeZdZUdZedZded<ded<ded<ded <d Zd ed <d Zd ed<dZ ded<dZ d ed<dZ ded<dZ ded<dZ ded<dZded< d"fd Zed#dZed#dZed$dZeeeed d%d Z d& d'd!ZxZS)(ArangoGraphQAChainaChain for question-answering against a graph by generating AQL statements. *Security note*: Make sure that the database connection uses credentials that are narrowly-scoped to only include necessary permissions. Failure to do so may result in data corruption or loss, since the calling code may attempt commands that would result in deletion, mutation of data if appropriately prompted or reading sensitive data if such data is present in the database. The best way to guard against such negative outcomes is to (as appropriate) limit the permissions granted to the credentials used with this tool. See https://python.langchain.com/docs/security for more information. T)excludergraphr aql_generation_chain aql_fix_chainqa_chainquerystr input_keyresult output_key inttop_k aql_examplesFboolreturn_aql_queryreturn_aql_resultmax_aql_generation_attemptsallow_dangerous_requestsc Vt|di||jdur tdy)zInitialize the chain.TaIn order to use this chain, you must acknowledge that it can make dangerous requests by setting `allow_dangerous_requests` to `True`.You must narrowly scope the permissions of the database connection to only include necessary permissions. Failure to do so may result in data corruption or loss or reading sensitive data if such data is present in the database.Only use this chain if you understand the risks and have taken the necessary precautions. See https://python.langchain.com/docs/security for more information.N)super__init__r) ValueError)selfkwargs __class__s v/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/chains/graph_qa/arangodb.pyr-zArangoGraphQAChain.__init__Ks9 "6"  ( ( 4W   5c|jgSN)rr/s r2 input_keyszArangoGraphQAChain.input_keys[sr3c|jgSr5)rr6s r2 output_keyszArangoGraphQAChain.output_keys_s  r3cy)Ngraph_aql_chainr+r6s r2 _chain_typezArangoGraphQAChain._chain_typecs r3) qa_promptaql_generation_promptaql_fix_promptc ht||}t||}t||}|d|||d|S)zInitialize from LLM.)llmprompt)rrrr+r ) clsrAr=r>r?r0rrrs r2from_llmzArangoGraphQAChain.from_llmgsOI6'C8MN S@  !5'    r3c|xstj}|j}||j}|jj |j j|j|d|}d}d}d} d} | | |jdzkrd} tj| |tj} | sK|jdd|j |j|d d|j td || d }|jd| d|j|j|dd|j d dlm}  |j j%||j&} | dz } | | |jdzkr| d|d}t||jdd|j |jt-| dd|j |j/|j j||| d|}|j0||j.j0i}|j2r||d<|j4r| |d<|S#| $r}|j(}|jdd|j |j|dd|j |j*j |j j||d|}Yd}~rd}~wwxYw)aw Generate an AQL statement from user input, use it retrieve a response from an ArangoDB Database instance, and respond to the user input in natural language. Users can modify the following ArangoGraphQAChain Class Variables: :var top_k: The maximum number of AQL Query Results to return :type top_k: int :var aql_examples: A set of AQL Query Examples that are passed to the AQL Generation Prompt Template to promote few-shot-learning. Defaults to an empty string. :type aql_examples: str :var return_aql_query: Whether to return the AQL Query in the output dictionary. Defaults to False. :type return_aql_query: bool :var return_aql_result: Whether to return the AQL Query in the output dictionary. Defaults to False :type return_aql_result: bool :var max_aql_generation_attempts: The maximum amount of AQL Generation attempts to be made prior to raising the last AQL Query Execution Error. Defaults to 3. :type max_aql_generation_attempts: int ) adb_schemar# user_input) callbacksr"Nz```(?i:aql)?(.*?)```zInvalid Response:  )endverbosered)colorrKrLzResponse is Invalid: rz AQL Query (z):)rLgreen)AQLQueryExecuteErrorzAQL Query Execution Error: yellowz )rF aql_query aql_errorz Maximum amount of AQL Query Generation attempts reached. Unable to execute the AQL Query due to the following error: z z AQL Result:)rFrGrR aql_resultrRrT)r get_noop_manager get_childrrrunrschemar#r(refindallDOTALLon_textrLr.arangorPrr! error_messagerrrrr%r&)r/inputs run_manager _run_managerrHrGaql_generation_outputrRrSrTaql_generation_attemptpatternmatchesrPemrs r2_callzArangoGraphQAChain._call}sAB#S&@&Q&Q&S  **, DNN+ !% 9 9 = ="jj// $ 1 1(   !>!    !"  &)I)IA)MM.Gjj*?KG$$(dDLL%$$)D$,,%!#89N8O!PQQ I  45R8$,, !   dDLL !  4 !ZZ--iD 2 #a ' "q  &)I)IA)MMr    A Q- ]dllK  O7dll  "jj//(&(     //6$--*B*B#CD  "+F;   ! !#-F<  {( OO $$1tT\\%$$X64<<% )-(:(:(>(>&*jj&7&7%.%. ( )?)% s&I$$K2)A>K--K2)r0rreturnNone)riz List[str])rir) rAr r=r r>r r?r r0rrirr5)r_Dict[str, Any]r`z$Optional[CallbackManagerForChainRun]rirk)__name__ __module__ __qualname____doc__rr__annotations__rrr!r#r%r&r(r)r-propertyr7r9r< classmethodrrrrDrh __classcell__)r1s@r2rrsR t,E;,""IsJE3OL##d"$t#()(%*d*    !!!! )64I-;   &  2  +      0=ATT:T  Tr3r)ro __future__rrYtypingrrrrlangchain.chains.baserlangchain.chains.llmr langchain_core.callbacksr langchain_core.language_modelsr langchain_core.promptsr pydanticr+langchain_community.chains.graph_qa.promptsrrr)langchain_community.graphs.arangodb_graphrrr+r3r2r~sD&" ,,')?<5 Bzzr3