tjdv ZdZddlZddlZddlmZmZddlmZddlm Z m Z m Z m Z m Z mZmZmZmZmZddlmZmZddlmZddlmZmZmZdd lmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%dd l&m'Z'dd l(m)Z)m*Z*dd l+m,Z,m-Z-m.Z.dd l/m0Z0m1Z1m2Z2ddl3m4Z4ddl5m6Z6m7Z7ddl8m9Z9ddl:m;Z;ddlZ>m?Z?m@Z@mAZAejBeCZDede deEdeEde de f dZFede deEdeEde de f dZGdede eEe ffdZHde eEe fdefdZIde eEe fdee de fdZJGd d!eZKdS)"z#Wrapper around Minimax chat models.N)asynccontextmanagercontextmanager) itemgetter) Any AsyncIteratorCallableDictIteratorListOptionalSequenceTypeUnion)AsyncCallbackManagerForLLMRunCallbackManagerForLLMRun)LanguageModelInput) BaseChatModelagenerate_from_streamgenerate_from_stream) AIMessageAIMessageChunk BaseMessageBaseMessageChunk ChatMessageChatMessageChunk HumanMessage SystemMessage ToolMessage)OutputParserLike)JsonOutputKeyToolsParserPydanticToolsParser)ChatGenerationChatGenerationChunk ChatResult)Runnable RunnableMapRunnablePassthrough)BaseTool)convert_to_secret_strget_from_dict_or_envconvert_to_openai_tool) get_fields) BaseModel ConfigDictField SecretStrmodel_validatorclientmethodurlkwargsreturnc+Kddlm}|j||fi|5}||VddddS#1swxYwYdS)aContext manager for connecting to an SSE stream. Args: client: The httpx client. method: The HTTP method. url: The URL to connect to. kwargs: Additional keyword arguments to pass to the client. Yields: An EventSource object. r EventSourceN httpx_sser:streamr3r4r5r6r:responses e/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_community/chat_models/minimax.pyconnect_httpx_sserA?s&%%%%% vs - -f - -$k(#####$$$$$$$$$$$$$$$$$$s 377c Kddlm}|j||fi|4d{V}||WVdddd{VdS#1d{VswxYwYdS)aAsync context manager for connecting to an SSE stream. Args: client: The httpx client. method: The HTTP method. url: The URL to connect to. kwargs: Additional keyword arguments to pass to the client. Yields: An EventSource object. rr9Nr;r>s r@aconnect_httpx_sserCRs &%%%%%v}VS33F33$$$$$$$xk(######$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$sA A  A messagect|tr d|jd}nt|tr$d|j|jdd}nt|t r d|jd}nct|tr1d|j|j|j p|jdd }ntd |j j d |S) z%Convert a LangChain messages to Dict.userrolecontent assistant tool_calls)rHrIrKsystemtoolname)rHrI tool_call_idrNzGot unknown type 'z'.) isinstancerrIradditional_kwargsgetrrrOrN TypeError __class____name__)rD message_dicts r@_convert_message_to_dictrWgs'<((M &7?CC GY ' 'M!377 EE  G] + + M (W_EE G[ ) )M#0LIG$=$A$A&$I$I   KW->-GKKKLLL dctc|d}|dd}|dkr0i}|dd}|||d<t||St||S) z$Convert a dict to LangChain message.rHrIrJrKNrIrQrG)rRrr)rYrHrIrQrKs r@_convert_dict_to_messager]s 776??Dggi$$G {WW\400  !.8 l +z4MiniMaxChat.validate_environment..s2   e}( %-(((rXryrvMINIMAX_API_HOST)r)r*r-itemsupdate)clsrdefault_valuess r@validate_environmentz MiniMaxChat.validate_environments%: "I.!  % %  !  )#4466    f%%%&:   ,  - . & & !"  rXr?cg}t|ts|}|dD]^}t|d}t|d}|t ||_|di}||jd}t||S) NchoicesrD finish_reason)rrDgeneration_infousage) token_usage model_name) generations llm_output)rPdictr]rRappendr"rer$)rhr?rresrDrrrs r@_create_chat_resultzMiniMaxChat._create_chat_results (D)) '}}HI&  C.s9~>>G"1I1IJJJO   wPPP    ll7B// &*  kjIIIIrXmessages is_streamr6c d|D}|j}||d<||di|jdi||rd|d<|S)z#Create API request body parameters.c,g|]}t|Srl)rW)rms r@ z:MiniMaxChat._create_payload_parameters..s!GGG1!44GGGrXrtoolsTr=rl)rf_reformat_function_parametersrRr)rhrrr6 message_dictspayloads r@_create_payload_parametersz&MiniMaxChat._create_payload_parameterss{HGhGGG &+  **6::gr+B+BCCC      % $GH rX tools_argc|D]X}|ddkrJt|ddts)tj|dd|dd<YdS)z,Reformat the function parameters to strings.typefunction parametersN)rPstrjsondumps)rtool_args r@rz)MiniMaxChat._reformat_function_parameterssv"  H:--j$\2C77-6:ZZ(666$\2   rXstop run_managerr=c |std||n|j}|r |j|f||d|}t|S|j|fi|}d} |j|j} d| dd} ddl} | | d 5} | |j | } | dddn #1swxYwY| }d |vr+d |d vr!|d d dkrtd|| S)a<Generate next turn in the conversation. Args: messages: The history of the conversation as a list of messages. Code chat does not support context. stop: The list of stop words (optional). run_manager: The CallbackManager for LLM run, it's not used at the moment. stream: Whether to stream the results or not. Returns: The ChatResult that contains outputs generated by the model. 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Args: tools: A list of tool definitions to bind to this chat model. Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic models, callables, and BaseTools will be automatically converted to their schema dictionary representation. **kwargs: Any additional parameters to pass to the :class: `~langchain.runnable.Runnable` constructor. c,g|]}t|Srlr+)rrMs r@rz*MiniMaxChat.bind_tools..s!JJJD1$77JJJrXrrl)superbind)rhrr6formatted_toolsrTs r@ bind_toolszMiniMaxChat.bind_toolss: KJEJJJuww|< AnswerWithJustification( # answer='A pound of bricks and a pound of feathers weigh the same.', # justification='The weight of the feathers is much less dense than the weight of the bricks, but since both weigh one pound, they weigh the same.' # ) Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True): .. code-block:: python from langchain_community.chat_models import MiniMaxChat from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = MiniMaxChat() structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True) structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers") # -> { # 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_function_8953642285', 'type': 'function', 'function': {'name': 'AnswerWithJustification', 'arguments': '{"answer": "A pound of bricks and a pound of feathers weigh the same.", "justification": "The weight of the feathers is much less dense than the weight of the bricks, but since both weigh one pound, they weigh the same."}'}}]}, response_metadata={'token_usage': {'total_tokens': 257}, 'model_name': 'abab6.5-chat', 'finish_reason': 'tool_calls'}, id='run-d897e037-2796-49f5-847e-f9f69dd390db-0', tool_calls=[{'name': 'AnswerWithJustification', 'args': {'answer': 'A pound of bricks and a pound of feathers weigh the same.', 'justification': 'The weight of the feathers is much less dense than the weight of the bricks, but since both weigh one pound, they weigh the same.'}, 'id': 'call_function_8953642285', 'type': 'tool_call'}]), # 'parsed': AnswerWithJustification(answer='A pound of bricks and a pound of feathers weigh the same.', justification='The weight of the feathers is much less dense than the weight of the bricks, but since both weigh one pound, they weigh the same.'), # 'parsing_error': None # } Example: Function-calling, dict schema (method="function_calling", include_raw=False): .. code-block:: python from langchain_community.chat_models import MiniMaxChat from pydantic import BaseModel from langchain_core.utils.function_calling import convert_to_openai_tool class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str dict_schema = convert_to_openai_tool(AnswerWithJustification) llm = MiniMaxChat() structured_llm = llm.with_structured_output(dict_schema) structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers") # -> { # 'answer': 'A pound of bricks and a pound of feathers both weigh the same, which is a pound.', # 'justification': 'The difference is that bricks are much denser than feathers, so a pound of bricks will take up much less space than a pound of feathers.' # } zReceived unsupported arguments T)rfirst_tool_onlyrrN)key_namerrawcdSrrl_s r@z4MiniMaxChat.with_structured_output..sRVrX)parsed parsing_errorcdSrrlrs r@rz4MiniMaxChat.with_structured_output..sdrX)rr) exception_key)r)rrPr issubclassr.rr!r,r r'assignrwith_fallbacksr&) rhrrr6is_pydantic_schemallm output_parserr parser_assign parser_noneparser_with_fallbacks r@with_structured_outputz"MiniMaxChat.with_structured_outputsB`  IGvGGHH H'55W*VY:W:Woovh''  .Ah $///MM .f55jA&IH4!4M  '/6!%((=8M.4NNKKKK#0#?#? _$@$$ 3'''*>> >& &rX)F)NNN)NN)>rU __module__ __qualname____doc__propertyr rrrirmrfrs__annotations__rerointrpfloatrqr0rrrryr{r r}r1r~boolr/ model_configr2 classmethodrrr.r$rr rr staticmethodrrrr r#rrrrrr rrr(r%rrrr __classcell__)rTs@r@rcrcs|~~@AT#s(^AAAXA3X c3h   X GS E3   JAKQE5D#(5#>#>#>L$sCx.>>>V!EEZc',eD &K&K&KhsmKKKJ!&Y!7!7!7OY777It/:L_(###$3[$#4JE$ /,BJzJJJJ$>C[)6:NQ c3h c3hD\%):>!% 4949{#49tCy!4967 49  49  49 49494949r%):> --{#-tCy!-67 -  - % & ----d%)?C!% 99{#9tCy!9;< 9  9  9 9999H%)?C ,,{#,tCy!,;< ,  , * + ,,,,\=d38nd9oxQRS== $i/ 0 ======." I'I'I'dDO+,I' I'  I' $eD)O&<< = I'I'I'I'I'I'I'I'rXrc)Lrrlogging contextlibrroperatorrtypingrrrr r r r r rrlangchain_core.callbacksrrlangchain_core.language_modelsr*langchain_core.language_models.chat_modelsrrrlangchain_core.messagesrrrrrrrrr"langchain_core.output_parsers.baser*langchain_core.output_parsers.openai_toolsr r!langchain_core.outputsr"r#r$langchain_core.runnablesr%r&r'langchain_core.toolsr(langchain_core.utilsr)r*%langchain_core.utils.function_callingr,langchain_core.utils.pydanticr-pydanticr.r/r0r1r2 getLoggerrUloggerrrArCrWr]rarcrlrXr@rs)) ::::::::                        >=====                      @?????SRRRRRRRRROOOOOOOOOO))))))LLLLLLLLHHHHHH444444  8 $ $$c$3$S$C$H$$$$$$ $$#&$25$$$$$(kd38n2 3$sCx. 3[ 3 3 3 3* c3h*(,-=(>*****"G 'G 'G 'G 'G '-G 'G 'G 'G 'G 'rX