dh%SrSSKJrJrJrJrJrJrJrJ r J r J r SSK J r Jr SSKJr SSKJr SSKJrJrJrJrJr SSKJrJrJrJr SSKJr SS K J!r! SS K"J#r# SS K$J%r% S r&"S S\5r'g)zMLX Chat Wrapper.) AnyCallableDictIteratorListLiteralOptionalSequenceTypeUnion)AsyncCallbackManagerForLLMRunCallbackManagerForLLMRun)LanguageModelInput) BaseChatModel) AIMessageAIMessageChunk BaseMessage HumanMessage SystemMessage)ChatGenerationChatGenerationChunk ChatResult LLMResult)Runnable)BaseTool)convert_to_openai_tool) MLXPipelinez4You are a helpful, respectful, and honest assistant.c ^\rSrSr%Sr\\S'\"\S9r \\S'Sr \ \S'S\ 4U4S jjr SS \ \S \\ \S \\S\ S \4 SjjrSS \ \S \\ \S \\S\ S \4 SjjrS S \ \S\S\\S \4SjjrS\S \4Sjr\S\S \4Sj5r\S \4Sj5rSS \ \S \\ \S \\S\ S \\ 4 Sjjr!SS.S\"\#\$\\ 4\%\&\'4S\\#\\\(S\4S\ S \)\*\44U4Sjjjr+Sr,U=r-$)!ChatMLX,aMLX chat models. Works with `MLXPipeline` LLM. To use, you should have the ``mlx-lm`` python package installed. Example: .. code-block:: python from langchain_community.chat_models import chatMLX from langchain_community.llms import MLXPipeline llm = MLXPipeline.from_model_id( model_id="mlx-community/quantized-gemma-2b-it", ) chat = chatMLX(llm=llm) llmcontentsystem_messageN tokenizerkwargsc \>[TU]"S0UD6 URRUlg)N)super__init__r!r%)selfr& __class__s [/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/chat_models/mlx.pyr*ChatMLX.__init__Ds$ "6"++messagesstop run_managerreturnc URU5nURR"SU/X#S.UD6nURU5$N)promptsr1r2r()_to_chat_promptr! _generate_to_chat_resultr+r0r1r2r& llm_input llm_results r-r8ChatMLX._generateHsO((2 XX'' Kd GM ##J//r/c# URU5nURR"SU/X#S.UD6IShvN nURU5$N7fr5)r7r! _agenerater9r:s r-r?ChatMLX._agenerateUs\((2 88.. Kd GM  ##J// s5AA Atokenizereturn_tensorscU(d [S5e[US[5(d [S5eUVs/sHo@RU5PM nnURR UUSUS9$s snf)zHConvert a list of messages into a prompt format expected by wrapped LLM.z+At least one HumanMessage must be provided!z$Last message must be a HumanMessage!T)rAadd_generation_promptrB) ValueError isinstancer_to_chatml_formatr%apply_chat_template)r+r0rArBmmessages_dictss r-r7ChatMLX._to_chat_promptbs{JK K(2, 55CD D=EFX003XF~~11 "&) 2  GsA1messagec[U[5(aSnOG[U[5(aSnO/[U[5(aSnO[ S[ U535eX!R S.$)z+Convert LangChain message to ChatML format.system assistantuserzUnknown message type: )roler#)rGrrrrFtyper#)r+rMrRs r-rHChatMLX._to_chatml_formatws^ g} - -D  + +D  . .D5d7m_EF F99r/r<c/nURSH9n[[URS9URS9nUR U5 M; [ XRS9$)Nrr")rMgeneration_info) generations llm_output)rWrrtextrVappendrrX)r<chat_generationsgchat_generations r-r9ChatMLX._to_chat_resultsc''*A,!!&&11CTCTO  # #O 4 + (5J5J  r/cg)Nzmlx-chat-wrapperr()r+s r- _llm_typeChatMLX._llm_types!r/c +2# SSKJn SSKJn SSKJn SSKJnJn SSKJn URSURR5n U RSS5n U RSS 5n U RS S5n U RS S5n U RS S 5nU RSS5nU RSS5nURUSSS9nURUS5nURRnU"U =(d SXU5nU"SX5n[U"UURR UUS9[#U 55HuunnnSn[%U[&5(d*URR)UR+55nOURR)U5nU(a+[-[/US9S9nU(aUR1UUS9 Uv UU:Xd UcMUU;dM g g![a [S5ef=f7f)Nr) generate_step)make_logits_processors make_samplerzTCould not import mlx_lm python package. Please install it with `pip install mlx_lm`. model_kwargstempg max_tokensdrepetition_penaltyrepetition_context_sizetop_pg?min_pmin_tokens_to_keepTnp)rArB)samplerlogits_processorsr")rM)chunk)mlx.corecore mlx_lm.utilsrcmlx_lm.sample_utilsrdre ImportErrorgetr!pipeline_kwargsr7arrayr% eos_token_idzipmodelrangerGintdecodeitemrron_llm_new_token)r+r0r1r2r&mxrcrdrerfrgmax_new_tokensrjrkrlrmrnr; prompt_tokensr|rqrrtokenprobnrYrss r-_streamChatMLX._streams  .  ! P 2zz.$((2J2JK "&&vs3*..|SA.:.>.> $/ 2>1A1A %t2 $''5#''5"."2"23G"K((DQU(V 1. ~~22 t{sE:LM2 $ !$ "3   . !! MUD1#'DeS))~~,,UZZ\:~~,,U3+N44PQ00U0C  $)9ddl3! = ?  s( HG>G H1H9H>HH) tool_choicetoolsrautononec >UVs/sHn[U5PM nnUbU(a[U5S:wa[S[U5S35e[U[5(aUS;aSSU0S.nOo[U[ 5(aUSnOT[U[ 5(a1USSSUSS:wa[S US USSSS 35eO[S U35eX#S '[TU] "SSU0UD6$s snf)aBind tool-like objects to this chat model. Assumes model is compatible with OpenAI tool-calling API. Args: tools: A list of tool definitions to bind to this chat model. Supports any tool definition handled by :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`. tool_choice: Which tool to require the model to call. Must be the name of the single provided function or "auto" to automatically determine which function to call (if any), or a dict of the form: {"type": "function", "function": {"name": <>}}. **kwargs: Any additional parameters to pass to the :class:`~langchain.runnable.Runnable` constructor. rozKWhen specifying `tool_choice`, you must provide exactly one tool. Received z tools.rfunctionname)rSrrz Tool choice z/ was specified, but the only provided tool was .zEUnrecognized tool_choice type. Expected str, bool or dict. Received: rrr() rlenrFrGstrbooldictr)bind)r+rrr&toolformatted_toolsr,s r- bind_toolsChatMLX.bind_toolssS0EJJED1$7EJ  "{?#q( &&)/&:%;7D+s++&66 *%+[$9#KK..-a0 K..#A&z26:":.v67%&{m4-- 0{# 0tCy! 067 0  0  0 %)?C 0{# 0tCy! 0;< 0  0  0 (,  {#  !   * : : :  I  *    "3"" %):> F{#FtCy!F67 F  F % & FXRV 6=d38ndHhFGH6=eD#w~/F$LMN 6=  6= $k1 2 6=6=r/rN)(rtypingrrrrrrr r r r langchain_core.callbacks.managerr rlangchain_core.language_modelsr*langchain_core.language_models.chat_modelsrlangchain_core.messagesrrrrrlangchain_core.outputsrrrrlangchain_core.runnablesrlangchain_core.toolsr%langchain_core.utils.function_callingr%langchain_community.llms.mlx_pipelinerrrr(r/r-rsZ   >D .)H=Ri=mi=r/