hh%dZddlmZmZmZmZmZmZmZm Z m Z m Z ddl m Z mZddlmZddlmZddlmZmZmZmZmZddlmZmZmZmZddlmZdd l m!Z!dd l"m#Z#dd l$m%Z%d Z&Gd deZ'dS)zMLX Chat Wrapper.) AnyCallableDictIteratorListLiteralOptionalSequenceTypeUnion)AsyncCallbackManagerForLLMRunCallbackManagerForLLMRun)LanguageModelInput) BaseChatModel) AIMessageAIMessageChunk BaseMessage HumanMessage SystemMessage)ChatGenerationChatGenerationChunk ChatResult LLMResult)Runnable)BaseToolconvert_to_openai_tool) MLXPipelinez4You are a helpful, respectful, and honest assistant.c veZdZUdZeed<eeZeed<dZ e ed<de ffd Z dd e e d ee ed eede d ef d Z dd e e d ee ed eede d ef dZ dd e e dedeed efdZde d efdZeded efdZed efdZ dd e e d ee ed eede d eef dZ ddde!e"e#ee fe$e%e&fdee"eee'defde d e(e)e fffdZ*xZ+S) ChatMLXaMLX 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 \tjdi||jj|_dS)N)super__init__r!r%)selfr& __class__s `/var/www/FlaskApp/flask-venv/lib/python3.11/site-packages/langchain_community/chat_models/mlx.pyr*zChatMLX.__init__Ds0""6"""+messagesstop run_managerreturnc ||}|jjd|g||d|}||SN)promptsr0r1r()_to_chat_promptr! _generate_to_chat_resultr+r/r0r1r& llm_input llm_results r-r7zChatMLX._generateHs^((22 'TX' Kd   GM  ##J///r.cK||}|jjd|g||d|d{V}||Sr4)r6r! _agenerater8r9s r-r=zChatMLX._agenerateUs((22 .48. Kd   GM        ##J///r.Ftokenizereturn_tensorsc|stdt|dtstdfd|D}j||d|S)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!c:g|]}|Sr()_to_chatml_format).0mr+s r- z+ChatMLX._to_chat_prompt..os'FFF$0033FFFr.T)r>add_generation_promptr?) ValueError isinstancerr%apply_chat_template)r+r/r>r?messages_dictss` r-r6zChatMLX._to_chat_promptbs LJKK K(2, 55 ECDD DFFFFXFFF~11 "&) 2   r.messagect|trd}nOt|trd}n7t|trd}nt dt |||jdS)z+Convert LangChain message to ChatML format.system assistantuserzUnknown message type: )roler#)rIrrrrHtyper#)r+rLrQs r-rCzChatMLX._to_chatml_formatws g} - - GDD  + + GDD  . . GDDEd7mmEEFF F999r.r;cg}|jdD]@}tt|j|j}||At ||jS)Nrr")rLgeneration_info) generations llm_output)rUrrtextrTappendrrV)r;chat_generationsgchat_generations r-r8zChatMLX._to_chat_results'* 5 5A,!!&1111CTO  # #O 4 4 4 4(Z5J    r.cdS)Nzmlx-chat-wrapperr()r+s r- _llm_typezChatMLX._llm_types!!r.c+Kddlm}ddlm} ddlm}ddlm}m}ddlm}n#t$rtdwxYw|d|j j } | dd} | dd } | d d} | d d} | d d }| dd}| dd}| |dd}| |d}|j j}|| pd|||}|d| | }t|||j j||t#| D]\\}}}d}t%|t&s-|j |}n|j |}|r;t-t/|}|r||||V||ks|||vrdSdS)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)r>r?)samplerlogits_processorsr")rL)chunk)mlx.corecore mlx_lm.utilsr_mlx_lm.sample_utilsr`ra ImportErrorgetr!pipeline_kwargsr6arrayr% eos_token_idzipmodelrangerIintdecodeitemrron_llm_new_token)r+r/r0r1r&mxr_r`rarbrcmax_new_tokensrfrgrhrirjr: prompt_tokensrxrmrntokenprobnrWros r-_streamzChatMLX._streams ......  ! ! ! ! ! ! P P P P P P P P 2 2 2 2 2 2 2   ?   zz.$(2JKK "&&vs33*..|SAA.:.>.> $/ / 2>1A1A %t2 2 $''55#''55"."2"23G"K"K((DQU(VV 1.. ~2 ,t{sE5:LMM22 $&=  !$ M"3     . ! !! !    MUD1#'DeS)) 4~,,UZZ\\::~,,U33 +N44P4P4PQQQD00U0CCC  $$)9ddll3  s%?) tool_choicetoolsrautononec 0d|D}||rt|dkr tdt|dt|tr |dvrdd|id }nt|tr |d }ntt|t rM|d dd|ddkr(td |d |d ddd ntd|||d<t jdd|i|S)a*Bind 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. c,g|]}t|Sr(r)rDtools r-rFz&ChatMLX.bind_tools..s!JJJD1$77JJJr.NrkzKWhen specifying `tool_choice`, you must provide exactly one tool. Received z tools.rfunctionname)rRrrz Tool choice z/ was specified, but the only provided tool was .zEUnrecognized tool_choice type. Expected str, bool or dict. Received: rrr()lenrHrIstrbooldictr)bind)r+rrr&formatted_toolsr,s r- bind_toolszChatMLX.bind_toolss0KJEJJJ  "{ "?##q(( D&)/&:&:DDD+s++ &666 *%+[$9##KK.. -a0 K.. #A&z26:":.v677%W{WW- 0 0{# 0tCy! 067 0  0  0 0 0 0 %)?C 0 0{# 0tCy! 0;< 0  0  0 0 0 0 (,   {#  !      * : : : : : :  I  *    \  "3"""X" %):> FF{#FtCy!F67 F  F % & FFFFXRV 6=6=6=d38ndHhFGH6=eD#w~/F$LMN 6=  6= $k1 2 6=6=6=6=6=6=6=6=6=6=r.r N)(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_pipelinerrr r(r.r-rs                        >=====DDDDDD .-----))))))HHHHHH======Ri=i=i=i=i=mi=i=i=i=i=r.