jzddlZddlmZddlmZddlmZmZmZm Z m Z m Z m Z m Z mZmZmZddlmZddlmZddlmZmZddlmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&dd l'm(Z(m)Z)m*Z*dd l+m,Z,dd l-m.Z.m/Z/m0Z0m1Z1dd l2m3Z3m4Z4m5Z5dd l6m7Z7m8Z8m9Z9ddl:m;Z;ddlm?Z?ddl@mAZAmBZBmCZCddlDmEZEGddeZFde)deGfdZHde(deGfdZIde eJefdefdZKdedefdZLdedeGfdZMde eJefd eedefd!ZNy)"N) itemgetter)Path) AnyCallableDictIteratorListMappingOptionalSequenceTypeUnioncast)CallbackManagerForLLMRun)LanguageModelInput) BaseChatModelgenerate_from_stream) AIMessageAIMessageChunk BaseMessageBaseMessageChunk ChatMessageChatMessageChunkFunctionMessageFunctionMessageChunk HumanMessageHumanMessageChunk SystemMessageSystemMessageChunk ToolMessageToolMessageChunk)InvalidToolCallToolCall ToolCallChunk)OutputParserLike)JsonOutputKeyToolsParserPydanticToolsParsermake_invalid_tool_callparse_tool_call)ChatGenerationChatGenerationChunk ChatResult)Runnable RunnableMapRunnablePassthrough)BaseTool)convert_to_openai_tool)is_basemodel_subclass) BaseModelFieldmodel_validator)SelfcjeZdZUdZdZeed<eed< dZe eed< dZ e eed< dZ e ed< d Z e ed < d Ze ed < d Zeed < dZeed< dZeed< dZeed< dZe e ed< dZe ed< dZe e ed< dZe eed< dZe ed< dZeed< dZeed< dZe e ed< dZeed< dZe eeed< d Z eed!< d"Z!e ed#< d$Z"e ed%< d Z#eed&< d'Z$eed(< d)Z%eed*< e&e'+Z(e)eefed,< d Z*eed-< dZ+e e,ee-fed.< dZ.eed/< d Z/eed0< e0d12d3e1fd4Z2de eed3e)eeffd5Z3d6ee4d3ee)eeffd7Z5d8e'd3e6fd9Z7 dId6ee4de eed:e e8d;ed3e6f d<Z9 dId6ee4de eed:e e8d;ed3e:e;f d=Zd?e=e,e)eefe>e?e@eAfd@e e,e'eefd;ed3eBeCe4fffdAZD dJddBdCe e,e)e>e?fdDed;ed3eBeCe,e)e?fffdEZEeFd3e)eeffdFZGeFd3efdGZHeFd3e)eeffdHZIxZJS)K ChatLlamaCppzllama.cpp model. To use, you should have the llama-cpp-python library installed, and provide the path to the Llama model as a named parameter to the constructor. Check out: https://github.com/abetlen/llama-cpp-python Nclient model_path lora_base lora_pathin_ctxn_partsseedTf16_kvF logits_all vocab_only use_mlock n_threadsn_batch n_gpu_layerssuffix max_tokensg? temperaturegffffff?top_plogprobsechostopg?repeat_penalty(top_k@last_n_tokens_sizeuse_mmapg?rope_freq_scaleg@rope_freq_base)default_factory model_kwargs streaming grammar_pathgrammarverboseafter)modereturnc ddlm}m}|j}gd}|Dcic]}|t ||}}|j |j |d<|j|j ||fi||_ |jr6|jr*|j}|j} td |d | d t|jtr"|j!|j|_ |S|jr"|j#|j|_ |S |S#t$r tdwxYwcc}w#t$r}td|d|d}~wwxYw) z4Validate that llama-cpp-python library is installed.r)Llama LlamaGrammarzCould not import llama-cpp-python library. Please install the llama-cpp-python library to use this embedding model: pip install llama-cpp-python)rWrXr<r;r=r?r@rArBrCrDrErGrVrUr^NrHz&Could not load Llama model from path: z. Received error zCCan only pass in one of grammar and grammar_path. Received grammar=z and grammar_path=.) llama_cpprcrd ImportErrorr:getattrrHupdaterZr9 Exception ValueErrorr]r\ isinstancestr from_string from_file) selfrcrdr:model_param_namesk model_paramser]r\s r/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/chat_models/llamacpp.pyvalidate_environmentz!ChatLlamaCpp.validate_environments  5__  $6GG5F74++5F G    (+/+<+G"1IJOS .1*o + /RC   s # 'll7B/ ; kjIIr} run_managerkwargsc &i|j||}|jr1|jds |j|fd|i|}t |S|j |}|j jdd|i|}|j|S)N tool_choicerr~) r|r[r_streamrrr9create_chat_completionr) rpr~rPrrr{ stream_iterrrs ru _generatezChatLlamaCpp._generates:D((.9&9 >>&**]";&$,,xS[SFSK' 4 4228< 54;;55W}WPVW''11r}c+BKi|j||}|j|}|jjd |dd|}t}d} |D]} | dz } t | t s| j} t| ddk(r:| dd} | dHt| d|} i} | jdx} r| | d<| jd}|r|| d<| j}t| | xsd } |r|j| j| | | yw) NT)r~streamrrdeltarrNr)chunkrNr)r|rr9rrrlr model_dumplen_convert_delta_to_message_chunkr __class__r+on_llm_new_tokentext)rpr~rPrrr{rresultdefault_chunk_classcountrchoicerrrNs rurzChatLlamaCpp._stream+sY:D((.9&9228< 333 "4 39 7EE QJEeT*((*5#$)9%a(Fg&3w!4E!O & ? ;;};3@0zz*-H.6 +"'// '/F$E,,UZZux,XK1sDDrtoolsrc f|Dcgc] }t|}}|Dcgc] }|dd }}rttr&tfd|Dst dd|dtt r/|Dcgc]}|ddk(s|} }| s\t dd|dtt r,t|dkDrt dt|d |d nt d |d <|Dcgc] }t|}}t |$dd |i|Scc}wcc}wcc}wcc}w)aBind tool-like objects to this chat model tool_choice: does not currently support "any", "auto" choices like OpenAI tool-calling API. should be a dict of the form to force this tool {"type": "function", "function": {"name": <>}}. functionnamec34K|]}dd|k(yw)rrNr).0rrs ru z*ChatLlamaCpp.bind_tools..fs$HRK +F3t; szTool choice tool_choice=z1 was specified, but the only provided tools were rerzQtool_choice=True can only be specified when a single tool is passed in. Received z tools.rzUnrecognized tool_choice type. Expected dict having format like this {"type": "function", "function": {"name": <>}}Received: rrr) r1rlranyrkrmboolrsuperbind) rprrrtoolformatted_toolsft tool_namesfchosenrs ` ru bind_toolszChatLlamaCpp.bind_toolsUsEJJED1$7EJ7FGbnV, G +t,HR%3{n5//9l!=K-..!!J-2G;2VA$3{n5//9l!=K.'!+$//25zl'C.a0  !!, / !,}DIJED1$7EJw|", "<|eot_id|>"], ) structured_llm = llm.with_structured_output(AnswerWithJustification) structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers") # -> AnswerWithJustification( # answer='They weigh the same', # justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.' # ) Example: Pydantic schema (include_raw=True): .. code-block:: python from langchain_community.chat_models import ChatLlamaCpp from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = ChatLlamaCpp( temperature=0., model_path="./SanctumAI-meta-llama-3-8b-instruct.Q8_0.gguf", n_ctx=10000, n_gpu_layers=4, n_batch=200, max_tokens=512, n_threads=multiprocessing.cpu_count() - 1, repeat_penalty=1.5, top_p=0.5, stop=["<|end_of_text|>", "<|eot_id|>"], ) 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_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}), # 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'), # 'parsing_error': None # } Example: dict schema (include_raw=False): .. code-block:: python from langchain_community.chat_models import ChatLlamaCpp 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 = ChatLlamaCpp( temperature=0., model_path="./SanctumAI-meta-llama-3-8b-instruct.Q8_0.gguf", n_ctx=10000, n_gpu_layers=4, n_batch=200, max_tokens=512, n_threads=multiprocessing.cpu_count() - 1, repeat_penalty=1.5, top_p=0.5, stop=["<|end_of_text|>", "<|eot_id|>"], ) structured_llm = llm.with_structured_output(dict_schema) structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers") # -> { # 'answer': 'They weigh the same', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' # } zReceived unsupported arguments zJschema must be specified when method is 'function_calling'. Received None.rr)typerrT)rfirst_tool_only)key_namerrawcyrr_s ruz5ChatLlamaCpp.with_structured_output..*sRVr})parsed parsing_errorcyrrrs rurz5ChatLlamaCpp.with_structured_output..,sdr})rr) exception_key)r)rkrlrr2r1rr'rr r&r/assignrwith_fallbacksr.) rprrris_pydantic_schema tool_namerllm output_parser parser_assign parser_noneparser_with_fallbacks ruwith_structured_outputz#ChatLlamaCpp.with_structured_outputs#X >vhGH H'5W:OPV:W >! +62:>vF ) 7JK oovhKo@ .AD&)*D/M5"DM /66!%(=8M.44NKK#0#?#? _$@$ 3'*>> >& &r}c:id|ji|jS)zReturn a dictionary of identifying parameters. This information is used by the LangChain callback system, which is used for tracing purposes make it possible to monitor LLMs. r:)r:ryrps ru_identifying_paramsz ChatLlamaCpp._identifying_params4s/ T__-  ""   r}cy)z7Get the type of language model used by this chat model.zllama-cpp-pythonrrs ru _llm_typezChatLlamaCpp._llm_typeDs"r}c|j|j|j|j|j|j |j d}|jr|j|d<|S)z>Get the default parameters for calling create_chat_completion.)rKrLrMrSrNrxrQr])rKrLrMrSrNrPrQr])rpr{s ruryzChatLlamaCpp._default_paramsIs\//++ZZZZ "ii"11  << $ F9  r})NNr)K__name__ __module__ __qualname____doc__r9r__annotations__rmr;r r<r=intr?r@rArrBrCrDrErGrHrIrKrLfloatrMrNrOrPr rQrSrUrVrWrXr4rrZrr[r\rrr]r^r5r6rvr|rrr,rrrrr+rr r r3rr0r-rrrpropertyrrry __classcell__)rs@rur8r8=sOFCO+#Ix}#0#Ix}#AE3GS?D#N-FD1J@J/It,#Ix}#CGS/#'L(3-&F FHSM SJ3K.E5."Hhsm"ND$% $D(49 $@NE2E3O.  MHd1 OU )#NE#+#(#>L$sCx.>?It8/3L(5d+,3 GS GT)'":d:#:xHT#Y$7DcN$[) d38n JDJZJ&%):> 2{#2tCy!267 2  2  20%):> ({#(tCy!(67 (  ( % & (\9= 1=d38nd9oxQRS1=eD$O45 1=  1= $k1 2 1=j:>j'" j'tT)_456j' j'  j' $eD)O&<< = j'X  T#s(^    "3"" c3h  r}r8 tool_callracLd|d|dtj|dddSNridrargs)r arguments)rrr)jsondumps)rs ru!_lc_tool_call_to_openai_tool_callrZs4of%If$56  r}invalid_tool_callc&d|d|d|dddSrr)rs ru)_lc_invalid_tool_call_to_openai_tool_callres.%%f-*62  r}_dictc |jd}|jd}|jd}|dk(rt|jdd||S|dk(r|jddxsd}i}|jd x}rt||d <g}g}|jd x} r.| |d <| D]$} t| d } | s|j | &t||||||S|dk(rt|jdd||S|dk(r;t|jddtt|jd|S|dk(rKi}d|vr|d|d<t|jddtt|jd|||St|jddtt||S#t $r1} t | t| } |j | Yd } ~ Ld } ~ wwxYw)zConvert a dictionary to a LangChain message. Args: _dict: The dictionary. Returns: The LangChain message. rolerrusercontent)rrr assistant function_call tool_callsT) return_idN)radditional_kwargsrrrinvalid_tool_callssystemrrrrr tool_call_id)rrrrrrrr)rrrr)rrjr(rmrrrrr r)rrrid_rrrrrraw_tool_calls raw_tool_calltcrt invalid_tcs rurrrs& 99V D 99V D ))D/C v~EIIi$<4PP  ))Ir*0b"$!IIo6 6= 615m1D o . "YY|4 4> 4.< l +!/  .($GB  "))"-"0/!1     UYYy"%=DSQQ  IIi,4UYYv=N3OTW    U?(-f f %IIi,c599^#<=/   IIi,4T?s  E!:!7 s1v!NJ&--j99:s7 G  H&G??Hrc|rIt|tr9g}|D]0}t|tr d|vr |ddk(r |j|2|S|}|S)zFormat message content.rtool_use)rllistrr)rformatted_contentblocks ru_format_message_contentrsb:gt,E5$'eO&MZ/!((/ $ r}rc dt|ji}|jxs|jj dx}||d<t |t r|j|d<|St |trd|d<|St |trd|d<d|jvr|jd|d<|js |jrK|jDcgc] }t|c}|jDcgc] }t|c}z|d<ngd|jvrX|jd|d<hd }|dDcgc]+}|jDcic] \}}||vs ||c}}-c}}}|d<n d|vsd|vr |dxsd|d<|St |trd |d<|St |t rd |d<|St |t"rBd |d<|j$|d <hd}|jDcic] \}}||vs ||}}}|St'd|cc}wcc}wcc}}wcc}}}wcc}}w)zConvert a LangChain message to a dictionary. Args: message: The LangChain message. Returns: The dictionary. rrNrrrrr>rrrrrrr>rrrzGot unknown type )rrrrrrlrrrrrrrritemsrrr r TypeError) r message_dictrr tool_call_supported_propsrrrvsupported_propss rurrs *7??;$L E 9 9 = =f EER# V';'&|| VL K G\ *% VH G GY '* V g77 7,3,E,Eo,VL )   !;!;@G@R@R*@R"1"5@R*"444B:"=4*L & W66 6)0)B)B<)PL &(B %".l!;*!;I#,//"3V"3$!QqH)H4 H.+H.0H4? H; H;.H4 default_classc|jd}tt|jd}tt|jdxsd}i}|jdr!t|d}d|vr |dd|d<||d<g}|jdx}rc||d<|D]Y} t | djd| djd | jd| d  } |j | [|d k(s |tk(r t|| S|dk(s |tk(rt||||S|dk(s |tk(r t|| S|dk(s |tk(rt||d|S|dk(s |tk(rt||d|S|s |tk(rt|||S||| S#t $rYwxYw)Nrrrrrrrrrindex)rrrrr)rrr)rrrtool_call_chunksrrrr)rrrr) rrrmrr$rKeyErrorrrrrr!r) rrrrrrrrrrtcrs rurrs ))D/C UYYv& 'D3 ),23G  yy!U?34 ] "}V'<'D$&M& !-:/*<00~0*8,'!C )Z,,V4Z,,[9wwt}g,  !'' 2" v~*;; S99    ?/-    ].@@!'c::  }0DD#G%-CPP =,<<%*?C   "22dsCCW55/  s(AF44 GG)Oroperatorrpathlibrtypingrrrrr r r r r rrlangchain_core.callbacksrlangchain_core.language_modelsr*langchain_core.language_models.chat_modelsrrlangchain_core.messagesrrrrrrrrrrrrr r!langchain_core.messages.toolr"r#r$"langchain_core.output_parsers.baser%*langchain_core.output_parsers.openai_toolsr&r'r(r)langchain_core.outputsr*r+r,langchain_core.runnablesr-r.r/langchain_core.toolsr0%langchain_core.utils.function_callingr1langchain_core.utils.pydanticr2pydanticr3r4r5typing_extensionsr6r8rrrrmrrrrrr}rur/s*     >= RQ? 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