ih>=dZddlmZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZddlmZddlmZddlmZmZdd lmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%dd l&m'Z'm(Z(dd l)m*Z*m+Z+m,Z,dd l-m.Z.m/Z/m0Z0dd l1m2Z2m3Z3ddl4m5Z5ddl6m7Z7m8Z8m9Z9m:Z:m;Z;ddleee?efee>efZ@eee>fZAejeCZDddZEGddeZFy)zWrapper around Perplexity APIs.) annotationsN) itemgetter) AnyDictIteratorListLiteralMappingOptionalTupleTypeTypeVarUnion)CallbackManagerForLLMRun)LanguageModelInput) BaseChatModelgenerate_from_stream) AIMessageAIMessageChunk BaseMessageBaseMessageChunk ChatMessageChatMessageChunkFunctionMessageChunk HumanMessageHumanMessageChunk SystemMessageSystemMessageChunkToolMessageChunk)JsonOutputParserPydanticOutputParser)ChatGenerationChatGenerationChunk ChatResult)Runnable RunnableMapRunnablePassthrough)from_envget_pydantic_field_names)is_basemodel_subclass) BaseModel ConfigDictField TypeAdaptermodel_validator)Self_BM)boundc<t|txr t|SN) isinstancetyper*)objs h/var/www/html/dev/engine/venv/lib/python3.12/site-packages/langchain_community/chat_models/perplexity.py_is_pydantic_classr9:s c4 ?%:3%??ceZdZUdZdZded<dZded< dZd ed < ee Z d ed < ee dddZ ded< eddZ ded< dZded< dZded< dZded< ed !Zed3d"Zed#$ed4d%Zed&$d5d'Zed6d(Zd7d)Z d8d*Z d9d+Z d: d;d,Z d: dd/Z d?d0ddd1 d@d2Z!y)AChatPerplexitya@`Perplexity AI` Chat models API. To use, you should have the ``openai`` python package installed, and the environment variable ``PPLX_API_KEY`` set to your API key. Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved on this class. Example: .. code-block:: python from langchain_community.chat_models import ChatPerplexity chat = ChatPerplexity( model="llama-3.1-sonar-small-128k-online", temperature=0.7, ) Nrclientz!llama-3.1-sonar-small-128k-onlinestrmodelgffffff?float temperature)default_factoryDict[str, Any] model_kwargs PPLX_API_KEY)defaultapi_key)rBaliasz Optional[str] pplx_api_keytimeout)rHz+Optional[Union[float, Tuple[float, float]]]request_timeoutint max_retriesFbool streamingz Optional[int] max_tokensT)populate_by_namec ddiS)NrIrEselfs r8 lc_secretszChatPerplexity.lc_secretsls //r:before)modec `t|}|jdi}t|D]M}||vrtd|d||vstj d|d|d|d|j |||<O|j|j}|rtd|d ||d<|S) z>Build extra kwargs from additional params that were passed in.rDzFound z supplied twice.z WARNING! z1 is not a default parameter. zJ was transferred to model_kwargs. Please confirm that z is what you intended.z Parameters za should be specified explicitly. Instead they were passed in as part of `model_kwargs` parameter.) r)getlist ValueErrorloggerwarningpop intersectionkeys)clsvaluesall_required_field_namesextra field_nameinvalid_model_kwargss r8 build_extrazChatPerplexity.build_extraps$2.v, ;JU" 6*5E!FGG!99!*.L!))3 4JN %+JJz$:j! ; 8DDUZZ\R 234ST  "'~ r:afterc ddl} |j|jd|_|S#t$r tdwxYw#t $r t dwxYw)z?Validate that api key and python package exists in environment.rNzTCould not import openai python package. Please install it with `pip install openai`.zhttps://api.perplexity.ai)rGbase_urlz`openai` has no `ChatCompletion` attribute, this is likely due to an old version of the openai package. Try upgrading it with `pip install --upgrade openai`.)openai ImportErrorOpenAIrIr=AttributeErrorr])rVrms r8validate_environmentz#ChatPerplexity.validate_environments|    --))4O(DK  ?   7  s+"AAAcb|j|j|jd|jS)z:Get the default parameters for calling PerplexityChat API.)rQstreamrA)rQrPrArDrUs r8_default_paramszChatPerplexity._default_paramss5//nn++    r:c:t|tr|j|jd}|St|trd|jd}|St|t rd|jd}|St|t rd|jd}|Std|)N)rolecontentsystemuser assistantzGot unknown type )r5rrvrwrrr TypeError)rVmessage message_dicts r8_convert_message_to_dictz'ChatPerplexity._convert_message_to_dicts g{ +$+LLW__ML /$,IL  .$*wGL   +$/GOOLL/y9: :r:ct|j}|d|vr td||d<|Dcgc]}|j|}}||fScc}w)Nstopz2`stop` found in both the input and default params.)dict_invocation_paramsr]r~)rVmessagesrparamsm message_dictss r8_create_message_dictsz$ChatPerplexity._create_message_dictssfd--.   !UVV!F6NCKLa66q9L Lf$$MsAc@|jd}|jdxsd}i}|jdr!t|d}d|vr |dd|d<||d<|jdr|d|d<|dk(s |tk(r t|S|d k(s |tk(r t|| S|d k(s |tk(r t |S|d k(s |t k(rt ||d S|dk(s |t k(rt ||dS|s |tk(r t||S||S)Nrvrw function_callname tool_callsry)rwrzrwadditional_kwargsrxfunction)rwrtool tool_call_id)rwr)rwrv)r[rrrrrrr)rV_dict default_classrvrwrrs r8_convert_delta_to_message_chunkz.ChatPerplexity._convert_delta_to_message_chunks@yy ))I&,""$ 99_ % !78M&=+@+H(* f%1> o . 99\ ".3L.A l + 6>].??$W5 5 [ M^$C!'EVW W X 2D!D%g6 6 Z =4H#H'eFmL L V^}0@@#G%BWX X ]&66#G$? ? 1 1r:c+K|j||\}}i||}t}|jdd|r||d<|jjj j d|dd|}d} |D]} t| ts| j} t| ddk(r5| dd} | jdg} |j| d|} | r| xjd| izc_ d } | jd } | t| nd}| j}t| | } |r|j| j | | yw)Nrsstop_sequencesT)rrschoicesr citationsdeltaF finish_reason)r)r|generation_info)chunkrT)rrr`r=chat completionscreater5rlenr[rr __class__r#on_llm_new_tokentext)rVrr run_managerkwargsrrdefault_chunk_class stream_resp first_chunkrchoicerrrs r8_streamzChatPerplexity._streamsw!% : :8T J v%F%f%, 8T" '+F# $9dkk&&2299 "4 39    EeT* 5#$)9%a(F +r2I88w!4E''K+CC'# "JJ7M5B5N=1TX #(// 'WE,,UZZu,EK- sEEc |jr#|j|f||d|}|r t|S|j||\}}i||}|jj j jdd|i|}t|jdjjd|ji} tt| gS) N)rrrrrr)r|) generationsrT)rPrrrr=rrrrrr|rwrr$r") rVrrrr stream_iterrrresponser|s r8 _generatezChatPerplexity._generate s >>&$,,#@FK+K88 $ : :8T J v%F%f%64;;##//66X XQWX$$Q'//77*H,>,>? ~g'F&GHHr:c>d|ji}i||jS)z,Get the parameters used to invoke the model.r?)r?rt)rV pplx_credss r8rz!ChatPerplexity._invocation_paramss, TZZ& 6*5 4 455r:cy)zReturn type of chat model.perplexitychatrTrUs r8 _llm_typezChatPerplexity._llm_type's r: json_schema)method include_rawstrictc t|dk(r| tdt|}|rt|dr|j}nY|r|j }nFt |t r|}n3t|jdk(rt|}|j}|jddid} |r t|n t} ntd |d |r^tjt!d | zd } tjd} | j#| gd} t%| | zS| | zS)aPModel wrapper that returns outputs formatted to match the given schema for Preplexity. Currently, Preplexity only supports "json_schema" method for structured output as per their official documentation: https://docs.perplexity.ai/guides/structured-outputs Args: schema: The output schema. Can be passed in as: - a JSON Schema, - a TypedDict class, - or a Pydantic class method: The method for steering model generation, currently only support: - "json_schema": Use the JSON Schema to parse the model output include_raw: If False then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If True then both the raw model response (a BaseMessage) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a dict with keys "raw", "parsed", and "parsing_error". kwargs: Additional keyword args aren't supported. Returns: A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`. | If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is False then Runnable outputs a dict. | If ``include_raw`` is True, then Runnable outputs a dict with keys: - "raw": BaseMessage - "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above. - "parsing_error": Optional[BaseException] rzIschema must be specified when method is not 'json_schema'. Received None.model_json_schema_TypedDictMetaschema)r6r)response_format)pydantic_objectzSUnrecognized method argument. Expected 'json_schema' Received: ''rawcyr4rT_s r8z7ChatPerplexity.with_structured_output..r:)parsed parsing_errorcyr4rTrs r8rz7ChatPerplexity.with_structured_output..rr:)rr) exception_key)r)r]r9hasattrrrr5rr6__name__r.rbindr!r r'assignrwith_fallbacksr&)rVrrrrris_pydantic_schemaradapterllm output_parser parser_assign parser_noneparser_with_fallbacks r8with_structured_outputz%ChatPerplexity.with_structured_output,sh` ] "~ %"4F!; !g+'#)":":"<#"(--/FD)"(f&&*::%f-")"5"5"7)))$,o#>!C&%V<%'  XQ   /66!%(=8M.44NKK#0#?#? _$@$ 3'*>> >& &r:)returnzDict[str, str])rdrCrr)rr0)rrC)r|rrrC)rList[BaseMessage]rOptional[List[str]]rz+Tuple[List[Dict[str, Any]], Dict[str, Any]])rMapping[str, Any]rzType[BaseMessageChunk]rr)NN) rrrrr"Optional[CallbackManagerForLLMRun]rrrzIterator[ChatGenerationChunk]) rrrrrrrrrr$)rr)rr>r4) rzOptional[_DictOrPydanticClass]rzLiteral['json_schema']rrOrzOptional[bool]rrrz-Runnable[LanguageModelInput, _DictOrPydantic])"r __module__ __qualname____doc__r=__annotations__r?rAr-rrDr(rIrKrNrPrQr, model_configpropertyrWr/ classmethodrirqrtr~rrrrrrrrTr:r8r<r<>sZ$FC4E34K+#(#>L.>V"' >i#L-=CH IDO@RK<It/ $J $/L00(#$2'"#*    %) %1D % 4 %2&27M2 2@%):> '#'"'8 '  ' ' 'X%):> I#I"I8 I  I  I,66  26^'*7!!% ^'.^'' ^'  ^'  ^'^' 7^'r:r<)r7rrrO)Gr __future__rloggingoperatorrtypingrrrrr r r r r rrlangchain_core.callbacksrlangchain_core.language_modelsr*langchain_core.language_models.chat_modelsrrlangchain_core.messagesrrrrrrrrrrrrlangchain_core.output_parsersr r!langchain_core.outputsr"r#r$langchain_core.runnablesr%r&r'langchain_core.utilsr(r)langchain_core.utils.pydanticr*pydanticr+r,r-r.r/typing_extensionsr0r1r>_DictOrPydanticClass_DictOrPydantic getLoggerrr^r9r<rTr:r8rs%"    >=    QRROOCPO" e9%T#s(^T#Y<=c "   8 $@L']L'r: