NgddlZddlmZddlmZmZmZmZmZm Z m Z m Z m Z m Z mZmZddlZddlmZddlmZddlmZmZddlmZmZmZmZmZmZmZm Z ddl!m"Z"m#Z#dd l$m%Z%dd l&m'Z'm(Z(m)Z)m*Z*dd l+m,Z,m-Z-m.Z.dd l/m0Z0m1Z1m2Z2dd l3m4Z4ddl5m6Z6m7Z7ddl8m9Z9ddl:m;Z;ddlZ>m?Z?ddlm@Z@dedeeAeffdZBdeedeeeAeffdZCdedeDfdZEGddeZFGddeZGdS)N) itemgetter) AnyCallableDictIteratorListLiteralOptionalSequenceTupleTypeUnioncast)CallbackManagerForLLMRun)LanguageModelInput) BaseChatModelgenerate_from_stream) AIMessageAIMessageChunk BaseMessageBaseMessageChunk ChatMessage HumanMessage SystemMessage ToolMessage)JsonOutputParserPydanticOutputParser)OutputParserLike)JsonOutputKeyToolsParserPydanticToolsParsermake_invalid_tool_callparse_tool_call)ChatGenerationChatGenerationChunk ChatResult)Runnable RunnableMapRunnablePassthrough)BaseTool)convert_to_secret_strget_from_dict_or_envconvert_to_openai_tool)is_basemodel_subclass) BaseModelField SecretStr)Responsemessagereturnci}t|tr|j|jd}nt|tr d|jd}nt|t r d|jd}nt|t r5d|jd}d|jvr!|jd|d<|ddkrd|d<n8t|trd |j|j d }ntd ||S) z convert a BaseMessage to a dictionary with Role / content Args: message: BaseMessage Returns: messages_dict: role / content dict )rolecontentsystemuser assistant tool_callsr7Ntool)r6r7 tool_call_idGot unknown type ) isinstancerr6r7rrradditional_kwargsrr> TypeError)r3 message_dicts e/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/chat_models/sambanova.py_convert_message_to_dictrE:s$&L';''7 ' II G] + +7 (W_EE G\ * *7 &7?CC GY ' ' 7 +HH 74 4 4)0)B<)PL &I&",,*. Y' G[ ) )7#0  5G55666 messagescd|D}|S)z Convert a list of BaseMessages to a list of dictionaries with Role / content Args: messages: list of BaseMessages Returns: messages_dicts: list of role / content dicts c,g|]}t|S)rE).0ms rD z)_create_message_dicts..fs!CCCQ-a00CCCrFrJ)rG message_dictss rD_create_message_dictsrO\sDC(CCCM rFobjcJt|tot|SN)r@typer.)rPs rD_is_pydantic_classrTjs c4 ?%:3%?%??rFceZdZUdZedZeed< eedZ eed< edZ eed< edZ e ed < ed Z eed < ed Zeed < edZeeed< edZeeed< eddiZeeefed< eiZeeefed< GddZede fdZedeeeffdZedeeeffdZedefdZdeddffd Zdddde e!eeefe"ee#d efe$fd!ee!eeefe efd"ee dede%e&e'ff fd#Z( d5d$dd%d&ee!eeefe"e)fd'e*d(d)e dede%e&e!eeefe)fff d*Z+ d6d+e,eeefd,ee,ed e dede-f d-Z.d.e-de/fd/Z0d.e-de1e2fd0Z3 d7d1e,e'd,ee,ed2ee4dede5f d3Z6 d7d1e,e'd,ee,ed2ee4dede1e7f d4Z8xZ9S)8ChatSambaNovaCloudu SambaNova Cloud chat model. Setup: To use, you should have the environment variables: `SAMBANOVA_URL` set with your SambaNova Cloud URL. `SAMBANOVA_API_KEY` set with your SambaNova Cloud API Key. http://cloud.sambanova.ai/ Example: .. code-block:: python ChatSambaNovaCloud( sambanova_url = SambaNova cloud endpoint URL, sambanova_api_key = set with your SambaNova cloud API key, model = model name, max_tokens = max number of tokens to generate, temperature = model temperature, top_p = model top p, top_k = model top k, stream_options = include usage to get generation metrics ) Key init args — completion params: model: str The name of the model to use, e.g., Meta-Llama-3-70B-Instruct. streaming: bool Whether to use streaming handler when using non streaming methods max_tokens: int max tokens to generate temperature: float model temperature top_p: float model top p top_k: int model top k stream_options: dict stream options, include usage to get generation metrics Key init args — client params: sambanova_url: str SambaNova Cloud Url sambanova_api_key: str SambaNova Cloud api key Instantiate: .. code-block:: python from langchain_community.chat_models import ChatSambaNovaCloud chat = ChatSambaNovaCloud( sambanova_url = SambaNova cloud endpoint URL, sambanova_api_key = set with your SambaNova cloud API key, model = model name, max_tokens = max number of tokens to generate, temperature = model temperature, top_p = model top p, top_k = model top k, stream_options = include usage to get generation metrics ) Invoke: .. code-block:: python messages = [ SystemMessage(content="your are an AI assistant."), HumanMessage(content="tell me a joke."), ] response = chat.invoke(messages) Stream: .. code-block:: python for chunk in chat.stream(messages): print(chunk.content, end="", flush=True) Async: .. code-block:: python response = chat.ainvoke(messages) await response Tool calling: .. code-block:: python from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field( ..., description="The city and state, e.g. Los Angeles, CA" ) llm_with_tools = llm.bind_tools([GetWeather, GetPopulation]) ai_msg = llm_with_tools.invoke("Should I bring my umbrella today in LA?") ai_msg.tool_calls .. code-block:: none [ { 'name': 'GetWeather', 'args': {'location': 'Los Angeles, CA'}, 'id': 'call_adf61180ea2b4d228a' } ] Structured output: .. code-block:: python from typing import Optional from pydantic import BaseModel, Field class Joke(BaseModel): '''Joke to tell user.''' setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") structured_model = llm.with_structured_output(Joke) structured_model.invoke("Tell me a joke about cats") .. code-block:: python Joke(setup="Why did the cat join a band?", punchline="Because it wanted to be the purr-cussionist!") See `ChatSambanovaCloud.with_structured_output()` for more. Token usage: .. code-block:: python response = chat.invoke(messages) print(response.response_metadata["usage"]["prompt_tokens"] print(response.response_metadata["usage"]["total_tokens"] Response metadata .. code-block:: python response = chat.invoke(messages) print(response.response_metadata) r<default sambanova_urlsambanova_api_keyzMeta-Llama-3.1-8B-InstructmodelF streaming max_tokensffffff? temperatureNtop_ptop_k include_usageTstream_optionsadditional_headersceZdZdZdS)ChatSambaNovaCloud.ConfigTN__name__ __module__ __qualname__populate_by_namerJrFrDConfigrgrFrmr4cdSz9Return whether this model can be serialized by Langchain.FrJclss rDis_lc_serializablez%ChatSambaNovaCloud.is_lc_serializable! urFc ddiS)NrZrJselfs rD lc_secretszChatSambaNovaCloud.lc_secrets&s#%899rFc\|j|j|j|j|j|j|jdS)Return 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\r^r`rarbrdr{rvs rD_identifying_paramsz&ChatSambaNovaCloud._identifying_params*s8Z/+ZZ"1   rFcdS)7Get the type of language model used by this chat model.zsambanovacloud-chatmodelrJrvs rD _llm_typezChatSambaNovaCloud._llm_type;s *)rFkwargsc t|ddd|d<tt|dd|d<tjdi|dS) 'init and validate environment variablesrY SAMBANOVA_URLz,https://api.sambanova.ai/v1/chat/completionsrWrZSAMBANOVA_API_KEYNrJ)r+r*super__init__rwr __class__s rDrzChatSambaNovaCloud.__init__@su"6   B # # #  '< )<>Q R R' ' "# ""6"""""rF) tool_choiceparallel_tool_callstools.rrc Fd|D}|rlt|tr|dvrd}nRt|tr|rd}n8t|trt dt d|d}||d<||d<t jd d |i|S) zBind tool-like objects to this chat model tool_choice: does not currently support "any", choice like should be one of ["auto", "none", "required"] c,g|]}t|SrJr,)rKr=s rDrMz1ChatSambaNovaCloud.bind_tools..[s!JJJD1$77JJJrF)autononerequiredrrz7tool_choice must be one of ['auto', 'none', 'required']z;Unrecognized tool_choice type. Expected str, boolReceived: rrrrJ)r@strbooldict ValueErrorrbind)rwrrrrformatted_toolsrs rD bind_toolszChatSambaNovaCloud.bind_toolsMsKJEJJJ  !+s++ &BBB"(KK.. -",KK..  M!/!,// !K +}(;$%uww|< AnswerWithJustification( # answer='They weigh the same', # justification='A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same.' # ) Example: schema=Pydantic class, method="function_calling", include_raw=True: .. code-block:: python from langchain_community.chat_models import ChatSambaNovaCloud from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = ChatSambaNovaCloud(model="Meta-Llama-3.1-70B-Instruct", temperature=0) 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': [{'function': {'arguments': '{"answer": "They weigh the same.", "justification": "A pound is a unit of weight or mass, so one pound of bricks and one pound of feathers both weigh the same amount."}', 'name': 'AnswerWithJustification'}, 'id': 'call_17a431fc6a4240e1bd', 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'usage': {'acceptance_rate': 5, 'completion_tokens': 53, 'completion_tokens_after_first_per_sec': 343.7964936837758, 'completion_tokens_after_first_per_sec_first_ten': 439.1205661878638, 'completion_tokens_per_sec': 162.8511306784833, 'end_time': 1731527851.0698032, 'is_last_response': True, 'prompt_tokens': 213, 'start_time': 1731527850.7137961, 'time_to_first_token': 0.20475482940673828, 'total_latency': 0.32545061111450196, 'total_tokens': 266, 'total_tokens_per_sec': 817.3283162354066}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731527850}, id='95667eaf-447f-4b53-bb6e-b6e1094ded88', tool_calls=[{'name': 'AnswerWithJustification', 'args': {'answer': 'They weigh the same.', 'justification': 'A pound is a unit of weight or mass, so one pound of bricks and one pound of feathers both weigh the same amount.'}, 'id': 'call_17a431fc6a4240e1bd', 'type': 'tool_call'}]), # 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='A pound is a unit of weight or mass, so one pound of bricks and one pound of feathers both weigh the same amount.'), # 'parsing_error': None # } Example: schema=TypedDict class, method="function_calling", include_raw=False: .. code-block:: python # IMPORTANT: If you are using Python <=3.8, you need to import Annotated # from typing_extensions, not from typing. from typing_extensions import Annotated, TypedDict from langchain_community.chat_models import ChatSambaNovaCloud class AnswerWithJustification(TypedDict): '''An answer to the user question along with justification for the answer.''' answer: str justification: Annotated[ Optional[str], None, "A justification for the answer." ] llm = ChatSambaNovaCloud(model="Meta-Llama-3.1-70B-Instruct", temperature=0) structured_llm = llm.with_structured_output(AnswerWithJustification) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'answer': 'They weigh the same', # 'justification': 'A pound is a unit of weight or mass, so one pound of bricks and one pound of feathers both weigh the same amount.' # } Example: schema=OpenAI function schema, method="function_calling", include_raw=False: .. code-block:: python from langchain_community.chat_models import ChatSambaNovaCloud oai_schema = { 'name': 'AnswerWithJustification', 'description': 'An answer to the user question along with justification for the answer.', 'parameters': { 'type': 'object', 'properties': { 'answer': {'type': 'string'}, 'justification': {'description': 'A justification for the answer.', 'type': 'string'} }, 'required': ['answer'] } } llm = ChatSambaNovaCloud(model="Meta-Llama-3.1-70B-Instruct", temperature=0) structured_llm = llm.with_structured_output(oai_schema) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'answer': 'They weigh the same', # 'justification': 'A pound is a unit of weight or mass, so one pound of bricks and one pound of feathers both weigh the same amount.' # } Example: schema=Pydantic class, method="json_mode", include_raw=True: .. code-block:: from langchain_community.chat_models import ChatSambaNovaCloud from pydantic import BaseModel class AnswerWithJustification(BaseModel): answer: str justification: str llm = ChatSambaNovaCloud(model="Meta-Llama-3.1-70B-Instruct", temperature=0) structured_llm = llm.with_structured_output( AnswerWithJustification, method="json_mode", include_raw=True ) structured_llm.invoke( "Answer the following question. " "Make sure to return a JSON blob with keys 'answer' and 'justification'. " "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{ "answer": "They are the same weight", "justification": "A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities." }', additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 5.3125, 'completion_tokens': 79, 'completion_tokens_after_first_per_sec': 292.65701089829776, 'completion_tokens_after_first_per_sec_first_ten': 346.43324678555325, 'completion_tokens_per_sec': 200.012158915008, 'end_time': 1731528071.1708555, 'is_last_response': True, 'prompt_tokens': 70, 'start_time': 1731528070.737394, 'time_to_first_token': 0.16693782806396484, 'total_latency': 0.3949759876026827, 'total_tokens': 149, 'total_tokens_per_sec': 377.2381225105847}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731528070}, id='83208297-3eb9-4021-a856-ca78a15758df'), # 'parsed': AnswerWithJustification(answer='They are the same weight', justification='A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities.'), # 'parsing_error': None # } Example: schema=None, method="json_mode", include_raw=True: .. code-block:: from langchain_community.chat_models import ChatSambaNovaCloud llm = ChatSambaNovaCloud(model="Meta-Llama-3.1-70B-Instruct", temperature=0) structured_llm = llm.with_structured_output(method="json_mode", include_raw=True) structured_llm.invoke( "Answer the following question. " "Make sure to return a JSON blob with keys 'answer' and 'justification'. " "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{ "answer": "They are the same weight", "justification": "A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities." }', additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 4.722222222222222, 'completion_tokens': 79, 'completion_tokens_after_first_per_sec': 357.1315485254867, 'completion_tokens_after_first_per_sec_first_ten': 416.83279609305305, 'completion_tokens_per_sec': 240.92819585198137, 'end_time': 1731528164.8474727, 'is_last_response': True, 'prompt_tokens': 70, 'start_time': 1731528164.4906917, 'time_to_first_token': 0.13837409019470215, 'total_latency': 0.3278985247892492, 'total_tokens': 149, 'total_tokens_per_sec': 454.4088757208256}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731528164}, id='15261eaf-8a25-42ef-8ed5-f63d8bf5b1b0'), # 'parsed': { # 'answer': 'They are the same weight', # 'justification': 'A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities.'}, # }, # 'parsing_error': None # } Example: schema=None, method="json_schema", include_raw=True: .. code-block:: from langchain_community.chat_models import ChatSambaNovaCloud class AnswerWithJustification(BaseModel): answer: str justification: str llm = ChatSambaNovaCloud(model="Meta-Llama-3.1-70B-Instruct", temperature=0) structured_llm = llm.with_structured_output(AnswerWithJustification, method="json_schema", include_raw=True) structured_llm.invoke( "Answer the following question. " "Make sure to return a JSON blob with keys 'answer' and 'justification'. " "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{ "answer": "They are the same weight", "justification": "A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities." }', additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 5.3125, 'completion_tokens': 79, 'completion_tokens_after_first_per_sec': 292.65701089829776, 'completion_tokens_after_first_per_sec_first_ten': 346.43324678555325, 'completion_tokens_per_sec': 200.012158915008, 'end_time': 1731528071.1708555, 'is_last_response': True, 'prompt_tokens': 70, 'start_time': 1731528070.737394, 'time_to_first_token': 0.16693782806396484, 'total_latency': 0.3949759876026827, 'total_tokens': 149, 'total_tokens_per_sec': 377.2381225105847}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731528070}, id='83208297-3eb9-4021-a856-ca78a15758df'), # 'parsed': AnswerWithJustification(answer='They are the same weight', justification='A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities.'), # 'parsing_error': None # } NzReceived unsupported arguments rzL`schema` must be specified when method is `function_calling`. Received None.functionname)rT)rfirst_tool_only)key_namerr)pydantic_objectrzI`schema` must be specified when method is not `json_mode`. Received None.z\Unrecognized method argument. Expected one of `function_calling` or `json_mode`. Received: ``rawcdSrRrJ_s rDz;ChatSambaNovaCloud.with_structured_output..sRVrF)parsed parsing_errorcdSrRrJrs rDrz;ChatSambaNovaCloud.with_structured_output..sdrF)rr) exception_key)r)rrTr-rr rrr r/rrr(assignrwith_fallbacksr') rwrrrris_pydantic_schema tool_namellm output_parser parser_assign parser_noneparser_with_fallbacks rDwith_structured_outputz)ChatSambaNovaCloud.with_structured_outputts?n  GvGGHH H/77 ' ' '~ %/v66zB6JI//6( /BBC! 7J!($(888 !9&!!! { " "C" 3d9ov66 4V L L L 0 2 2 } $ $~ %C " 3d9ov66 4V L L L 0 2 2 5+1555   '/6!%((=8M.4NNKKKK#0#?#? _$@$$ 3'''*>> >& &rFmessages_dictsstopc |r-||j||j|j|j|jd|jd |}n%||j||j|j|j|jd|}t j}||j d|j dd|j ||}|j dkr!td |j d |jd |S) a* Performs a post request to the LLM API. Args: messages_dicts: List of role / content dicts to use as input. stop: list of stop tokens streaming: wether to do a streaming call Returns: An iterator of response dicts. T rGr^rr[r`rarbstreamrd)rGr^rr[r`rarbBearer application/json Authorizationz Content-Typeheadersjsonr1Sambanova /complete call failed with status code .)r^r[r`rarbrdrequestsSessionpostrYrZget_secret_valuere status_code RuntimeErrortext)rwrrr\rdata http_sessionresponses rD_handle_requestz"ChatSambaNovaCloud._handle_requestsB$  *"o#/"&"5   DD+"o#/   D ')) $$  !V4+A+R+R+T+T!V!V 2) %     3 & &+'+++=###  rFrc  |}|drtd|jd|dn,#t$r}td|d|jd}~wwxYw|ddd d d }|d }i}g}g}|ddd d }|r||d <|D]} t | d dtr7tj| d di| d d< | t| d#t$r:}| t| t|Yd}~d}~wwxYwt|||||ddd|d|d|d|dd|d} | S) Process a non streaming response from the api Args: response: A request Response object Returns generation: an AIMessage with model generation errorrr;Sambanova /complete call failed couldn't get JSON response response: Nchoicesrr3r7r<r;r argumentsT) return_id finish_reasonusager[system_fingerprintcreatedrr model_namerrid)r7rAr;invalid_tool_callsresponse_metadatar)rgetrr Exceptionrr@rdumpsappendr"r!rr) rwr response_dicter7rAr;rraw_tool_calls raw_tool_callr3s rD_process_responsez$ChatSambaNovaCloud._process_responses $MMOOM  )) "/+///$'''     -a--%]--    *1-i8<>M*-k:%%omt&T&T&TUUUU &--.}c!ffEE/!1!.y!9!!<_!M&**733+G4&34H&I(3 T"    s0AA A1A,,A11$E F 0FFc#K ddl}n#t$rtdwxYw||}|D]}|jdkr t d|jd|jd |jdkrFt|jtrtj |j}n t d|jd|jd| dr t d|jd|jdt|d dkrV|d d d }|d dd d }|d }t||i} nJd}|d }|| d|d|d|dd} t||| i} | V#t$r} t d| d|jd} ~ wwxYwdS) Process a streaming response from the api Args: response: An iterable request Response object Yields: generation: an AIMessageChunk with model partial generation rNTcould not import sseclient libraryPlease install it with `pip install sseclient-py`. error_eventrr[DONE]rrrdeltar7r)r7rrAr<rr[rrrr7rrrA3Error getting content chunk raw streamed response: data: ) sseclient ImportError SSEClienteventseventrrrr@rrloadsrlenrr) rwrrclientrrrr7rchunkmetadatars rD_process_stream_responsez+ChatSambaNovaCloud._process_stream_response$s         E   $$X..]]__7 7 E{m++"%+%%z%%% / :))!%*c22#z%*55*-'3--$z--- xx((*-'3--$z--- 4 ?++a//(,Y(:(>(>(O(O "&y/!"4W"=i"H!$Z .$+b!!!#%!$Z-:%)XXg%6%6*.w-267K2L'+I $$!/$+!.6.0 !!!  KKK   "*!**"Z** g7 7 s! #>EG G;G66G;rG run_managerc |jr"|j|f||d|}|rt|St|}|j||fddi|}||}t |d|jdi} t| gS)a Call SambaNovaCloud models. Args: messages: the prompt composed of a list of messages. stop: a list of strings on which the model should stop generating. If generation stops due to a stop token, the stop token itself SHOULD BE INCLUDED as part of the output. This is not enforced across models right now, but it's a good practice to follow since it makes it much easier to parse the output of the model downstream and understand why generation stopped. run_manager: A run manager with callbacks for the LLM. Returns: result: ChatResult with model generation rrr\Fr)r3generation_info generations) r\_streamrrOrrr#rr%) rwrGrrr stream_iterrrr3 generations rD _generatezChatSambaNovaCloud._generatess. > 9&$,#@FK 9+K888.x88'4'XXXQWXX((22#!:?!K   zl3333rFc+Kt|}|j||fddi|}||D]4}t|}|r||j||V5dS)a Stream the output of the SambaNovaCloud chat model. Args: messages: the prompt composed of a list of messages. stop: a list of strings on which the model should stop generating. If generation stops due to a stop token, the stop token itself SHOULD BE INCLUDED as part of the output. This is not enforced across models right now, but it's a good practice to follow since it makes it much easier to parse the output of the model downstream and understand why generation stopped. run_manager: A run manager with callbacks for the LLM. Yields: chunk: ChatGenerationChunk with model partial generation r\Tr3rN)rOrrr$on_llm_new_tokenr) rwrGrrrrrai_message_chunkrs rDrzChatSambaNovaCloud._streams./x88'4'WWWPVWW $ = =h G G   '0@AAAE F,,UZu,EEEKKKK   rFrRNFNN):rirjrk__doc__r0rYr__annotations__r1rZr[r\rr^intr`floatrar rbrdrrrerm classmethodrspropertyrxr|rrr rr rr)r&rrrr/r rrr2rrrrrrrr%r r$r __classcell__rs@rDrVrVnsOObr***M3***#(52#?#?#?y???!;<<? %= &d^ %=  %= $k1 2%=%=%=%=%=%=RDHv' !v'v'v'tCH~tI>?@v' : v'v'v' $eDcNI,E&FF Gv'v'v'v'v %) ;;T#s(^,;tCy!; ;  ;  ;;;;z9(9y9999vM M " #MMMMd%):> &4&4{#&4tCy!&467 &4  &4  &4&4&4&4V%):> {#tCy!67    % & rFrVc eZdZUdZedZeed< eedZ eed< eddZ eed< eddZ eed < ed Z e eed < ed Zeed < edZeed< edZe eed< ed Ze eed< ed Ze eed< ed Ze eed< edZe eed< eddiZeeefed< edddddZeeefed< d Ze eeefed< Gdd Zed!efd"Ze d!eeeffd#Z!e d!eeeffd$Z"e d!efd%Z#d&ed!d ffd' Z$d(e%d!efd)Z&d*e'e%d!efd+Z(d,ed!e)eeffd-Z* d6d*e'e%d.e e'ed e ed!e+fd/Z,d0e+d!e-fd1Z.d0e+d!e/e0fd2Z1 d7d*e'e%d.e e'ed3e e2d&ed!e3f d4Z4 d7d*e'e%d.e e'ed3e e2d&ed!e/e5f d5Z6xZ7S)8ChatSambaStudiou\ SambaStudio chat model. Setup: To use, you should have the environment variables: ``SAMBASTUDIO_URL`` set with your SambaStudio deployed endpoint URL. ``SAMBASTUDIO_API_KEY`` set with your SambaStudio deployed endpoint Key. https://docs.sambanova.ai/sambastudio/latest/index.html Example: .. code-block:: python ChatSambaStudio( sambastudio_url = set with your SambaStudio deployed endpoint URL, sambastudio_api_key = set with your SambaStudio deployed endpoint Key. model = model or expert name (set for CoE endpoints), max_tokens = max number of tokens to generate, temperature = model temperature, top_p = model top p, top_k = model top k, do_sample = wether to do sample process_prompt = wether to process prompt (set for CoE generic v1 and v2 endpoints) stream_options = include usage to get generation metrics special_tokens = start, start_role, end_role, end special tokens (set for CoE generic v1 and v2 endpoints when process prompt set to false or for StandAlone v1 and v2 endpoints) model_kwargs: Optional = Extra Key word arguments to pass to the model. ) Key init args — completion params: model: str The name of the model to use, e.g., Meta-Llama-3-70B-Instruct-4096 (set for CoE endpoints). streaming: bool Whether to use streaming max_tokens: inthandler when using non streaming methods max tokens to generate temperature: float model temperature top_p: float model top p top_k: int model top k do_sample: bool wether to do sample process_prompt: wether to process prompt (set for CoE generic v1 and v2 endpoints) stream_options: dict stream options, include usage to get generation metrics special_tokens: dict start, start_role, end_role and end special tokens (set for CoE generic v1 and v2 endpoints when process prompt set to false or for StandAlone v1 and v2 endpoints) default to llama3 special tokens model_kwargs: dict Extra Key word arguments to pass to the model. Key init args — client params: sambastudio_url: str SambaStudio endpoint Url sambastudio_api_key: str SambaStudio endpoint api key Instantiate: .. code-block:: python from langchain_community.chat_models import ChatSambaStudio chat = ChatSambaStudio=( sambastudio_url = set with your SambaStudio deployed endpoint URL, sambastudio_api_key = set with your SambaStudio deployed endpoint Key. model = model or expert name (set for CoE endpoints), max_tokens = max number of tokens to generate, temperature = model temperature, top_p = model top p, top_k = model top k, do_sample = wether to do sample process_prompt = wether to process prompt (set for CoE generic v1 and v2 endpoints) stream_options = include usage to get generation metrics special_tokens = start, start_role, end_role, and special tokens (set for CoE generic v1 and v2 endpoints when process prompt set to false or for StandAlone v1 and v2 endpoints) model_kwargs: Optional = Extra Key word arguments to pass to the model. ) Invoke: .. code-block:: python messages = [ SystemMessage(content="your are an AI assistant."), HumanMessage(content="tell me a joke."), ] response = chat.invoke(messages) Stream: .. code-block:: python for chunk in chat.stream(messages): print(chunk.content, end="", flush=True) Async: .. code-block:: python response = chat.ainvoke(messages) await response Token usage: .. code-block:: python response = chat.invoke(messages) print(response.response_metadata["usage"]["prompt_tokens"] print(response.response_metadata["usage"]["total_tokens"] Response metadata .. code-block:: python response = chat.invoke(messages) print(response.response_metadata) r<rWsambastudio_urlsambastudio_api_keyT)rXexcludebase_url streaming_urlNr[Fr\r]r^r_r`rarb do_sampleprocess_promptrcrdz<|begin_of_text|>z;<|begin_of_text|><|start_header_id|>{role}<|end_header_id|>z <|eot_id|>z.<|start_header_id|>assistant<|end_header_id|> )start start_roleend_roleendspecial_tokens model_kwargsceZdZdZdS)ChatSambaStudio.ConfigTNrhrJrFrDrmr)grnrFrmr4cdSrprJrqs rDrsz"ChatSambaStudio.is_lc_serializablejrtrFc dddS)Nrr)rrrJrvs rDrxzChatSambaStudio.lc_secretsos 1#8   rFc |j|j|j|j|j|j|j|j|j|j |j d S)rz r[r\r^r`rarbr r!rdr&r'r-rvs rDr|z#ChatSambaStudio._identifying_paramsvsOZ/+ZZ"1"1"1 -   rFcdS)r~zsambastudio-chatmodelrJrvs rDrzChatSambaStudio._llm_types '&rFrc t|dd|d<tt|dd|d<||d\|d<|d<tjdi|dS) rrSAMBASTUDIO_URLrSAMBASTUDIO_API_KEYrrNrJ)r+r*_get_sambastudio_urlsrrrs rDrzChatSambaStudio.__init__s$8 %'8% %  !)> )>@U V V) ) $%7;6P6P $ %7 7 3zF?3 ""6"""""rFr3c$t|tr|j}nrt|trd}nZt|trd}nBt|t rd}n*t|t rd}ntd||S)z Get the role of LangChain BaseMessage Args: message: LangChain BaseMessage Returns: str: Role of the LangChain BaseMessage r8r9r:r=r?)r@rr6rrrrrB)rwr3r6s rD _get_rolezChatSambaStudio._get_roles g{ + + ;
}|d|j|||jd?t j|}nw|jd}|D]W}||jd||z }|d|jdz }||jd z }X||jd z }|S) a Convert a list of BaseMessages to a: - dumped json string with Role / content dict structure when process_prompt is true, - string with special tokens if process_prompt is false for generic V1 and V2 endpoints Args: messages: list of BaseMessages Returns: str: string to send as model input depending on process_prompt param zsambaverse-conversation-id)conversation_idrGrG) message_idr6r7r"r#)r6 r$r%) r!rrr4r7rrr&format)rwrG messages_dictr3messages_strings rD_messages_to_stringz#ChatSambaStudio._messages_to_strings'   :#?--M$  j)00&-j $w 7 7#*?#j77OO"1':O# C C4#6|#D#K#K00$L$$ #9w#9#9#994#6z#BB t259 9OrFurlcd|vr|}|}n[d|vr|dd}|}n>|}d|vr)d|d}ntd||fS)a3 Get streaming and non streaming URLs from the given URL Args: url: string with sambastudio base or streaming endpoint url Returns: base_url: string with url to do non streaming calls streaming_url: string with url to do streaming calls openairzstream/r<genericzgeneric/streamUnsupported URL)replacejoinsplitr)rwr=r stream_urls rDr2z%ChatSambaStudio._get_sambastudio_urlss s??HJJ3;;y"55 ##!1!6!6syy7K7K!L!LJJ$%6777##rFrc d|jvrxt|}||j||j|j|j|j||jd }d|D}d|j dd}nnd|jvrd| |d g}|j|j |j|j|j|j|j d }|j i||j}d |D}||d }d|j i}nd|jvr|j|j |j|j|j|j|j d }|j i||j}d|D}|r| ||d}n| |g|d}d|j i}ntd|jdt!j} |r| |j||d} n| |j||d} | jdkr t-d| jd| jd| S)a Performs a post request to the LLM API. Args: messages_dicts: List of role / content dicts to use as input. stop: list of stop tokens streaming: wether to do a streaming call Returns: A request Response object r?rci|] \}}||| SrRrJrKkeyvalues rD z3ChatSambaStudio._handle_request..s#SSS:3ARCARARARrFrrrapi/v2/predict/genericitem0)rrJ) select_expertr!max_tokens_to_generater`rarbr Nci|] \}}||| SrRrJrHs rDrKz3ChatSambaStudio._handle_request..-s#WWWZS%UEVc5EVEVEVrF)itemsparamsrIapi/predict/genericcbi|],\}}||t|jt|d-S)N)rSrJ)rSrirrHs rDrKz3ChatSambaStudio._handle_request..>sEC$d5kk2SZZHH$$$rF)instancerR) instancesrRrA9only openai, generic v1 and generic v2 APIs are supportedTrFrrr)rrOr^r[r`rarbrdrQrrr<r!r r'rrrrrrrrr) rwrGrr\rrrrQrRrrs rDrzChatSambaStudio._handle_requestsB& t+ + +28<>"A"A 2GG&)= = =#d.F.Fx.P.PQQRE!%"&"5*./#/!^&&F ,8F8d&78WW6<<>>WWWF"f55Dd6GGIIJGG#d&: : :!%"&"5*./#/!^F ,8F8d&78"(,,..F   $ 8 8 B B$ #'":":8"D"D!E$d6GGIIJGGL$"6LLL   '))  #(("G$t)HH$(( wT%)H  3 & &$'$$=$$$  rFrc |}n,#t$r}td|d|jd}~wwxYwd|jvrb|dddd}|d }|ddd |d |d |d |dd}nd|jvr=|dddd}|ddd }|dd}n:d|jvr|ddd}d}|}nt d|jdt|i||S)rrrNr?rrr3r7rrrr[rrrrLrQrJ completionrS predictionsrArW)r7rArr)rrrrrrrr)rwrrrr7rrs rDrz!ChatSambaStudio._process_responsefs $MMOOMM   -a--%]--   t+ + +#I.q1),B,B?,S,SM&*9oa&8&A)&LG!%dB')HH&(G!%dB1>)-'):):.27m6:;O6P+/ ? ((H 88G,,8&(G!%dB1>)-'):):.27m6:;O6P+/ ? ((H-$+!.6.0 !&.a..!&..q< < ~&)= = = ++--6 6 5:d++D"8nW5a8A.QGh03D9BH~g.q1':;MN%&-1(^G-DQ-G-P-T-T -..'+8nW&=a&@&I&M&Mh&W&W7;H~g7Nq7Q$+8""%#&;"<"<;?>';RST;U$+<""%#&?"@"@6:8nW6Ma6P$+7""%#&:";";.28nW.Ea.H.Q.U.U$0/"/"-1N7,CA,Fw,O,S,S$.-"-"9=Xw8OPQ8R$+9""%#&<"="=7;H~g7Nq7Q$+8""%#&;"<"<@DXw@W$%A"")A++.3/M+N+N37>'3J13M$+4""%#&7"8"87&& "$"$H$&( '*2*, !&(a((!%((e6 6 r#d&: : : ++--6 6 5:d++D"8n[9!<^LGBH~k2156HI%&-1(^K-H-K-O-O -..'+8n[&A!&D&H&H&R&R7;H~k7R$%8""%#&;"<"<;?>+;V$%<""%#&?"@"@6:8n[6Q$%7""%#&:";";.28n[.I!.L.P.P$0/"/"-1N;,G,J,N,N$.-"-"9=X{8S$%9""%#&<"="=7;H~k7R$%8""%#&;"<"<@DX$/A""#A%%(S)G%H%H37>+3Nq3Q3U3U$54"4"3&& "$"$H$&( '*2*, !&(a((!%((e6 6 rL$"6LLL sS # F H I"H<<I(I0S S>$S99S>%H \11 ];]]rc |jr"|j|f||d|}|rt|S|||d}||}t |}t |gS)a Call SambaStudio models. Args: messages: the prompt composed of a list of messages. stop: a list of strings on which the model should stop generating. If generation stops due to a stop token, the stop token itself SHOULD BE INCLUDED as part of the output. This is not enforced across models right now, but it's a good practice to follow since it makes it much easier to parse the output of the model downstream and understand why generation stopped. run_manager: A run manager with callbacks for the LLM. Returns: result: ChatResult with model generation rFr\r r)r\rrrrr#r%) rwrGrrrrrr3rs rDr zChatSambaStudio._generateps. > 9&$,#@FK 9+K888''$%'HH((22#G444 zl3333rFc+K|||d}||D]4}t|}|r||j||V5dS)a Stream the output of the SambaStudio model. Args: messages: the prompt composed of a list of messages. stop: a list of strings on which the model should stop generating. If generation stops due to a stop token, the stop token itself SHOULD BE INCLUDED as part of the output. This is not enforced across models right now, but it's a good practice to follow since it makes it much easier to parse the output of the model downstream and understand why generation stopped. run_manager: A run manager with callbacks for the LLM. Yields: chunk: ChatGenerationChunk with model partial generation Trnr r N)rrr$r r)rwrGrrrrrrs rDrzChatSambaStudio._streams.''$$'GG $ = =h G G   '0@AAAE F,,UZu,EEEKKKK   rFrr)8rirjrkrr0rrrr1rrrr[r r\rr^rr`rrarbr r!rdrrr&r'rmrrsrrxr|rrrr4rr<r r2r2rrrrrrrr%r r$rrrs@rDrrsjrrh!5,,,OS,,,%*U99R==%A%A%AAAAE"d333Hc333'r4888M3888# 5...E8C=...DeE***It***KeD)))J))) #(5#5#5#5K%555"U4000E8E?000 5...E8C=... %d 3 3 3Ix~333 %*U4%8%8%8NHTN888F%*UOT3J%K%K%KNDcNKKKA%*U(W$D   &&&NDcN( .2L(4S>*1112        4[ DcN   X  T#s(^   X ('3'''X' # # # # # # # #0&D,=&#&&&&P$$sCx$$$$:%)$) ll{#ltCy!lD> l  llll\7 (7 y7 7 7 7 rO O " #OOOOh%):> 4 4{# 4tCy! 467 4  4  4 4 4 4J%):> {#tCy!67    % & rFr)Hroperatorrtypingrrrrrr r r r r rrrlangchain_core.callbacksrlangchain_core.language_modelsr*langchain_core.language_models.chat_modelsrrlangchain_core.messagesrrrrrrrrlangchain_core.output_parsersrr"langchain_core.output_parsers.baser*langchain_core.output_parsers.openai_toolsrr r!r"langchain_core.outputsr#r$r%langchain_core.runnablesr&r'r(langchain_core.toolsr)langchain_core.utilsr*r+%langchain_core.utils.function_callingr-langchain_core.utils.pydanticr.pydanticr/r0r1r2rrErOrrTrVrrJrFrDrs                             >=====                    @????? 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