a dgG@sddlZddlZddlmZddlmZmZmZmZm Z m Z m Z m Z m Z mZddlmZddlmZddlmZmZddlmZddlmZmZmZdd lmZdd lmZdd l m!Z!dd l"m#Z#m$Z$dd l%m&Z&ddl'm(Z(m)Z)ddl*m+Z+ddl,m-Z-ddl.m/Z/ddl0m1Z1m2Z2ddl3m4Z4dZ5dddddddidgddZ6e de4dZ7eee7fZ8ee9d d!d"Z:eed#d$d%Z;Gd&d'd'e Zed+d,d-d.Gd/d0d0eZ?dS)1N) itemgetter) AnyCallableDictListOptionalSequenceType TypedDictTypeVarUnion) ChatOllama) deprecated)AsyncCallbackManagerForLLMRunCallbackManagerForLLMRun)LanguageModelInput) AIMessage BaseMessageToolCall)OutputParserLike)JsonOutputParser)PydanticOutputParser)ChatGeneration ChatResult)SystemMessagePromptTemplate)RunnableRunnableLambda) RunnableMap)RunnablePassthrough)BaseTool)is_basemodel_instanceis_basemodel_subclass) BaseModela%You have access to the following tools: {tools} You must always select one of the above tools and respond with only a JSON object matching the following schema: {{ "tool": , "tool_input": }} Z__conversational_responsezNRespond conversationally if no other tools should be called for a given query.objectresponsestringz$Conversational response to the user.)type description)r& propertiesrequired)namer' parameters_BM)bound)objreturncCst|tot|pt|jvSN) isinstancer&r!r" __bases__)r.r3z/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_experimental/llms/ollama_functions.py_is_pydantic_classLs r5)toolr/cCsd}t|r"|}|d}nt|trF|j}|}|j}n\t|rj| }|}|j}n8t|t rd|vrd|vr| St d|d|d||d}|r||d <|S) z!Convert a tool to an Ollama tool.Ntitler*r+zCannot convert z! to an Ollama tool. z0 needs to be a Pydantic class, model, or a dict.)r*r+r') r5Zmodel_constructZmodel_json_schemar1rZtool_call_schemaget_namer'r Zget_input_schemadictcopy ValueError)r6r'schemar*Z definitionr3r3r4convert_to_ollama_toolRs.      r=c@s.eZdZUeed<eeed<eeed<dS)_AllReturnTyperawparsed parsing_errorN)__name__ __module__ __qualname__r__annotations__r_DictOrPydantic BaseExceptionr3r3r3r4r>ns  r>)messager/cCst|trv|j}|j}t|dkr>|d}|d}t|Sd|vrnd|dvr^|ddStd|ntdtd|d S) z)Extract `function_call` from `AIMessage`.rargs function_call argumentsz;`arguments` missing from `function_call` within AIMessage: z.`tool_calls` missing from AIMessage: {message}z-`message` is not an instance of `AIMessage`: N) r1radditional_kwargs tool_callslengetjsondumpsr;)rHkwargsrNZ tool_callrJr3r3r4parse_responsets      rTz0.0.64z1.0zlangchain_ollama.ChatOllama)ZsinceZremovalZalternative_importc s eZdZUdZeZeed<eddfdd Z e e e eefe eeefeeeefddd Zd d e e e efeeeee e effd d dZdeeeeeeeeedfdd Zdeeeeeeeeedfdd ZeedddZZ S)OllamaFunctionsz)Function chat model that uses Ollama API.tool_system_prompt_templateN)rSr/c stjfi|dSr0)super__init__)selfrS __class__r3r4rXszOllamaFunctions.__init__)toolsrSr/cKs|jfd|i|S)N functions)bind)rYr\rSr3r3r4 bind_toolsszOllamaFunctions.bind_toolsF) include_raw)r<r`rSr/c Ks|rtd|t|}|dur*td|j|gdd}|rJt|d}nt}tt|B}|rtjt d|Bdd d }tjd d d } |j | gd d} t |d| BS||BSdS)aModel wrapper that returns outputs formatted to match the given schema. Args: schema: The output schema as a dict or a Pydantic class. If a Pydantic class then the model output will be an object of that class. If a dict then the model output will be a dict. With a Pydantic class the returned attributes will be validated, whereas with a dict they will not be. 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". Returns: A Runnable that takes any ChatModel input and returns as output: If include_raw is True then a dict with keys: raw: BaseMessage parsed: Optional[_DictOrPydantic] parsing_error: Optional[BaseException] If include_raw is False then just _DictOrPydantic is returned, where _DictOrPydantic depends on the schema: If schema is a Pydantic class then _DictOrPydantic is the Pydantic class. If schema is a dict then _DictOrPydantic is a dict. Example: Pydantic schema (include_raw=False): .. code-block:: python from langchain_experimental.llms import OllamaFunctions from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = OllamaFunctions(model="phi3", format="json", temperature=0) 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_experimental.llms import OllamaFunctions from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = OllamaFunctions(model="phi3", format="json", 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': [{'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 (method="include_raw=False): .. code-block:: python from langchain_experimental.llms import OllamaFunctions, convert_to_ollama_tool from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str dict_schema = convert_to_ollama_tool(AnswerWithJustification) llm = OllamaFunctions(model="phi3", format="json", temperature=0) 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 NzJschema must be specified when method is 'function_calling'. 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Response: r6c3s|]}|dkr|VqdSr*Nr3rkcalled_tool_namer3r4 Krez,OllamaFunctions._generate..r* tool_inputr$z Failed to parse a response from output: contentrH generationsZcall_-)r*rJid)rrN)rPr;appendDEFAULT_RESPONSE_FUNCTIONr from_templaterVrarQrRrW _generatertextr1strloadsJSONDecodeErrormodelnextrrrruuiduuid4replace)rYrgrhrirSr]system_message_prompt_templatesystem_messageresponse_messagechat_generation_contentparsed_chat_result called_toolr$called_tool_argumentsresponse_message_with_functionsrZr|rSr4rs                 zOllamaFunctions._generatecsdg}dvrd=dvrHfdd|D}|s@tdd=n|sV|tt|drpdd|D}t|j}|jt j |dd d }t j |g|f||d IdH}|j dj} t| tstd zt | } Wn,t jytd |jd| Yn0| d| d} tfdd|Dd} | durZtd|jd| | dtdkrttt| dddgdStdd| rt | nddid} tt| dgdS)Nr]rKcs$g|]}|dddkr|qSrjr3rkrnr3r4rosz.OllamaFunctions._agenerate..rprcSsg|] }t|qSr3rqrkr3r3r4rorerrrsrurvrwrxryr6r~c3s|]}|dkr|VqdSrzr3rkr{r3r4r}rez-OllamaFunctions._agenerate..z%Failed to parse a function call from rr*r$rrrr)r*rL)rrM)rPr;rrr5rrrVrarQrRrW _ageneraterrr1rrrrrrrr)rYrgrhrirSr]rrrrrrrrrZrr4r|s               zOllamaFunctions._agenerate)r/cCsdS)NZollama_functionsr3)rYr3r3r4 _llm_typeszOllamaFunctions._llm_type)NN)NN)!rBrCrD__doc__DEFAULT_SYSTEM_TEMPLATErVrrErrXrr rr r"rrrrrr_boolrfrrrrrrrpropertyr __classcell__r3r3rZr4rUsF     b PrU)@rQroperatorrtypingrrrrrrr r r r Z&langchain_community.chat_models.ollamar Zlangchain_core._apirZlangchain_core.callbacksrrZlangchain_core.language_modelsrZlangchain_core.messagesrrrZ"langchain_core.output_parsers.baserZ"langchain_core.output_parsers.jsonrZ&langchain_core.output_parsers.pydanticrZlangchain_core.outputsrrZlangchain_core.promptsrZlangchain_core.runnablesrrZlangchain_core.runnables.baserZ$langchain_core.runnables.passthroughrZlangchain_core.toolsrZlangchain_core.utils.pydanticr r!Zpydanticr"rrr,rFrr5r=r>rrTrUr3r3r3r4sR 0