"j%SSKJr SSKrSSKJrJrJr SSKJ r J r SSK J r SSK Jr SSKJrJrJrJrJrJrJrJr SSKJrJr SS KJrJr SS KJrJ r SS K!J"r"J#r#J$r$ S S K%J&r&J'r' S SK(J)r)J*r*J+r+J,r,J-r-J.r.J/r/ S SK0J1r1 S SK2J3r3J4r4 S SK5J6r6 S SK7J8r8 S SK9J:r: S SK;J\\4\3/\?S-4\\4\3/\\?S-4-\/\?S-4-\/\\?S-4-r@S\AS'\\\4\3\>4\4rBS\AS'\\>\4rCS\AS'\B\3\>4\C\>-rDS\AS'\\\4\3\>4\S4\\\4\3\>4S4-rES\AS'\\4\3S/\\S SS44rFS\AS!'\\4\3\GS/\\GS\GS-4rHS\AS"'\\4\3S/\I\\I-4rJS\AS#'\S$\\?-\K\?\4-\J\3-rLS\AS%'S=S&jrMS>S'jrN\\4\3/\\ToolDefinition | Noner5r6r7allr8c DUR5Hup#X ;a gXn[U[5(a`[U[5(aK[[ [[ [ 4U5[ [[ [ 4U55(d gMXC:wdM g g)zMCheck whether *metadata* deeply includes all key-value pairs from *selector*.FT)items isinstancedict_metadata_includesrstrr )metadataselectorkeyexpectedactuals N/home/mande/repo/quber/.venv/lib/python3.13/site-packages/pydantic_ai/tools.pyrGrGs!)    h % %*VT*B*B%d4S>6&BDcSVhYaDbccd  * cj# US:Xag[U5(a/U"X5n[R"U5(a UIShvN $U$[U[5(a UR =(d 0n[ X@5$[U[5(aURU:H$URU;$Nn7f)aCheck whether a tool definition matches a [`ToolSelector`][pydantic_ai.tools.ToolSelector]. Args: selector: The selector to check against. ctx: The current run context. tool_def: The tool definition to test. Returns: `True` if the tool matches the selector. rBTN) callableinspect isawaitablerErFrIrGrHname)rJctxtool_defresultrIs rNr9r9s5#(   v & &<  (D!!#+#4#4#:!(55(C  }}(( ==H $$ sAB3B1A/B3r;r:)googlenumpysphinxautor/Ac<^\rSrSrSU4SjjrSU4SjjrSrU=r$)GenerateToolJsonSchemaicT>[T U]U5nUSn[U[5(dU$[R "U5n0nUR R5H7upgURU5=ncMURURU5 M9 U(a[SURSS555(aZ[USURS55VV s/sH+upySU 0URU;aSXWR0O0EPM- sn nUS'U$s sn nf) Nclsc3(# UHoSLv M g7f)N).0values rN 5GenerateToolJsonSchema.enum_schema..0sY=XE4/=Xsenumrbmembersconst descriptionanyOf)super enum_schema issubclassr&renum_member_docstrings __members__rDget setdefaultrTrBzippop) selfschema json_schemaenum_clsdeclared docstringsrTmember docstringrd __class__s rNrm"GenerateToolJsonSchema.enum_schemasg)&1  &e}($;<<  00:%' $00668LD%\\$// <%%fkk9=9 #Y[__VUW=XYYY&) ):KOOF[TU]U5nURS05HnUSURSS5 M U$)N propertiestitle)rl_named_required_fields_schemarqrt)runamed_required_fieldsspr}s rNr4GenerateToolJsonSchema._named_required_fields_schema7sF G 12G H|R(A lOA  " "7D 1)rOrb)rvzcore_schema.EnumSchemareturnr)rzSequence[tuple[str, bool, Any]]rr)__name__ __module__ __qualname____firstlineno__rmr__static_attributes__ __classcell__)r}s@rNr^r^s!FrOr^ToolAgentDepsTT)r@ contravariantc4UbUS:a[SU35egg)Nrzmax_retries must be >= 0, got r)) max_retriess rN_validate_max_retriesrCs);?8 FGG$3rOc4UbUS::a[SU35egg)Nrztimeout must be > 0, got r))timeouts rN_validate_timeoutrHs)w!|3G9=>> ,rOF)initc\rSrSr%SrS\S'S\S'S\S'S \S 'S \S 'S \S'S\S'S\S'S\S'S\S'S\S'S\S'S\S'S\S'S\S'S\S'S\S'S S S S S S S!S"\S S"S"S S S"S S S#.S)S$jjr\S*S+S%jj5r \ S,S&j5r S-S'jr S(r g ).r<iMzA tool function for an agent.zToolFuncEither[ToolAgentDepsT]functionbool takes_ctx int | NonerrHrT str | Nonerj&ToolPrepareFunc[ToolAgentDepsT] | Nonepreparez-ArgsValidatorFunc[ToolAgentDepsT, ...] | Noneargs_validatorr/docstring_formatrequire_parameter_descriptions bool | Nonestrict sequentialrequires_approvaldict[str, Any] | NonerI float | Noner defer_loadinginclude_return_schemaz_function_schema.FunctionSchemafunction_schemaNr[F)rrrTrjrrrrschema_generatorrrrrIrrrrc [U5 [U5 XlU=(d URUlU=(d# [ R "UU URUUU S9UlUR RUlX0lUbUOUR RUl X`l Xpl Xl Xl XlXlXlXlXlUUlUUlg)aCreate a new tool instance. Example usage: ```python {noqa="I001"} from pydantic_ai import Agent, RunContext, Tool async def my_tool(ctx: RunContext[int], x: int, y: int) -> str: return f'{ctx.deps} {x} {y}' agent = Agent('test', tools=[Tool(my_tool)]) ``` or with a custom prepare method: ```python {noqa="I001"} from pydantic_ai import Agent, RunContext, Tool from pydantic_ai.tools import ToolDefinition async def my_tool(ctx: RunContext[int], x: int, y: int) -> str: return f'{ctx.deps} {x} {y}' async def prep_my_tool( ctx: RunContext[int], tool_def: ToolDefinition ) -> ToolDefinition | None: # only register the tool if `deps == 42` if ctx.deps == 42: return tool_def agent = Agent('test', tools=[Tool(my_tool, prepare=prep_my_tool)]) ``` Args: function: The Python function to call as the tool. takes_ctx: Whether the function takes a [`RunContext`][pydantic_ai.tools.RunContext] first argument, this is inferred if unset. max_retries: Maximum number of retries allowed for this tool, set to the agent default if `None`. name: Name of the tool, inferred from the function if `None`. description: Description of the tool, inferred from the function if `None`. prepare: custom method to prepare the tool definition for each step, return `None` to omit this tool from a given step. This is useful if you want to customise a tool at call time, or omit it completely from a step. See [`ToolPrepareFunc`][pydantic_ai.tools.ToolPrepareFunc]. args_validator: custom method to validate tool arguments after schema validation has passed, before execution. The validator receives the already-validated and type-converted parameters, with `RunContext` as the first argument. Raise [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] to ask the model to correct the arguments and try again, or [`ToolFailed`][pydantic_ai.exceptions.ToolFailed] to report a terminal failure the model should adapt to instead of retrying. Return `None` on success. See [`ArgsValidatorFunc`][pydantic_ai.tools.ArgsValidatorFunc]. docstring_format: The format of the docstring, see [`DocstringFormat`][pydantic_ai.tools.DocstringFormat]. Defaults to `'auto'`, such that the format is inferred from the structure of the docstring. require_parameter_descriptions: If True, raise an error if a parameter description is missing. Defaults to False. schema_generator: The JSON schema generator class to use. Defaults to `GenerateToolJsonSchema`. strict: Whether to enforce (vendor-specific) strict schema adherence for tool calls (supported by OpenAI, Anthropic, Google, and Bedrock). See [`ToolDefinition`][pydantic_ai.tools.ToolDefinition] for more info. sequential: Whether this tool acts as a barrier that runs alone, not overlapping with other tool calls. See [`ToolDefinition`][pydantic_ai.tools.ToolDefinition] for more info. Defaults to False. requires_approval: Whether this tool requires human-in-the-loop approval. Defaults to False. See the [tools documentation](../deferred-tools.md#human-in-the-loop-tool-approval) for more info. metadata: Optional metadata for the tool. This is not sent to the model but can be used for filtering and tool behavior customization. timeout: Timeout in seconds for tool execution. If the tool takes longer, a retry prompt is returned to the model. Defaults to None (no timeout). defer_loading: Whether to hide this tool until it's revealed by tool search, `load_capability`, or another tool's `ToolReturn.tools`. Defaults to False. See [Tool Search](../tools-advanced.md#tool-search) for more info. include_return_schema: Whether to include the return schema in the tool definition sent to the model. If `None`, defaults to `False` unless the [`IncludeToolReturnSchemas`][pydantic_ai.capabilities.IncludeToolReturnSchemas] capability is used. function_schema: The function schema to use for the tool. If not provided, it will be generated. ) tool_namerrrN)rrrrrTrrrrrjrrrrrrrrIrrr)rurrrrTrjrrrrrrrrrIrrrrs rN__init__ Tool.__init__hsz k*'" -H-- . 2B2R2R  ii-+I 3 --77&*5*A;tG[G[GgGg , 0.L+ $!2  *%:"rOc [R"UUU[[R"5S9UU[ R "U5S9nU"UUUUUUUS9n U $)a Creates a Pydantic tool from a function and a JSON schema. Args: function: The function to call. This will be called with keywords only. Schema validation of the arguments is skipped, but a custom `args_validator` will still run if provided. name: The unique name of the tool that clearly communicates its purpose description: Used to tell the model how/when/why to use the tool. You can provide few-shot examples as a part of the description. json_schema: The schema for the function arguments takes_ctx: An optional boolean parameter indicating whether the function accepts the context object as an argument. sequential: Whether this tool acts as a barrier that runs alone, not overlapping with other tool calls. See [`ToolDefinition`][pydantic_ai.tools.ToolDefinition] for more info. Defaults to False. args_validator: custom method to validate tool arguments after schema validation has passed, before execution. The validator receives the already-validated and type-converted parameters, with `RunContext` as the first argument. Raise [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] to ask the model to correct the arguments and try again, or [`ToolFailed`][pydantic_ai.exceptions.ToolFailed] to report a terminal failure the model should adapt to instead of retrying. Return `None` on success. See [`ArgsValidatorFunc`][pydantic_ai.tools.ArgsValidatorFunc]. Returns: A Pydantic tool that calls the function )rv)rrTrj validatorrwris_async)rrTrjrrr)rFunctionSchemarr any_schemaris_async_callable) r`rrTrjrwrrrrtools rN from_schemaTool.from_schemaskJ+99#%[-C-C-EF#--h7  #+!)  rOc>[URURURRUR UR URURURUR(aSOSURRURS9 $)N unapprovedr) rTrjparameters_json_schemarrrIrrkind return_schemar) r>rTrjrrwrrrIrrrrrrus rNrV Tool.tool_defsx((#'#7#7#C#C;;]]LL,,!%!7!7Z..<<"&"<"<  rOc# URnURb8URX5n[R"U5(a UIShvN $U$U$N7f)zGet the tool definition. By default, this method creates a tool definition, then either returns it, or calls `self.prepare` if it's set. Returns: return a `ToolDefinition` or `None` if the tools should not be registered for this run. N)rVrrRrS)rurUrVrWs rNprepare_tool_defTool.prepare_tool_def*sP== << #\\#0F""6**#|#MO$sA A A A)rrrjrrrrrrIrTrrrrrrr)$rz*ToolFuncEither[ToolAgentDepsT, ToolParams]rrrrrTrrjrrrrz4ArgsValidatorFunc[ToolAgentDepsT, ToolParams] | Nonerr/rrrztype[GenerateJsonSchema]rrrrrrrIrrrrrrrrz&_function_schema.FunctionSchema | None)FFN)rzCallable[..., Any]rTrHrjrrwrrrrrrz"ArgsValidatorFunc[Any, ...] | Nonerr)rr>)rUzRunContext[ToolAgentDepsT]rrA)rrrr__doc____annotations__r^r classmethodrpropertyrVrrrbrOrNr<r<Ms',,O I 33AA%%$(( ## &&44"&"&"&:>OS,2/45K" "'*. $#-1BF)v;<v; v;  v;  v; v;8v;Mv;*v;)-v;3v;v;v; v; (!v;"#v;$%v;& +'v;(@)v;p  =A7$77 7 % 7  77;7 77r    rOr<r=)routputexternalrToolKind)reprkw_onlyc\rSrSr%SrS\S'\"SS9rS\S'S rS \S 'S r S \S 'S r S \S'Sr S\S'\"SS9r S\S'S r S\S'S rS\S'SrS\S'S rS\S'S rS \S'S rS\S'S rS \S!'S rS \S"'S rS \S#'S rS \S$'\S)S%j5rS*S&jr\S+S'j5r\R:rS(rg ),r>iJz`Definition of a tool passed to a model. This is used for both function tools and output tools. rHrTc S0S.$)Nobject)typerrbrbrOrNToolDefinition.Ts V^npMqrO)default_factoryr=rNrrjouter_typed_dict_keyrrFrrrr?rrrrIrrrzoAnnotated[str | None, Field(validation_alias=AliasChoices('unless_native', 'prefer_native', 'prefer_builtin'))] unless_native with_nativezToolPartKind | None tool_kindzObjectJsonSchema | Nonerr toolset_id capability_idcj[R"URURURS9$)uThe function signature shape for this tool. Lazily computed from `parameters_json_schema` and `return_schema` on first access. Name and description are not stored on the signature — pass them at render time via `sig.render(body, name=td.name, description=td.description)`. )rTparameters_schemar)r+rrTrrrs rNfunction_signature!ToolDefinition.function_signatures0!,,"99,,  rOc jURR"U4URURS.UD6$)zRender the function signature with this tool's name and description. 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See the [tools documentation](../deferred-tools.md#deferred-tools) for more info. )rr)rrs rNdeferToolDefinition.defers yy666rOrb)rr+)rrHrr rrH)rr) rrrrrrrrrjrrrrrIrrrrrrrrrr rrrrrdataclasses_no_defaults_repr__repr__rrbrOrNr>r>Jsl I/4Eq/r,r4"K"&'+*+ FK"J:.D(.'+H#* !G\   M4  #K" ,&*I")".2M*1*.;-"J ! !%M:$    l7722HrO)rIdict[str, Any]rJrrr)rJzToolSelector[AgentDepsT]rUzRunContext[AgentDepsT]rVr>rr)rrrNone)rrrr)\ __future__r _annotationsrRcollections.abcrrr dataclassesrrrgr functoolsr typingr r r rrrrrpydanticrrpydantic.json_schemarr pydantic_corerrtyping_extensionsrrrrr _deferredrr r!r"r#r$r% _json_schemar& _run_contextr'r( exceptionsr*rr+messagesr, native_toolsr-__all__r4rHr0rr1r2r3r.r5listr6rr7rFr8rGr9r;r:r/r\r^rrrrr<r=rr>rbrOrNrs299(%XXX(D666&20!1", 6|S 1 $ j$ %sTz 12 :&'3:)>>?@r3:~r9S4Z(()*) &k*Z2H*2T&UWZ&Z[[$JO4 y4,J ,BCmT^F__ _ [J/; DEF9& -. )+ ,.> DEG4' T"234 d#$%-=(>>@)6' -.9T?)"%.8C=84S>IL\]gLhh ih" %&% %% %>% i(:T(ABEWWZ^^^  0.2LLL %%HII CL)/)X)6N6H ?  m7> "mm`#38n),LM)M t$K3K3%K3rO