"j:SSKJr SSKJrJrJr SSKJrJr SSK J r J r SSK r SSK Jr SSKJr SS KJrJr SS KJrJr SS KJr SS KJrJr SS KJr SSKJrJ r J!r!J"r"J#r#J$r$J%r%J&r&J'r'J(r(J)r) SSK*J+r+J,r, \"SS9"SS\,\55r-"SS\+\5r.g)) annotations) AwaitableCallableSequence) dataclassreplace)AnyoverloadN)GenerateJsonSchema)_utils)AgentInstructionsnormalize_instructions) AgentDepsT RunContext)SystemPromptRunner) ModelRetry UserError)InstructionPart) ArgsValidatorFuncDocstringFormatGenerateToolJsonSchemaSystemPromptFuncToolToolDefinitionToolFuncContextToolFuncEither ToolFuncPlain ToolParamsToolPrepareFunc)AbstractToolset ToolsetToolT)kw_onlycL\rSrSr%SrS\S'S\S'SrS\S 'SrS \S 'S rg) FunctionToolsetTool zWA tool definition for a function toolset tool that keeps track of the function to call.zBCallable[[dict[str, Any], RunContext[AgentDepsT]], Awaitable[Any]] call_funcboolis_asyncN float | Nonetimeout str | None original_name) __name__ __module__ __qualname____firstlineno____doc____annotations__r,r.__static_attributes__r/Z/home/mande/repo/quber/.venv/lib/python3.13/site-packages/pydantic_ai/toolsets/function.pyr&r& s0aQQN G\ !%M:$r7r&c\rSrSr%SrS\S'S\S'S\S'S \S 'S \S 'S \S'S\S'S \S'S\S'/4SSSS\SSSSSSSSS. S+Sjjjr\S,Sj5r \ S-Sj5r \ SSSSSSSSSSSSSSSS.S.Sjj5r S/SSSSSSSSSSSSSSSS.S0Sjjjr \ S1Sj5r \ SSSSSSSSSSSSSSSS.S2Sjj5r S/SSSSSSSSSSSSSSSS.S3S jjjr S4S!jr S5S6S"jjrS7S#jrS8S$jrS9S%jrSS&.S:S'jjrS;S(jrSr,r@rArCstrict sequentialrequires_approvalmetadata defer_loadingrFid instructionsc X lX0lXlX@lXPlX`lXplXlXlXl Xl Xl [U5Vs/sH3n[U[[45(aUO[ [""U5PM5 snUl0UlUH<n[U[(5(aUR+U5 M+UR-U5 M> gs snf)a Build a new function toolset. Args: tools: The tools to add to the toolset. max_retries: The maximum number of retries for each tool during a run. If `None`, inherits the agent's default retry count at runtime. Applies to all tools, unless overridden when adding a tool. timeout: Timeout in seconds for tool execution. If a tool takes longer than this, a retry prompt is returned to the model. Individual tools can override this with their own timeout. Defaults to None (no timeout). docstring_format: Format of tool docstring, see [`DocstringFormat`][pydantic_ai.tools.DocstringFormat]. Defaults to `'auto'`, such that the format is inferred from the structure of the docstring. Applies to all tools, unless overridden when adding a tool. require_parameter_descriptions: If True, raise an error if a parameter description is missing. Defaults to False. Applies to all tools, unless overridden when adding a tool. schema_generator: The JSON schema generator class to use for this tool. Defaults to `GenerateToolJsonSchema`. Applies to all tools, unless overridden when adding a tool. 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. Applies to all tools, unless overridden when adding a tool. 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. Applies to all tools, unless overridden when adding a tool. metadata: Optional metadata for the tool. This is not sent to the model but can be used for filtering and tool behavior customization. Applies to all tools, unless overridden when adding a tool, which will be merged with the toolset's metadata. defer_loading: Whether to hide tools from the model until they're revealed by tool search, `load_capability`, or another tool's `ToolReturn.tools`. See [Tool Search](../tools-advanced.md#tool-search) for more info. Applies to all tools, unless overridden when adding a tool. include_return_schema: Whether to include return schemas in tool definitions sent to the model. If `None`, defaults to `False` unless the [`IncludeToolReturnSchemas`][pydantic_ai.capabilities.IncludeToolReturnSchemas] capability is used. Applies to all tools, unless overridden when adding a tool. id: An optional unique ID for the toolset. A toolset needs to have an ID in order to be used in a durable execution environment like Temporal, in which case the ID will be used to identify the toolset's activities within the workflow. instructions: Instructions for this toolset that are automatically included in the model request. Can be a string, an [`InstructionPart`][pydantic_ai.messages.InstructionPart] declaring the part's [`name`][pydantic_ai.messages.InstructionPart.name] and whether it is [`dynamic`][pydantic_ai.messages.InstructionPart.dynamic], a function (sync or async, with or without `RunContext`), or a sequence of these. N)r>r,r?r@rArCrHrIrJrKrDrFr isinstancestrrrr _instructionsr<radd_tool add_function)selfr<r>r,r@rArCrHrIrJrKrLrFrMrN instructiontools r8__init__FunctionToolset.__init__Bsz'  0.L+ 0 $!2 +%:" 6lC \  D +_'=>> #J/ < = D \  D$%% d#!!$' \ s:C"cUR$N)r?)rUs r8rMFunctionToolset.ids xxr7cgr[r/rUfuncs r8rWFunctionToolset.toolsqtr7)name descriptionretriesprepareargs_validatorr@rArCrHrIrJrKr,rLrFcgr[r/rUrarbrcrdrer@rArCrHrIrJrKr,rLrFs r8rWr`s (hkr7cr^^^^^^^^^ ^ ^ ^ ^ ^^^SUUUUUU UUUU UU UU U U4SjjnUcU$U"U5$)aDecorator to register a tool function which takes [`RunContext`][pydantic_ai.tools.RunContext] as its first argument. Can decorate a sync or async functions. The docstring is inspected to extract both the tool description and description of each parameter, [learn more](../tools.md#function-tools-and-schema). We can't add overloads for every possible signature of tool, since the return type is a recursive union so the signature of functions decorated with `@toolset.tool` is obscured. Example: ```python from pydantic_ai import Agent, FunctionToolset, RunContext toolset = FunctionToolset() @toolset.tool def foobar(ctx: RunContext[int], x: int) -> int: return ctx.deps + x @toolset.tool(retries=2) async def spam(ctx: RunContext[str], y: float) -> float: return ctx.deps + y agent = Agent('test', toolsets=[toolset], deps_type=int) result = agent.run_sync('foobar', deps=1) print(result.output) #> {"foobar":1,"spam":1.0} ``` Args: func: The tool function to register. name: The name of the tool, defaults to the function name. description: The description of the tool,defaults to the function docstring. retries: The number of retries to allow for this tool, defaults to the agent's default retries, which defaults to 1. 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]. If `None`, the default value is determined by the toolset. require_parameter_descriptions: If True, raise an error if a parameter description is missing. If `None`, the default value is determined by the toolset. schema_generator: The JSON schema generator class to use for this tool. If `None`, the default value is determined by the toolset. 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. If `None`, the default value is determined by the toolset. 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. If `None`, the default value is determined by the toolset. 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. If `None`, the default value is determined by the toolset. metadata: Optional metadata for the tool. This is not sent to the model but can be used for filtering and tool behavior customization. If `None`, the default value is determined by the toolset. If provided, it will be merged with the toolset's metadata. 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`. See [Tool Search](../tools-advanced.md#tool-search) for more info. If `None`, the default value is determined by the toolset. include_return_schema: Whether to include the return schema in the tool definition sent to the model. If `None`, the default value is determined by the toolset. cF>T RUSTTT TTTT T TTT TTTTS9 U$)NTr_ takes_ctxrarbrcrdrer@rArCrHrIrJrKr,rLrFrTfunc_rerLrbr@rFrKrardrArJrcrCrUrIrHr,s r8tool_decorator,FunctionToolset.tool..tool_decoratorsR   '-!1/M!1%"3!+&;#  &Lr7)rn'ToolFuncContext[AgentDepsT, ToolParams]returnrqr/rUr_rarbrcrdrer@rArCrHrIrJrKr,rLrFros` ``````````````` r8rWr`s:| :  4   0"&~G>$3GGr7cgr[r/r^s r8 tool_plainFunctionToolset.tool_plain2s[^r7cgr[r/rgs r8rurv5s (LOr7cr^^^^^^^^^ ^ ^ ^ ^ ^^^SUUUUUU UUUU UU UU U U4SjjnUcU$U"U5$)uODecorator to register a tool function which DOES NOT take `RunContext` as an argument. Can decorate a sync or async functions. The docstring is inspected to extract both the tool description and description of each parameter, [learn more](../tools.md#function-tools-and-schema). We can't add overloads for every possible signature of tool, since the return type is a recursive union so the signature of functions decorated with `@toolset.tool_plain` is obscured. Example: ```python from pydantic_ai import Agent, FunctionToolset toolset = FunctionToolset() @toolset.tool_plain def foobar(x: int) -> int: return x + 1 @toolset.tool_plain(retries=2) async def spam(y: float) -> float: return y * 2.0 agent = Agent('test', toolsets=[toolset]) result = agent.run_sync('foobar') print(result.output) #> {"foobar":1,"spam":0.0} ``` Args: func: The tool function to register. name: The name of the tool, defaults to the function name. description: The description of the tool, defaults to the function docstring. retries: The number of retries to allow for this tool, defaults to the toolset's default retries, which defaults to the agent's default. 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`][pydantic_ai.tools.RunContext] as the first argument — even though the tool function itself does not take `RunContext` when using `tool_plain`. 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]. If `None`, the default value is determined by the toolset. require_parameter_descriptions: If True, raise an error if a parameter description is missing. If `None`, the default value is determined by the toolset. schema_generator: The JSON schema generator class to use for this tool. If `None`, the default value is determined by the toolset. 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. If `None`, the default value is determined by the toolset. 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. If `None`, the default value is determined by the toolset. 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. If `None`, the default value is determined by the toolset. metadata: Optional metadata for the tool. This is not sent to the model but can be used for filtering and tool behavior customization. If `None`, the default value is determined by the toolset. If provided, it will be merged with the toolset's metadata. 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`. See [Tool Search](../tools-advanced.md#tool-search) for more info. If `None`, the default value is determined by the toolset. include_return_schema: Whether to include the return schema in the tool definition sent to the model. If `None`, the default value is determined by the toolset. cF>T RUSTTT TTTT T TTT TTTTS9 U$)NFrjrlrms r8ro2FunctionToolset.tool_plain..tool_decoratorsR   '-!1/M!1%"3!+&;#  &Lr7)rnToolFuncPlain[ToolParams]rrr{r/rss` ``````````````` r8rurvKs:~ ,  &   2"&~G>$3GGr7cNURR[U55 U$)a_Decorator to register an instructions function for this toolset. The function can be sync or async, and can optionally take a [`RunContext`][pydantic_ai.tools.RunContext] as its first argument. Example: ```python from pydantic_ai import FunctionToolset, RunContext toolset = FunctionToolset[int]() @toolset.instructions def my_instructions(ctx: RunContext[int]) -> str: return 'Always use the search tool when looking for information.' @toolset.tool def search(ctx: RunContext[int], query: str) -> str: return f'Results for: {query}' ``` Args: func: The instructions function to register. )rRappendrr^s r8rNFunctionToolset.instructionss#8 !!"4T":; r7cUc URnU c URn U c URn U c URn U c URn U c UR n Uc UR nUc URn[["U40SU_SU_SU_SU_SU_SU_SU_SU _S U _S U _S U _S U _S U_SU_SU_SU_6nURU5 U$)aAdd a function as a tool to the toolset. Can take a sync or async function. The docstring is inspected to extract both the tool description and description of each parameter, [learn more](../tools.md#function-tools-and-schema). Args: func: The tool function to register. takes_ctx: Whether the function takes a [`RunContext`][pydantic_ai.tools.RunContext] as its first argument. If `None`, this is inferred from the function signature. name: The name of the tool, defaults to the function name. description: The description of the tool, defaults to the function docstring. retries: The number of retries to allow for this tool, defaults to the agent's default retries, which defaults to 1. 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]. If `None`, the default value is determined by the toolset. require_parameter_descriptions: If True, raise an error if a parameter description is missing. If `None`, the default value is determined by the toolset. schema_generator: The JSON schema generator class to use for this tool. If `None`, the default value is determined by the toolset. 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. If `None`, the default value is determined by the toolset. 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. If `None`, the default value is determined by the toolset. 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. If `None`, the default value is determined by the toolset. defer_loading: Whether to hide this tool until it's revealed by tool search, `load_capability`, or another tool's `ToolReturn.tools`. See [Tool Search](../tools-advanced.md#tool-search) for more info. If `None`, the default value is determined by the toolset. metadata: Optional metadata for the tool. This is not sent to the model but can be used for filtering and tool behavior customization. If `None`, the default value is determined by the toolset. If provided, it will be merged with the toolset's metadata. 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). include_return_schema: Whether to include the return schema in the tool definition sent to the model. If `None`, the default value is determined by the toolset. rkrarbr>rdrer@rArCrHrIrJrKr,rLrF) r@rArCrHrIrJrDrFrrrS)rUr_rkrarbrcrdrer@rArCrHrIrJrLrKr,rFrWs r8rTFunctionToolset.add_functionsQL  ##44  ) 1-1-P-P *  ##44  >[[F  J  $ $ 6 6    //M ($($>$> !J    $      * . ,J .  " 0   (! "#8# & d r7cXURUR;a[SUR<35eURcURbURUlURb'URUR=(d 0-UlXRUR'g)z=Add a tool to the toolset. Args: tool: The tool to add. z(Tool name conflicts with existing tool: N)rar<rr>rK)rUrWs r8rSFunctionToolset.add_toolQs 99 "FtyymTU U    #(8(8(D#//D  == $ MMT]]-@bADM $ 499r7c# UR(dg/nURHn[U[5(aURU5 M+[U[5(a2UR 5(aUR[USS95 MpMrUR U5IShvN nU(dMUR 5(dMUR[USS95 M U=(d S$NL7f)NF)contentdynamicT)rRrPrr}rQstriprun)rUctxpartsrVresults r8get_instructions FunctionToolset.get_instructions_s!!')--K+77 [)K--$$&&LLe!TU' +s336fllnnLL!NO.}4sB'C8)C6* C87C8)C8c # 0nURR5Hup4URb URO URnUc URn[UUURR US5US9nUR U5IShvN nU(dMURnX;a)X:wa[SU<SU<S35e[SU<S35eURXGXS5X('M U$Na7f)Nr) tool_nameretryr>zRenaming tool z to z conflicts with existing tool.z2Tool name conflicts with previously renamed tool: .) r<itemsr>rrcgetprepare_tool_defrar _tool_for) rUrr<r.rWr> run_contexttool_defnew_names r8 get_toolsFunctionToolset.get_toolsrs46#'::#3#3#5 M.2.>.>.J$**PTP`P`K"!oo !'kkoomQ7' K "22;??H}}H ,#n]4ET(Us$tuu#&XYaXdde$fgg"nnT[XEO+$6, @sBC6C4A"C6)r.cUbUO URnURUnURb URO URnURXAUbXS5$URU5$)a Build the tool to call for a tool definition that was already prepared elsewhere. Used by [durable execution](../durable_execution/overview.md) to rebuild the tool inside the durable unit (e.g. a Temporal activity) from the tool definition that [`get_tools()`][pydantic_ai.toolsets.AbstractToolset.get_tools] produced outside it, instead of running the tool's `prepare` function a second time against a different run context. Args: tool_def: The prepared tool definition to build the tool from. ctx: The run context used to resolve the tool's retry budget. original_name: The name this toolset holds the tool under, from the built tool's `original_name`. Defaults to `tool_def.name`, which is only the same when no `prepare` function renamed the tool. This is specific to `FunctionToolset` because per-tool preparation runs inside its own `get_tools()` and can change the exposed name without changing the key in `tools`. Raises: KeyError: If the toolset holds no tool under that name. )rar<r>r)rUrrr.rWr>s r8tool_for_tool_def!FunctionToolset.tool_for_tool_defso,*7)B  zz-(*.*:*:*Fd&&DL\L\ ~~ ;+BK  HKYf  r7c [UUUURRURURRURR UR US9 $)N) toolsetrr>reargs_validator_funcr(r*r,r.)r&function_schema validatorrecallr*r,)rUrWrr>r.s r8rFunctionToolset._tool_fors\##//99 $ 3 3**//))22$$'  r7c.# [U[5(deURRbURRO URnUbY[R "U5 [ R"5 URX#5IShvN sSSS5 sSSS5 $URX#5IShvN $N/!,(df  O=fSSS5 g!,(df  g=f![a [SUS35Sef=fNO7f)NzTimed out after z seconds.) rPr&rr,anyio fail_afterr abandon_threads_on_cancelr( TimeoutErrorr)rUra tool_argsrrWr,s r8 call_toolFunctionToolset.call_tools$ 34444,0==+@+@+L$--''RVR^R^   R%%g.0P0P0R!% !??1S0R..  77 7 @1S0R... 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