i1$SSKrSSKrSSKJrJrJr SSKJrJrJ r J r J r J r SSK JrJrJr SSKJrJrJrJrJr SSKJrJrJrJr SSKJr SSKJrJ r SS K!J"r" SS K#J$r$J%r% SS K&J'r'J(r( SS K)J*r* SS K+J,r, SSK-J.r. SSK/J0r0 SSK1J2r2 SSK3J4r4J5r5 SSK6J7r7 SSK8J9r9 SSK:J;r; SSKr>J?r? SSK@JArAJBrB \C\;-rD\C\E\;-rF\?"S\9S9"SS\>55rG\?"S\9S9"SS\;55rH\R"5 \R"S\9SS 9 \?"S!\9S9"S"S#\G55rKSSS5 \R"5 \R"S\9S$S 9 \?"S%\9S9"S&S'\H55rLSSS5 \ "S(\G\H-S)9rM\E\MrNS*rO\\P-\\M/\4-\\M\4-rQSMS+\MS,\PS-\S.\4S/jjrRS0\QS-S.\4S1jrSSNS2\S3\\S4\TS.\U4S5jjrVS2\S.\4S6jrWS7\\S.S4S8jrX\?"S9\9S9SSSSSSSSSSS:S;SS<. S2\P\-\\M\0\7/\4-\\M\0\7/\\4-\\M\0\7/\\\44-\\M\0\7/\\\\44-S3\\\-\C\P\4-\B-S0\QS-S=\F\Y\P\F4-S-S>\ S-S?\ S-S@\NS-SA\E\S-SB\4S-SC\2S-SD\Z\PS-SE\Z\PS-SF\USG\ SHSI\PS-SJ\S.\,4"SKjj5r[\[r\/SLQr]g!,(df  GN=f!,(df  GN`=f)ON) AwaitableCallableSequence) AnnotatedAnyLiteralTypeVarcastget_type_hints) BaseChatModelLanguageModelInputLanguageModelLike) AIMessage AnyMessage BaseMessage SystemMessage ToolMessage)RunnableRunnableBindingRunnableConfigRunnableSequence)BaseTool)RunnableCallable RunnableLike)MISSING) ErrorCodecreate_error_message)END StateGraph) add_messages)CompiledStateGraph)RemainingSteps)Runtime) BaseStore) CheckpointerSend)ContextT)LangGraphDeprecatedSinceV10) BaseModel) NotRequired TypedDict deprecated)ToolCallWithContextToolNodezxAgentState has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentState`.categorycD\rSrSr%Sr\\\\4\ S'\ \ \ S'Sr g) AgentState5The state of the agent.messagesremaining_stepsN) __name__ __module__ __qualname____firstlineno____doc__rrrr __annotations__r*r"__static_attributes__r7`/home/james-whalen/.local/lib/python3.13/site-packages/langgraph/prebuilt/chat_agent_executor.pyr2r25s' "-|;<< 00r?r2zAgentStatePydantic has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentStatePydantic`.cB\rSrSr%Sr\\\\4\ S'Sr \ \ S'Sr g)AgentStatePydanticAr4r5r6r7N) r8r9r:r;r<rrrr r=r6r"r>r7r?r@rBrBAs& "-|;<<&(O^(r?rBignorez/AgentState has been moved to langchain.agents.*)r0messagezAgentStateWithStructuredResponse has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentStateWithStructuredResponse`.c$\rSrSr%Sr\\S'Srg) AgentStateWithStructuredResponseT2The state of the agent with a structured response.structured_responser7Nr8r9r:r;r<StructuredResponser=r>r7r?r@rHrHT A//r?rHz7AgentStatePydantic has been moved to langchain.agents.*zAgentStateWithStructuredResponsePydantic has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentStateWithStructuredResponsePydantic`.c$\rSrSr%Sr\\S'Srg)(AgentStateWithStructuredResponsePydanticerJrKr7NrLr7r?r@rPrPerNr?rP StateSchema)boundPromptstatekeydefaultreturncf[U[5(aURX5$[XU5$N) isinstancedictgetgetattr)rUrVrWs r@_get_state_valuer_|s5 eT " "  #U )r?promptc^^Tc[S[S9nU$[T[5(a[ TS9m[U4Sj[S9nU$[T[5(a[U4Sj[S9nU$[ R "T5(a[ST[S9nU$[T5(a[T[S9nU$[T[5(aTnU$[S[T535e)Nc[US5$Nr5r_)rUs r@&_get_prompt_runnable..s *5*=r?namecontentc$>T/[US5-$rcrd)rU_system_messages r@rerfs?+.>uj.QQr?c$>T/[US5-$rcrd)rUr`s r@rerfs6(%5eZ%HHr?z"Got unexpected type for `prompt`: ) rPROMPT_RUNNABLE_NAMEr[strrinspectiscoroutinefunctioncallabler ValueErrortype)r`prompt_runnablerls` @r@_get_prompt_runnablervs ~* =DX < 7 FC '4V'D* Q% 2 + FM * ** H% ( !  $ $V , ,*  %   &  * %   FH % %  =d6l^LMMr?modeltools num_builtincn[U[5(a[SUR5U5n[U[5(dgSUR ;agUR Sn[ U5[ U5U- :wa&[S[ U5S[ U5U- 35e[SU55n[5nUHPnURS5S:Xa USS nOURS 5(aUS nOM?URU5 MR XE- =n(a[S US 35eg ) Nc3`# UH$n[U[[45(dM Uv M& g7frZr[rr .0steps r@ %_should_bind_tools..( 'Dd_m$DE'. .Trxz`Number of tools in the model.bind_tools() and tools passed to create_react_agent must match Got z tools, expected c38# UHoRv M g7frZrg)r~tools r@rrs154YY5srtfunctionrhzMissing tools 'z' in the model.bind_tools()F) r[rnextstepsrkwargslenrssetr]add) rwrxry bound_tools tool_namesbound_tool_names bound_toolbound_tool_name missing_toolss r@_should_bind_toolsrsB%)** !KK     e_ - -ell",,w'K 5zS% 33 J<0[1AK1O0P R  1511Ju! >>& !Z /(4V.rrzXExpected `model` to be a ChatModel or RunnableBinding (e.g. model.bind_tools(...)), got ) r[rrrrrSr TypeErrorrt)rws r@ _get_modelrs{%)** !KK    %))  e] + +fgklqgrfs t   Lr?r5cUVVs/sH0n[U[5(dMURHnUPM M2 nnnUVs1sH&n[U[5(dMURiM( nnUVs/sHnUSU;dMUPM nnU(dg[ SUSSS3[ RS9n[U5es snnfs snfs snf)zLValidate that all tool calls in AIMessages have a corresponding ToolMessage.idNz{Found AIMessages with tool_calls that do not have a corresponding ToolMessage. Here are the first few of those tool calls: z. Every tool call (LLM requesting to call a tool) in the message history MUST have a corresponding ToolMessage (result of a tool invocation to return to the LLM) - this is required by most LLM providers.)rF error_code) r[r tool_callsr tool_call_idrrINVALID_CHAT_HISTORYrs)r5rF tool_callall_tool_callstool_call_ids_with_resultstool_calls_without_results error_messages r@_validate_chat_historyrs  G gy ) !++I , -5",4 7K8XH" ("'I T?"< < '" &(77QRTST7U6VWgg11 M ] ##1 ""s"CCC!C7 C C zcreate_react_agent has been moved to `langchain.agents`. Please update your import to `from langchain.agents import create_agent`.Fv2) r`response_formatpre_model_hookpost_model_hook state_schemacontext_schema checkpointerstoreinterrupt_beforeinterrupt_afterdebugversionrhrrrrrrrrrrrv1rrhdeprecated_kwargsc ^^^^^^^ ^^'^(^)^*^+^,^-^.^/URS[5=n[La[R"S[S9 UcUn[ U5S:a[ SU35eT S;a[ST S 35eTbPS S 1nTbURS 5 [[T55nU[U5- =n(a[S US35eTcTb[O[m/n[U[5(a&[UR R#55nUnOUVs/sHn[U[$5(dMUPM nn[UVs/sHn[U[$5(aMUPM sn5n[UR R#55n[T[&[(45(+=(a [+T5m-T-=(a [,R."T5m,[ U5S:nT-(d[T[&5(aSSKJn [7[8U"T55m[;TU[ U5S9(a4[ UU-5S:a"[7[8T5R=UU-5m[?T5T-m/OSm/UVs1sH"nUR@(dMURBiM$ snm.S[DS[F[HS[J4U-UUU/4Sjjm*S[DS[F[HS[J4U,U-UUU/4Sjjm(S[DS[LS[N4U.4Sjjm'S[DS[D4UU4Sjjm)S[DS[F[HS[PS[D4U'U)U*U,U-UU/4SjjnS[DS[F[HS[PS[D4U'U(U)U-UU/4SjjnTbT[T[R5(a1[UT[V5(aSSK,J-n U"S[[\S4TS 9nO"S!ST5nUnOTnS[DS[F[HS[PS[D4U*U,U4S"jjnS[DS[F[HS[PS[D4U(U4S#jjn U(d[_TUS$9n!U!RaS%[cUU5US&9 Tb'U!RaS'T5 U!ReS'S%5 S'm+OS%m+U!RgT+5 Tb$U!RaS(T5 U!ReS%S(5 TbDU!RaS)[cUU 55 TbU!ReS(S)5 OU!ReS%S)5 U!RiUU U U U TS*9$S[DS[&[[j-4UUU 4S+jjn"[_T=(d [US$9n!U!RaS%[cUU5US&9 U!RaS,U5 Tb'U!RaS'T5 U!ReS'S%5 S'm+OS%m+U!RgT+5 /n#T+S,/n$Tb6U!RaS(T5 U#RmS(5 U!ReS%S(5 OU#RmS,5 TbCU!RaS)[cUU 55 TbU$RmS)5 O@U#RmS)5 O.TbU$Rm[n5 OU#Rm[n5 Tb4S[DS[&[[j-4U+U4S-jjn%U!RqS(U%U$S.9 U!RqS%U"U#S.9 S[DS[&4U+U.4S/jjn&T.(aU!RqS,U&T+[n/S.9 OU!ReS,T+5 U!RiUU U U U TS*9$s snfs snf![4a [5S5ef=fs snf)0a&Creates an agent graph that calls tools in a loop until a stopping condition is met. For more details on using `create_react_agent`, visit [Agents](https://langchain-ai.github.io/langgraph/agents/overview/) documentation. Args: model: The language model for the agent. Supports static and dynamic model selection. - **Static model**: A chat model instance (e.g., `ChatOpenAI()`) or string identifier (e.g., `"openai:gpt-4"`) - **Dynamic model**: A callable with signature `(state, runtime) -> BaseChatModel` that returns different models based on runtime context If the model has tools bound via `.bind_tools()` or other configurations, the return type should be a Runnable[LanguageModelInput, BaseMessage] Coroutines are also supported, allowing for asynchronous model selection. Dynamic functions receive graph state and runtime, enabling context-dependent model selection. Must return a `BaseChatModel` instance. For tool calling, bind tools using `.bind_tools()`. Bound tools must be a subset of the `tools` parameter. Dynamic model example: ```python from dataclasses import dataclass @dataclass class ModelContext: model_name: str = "gpt-3.5-turbo" # Instantiate models globally gpt4_model = ChatOpenAI(model="gpt-4") gpt35_model = ChatOpenAI(model="gpt-3.5-turbo") def select_model(state: AgentState, runtime: Runtime[ModelContext]) -> ChatOpenAI: model_name = runtime.context.model_name model = gpt4_model if model_name == "gpt-4" else gpt35_model return model.bind_tools(tools) ``` !!! note "Dynamic Model Requirements" Ensure returned models have appropriate tools bound via `.bind_tools()` and support required functionality. Bound tools must be a subset of those specified in the `tools` parameter. tools: A list of tools or a `ToolNode` instance. If an empty list is provided, the agent will consist of a single LLM node without tool calling. prompt: An optional prompt for the LLM. Can take a few different forms: - str: This is converted to a SystemMessage and added to the beginning of the list of messages in state["messages"]. - SystemMessage: this is added to the beginning of the list of messages in state["messages"]. - Callable: This function should take in full graph state and the output is then passed to the language model. - Runnable: This runnable should take in full graph state and the output is then passed to the language model. response_format: An optional schema for the final agent output. If provided, output will be formatted to match the given schema and returned in the 'structured_response' state key. If not provided, `structured_response` will not be present in the output state. Can be passed in as: - an OpenAI function/tool schema, - a JSON Schema, - a TypedDict class, - or a Pydantic class. - a tuple (prompt, schema), where schema is one of the above. The prompt will be used together with the model that is being used to generate the structured response. !!! Important `response_format` requires the model to support `.with_structured_output` !!! Note The graph will make a separate call to the LLM to generate the structured response after the agent loop is finished. This is not the only strategy to get structured responses, see more options in [this guide](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/). pre_model_hook: An optional node to add before the `agent` node (i.e., the node that calls the LLM). Useful for managing long message histories (e.g., message trimming, summarization, etc.). Pre-model hook must be a callable or a runnable that takes in current graph state and returns a state update in the form of ```python # At least one of `messages` or `llm_input_messages` MUST be provided { # If provided, will UPDATE the `messages` in the state "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), ...], # If provided, will be used as the input to the LLM, # and will NOT UPDATE `messages` in the state "llm_input_messages": [...], # Any other state keys that need to be propagated ... } ``` !!! Important At least one of `messages` or `llm_input_messages` MUST be provided and will be used as an input to the `agent` node. The rest of the keys will be added to the graph state. !!! Warning If you are returning `messages` in the pre-model hook, you should OVERWRITE the `messages` key by doing the following: ```python { "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), *new_messages] ... } ``` post_model_hook: An optional node to add after the `agent` node (i.e., the node that calls the LLM). Useful for implementing human-in-the-loop, guardrails, validation, or other post-processing. Post-model hook must be a callable or a runnable that takes in current graph state and returns a state update. !!! Note Only available with `version="v2"`. state_schema: An optional state schema that defines graph state. Must have `messages` and `remaining_steps` keys. Defaults to `AgentState` that defines those two keys. !!! Note `remaining_steps` is used to limit the number of steps the react agent can take. Calculated roughly as `recursion_limit` - `total_steps_taken`. If `remaining_steps` is less than 2 and tool calls are present in the response, the react agent will return a final AI Message with the content "Sorry, need more steps to process this request.". No `GraphRecusionError` will be raised in this case. context_schema: An optional schema for runtime context. checkpointer: An optional checkpoint saver object. This is used for persisting the state of the graph (e.g., as chat memory) for a single thread (e.g., a single conversation). store: An optional store object. This is used for persisting data across multiple threads (e.g., multiple conversations / users). interrupt_before: An optional list of node names to interrupt before. Should be one of the following: "agent", "tools". This is useful if you want to add a user confirmation or other interrupt before taking an action. interrupt_after: An optional list of node names to interrupt after. Should be one of the following: "agent", "tools". This is useful if you want to return directly or run additional processing on an output. debug: A flag indicating whether to enable debug mode. version: Determines the version of the graph to create. Can be one of: - `"v1"`: The tool node processes a single message. All tool calls in the message are executed in parallel within the tool node. - `"v2"`: The tool node processes a tool call. Tool calls are distributed across multiple instances of the tool node using the [Send](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API. name: An optional name for the CompiledStateGraph. This name will be automatically used when adding ReAct agent graph to another graph as a subgraph node - particularly useful for building multi-agent systems. !!! warning "`config_schema` Deprecated" The `config_schema` parameter is deprecated in v0.6.0 and support will be removed in v2.0.0. Please use `context_schema` instead to specify the schema for run-scoped context. Returns: A compiled LangChain runnable that can be used for chat interactions. The "agent" node calls the language model with the messages list (after applying the prompt). If the resulting AIMessage contains `tool_calls`, the graph will then call the ["tools"][langgraph.prebuilt.tool_node.ToolNode]. The "tools" node executes the tools (1 tool per `tool_call`) and adds the responses to the messages list as `ToolMessage` objects. The agent node then calls the language model again. The process repeats until no more `tool_calls` are present in the response. The agent then returns the full list of messages as a dictionary containing the key "messages". ``` mermaid sequenceDiagram participant U as User participant A as LLM participant T as Tools U->>A: Initial input Note over A: Prompt + LLM loop while tool_calls present A->>T: Execute tools T-->>A: ToolMessage for each tool_calls end A->>U: Return final state ``` Example: ```python from langgraph.prebuilt import create_react_agent def check_weather(location: str) -> str: '''Return the weather forecast for the specified location.''' return f"It's always sunny in {location}" graph = create_react_agent( "anthropic:claude-3-7-sonnet-latest", tools=[check_weather], prompt="You are a helpful assistant", ) inputs = {"messages": [{"role": "user", "content": "what is the weather in sf"}]} for chunk in graph.stream(inputs, stream_mode="updates"): print(chunk) ``` config_schemazW`config_schema` is deprecated and will be removed. Please use `context_schema` instead.r/Nrz7create_react_agent() got unexpected keyword arguments: rzInvalid version z'. Supported versions are 'v1' and 'v2'.r5r6rKzMissing required key(s) z in state_schema)init_chat_modelzsPlease install langchain (`pip install langchain`) to use ':' string syntax for `model` parameter.)ryrUruntimerXc>>T(a[T5T"X5-$T$)zBResolve the model to use, handling both static and dynamic models.rv)rUris_dynamic_modelrwr` static_models r@_resolve_model*create_react_agent.._resolve_modelCs# '/%2GG G r?c># T(aT"X5IShvN n[T5U-$T(a[T5T"X5-$T$N/7f)zHAsync resolve the model to use, handling both static and dynamic models.Nr)rUrresolved_modelis_async_dynamic_modelrrwr`rs r@_aresolve_model+create_react_agent.._aresolve_modelLsK "#(#88N'/.@ @ '/%2GG G  9sAA0Aresponsec>[U[5=(a URn[U[5(a[U4SjUR55OSn[ USS5nUbUS:aU(agUS:aU(agg)Nc32># UH oST;v M g7frhNr7r~callshould_return_directs r@rEcreate_react_agent.._are_more_steps_needed..[sUATV 44ATFr6T)r[rrallr_)rUrhas_tool_callsall_tools_return_directr6rs r@_are_more_steps_needed2create_react_agent.._are_more_steps_neededXs|#Hi8PX=P=P(I.. UATATU U +52CTJ  &"'> 1$r?c>Tb%[US5=(d [US5nSU3nO[US5nSU3nUc [U5e[U5 [T[5(a[ T[ 5(aXlU$XS'U$)Nllm_input_messagesr5zUExpected input to call_model to have 'llm_input_messages' or 'messages' key, but got z=Expected input to call_model to have 'messages' key, but got )r_rsrr[rt issubclassr)r5)rUr5 error_msgrrs r@_get_model_input_state2create_react_agent.._get_model_input_statehs  % (<=5!%4 ppuovwI'z:HOPUwW   Y' 'x( lD ) )jy.Q.Q%N !)*  r?configc8>T (a Sn[U5eT"U5nT (a(T "X5n[[URXB55nO[[T RXB55nT UlT"X5(aS[UR SS9/0$SU/0$)NAsync model callable provided but agent invoked synchronously. Use agent.ainvoke() or agent.astream(), or provide a sync model callable.r5/Sorry, need more steps to process this request.rrj) RuntimeErrorr rinvokerhr)rUrrmsg model_input dynamic_modelrrrrrrrhrs r@ call_model&create_react_agent..call_models "1  s# #,U3 *5:MI}';';K'PQHI|':':;'OPH !% 2 2#;; Q XJ''r?cT># T"U5nT (a8T"X5IShvN n[[URX25IShvN 5nO'[[T RX25IShvN 5nT UlT"X5(aS[URSS9/0$SU/0$NNbN<7f)Nr5rr)r rainvokerhr) rUrrrrrrrrrrhrs r@ acall_model'create_react_agent..acall_models-U3 #2%"AAMI]-B-B;-W'WXHI\-A-A+-V'VWH !% 2 2#;; Q XJ''#B'W'Vs3B(B""B(B$ 'B()B& *9B($B(&B() create_modelCallModelInputSchema.)r__base__c&\rSrSr%\\\S'Srg)0create_react_agent..CallModelInputSchemairr7N)r8r9r:r;listrr=r>r7r?r@rrs $($44r?c0>T (a Sn[U5e[US5nT n[T [5(aT upe[ US9/[ U5-nT "X5n[ U5R[[U55nURXB5n SU 0$)Nrr5rirK) rr_r[tuplerrrwith_structured_outputr StructuredResponseSchemar) rUrrrr5structured_response_schema system_promptrmodel_with_structured_outputrrrrs r@generate_structured_response8create_react_agent..generate_structured_responses "\ s# ##E:6%4" ou - -8G 5M%m<=XNH'7'1 ( )+E F  % 066xH%x00r?c8># [US5nT n[T [5(aT upT[US9/[ U5-nT "X5IShvN n[ U5R [[U55nURX25IShvN nSU0$NIN 7f)Nr5rirK) r_r[rrrrrr rr) rUrrr5rrrrrrrs r@agenerate_structured_response9create_react_agent..agenerate_structured_responses$E:6%4" ou - -8G 5M%m<=XNH.u>>'1 ( )+E F  % 6==hOO%x00? Ps%A B B AB B BB)rragent) input_schemarrr)rrrrrrhc >[US5nUSn[U[5(aUR(dTbgTbg[$TS:XagTS:Xa4TbgURVs/sHn[ S[ SUUS 95PM sn$gs snf) Nr5rrrrxrtool_call_with_context__typerrU)r_r[rrrr&r-)rUr5 last_messagerrrrs r@should_continue+create_react_agent..should_continue+s#E:6| , 22,:Q:Q*( ,5 $D".,!- 7 7 !8+#;&*"'!8  ! s&B rxc >[US5nUVs/sH&n[U[5(dMURPM( nn[ S[ U555nUR Vs/sHoUSU;dM UPM nnU(a&UVs/sHn[S[SUUS95PM sn$[US[5(aT$T bg[$s snfs snfs snf) a;Route to the next node after post_model_hook. Routes to one of: * "tools": if there are pending tool calls without a corresponding message. * "generate_structured_response": if no pending tool calls exist and response_format is specified. * END: if no pending tool calls exist and no response_format is specified. r5c3T# UHn[U[5(dMUv M g7frZ)r[r)r~ms r@rEcreate_react_agent..post_model_hook_router..s#-aAy1I-s( (rrxrrrr) r_r[rrrreversedrr&r-r) rUr5r tool_messageslast_ai_messagecpending_tool_callsr entrypointrs r@post_model_hook_router2create_react_agent..post_model_hook_routers(z:H(0(01Jq+4N ###H-#O+55"5a4 9U5 ""!3 !3+#;&*"'!3  HRL+66!! ,5 7 " sCC* C:C C)path_mapc>>[[US55H2n[U[5(d OURT;dM,[ s $ [W[ 5(a;UR(a*[U4SjUR55(a[ $T$)Nr5c32># UH oST;v M g7frr7rs r@rCcreate_react_agent..route_tool_responses..sQLD<#77Lr) r r_r[rrhrrrany)rUrrrs r@route_tool_responses0create_react_agent..route_tool_responsessq*5*=>Aa--vv-- ? a # # QALLQQQ r?)9poprwarningswarnr(rrrsrrr rHr2r[r.r tools_by_namevaluesr\rorrrrprqlangchain.chat_modelsr ImportErrorr r r bind_toolsrv return_directrhrRr#r'rrboolrrtrr)pydanticrrradd_noderadd_edgeset_entry_pointcompiler&appendradd_conditional_edges)0rwrxr`rrrrrrrrrrrrhrr required_keys schema_keys missing_keysllm_builtin_tools tool_classes tool_nodettool_calling_enabledrrrrrrrrworkflowr agent_pathspost_model_hook_pathsrrrrrrrrrrrs0` ````` `` @@@@@@@@@r@create_react_agentr5 sJ+..HH   e0  !*N !EFWEX Y  l"wi'N O  #%67  &   3 4.67 (3{+;; ;< ;7 ~EUVW W* -  %'%""E//6689  (-E1At1DQEJAjD6IaJK I33::<= %ec8_==Q(5/-T'2M2Me2T|,q0  eS ! ! (>?E ulDU@V WL#4459.9900E)=V(Du(L  -9LLqAOOFAFFLL  %,X%6       %,X%6     k[T kk2!(!(%,X%6!(@N!( !(!(F((%,X%6(@N( ((8! lD ) )jy.Q.Q -'&$($4c#:%L 5| 50L# 11%,X%61@N1 11211%,X%61@N1 11$ <W  Z 5%   %   . ?   . 8)J J  ,  &   / A   g'8 9  &   . 01 *!!"35ST!!'+IJ%-+    {sT$Z/?>!/ZH  [1!  gy)!*N;*G4%   Z(K'1"+_=,-'#457#" * ,-    & ! ( ()G H   = >  & ! 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