From 68c2320fb88ee61d73547668c9a7bbee5dfa76b5 Mon Sep 17 00:00:00 2001 From: vedantsahai18 Date: Thu, 14 Nov 2024 16:10:41 -0500 Subject: [PATCH 01/28] fix(agents-api): added anthropic call in chat endpoint + typespec changed to include imageurl in ChatInput --- agents-api/agents_api/autogen/Chat.py | 69 +++- agents-api/agents_api/autogen/Entries.py | 44 ++- agents-api/agents_api/autogen/Tasks.py | 46 ++- .../agents_api/routers/sessions/chat.py | 198 ++++++++-- .../integrations/autogen/Chat.py | 69 +++- .../integrations/autogen/Entries.py | 44 ++- .../integrations/autogen/Tasks.py | 46 ++- typespec/entries/models.tsp | 22 +- .../@typespec/openapi3/openapi-1.0.0.yaml | 344 ++++++++++++++++++ 9 files changed, 834 insertions(+), 48 deletions(-) diff --git a/agents-api/agents_api/autogen/Chat.py b/agents-api/agents_api/autogen/Chat.py index d013ae8b7..380476806 100644 --- a/agents-api/agents_api/autogen/Chat.py +++ b/agents-api/agents_api/autogen/Chat.py @@ -165,7 +165,58 @@ class Content(BaseModel): """ +class ContentItem(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + class ContentModel(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] + + +class ContentModel1(Content): + pass + + +class ContentModel2(ContentModel): + """ + Anthropic image content part + """ + + +class ContentModel3(Content): + pass + + +class ContentModel4(ContentModel): + """ + Anthropic image content part + """ + + +class ContentModel5(Content): + pass + + +class ContentModel6(ContentModel): + """ + Anthropic image content part + """ + + +class ContentModel7(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -193,7 +244,8 @@ class Delta(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[ContentModel1 | ContentModel7 | ContentModel2] | None, + Field(...), ] = None """ The content parts of the message @@ -258,7 +310,8 @@ class Message(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[Content | ContentModel7 | ContentModel] | None, + Field(...), ] = None """ The content parts of the message @@ -305,7 +358,8 @@ class MessageModel(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[ContentModel3 | ContentModel7 | ContentModel4] | None, + Field(...), ] = None """ The content parts of the message @@ -405,6 +459,15 @@ class SingleChatOutput(BaseChatOutput): message: MessageModel +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str + + class TokenLogProb(BaseTokenLogProb): model_config = ConfigDict( populate_by_name=True, diff --git a/agents-api/agents_api/autogen/Entries.py b/agents-api/agents_api/autogen/Entries.py index f001fc880..829818e37 100644 --- a/agents-api/agents_api/autogen/Entries.py +++ b/agents-api/agents_api/autogen/Entries.py @@ -28,7 +28,7 @@ class BaseEntry(BaseModel): """ name: str | None = None content: ( - list[Content | ContentModel] + list[Content | ContentModel3 | ContentModel] | Tool | ChosenFunctionCall | ChosenComputer20241022 @@ -37,7 +37,7 @@ class BaseEntry(BaseModel): | str | ToolResponse | list[ - list[Content | ContentModel] + list[ContentModel1 | ContentModel3 | ContentModel2] | Tool | ChosenFunctionCall | ChosenComputer20241022 @@ -95,7 +95,38 @@ class Content(BaseModel): """ +class ContentItem(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + class ContentModel(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] + + +class ContentModel1(Content): + pass + + +class ContentModel2(ContentModel): + """ + Anthropic image content part + """ + + +class ContentModel3(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -168,3 +199,12 @@ class Relation(BaseModel): head: UUID relation: str tail: UUID + + +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str diff --git a/agents-api/agents_api/autogen/Tasks.py b/agents-api/agents_api/autogen/Tasks.py index e62e6d3c3..2fad6d63d 100644 --- a/agents-api/agents_api/autogen/Tasks.py +++ b/agents-api/agents_api/autogen/Tasks.py @@ -85,6 +85,14 @@ class Content(BaseModel): """ +class ContentItem(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + class ContentModel(BaseModel): model_config = ConfigDict( populate_by_name=True, @@ -99,14 +107,33 @@ class ContentModel(BaseModel): """ -class ContentModel1(Content): +class ContentModel1(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] + + +class ContentModel2(Content): pass -class ContentModel2(ContentModel): +class ContentModel3(ContentModel): pass +class ContentModel4(ContentModel1): + """ + Anthropic image content part + """ + + class CreateTaskRequest(BaseModel): """ Payload for creating a task @@ -655,7 +682,8 @@ class PromptItem(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - list[str] | list[Content | ContentModel] | str | None, Field(...) + list[str] | list[Content | ContentModel | ContentModel1] | str | None, + Field(...), ] """ The content parts of the message @@ -861,6 +889,18 @@ class SleepStep(BaseModel): """ +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str + """ + A valid jinja template. + """ + + class SwitchStep(BaseModel): model_config = ConfigDict( populate_by_name=True, diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 17a93799d..5ef4bde56 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -1,9 +1,17 @@ -from typing import Annotated, Optional +from datetime import datetime +from typing import Annotated, Callable, Optional from uuid import UUID, uuid4 +from anthropic import AsyncAnthropic +from anthropic.types.beta.beta_message import BetaMessage from fastapi import BackgroundTasks, Depends, Header +from langchain_core.tools import BaseTool +from langchain_core.tools.convert import tool as tool_decorator +from litellm import ChatCompletionMessageToolCall, Function, Message +from litellm.types.utils import Choices, ModelResponse from starlette.status import HTTP_201_CREATED +from ...activities.utils import get_handler_with_filtered_params from ...autogen.openapi_model import ( ChatInput, ChatResponse, @@ -11,18 +19,78 @@ CreateEntryRequest, MessageChatResponse, ) +from ...autogen.Tools import Tool from ...clients import litellm from ...common.protocol.developers import Developer from ...common.protocol.sessions import ChatContext from ...common.utils.datetime import utcnow from ...common.utils.template import render_template from ...dependencies.developer_id import get_developer_data +from ...env import anthropic_api_key from ...models.chat.gather_messages import gather_messages from ...models.chat.prepare_chat_context import prepare_chat_context from ...models.entry.create_entries import create_entries from .metrics import total_tokens_per_user from .router import router +COMPUTER_USE_BETA_FLAG = "computer-use-2024-10-22" + + +def format_tool(tool: Tool) -> dict: + if tool.type == "computer_20241022": + return { + "type": tool.type, + "name": tool.name, + "display_width_px": tool.computer_20241022 + and tool.computer_20241022.display_width_px, + "display_height_px": tool.computer_20241022 + and tool.computer_20241022.display_height_px, + "display_number": tool.computer_20241022 + and tool.computer_20241022.display_number, + } + + if tool.type in ["bash_20241022", "text_editor_20241022"]: + return tool.model_dump(include={"type", "name"}) + + if tool.type == "function": + return { + "type": "function", + "function": { + "name": tool.name, + "description": tool.description, + "parameters": tool.function and tool.function.parameters, + }, + } + + # For other tool types, we need to translate them to the OpenAI function tool format + formatted = { + "type": "function", + "function": {"name": tool.name, "description": tool.description}, + } + + if tool.type == "system": + handler: Callable = get_handler_with_filtered_params(tool.system) + + lc_tool: BaseTool = tool_decorator(handler) + + json_schema: dict = lc_tool.get_input_jsonschema() + + formatted["function"]["description"] = formatted["function"][ + "description" + ] or json_schema.get("description") + + formatted["function"]["parameters"] = json_schema + + # # FIXME: Implement integration tools + # elif tool.type == "integration": + # raise NotImplementedError("Integration tools are not supported") + + # # FIXME: Implement API call tools + # elif tool.type == "api_call": + # raise NotImplementedError("API call tools are not supported") + + return formatted + @router.post( "/sessions/{session_id}/chat", @@ -106,27 +174,9 @@ async def chat( # Get the tools tools = settings.get("tools") or chat_context.get_active_tools() - tools = [tool.model_dump(mode="json") for tool in tools] - - # Convert anthropic tools to `function` - for tool in tools: - if tool.get("type") == "computer_20241022": - tool["function"] = { - "name": tool["name"], - "parameters": tool.pop("computer_20241022"), - } - - elif tool.get("type") == "bash_20241022": - tool["function"] = { - "name": tool["name"], - "parameters": tool.pop("bash_20241022"), - } - - elif tool.get("type") == "text_editor_20241022": - tool["function"] = { - "name": tool["name"], - "parameters": tool.pop("text_editor_20241022"), - } + + # Format tools for litellm + formatted_tools = [format_tool(tool) for tool in tools] # FIXME: Truncate chat messages in the chat context # SCRUM-7 @@ -144,16 +194,104 @@ async def chat( for m in messages ] - # Get the response from the model - model_response = await litellm.acompletion( - messages=messages, - tools=tools or None, - user=str(developer.id), # For tracking usage - tags=developer.tags, # For filtering models in litellm - custom_api_key=x_custom_api_key, - **settings, + # Check if using Claude model and has specific tool types + is_claude_model = settings["model"].lower().startswith("claude-3.5") + has_special_tools = any( + tool["type"] in ["computer_20241022", "bash_20241022", "text_editor_20241022"] + for tool in formatted_tools ) + if is_claude_model and has_special_tools: + # Use Anthropic API directly + client = AsyncAnthropic(api_key=anthropic_api_key) + + # Filter tools for specific types + filtered_tools = [ + tool + for tool in formatted_tools + if tool["type"] + in ["computer_20241022", "bash_20241022", "text_editor_20241022"] + ] + + # Format messages for Claude + claude_messages = [] + for msg in messages: + # Skip messages that are not assistant or user + if msg["role"] not in ["assistant", "user"]: + continue + + claude_messages.append({"role": msg["role"], "content": msg["content"]}) + + # Call Claude API + claude_response: BetaMessage = await client.beta.messages.create( + model="claude-3-5-sonnet-20241022", + messages=claude_messages, + tools=filtered_tools, + max_tokens=settings.get("max_tokens", 1024), + betas=[COMPUTER_USE_BETA_FLAG], + ) + + # Convert Claude response to litellm format + text_block = next( + (block for block in claude_response.content if block.type == "text"), + None, + ) + + if claude_response.stop_reason == "tool_use": + choice = Choices( + message=Message( + role="assistant", + content=text_block.text if text_block else None, + tool_calls=[ + ChatCompletionMessageToolCall( + type="function", + function=Function( + name=block.name, + arguments=block.input, + ), + ) + for block in claude_response.content + if block.type == "tool_use" + ], + ), + finish_reason="tool_calls", + ) + else: + assert ( + text_block + ), "Claude should always return a text block for stop_reason=stop" + choice = Choices( + message=Message( + role="assistant", + content=text_block.text, + ), + finish_reason="stop", + ) + + model_response = ModelResponse( + id=claude_response.id, + choices=[choice], + created=int(datetime.now().timestamp()), + model=claude_response.model, + object="text_completion", + usage={ + "total_tokens": claude_response.usage.input_tokens + + claude_response.usage.output_tokens + }, + ) + else: + # FIXME: hardcoded tool to a None value as the tool calls are not implemented yet + formatted_tools = None + # Use litellm for other models + model_response = await litellm.acompletion( + messages=messages, + tools=formatted_tools or None, + user=str(developer.id), + tags=developer.tags, + custom_api_key=x_custom_api_key, + **settings, + ) + # Save the input and the response to the session history if chat_input.save: new_entries = [ diff --git a/integrations-service/integrations/autogen/Chat.py b/integrations-service/integrations/autogen/Chat.py index d013ae8b7..380476806 100644 --- a/integrations-service/integrations/autogen/Chat.py +++ b/integrations-service/integrations/autogen/Chat.py @@ -165,7 +165,58 @@ class Content(BaseModel): """ +class ContentItem(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + class ContentModel(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] + + +class ContentModel1(Content): + pass + + +class ContentModel2(ContentModel): + """ + Anthropic image content part + """ + + +class ContentModel3(Content): + pass + + +class ContentModel4(ContentModel): + """ + Anthropic image content part + """ + + +class ContentModel5(Content): + pass + + +class ContentModel6(ContentModel): + """ + Anthropic image content part + """ + + +class ContentModel7(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -193,7 +244,8 @@ class Delta(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[ContentModel1 | ContentModel7 | ContentModel2] | None, + Field(...), ] = None """ The content parts of the message @@ -258,7 +310,8 @@ class Message(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[Content | ContentModel7 | ContentModel] | None, + Field(...), ] = None """ The content parts of the message @@ -305,7 +358,8 @@ class MessageModel(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[ContentModel3 | ContentModel7 | ContentModel4] | None, + Field(...), ] = None """ The content parts of the message @@ -405,6 +459,15 @@ class SingleChatOutput(BaseChatOutput): message: MessageModel +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str + + class TokenLogProb(BaseTokenLogProb): model_config = ConfigDict( populate_by_name=True, diff --git a/integrations-service/integrations/autogen/Entries.py b/integrations-service/integrations/autogen/Entries.py index f001fc880..829818e37 100644 --- a/integrations-service/integrations/autogen/Entries.py +++ b/integrations-service/integrations/autogen/Entries.py @@ -28,7 +28,7 @@ class BaseEntry(BaseModel): """ name: str | None = None content: ( - list[Content | ContentModel] + list[Content | ContentModel3 | ContentModel] | Tool | ChosenFunctionCall | ChosenComputer20241022 @@ -37,7 +37,7 @@ class BaseEntry(BaseModel): | str | ToolResponse | list[ - list[Content | ContentModel] + list[ContentModel1 | ContentModel3 | ContentModel2] | Tool | ChosenFunctionCall | ChosenComputer20241022 @@ -95,7 +95,38 @@ class Content(BaseModel): """ +class ContentItem(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + class ContentModel(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] + + +class ContentModel1(Content): + pass + + +class ContentModel2(ContentModel): + """ + Anthropic image content part + """ + + +class ContentModel3(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -168,3 +199,12 @@ class Relation(BaseModel): head: UUID relation: str tail: UUID + + +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str diff --git a/integrations-service/integrations/autogen/Tasks.py b/integrations-service/integrations/autogen/Tasks.py index e62e6d3c3..2fad6d63d 100644 --- a/integrations-service/integrations/autogen/Tasks.py +++ b/integrations-service/integrations/autogen/Tasks.py @@ -85,6 +85,14 @@ class Content(BaseModel): """ +class ContentItem(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + class ContentModel(BaseModel): model_config = ConfigDict( populate_by_name=True, @@ -99,14 +107,33 @@ class ContentModel(BaseModel): """ -class ContentModel1(Content): +class ContentModel1(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] + + +class ContentModel2(Content): pass -class ContentModel2(ContentModel): +class ContentModel3(ContentModel): pass +class ContentModel4(ContentModel1): + """ + Anthropic image content part + """ + + class CreateTaskRequest(BaseModel): """ Payload for creating a task @@ -655,7 +682,8 @@ class PromptItem(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - list[str] | list[Content | ContentModel] | str | None, Field(...) + list[str] | list[Content | ContentModel | ContentModel1] | str | None, + Field(...), ] """ The content parts of the message @@ -861,6 +889,18 @@ class SleepStep(BaseModel): """ +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str + """ + A valid jinja template. + """ + + class SwitchStep(BaseModel): model_config = ConfigDict( populate_by_name=True, diff --git a/typespec/entries/models.tsp b/typespec/entries/models.tsp index 61f47bce2..95fbadb55 100644 --- a/typespec/entries/models.tsp +++ b/typespec/entries/models.tsp @@ -50,7 +50,25 @@ model ChatMLImageContentPart { type: "image_url" = "image_url"; } -alias ChatMLContentPart = ChatMLTextContentPart | ChatMLImageContentPart; +model ChatMLAnthropicImageSource { + type: "base64" = "base64"; + media_type: string; + data: T; +} + +model ChatMLAnthropicInnerImageContentPart { + type: "image" = "image"; + source: ChatMLAnthropicImageSource; +} + +/** Anthropic image content part */ +model ChatMLAnthropicImageContentPart { + tool_use_id: string; + type: "tool_result" = "tool_result"; + content: ChatMLAnthropicInnerImageContentPart[]; +} + +alias ChatMLContentPart = ChatMLTextContentPart | ChatMLImageContentPart | ChatMLAnthropicImageContentPart; model ChatMLMessage { /** The role of the message */ @@ -119,4 +137,4 @@ model History { session_id: Session.id; ...HasCreatedAt; -} +} \ No newline at end of file diff --git a/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml b/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml index d83ce87f5..93b74354b 100644 --- a/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml +++ b/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml @@ -1836,6 +1836,49 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -1936,6 +1979,49 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -2167,6 +2253,49 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -2317,6 +2446,49 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -2809,6 +2981,49 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part - $ref: '#/components/schemas/Tools.Tool' - $ref: '#/components/schemas/Tools.ChosenFunctionCall' - $ref: '#/components/schemas/Tools.ChosenComputer20241022' @@ -2861,6 +3076,49 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part - $ref: '#/components/schemas/Tools.Tool' - $ref: '#/components/schemas/Tools.ChosenFunctionCall' - $ref: '#/components/schemas/Tools.ChosenComputer20241022' @@ -4784,6 +5042,49 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + $ref: '#/components/schemas/Common.JinjaTemplate' + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -4934,6 +5235,49 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + $ref: '#/components/schemas/Common.JinjaTemplate' + description: Anthropic image content part nullable: true description: The content parts of the message name: From a437681213e309ea8389464929ec0522088c7f3c Mon Sep 17 00:00:00 2001 From: Ahmad-mtos Date: Fri, 15 Nov 2024 19:14:30 +0300 Subject: [PATCH 02/28] fix: change format of past messages for anthropic + add key mappings to remote_browser --- agents-api/agents_api/autogen/Chat.py | 59 ++- agents-api/agents_api/autogen/Entries.py | 25 +- agents-api/agents_api/autogen/Tasks.py | 26 +- .../agents_api/routers/sessions/chat.py | 184 ++++--- .../integrations/autogen/Chat.py | 59 ++- .../integrations/autogen/Entries.py | 25 +- .../integrations/autogen/Tasks.py | 26 +- .../utils/integrations/remote_browser.py | 10 + typespec/entries/models.tsp | 2 +- .../@typespec/openapi3/openapi-1.0.0.yaml | 448 +++++++++++------- 10 files changed, 604 insertions(+), 260 deletions(-) diff --git a/agents-api/agents_api/autogen/Chat.py b/agents-api/agents_api/autogen/Chat.py index 380476806..042f9164d 100644 --- a/agents-api/agents_api/autogen/Chat.py +++ b/agents-api/agents_api/autogen/Chat.py @@ -165,7 +165,11 @@ class Content(BaseModel): """ -class ContentItem(BaseModel): +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -173,6 +177,30 @@ class ContentItem(BaseModel): source: Source +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + +class ContentItemModel3(Content): + pass + + +class ContentItemModel4(ContentItemModel): + pass + + +class ContentItemModel5(Content): + pass + + +class ContentItemModel6(ContentItemModel): + pass + + class ContentModel(BaseModel): """ Anthropic image content part @@ -183,38 +211,59 @@ class ContentModel(BaseModel): ) tool_use_id: str type: Literal["tool_result"] = "tool_result" - content: list[ContentItem] + content: list[ContentItem] | list[ContentItemModel] class ContentModel1(Content): pass -class ContentModel2(ContentModel): +class ContentModel2(BaseModel): """ Anthropic image content part """ + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + class ContentModel3(Content): pass -class ContentModel4(ContentModel): +class ContentModel4(BaseModel): """ Anthropic image content part """ + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel3] | list[ContentItemModel4] + class ContentModel5(Content): pass -class ContentModel6(ContentModel): +class ContentModel6(BaseModel): """ Anthropic image content part """ + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel5] | list[ContentItemModel6] + class ContentModel7(BaseModel): model_config = ConfigDict( diff --git a/agents-api/agents_api/autogen/Entries.py b/agents-api/agents_api/autogen/Entries.py index 829818e37..de37e77d8 100644 --- a/agents-api/agents_api/autogen/Entries.py +++ b/agents-api/agents_api/autogen/Entries.py @@ -95,7 +95,11 @@ class Content(BaseModel): """ -class ContentItem(BaseModel): +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -103,6 +107,14 @@ class ContentItem(BaseModel): source: Source +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + class ContentModel(BaseModel): """ Anthropic image content part @@ -113,18 +125,25 @@ class ContentModel(BaseModel): ) tool_use_id: str type: Literal["tool_result"] = "tool_result" - content: list[ContentItem] + content: list[ContentItem] | list[ContentItemModel] class ContentModel1(Content): pass -class ContentModel2(ContentModel): +class ContentModel2(BaseModel): """ Anthropic image content part """ + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + class ContentModel3(BaseModel): model_config = ConfigDict( diff --git a/agents-api/agents_api/autogen/Tasks.py b/agents-api/agents_api/autogen/Tasks.py index 2fad6d63d..8e98caaab 100644 --- a/agents-api/agents_api/autogen/Tasks.py +++ b/agents-api/agents_api/autogen/Tasks.py @@ -9,6 +9,7 @@ from pydantic import AwareDatetime, BaseModel, ConfigDict, Field, StrictBool from .Chat import ChatSettings +from .Common import JinjaTemplate from .Tools import ( ChosenBash20241022, ChosenComputer20241022, @@ -85,7 +86,11 @@ class Content(BaseModel): """ -class ContentItem(BaseModel): +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -93,6 +98,14 @@ class ContentItem(BaseModel): source: Source +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + class ContentModel(BaseModel): model_config = ConfigDict( populate_by_name=True, @@ -117,7 +130,7 @@ class ContentModel1(BaseModel): ) tool_use_id: str type: Literal["tool_result"] = "tool_result" - content: list[ContentItem] + content: list[ContentItem] | list[ContentItemModel] class ContentModel2(Content): @@ -128,11 +141,18 @@ class ContentModel3(ContentModel): pass -class ContentModel4(ContentModel1): +class ContentModel4(BaseModel): """ Anthropic image content part """ + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + class CreateTaskRequest(BaseModel): """ diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 5ef4bde56..57ba970e1 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -1,3 +1,4 @@ +import json from datetime import datetime from typing import Annotated, Callable, Optional from uuid import UUID, uuid4 @@ -36,6 +37,111 @@ COMPUTER_USE_BETA_FLAG = "computer-use-2024-10-22" +async def request_anthropic( + messages: list[dict], formatted_tools: list[dict], settings: dict +) -> ModelResponse: + # Use Anthropic API directly + client = AsyncAnthropic(api_key=anthropic_api_key) + + # Filter tools for specific types + filtered_tools = [ + tool + for tool in formatted_tools + if tool["type"] + in ["computer_20241022", "bash_20241022", "text_editor_20241022"] + ] + + # Format messages for Claude + claude_messages = [] + for msg in messages: + # Skip messages that are not assistant or user + if msg["role"] not in ["assistant", "user"]: + continue + + # Transform the message content and tool calls + if msg["role"] == "assistant": + transformed_content = [{"text": msg["content"], "type": "text"}] + transformed_content.extend( + { + "id": f"{tool_call['id']}", + "input": json.loads(tool_call["function"]["arguments"]), + "name": tool_call["function"]["name"], + "type": "tool_use", + } + for tool_call in msg.get("tool_calls", []) + ) + claude_message = { + "role": msg["role"], + "content": transformed_content, + } + else: + try: + transformed_content = json.loads(msg["content"]) + except Exception: + transformed_content = msg["content"] + claude_message = {"role": msg["role"], "content": transformed_content} + + claude_messages.append(claude_message) + # Call Claude API + claude_response: BetaMessage = await client.beta.messages.create( + model="claude-3-5-sonnet-20241022", + messages=claude_messages, + tools=filtered_tools, + max_tokens=settings.get("max_tokens", 1024), + betas=[COMPUTER_USE_BETA_FLAG], + ) + # Convert Claude response to litellm format + text_block = next( + (block for block in claude_response.content if block.type == "text"), + None, + ) + + if claude_response.stop_reason == "tool_use": + choice = Choices( + message=Message( + role="assistant", + content=text_block.text if text_block else None, + tool_calls=[ + ChatCompletionMessageToolCall( + type="function", + function=Function( + name=block.name, + arguments=block.input, + ), + ) + for block in claude_response.content + if block.type == "tool_use" + ], + ), + finish_reason="tool_calls", + ) + else: + assert ( + text_block + ), "Claude should always return a text block for stop_reason=stop" + choice = Choices( + message=Message( + role="assistant", + content=text_block.text, + ), + finish_reason="stop", + ) + + model_response = ModelResponse( + id=claude_response.id, + choices=[choice], + created=int(datetime.now().timestamp()), + model=claude_response.model, + object="text_completion", + usage={ + "total_tokens": claude_response.usage.input_tokens + + claude_response.usage.output_tokens + }, + ) + + return model_response + + def format_tool(tool: Tool) -> dict: if tool.type == "computer_20241022": return { @@ -202,83 +308,7 @@ async def chat( ) if is_claude_model and has_special_tools: - # Use Anthropic API directly - client = AsyncAnthropic(api_key=anthropic_api_key) - - # Filter tools for specific types - filtered_tools = [ - tool - for tool in formatted_tools - if tool["type"] - in ["computer_20241022", "bash_20241022", "text_editor_20241022"] - ] - - # Format messages for Claude - claude_messages = [] - for msg in messages: - # Skip messages that are not assistant or user - if msg["role"] not in ["assistant", "user"]: - continue - - claude_messages.append({"role": msg["role"], "content": msg["content"]}) - - # Call Claude API - claude_response: BetaMessage = await client.beta.messages.create( - model="claude-3-5-sonnet-20241022", - messages=claude_messages, - tools=filtered_tools, - max_tokens=settings.get("max_tokens", 1024), - betas=[COMPUTER_USE_BETA_FLAG], - ) - - # Convert Claude response to litellm format - text_block = next( - (block for block in claude_response.content if block.type == "text"), - None, - ) - - if claude_response.stop_reason == "tool_use": - choice = Choices( - message=Message( - role="assistant", - content=text_block.text if text_block else None, - tool_calls=[ - ChatCompletionMessageToolCall( - type="function", - function=Function( - name=block.name, - arguments=block.input, - ), - ) - for block in claude_response.content - if block.type == "tool_use" - ], - ), - finish_reason="tool_calls", - ) - else: - assert ( - text_block - ), "Claude should always return a text block for stop_reason=stop" - choice = Choices( - message=Message( - role="assistant", - content=text_block.text, - ), - finish_reason="stop", - ) - - model_response = ModelResponse( - id=claude_response.id, - choices=[choice], - created=int(datetime.now().timestamp()), - model=claude_response.model, - object="text_completion", - usage={ - "total_tokens": claude_response.usage.input_tokens - + claude_response.usage.output_tokens - }, - ) + model_response = await request_anthropic(messages, formatted_tools, settings) else: # FIXME: hardcoded tool to a None value as the tool calls are not implemented yet formatted_tools = None diff --git a/integrations-service/integrations/autogen/Chat.py b/integrations-service/integrations/autogen/Chat.py index 380476806..042f9164d 100644 --- a/integrations-service/integrations/autogen/Chat.py +++ b/integrations-service/integrations/autogen/Chat.py @@ -165,7 +165,11 @@ class Content(BaseModel): """ -class ContentItem(BaseModel): +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -173,6 +177,30 @@ class ContentItem(BaseModel): source: Source +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + +class ContentItemModel3(Content): + pass + + +class ContentItemModel4(ContentItemModel): + pass + + +class ContentItemModel5(Content): + pass + + +class ContentItemModel6(ContentItemModel): + pass + + class ContentModel(BaseModel): """ Anthropic image content part @@ -183,38 +211,59 @@ class ContentModel(BaseModel): ) tool_use_id: str type: Literal["tool_result"] = "tool_result" - content: list[ContentItem] + content: list[ContentItem] | list[ContentItemModel] class ContentModel1(Content): pass -class ContentModel2(ContentModel): +class ContentModel2(BaseModel): """ Anthropic image content part """ + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + class ContentModel3(Content): pass -class ContentModel4(ContentModel): +class ContentModel4(BaseModel): """ Anthropic image content part """ + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel3] | list[ContentItemModel4] + class ContentModel5(Content): pass -class ContentModel6(ContentModel): +class ContentModel6(BaseModel): """ Anthropic image content part """ + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel5] | list[ContentItemModel6] + class ContentModel7(BaseModel): model_config = ConfigDict( diff --git a/integrations-service/integrations/autogen/Entries.py b/integrations-service/integrations/autogen/Entries.py index 829818e37..de37e77d8 100644 --- a/integrations-service/integrations/autogen/Entries.py +++ b/integrations-service/integrations/autogen/Entries.py @@ -95,7 +95,11 @@ class Content(BaseModel): """ -class ContentItem(BaseModel): +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -103,6 +107,14 @@ class ContentItem(BaseModel): source: Source +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + class ContentModel(BaseModel): """ Anthropic image content part @@ -113,18 +125,25 @@ class ContentModel(BaseModel): ) tool_use_id: str type: Literal["tool_result"] = "tool_result" - content: list[ContentItem] + content: list[ContentItem] | list[ContentItemModel] class ContentModel1(Content): pass -class ContentModel2(ContentModel): +class ContentModel2(BaseModel): """ Anthropic image content part """ + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + class ContentModel3(BaseModel): model_config = ConfigDict( diff --git a/integrations-service/integrations/autogen/Tasks.py b/integrations-service/integrations/autogen/Tasks.py index 2fad6d63d..8e98caaab 100644 --- a/integrations-service/integrations/autogen/Tasks.py +++ b/integrations-service/integrations/autogen/Tasks.py @@ -9,6 +9,7 @@ from pydantic import AwareDatetime, BaseModel, ConfigDict, Field, StrictBool from .Chat import ChatSettings +from .Common import JinjaTemplate from .Tools import ( ChosenBash20241022, ChosenComputer20241022, @@ -85,7 +86,11 @@ class Content(BaseModel): """ -class ContentItem(BaseModel): +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -93,6 +98,14 @@ class ContentItem(BaseModel): source: Source +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + class ContentModel(BaseModel): model_config = ConfigDict( populate_by_name=True, @@ -117,7 +130,7 @@ class ContentModel1(BaseModel): ) tool_use_id: str type: Literal["tool_result"] = "tool_result" - content: list[ContentItem] + content: list[ContentItem] | list[ContentItemModel] class ContentModel2(Content): @@ -128,11 +141,18 @@ class ContentModel3(ContentModel): pass -class ContentModel4(ContentModel1): +class ContentModel4(BaseModel): """ Anthropic image content part """ + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + class CreateTaskRequest(BaseModel): """ diff --git a/integrations-service/integrations/utils/integrations/remote_browser.py b/integrations-service/integrations/utils/integrations/remote_browser.py index 11e3e24a3..6e5d63375 100644 --- a/integrations-service/integrations/utils/integrations/remote_browser.py +++ b/integrations-service/integrations/utils/integrations/remote_browser.py @@ -231,6 +231,16 @@ async def press_key(self, key_combination: str) -> RemoteBrowserOutput: """Press a key or key combination""" # Split combination into individual keys keys = key_combination.split("+") + keys = [ + "Enter" + if k == "Return" + else "Control" + if k == "ctrl" + else "PageDown" + if k == "Page_Down" + else k + for k in keys + ] # Press modifier keys first for key in keys[:-1]: diff --git a/typespec/entries/models.tsp b/typespec/entries/models.tsp index 95fbadb55..9821efa9b 100644 --- a/typespec/entries/models.tsp +++ b/typespec/entries/models.tsp @@ -65,7 +65,7 @@ model ChatMLAnthropicInnerImageContentPart { model ChatMLAnthropicImageContentPart { tool_use_id: string; type: "tool_result" = "tool_result"; - content: ChatMLAnthropicInnerImageContentPart[]; + content: ChatMLTextContentPart[] | ChatMLAnthropicInnerImageContentPart[]; } alias ChatMLContentPart = ChatMLTextContentPart | ChatMLImageContentPart | ChatMLAnthropicImageContentPart; diff --git a/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml b/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml index 93b74354b..4952c905b 100644 --- a/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml +++ b/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml @@ -1850,34 +1850,50 @@ components: - tool_result default: tool_result content: - type: array - items: - type: object - required: - - type - - source - properties: - type: - type: string - enum: - - image - default: image - source: + anyOf: + - type: array + items: type: object required: + - text - type - - media_type - - data properties: + text: + type: string type: type: string enum: - - base64 - default: base64 - media_type: - type: string - data: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string description: Anthropic image content part nullable: true description: The content parts of the message @@ -1993,34 +2009,50 @@ components: - tool_result default: tool_result content: - type: array - items: - type: object - required: - - type - - source - properties: - type: - type: string - enum: - - image - default: image - source: + anyOf: + - type: array + items: type: object required: + - text - type - - media_type - - data properties: + text: + type: string type: type: string enum: - - base64 - default: base64 - media_type: - type: string - data: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string description: Anthropic image content part nullable: true description: The content parts of the message @@ -2267,34 +2299,50 @@ components: - tool_result default: tool_result content: - type: array - items: - type: object - required: - - type - - source - properties: - type: - type: string - enum: - - image - default: image - source: + anyOf: + - type: array + items: type: object required: + - text - type - - media_type - - data properties: + text: + type: string type: type: string enum: - - base64 - default: base64 - media_type: - type: string - data: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string description: Anthropic image content part nullable: true description: The content parts of the message @@ -2460,34 +2508,50 @@ components: - tool_result default: tool_result content: - type: array - items: - type: object - required: - - type - - source - properties: - type: - type: string - enum: - - image - default: image - source: + anyOf: + - type: array + items: type: object required: + - text - type - - media_type - - data properties: + text: + type: string type: type: string enum: - - base64 - default: base64 - media_type: - type: string - data: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string description: Anthropic image content part nullable: true description: The content parts of the message @@ -2995,34 +3059,50 @@ components: - tool_result default: tool_result content: - type: array - items: - type: object - required: - - type - - source - properties: - type: - type: string - enum: - - image - default: image - source: + anyOf: + - type: array + items: type: object required: + - text - type - - media_type - - data properties: + text: + type: string type: type: string enum: - - base64 - default: base64 - media_type: - type: string - data: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string description: Anthropic image content part - $ref: '#/components/schemas/Tools.Tool' - $ref: '#/components/schemas/Tools.ChosenFunctionCall' @@ -3090,34 +3170,50 @@ components: - tool_result default: tool_result content: - type: array - items: - type: object - required: - - type - - source - properties: - type: - type: string - enum: - - image - default: image - source: + anyOf: + - type: array + items: type: object required: + - text - type - - media_type - - data properties: + text: + type: string type: type: string enum: - - base64 - default: base64 - media_type: - type: string - data: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string description: Anthropic image content part - $ref: '#/components/schemas/Tools.Tool' - $ref: '#/components/schemas/Tools.ChosenFunctionCall' @@ -5056,34 +5152,50 @@ components: - tool_result default: tool_result content: - type: array - items: - type: object - required: - - type - - source - properties: - type: - type: string - enum: - - image - default: image - source: + anyOf: + - type: array + items: type: object required: + - text - type - - media_type - - data properties: + text: + $ref: '#/components/schemas/Common.JinjaTemplate' type: type: string enum: - - base64 - default: base64 - media_type: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: type: string - data: - $ref: '#/components/schemas/Common.JinjaTemplate' + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + $ref: '#/components/schemas/Common.JinjaTemplate' description: Anthropic image content part nullable: true description: The content parts of the message @@ -5249,34 +5361,50 @@ components: - tool_result default: tool_result content: - type: array - items: - type: object - required: - - type - - source - properties: - type: - type: string - enum: - - image - default: image - source: + anyOf: + - type: array + items: type: object required: + - text - type - - media_type - - data properties: + text: + $ref: '#/components/schemas/Common.JinjaTemplate' type: type: string enum: - - base64 - default: base64 - media_type: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: type: string - data: - $ref: '#/components/schemas/Common.JinjaTemplate' + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + $ref: '#/components/schemas/Common.JinjaTemplate' description: Anthropic image content part nullable: true description: The content parts of the message From cb482373e3504a810c1fc2ca42d06672eeea87c5 Mon Sep 17 00:00:00 2001 From: Ahmad-mtos Date: Fri, 15 Nov 2024 23:14:23 +0300 Subject: [PATCH 03/28] fix: fix empty assistant text bug --- agents-api/agents_api/routers/sessions/chat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 57ba970e1..4ec15a93f 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -60,7 +60,7 @@ async def request_anthropic( # Transform the message content and tool calls if msg["role"] == "assistant": - transformed_content = [{"text": msg["content"], "type": "text"}] + transformed_content = [{"text": "Let's do this action" if msg["content"] == [] else msg["content"], "type": "text"}] transformed_content.extend( { "id": f"{tool_call['id']}", From 1bdc8755cfd95781d281a5a29d2651b10dfee168 Mon Sep 17 00:00:00 2001 From: Ahmad-mtos Date: Fri, 15 Nov 2024 20:15:03 +0000 Subject: [PATCH 04/28] refactor: Lint agents-api (CI) --- agents-api/agents_api/routers/sessions/chat.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 4ec15a93f..36f96014d 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -60,7 +60,14 @@ async def request_anthropic( # Transform the message content and tool calls if msg["role"] == "assistant": - transformed_content = [{"text": "Let's do this action" if msg["content"] == [] else msg["content"], "type": "text"}] + transformed_content = [ + { + "text": "Let's do this action" + if msg["content"] == [] + else msg["content"], + "type": "text", + } + ] transformed_content.extend( { "id": f"{tool_call['id']}", From 615bd2795c61f641dd21012a7f18940666c86001 Mon Sep 17 00:00:00 2001 From: vedantsahai18 Date: Sat, 16 Nov 2024 00:25:46 -0500 Subject: [PATCH 05/28] updated format tool call --- .../agents_api/routers/sessions/chat.py | 61 +------------------ 1 file changed, 1 insertion(+), 60 deletions(-) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 36f96014d..7033a4885 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -6,13 +6,10 @@ from anthropic import AsyncAnthropic from anthropic.types.beta.beta_message import BetaMessage from fastapi import BackgroundTasks, Depends, Header -from langchain_core.tools import BaseTool -from langchain_core.tools.convert import tool as tool_decorator from litellm import ChatCompletionMessageToolCall, Function, Message from litellm.types.utils import Choices, ModelResponse from starlette.status import HTTP_201_CREATED -from ...activities.utils import get_handler_with_filtered_params from ...autogen.openapi_model import ( ChatInput, ChatResponse, @@ -33,6 +30,7 @@ from ...models.entry.create_entries import create_entries from .metrics import total_tokens_per_user from .router import router +from ...activities.task_steps.prompt_step import format_tool COMPUTER_USE_BETA_FLAG = "computer-use-2024-10-22" @@ -148,63 +146,6 @@ async def request_anthropic( return model_response - -def format_tool(tool: Tool) -> dict: - if tool.type == "computer_20241022": - return { - "type": tool.type, - "name": tool.name, - "display_width_px": tool.computer_20241022 - and tool.computer_20241022.display_width_px, - "display_height_px": tool.computer_20241022 - and tool.computer_20241022.display_height_px, - "display_number": tool.computer_20241022 - and tool.computer_20241022.display_number, - } - - if tool.type in ["bash_20241022", "text_editor_20241022"]: - return tool.model_dump(include={"type", "name"}) - - if tool.type == "function": - return { - "type": "function", - "function": { - "name": tool.name, - "description": tool.description, - "parameters": tool.function and tool.function.parameters, - }, - } - - # For other tool types, we need to translate them to the OpenAI function tool format - formatted = { - "type": "function", - "function": {"name": tool.name, "description": tool.description}, - } - - if tool.type == "system": - handler: Callable = get_handler_with_filtered_params(tool.system) - - lc_tool: BaseTool = tool_decorator(handler) - - json_schema: dict = lc_tool.get_input_jsonschema() - - formatted["function"]["description"] = formatted["function"][ - "description" - ] or json_schema.get("description") - - formatted["function"]["parameters"] = json_schema - - # # FIXME: Implement integration tools - # elif tool.type == "integration": - # raise NotImplementedError("Integration tools are not supported") - - # # FIXME: Implement API call tools - # elif tool.type == "api_call": - # raise NotImplementedError("API call tools are not supported") - - return formatted - - @router.post( "/sessions/{session_id}/chat", status_code=HTTP_201_CREATED, From c7e0c60d1d3b38dc080c0b9a869a75a270f33899 Mon Sep 17 00:00:00 2001 From: Vedantsahai18 Date: Sat, 16 Nov 2024 05:26:31 +0000 Subject: [PATCH 06/28] refactor: Lint agents-api (CI) --- agents-api/agents_api/routers/sessions/chat.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 7033a4885..fbc7e6edf 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -10,6 +10,7 @@ from litellm.types.utils import Choices, ModelResponse from starlette.status import HTTP_201_CREATED +from ...activities.task_steps.prompt_step import format_tool from ...autogen.openapi_model import ( ChatInput, ChatResponse, @@ -30,7 +31,6 @@ from ...models.entry.create_entries import create_entries from .metrics import total_tokens_per_user from .router import router -from ...activities.task_steps.prompt_step import format_tool COMPUTER_USE_BETA_FLAG = "computer-use-2024-10-22" @@ -146,6 +146,7 @@ async def request_anthropic( return model_response + @router.post( "/sessions/{session_id}/chat", status_code=HTTP_201_CREATED, From e4dc3f4c37d8c4e7a94e29adae2632c2db96c0f0 Mon Sep 17 00:00:00 2001 From: vedantsahai18 Date: Sat, 16 Nov 2024 04:50:16 -0500 Subject: [PATCH 07/28] feat(litellm): added gemini models --- .env.example | 1 + llm-proxy/docker-compose.yml | 2 +- llm-proxy/litellm-config.yaml | 13 +++++++++++++ 3 files changed, 15 insertions(+), 1 deletion(-) diff --git a/.env.example b/.env.example index 96fa0abc1..ab4882297 100644 --- a/.env.example +++ b/.env.example @@ -34,6 +34,7 @@ LITELLM_REDIS_PASSWORD= # HUGGING_FACE_HUB_TOKEN= # ANTHROPIC_API_KEY= # GROQ_API_KEY= +# GEMINI_API_KEY= # CLOUDFLARE_API_KEY= # CLOUDFLARE_ACCOUNT_ID= # NVIDIA_NIM_API_KEY= diff --git a/llm-proxy/docker-compose.yml b/llm-proxy/docker-compose.yml index 0b084c385..0b9800b02 100644 --- a/llm-proxy/docker-compose.yml +++ b/llm-proxy/docker-compose.yml @@ -13,7 +13,7 @@ x--litellm-base: &litellm-base - LITELLM_MASTER_KEY=${LITELLM_MASTER_KEY} - DATABASE_URL=${LITELLM_DATABASE_URL:-postgresql://${LITELLM_POSTGRES_USER:-llmproxy}:${LITELLM_POSTGRES_PASSWORD}@${LITELLM_POSTGRES_HOST:-litellm-db}:${LITELLM_POSTGRES_PORT:-5432}/${LITELLM_POSTGRES_DB:-litellm}?sslmode=${LITELLM_POSTGRES_SSLMODE:-prefer_ssl}} - REDIS_URL=${LITELLM_REDIS_URL:-${LITELLM_REDIS_PROTOCOL:-redis}://${LITELLM_REDIS_USER:-default}:${LITELLM_REDIS_PASSWORD:-${LITELLM_REDIS_PASSWORD}}@${LITELLM_REDIS_HOST:-litellm-redis}:${LITELLM_REDIS_PORT:-6379}} - + - GEMINI_API_KEY=${GEMINI_API_KEY} - OPENAI_API_KEY=${OPENAI_API_KEY} - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} - GROQ_API_KEY=${GROQ_API_KEY} diff --git a/llm-proxy/litellm-config.yaml b/llm-proxy/litellm-config.yaml index 3e0127036..76295f715 100644 --- a/llm-proxy/litellm-config.yaml +++ b/llm-proxy/litellm-config.yaml @@ -6,6 +6,19 @@ model_list: # ------------------- # Gemini models + +- model_name: gemini-pro + litellm_params: + model: gemini/gemini-1.5-pro + api_key: os.environ/GEMINI_API_KEY + tags: ["paid"] + +- model_name: gemini-1.5-pro-latest + litellm_params: + model: gemini/gemini-1.5-pro-latest + api_key: os.environ/GEMINI_API_KEY + tags: ["paid"] + - model_name: gemini-1.5-pro litellm_params: model: vertex_ai_beta/gemini-1.5-pro From c118e9c30b444456510d3e2b42ee1d5d3e33d1f6 Mon Sep 17 00:00:00 2001 From: Ahmad-mtos Date: Sun, 17 Nov 2024 08:19:34 +0300 Subject: [PATCH 08/28] fix: fix typespec naming + execute integration hot fix --- agents-api/agents_api/activities/execute_integration.py | 5 ++++- typespec/entries/models.tsp | 8 ++++---- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/agents-api/agents_api/activities/execute_integration.py b/agents-api/agents_api/activities/execute_integration.py index 2badce0f9..d8b36fffb 100644 --- a/agents-api/agents_api/activities/execute_integration.py +++ b/agents-api/agents_api/activities/execute_integration.py @@ -49,7 +49,10 @@ async def execute_integration( arguments=arguments, ) - if "error" in integration_service_response: + if ( + "error" in integration_service_response + and integration_service_response["error"] + ): raise Exception(integration_service_response["error"]) return integration_service_response diff --git a/typespec/entries/models.tsp b/typespec/entries/models.tsp index 9821efa9b..7f8c8b9fa 100644 --- a/typespec/entries/models.tsp +++ b/typespec/entries/models.tsp @@ -56,19 +56,19 @@ model ChatMLAnthropicImageSource { data: T; } -model ChatMLAnthropicInnerImageContentPart { +model ChatMLAnthropicImageContentPart { type: "image" = "image"; source: ChatMLAnthropicImageSource; } /** Anthropic image content part */ -model ChatMLAnthropicImageContentPart { +model ChatMLAnthropicContentPart { tool_use_id: string; type: "tool_result" = "tool_result"; - content: ChatMLTextContentPart[] | ChatMLAnthropicInnerImageContentPart[]; + content: ChatMLTextContentPart[] | ChatMLAnthropicImageContentPart[]; } -alias ChatMLContentPart = ChatMLTextContentPart | ChatMLImageContentPart | ChatMLAnthropicImageContentPart; +alias ChatMLContentPart = ChatMLTextContentPart | ChatMLImageContentPart | ChatMLAnthropicContentPart; model ChatMLMessage { /** The role of the message */ From 1ea618859f505d0397f1e1e66aae9f15eaace067 Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Sun, 17 Nov 2024 15:08:55 +0300 Subject: [PATCH 09/28] Fix create session system tool --- agents-api/agents_api/activities/execute_system.py | 9 +++++++++ agents-api/agents_api/routers/sessions/chat.py | 2 +- 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/agents-api/agents_api/activities/execute_system.py b/agents-api/agents_api/activities/execute_system.py index 02623c3a6..d31684107 100644 --- a/agents-api/agents_api/activities/execute_system.py +++ b/agents-api/agents_api/activities/execute_system.py @@ -2,6 +2,7 @@ from typing import Any from uuid import UUID +from ..autogen.Sessions import CreateSessionRequest from beartype import beartype from box import Box, BoxList from fastapi.background import BackgroundTasks @@ -90,6 +91,14 @@ async def execute_system( chat_input=chat_input, ) + if system.operation == "create" and system.resource == "session": + developer_id = arguments.pop("developer_id") + session_id = arguments.pop("session_id", None) + data = CreateSessionRequest(**arguments) + return handler( + developer_id=developer_id, session_id=session_id, data=data + ) + # Handle regular operations if asyncio.iscoroutinefunction(handler): return await handler(**arguments) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index fbc7e6edf..5716165ba 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -79,7 +79,7 @@ async def request_anthropic( "role": msg["role"], "content": transformed_content, } - else: + elif msg["role"] == "user": try: transformed_content = json.loads(msg["content"]) except Exception: From 805b5e3709a0cf7a46e853b5c35f1fd5ee3e2744 Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Sun, 17 Nov 2024 12:09:41 +0000 Subject: [PATCH 10/28] refactor: Lint agents-api (CI) --- agents-api/agents_api/activities/execute_system.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/agents-api/agents_api/activities/execute_system.py b/agents-api/agents_api/activities/execute_system.py index d31684107..ea08e017e 100644 --- a/agents-api/agents_api/activities/execute_system.py +++ b/agents-api/agents_api/activities/execute_system.py @@ -2,7 +2,6 @@ from typing import Any from uuid import UUID -from ..autogen.Sessions import CreateSessionRequest from beartype import beartype from box import Box, BoxList from fastapi.background import BackgroundTasks @@ -15,6 +14,7 @@ TextOnlyDocSearchRequest, VectorDocSearchRequest, ) +from ..autogen.Sessions import CreateSessionRequest from ..autogen.Tools import SystemDef from ..common.protocol.tasks import StepContext from ..common.storage_handler import auto_blob_store @@ -95,9 +95,7 @@ async def execute_system( developer_id = arguments.pop("developer_id") session_id = arguments.pop("session_id", None) data = CreateSessionRequest(**arguments) - return handler( - developer_id=developer_id, session_id=session_id, data=data - ) + return handler(developer_id=developer_id, session_id=session_id, data=data) # Handle regular operations if asyncio.iscoroutinefunction(handler): From a59934ac972546739e0add3db1cc06dc02bef370 Mon Sep 17 00:00:00 2001 From: Ahmad-mtos Date: Sun, 17 Nov 2024 21:01:55 +0300 Subject: [PATCH 11/28] fix(agents-api): Remove screenshot after every action --- .../agents_api/routers/sessions/chat.py | 3 +- .../utils/integrations/remote_browser.py | 43 +++++++------------ 2 files changed, 18 insertions(+), 28 deletions(-) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 5716165ba..958094d14 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -55,7 +55,8 @@ async def request_anthropic( # Skip messages that are not assistant or user if msg["role"] not in ["assistant", "user"]: continue - + + # FIXME: return the tool call ids (save assistant message in entries as json dump) # Transform the message content and tool calls if msg["role"] == "assistant": transformed_content = [ diff --git a/integrations-service/integrations/utils/integrations/remote_browser.py b/integrations-service/integrations/utils/integrations/remote_browser.py index 6e5d63375..21ecc3a59 100644 --- a/integrations-service/integrations/utils/integrations/remote_browser.py +++ b/integrations-service/integrations/utils/integrations/remote_browser.py @@ -104,26 +104,26 @@ async def _reset_mouse(self) -> None: window.$$julep$$_initialized = true; """) - @staticmethod - def _with_error_and_screenshot(f): - @wraps(f) - async def wrapper(self: "PlaywrightActions", *args, **kwargs): - try: - result: RemoteBrowserOutput = await f(self, *args, **kwargs) - await self._wait_for_load() + # @staticmethod + # def _with_error_and_screenshot(f): + # @wraps(f) + # async def wrapper(self: "PlaywrightActions", *args, **kwargs): + # try: + # result: RemoteBrowserOutput = await f(self, *args, **kwargs) + # await self._wait_for_load() - screenshot: RemoteBrowserOutput = await self.take_screenshot() + # screenshot: RemoteBrowserOutput = await self.take_screenshot() - return RemoteBrowserOutput( - output=result.output, - base64_image=screenshot.base64_image, - system=result.system or f.__name__, - ) + # return RemoteBrowserOutput( + # output=result.output, + # base64_image=screenshot.base64_image, + # system=result.system or f.__name__, + # ) - except Exception as e: - return RemoteBrowserOutput(error=str(e)) + # except Exception as e: + # return RemoteBrowserOutput(error=str(e)) - return wrapper + # return wrapper async def _get_screen_size(self) -> tuple[int, int]: """Get the current browser viewport size""" @@ -198,7 +198,6 @@ def _overlay_cursor(self, screenshot_bytes: bytes, x: int, y: int) -> bytes: # --- # Actions - @_with_error_and_screenshot async def navigate(self, url: str) -> RemoteBrowserOutput: """Navigate to a specific URL""" await self.page.goto(url) @@ -208,7 +207,6 @@ async def navigate(self, url: str) -> RemoteBrowserOutput: output=url, ) - @_with_error_and_screenshot async def refresh(self) -> RemoteBrowserOutput: """Refresh the current page""" await self.page.reload() @@ -218,7 +216,6 @@ async def refresh(self) -> RemoteBrowserOutput: output="Refreshed page", ) - @_with_error_and_screenshot async def cursor_position(self) -> RemoteBrowserOutput: """Get current mouse coordinates""" x, y = await self._get_mouse_coordinates() @@ -226,7 +223,6 @@ async def cursor_position(self) -> RemoteBrowserOutput: output=f"X={x}, Y={y}", ) - @_with_error_and_screenshot async def press_key(self, key_combination: str) -> RemoteBrowserOutput: """Press a key or key combination""" # Split combination into individual keys @@ -257,7 +253,6 @@ async def press_key(self, key_combination: str) -> RemoteBrowserOutput: output=f"Pressed {key_combination}", ) - @_with_error_and_screenshot async def type_text(self, text: str) -> RemoteBrowserOutput: """Type a string of text""" await self.page.keyboard.type(text) @@ -266,7 +261,6 @@ async def type_text(self, text: str) -> RemoteBrowserOutput: output=f"Typed {text}", ) - @_with_error_and_screenshot async def mouse_move(self, coordinate: tuple[int, int]) -> RemoteBrowserOutput: """Move mouse to specified coordinates""" await self.mouse.move(*coordinate) @@ -275,7 +269,6 @@ async def mouse_move(self, coordinate: tuple[int, int]) -> RemoteBrowserOutput: output=f"Moved mouse to {coordinate}", ) - @_with_error_and_screenshot async def left_click(self) -> RemoteBrowserOutput: """Perform left mouse click""" x, y = await self._get_mouse_coordinates() @@ -285,7 +278,6 @@ async def left_click(self) -> RemoteBrowserOutput: output="Left clicked", ) - @_with_error_and_screenshot async def left_click_drag(self, coordinate: tuple[int, int]) -> RemoteBrowserOutput: """Click and drag to specified coordinates""" await self.mouse.down() @@ -296,7 +288,6 @@ async def left_click_drag(self, coordinate: tuple[int, int]) -> RemoteBrowserOut output=f"Left clicked and dragged to {coordinate}", ) - @_with_error_and_screenshot async def right_click(self) -> RemoteBrowserOutput: """Perform right mouse click""" x, y = await self._get_mouse_coordinates() @@ -306,7 +297,6 @@ async def right_click(self) -> RemoteBrowserOutput: output="Right clicked", ) - @_with_error_and_screenshot async def middle_click(self) -> RemoteBrowserOutput: """Perform middle mouse click""" x, y = await self._get_mouse_coordinates() @@ -316,7 +306,6 @@ async def middle_click(self) -> RemoteBrowserOutput: output="Middle clicked", ) - @_with_error_and_screenshot async def double_click(self) -> RemoteBrowserOutput: """Perform double click""" x, y = await self._get_mouse_coordinates() From 494dee096c9c46f6d17acc03adb95c4361505925 Mon Sep 17 00:00:00 2001 From: Ahmad-mtos Date: Sun, 17 Nov 2024 18:02:38 +0000 Subject: [PATCH 12/28] refactor: Lint agents-api (CI) --- agents-api/agents_api/routers/sessions/chat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 958094d14..f6e7807b4 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -55,7 +55,7 @@ async def request_anthropic( # Skip messages that are not assistant or user if msg["role"] not in ["assistant", "user"]: continue - + # FIXME: return the tool call ids (save assistant message in entries as json dump) # Transform the message content and tool calls if msg["role"] == "assistant": From 1b900d45f9adf699c6b2a857e1d658d849ef6dc4 Mon Sep 17 00:00:00 2001 From: Diwank Singh Tomer Date: Sun, 17 Nov 2024 23:43:49 +0530 Subject: [PATCH 13/28] fix(agents-api): Fix state machine for init_branch -> init_branch Signed-off-by: Diwank Singh Tomer --- agents-api/agents_api/common/protocol/tasks.py | 9 ++++++++- agents-api/gunicorn_conf.py | 6 +++++- integrations-service/gunicorn_conf.py | 6 +++++- 3 files changed, 18 insertions(+), 3 deletions(-) diff --git a/agents-api/agents_api/common/protocol/tasks.py b/agents-api/agents_api/common/protocol/tasks.py index 6a379b6a4..7619cf7d8 100644 --- a/agents-api/agents_api/common/protocol/tasks.py +++ b/agents-api/agents_api/common/protocol/tasks.py @@ -75,7 +75,14 @@ valid_transitions: dict[TransitionType, list[TransitionType]] = { # Start state "init": ["wait", "error", "step", "cancelled", "init_branch", "finish"], - "init_branch": ["wait", "error", "step", "cancelled", "finish_branch"], + "init_branch": [ + "wait", + "error", + "step", + "cancelled", + "init_branch", + "finish_branch", + ], # End states "finish": [], "error": [], diff --git a/agents-api/gunicorn_conf.py b/agents-api/gunicorn_conf.py index f50b39c3d..d7c197e6d 100644 --- a/agents-api/gunicorn_conf.py +++ b/agents-api/gunicorn_conf.py @@ -1,7 +1,11 @@ import multiprocessing +import os + +TESTING = os.getenv("TESTING", "false").lower() == "true" +DEBUG = os.getenv("DEBUG", "false").lower() == "true" # Gunicorn config variables -workers = multiprocessing.cpu_count() - 1 +workers = multiprocessing.cpu_count() - 1 if not (TESTING or DEBUG) else 1 worker_class = "uvicorn.workers.UvicornWorker" bind = "0.0.0.0:8080" keepalive = 120 diff --git a/integrations-service/gunicorn_conf.py b/integrations-service/gunicorn_conf.py index 30f5a2e4f..fe058fe6d 100644 --- a/integrations-service/gunicorn_conf.py +++ b/integrations-service/gunicorn_conf.py @@ -1,7 +1,11 @@ import multiprocessing +import os + +TESTING = os.getenv("TESTING", "false").lower() == "true" +DEBUG = os.getenv("DEBUG", "false").lower() == "true" # Gunicorn config variables -workers = multiprocessing.cpu_count() - 1 +workers = multiprocessing.cpu_count() - 1 if not (TESTING or DEBUG) else 1 worker_class = "uvicorn.workers.UvicornWorker" bind = "0.0.0.0:8000" keepalive = 120 From 3c715954d085194973ba2780eff49213fca44fae Mon Sep 17 00:00:00 2001 From: Diwank Singh Tomer Date: Sun, 17 Nov 2024 23:49:32 +0530 Subject: [PATCH 14/28] fix(agents-api): Fix branched step types not passing subworkflows Signed-off-by: Diwank Singh Tomer --- .../workflows/task_execution/helpers.py | 25 +++++++++++++++---- 1 file changed, 20 insertions(+), 5 deletions(-) diff --git a/agents-api/agents_api/workflows/task_execution/helpers.py b/agents-api/agents_api/workflows/task_execution/helpers.py index 7fbc2c008..6d47b3a1f 100644 --- a/agents-api/agents_api/workflows/task_execution/helpers.py +++ b/agents-api/agents_api/workflows/task_execution/helpers.py @@ -66,7 +66,10 @@ async def execute_switch_branch( case_wf_name = f"`{context.cursor.workflow}`[{context.cursor.step}].case" case_task = execution_input.task.model_copy() - case_task.workflows = [Workflow(name=case_wf_name, steps=[chosen_branch.then])] + case_task.workflows = [ + Workflow(name=case_wf_name, steps=[chosen_branch.then]), + *case_task.workflows, + ] case_execution_input = execution_input.model_copy() case_execution_input.task = case_task @@ -99,7 +102,10 @@ async def execute_if_else_branch( if_else_wf_name += ".then" if condition else ".else" if_else_task = execution_input.task.model_copy() - if_else_task.workflows = [Workflow(name=if_else_wf_name, steps=[chosen_branch])] + if_else_task.workflows = [ + Workflow(name=if_else_wf_name, steps=[chosen_branch]), + *if_else_task.workflows, + ] if_else_execution_input = execution_input.model_copy() if_else_execution_input.task = if_else_task @@ -132,7 +138,10 @@ async def execute_foreach_step( f"`{context.cursor.workflow}`[{context.cursor.step}].foreach[{i}]" ) foreach_task = execution_input.task.model_copy() - foreach_task.workflows = [Workflow(name=foreach_wf_name, steps=[do_step])] + foreach_task.workflows = [ + Workflow(name=foreach_wf_name, steps=[do_step]), + *foreach_task.workflows, + ] foreach_execution_input = execution_input.model_copy() foreach_execution_input.task = foreach_task @@ -170,7 +179,10 @@ async def execute_map_reduce_step( f"`{context.cursor.workflow}`[{context.cursor.step}].mapreduce[{i}]" ) map_reduce_task = execution_input.task.model_copy() - map_reduce_task.workflows = [Workflow(name=workflow_name, steps=[map_defn])] + map_reduce_task.workflows = [ + Workflow(name=workflow_name, steps=[map_defn]), + *map_reduce_task.workflows, + ] map_reduce_execution_input = execution_input.model_copy() map_reduce_execution_input.task = map_reduce_task @@ -234,7 +246,10 @@ async def execute_map_reduce_step_parallel( # Note: Added PAR: prefix to easily identify parallel batches in logs workflow_name = f"PAR:`{context.cursor.workflow}`[{context.cursor.step}].mapreduce[{i}][{j}]" map_reduce_task = execution_input.task.model_copy() - map_reduce_task.workflows = [Workflow(name=workflow_name, steps=[map_defn])] + map_reduce_task.workflows = [ + Workflow(name=workflow_name, steps=[map_defn]), + *map_reduce_task.workflows, + ] map_reduce_execution_input = execution_input.model_copy() map_reduce_execution_input.task = map_reduce_task From 8c6329b180bdadaf013caf3c98e4cc3e89f6ab84 Mon Sep 17 00:00:00 2001 From: Ahmad-mtos Date: Mon, 18 Nov 2024 14:28:21 +0300 Subject: [PATCH 15/28] fix(agents-api + llmproxy): Add litellm fork instead of sdk --- .../activities/task_steps/prompt_step.py | 1 + .../agents_api/routers/sessions/chat.py | 36 +- agents-api/poetry.lock | 649 +++++++++--------- agents-api/pyproject.toml | 2 +- llm-proxy/docker-compose.yml | 2 +- 5 files changed, 340 insertions(+), 350 deletions(-) diff --git a/agents-api/agents_api/activities/task_steps/prompt_step.py b/agents-api/agents_api/activities/task_steps/prompt_step.py index ad16ed6bd..8a4f74c18 100644 --- a/agents-api/agents_api/activities/task_steps/prompt_step.py +++ b/agents-api/agents_api/activities/task_steps/prompt_step.py @@ -26,6 +26,7 @@ def format_tool(tool: Tool) -> dict: + # FIXME: Wrong format for computer_20241022 for litellm if tool.type == "computer_20241022": return { "type": tool.type, diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index f6e7807b4..0a8cd9a8b 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -231,8 +231,11 @@ async def chat( # Get the tools tools = settings.get("tools") or chat_context.get_active_tools() + # Check if using Claude model and has specific tool types + is_claude_model = settings["model"].lower().startswith("claude-3.5") + # Format tools for litellm - formatted_tools = [format_tool(tool) for tool in tools] + formatted_tools = tools if is_claude_model else [format_tool(tool) for tool in tools] # FIXME: Truncate chat messages in the chat context # SCRUM-7 @@ -250,27 +253,24 @@ async def chat( for m in messages ] - # Check if using Claude model and has specific tool types - is_claude_model = settings["model"].lower().startswith("claude-3.5") has_special_tools = any( tool["type"] in ["computer_20241022", "bash_20241022", "text_editor_20241022"] for tool in formatted_tools ) - - if is_claude_model and has_special_tools: - model_response = await request_anthropic(messages, formatted_tools, settings) - else: - # FIXME: hardcoded tool to a None value as the tool calls are not implemented yet - formatted_tools = None - # Use litellm for other models - model_response = await litellm.acompletion( - messages=messages, - tools=formatted_tools or None, - user=str(developer.id), - tags=developer.tags, - custom_api_key=x_custom_api_key, - **settings, - ) + print("*" * 100) + print("modell", settings["model"]) + print("*" * 100) + + # formatted_tools = None + # Use litellm for other models + model_response = await litellm.acompletion( + messages=messages, + tools=formatted_tools or None, + user=str(developer.id), + tags=developer.tags, + custom_api_key=x_custom_api_key, + **settings, + ) # Save the input and the response to the session history if chat_input.save: diff --git a/agents-api/poetry.lock b/agents-api/poetry.lock index e144ca0f7..b59a9d4e9 100644 --- a/agents-api/poetry.lock +++ b/agents-api/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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= "^1.52.3" numpy = ">=2.0.0,<2.1.0" tiktoken = "^0.7.0" tenacity = "^9.0.0" @@ -51,6 +50,7 @@ uvloop = "^0.21.0" anthropic = "^0.37.1" simsimd = "^5.9.4" langchain-core = "^0.3.14" +litellm = {git = "https://github.com/julep-ai/litellm.git", rev = "fix_anthropic_tool_image_content"} [tool.poetry.group.dev.dependencies] ipython = "^8.26.0" ruff = "^0.5.5" diff --git a/llm-proxy/docker-compose.yml b/llm-proxy/docker-compose.yml index 0b9800b02..6b7026e73 100644 --- a/llm-proxy/docker-compose.yml +++ b/llm-proxy/docker-compose.yml @@ -1,7 +1,7 @@ name: julep-llm-proxy x--litellm-base: &litellm-base - image: ghcr.io/berriai/litellm-database:main-v1.52.0-stable + image: julepai/litellm-database:fix_anthropic_tool_image_content restart: unless-stopped hostname: litellm ports: From 1fd3f8c52e6fe682ad2e5d1248d833dba96c74ee Mon Sep 17 00:00:00 2001 From: Ahmad-mtos Date: Mon, 18 Nov 2024 11:29:27 +0000 Subject: [PATCH 16/28] refactor: Lint agents-api (CI) --- agents-api/agents_api/routers/sessions/chat.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 0a8cd9a8b..4c48fc44d 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -235,7 +235,9 @@ async def chat( is_claude_model = settings["model"].lower().startswith("claude-3.5") # Format tools for litellm - formatted_tools = tools if is_claude_model else [format_tool(tool) for tool in tools] + formatted_tools = ( + tools if is_claude_model else [format_tool(tool) for tool in tools] + ) # FIXME: Truncate chat messages in the chat context # SCRUM-7 From 97cde47a743d7b2b7fe0632abd3b4be0d7090fe4 Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Tue, 19 Nov 2024 12:45:30 +0300 Subject: [PATCH 17/28] Fix chat endpoint for computer tool --- .../agents_api/routers/sessions/chat.py | 147 ++++-------------- 1 file changed, 26 insertions(+), 121 deletions(-) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 0a8cd9a8b..f7bd28e5c 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -34,120 +34,6 @@ COMPUTER_USE_BETA_FLAG = "computer-use-2024-10-22" - -async def request_anthropic( - messages: list[dict], formatted_tools: list[dict], settings: dict -) -> ModelResponse: - # Use Anthropic API directly - client = AsyncAnthropic(api_key=anthropic_api_key) - - # Filter tools for specific types - filtered_tools = [ - tool - for tool in formatted_tools - if tool["type"] - in ["computer_20241022", "bash_20241022", "text_editor_20241022"] - ] - - # Format messages for Claude - claude_messages = [] - for msg in messages: - # Skip messages that are not assistant or user - if msg["role"] not in ["assistant", "user"]: - continue - - # FIXME: return the tool call ids (save assistant message in entries as json dump) - # Transform the message content and tool calls - if msg["role"] == "assistant": - transformed_content = [ - { - "text": "Let's do this action" - if msg["content"] == [] - else msg["content"], - "type": "text", - } - ] - transformed_content.extend( - { - "id": f"{tool_call['id']}", - "input": json.loads(tool_call["function"]["arguments"]), - "name": tool_call["function"]["name"], - "type": "tool_use", - } - for tool_call in msg.get("tool_calls", []) - ) - claude_message = { - "role": msg["role"], - "content": transformed_content, - } - elif msg["role"] == "user": - try: - transformed_content = json.loads(msg["content"]) - except Exception: - transformed_content = msg["content"] - claude_message = {"role": msg["role"], "content": transformed_content} - - claude_messages.append(claude_message) - # Call Claude API - claude_response: BetaMessage = await client.beta.messages.create( - model="claude-3-5-sonnet-20241022", - messages=claude_messages, - tools=filtered_tools, - max_tokens=settings.get("max_tokens", 1024), - betas=[COMPUTER_USE_BETA_FLAG], - ) - # Convert Claude response to litellm format - text_block = next( - (block for block in claude_response.content if block.type == "text"), - None, - ) - - if claude_response.stop_reason == "tool_use": - choice = Choices( - message=Message( - role="assistant", - content=text_block.text if text_block else None, - tool_calls=[ - ChatCompletionMessageToolCall( - type="function", - function=Function( - name=block.name, - arguments=block.input, - ), - ) - for block in claude_response.content - if block.type == "tool_use" - ], - ), - finish_reason="tool_calls", - ) - else: - assert ( - text_block - ), "Claude should always return a text block for stop_reason=stop" - choice = Choices( - message=Message( - role="assistant", - content=text_block.text, - ), - finish_reason="stop", - ) - - model_response = ModelResponse( - id=claude_response.id, - choices=[choice], - created=int(datetime.now().timestamp()), - model=claude_response.model, - object="text_completion", - usage={ - "total_tokens": claude_response.usage.input_tokens - + claude_response.usage.output_tokens - }, - ) - - return model_response - - @router.post( "/sessions/{session_id}/chat", status_code=HTTP_201_CREATED, @@ -253,13 +139,32 @@ async def chat( for m in messages ] - has_special_tools = any( - tool["type"] in ["computer_20241022", "bash_20241022", "text_editor_20241022"] - for tool in formatted_tools - ) - print("*" * 100) - print("modell", settings["model"]) - print("*" * 100) + # has_special_tools = any( + # tool["type"] in ["computer_20241022", "bash_20241022", "text_editor_20241022"] + # for tool in formatted_tools + # ) + + + # FIXME: Hack to make the computer use tools compatible with litellm + # Issue was: litellm expects type to be `computer_20241022` and spec to be + # `function` (see: https://docs.litellm.ai/docs/providers/anthropic#computer-tools) + # but we don't allow that (spec should match type). + for i, tool in enumerate(formatted_tools): + if tool.type == "computer_20241022": + function = tool.computer_20241022 + tool = { + "type": tool.type, + "function": { + "name": tool.name, + "parameters": { + k: v + for k, v in function.model_dump().items() + if k not in ["name", "type"] + }, + }, + } + formatted_tools[i] = tool + # formatted_tools = None # Use litellm for other models From a891d2844f44503306e6df5057beb34994d5fe39 Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Tue, 19 Nov 2024 09:46:33 +0000 Subject: [PATCH 18/28] refactor: Lint agents-api (CI) --- agents-api/agents_api/routers/sessions/chat.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 3a61757dd..4df113c9f 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -34,6 +34,7 @@ COMPUTER_USE_BETA_FLAG = "computer-use-2024-10-22" + @router.post( "/sessions/{session_id}/chat", status_code=HTTP_201_CREATED, @@ -146,9 +147,8 @@ async def chat( # for tool in formatted_tools # ) - # FIXME: Hack to make the computer use tools compatible with litellm - # Issue was: litellm expects type to be `computer_20241022` and spec to be + # Issue was: litellm expects type to be `computer_20241022` and spec to be # `function` (see: https://docs.litellm.ai/docs/providers/anthropic#computer-tools) # but we don't allow that (spec should match type). for i, tool in enumerate(formatted_tools): @@ -167,7 +167,6 @@ async def chat( } formatted_tools[i] = tool - # formatted_tools = None # Use litellm for other models model_response = await litellm.acompletion( From d3c5956b65890af7034b6d4ac052c1994479eb7b Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Tue, 19 Nov 2024 13:09:25 +0300 Subject: [PATCH 19/28] Fix background tasks in execute system --- .../agents_api/activities/execute_system.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/agents-api/agents_api/activities/execute_system.py b/agents-api/agents_api/activities/execute_system.py index ea08e017e..9dcc24780 100644 --- a/agents-api/agents_api/activities/execute_system.py +++ b/agents-api/agents_api/activities/execute_system.py @@ -65,12 +65,16 @@ async def execute_system( # Handle special cases for doc operations if system.operation == "create" and system.subresource == "doc": arguments["x_developer_id"] = arguments.pop("developer_id") - return await handler( + bg_runner = BackgroundTasks() + res = await handler( data=CreateDocRequest(**arguments.pop("data")), - background_tasks=BackgroundTasks(), + background_tasks=bg_runner, **arguments, ) + await bg_runner() + return res + # Handle search operations if system.operation == "search" and system.subresource == "doc": arguments["x_developer_id"] = arguments.pop("developer_id") @@ -83,14 +87,18 @@ async def execute_system( session_id = arguments.pop("session_id") x_custom_api_key = arguments.pop("x_custom_api_key", None) chat_input = ChatInput(**arguments) - return await handler( + bg_runner = BackgroundTasks() + res = await handler( developer=developer, session_id=session_id, - background_tasks=BackgroundTasks(), + background_tasks=bg_runner, x_custom_api_key=x_custom_api_key, chat_input=chat_input, ) + await bg_runner() + return res + if system.operation == "create" and system.resource == "session": developer_id = arguments.pop("developer_id") session_id = arguments.pop("session_id", None) From 22086bcf9390be7f21cba19cdb5bc1e689cac307 Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Tue, 19 Nov 2024 15:16:44 +0300 Subject: [PATCH 20/28] fix(agents-api): Make SystemDef a RemoteObject in task_execution --- .../agents_api/activities/execute_system.py | 54 +++++++++++-------- .../agents_api/autogen/openapi_model.py | 5 ++ .../workflows/task_execution/__init__.py | 2 +- 3 files changed, 37 insertions(+), 24 deletions(-) diff --git a/agents-api/agents_api/activities/execute_system.py b/agents-api/agents_api/activities/execute_system.py index 9dcc24780..a8ac893ab 100644 --- a/agents-api/agents_api/activities/execute_system.py +++ b/agents-api/agents_api/activities/execute_system.py @@ -7,15 +7,16 @@ from fastapi.background import BackgroundTasks from temporalio import activity -from ..autogen.Chat import ChatInput -from ..autogen.Docs import ( +from ..autogen.openapi_model import ( + ChatInput, + SystemDef, + CreateSessionRequest, CreateDocRequest, HybridDocSearchRequest, TextOnlyDocSearchRequest, VectorDocSearchRequest, ) -from ..autogen.Sessions import CreateSessionRequest -from ..autogen.Tools import SystemDef + from ..common.protocol.tasks import StepContext from ..common.storage_handler import auto_blob_store from ..env import testing @@ -23,7 +24,7 @@ from .utils import get_handler -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def execute_system( context: StepContext, @@ -80,24 +81,31 @@ async def execute_system( arguments["x_developer_id"] = arguments.pop("developer_id") search_params = _create_search_request(arguments) return await handler(search_params=search_params, **arguments) - - # Handle chat operations - if system.operation == "chat" and system.resource == "session": - developer = get_developer(developer_id=arguments.pop("developer_id")) - session_id = arguments.pop("session_id") - x_custom_api_key = arguments.pop("x_custom_api_key", None) - chat_input = ChatInput(**arguments) - bg_runner = BackgroundTasks() - res = await handler( - developer=developer, - session_id=session_id, - background_tasks=bg_runner, - x_custom_api_key=x_custom_api_key, - chat_input=chat_input, - ) - - await bg_runner() - return res + + try: + # Handle chat operations + if system.operation == "chat" and system.resource == "session": + developer = get_developer(developer_id=arguments.get("developer_id")) + session_id = arguments.get("session_id") + x_custom_api_key = arguments.get("x_custom_api_key", None) + chat_input = ChatInput(**arguments) + bg_runner = BackgroundTasks() + res = await handler( + developer=developer, + session_id=session_id, + background_tasks=bg_runner, + x_custom_api_key=x_custom_api_key, + chat_input=chat_input, + ) + + await bg_runner() + return res + except Exception as e: + activity.logger.error(f"Error in execute_system_call: {e}") + activity.logger.error(f"arguments: {arguments}") + activity.logger.error(f"session_id: {session_id}") + activity.logger.error(f"chat_input: {chat_input}") + raise if system.operation == "create" and system.resource == "session": developer_id = arguments.pop("developer_id") diff --git a/agents-api/agents_api/autogen/openapi_model.py b/agents-api/agents_api/autogen/openapi_model.py index 3e44a71b2..b53b4f51f 100644 --- a/agents-api/agents_api/autogen/openapi_model.py +++ b/agents-api/agents_api/autogen/openapi_model.py @@ -354,6 +354,11 @@ def validate_subworkflows(self): # Create models # ------------- +from ..common.storage_handler import RemoteObject + +class SystemDef(SystemDef): + arguments: dict[str, Any] | None | RemoteObject = None + class CreateTransitionRequest(Transition): # The following fields are optional in this diff --git a/agents-api/agents_api/workflows/task_execution/__init__.py b/agents-api/agents_api/workflows/task_execution/__init__.py index 05f4ce795..411681cfb 100644 --- a/agents-api/agents_api/workflows/task_execution/__init__.py +++ b/agents-api/agents_api/workflows/task_execution/__init__.py @@ -39,8 +39,8 @@ WaitForInputStep, WorkflowStep, YieldStep, + SystemDef ) - from ...autogen.Tools import SystemDef from ...common.protocol.remote import RemoteList from ...common.protocol.tasks import ( ExecutionInput, From 61f66f12de156a1ee552604bdab70cf969e27286 Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Tue, 19 Nov 2024 15:22:09 +0300 Subject: [PATCH 21/28] Import all autogen code from openapi_model --- agents-api/agents_api/activities/task_steps/prompt_step.py | 2 +- agents-api/agents_api/models/chat/gather_messages.py | 3 +-- .../agents_api/routers/tasks/create_task_execution.py | 2 +- agents-api/tests/test_execution_queries.py | 7 ++++--- agents-api/tests/test_session_queries.py | 7 +++++-- 5 files changed, 12 insertions(+), 9 deletions(-) diff --git a/agents-api/agents_api/activities/task_steps/prompt_step.py b/agents-api/agents_api/activities/task_steps/prompt_step.py index 8a4f74c18..3d65ad93f 100644 --- a/agents-api/agents_api/activities/task_steps/prompt_step.py +++ b/agents-api/agents_api/activities/task_steps/prompt_step.py @@ -11,7 +11,7 @@ from temporalio import activity from temporalio.exceptions import ApplicationError -from ...autogen.Tools import Tool +from ...autogen.openapi_model import Tool from ...clients import ( litellm, # We dont directly import `acompletion` so we can mock it ) diff --git a/agents-api/agents_api/models/chat/gather_messages.py b/agents-api/agents_api/models/chat/gather_messages.py index 1a2216a0a..17955d183 100644 --- a/agents-api/agents_api/models/chat/gather_messages.py +++ b/agents-api/agents_api/models/chat/gather_messages.py @@ -6,8 +6,7 @@ from pycozo.client import QueryException from pydantic import ValidationError -from ...autogen.Chat import ChatInput -from ...autogen.openapi_model import DocReference, History +from ...autogen.openapi_model import DocReference, History, ChatInput from ...clients import litellm from ...common.protocol.developers import Developer from ...common.protocol.sessions import ChatContext diff --git a/agents-api/agents_api/routers/tasks/create_task_execution.py b/agents-api/agents_api/routers/tasks/create_task_execution.py index 24de79522..f8cbac12a 100644 --- a/agents-api/agents_api/routers/tasks/create_task_execution.py +++ b/agents-api/agents_api/routers/tasks/create_task_execution.py @@ -10,11 +10,11 @@ from starlette.status import HTTP_201_CREATED from temporalio.client import WorkflowHandle -from ...autogen.Executions import Execution from ...autogen.openapi_model import ( CreateExecutionRequest, ResourceCreatedResponse, UpdateExecutionRequest, + Execution, ) from ...clients.temporal import run_task_execution_workflow from ...dependencies.developer_id import get_developer_id diff --git a/agents-api/tests/test_execution_queries.py b/agents-api/tests/test_execution_queries.py index 42904776d..f072bf6e9 100644 --- a/agents-api/tests/test_execution_queries.py +++ b/agents-api/tests/test_execution_queries.py @@ -3,10 +3,11 @@ from temporalio.client import WorkflowHandle from ward import test -from agents_api.autogen.Executions import ( - CreateExecutionRequest, +from agents_api.autogen.openapi_model import ( + CreateTransitionRequest, + Execution, + CreateExecutionRequest ) -from agents_api.autogen.openapi_model import CreateTransitionRequest, Execution from agents_api.models.execution.create_execution import create_execution from agents_api.models.execution.create_execution_transition import ( create_execution_transition, diff --git a/agents-api/tests/test_session_queries.py b/agents-api/tests/test_session_queries.py index c0036d9b3..7e02caa7d 100644 --- a/agents-api/tests/test_session_queries.py +++ b/agents-api/tests/test_session_queries.py @@ -3,8 +3,11 @@ from ward import test -from agents_api.autogen.openapi_model import CreateOrUpdateSessionRequest, Session -from agents_api.autogen.Sessions import CreateSessionRequest +from agents_api.autogen.openapi_model import ( + CreateOrUpdateSessionRequest, + Session, + CreateSessionRequest, +) from agents_api.models.session.create_or_update_session import create_or_update_session from agents_api.models.session.create_session import create_session from agents_api.models.session.delete_session import delete_session From 057e60b372f81fa7b517f90a062652fd6ff9786d Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Tue, 19 Nov 2024 12:23:19 +0000 Subject: [PATCH 22/28] refactor: Lint agents-api (CI) --- agents-api/agents_api/activities/execute_system.py | 9 ++++----- agents-api/agents_api/autogen/openapi_model.py | 1 + agents-api/agents_api/models/chat/gather_messages.py | 2 +- .../agents_api/routers/tasks/create_task_execution.py | 2 +- .../agents_api/workflows/task_execution/__init__.py | 2 +- agents-api/tests/test_execution_queries.py | 2 +- agents-api/tests/test_session_queries.py | 2 +- 7 files changed, 10 insertions(+), 10 deletions(-) diff --git a/agents-api/agents_api/activities/execute_system.py b/agents-api/agents_api/activities/execute_system.py index a8ac893ab..549fdce23 100644 --- a/agents-api/agents_api/activities/execute_system.py +++ b/agents-api/agents_api/activities/execute_system.py @@ -9,14 +9,13 @@ from ..autogen.openapi_model import ( ChatInput, - SystemDef, - CreateSessionRequest, CreateDocRequest, + CreateSessionRequest, HybridDocSearchRequest, + SystemDef, TextOnlyDocSearchRequest, VectorDocSearchRequest, ) - from ..common.protocol.tasks import StepContext from ..common.storage_handler import auto_blob_store from ..env import testing @@ -81,8 +80,8 @@ async def execute_system( arguments["x_developer_id"] = arguments.pop("developer_id") search_params = _create_search_request(arguments) return await handler(search_params=search_params, **arguments) - - try: + + try: # Handle chat operations if system.operation == "chat" and system.resource == "session": developer = get_developer(developer_id=arguments.get("developer_id")) diff --git a/agents-api/agents_api/autogen/openapi_model.py b/agents-api/agents_api/autogen/openapi_model.py index b53b4f51f..f10f6a7de 100644 --- a/agents-api/agents_api/autogen/openapi_model.py +++ b/agents-api/agents_api/autogen/openapi_model.py @@ -356,6 +356,7 @@ def validate_subworkflows(self): from ..common.storage_handler import RemoteObject + class SystemDef(SystemDef): arguments: dict[str, Any] | None | RemoteObject = None diff --git a/agents-api/agents_api/models/chat/gather_messages.py b/agents-api/agents_api/models/chat/gather_messages.py index 17955d183..e6ffdb48e 100644 --- a/agents-api/agents_api/models/chat/gather_messages.py +++ b/agents-api/agents_api/models/chat/gather_messages.py @@ -6,7 +6,7 @@ from pycozo.client import QueryException from pydantic import ValidationError -from ...autogen.openapi_model import DocReference, History, ChatInput +from ...autogen.openapi_model import ChatInput, DocReference, History from ...clients import litellm from ...common.protocol.developers import Developer from ...common.protocol.sessions import ChatContext diff --git a/agents-api/agents_api/routers/tasks/create_task_execution.py b/agents-api/agents_api/routers/tasks/create_task_execution.py index f8cbac12a..ecc0aa470 100644 --- a/agents-api/agents_api/routers/tasks/create_task_execution.py +++ b/agents-api/agents_api/routers/tasks/create_task_execution.py @@ -12,9 +12,9 @@ from ...autogen.openapi_model import ( CreateExecutionRequest, + Execution, ResourceCreatedResponse, UpdateExecutionRequest, - Execution, ) from ...clients.temporal import run_task_execution_workflow from ...dependencies.developer_id import get_developer_id diff --git a/agents-api/agents_api/workflows/task_execution/__init__.py b/agents-api/agents_api/workflows/task_execution/__init__.py index 411681cfb..67d1f9546 100644 --- a/agents-api/agents_api/workflows/task_execution/__init__.py +++ b/agents-api/agents_api/workflows/task_execution/__init__.py @@ -34,12 +34,12 @@ SleepFor, SleepStep, SwitchStep, + SystemDef, ToolCallStep, TransitionTarget, WaitForInputStep, WorkflowStep, YieldStep, - SystemDef ) from ...common.protocol.remote import RemoteList from ...common.protocol.tasks import ( diff --git a/agents-api/tests/test_execution_queries.py b/agents-api/tests/test_execution_queries.py index f072bf6e9..0ea72ff95 100644 --- a/agents-api/tests/test_execution_queries.py +++ b/agents-api/tests/test_execution_queries.py @@ -4,9 +4,9 @@ from ward import test from agents_api.autogen.openapi_model import ( + CreateExecutionRequest, CreateTransitionRequest, Execution, - CreateExecutionRequest ) from agents_api.models.execution.create_execution import create_execution from agents_api.models.execution.create_execution_transition import ( diff --git a/agents-api/tests/test_session_queries.py b/agents-api/tests/test_session_queries.py index 7e02caa7d..8d7c07b36 100644 --- a/agents-api/tests/test_session_queries.py +++ b/agents-api/tests/test_session_queries.py @@ -5,8 +5,8 @@ from agents_api.autogen.openapi_model import ( CreateOrUpdateSessionRequest, - Session, CreateSessionRequest, + Session, ) from agents_api.models.session.create_or_update_session import create_or_update_session from agents_api.models.session.create_session import create_session From 25aa9a0f2fcea26518f360f671faddeee53e387b Mon Sep 17 00:00:00 2001 From: Ahmad-mtos Date: Tue, 19 Nov 2024 22:07:49 +0300 Subject: [PATCH 23/28] fix(agents-api, llm-proxy): Fix loading data from blob-store. Update llm-proxy image --- .../agents_api/activities/embed_docs.py | 2 +- .../activities/excecute_api_call.py | 2 +- .../agents_api/activities/execute_system.py | 41 ++++++++----------- .../activities/task_steps/base_evaluate.py | 2 +- .../activities/task_steps/cozo_query_step.py | 2 +- .../activities/task_steps/evaluate_step.py | 2 +- .../activities/task_steps/for_each_step.py | 2 +- .../activities/task_steps/get_value_step.py | 2 +- .../activities/task_steps/if_else_step.py | 2 +- .../activities/task_steps/log_step.py | 2 +- .../activities/task_steps/map_reduce_step.py | 2 +- .../activities/task_steps/prompt_step.py | 2 +- .../task_steps/raise_complete_async.py | 2 +- .../activities/task_steps/return_step.py | 2 +- .../activities/task_steps/set_value_step.py | 2 +- .../activities/task_steps/switch_step.py | 2 +- .../activities/task_steps/tool_call_step.py | 2 +- .../task_steps/wait_for_input_step.py | 2 +- .../activities/task_steps/yield_step.py | 2 +- .../agents_api/common/protocol/tasks.py | 6 ++- .../agents_api/common/storage_handler.py | 3 ++ llm-proxy/docker-compose.yml | 3 +- 22 files changed, 45 insertions(+), 44 deletions(-) diff --git a/agents-api/agents_api/activities/embed_docs.py b/agents-api/agents_api/activities/embed_docs.py index 0dbf7f03b..80765bb79 100644 --- a/agents-api/agents_api/activities/embed_docs.py +++ b/agents-api/agents_api/activities/embed_docs.py @@ -13,7 +13,7 @@ from .types import EmbedDocsPayload -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def embed_docs( payload: EmbedDocsPayload, cozo_client=None, max_batch_size: int = 100 diff --git a/agents-api/agents_api/activities/excecute_api_call.py b/agents-api/agents_api/activities/excecute_api_call.py index 6c60a5cc1..0232eba2f 100644 --- a/agents-api/agents_api/activities/excecute_api_call.py +++ b/agents-api/agents_api/activities/excecute_api_call.py @@ -20,7 +20,7 @@ class RequestArgs(TypedDict): headers: Optional[dict[str, str]] -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def execute_api_call( api_call: ApiCallDef, diff --git a/agents-api/agents_api/activities/execute_system.py b/agents-api/agents_api/activities/execute_system.py index 549fdce23..6ff22de6f 100644 --- a/agents-api/agents_api/activities/execute_system.py +++ b/agents-api/agents_api/activities/execute_system.py @@ -81,30 +81,23 @@ async def execute_system( search_params = _create_search_request(arguments) return await handler(search_params=search_params, **arguments) - try: - # Handle chat operations - if system.operation == "chat" and system.resource == "session": - developer = get_developer(developer_id=arguments.get("developer_id")) - session_id = arguments.get("session_id") - x_custom_api_key = arguments.get("x_custom_api_key", None) - chat_input = ChatInput(**arguments) - bg_runner = BackgroundTasks() - res = await handler( - developer=developer, - session_id=session_id, - background_tasks=bg_runner, - x_custom_api_key=x_custom_api_key, - chat_input=chat_input, - ) - - await bg_runner() - return res - except Exception as e: - activity.logger.error(f"Error in execute_system_call: {e}") - activity.logger.error(f"arguments: {arguments}") - activity.logger.error(f"session_id: {session_id}") - activity.logger.error(f"chat_input: {chat_input}") - raise + # Handle chat operations + if system.operation == "chat" and system.resource == "session": + developer = get_developer(developer_id=arguments.get("developer_id")) + session_id = arguments.get("session_id") + x_custom_api_key = arguments.get("x_custom_api_key", None) + chat_input = ChatInput(**arguments) + bg_runner = BackgroundTasks() + res = await handler( + developer=developer, + session_id=session_id, + background_tasks=bg_runner, + x_custom_api_key=x_custom_api_key, + chat_input=chat_input, + ) + + await bg_runner() + return res if system.operation == "create" and system.resource == "session": developer_id = arguments.pop("developer_id") diff --git a/agents-api/agents_api/activities/task_steps/base_evaluate.py b/agents-api/agents_api/activities/task_steps/base_evaluate.py index 2c56b315f..583e6c6c1 100644 --- a/agents-api/agents_api/activities/task_steps/base_evaluate.py +++ b/agents-api/agents_api/activities/task_steps/base_evaluate.py @@ -63,7 +63,7 @@ def _recursive_evaluate(expr, evaluator: SimpleEval): raise ValueError(f"Invalid expression: {expr}") -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def base_evaluate( exprs: Any, diff --git a/agents-api/agents_api/activities/task_steps/cozo_query_step.py b/agents-api/agents_api/activities/task_steps/cozo_query_step.py index 7a13ae6ec..16e9a53d8 100644 --- a/agents-api/agents_api/activities/task_steps/cozo_query_step.py +++ b/agents-api/agents_api/activities/task_steps/cozo_query_step.py @@ -8,7 +8,7 @@ from ...env import testing -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def cozo_query_step( query_name: str, diff --git a/agents-api/agents_api/activities/task_steps/evaluate_step.py b/agents-api/agents_api/activities/task_steps/evaluate_step.py index 4458bbd2d..06d3c7262 100644 --- a/agents-api/agents_api/activities/task_steps/evaluate_step.py +++ b/agents-api/agents_api/activities/task_steps/evaluate_step.py @@ -9,7 +9,7 @@ from ...env import testing -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def evaluate_step( context: StepContext, diff --git a/agents-api/agents_api/activities/task_steps/for_each_step.py b/agents-api/agents_api/activities/task_steps/for_each_step.py index df01c1ca8..76c74b3d6 100644 --- a/agents-api/agents_api/activities/task_steps/for_each_step.py +++ b/agents-api/agents_api/activities/task_steps/for_each_step.py @@ -11,7 +11,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def for_each_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/activities/task_steps/get_value_step.py b/agents-api/agents_api/activities/task_steps/get_value_step.py index dc9b73832..ca38bc4fe 100644 --- a/agents-api/agents_api/activities/task_steps/get_value_step.py +++ b/agents-api/agents_api/activities/task_steps/get_value_step.py @@ -8,7 +8,7 @@ # TODO: We should use this step to query the parent workflow and get the value from the workflow context # SCRUM-1 -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def get_value_step( context: StepContext, diff --git a/agents-api/agents_api/activities/task_steps/if_else_step.py b/agents-api/agents_api/activities/task_steps/if_else_step.py index 9b90647de..3d0a9739f 100644 --- a/agents-api/agents_api/activities/task_steps/if_else_step.py +++ b/agents-api/agents_api/activities/task_steps/if_else_step.py @@ -11,7 +11,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def if_else_step(context: StepContext) -> StepOutcome: # NOTE: This activity is only for logging, so we just evaluate the expression diff --git a/agents-api/agents_api/activities/task_steps/log_step.py b/agents-api/agents_api/activities/task_steps/log_step.py index 4c5158279..527a6f45f 100644 --- a/agents-api/agents_api/activities/task_steps/log_step.py +++ b/agents-api/agents_api/activities/task_steps/log_step.py @@ -11,7 +11,7 @@ from ...env import testing -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def log_step(context: StepContext) -> StepOutcome: # NOTE: This activity is only for logging, so we just evaluate the expression diff --git a/agents-api/agents_api/activities/task_steps/map_reduce_step.py b/agents-api/agents_api/activities/task_steps/map_reduce_step.py index 904a7082a..8237e37af 100644 --- a/agents-api/agents_api/activities/task_steps/map_reduce_step.py +++ b/agents-api/agents_api/activities/task_steps/map_reduce_step.py @@ -13,7 +13,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def map_reduce_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/activities/task_steps/prompt_step.py b/agents-api/agents_api/activities/task_steps/prompt_step.py index 3d65ad93f..692da9a9f 100644 --- a/agents-api/agents_api/activities/task_steps/prompt_step.py +++ b/agents-api/agents_api/activities/task_steps/prompt_step.py @@ -86,7 +86,7 @@ def format_tool(tool: Tool) -> dict: @activity.defn -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def prompt_step(context: StepContext) -> StepOutcome: # Get context data diff --git a/agents-api/agents_api/activities/task_steps/raise_complete_async.py b/agents-api/agents_api/activities/task_steps/raise_complete_async.py index adae15b7e..640d6ae4e 100644 --- a/agents-api/agents_api/activities/task_steps/raise_complete_async.py +++ b/agents-api/agents_api/activities/task_steps/raise_complete_async.py @@ -11,7 +11,7 @@ @activity.defn -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def raise_complete_async(context: StepContext, output: Any) -> None: activity_info = activity.info() diff --git a/agents-api/agents_api/activities/task_steps/return_step.py b/agents-api/agents_api/activities/task_steps/return_step.py index e58c4d7e7..e00ad6d20 100644 --- a/agents-api/agents_api/activities/task_steps/return_step.py +++ b/agents-api/agents_api/activities/task_steps/return_step.py @@ -11,7 +11,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def return_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/activities/task_steps/set_value_step.py b/agents-api/agents_api/activities/task_steps/set_value_step.py index 707dbdeb0..dd2553abd 100644 --- a/agents-api/agents_api/activities/task_steps/set_value_step.py +++ b/agents-api/agents_api/activities/task_steps/set_value_step.py @@ -11,7 +11,7 @@ # TODO: We should use this step to signal to the parent workflow and set the value on the workflow context # SCRUM-2 -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def set_value_step( context: StepContext, diff --git a/agents-api/agents_api/activities/task_steps/switch_step.py b/agents-api/agents_api/activities/task_steps/switch_step.py index 413611e27..95c136890 100644 --- a/agents-api/agents_api/activities/task_steps/switch_step.py +++ b/agents-api/agents_api/activities/task_steps/switch_step.py @@ -11,7 +11,7 @@ from ..utils import get_evaluator -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def switch_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/activities/task_steps/tool_call_step.py b/agents-api/agents_api/activities/task_steps/tool_call_step.py index 03525e5ed..7992de19e 100644 --- a/agents-api/agents_api/activities/task_steps/tool_call_step.py +++ b/agents-api/agents_api/activities/task_steps/tool_call_step.py @@ -47,7 +47,7 @@ def construct_tool_call( @activity.defn -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def tool_call_step(context: StepContext) -> StepOutcome: assert isinstance(context.current_step, ToolCallStep) diff --git a/agents-api/agents_api/activities/task_steps/wait_for_input_step.py b/agents-api/agents_api/activities/task_steps/wait_for_input_step.py index d9839bc8e..db3e41055 100644 --- a/agents-api/agents_api/activities/task_steps/wait_for_input_step.py +++ b/agents-api/agents_api/activities/task_steps/wait_for_input_step.py @@ -8,7 +8,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def wait_for_input_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/activities/task_steps/yield_step.py b/agents-api/agents_api/activities/task_steps/yield_step.py index ec6c08353..8480beb93 100644 --- a/agents-api/agents_api/activities/task_steps/yield_step.py +++ b/agents-api/agents_api/activities/task_steps/yield_step.py @@ -10,7 +10,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def yield_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/common/protocol/tasks.py b/agents-api/agents_api/common/protocol/tasks.py index 7619cf7d8..05964b48a 100644 --- a/agents-api/agents_api/common/protocol/tasks.py +++ b/agents-api/agents_api/common/protocol/tasks.py @@ -82,6 +82,7 @@ "cancelled", "init_branch", "finish_branch", + "finish", ], # End states "finish": [], @@ -242,14 +243,17 @@ def model_dump(self, *args, **kwargs) -> dict[str, Any]: def prepare_for_step(self, *args, **kwargs) -> dict[str, Any]: current_input = self.current_input + inputs = self.inputs if activity.in_activity(): + inputs = [load_from_blob_store_if_remote(input) for input in inputs] current_input = load_from_blob_store_if_remote(current_input) # Merge execution inputs into the dump dict dump = self.model_dump(*args, **kwargs) + dump["inputs"] = inputs prepared = dump | {"_": current_input} - for i, input in enumerate(self.inputs): + for i, input in enumerate(inputs): prepared = prepared | {f"_{i}": input} if i >= 100: break diff --git a/agents-api/agents_api/common/storage_handler.py b/agents-api/agents_api/common/storage_handler.py index 99b30707f..894b0ff72 100644 --- a/agents-api/agents_api/common/storage_handler.py +++ b/agents-api/agents_api/common/storage_handler.py @@ -41,6 +41,9 @@ def load_from_blob_store_if_remote(x: Any | RemoteObject) -> Any: fetched = s3.get_object(x.key) return deserialize(fetched) + elif isinstance(x, RemoteList): + x = list(x) + return x diff --git a/llm-proxy/docker-compose.yml b/llm-proxy/docker-compose.yml index 6b7026e73..08491557c 100644 --- a/llm-proxy/docker-compose.yml +++ b/llm-proxy/docker-compose.yml @@ -1,7 +1,7 @@ name: julep-llm-proxy x--litellm-base: &litellm-base - image: julepai/litellm-database:fix_anthropic_tool_image_content + image: julepai/litellm-database:fix_anthropic_tool_image_content-amd64 restart: unless-stopped hostname: litellm ports: @@ -52,6 +52,7 @@ services: <<: *litellm-base profiles: - self-hosted-db + platform: linux/amd64 depends_on: - litellm-db From bea2681fd441c0606e37216bc01b1a89729bb323 Mon Sep 17 00:00:00 2001 From: vedantsahai18 Date: Fri, 22 Nov 2024 00:56:55 -0500 Subject: [PATCH 24/28] fix(agents-api): exec_system + chat bug fixes --- .../agents_api/activities/execute_system.py | 3 -- .../agents_api/routers/sessions/chat.py | 31 +++++++------------ 2 files changed, 12 insertions(+), 22 deletions(-) diff --git a/agents-api/agents_api/activities/execute_system.py b/agents-api/agents_api/activities/execute_system.py index 2839190c3..e42891d92 100644 --- a/agents-api/agents_api/activities/execute_system.py +++ b/agents-api/agents_api/activities/execute_system.py @@ -68,12 +68,9 @@ async def execute_system( if system.operation == "create" and system.subresource == "doc": arguments["x_developer_id"] = arguments.pop("developer_id") bg_runner = BackgroundTasks() - res = await handler( - bg_runner = BackgroundTasks() res = await handler( data=CreateDocRequest(**arguments.pop("data")), background_tasks=bg_runner, - background_tasks=bg_runner, **arguments, ) await bg_runner() diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 4df113c9f..200832f4f 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -1,16 +1,11 @@ -import json from datetime import datetime from typing import Annotated, Callable, Optional from uuid import UUID, uuid4 -from anthropic import AsyncAnthropic from anthropic.types.beta.beta_message import BetaMessage from fastapi import BackgroundTasks, Depends, Header -from litellm import ChatCompletionMessageToolCall, Function, Message -from litellm.types.utils import Choices, ModelResponse from starlette.status import HTTP_201_CREATED -from ...activities.task_steps.prompt_step import format_tool from ...autogen.openapi_model import ( ChatInput, ChatResponse, @@ -18,14 +13,12 @@ CreateEntryRequest, MessageChatResponse, ) -from ...autogen.Tools import Tool from ...clients import litellm from ...common.protocol.developers import Developer from ...common.protocol.sessions import ChatContext from ...common.utils.datetime import utcnow from ...common.utils.template import render_template from ...dependencies.developer_id import get_developer_data -from ...env import anthropic_api_key from ...models.chat.gather_messages import gather_messages from ...models.chat.prepare_chat_context import prepare_chat_context from ...models.entry.create_entries import create_entries @@ -122,9 +115,9 @@ async def chat( is_claude_model = settings["model"].lower().startswith("claude-3.5") # Format tools for litellm - formatted_tools = ( - tools if is_claude_model else [format_tool(tool) for tool in tools] - ) + # formatted_tools = ( + # tools if is_claude_model else [format_tool(tool) for tool in tools] + # ) # FIXME: Truncate chat messages in the chat context # SCRUM-7 @@ -142,17 +135,13 @@ async def chat( for m in messages ] - # has_special_tools = any( - # tool["type"] in ["computer_20241022", "bash_20241022", "text_editor_20241022"] - # for tool in formatted_tools - # ) - # FIXME: Hack to make the computer use tools compatible with litellm # Issue was: litellm expects type to be `computer_20241022` and spec to be # `function` (see: https://docs.litellm.ai/docs/providers/anthropic#computer-tools) # but we don't allow that (spec should match type). - for i, tool in enumerate(formatted_tools): - if tool.type == "computer_20241022": + formatted_tools = [] + for i, tool in enumerate(tools): + if tool.type == "computer_20241022" and tool.computer_20241022: function = tool.computer_20241022 tool = { "type": tool.type, @@ -165,9 +154,13 @@ async def chat( }, }, } - formatted_tools[i] = tool + formatted_tools.append(tool) + + # If not using Claude model, + + if not is_claude_model: + formatted_tools = None - # formatted_tools = None # Use litellm for other models model_response = await litellm.acompletion( messages=messages, From dbe6ae9134288bbc5054aa5b8b1a0979a7c066b9 Mon Sep 17 00:00:00 2001 From: vedantsahai18 Date: Fri, 22 Nov 2024 01:16:55 -0500 Subject: [PATCH 25/28] fix(agents-api): updated the prompt step --- .../activities/task_steps/prompt_step.py | 168 +++++------------- agents-api/agents_api/env.py | 1 - agents-api/docker-compose.yml | 1 - 3 files changed, 42 insertions(+), 128 deletions(-) diff --git a/agents-api/agents_api/activities/task_steps/prompt_step.py b/agents-api/agents_api/activities/task_steps/prompt_step.py index 692da9a9f..0b67a2aa9 100644 --- a/agents-api/agents_api/activities/task_steps/prompt_step.py +++ b/agents-api/agents_api/activities/task_steps/prompt_step.py @@ -1,13 +1,10 @@ -from datetime import datetime from typing import Callable -from anthropic import AsyncAnthropic # Import AsyncAnthropic client from anthropic.types.beta.beta_message import BetaMessage from beartype import beartype from langchain_core.tools import BaseTool from langchain_core.tools.convert import tool as tool_decorator -from litellm import ChatCompletionMessageToolCall, Function, Message -from litellm.types.utils import Choices, ModelResponse +from litellm.types.utils import ModelResponse from temporalio import activity from temporalio.exceptions import ApplicationError @@ -18,7 +15,6 @@ from ...common.protocol.tasks import StepContext, StepOutcome from ...common.storage_handler import auto_blob_store from ...common.utils.template import render_template -from ...env import anthropic_api_key, debug from ..utils import get_handler_with_filtered_params from .base_evaluate import base_evaluate @@ -26,22 +22,6 @@ def format_tool(tool: Tool) -> dict: - # FIXME: Wrong format for computer_20241022 for litellm - if tool.type == "computer_20241022": - return { - "type": tool.type, - "name": tool.name, - "display_width_px": tool.computer_20241022 - and tool.computer_20241022.display_width_px, - "display_height_px": tool.computer_20241022 - and tool.computer_20241022.display_height_px, - "display_number": tool.computer_20241022 - and tool.computer_20241022.display_number, - } - - if tool.type in ["bash_20241022", "text_editor_20241022"]: - return tool.model_dump(include={"type", "name"}) - if tool.type == "function": return { "type": "function", @@ -163,114 +143,50 @@ async def prompt_step(context: StepContext) -> StepOutcome: for fmt_tool, orig_tool in zip(formatted_tools, context.tools) } - # Check if the model is Anthropic - if agent_model.lower().startswith("claude-3.5") and any( - tool["type"] in ["computer_20241022", "bash_20241022", "text_editor_20241022"] - for tool in formatted_tools - ): - # Retrieve the API key from the environment variable - betas = [COMPUTER_USE_BETA_FLAG] - # Use Anthropic API directly - client = AsyncAnthropic(api_key=anthropic_api_key) - - # Reformat the prompt for Anthropic - # Anthropic expects a list of messages with role and content (and no name etc) - prompt = [{"role": "user", "content": message["content"]} for message in prompt] - - # Filter tools for specific types - filtered_tools = [ - tool - for tool in formatted_tools - if tool["type"] - in ["computer_20241022", "bash_20241022", "text_editor_20241022"] - ] - - # Claude Response - claude_response: BetaMessage = await client.beta.messages.create( - model="claude-3-5-sonnet-20241022", - messages=prompt, - tools=filtered_tools, - max_tokens=1024, - betas=betas, - ) - - # Claude returns [ToolUse | TextBlock] - # We need to convert tool_use to tool_calls - # And set content = TextBlock.text - # But we need to ensure no more than one text block is returned - if ( - len([block for block in claude_response.content if block.type == "text"]) - > 1 - ): - raise ApplicationError("Claude should only return one message") - - text_block = next( - (block for block in claude_response.content if block.type == "text"), - None, - ) - - stop_reason = claude_response.stop_reason - - if stop_reason == "tool_use": - choice = Choices( - message=Message( - role="assistant", - content=text_block.text if text_block else None, - tool_calls=[ - ChatCompletionMessageToolCall( - type="function", - function=Function( - name=block.name, - arguments=block.input, - ), - ) - for block in claude_response.content - if block.type == "tool_use" - ], - ), - finish_reason="tool_calls", - ) - else: - assert ( - text_block - ), "Claude should always return a text block for stop_reason=stop" - - choice = Choices( - message=Message( - role="assistant", - content=text_block.text, - ), - finish_reason="stop", - ) - - response: ModelResponse = ModelResponse( - id=claude_response.id, - choices=[choice], - created=int(datetime.now().timestamp()), - model=claude_response.model, - object="text_completion", - ) - - else: - # FIXME: hardcoded tool to a None value as the tool calls are not implemented yet + # Check if using Claude model and has specific tool types + is_claude_model = agent_model.lower().startswith("claude-3.5") + + # FIXME: Hack to make the computer use tools compatible with litellm + # Issue was: litellm expects type to be `computer_20241022` and spec to be + # `function` (see: https://docs.litellm.ai/docs/providers/anthropic#computer-tools) + # but we don't allow that (spec should match type). + formatted_tools = [] + for i, tool in enumerate(context.tools): + if tool.type == "computer_20241022" and tool.computer_20241022: + function = tool.computer_20241022 + tool = { + "type": tool.type, + "function": { + "name": tool.name, + "parameters": { + k: v + for k, v in function.model_dump().items() + if k not in ["name", "type"] + }, + }, + } + formatted_tools.append(tool) + + if not is_claude_model: formatted_tools = None - # Use litellm for other models - completion_data: dict = { - "model": agent_model, - "tools": formatted_tools or None, - "messages": prompt, - **agent_default_settings, - **passed_settings, - } - extra_body = { - "cache": {"no-cache": debug or context.current_step.disable_cache}, - } + # Use litellm for other models + completion_data: dict = { + "model": agent_model, + "tools": formatted_tools or None, + "messages": prompt, + **agent_default_settings, + **passed_settings, + } - response: ModelResponse = await litellm.acompletion( - **completion_data, - extra_body=extra_body, - ) + extra_body = { + "cache": {"no-cache": debug or context.current_step.disable_cache}, + } + + response: ModelResponse = await litellm.acompletion( + **completion_data, + extra_body=extra_body, + ) if context.current_step.unwrap: if len(response.choices) != 1: diff --git a/agents-api/agents_api/env.py b/agents-api/agents_api/env.py index e33e3527a..3eb340cf4 100644 --- a/agents-api/agents_api/env.py +++ b/agents-api/agents_api/env.py @@ -25,7 +25,6 @@ hostname: str = env.str("AGENTS_API_HOSTNAME", default="localhost") public_port: int = env.int("AGENTS_API_PUBLIC_PORT", default=80) api_prefix: str = env.str("AGENTS_API_PREFIX", default="") -anthropic_api_key: str = env.str("ANTHROPIC_API_KEY", default=None) # Tasks # ----- diff --git a/agents-api/docker-compose.yml b/agents-api/docker-compose.yml index 59a8cfd7d..23cb6dd61 100644 --- a/agents-api/docker-compose.yml +++ b/agents-api/docker-compose.yml @@ -28,7 +28,6 @@ x--shared-environment: &shared-environment S3_ENDPOINT: ${S3_ENDPOINT:-http://seaweedfs:8333} S3_ACCESS_KEY: ${S3_ACCESS_KEY} S3_SECRET_KEY: ${S3_SECRET_KEY} - ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY} x--base-agents-api: &base-agents-api image: julepai/agents-api:${TAG:-dev} From bc0e0d4bf6b7bd5af064aa1fcc8ae90371dc00ac Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Fri, 22 Nov 2024 09:48:00 +0300 Subject: [PATCH 26/28] Add missing debug var from env --- agents-api/agents_api/activities/task_steps/prompt_step.py | 1 + 1 file changed, 1 insertion(+) diff --git a/agents-api/agents_api/activities/task_steps/prompt_step.py b/agents-api/agents_api/activities/task_steps/prompt_step.py index 0b67a2aa9..8da0e49c7 100644 --- a/agents-api/agents_api/activities/task_steps/prompt_step.py +++ b/agents-api/agents_api/activities/task_steps/prompt_step.py @@ -17,6 +17,7 @@ from ...common.utils.template import render_template from ..utils import get_handler_with_filtered_params from .base_evaluate import base_evaluate +from ...env import debug COMPUTER_USE_BETA_FLAG = "computer-use-2024-10-22" From bd5ba4fa787c29e8d827eb3aa7df05e6033b88de Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Fri, 22 Nov 2024 06:48:49 +0000 Subject: [PATCH 27/28] refactor: Lint agents-api (CI) --- agents-api/agents_api/activities/task_steps/prompt_step.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents-api/agents_api/activities/task_steps/prompt_step.py b/agents-api/agents_api/activities/task_steps/prompt_step.py index 8da0e49c7..bf2b413ae 100644 --- a/agents-api/agents_api/activities/task_steps/prompt_step.py +++ b/agents-api/agents_api/activities/task_steps/prompt_step.py @@ -15,9 +15,9 @@ from ...common.protocol.tasks import StepContext, StepOutcome from ...common.storage_handler import auto_blob_store from ...common.utils.template import render_template +from ...env import debug from ..utils import get_handler_with_filtered_params from .base_evaluate import base_evaluate -from ...env import debug COMPUTER_USE_BETA_FLAG = "computer-use-2024-10-22" From 65e7f39f2da3e954bd8edfc5603cedbe0a222f18 Mon Sep 17 00:00:00 2001 From: HamadaSalhab Date: Fri, 22 Nov 2024 10:05:49 +0300 Subject: [PATCH 28/28] Fix browser use cookbook --- cookbooks/06-browser-use.ipynb | 127 ++++++++++++++++++++++++++++++--- 1 file changed, 116 insertions(+), 11 deletions(-) diff --git a/cookbooks/06-browser-use.ipynb b/cookbooks/06-browser-use.ipynb index 28b6c35f6..62e024305 100644 --- a/cookbooks/06-browser-use.ipynb +++ b/cookbooks/06-browser-use.ipynb @@ -138,7 +138,18 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: Ignoring invalid distribution ~ulep (/Users/hamadasalhab/Documents/repos/julep-ai/julep/agents-api/.venv/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~ulep (/Users/hamadasalhab/Documents/repos/julep-ai/julep/agents-api/.venv/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~ulep (/Users/hamadasalhab/Documents/repos/julep-ai/julep/agents-api/.venv/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], "source": [ "!pip install julep -U --quiet" ] @@ -187,7 +198,7 @@ "api_key = os.getenv(\"JULEP_API_KEY\")\n", "\n", "# Create a Julep client\n", - "client = Client(api_key=api_key, environment=\"dev\")" + "client = Client(api_key=api_key, environment=\"local_multi_tenant\")" ] }, { @@ -232,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -526,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -534,7 +545,11 @@ "task = client.tasks.create_or_update(\n", " task_id=TASK_UUID,\n", " agent_id=AGENT_UUID,\n", - " **task_def\n", + " **task_def,\n", + " extra_body={\n", + " \"run_browser\": task_def[\"run_browser\"],\n", + " \"check_goal_status\": task_def[\"check_goal_status\"],\n", + " }\n", ")" ] }, @@ -554,14 +569,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started an execution. Execution ID: 8fbb3530-de78-46d3-93f1-b40d632d5367\n" + ] + } + ], "source": [ "execution = client.executions.create(\n", " task_id=task.id,\n", " input={\n", - " \"agent_id\": AGENT_UUID,\n", + " \"agent_id\": str(AGENT_UUID),\n", " \"goal\": \"Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\",\n", " }\n", ")\n", @@ -625,9 +648,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transition type: init\n", + "Transition output: {'agent_id': '07648b48-4ca7-4076-81db-c29eccc15f80', 'goal': \"Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\"}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'auto_run_tools': False, 'context_overflow': None, 'created_at': '2024-11-22T07:04:47.809701Z', 'id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'kind': None, 'metadata': {}, 'render_templates': True, 'situation': '', 'summary': None, 'token_budget': None, 'updated_at': '2024-11-22T07:04:47Z'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'avgCpuUsage': None, 'contextId': None, 'createdAt': '2024-11-22T07:04:51.047247+00:00', 'endedAt': None, 'expiresAt': '2024-11-22T07:19:51.009+00:00', 'id': 'a959cfd1-1b96-4c15-b025-863c31ffcea5', 'keepAlive': None, 'memoryUsage': None, 'projectId': 'c35ee022-883e-4070-9f3c-89607393214b', 'proxyBytes': 0, 'startedAt': '2024-11-22T07:04:51.009+00:00', 'status': 'RUNNING'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'browser_session_id': 'a959cfd1-1b96-4c15-b025-863c31ffcea5'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'urls': {'debuggerFullscreenUrl': 'https://www.browserbase.com/devtools-fullscreen/inspector.html?wss=connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/page/6B58334B1B83E8419C546ECD99DF70C8?debug=true', 'debuggerUrl': 'https://www.browserbase.com/devtools/inspector.html?wss=connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/page/6B58334B1B83E8419C546ECD99DF70C8?debug=true', 'wsUrl': 'wss://connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/browser/bf6e1919-d6b7-44ee-8ec3-5cd8dd2faab8'}}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'debugger_url': 'https://www.browserbase.com/devtools/inspector.html?wss=connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/page/6B58334B1B83E8419C546ECD99DF70C8?debug=true'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'connect_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'base64_image': None, 'error': None, 'output': 'https://www.google.com', 'system': None}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': \"\\n\\n* You are utilising a headless chrome browser to interact with the internet.\\n* You can use the computer tool to interact with the browser.\\n* You have access to only the browser.\\n* You are already inside the browser.\\n* You can't open new tabs or windows.\\n* For now, rely on screenshots as the only way to see the browser.\\n* You can't don't have access to the browser's UI.\\n* YOU CANNOT WRITE TO THE SEARCH BAR OF THE BROWSER.\\n\\n\\n*Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\\n\", 'role': 'user'}], 'workflow_label': 'run_browser'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': \"\\n\\n* You are utilising a headless chrome browser to interact with the internet.\\n* You can use the computer tool to interact with the browser.\\n* You have access to only the browser.\\n* You are already inside the browser.\\n* You can't open new tabs or windows.\\n* For now, rely on screenshots as the only way to see the browser.\\n* You can't don't have access to the browser's UI.\\n* YOU CANNOT WRITE TO THE SEARCH BAR OF THE BROWSER.\\n\\n\\n*Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\\n\", 'role': 'user'}], 'workflow_label': 'run_browser'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'choices': [{'finish_reason': 'tool_calls', 'index': 0, 'logprobs': None, 'message': {'content': \"I'll help you navigate to JulepAI's Github repository to check its stars. Let me break this down into steps:\\n\\n1. First, let me take a screenshot to see where we are:\", 'created_at': None, 'id': None, 'name': None, 'role': 'assistant', 'tool_call_id': None, 'tool_calls': [{'api_call': None, 'bash_20241022': None, 'computer_20241022': None, 'function': {'arguments': '{\"action\": \"screenshot\"}', 'name': 'computer'}, 'id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ', 'integration': None, 'system': None, 'text_editor_20241022': None, 'type': 'function'}]}, 'tool_calls': None}], 'created_at': '2024-11-22T07:05:08.603440Z', 'docs': [], 'id': 'cde04753-fd04-4016-a812-e13d4ac2e3fc', 'jobs': [], 'usage': {'completion_tokens': 95, 'prompt_tokens': 1335, 'total_tokens': 1430}}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'content': \"I'll help you navigate to JulepAI's Github repository to check its stars. Let me break this down into steps:\\n\\n1. First, let me take a screenshot to see where we are:\", 'tool_calls': [{'action': 'screenshot', 'coordinate': None, 'text': None, 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}]}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: {'action': 'screenshot', 'coordinate': None, 'text': None, 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'base64_image': 'data:image/png;base64,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', 'error': None, 'output': None, 'system': 'take_screenshot'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: [{'base64_image': 'data:image/png;base64,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+jYmDynJyQkyGq1yTufTxSIi4tXakpqpsZfs7ubysfLO8dlKpSvoOeefUYDnnlKv86arXGfjVejBg1ybUhK0l8LF+nXWbPUtUsXlSpZSu7uufeEiI2Nk89VDbfSpUvlvkFF8FjF3Kxfv77Rc889e3TS5K+P3HXXXXWLuj7ZKVumjLw8vfTeO29nmfbPseOyWq16eeBLKhYQUGjrLF26jBISEhQRGZnvcl1cXFS8WLF8zTtv/ny1a9fWcVvMmbNn9eusWY7pHe69V9O++16zf5ujBg3q5/oUC1dnlyyDFwIAkBGDAAIAsmhQr4HMZrPWr98gSXJ3d1epkiVVPEPX7mIBAfLw8Mg0mJvdbtfps2dUPrBcntN9fHzk6empi5f+HZQvN2XLlFGdOrX13jtvZ/pp0uSuLPMOGvyaFi9Z6vjdyclJPbt3U0pKqi5eCsrUkMz480DfPpKkX2fN0sAXX9T/Pfes7r+/l+rXq5dr3cqUKaPLV65kem3rtm0KCQ3N17YVpRo1a56/++67/5GkXbt21Xnu2QHumzdv2lfE1cpWh/btdSU4WHP++EM2m00pKSmaMHGS9u3brxo1qqtataqaNHmKkpKSZLfbtXDhYv32++/Xtc7q1aqqUsWK+n7GD0pOTlZMTIyWr1hRSFskubq66vSZM4qLTxvQ79eZaY1/q9UqSSperJiaNLlLixYvUcf27XMtq3z5crJYLI7BNvfu3afjJ04WWl0BALc/AgAAQBYBAf56sF8/fTv9e23dtk0Wi0VWq1Wbt26TJLl7pHVp7t2zp36ZOUthYeGy2Wz6Y948xUTHqEOHe/M1vUOHe/XbnDkKj4iQxWLRn38uyLFO3bp10YqVq7Rv335JaU8qGPXBGIVnM4h940aNNHP2bB06nDaYYXJysubOmy8vLy9Vrlwp14aklNYoO3LkqKxWq86dP6dFS5bIarPmWLf7unTS2rXrdP7CeUnSjh07NebjT2Wz5rzMraJK5cph076b7tS5c5e9knTkyJGqz//f/xVfuXLFTkm3VDeFUqVKaczoUVq4eInuf6C/HnjwYUVHRatGjeoyOzlp9HvvKSoqSn37P6S+/R/S4mVL1bLFPde1TpPJpDeGva49e/fq/gf66eln/0++Pr6O8QKu15P/eVwJCQnq2+9BPfafJ1WmdNpYjFeuBDvm6dypk8xms9q0bZNrWYGBgXr80Uf1zvsj1b3X/Zo+4weVL1+uUOoJALgzmOy53YQHALjtJKekFko5drtdM2fP1tx585WYmHZFtUSJEnrqif+oW9f70taVnKzPv5igvzdslLOzs/z8fPXG0KFq2rRJvqZHRUdr1OgPdPjIUbm5uald2zZatXqNfvh+miqUr6CRoz9UQLEADX11sCRp7vz5+vmXmXJyMslqtan/Aw84Rk/PyGaz6ddZs7Tgr0X/u9XAqiqVK+mVQYPUsEHaPeF79uzV5199peioaNntdjW7+269OWyovLy8tGPHTo0d/1/FxMTI399fbdq00sqVq/TX/Hnat2+/3h05SiuXLcm0r7759jstXLxYLi4ucnd306uDX1GbVq20c+euLPO/+dbbqlWjhv7vuWev+31yc83/venSv8dHgL9fSmJiomuXLl32zv9zQd3g4OCwN4a9fnHBggX3SFJgYGDwf//7+akH+vVrYTKZbrkLBlFRUXJxcZHXVU+LkKSEhAQlJ6coIMC/UNZltVolk0kREREqXqyYfp87Vxs2btKUSRPzXjifwiMi5OnpKY9sxgtY8NdC7dy1Wx+P+SBfZSUmJio1NdUx6OWNUtBjDwBQ9AgAAOAOU1gBQDqr1arw8HC5ubvL18cn2yuf8fHxik9IUMkSJQo03Wqzyel/DSsfX1+dOnVKg18bqkUL5svDwyPb+tjtdoWHR8jX10eurq651t1msyk8IkJenp7y9PTMdp6cGpJWm03h4eEqFhAgZ+f8DZmTlJSk2Lg4lShevNCuEOelMAKAufPm13Zzc/MICQkJeeftt07MmjWrpc1mcypevETEJ59+cviJJ55saTabDTtu0Fvvvqc6tWqrTZtWuhJ8RRO+nKRHH3lID/brd0PXGx8frzNnz+qTT8fplUED1aplyxu6voIiAACA2w8BAADcYQo7ALiRZs7+TadOnVb/fg/Ibrfr22nT5O8foDEfMNJ5fhVmACBJUVFRkR9+MPrgtGnTWlksFmdfX9+4kSNH7X7hxRdbuLq65j4a4h3q3Llz+vnXmTp69B/5+PqoS+dO6te3r5ycbmzHiOXLV+jrb75V75499cLzz93QdV0LAgAAuP0QAADAHeZ2CgBiY2P1868ztWPHLplMJjW9+y4989RTWUbUR84KIwCYN//POq6uro6+5/HxcXHjxo7b/dVXX7ZITk528/T0THxz+PDtQ4YMbe7h4ZF9VwoYDgEAANx+CAAA4A5zOwUAuH43IgCQpKSkpMTJkyZtHzv202ZxcXFerq6uKYNeeWXrO++8e5ePj8+NvbkctwUCAAC4/dxyg/oAAICi5+7u7vHakCGtPv7k093+/v4xKSkprhO/+qrNW2+N2B8Rkc2jFwAAwC2PAAAAAGTLxcXF9fnnn2/9xYQJB0qWLBVutVrNM77/vs3QIUOOXcn4nDoAAHBbIAAAAAA5cnJyMj/22OOtpkyZcqJChQqX7Xa7ac6c31oOenng+XPnzl0o6voBAID8IwAAAAC5MplMTr3vv7/Ft9OmBdWoUeOcJC1ZsqTZCy88H3Hsn39OFnX9AABA/hAAAACAfOnYsVPT6d/PiGnYsNEJSVr/99+Nnnvu2dS9e/ceKeq6AQCAvBEAAABgYCaTqUBPA7rnnnsafD/je2uLFi0OS9KuXbvq/N//Pee2ZfPm/TemhgAAoLAQAAAAgAJp0KBh7e+mf+/RqVPnvZJ0+NChav/3f88VW7ly5U5JPF4YAIBbFAEAANxhzE58tRuFk5OpwMs4m82yyy673V7whTOoUaNG1W+nTSvVp0+fHZJ0+vTpCi+9+ELF+fPnbbXb7bbrKRu3NpNM13TsAQCKHmeJAHCHMZv5ajcKZ7O5wMsU5vFRvnz5cpMmf135iSee2Ozk5GQLCgoqPfiVwbV//vnnzVar1VJoK8ItwySTbLLJxdm5qKsCALgGnCUCwB3GZDLJ1cWZK3R3MCentPfYZLq299jd1bXQ6lKqVKlS//38i7ovvvjSJmdnZ0t4eFix4W++0fibqVM3p6SkJBfailD0TJLJqXCPHwDAzUV8CwB3IJPJxBU65IuTk5PcXF3k6upyzWWULlUy4LPPxjXx9/fbPGHChBbR0dE+77//XvOEhPitw4YNa+7h4eFZiFUGAADXiB4AAADgunl7e3uPHDmy+ahRo7Z6e3vHJyQkeIwZM6bVyJEjd8bExMQUdf0AAAABAAAAKCTu7u4eb7zxRuvPPvtsd0BAQHRKSorrhAkT2gwfPnx/RERERFHXDwAAoyMAAAAA18Vut9ssFktqUlJSYmJiYtJDDz1Ud8SIEXs9PDySrFaredq0aW0GDx587MqVK8FFXVcAAIyMG0QBAEC+HTx48NjGjRuD4+LibNHR0abY2FhTdHS0OTY21iU2NtY1NjbWNSEhwTU+Pr56amqqsyTZ7XbTrFmzWsbGxu6cOHFiSuXKlSsU9XYAAGBEBAAAACDfgoKCYoYPH353fHx8gQf2W7RoUbP4+Pj9X331VWL9+vVr3oj6AQCAnBEAAACAbEVHR0dv27bt+L333tvAzc3NXZJatWpVu3Xr1sdWrlx5lyS5u7sne3t7x7u7u6d6eHgke3p6Jnt5eSV7e3un+vj4pPj6+lp8fX2tvr6+Nl9fX5OPj48pKSnJXLRbBgCAMREAAABgYCaTKdvXY2JiYkaOHLl//vz5tRYtWnSmcePGdSTJx8fH57HHHotbs2aN1Wq1mu+9997D48aN8/T29vbw8PBwc3Nz83Z1dXV1cXFxdnZ2djabzWZJ2a8EAADcVAwCCACAAdnt9hwb5ZGRkZEjRozY//XXX7e+ePFi6T/++CMk4/Ru3brVbNCgwSlJOnz4cKCzs7Nz1apVK5UtW7ZMsWLFinl7e3u7ubm5m81mZ9H4BwDglkEAAAAAHMLDw8OHDRt2ZNq0aa2tVqtZkubPn1/p/PnzF9PnKVOmTOmHHnooSJIuXLhQ5o8//riYU3kAAODWQQAAAAAkSSEhISGvvfbasR9//LGVzWZzcnZ2tkjSsWPHKi1atOhUxnn79etXoWLFipclad68eRUuXboUVBR1BgAA+UcAAACAgdntdplMJqfLly9fGTRo0JmZM2e2stvtpmrVql0YPXr0ppIlS0bY7XbTb7/9ViwqKioyfbmaNWtW7tWr10lJOnz4cJVly5adynktAADgVkAAAACAgTk5OdnPnz8f9NJLL12YO3fuPZJUvXr1C998803Im2++2bJjx47HJWnXrl01161bdyzDcubHHnvM19/fP8ZmsznNmjXLNyYmJqaotgMAAOSNAAAAAAO7ePGi30svvRS1cOHCZpJUu3bts99//314586dm7q6uro9/vjjJg8Pj6SkpCS3WbNmmZKTk5PSl7377rtrdujQ4R9J2r59e82NGzf+U1TbAQAA8kYAAACAgR04cKD66tWrG0tS/fr1T82YMSOmXbt2jdOnt2/fvlazZs2OS9KaNWtq79mz53j6NHd3d4/HH3/c6ubmlpKQkOAxa9Ysa2pqasrN3gYAAJA/BAAAAEB33XXX8R9//DGpZcuWDTO+7ufn5//4449HmUwme2RkpN9vv/0WZbfbbenTO3bsWKtJkyYnJGnlypW19u/ff+Jm1x0AAOQPAQAAAAbXvHnzoz/++KOtadOm9bKb3rNnz+p16tQ5I0kLFy6sfuLEibPp04oVK1bs0UcfDZOksLCwYnPmzAmTZL8Z9QYAAAVDAAAAgIG1adPm4A8//ODSsGHD2jnNU758+cD+/fufl6SzZ88G/vnnn+czTu/Tp0/VWrVqnZWkBQsWVD19+vT5bIoBAABFjAAAAAADstvtpg4dOuyfMWOGV926davnNf+DDz5YNjAwMESSfv/997IhISEh6dN8fX296tatGyJJJ0+erLBw4cKzN6ziAADgmhEAAABgQP369dv53Xff+dWoUaNqfuavV69ete7dux+XpAMHDlRbvnz58cuXL1+ZMmXKht69ewctXbq0Ufq8f/zxR4moqKjIG1V3AABwbUx2u5379AAAMJjIyMiIgICAYgVZZs2aNbsfeOCB2rGxsV7Vq1e/4OLiYjl27Fglm83mJEnu7u7JzZo1O/7MM89EPfroo008PT29bkztAQDAtSAAAAAA+RIfHx//8MMP/7N06dKmGV8PCAiI7tSp0z9PPPGEvX379rX9/f39i6iKAAAgF9wCAOCOcTkoUVs2hmb5CQlOuuYyD+yN1IBHNxdiLfO2YW2whg3aVWjlHT0crb/mXtDGv0MUH2cptHJz894be7Vs0aUCL2ezSTu3hWvh/Ivav+fW6EE+6Lnt2rE1LNtpQZcSdeRQdIHKy3icbt8SprOn4zNNL4xjbsPaYA0duLNAdcn4ExmRku38e3bEez3yyGNJzs7OFpPJZK9cuXLQq6++umHp0qXnq5R9o5GHS7MWhdX4Dw1J1vNPbFWbu5bfMsfC1aZ8eUw/fHuySOvw9tA9Wrk06LrKuNbPa24K8v4V9nceACBnzkVdAQAoLMsXXdInow+qXgP/TK+/+kYddbyvzDWVGR2Vqp3bwguhdjkLvpKkH749qbdG1ZeUduJ8YO/1N3isVrsGP79D69dcUdPmxRURnqKgiwn6cU5rNbwr4LrLz82hA1GqVsOnQMtERqTo4V7rZbXaVbW6jw4diFTlKt768fc2cnd30s5t4Tp5PEaPPVXlBtU6zdzZ51S2nKdatyspSdq7K0K9H6iQ7bxLFlzUkUNRmjC1Wb7Lz3ic2u3S6ZOxqljZSzPnt5N/gEuhHHOhIcnatzvvYyinz8y7HzZUsxbFM72WkmLTm6/u0vjJzavee++9hxo3bhz9zDPPlK1du3Yrs9ns/NE7a1W/0bWHbVf7bMwhxcdb9OOc1ipfwbPQyi1Mp07Eysu7cE+lrv4+yMvB/VGqU9//utZ5LZ/XvOT2/t2o7zwAQN4IAADcUarV8NGClR2KuhoFEhqSpK8n/JPvE/78+mPWOe3cGqY127qqTFl3SdL4jw7rtRd3at2O+wp1XYXh6wn/qHgJN/22sJ0kKTXVrt6d1ui7r49r8LDa2rc7QquXX77hAcCSvy6pUZMARwCQm+cH1bimdWQ8TlNSbOp57xr9MuOUBg/L8Ul8N0x+PzOurk7adrCHJJVd1X5V2Rtdr+P/xOihxyupes3CbZje6m7U98HNltv7d6dsIwDcjrgFAIAhLF14Sd3br1FSolWS9NX4o3rmkc2y26X+3f/W558cUbumK1Snwl8aOnCnkpJs2ZazaX2IurZZrZqBC9S709pMXVv7d/9bH7yzX01rLdb0KSckSdMmn1CTmovVoMpCDX91t5KTM5f72y9n9WT/TbLZpOb1ljqu/Kam2vT+8H2qX3mhmtVdkqmL7/mz8Xqk9wZVL/OnOrVYqa2bQrOt62+/nNFT/1fN0fiXpP97uYb6P1rRsR8unEvQf/ptVO3yC9Su6XLNnX0uUxnTp5xQq0bLVK/SX/q//2xVWGhyprrfU3+pGlRZqLEfHFLHe1Zm6c4uSYkJVr3xym7VKrdA99Rfqu+nZt9lOjIiRQHFXB2/u7iYNOazu9SgUYDeHLxbE8Yd0c5tYWpeb6msVrsSE6x6a8geNaq2SE1rLdZH7x9QamrasDbLFwfp0fs36Pkntqph1UX53m+dWqzUhrVX9O2k43rhya2O13fvCNd9rVepRtk/9exjWxzHxzcTj+vtoXsc83352VE1rr5IdSr8pRee3KqoyNRstzUjV1cnVajkpYjw5Gyn//z9abVosFS1yi3Qgz3WZ9rHB/dFqce9a1St1Hy1v3uFVi+/nGX55GSbBjy6RR+PPJhnXTL6eORBDRu0S13brNZzj2+R1WpX83pLdepEnKS0WxW6tlmtWuUW6P/+s1WxMf9ua0qKTe8O26tG1RapbsW/9NqLaZ+po4ej1bLhskz7pWeHNVq5NHO9+3f/W/v3RGjsB4fUv/vfklTg9zuj3JZdvOCinnnk31suzp6OV/N6S5WcbFNIcJKa11uqj0ceVN2Kf+nQ/qgsZV84F6/+3f9WzcAF6tftb108n+CYtn5NsDo0X6la5Rbo0T4bFHQp0TEtu2Mlp++DjHLb77l91oIuJurphzerbsW/1KTmYk396niWsiUpPs6iQc9tV50Kf6lRtUUa9+GhbOfLbZ9m9/6lu1HfecMG7dIXY484fv/z9/N6uNf6PJfftjlM3dqlfaff22yF/l4dLEn5eu8B4HZEAADgjhIfZ9HGdSGOn727IiRJ3XuXk7e3syZ9/o9OnYjTpM//0bC368lkSrsPeu/uCM1Z1E6L13bUzm3hmjLhnyxlnzsTr6cf3qwhI+ro4Jn79fjTVfRonw2KCE+7X/pyUKK2bQ7TlB9aqP+jlTR39jn9MO2k5i/voM37uuvUiVhN/epYpjIferySZsxuJScnacPubmraPK3b9YljsapTz0+7jvbUk89WczQyLRa7nui/US3bltSR8300/P36eu7xLYqLzXpv/9nTcapVxy/TawHFXPXK67Xl7mGWxWLXf/ptVP2GAdp7vLe+mNJMI0fs08Z1aY93nzv7nL6ZeFwzZrfWjsM95R/gqv/7zxZJ0pFD0XpryG6N+ayxdhzuKS9vZx07GiOLJWtw8t6bexUWmqRdR3tp1p/t9NX4o9neU//M89W1avllvfzsdm3eECq7XWrWorju7Vxan3zRRIOH1VHT5sW1YXc3mc0mvffmXp09Had1O7pq8bpO2rQ+RF98evjf4+DvEDVtXlxzl7bP935bur6z2nYorf8bWEOTv7/H8fqm9SH68fc2Wr+zmw7uj9T8OWlBSXRUiiMU2bA2WD98e1JL13fWrqM9FR9v0fiPsm88xUSnavniIC1deEnjPjykvbsi9MSArE/j274lTJ+NOaRZf7bT4XN9VKmKl94fvldSWg+J/3tii3r2Ka9jQQ9ozGeNNei57Qq6+G8j02Kx66Wntyk11abh72d/tdVqsSsiPMXxk75PIsKTtWzRJQ0ZUUdjPrtLdrt06UKCUlNtSkmx6dnHtqhL97LacbinHnq8ki6c+zeY+HrCMR05FK2Ne7ppy/4eOnwgSjO+OaE69fxkMknLF6fdb35of5SOHopWq7aZe1vM/qud6jX01/D362v2X2k9Qgryfl8tr2WvXM64z2y6dCFBdnva/rt0IUGhIUmas7B9tlezd++I0Afj0j4HFSp5achLaWMvnD4Zpxee2qoPxjbSgdP3q0GjAA3937ScjpWcvg/S5bXfc/usDXlph6pW99bBM/dr9l/t9N+PD2V7m8ikz/9ReFiy9hzrpSXrOun3mWe1eMHFAu3T7N6/dDfqO69lm5L67Zezjt8Xzr+gps2L57p8YoJVTz20Sa+9WUfHLvXVS6/W0otPb5XVas/Xew8AtyMCAAB3lOArSZr8xT+Onz9mpTXUTCbpv5Pv1o/fndQr/7ddz79cQw0a+zuWe2JAVZUN9FC1Gj4a+FqtbE94f/vljNp1KK3uvcvJzc1Jjz1VRTVq+WjBH+cd87z6Rm3d06qEAoq56tcfTuuJAVUVUMxVdrv06JNVtGxh5oG2zGaTXFzTvord3Z3k9L9v5br1/fT401Xk7mFWj/vL6crlJCUl2bR1Y6jCQpP1zPPVlZhgVYvWJVWylLu2bAzJUt+Y6FTH/ckXzydo2KBdjp+Q4CRt3RiqiPBkvflePXl4mnX3PcX15LPV9NP0U5KkX384rRcH11Ttur7y8nbW6E8baf+eSB09HK3Ff15U+05ldF+PQHl4mjXwtVrZvh+JCVbN++2cBg2tLZvNrhIl3dS1Z6CWLsw64FiDxv76e0dX+fm56Pkntqh53SWa9dMZSWm9AZydTXJyMsnd3UnJyTb9PvOs3vuooYoVd1XZQA+9PaqBfv7+lKO8OvX89NKrNVWztm++95ubm5NMJpPMzia5uv77J/K1N+sosJyHAst76O7mxXXmVFyW+lutdlmtdl2+lChPL2d9/X0LDRqafZf+qMgUzfvtnP78/bw2rAtW2XIecnc3Z5nv7nuKa8/xXipV2l3nzsSpdl0/nTweK0natilUKck2vTyklpydTWrXsbTe/6ihEhL+bRi98couRUYk67tfWsrFxZRtXY4djVG7pssdPyNH7HNM6/9oJXXvXU6B5T0yLbNja5gSEy0aMqKu/Pxd1LVnoOpmGEfglddr648lacFLSHCiatX11akTafXu93BFLfrzgqS0IKBzt7Ly9sl8R6Kra9r74Py/96Gg73dG+Vk2L6M/bawGjf3l7pH1Per7UAXVb+gv/wAXDX+vnrZuShtE8Y9ZZ9WidUk1alJMSYlWPTGgqjatD1FsjCXHYyWn74P87Pe8Pmu/zGurdz9sqOAraeM0lC3nqZPHY7Jsj9VqV1KiVaEhSSpf0VOL13VS63alCrRPr37/MrpR33k97i+viPBk7dkZocQEqzasDVbfhyrmury7h1kHTt+vjveV1bkz8apWw0dxsRYFX/53LIvc3nsAuB0xBgCAO0rV6t6as6hdttMqVfHS/f0q6PeZZzVv6b05llGpipeCryRmef3C+YQsV4Gq1/TV2TP/NgZNpn8bWRfPJ2jqV8c045sTjteKl3DL76Y4mM1pZVotNl28EK+EeIs6tViRaZ6Y6Kxdzb19XBQWknYi6+nlrKbNiysp0ar3h+/TwNdq6cL5eFWq4iVn53/rXL2mj9auTOuOfeFcfKaBwbx9nFW6rLvOnYlX8JVEVayc9yPeg68kKTXVrpee3prp9ft6BGY7f/mKnvp0QhN9MK6xli28pLeG7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', 'error': None, 'output': None, 'system': 'take_screenshot'}]\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'contents': [{'image_url': {'url': 'data:image/png;base64,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u7du9WiRQutXr1a0dHR6ty5s6pVq6a///5bR44cUcWKFdWtWzc5O6c1G+x2u9atW6fjx4+rePHi6tatW6a/2UVhy5Ytuv/++1W7dm1JUp06dfTSSy/pm2++cQQAOe2XRYsW6dSpU4qJidG8efNUt25dxy1ja9euVUpKiho1apTpeMvPud+xY8e0bt06lShRQl26dNHq1avVpUsX+fr6Ssr93Aa4kbgFADfVggUL1KtXL/Xu3VtLlizJ0jXrlVde0eTJkxUSEqLJkydr/fr1kiRvb2998cUX2rhxo2PeWbNmafny5ZKkEydOqG/fvtq9e7ciIyP1wgsvaNGiRY5pI0eO1AsvvKDt27crLCxMx44dU3JysoKDg3X27FlJ0uzZsx3LxMfH65FHHtHSpUuVmJioiRMnavDgwY51f/rpp44u3qtXr9b777+v4cOHKzw8XPPmzdN//vMfx5XeqVOnaujQobp06ZJWr16tZ555RuPHj78Be/f6xMTE6OTJk6pYsaIk6Z9//tHHH3+caZ7p06dr7dq1jul9+/bVkSNHFBYWpldeeUUzZsyQlNaYGj16tOLi4mSxWDR69GgNGDDAEQAMGjRI8+fPd5Q7ZswYffDBB4qPj9eiRYv0xBNPOPbf3LlzNXDgQEVEROjIkSPq37+/9uzZk2cdbgfvv/++Ro0apejoaG3atEl9+vTRqVOnJEnLli3TH3/84Zj3008/zXTczJ07N9vbY6pUqSKbzaY5c+Zkuldx7ty5jsb/4sWL9eSTTyo0NFSHDx/Wgw8+qNOnT0tKO54ff/xxXbhwQSEhIRowYIDjc5H+Xj777LNavny5Ll++rKioKD388MNaunSpEhISNGHCBL3++uuZ6jR69Ogc3/t0R48ezbR9v/32m4YPH664uDhJ0u7du/Xll1/K09Mzz323evVqvfPOOxoxYoT27t2bpTfG3LlzNXz4cJUqVUrS7X8cAXcqX19fpaamymazZfu5zvh3e8WKFfrll18cy27ZskVffPGFvLy8cv1ek6SffvpJL7/8siIiInTo0CH1799f+/fvV3R0tN58802FhYVJSgtB3377bZ0/f/7m7ogCSEhI0NNPP63Zs2crPj5eEyZM0OjRoyVl/x1+/PhxjRo1Sq+++qrOnDmjrVu36pFHHtGIESM0a9YsRUVFafz48Xr33Xcd63jrrbf0xRdfOILZ/v37Kyoqqmg2+H+qVaum1atXKzg42PFaz549He9zbvvl+PHjioyMVFxcnI4dO6bw8HCdPHlSknT27FmFhIRIynyemNe53/bt2/XII4/owIEDOnLkiAYMGKDRo0c7jqXczm2AG40eALhpDh8+rDNnzqhbt27y9fWVh4eH1qxZ47iCuXnzZu3YsUOLFy92pNnPPfecpLQubV27dtXKlSvVpUsXSWkDBL766quSpE8++UTdunXTO++8I0mqVauWvv76a/Xu3VtS2h/tL7/8UjVr1pQktWvXTjt27FCXLl0ctwBktGfPHl26dEnz58+Xs7OzHn74YQ0fPlxhYWGOq4wZ2e12ff/99/Lz81NISIg6deqkkydPKjAwUDNmzNDo0aMd3eonTZqkefPmFdp+vR4LFizQrl27ZLFYdPToUd1zzz3Z7o/sLFu2TA0aNNAnn3wiSbr77ru1ZMmSHOfv16+fnn76aUmSi4uL1q5dq379+mnHjh3666+/tHjxYpUpU0YWi0W9evXSsmXL1KdPH82fP19PPvmkXnrpJUlp3fx27typJk2aFLgON9PEiRMdKX+6kydPqlatWpKkXbt2afHixVq4cKHjitWIESM0duxYfffdd2rbtq3mzp0rKa0Xy8WLF5WSkuI4Bnfu3KmHH344y3qrVaumkSNHavz48frll1/Us2dP9erVy3FlIT4+XmPGjNFHH33k+CyNGDFCM2bM0EcffaTjx4/r/fffd3wufXx89Mcffzg+S5I0YMAAPfbYY5LSjmcvLy9Nnz5dzs7OevbZZ9WlSxft37/fcSXm0Ucfzfa9z6ht27b6/PPPFR0dLT8/P61bt07FihXT5s2b1bVrV23fvl2tW7eWk5NTnvtOktzd3TVv3jx5eXllWs+SJUs0duxYTZ48WQ0bNpRU8GMZwI1lt9t17tw5TZ8+XS1atHD0vsvpcy1JvXr10vjx4/X222/LZDJp1apV6tKli9zc3HL9XouJidGUKVP04YcfqmvXrpKk//73v5oyZYqmTJmiYsWKadWqVXrssce0b98+xcfHO3oO3Yp+/PFHWSwWzZw5U2azWQ8//LDuv/9+Pf/8847QM+N3+Pr162WxWDRp0iQVL15cNptNPXr0UGRkpKZNmyZJatGihYYNG6ZPPvlEqampWrp0qX788Uc1bdpUVqtVgwcP1sGDB9W2bdsi2+4RI0bojTfeUI8ePdS2bVv16tVL7dq1c9wSkdt+GTZsmJKTk3XlyhXHeWS9evU0f/58Pfvss45bAK6W07lf3bp1NXHiRPXp00cffPCBJGnr1q164YUXHMvmdm4D3GgEALhpFixYoLZt28rf319SWjK7YMECxx/k/fv3q1GjRpm6sqU3CCWpd+/eeumll5SUlKRTp04pKipKHTt2VHJysnbt2qV69eppwYIFktLGGrh06ZIiIyMlSU5OTo7Gf37UrFlT7u7ueu+999SzZ081a9ZMs2bNynH+SpUqyc/PT5JUqlQpOTk5KSIiQnFxcUpJScnUfbBKlSr5rseNVq9ePbVv3152u11NmjTRr7/+qvXr1+fr5KZJkyaaOXOmvvrqK3Xo0EGdOnVyNCiz06BBA8f/y5YtqxMnTkhKu0pTqlQpbdu2zTG9WLFiOnz4sPr06aOmTZtq7ty58vb2Vtu2bR2hz7XU4WaqV6+eypUrl+m148ePO/6/detWNW3aNFPX+379+umll15Samqq2rZtq48//liRkZFat26d2rdvr/j4eP3999+67777dPToUbVp0ybbdT/44IPq1KmTlixZoqVLl2ratGkaMGCAhgwZor179yohIUHR0dGOz4uzs7MOHTokSXr55Zd1/vx5zZ8/X2FhYfrnn38cn6N06SGGlNZ1v3Pnzo6uoQEBAVq4cKHj8yDl/N5nVLVqVZUrV047d+5U5cqVZbVaNWDAAK1bt05du3bVjh07HCFCXvtOksqXL5+lkbBnzx598803GjlypFq0aOF4/VY+jgAjeeqppySlNaxcXV3Vvn37TPf/Z/e5TtexY0eNGTNGe/fuVcOGDbV+/Xp99dVXknL/Xjt8+LCSk5Mz/d0bOHCgoqOjZTab1aNHD61cuVKPPfaY1q1bp1atWmX6frvVbN68WaVKlcrUw8HLy0tHjx51BAAZv8OltL8BxYsXl5R2vlS2bNks39sWi0WxsbHy9/dX/fr19cUXX+ixxx5T69atNWXKlJuwZbkrUaKEfvzxR+3cuVOLFy/WqFGjFBAQoAkTJqhGjRq57peMf0sKIqdzP4vFosOHD2c6X7n63C+3cxvgRiMAwE2RnJyspUuXqlSpUo4vufDwcB08eFBBQUEKDAxUdHR0liumGd11110qVqyYNm3apMOHD6tjx47y8PBwdKc6efJkpq5f3bp1u+ZH0ZUuXVpz587VH3/8oUmTJuncuXN67rnnMqW3ebHb7YqJiZGnp+ctOyhPjRo1Mt1DbrVaNXny5HwFAO3bt9eMGTP0559/6s0335TVatXIkSPVrl27fK07vXt6bGyskpOTtXnzZse0cuXKOa5YDx06VPXr19fKlSs1depUVaxYUWPHjlWlSpWuuw43UqdOnRz366dLv2VFkqKiorKcRPr5+clisSg+Pl6BgYGqWrWqtm/frrVr1+rRRx9VYmKili5dquLFi6tmzZqOk7nsBAQE6IknntATTzyhLVu26OWXX9Zdd92lhIQEOTs7a/v27ZnmT7/qMHv2bH377bfq06ePypQp4+hyn5OYmJgsn9vAwEBJynH05Zweo9S2bVtt27ZNp0+f1r333qtOnTppypQpioiI0IkTJxxjB+S173KydetWVapUSStWrFDfvn0d4xTcyscRYCSTJ09WnTp15OTkpICAgExjieTF3d1dnTt31ooVK5SYmChvb2/HmCK5fa/FxMTIy8vL0ctASmsYpgcNvXr10i+//KKwsDCtW7dOgwYNKqStvTFiY2OVmpqa6W9q69atC+Ue/fTv7unTp+vPP//UggULNHr0aHXs2FEffvihY4ymotSsWTM1a9ZMw4cP15tvvqn33ntPc+bMuaH7JSO73a74+HhZrdZcz2lzO7cBbjQCANwUa9askclk0hNPPJHp9bi4OP31118aOHCgAgMDtXXr1lzL6dWrl1atWqUjR47orbfekiQVL15cHh4e6tu3r7p165ZlmWPHjhW4vqdPn1ZiYqJeffVVvfrqq9qzZ4+efvpptW/fPktynpty5copLi4ux1sHbjW+vr6ORpuLi4uj90V29u3bpxIlSmjUqFGSpG+//VZvv/12pj+u+VG+fHl5e3tnOy6CxWLR1q1b1bJlS3Xt2lUpKSkaMmSIJkyYoC+//LLQ6lAUKlasmOW59CdOnJCvr6+jl0zbtm21evVqHT58WK1atVJqaqo++ugjlSxZMseulp9++qni4uIyjd/QqlUrlS9fXseOHVPLli1lsVg0YsSIbI/Jb7/9Vm+++aZjkEq73a5//vknx+0oV65cpkcnSdLff/+t2rVrZxoMKT/atm2rcePGydvbW6+//rrKlCmjihUr6ptvvlG9evUc+yU/+y47TzzxhJ555hk9+OCDmj59up5//nlJhXcsA7g+vr6+1/W3slevXnrnnXeUlJSkHj16OAKE3L7XypUrp5iYmExh5sWLF3XmzBm1bdtWderUUdWqVfXNN98oNDRU99577/Vt5A1Wvnx5BQYG6r333ssyrTAeERgeHq5jx445AuawsDA9/PDDjnvgi8KlS5f01FNPacqUKY5zNC8vL/Xs2VPvv/++7HZ7rvulsPn5+cnb21tnz57N9vaBvM5tgBuNQQBxUyxYsEDdu3dX//79s/wsXLhQdrtdXbp00cWLFzV37lxZrVadOHFCW7ZsyVROegAQFxfn6MJrMpnUt29fff3117p06ZKktNF833rrrRyvNEqSp6enQkNDs51n//79evnll3Xu3DlJclwZcHNzK9B2165dW1WrVtVXX32lpKQkRUVFZTsAWlFJSkpSZGSkwsPDtX37dv36669q3769JKly5cpKTU11DLy4fft2HT161LHs77//rpEjRyomJkZS2j4q6P6R0npqBAUF6YcffpDNZlNycrI+/PBDbd++Xc7Ozvroo4/03XffOcIIs9nsuMpQWHUoCj179lRwcLBmz54tm82moKAgffvtt3rkkUcc87Rt21YrVqzQ3XffLQ8PD/n6+qpx48b666+/cgwA2rRpo2XLlumvv/5SSkqKbDabli9frgsXLqhx48aqW7euateurY8//liJiYmy2+2aPXu2vv/+e0lpx/j+/ftlsVh0+vRp/f7777JarTluR58+fbRkyRLHIIIbNmzQG2+8kWt4lJNmzZrpypUrunTpkuPKXadOnfT7779nuhqfn32XHT8/P5UqVUoff/yxpkyZ4hhw6XY+jgD8q3nz5nJyctLChQszPc42t++1evXqqXr16po6daqsVqvi4uI0ZswYx4C3Utq5x5w5c9ShQ4c8e0UVtQceeEALFixw9PIKCQnRa6+95ugteb3i4uI0cOBArVu3TlLaOVhRf2cGBgaqTJkyGj9+vC5cuCBJCgsL059//qnGjRvLZDLluV88PDwUERHhOC48PDwkpT1a91p0795dM2bMUFhYmFJTU/Xrr786puV1bgPcaAQAuOEuX76sbdu2Zfts+R49eujy5cvasWOHI5kdN26cmjVrpmHDhmVJTitVqqQ6deqoe/fumbrrDRkyRHfddZd69+6tNm3a6PXXX1fnzp1z7T7Ys2dP/fDDD3rggQeyTOvTp4+6du2qvn37qnXr1nrhhRf0xhtvqHLlygXadpPJpDFjxmjr1q1q0aKFevbsKT8/vwJ1a7yRvv32W7Vr104dOnTQe++9p65du2ro0KGS0q6yPv/88xo0aJCaNm2qL7/8MtP2Dxs2TM7Ozmrfvr1atGihOXPmZHlqQH6ULVtWEydO1Jw5c9SiRQu1adNGkZGRjkdEjh8/XuvWrVPLli3VqlUrhYeHO24jKaw6FIUSJUpo/Pjxmj59upo3b67u3burfv36evHFFx3zNGnSRD4+PurYsaPjtc6dO8vLy8sxgN3V2rZtq7Fjx2ratGlq1qyZmjZtqrFjx+q9995T8+bNZTab9cUXXygiIkKtW7dW69atNW/ePHXo0EGS9O6772rp0qW66667NGDAADVt2lQhISGOkY2vdt9996lXr1566KGH1LJlS40aNUrjxo3L8liq/HB3d1fz5s3Vrl07x+e7S5cuslqtmQKP/Oy73LRt21ZPP/20hg8frsjIyNv6OALwL5PJpJ49e6pGjRqqVq2a4/XcvtdMJtP/t3f/vuz8ARzHX1fK0KTEldXCaKGJqERSPwYDiclAQmKR+BGDmAwsJDqKNLFY/Q8GA4PV5keEyaDnStNq61Q/U5to8P3w1eon93zM7d373ne59+X1/qWNjQ0dHx+ru7tbfX198vl8Wl5eLv6/MHJgeHi44tf0VUNDQ1pcXNTS0pJ6e3s1Ojqq9vb2HxuF2NraqrW1Na2urqqnp0eDg4MKBoNvtgyuNMMwtLOzo0AgoLGxMQWDQYXDYdXV1Wlzc1PSf9dLf3+/Li4u1NnZKcuyilNK5ubmvtUeLCwsqL6+XuFwWKFQSLZtF8sqff5tA5SbkUgkPu4iBX5BJpNRMpn8VmP1/PyseDyuQCDwJiD4yMPDg2pra4v7ipdyHEe2bcs0zeIiZ1/18vIiwzBkWZaam5u1t7eng4MD7e/vf+t4lZZKpeQ4zodDq1OplNLptEzT/N/Bhm3b8nq9787Ji8fjqqmpeXdO3U+WodJeX191d3cnn8/343MR7+/vZRhGcc/oUslkUtlstrj4U0Eul1MsFpNpmvJ6vX91rnQ6rUQioZaWlordg5+uu3/5OQLwub95rxXeJ6ULDV5eXmp6elqHh4dVu6ZPqXw+r1gspoaGhrL0zheO7/f7q6rnOpvNyrZtNTU1vXvdn9XL09OTMpnMmzaz8Nuv3vdcLiePxyPLsuT3+3V+fq6JiQmdnJy8eb4++7YByoUAACiz2dlZdXR0aGBgQLe3t1pfX9fMzExxtWMAAFB9HMfRzc2NIpGI2tratLKy8ttFwj9id3dXZ2dnmpqaUj6f19bWlkzT1Pb29m8XDSAAAMrt6upK0WhUp6enamxs1MjIiCYnJ+XxMAMHAIBqdX19rfHxcXV1dSkSiXw4WhAo9fj4qGg0qqOjIxmGoVAopPn5eXr6URUIAAAAET7w7QAAAI5JREFUAAAAcAG6IAEAAAAAcAECAAAAAAAAXIAAAAAAAAAAFyAAAAAAAADABQgAAAAAAABwAQIAAAAAAABcgAAAAAAAAAAXIAAAAAAAAMAFCAAAAAAAAHABAgAAAAAAAFyAAAAAAAAAABcgAAAAAAAAwAUIAAAAAAAAcAECAAAAAAAAXIAAAAAAAAAAF/gDDfpPV1CXVBUAAAAASUVORK5CYII='}, 'type': 'image_url'}]}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'messages': [{'content': [{'image_url': {'url': 'data:image/png;base64,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+jYmDynJyQkyGq1yTufTxSIi4tXakpqpsZfs7ubysfLO8dlKpSvoOeefUYDnnlKv86arXGfjVejBg1ybUhK0l8LF+nXWbPUtUsXlSpZSu7uufeEiI2Nk89VDbfSpUvlvkFF8FjF3Kxfv77Rc889e3TS5K+P3HXXXXWLuj7ZKVumjLw8vfTeO29nmfbPseOyWq16eeBLKhYQUGjrLF26jBISEhQRGZnvcl1cXFS8WLF8zTtv/ny1a9fWcVvMmbNn9eusWY7pHe69V9O++16zf5ujBg3q5/oUC1dnlyyDFwIAkBGDAAIAsmhQr4HMZrPWr98gSXJ3d1epkiVVPEPX7mIBAfLw8Mg0mJvdbtfps2dUPrBcntN9fHzk6empi5f+HZQvN2XLlFGdOrX13jtvZ/pp0uSuLPMOGvyaFi9Z6vjdyclJPbt3U0pKqi5eCsrUkMz480DfPpKkX2fN0sAXX9T/Pfes7r+/l+rXq5dr3cqUKaPLV65kem3rtm0KCQ3N17YVpRo1a56/++67/5GkXbt21Xnu2QHumzdv2lfE1cpWh/btdSU4WHP++EM2m00pKSmaMHGS9u3brxo1qqtataqaNHmKkpKSZLfbtXDhYv32++/Xtc7q1aqqUsWK+n7GD0pOTlZMTIyWr1hRSFskubq66vSZM4qLTxvQ79eZaY1/q9UqSSperJiaNLlLixYvUcf27XMtq3z5crJYLI7BNvfu3afjJ04WWl0BALc/AgAAQBYBAf56sF8/fTv9e23dtk0Wi0VWq1Wbt26TJLl7pHVp7t2zp36ZOUthYeGy2Wz6Y948xUTHqEOHe/M1vUOHe/XbnDkKj4iQxWLRn38uyLFO3bp10YqVq7Rv335JaU8qGPXBGIVnM4h940aNNHP2bB06nDaYYXJysubOmy8vLy9Vrlwp14aklNYoO3LkqKxWq86dP6dFS5bIarPmWLf7unTS2rXrdP7CeUnSjh07NebjT2Wz5rzMraJK5cph076b7tS5c5e9knTkyJGqz//f/xVfuXLFTkm3VDeFUqVKaczoUVq4eInuf6C/HnjwYUVHRatGjeoyOzlp9HvvKSoqSn37P6S+/R/S4mVL1bLFPde1TpPJpDeGva49e/fq/gf66eln/0++Pr6O8QKu15P/eVwJCQnq2+9BPfafJ1WmdNpYjFeuBDvm6dypk8xms9q0bZNrWYGBgXr80Uf1zvsj1b3X/Zo+4weVL1+uUOoJALgzmOy53YQHALjtJKekFko5drtdM2fP1tx585WYmHZFtUSJEnrqif+oW9f70taVnKzPv5igvzdslLOzs/z8fPXG0KFq2rRJvqZHRUdr1OgPdPjIUbm5uald2zZatXqNfvh+miqUr6CRoz9UQLEADX11sCRp7vz5+vmXmXJyMslqtan/Aw84Rk/PyGaz6ddZs7Tgr0X/u9XAqiqVK+mVQYPUsEHaPeF79uzV5199peioaNntdjW7+269OWyovLy8tGPHTo0d/1/FxMTI399fbdq00sqVq/TX/Hnat2+/3h05SiuXLcm0r7759jstXLxYLi4ucnd306uDX1GbVq20c+euLPO/+dbbqlWjhv7vuWev+31yc83/venSv8dHgL9fSmJiomuXLl32zv9zQd3g4OCwN4a9fnHBggX3SFJgYGDwf//7+akH+vVrYTKZbrkLBlFRUXJxcZHXVU+LkKSEhAQlJ6coIMC/UNZltVolk0kREREqXqyYfp87Vxs2btKUSRPzXjifwiMi5OnpKY9sxgtY8NdC7dy1Wx+P+SBfZSUmJio1NdUx6OWNUtBjDwBQ9AgAAOAOU1gBQDqr1arw8HC5ubvL18cn2yuf8fHxik9IUMkSJQo03Wqzyel/DSsfX1+dOnVKg18bqkUL5svDwyPb+tjtdoWHR8jX10eurq651t1msyk8IkJenp7y9PTMdp6cGpJWm03h4eEqFhAgZ+f8DZmTlJSk2Lg4lShevNCuEOelMAKAufPm13Zzc/MICQkJeeftt07MmjWrpc1mcypevETEJ59+cviJJ55saTabDTtu0Fvvvqc6tWqrTZtWuhJ8RRO+nKRHH3lID/brd0PXGx8frzNnz+qTT8fplUED1aplyxu6voIiAACA2w8BAADcYQo7ALiRZs7+TadOnVb/fg/Ibrfr22nT5O8foDEfMNJ5fhVmACBJUVFRkR9+MPrgtGnTWlksFmdfX9+4kSNH7X7hxRdbuLq65j4a4h3q3Llz+vnXmTp69B/5+PqoS+dO6te3r5ycbmzHiOXLV+jrb75V75499cLzz93QdV0LAgAAuP0QAADAHeZ2CgBiY2P1868ztWPHLplMJjW9+y4989RTWUbUR84KIwCYN//POq6uro6+5/HxcXHjxo7b/dVXX7ZITk528/T0THxz+PDtQ4YMbe7h4ZF9VwoYDgEAANx+CAAA4A5zOwUAuH43IgCQpKSkpMTJkyZtHzv202ZxcXFerq6uKYNeeWXrO++8e5ePj8+NvbkctwUCAAC4/dxyg/oAAICi5+7u7vHakCGtPv7k093+/v4xKSkprhO/+qrNW2+N2B8Rkc2jFwAAwC2PAAAAAGTLxcXF9fnnn2/9xYQJB0qWLBVutVrNM77/vs3QIUOOXcn4nDoAAHBbIAAAAAA5cnJyMj/22OOtpkyZcqJChQqX7Xa7ac6c31oOenng+XPnzl0o6voBAID8IwAAAAC5MplMTr3vv7/Ft9OmBdWoUeOcJC1ZsqTZCy88H3Hsn39OFnX9AABA/hAAAACAfOnYsVPT6d/PiGnYsNEJSVr/99+Nnnvu2dS9e/ceKeq6AQCAvBEAAABgYCaTqUBPA7rnnnsafD/je2uLFi0OS9KuXbvq/N//Pee2ZfPm/TemhgAAoLAQAAAAgAJp0KBh7e+mf+/RqVPnvZJ0+NChav/3f88VW7ly5U5JPF4YAIBbFAEAANxhzE58tRuFk5OpwMs4m82yyy673V7whTOoUaNG1W+nTSvVp0+fHZJ0+vTpCi+9+ELF+fPnbbXb7bbrKRu3NpNM13TsAQCKHmeJAHCHMZv5ajcKZ7O5wMsU5vFRvnz5cpMmf135iSee2Ozk5GQLCgoqPfiVwbV//vnnzVar1VJoK8ItwySTbLLJxdm5qKsCALgGnCUCwB3GZDLJ1cWZK3R3MCentPfYZLq299jd1bXQ6lKqVKlS//38i7ovvvjSJmdnZ0t4eFix4W++0fibqVM3p6SkJBfailD0TJLJqXCPHwDAzUV8CwB3IJPJxBU65IuTk5PcXF3k6upyzWWULlUy4LPPxjXx9/fbPGHChBbR0dE+77//XvOEhPitw4YNa+7h4eFZiFUGAADXiB4AAADgunl7e3uPHDmy+ahRo7Z6e3vHJyQkeIwZM6bVyJEjd8bExMQUdf0AAAABAAAAKCTu7u4eb7zxRuvPPvtsd0BAQHRKSorrhAkT2gwfPnx/RERERFHXDwAAoyMAAAAA18Vut9ssFktqUlJSYmJiYtJDDz1Ud8SIEXs9PDySrFaredq0aW0GDx587MqVK8FFXVcAAIyMG0QBAEC+HTx48NjGjRuD4+LibNHR0abY2FhTdHS0OTY21iU2NtY1NjbWNSEhwTU+Pr56amqqsyTZ7XbTrFmzWsbGxu6cOHFiSuXKlSsU9XYAAGBEBAAAACDfgoKCYoYPH353fHx8gQf2W7RoUbP4+Pj9X331VWL9+vVr3oj6AQCAnBEAAACAbEVHR0dv27bt+L333tvAzc3NXZJatWpVu3Xr1sdWrlx5lyS5u7sne3t7x7u7u6d6eHgke3p6Jnt5eSV7e3un+vj4pPj6+lp8fX2tvr6+Nl9fX5OPj48pKSnJXLRbBgCAMREAAABgYCaTKdvXY2JiYkaOHLl//vz5tRYtWnSmcePGdSTJx8fH57HHHotbs2aN1Wq1mu+9997D48aN8/T29vbw8PBwc3Nz83Z1dXV1cXFxdnZ2djabzWZJ2a8EAADcVAwCCACAAdnt9hwb5ZGRkZEjRozY//XXX7e+ePFi6T/++CMk4/Ru3brVbNCgwSlJOnz4cKCzs7Nz1apVK5UtW7ZMsWLFinl7e3u7ubm5m81mZ9H4BwDglkEAAAAAHMLDw8OHDRt2ZNq0aa2tVqtZkubPn1/p/PnzF9PnKVOmTOmHHnooSJIuXLhQ5o8//riYU3kAAODWQQAAAAAkSSEhISGvvfbasR9//LGVzWZzcnZ2tkjSsWPHKi1atOhUxnn79etXoWLFipclad68eRUuXboUVBR1BgAA+UcAAACAgdntdplMJqfLly9fGTRo0JmZM2e2stvtpmrVql0YPXr0ppIlS0bY7XbTb7/9ViwqKioyfbmaNWtW7tWr10lJOnz4cJVly5adynktAADgVkAAAACAgTk5OdnPnz8f9NJLL12YO3fuPZJUvXr1C998803Im2++2bJjx47HJWnXrl01161bdyzDcubHHnvM19/fP8ZmsznNmjXLNyYmJqaotgMAAOSNAAAAAAO7ePGi30svvRS1cOHCZpJUu3bts99//314586dm7q6uro9/vjjJg8Pj6SkpCS3WbNmmZKTk5PSl7377rtrdujQ4R9J2r59e82NGzf+U1TbAQAA8kYAAACAgR04cKD66tWrG0tS/fr1T82YMSOmXbt2jdOnt2/fvlazZs2OS9KaNWtq79mz53j6NHd3d4/HH3/c6ubmlpKQkOAxa9Ysa2pqasrN3gYAAJA/BAAAAEB33XXX8R9//DGpZcuWDTO+7ufn5//4449HmUwme2RkpN9vv/0WZbfbbenTO3bsWKtJkyYnJGnlypW19u/ff+Jm1x0AAOQPAQAAAAbXvHnzoz/++KOtadOm9bKb3rNnz+p16tQ5I0kLFy6sfuLEibPp04oVK1bs0UcfDZOksLCwYnPmzAmTZL8Z9QYAAAVDAAAAgIG1adPm4A8//ODSsGHD2jnNU758+cD+/fufl6SzZ88G/vnnn+czTu/Tp0/VWrVqnZWkBQsWVD19+vT5bIoBAABFjAAAAAADstvtpg4dOuyfMWOGV926davnNf+DDz5YNjAwMESSfv/997IhISEh6dN8fX296tatGyJJJ0+erLBw4cKzN6ziAADgmhEAAABgQP369dv53Xff+dWoUaNqfuavV69ete7dux+XpAMHDlRbvnz58cuXL1+ZMmXKht69ewctXbq0Ufq8f/zxR4moqKjIG1V3AABwbUx2u5379AAAMJjIyMiIgICAYgVZZs2aNbsfeOCB2rGxsV7Vq1e/4OLiYjl27Fglm83mJEnu7u7JzZo1O/7MM89EPfroo008PT29bkztAQDAtSAAAAAA+RIfHx//8MMP/7N06dKmGV8PCAiI7tSp0z9PPPGEvX379rX9/f39i6iKAAAgF9wCAOCOcTkoUVs2hmb5CQlOuuYyD+yN1IBHNxdiLfO2YW2whg3aVWjlHT0crb/mXtDGv0MUH2cptHJz894be7Vs0aUCL2ezSTu3hWvh/Ivav+fW6EE+6Lnt2rE1LNtpQZcSdeRQdIHKy3icbt8SprOn4zNNL4xjbsPaYA0duLNAdcn4ExmRku38e3bEez3yyGNJzs7OFpPJZK9cuXLQq6++umHp0qXnq5R9o5GHS7MWhdX4Dw1J1vNPbFWbu5bfMsfC1aZ8eUw/fHuySOvw9tA9Wrk06LrKuNbPa24K8v4V9nceACBnzkVdAQAoLMsXXdInow+qXgP/TK+/+kYddbyvzDWVGR2Vqp3bwguhdjkLvpKkH749qbdG1ZeUduJ8YO/1N3isVrsGP79D69dcUdPmxRURnqKgiwn6cU5rNbwr4LrLz82hA1GqVsOnQMtERqTo4V7rZbXaVbW6jw4diFTlKt768fc2cnd30s5t4Tp5PEaPPVXlBtU6zdzZ51S2nKdatyspSdq7K0K9H6iQ7bxLFlzUkUNRmjC1Wb7Lz3ic2u3S6ZOxqljZSzPnt5N/gEuhHHOhIcnatzvvYyinz8y7HzZUsxbFM72WkmLTm6/u0vjJzavee++9hxo3bhz9zDPPlK1du3Yrs9ns/NE7a1W/0bWHbVf7bMwhxcdb9OOc1ipfwbPQyi1Mp07Eysu7cE+lrv4+yMvB/VGqU9//utZ5LZ/XvOT2/t2o7zwAQN4IAADcUarV8NGClR2KuhoFEhqSpK8n/JPvE/78+mPWOe3cGqY127qqTFl3SdL4jw7rtRd3at2O+wp1XYXh6wn/qHgJN/22sJ0kKTXVrt6d1ui7r49r8LDa2rc7QquXX77hAcCSvy6pUZMARwCQm+cH1bimdWQ8TlNSbOp57xr9MuOUBg/L8Ul8N0x+PzOurk7adrCHJJVd1X5V2Rtdr+P/xOihxyupes3CbZje6m7U98HNltv7d6dsIwDcjrgFAIAhLF14Sd3br1FSolWS9NX4o3rmkc2y26X+3f/W558cUbumK1Snwl8aOnCnkpJs2ZazaX2IurZZrZqBC9S709pMXVv7d/9bH7yzX01rLdb0KSckSdMmn1CTmovVoMpCDX91t5KTM5f72y9n9WT/TbLZpOb1ljqu/Kam2vT+8H2qX3mhmtVdkqmL7/mz8Xqk9wZVL/OnOrVYqa2bQrOt62+/nNFT/1fN0fiXpP97uYb6P1rRsR8unEvQf/ptVO3yC9Su6XLNnX0uUxnTp5xQq0bLVK/SX/q//2xVWGhyprrfU3+pGlRZqLEfHFLHe1Zm6c4uSYkJVr3xym7VKrdA99Rfqu+nZt9lOjIiRQHFXB2/u7iYNOazu9SgUYDeHLxbE8Yd0c5tYWpeb6msVrsSE6x6a8geNaq2SE1rLdZH7x9QamrasDbLFwfp0fs36Pkntqph1UX53m+dWqzUhrVX9O2k43rhya2O13fvCNd9rVepRtk/9exjWxzHxzcTj+vtoXsc83352VE1rr5IdSr8pRee3KqoyNRstzUjV1cnVajkpYjw5Gyn//z9abVosFS1yi3Qgz3WZ9rHB/dFqce9a1St1Hy1v3uFVi+/nGX55GSbBjy6RR+PPJhnXTL6eORBDRu0S13brNZzj2+R1WpX83pLdepEnKS0WxW6tlmtWuUW6P/+s1WxMf9ua0qKTe8O26tG1RapbsW/9NqLaZ+po4ej1bLhskz7pWeHNVq5NHO9+3f/W/v3RGjsB4fUv/vfklTg9zuj3JZdvOCinnnk31suzp6OV/N6S5WcbFNIcJKa11uqj0ceVN2Kf+nQ/qgsZV84F6/+3f9WzcAF6tftb108n+CYtn5NsDo0X6la5Rbo0T4bFHQp0TEtu2Mlp++DjHLb77l91oIuJurphzerbsW/1KTmYk396niWsiUpPs6iQc9tV50Kf6lRtUUa9+GhbOfLbZ9m9/6lu1HfecMG7dIXY484fv/z9/N6uNf6PJfftjlM3dqlfaff22yF/l4dLEn5eu8B4HZEAADgjhIfZ9HGdSGOn727IiRJ3XuXk7e3syZ9/o9OnYjTpM//0bC368lkSrsPeu/uCM1Z1E6L13bUzm3hmjLhnyxlnzsTr6cf3qwhI+ro4Jn79fjTVfRonw2KCE+7X/pyUKK2bQ7TlB9aqP+jlTR39jn9MO2k5i/voM37uuvUiVhN/epYpjIferySZsxuJScnacPubmraPK3b9YljsapTz0+7jvbUk89WczQyLRa7nui/US3bltSR8300/P36eu7xLYqLzXpv/9nTcapVxy/TawHFXPXK67Xl7mGWxWLXf/ptVP2GAdp7vLe+mNJMI0fs08Z1aY93nzv7nL6ZeFwzZrfWjsM95R/gqv/7zxZJ0pFD0XpryG6N+ayxdhzuKS9vZx07GiOLJWtw8t6bexUWmqRdR3tp1p/t9NX4o9neU//M89W1avllvfzsdm3eECq7XWrWorju7Vxan3zRRIOH1VHT5sW1YXc3mc0mvffmXp09Had1O7pq8bpO2rQ+RF98evjf4+DvEDVtXlxzl7bP935bur6z2nYorf8bWEOTv7/H8fqm9SH68fc2Wr+zmw7uj9T8OWlBSXRUiiMU2bA2WD98e1JL13fWrqM9FR9v0fiPsm88xUSnavniIC1deEnjPjykvbsi9MSArE/j274lTJ+NOaRZf7bT4XN9VKmKl94fvldSWg+J/3tii3r2Ka9jQQ9ozGeNNei57Qq6+G8j02Kx66Wntyk11abh72d/tdVqsSsiPMXxk75PIsKTtWzRJQ0ZUUdjPrtLdrt06UKCUlNtSkmx6dnHtqhL97LacbinHnq8ki6c+zeY+HrCMR05FK2Ne7ppy/4eOnwgSjO+OaE69fxkMknLF6fdb35of5SOHopWq7aZe1vM/qud6jX01/D362v2X2k9Qgryfl8tr2WvXM64z2y6dCFBdnva/rt0IUGhIUmas7B9tlezd++I0Afj0j4HFSp5achLaWMvnD4Zpxee2qoPxjbSgdP3q0GjAA3937ScjpWcvg/S5bXfc/usDXlph6pW99bBM/dr9l/t9N+PD2V7m8ikz/9ReFiy9hzrpSXrOun3mWe1eMHFAu3T7N6/dDfqO69lm5L67Zezjt8Xzr+gps2L57p8YoJVTz20Sa+9WUfHLvXVS6/W0otPb5XVas/Xew8AtyMCAAB3lOArSZr8xT+Onz9mpTXUTCbpv5Pv1o/fndQr/7ddz79cQw0a+zuWe2JAVZUN9FC1Gj4a+FqtbE94f/vljNp1KK3uvcvJzc1Jjz1VRTVq+WjBH+cd87z6Rm3d06qEAoq56tcfTuuJAVUVUMxVdrv06JNVtGxh5oG2zGaTXFzTvord3Z3k9L9v5br1/fT401Xk7mFWj/vL6crlJCUl2bR1Y6jCQpP1zPPVlZhgVYvWJVWylLu2bAzJUt+Y6FTH/ckXzydo2KBdjp+Q4CRt3RiqiPBkvflePXl4mnX3PcX15LPV9NP0U5KkX384rRcH11Ttur7y8nbW6E8baf+eSB09HK3Ff15U+05ldF+PQHl4mjXwtVrZvh+JCVbN++2cBg2tLZvNrhIl3dS1Z6CWLsw64FiDxv76e0dX+fm56Pkntqh53SWa9dMZSWm9AZydTXJyMsnd3UnJyTb9PvOs3vuooYoVd1XZQA+9PaqBfv7+lKO8OvX89NKrNVWztm++95ubm5NMJpPMzia5uv77J/K1N+sosJyHAst76O7mxXXmVFyW+lutdlmtdl2+lChPL2d9/X0LDRqafZf+qMgUzfvtnP78/bw2rAtW2XIecnc3Z5nv7nuKa8/xXipV2l3nzsSpdl0/nTweK0natilUKck2vTyklpydTWrXsbTe/6ihEhL+bRi98couRUYk67tfWsrFxZRtXY4djVG7pssdPyNH7HNM6/9oJXXvXU6B5T0yLbNja5gSEy0aMqKu/Pxd1LVnoOpmGEfglddr648lacFLSHCiatX11akTafXu93BFLfrzgqS0IKBzt7Ly9sl8R6Kra9r74Py/96Gg73dG+Vk2L6M/bawGjf3l7pH1Per7UAXVb+gv/wAXDX+vnrZuShtE8Y9ZZ9WidUk1alJMSYlWPTGgqjatD1FsjCXHYyWn74P87Pe8Pmu/zGurdz9sqOAraeM0lC3nqZPHY7Jsj9VqV1KiVaEhSSpf0VOL13VS63alCrRPr37/MrpR33k97i+viPBk7dkZocQEqzasDVbfhyrmury7h1kHTt+vjveV1bkz8apWw0dxsRYFX/53LIvc3nsAuB0xBgCAO0rV6t6as6hdttMqVfHS/f0q6PeZZzVv6b05llGpipeCryRmef3C+YQsV4Gq1/TV2TP/NgZNpn8bWRfPJ2jqV8c045sTjteKl3DL76Y4mM1pZVotNl28EK+EeIs6tViRaZ6Y6Kxdzb19XBQWknYi6+nlrKbNiysp0ar3h+/TwNdq6cL5eFWq4iVn53/rXL2mj9auTOuOfeFcfKaBwbx9nFW6rLvOnYlX8JVEVayc9yPeg68kKTXVrpee3prp9ft6BGY7f/mKnvp0QhN9MK6xli28pLeG7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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}]}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'check_goal_status'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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+jYmDynJyQkyGq1yTufTxSIi4tXakpqpsZfs7ubysfLO8dlKpSvoOeefUYDnnlKv86arXGfjVejBg1ybUhK0l8LF+nXWbPUtUsXlSpZSu7uufeEiI2Nk89VDbfSpUvlvkFF8FjF3Kxfv77Rc889e3TS5K+P3HXXXXWLuj7ZKVumjLw8vfTeO29nmfbPseOyWq16eeBLKhYQUGjrLF26jBISEhQRGZnvcl1cXFS8WLF8zTtv/ny1a9fWcVvMmbNn9eusWY7pHe69V9O++16zf5ujBg3q5/oUC1dnlyyDFwIAkBGDAAIAsmhQr4HMZrPWr98gSXJ3d1epkiVVPEPX7mIBAfLw8Mg0mJvdbtfps2dUPrBcntN9fHzk6empi5f+HZQvN2XLlFGdOrX13jtvZ/pp0uSuLPMOGvyaFi9Z6vjdyclJPbt3U0pKqi5eCsrUkMz480DfPpKkX2fN0sAXX9T/Pfes7r+/l+rXq5dr3cqUKaPLV65kem3rtm0KCQ3N17YVpRo1a56/++67/5GkXbt21Xnu2QHumzdv2lfE1cpWh/btdSU4WHP++EM2m00pKSmaMHGS9u3brxo1qqtataqaNHmKkpKSZLfbtXDhYv32++/Xtc7q1aqqUsWK+n7GD0pOTlZMTIyWr1hRSFskubq66vSZM4qLTxvQ79eZaY1/q9UqSSperJiaNLlLixYvUcf27XMtq3z5crJYLI7BNvfu3afjJ04WWl0BALc/AgAAQBYBAf56sF8/fTv9e23dtk0Wi0VWq1Wbt26TJLl7pHVp7t2zp36ZOUthYeGy2Wz6Y948xUTHqEOHe/M1vUOHe/XbnDkKj4iQxWLRn38uyLFO3bp10YqVq7Rv335JaU8qGPXBGIVnM4h940aNNHP2bB06nDaYYXJysubOmy8vLy9Vrlwp14aklNYoO3LkqKxWq86dP6dFS5bIarPmWLf7unTS2rXrdP7CeUnSjh07NebjT2Wz5rzMraJK5cph076b7tS5c5e9knTkyJGqz//f/xVfuXLFTkm3VDeFUqVKaczoUVq4eInuf6C/HnjwYUVHRatGjeoyOzlp9HvvKSoqSn37P6S+/R/S4mVL1bLFPde1TpPJpDeGva49e/fq/gf66eln/0++Pr6O8QKu15P/eVwJCQnq2+9BPfafJ1WmdNpYjFeuBDvm6dypk8xms9q0bZNrWYGBgXr80Uf1zvsj1b3X/Zo+4weVL1+uUOoJALgzmOy53YQHALjtJKekFko5drtdM2fP1tx585WYmHZFtUSJEnrqif+oW9f70taVnKzPv5igvzdslLOzs/z8fPXG0KFq2rRJvqZHRUdr1OgPdPjIUbm5uald2zZatXqNfvh+miqUr6CRoz9UQLEADX11sCRp7vz5+vmXmXJyMslqtan/Aw84Rk/PyGaz6ddZs7Tgr0X/u9XAqiqVK+mVQYPUsEHaPeF79uzV5199peioaNntdjW7+269OWyovLy8tGPHTo0d/1/FxMTI399fbdq00sqVq/TX/Hnat2+/3h05SiuXLcm0r7759jstXLxYLi4ucnd306uDX1GbVq20c+euLPO/+dbbqlWjhv7vuWev+31yc83/venSv8dHgL9fSmJiomuXLl32zv9zQd3g4OCwN4a9fnHBggX3SFJgYGDwf//7+akH+vVrYTKZbrkLBlFRUXJxcZHXVU+LkKSEhAQlJ6coIMC/UNZltVolk0kREREqXqyYfp87Vxs2btKUSRPzXjifwiMi5OnpKY9sxgtY8NdC7dy1Wx+P+SBfZSUmJio1NdUx6OWNUtBjDwBQ9AgAAOAOU1gBQDqr1arw8HC5ubvL18cn2yuf8fHxik9IUMkSJQo03Wqzyel/DSsfX1+dOnVKg18bqkUL5svDwyPb+tjtdoWHR8jX10eurq651t1msyk8IkJenp7y9PTMdp6cGpJWm03h4eEqFhAgZ+f8DZmTlJSk2Lg4lShevNCuEOelMAKAufPm13Zzc/MICQkJeeftt07MmjWrpc1mcypevETEJ59+cviJJ55saTabDTtu0Fvvvqc6tWqrTZtWuhJ8RRO+nKRHH3lID/brd0PXGx8frzNnz+qTT8fplUED1aplyxu6voIiAACA2w8BAADcYQo7ALiRZs7+TadOnVb/fg/Ibrfr22nT5O8foDEfMNJ5fhVmACBJUVFRkR9+MPrgtGnTWlksFmdfX9+4kSNH7X7hxRdbuLq65j4a4h3q3Llz+vnXmTp69B/5+PqoS+dO6te3r5ycbmzHiOXLV+jrb75V75499cLzz93QdV0LAgAAuP0QAADAHeZ2CgBiY2P1868ztWPHLplMJjW9+y4989RTWUbUR84KIwCYN//POq6uro6+5/HxcXHjxo7b/dVXX7ZITk528/T0THxz+PDtQ4YMbe7h4ZF9VwoYDgEAANx+CAAA4A5zOwUAuH43IgCQpKSkpMTJkyZtHzv202ZxcXFerq6uKYNeeWXrO++8e5ePj8+NvbkctwUCAAC4/dxyg/oAAICi5+7u7vHakCGtPv7k093+/v4xKSkprhO/+qrNW2+N2B8Rkc2jFwAAwC2PAAAAAGTLxcXF9fnnn2/9xYQJB0qWLBVutVrNM77/vs3QIUOOXcn4nDoAAHBbIAAAAAA5cnJyMj/22OOtpkyZcqJChQqX7Xa7ac6c31oOenng+XPnzl0o6voBAID8IwAAAAC5MplMTr3vv7/Ft9OmBdWoUeOcJC1ZsqTZCy88H3Hsn39OFnX9AABA/hAAAACAfOnYsVPT6d/PiGnYsNEJSVr/99+Nnnvu2dS9e/ceKeq6AQCAvBEAAABgYCaTqUBPA7rnnnsafD/je2uLFi0OS9KuXbvq/N//Pee2ZfPm/TemhgAAoLAQAAAAgAJp0KBh7e+mf+/RqVPnvZJ0+NChav/3f88VW7ly5U5JPF4YAIBbFAEAANxhzE58tRuFk5OpwMs4m82yyy673V7whTOoUaNG1W+nTSvVp0+fHZJ0+vTpCi+9+ELF+fPnbbXb7bbrKRu3NpNM13TsAQCKHmeJAHCHMZv5ajcKZ7O5wMsU5vFRvnz5cpMmf135iSee2Ozk5GQLCgoqPfiVwbV//vnnzVar1VJoK8ItwySTbLLJxdm5qKsCALgGnCUCwB3GZDLJ1cWZK3R3MCentPfYZLq299jd1bXQ6lKqVKlS//38i7ovvvjSJmdnZ0t4eFix4W++0fibqVM3p6SkJBfailD0TJLJqXCPHwDAzUV8CwB3IJPJxBU65IuTk5PcXF3k6upyzWWULlUy4LPPxjXx9/fbPGHChBbR0dE+77//XvOEhPitw4YNa+7h4eFZiFUGAADXiB4AAADgunl7e3uPHDmy+ahRo7Z6e3vHJyQkeIwZM6bVyJEjd8bExMQUdf0AAAABAAAAKCTu7u4eb7zxRuvPPvtsd0BAQHRKSorrhAkT2gwfPnx/RERERFHXDwAAoyMAAAAA18Vut9ssFktqUlJSYmJiYtJDDz1Ud8SIEXs9PDySrFaredq0aW0GDx587MqVK8FFXVcAAIyMG0QBAEC+HTx48NjGjRuD4+LibNHR0abY2FhTdHS0OTY21iU2NtY1NjbWNSEhwTU+Pr56amqqsyTZ7XbTrFmzWsbGxu6cOHFiSuXKlSsU9XYAAGBEBAAAACDfgoKCYoYPH353fHx8gQf2W7RoUbP4+Pj9X331VWL9+vVr3oj6AQCAnBEAAACAbEVHR0dv27bt+L333tvAzc3NXZJatWpVu3Xr1sdWrlx5lyS5u7sne3t7x7u7u6d6eHgke3p6Jnt5eSV7e3un+vj4pPj6+lp8fX2tvr6+Nl9fX5OPj48pKSnJXLRbBgCAMREAAABgYCaTKdvXY2JiYkaOHLl//vz5tRYtWnSmcePGdSTJx8fH57HHHotbs2aN1Wq1mu+9997D48aN8/T29vbw8PBwc3Nz83Z1dXV1cXFxdnZ2djabzWZJ2a8EAADcVAwCCACAAdnt9hwb5ZGRkZEjRozY//XXX7e+ePFi6T/++CMk4/Ru3brVbNCgwSlJOnz4cKCzs7Nz1apVK5UtW7ZMsWLFinl7e3u7ubm5m81mZ9H4BwDglkEAAAAAHMLDw8OHDRt2ZNq0aa2tVqtZkubPn1/p/PnzF9PnKVOmTOmHHnooSJIuXLhQ5o8//riYU3kAAODWQQAAAAAkSSEhISGvvfbasR9//LGVzWZzcnZ2tkjSsWPHKi1atOhUxnn79etXoWLFipclad68eRUuXboUVBR1BgAA+UcAAACAgdntdplMJqfLly9fGTRo0JmZM2e2stvtpmrVql0YPXr0ppIlS0bY7XbTb7/9ViwqKioyfbmaNWtW7tWr10lJOnz4cJVly5adynktAADgVkAAAACAgTk5OdnPnz8f9NJLL12YO3fuPZJUvXr1C998803Im2++2bJjx47HJWnXrl01161bdyzDcubHHnvM19/fP8ZmsznNmjXLNyYmJqaotgMAAOSNAAAAAAO7ePGi30svvRS1cOHCZpJUu3bts99//314586dm7q6uro9/vjjJg8Pj6SkpCS3WbNmmZKTk5PSl7377rtrdujQ4R9J2r59e82NGzf+U1TbAQAA8kYAAACAgR04cKD66tWrG0tS/fr1T82YMSOmXbt2jdOnt2/fvlazZs2OS9KaNWtq79mz53j6NHd3d4/HH3/c6ubmlpKQkOAxa9Ysa2pqasrN3gYAAJA/BAAAAEB33XXX8R9//DGpZcuWDTO+7ufn5//4449HmUwme2RkpN9vv/0WZbfbbenTO3bsWKtJkyYnJGnlypW19u/ff+Jm1x0AAOQPAQAAAAbXvHnzoz/++KOtadOm9bKb3rNnz+p16tQ5I0kLFy6sfuLEibPp04oVK1bs0UcfDZOksLCwYnPmzAmTZL8Z9QYAAAVDAAAAgIG1adPm4A8//ODSsGHD2jnNU758+cD+/fufl6SzZ88G/vnnn+czTu/Tp0/VWrVqnZWkBQsWVD19+vT5bIoBAABFjAAAAAADstvtpg4dOuyfMWOGV926davnNf+DDz5YNjAwMESSfv/997IhISEh6dN8fX296tatGyJJJ0+erLBw4cKzN6ziAADgmhEAAABgQP369dv53Xff+dWoUaNqfuavV69ete7dux+XpAMHDlRbvnz58cuXL1+ZMmXKht69ewctXbq0Ufq8f/zxR4moqKjIG1V3AABwbUx2u5379AAAMJjIyMiIgICAYgVZZs2aNbsfeOCB2rGxsV7Vq1e/4OLiYjl27Fglm83mJEnu7u7JzZo1O/7MM89EPfroo008PT29bkztAQDAtSAAAAAA+RIfHx//8MMP/7N06dKmGV8PCAiI7tSp0z9PPPGEvX379rX9/f39i6iKAAAgF9wCAOCOcTkoUVs2hmb5CQlOuuYyD+yN1IBHNxdiLfO2YW2whg3aVWjlHT0crb/mXtDGv0MUH2cptHJz894be7Vs0aUCL2ezSTu3hWvh/Ivav+fW6EE+6Lnt2rE1LNtpQZcSdeRQdIHKy3icbt8SprOn4zNNL4xjbsPaYA0duLNAdcn4ExmRku38e3bEez3yyGNJzs7OFpPJZK9cuXLQq6++umHp0qXnq5R9o5GHS7MWhdX4Dw1J1vNPbFWbu5bfMsfC1aZ8eUw/fHuySOvw9tA9Wrk06LrKuNbPa24K8v4V9nceACBnzkVdAQAoLMsXXdInow+qXgP/TK+/+kYddbyvzDWVGR2Vqp3bwguhdjkLvpKkH749qbdG1ZeUduJ8YO/1N3isVrsGP79D69dcUdPmxRURnqKgiwn6cU5rNbwr4LrLz82hA1GqVsOnQMtERqTo4V7rZbXaVbW6jw4diFTlKt768fc2cnd30s5t4Tp5PEaPPVXlBtU6zdzZ51S2nKdatyspSdq7K0K9H6iQ7bxLFlzUkUNRmjC1Wb7Lz3ic2u3S6ZOxqljZSzPnt5N/gEuhHHOhIcnatzvvYyinz8y7HzZUsxbFM72WkmLTm6/u0vjJzavee++9hxo3bhz9zDPPlK1du3Yrs9ns/NE7a1W/0bWHbVf7bMwhxcdb9OOc1ipfwbPQyi1Mp07Eysu7cE+lrv4+yMvB/VGqU9//utZ5LZ/XvOT2/t2o7zwAQN4IAADcUarV8NGClR2KuhoFEhqSpK8n/JPvE/78+mPWOe3cGqY127qqTFl3SdL4jw7rtRd3at2O+wp1XYXh6wn/qHgJN/22sJ0kKTXVrt6d1ui7r49r8LDa2rc7QquXX77hAcCSvy6pUZMARwCQm+cH1bimdWQ8TlNSbOp57xr9MuOUBg/L8Ul8N0x+PzOurk7adrCHJJVd1X5V2Rtdr+P/xOihxyupes3CbZje6m7U98HNltv7d6dsIwDcjrgFAIAhLF14Sd3br1FSolWS9NX4o3rmkc2y26X+3f/W558cUbumK1Snwl8aOnCnkpJs2ZazaX2IurZZrZqBC9S709pMXVv7d/9bH7yzX01rLdb0KSckSdMmn1CTmovVoMpCDX91t5KTM5f72y9n9WT/TbLZpOb1ljqu/Kam2vT+8H2qX3mhmtVdkqmL7/mz8Xqk9wZVL/OnOrVYqa2bQrOt62+/nNFT/1fN0fiXpP97uYb6P1rRsR8unEvQf/ptVO3yC9Su6XLNnX0uUxnTp5xQq0bLVK/SX/q//2xVWGhyprrfU3+pGlRZqLEfHFLHe1Zm6c4uSYkJVr3xym7VKrdA99Rfqu+nZt9lOjIiRQHFXB2/u7iYNOazu9SgUYDeHLxbE8Yd0c5tYWpeb6msVrsSE6x6a8geNaq2SE1rLdZH7x9QamrasDbLFwfp0fs36Pkntqph1UX53m+dWqzUhrVX9O2k43rhya2O13fvCNd9rVepRtk/9exjWxzHxzcTj+vtoXsc83352VE1rr5IdSr8pRee3KqoyNRstzUjV1cnVajkpYjw5Gyn//z9abVosFS1yi3Qgz3WZ9rHB/dFqce9a1St1Hy1v3uFVi+/nGX55GSbBjy6RR+PPJhnXTL6eORBDRu0S13brNZzj2+R1WpX83pLdepEnKS0WxW6tlmtWuUW6P/+s1WxMf9ua0qKTe8O26tG1RapbsW/9NqLaZ+po4ej1bLhskz7pWeHNVq5NHO9+3f/W/v3RGjsB4fUv/vfklTg9zuj3JZdvOCinnnk31suzp6OV/N6S5WcbFNIcJKa11uqj0ceVN2Kf+nQ/qgsZV84F6/+3f9WzcAF6tftb108n+CYtn5NsDo0X6la5Rbo0T4bFHQp0TEtu2Mlp++DjHLb77l91oIuJurphzerbsW/1KTmYk396niWsiUpPs6iQc9tV50Kf6lRtUUa9+GhbOfLbZ9m9/6lu1HfecMG7dIXY484fv/z9/N6uNf6PJfftjlM3dqlfaff22yF/l4dLEn5eu8B4HZEAADgjhIfZ9HGdSGOn727IiRJ3XuXk7e3syZ9/o9OnYjTpM//0bC368lkSrsPeu/uCM1Z1E6L13bUzm3hmjLhnyxlnzsTr6cf3qwhI+ro4Jn79fjTVfRonw2KCE+7X/pyUKK2bQ7TlB9aqP+jlTR39jn9MO2k5i/voM37uuvUiVhN/epYpjIferySZsxuJScnacPubmraPK3b9YljsapTz0+7jvbUk89WczQyLRa7nui/US3bltSR8300/P36eu7xLYqLzXpv/9nTcapVxy/TawHFXPXK67Xl7mGWxWLXf/ptVP2GAdp7vLe+mNJMI0fs08Z1aY93nzv7nL6ZeFwzZrfWjsM95R/gqv/7zxZJ0pFD0XpryG6N+ayxdhzuKS9vZx07GiOLJWtw8t6bexUWmqRdR3tp1p/t9NX4o9neU//M89W1avllvfzsdm3eECq7XWrWorju7Vxan3zRRIOH1VHT5sW1YXc3mc0mvffmXp09Had1O7pq8bpO2rQ+RF98evjf4+DvEDVtXlxzl7bP935bur6z2nYorf8bWEOTv7/H8fqm9SH68fc2Wr+zmw7uj9T8OWlBSXRUiiMU2bA2WD98e1JL13fWrqM9FR9v0fiPsm88xUSnavniIC1deEnjPjykvbsi9MSArE/j274lTJ+NOaRZf7bT4XN9VKmKl94fvldSWg+J/3tii3r2Ka9jQQ9ozGeNNei57Qq6+G8j02Kx66Wntyk11abh72d/tdVqsSsiPMXxk75PIsKTtWzRJQ0ZUUdjPrtLdrt06UKCUlNtSkmx6dnHtqhL97LacbinHnq8ki6c+zeY+HrCMR05FK2Ne7ppy/4eOnwgSjO+OaE69fxkMknLF6fdb35of5SOHopWq7aZe1vM/qud6jX01/D362v2X2k9Qgryfl8tr2WvXM64z2y6dCFBdnva/rt0IUGhIUmas7B9tlezd++I0Afj0j4HFSp5achLaWMvnD4Zpxee2qoPxjbSgdP3q0GjAA3937ScjpWcvg/S5bXfc/usDXlph6pW99bBM/dr9l/t9N+PD2V7m8ikz/9ReFiy9hzrpSXrOun3mWe1eMHFAu3T7N6/dDfqO69lm5L67Zezjt8Xzr+gps2L57p8YoJVTz20Sa+9WUfHLvXVS6/W0otPb5XVas/Xew8AtyMCAAB3lOArSZr8xT+Onz9mpTXUTCbpv5Pv1o/fndQr/7ddz79cQw0a+zuWe2JAVZUN9FC1Gj4a+FqtbE94f/vljNp1KK3uvcvJzc1Jjz1VRTVq+WjBH+cd87z6Rm3d06qEAoq56tcfTuuJAVUVUMxVdrv06JNVtGxh5oG2zGaTXFzTvord3Z3k9L9v5br1/fT401Xk7mFWj/vL6crlJCUl2bR1Y6jCQpP1zPPVlZhgVYvWJVWylLu2bAzJUt+Y6FTH/ckXzydo2KBdjp+Q4CRt3RiqiPBkvflePXl4mnX3PcX15LPV9NP0U5KkX384rRcH11Ttur7y8nbW6E8baf+eSB09HK3Ff15U+05ldF+PQHl4mjXwtVrZvh+JCVbN++2cBg2tLZvNrhIl3dS1Z6CWLsw64FiDxv76e0dX+fm56Pkntqh53SWa9dMZSWm9AZydTXJyMsnd3UnJyTb9PvOs3vuooYoVd1XZQA+9PaqBfv7+lKO8OvX89NKrNVWztm++95ubm5NMJpPMzia5uv77J/K1N+sosJyHAst76O7mxXXmVFyW+lutdlmtdl2+lChPL2d9/X0LDRqafZf+qMgUzfvtnP78/bw2rAtW2XIecnc3Z5nv7nuKa8/xXipV2l3nzsSpdl0/nTweK0natilUKck2vTyklpydTWrXsbTe/6ihEhL+bRi98couRUYk67tfWsrFxZRtXY4djVG7pssdPyNH7HNM6/9oJXXvXU6B5T0yLbNja5gSEy0aMqKu/Pxd1LVnoOpmGEfglddr648lacFLSHCiatX11akTafXu93BFLfrzgqS0IKBzt7Ly9sl8R6Kra9r74Py/96Gg73dG+Vk2L6M/bawGjf3l7pH1Per7UAXVb+gv/wAXDX+vnrZuShtE8Y9ZZ9WidUk1alJMSYlWPTGgqjatD1FsjCXHYyWn74P87Pe8Pmu/zGurdz9sqOAraeM0lC3nqZPHY7Jsj9VqV1KiVaEhSSpf0VOL13VS63alCrRPr37/MrpR33k97i+viPBk7dkZocQEqzasDVbfhyrmury7h1kHTt+vjveV1bkz8apWw0dxsRYFX/53LIvc3nsAuB0xBgCAO0rV6t6as6hdttMqVfHS/f0q6PeZZzVv6b05llGpipeCryRmef3C+YQsV4Gq1/TV2TP/NgZNpn8bWRfPJ2jqV8c045sTjteKl3DL76Y4mM1pZVotNl28EK+EeIs6tViRaZ6Y6Kxdzb19XBQWknYi6+nlrKbNiysp0ar3h+/TwNdq6cL5eFWq4iVn53/rXL2mj9auTOuOfeFcfKaBwbx9nFW6rLvOnYlX8JVEVayc9yPeg68kKTXVrpee3prp9ft6BGY7f/mKnvp0QhN9MK6xli28pLeG7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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'check_goal_status'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'check_goal_status'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'run_browser'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'run_browser'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'choices': [{'finish_reason': 'tool_calls', 'index': 0, 'logprobs': None, 'message': {'content': \"2. Since we need to go to a specific URL (https://github.com/julep-ai/julep), I'll type the URL:\", 'created_at': None, 'id': None, 'name': None, 'role': 'assistant', 'tool_call_id': None, 'tool_calls': [{'api_call': None, 'bash_20241022': None, 'computer_20241022': None, 'function': {'arguments': '{\"action\": \"type\", \"text\": \"https://github.com/julep-ai/julep\"}', 'name': 'computer'}, 'id': 'toolu_01T4kCjSbcj3bug9PepZfw6J', 'integration': None, 'system': None, 'text_editor_20241022': None, 'type': 'function'}]}, 'tool_calls': None}], 'created_at': '2024-11-22T07:05:25.267991Z', 'docs': [], 'id': 'e2117108-a359-450e-ab88-20ffc3e23e6b', 'jobs': [], 'usage': {'completion_tokens': 114, 'prompt_tokens': 2489, 'total_tokens': 2603}}\n", + "--------------------------------------------------\n" + ] + } + ], "source": [ "# Lists all the task steps that have been executed up to this point in time\n", "transitions = client.executions.transitions.list(execution_id=execution.id).items\n",