From add011d41418ef996407bf5a16b09016b5185b3d Mon Sep 17 00:00:00 2001 From: Dillon Laird Date: Thu, 21 Nov 2024 14:30:28 -0800 Subject: [PATCH] Adds VisionAgentV2 (#302) * added AgentMessage type for conversations * added VisionAgentV2 * added VisionAgentV2 to imports * made timeout 3 minutes * consolidated types and added AgentMessage to utils * fixed owl_v2_image name in prompts * fix flake8 * added abstract classes for coder and planner * add message to agentmessage functionality * added documentation * add make file * fix chat highlighting * fix issues with app * added readme for app * add matplotlib to deps * make local app work with conv agent * update v2 actions, make max_steps argument * don't add file names to non-user chats * fix typo * update action descriptions --- assets/screenshot.png | Bin 0 -> 4017796 bytes examples/chat/README.md | 25 + examples/chat/app.py | 17 +- examples/chat/chat-app/Makefile | 18 + examples/chat/chat-app/package-lock.json | 218 +- examples/chat/chat-app/package.json | 2 +- .../chat-app/src/components/ChatSection.tsx | 37 +- .../chat/chat-app/src/components/ui/card.tsx | 12 +- poetry.lock | 1870 +++++++++-------- vision_agent/agent/__init__.py | 3 +- vision_agent/agent/agent.py | 33 + vision_agent/agent/agent_utils.py | 81 +- vision_agent/agent/types.py | 51 + vision_agent/agent/vision_agent_coder_v2.py | 174 +- .../agent/vision_agent_planner_prompts_v2.py | 2 +- vision_agent/agent/vision_agent_planner_v2.py | 159 +- vision_agent/agent/vision_agent_prompts.py | 8 +- vision_agent/agent/vision_agent_prompts_v2.py | 46 + vision_agent/agent/vision_agent_v2.py | 215 ++ vision_agent/utils/execute.py | 2 +- 20 files changed, 1839 insertions(+), 1134 deletions(-) create mode 100644 assets/screenshot.png create mode 100644 examples/chat/README.md create mode 100644 examples/chat/chat-app/Makefile create mode 100644 vision_agent/agent/types.py create mode 100644 vision_agent/agent/vision_agent_prompts_v2.py create mode 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} } } diff --git a/examples/chat/chat-app/package.json b/examples/chat/chat-app/package.json index ab76050b..7ee1f1dd 100644 --- a/examples/chat/chat-app/package.json +++ b/examples/chat/chat-app/package.json @@ -10,7 +10,7 @@ }, "dependencies": { "@radix-ui/react-collapsible": "^1.1.1", - "@radix-ui/react-scroll-area": "^1.2.0", + "@radix-ui/react-scroll-area": "^1.2.1", "@radix-ui/react-slot": "^1.1.0", "@radix-ui/react-tabs": "^1.1.1", "class-variance-authority": "^0.7.0", diff --git a/examples/chat/chat-app/src/components/ChatSection.tsx b/examples/chat/chat-app/src/components/ChatSection.tsx index bc00150b..96a123e2 100644 --- a/examples/chat/chat-app/src/components/ChatSection.tsx +++ b/examples/chat/chat-app/src/components/ChatSection.tsx @@ -21,7 +21,13 @@ interface ChatSectionProps { } interface Message { - role: "assistant" | "user" | "observation"; + role: + | "assistant" + | "conversation" + | "coder" + | "planner" + | "user" + | "observation"; content: string; media?: string[]; } @@ -64,7 +70,8 @@ const checkContent = (content: string) => { return !(finalizePlanMatch || finalCodeMatch); }; -const formatAssistantContent = (content: string) => { +const formatAssistantContent = (role: string, content: string) => { + const responseMatch = content.match(/(.*?)<\/response>/s); const thinkingMatch = content.match(/(.*?)<\/thinking>/s); const pythonMatch = content.match(/(.*?)<\/execute_python>/s); const finalPlanJsonMatch = content.match(/(.*?)<\/json>/s); @@ -72,10 +79,13 @@ const formatAssistantContent = (content: string) => { const finalPlanJson = JSON.parse( finalPlanJsonMatch ? finalPlanJsonMatch[1] : "{}", ); + if ("plan" in finalPlanJson && "instructions" in finalPlanJson) { return ( <> -

    {finalPlanJson.plan}
    +
    + [{role.toUpperCase()}] {finalPlanJson.plan} +
               
                 {Array.isArray(finalPlanJson.instructions)
    @@ -87,10 +97,19 @@ const formatAssistantContent = (content: string) => {
         );
       }
     
    -  if (thinkingMatch || pythonMatch) {
    +  if (responseMatch || thinkingMatch || pythonMatch) {
         return (
           <>
    -        {thinkingMatch && 
    {thinkingMatch[1]}
    } + {thinkingMatch && ( +
    + [{role.toUpperCase()}] {thinkingMatch[1]} +
    + )} + {responseMatch && ( +
    + [{role.toUpperCase()}] {responseMatch[1]} +
    + )} {pythonMatch && (
                 {pythonMatch[1].trim()}
    @@ -115,8 +134,11 @@ export function MessageBubble({ message }: MessageBubbleProps) {
         >
           {message.role === "observation" ? (
             
    -      ) : message.role === "assistant" ? (
    -        formatAssistantContent(message.content)
    +      ) : message.role === "assistant" ||
    +        message.role === "conversation" ||
    +        message.role === "planner" ||
    +        message.role === "coder" ? (
    +        formatAssistantContent(message.role, message.content)
           ) : (
             message.content
           )}
    @@ -133,7 +155,6 @@ export function ChatSection({
       onUploadedResult,
     }: ChatSectionProps) {
       const [messages, setMessages] = useState([]);
    -  const [ws, setWs] = useState(null);
     
       const handleSubmit = async (e: React.FormEvent) => {
         e.preventDefault();
    diff --git a/examples/chat/chat-app/src/components/ui/card.tsx b/examples/chat/chat-app/src/components/ui/card.tsx
    index afa13ecf..f62edea5 100644
    --- a/examples/chat/chat-app/src/components/ui/card.tsx
    +++ b/examples/chat/chat-app/src/components/ui/card.tsx
    @@ -30,10 +30,10 @@ const CardHeader = React.forwardRef<
     CardHeader.displayName = "CardHeader"
     
     const CardTitle = React.forwardRef<
    -  HTMLParagraphElement,
    -  React.HTMLAttributes
    +  HTMLDivElement,
    +  React.HTMLAttributes
     >(({ className, ...props }, ref) => (
    -  

    + HTMLDivElement, + React.HTMLAttributes >(({ className, ...props }, ref) => ( -

    =2,<3)"] [[package]] name = "anyio" -version = "4.4.0" +version = "4.6.2.post1" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, - {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, + {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"}, + {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"}, ] [package.dependencies] @@ -54,9 +54,9 @@ sniffio = ">=1.1" typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} [package.extras] -doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] -test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] -trio = ["trio (>=0.23)"] +doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"] +trio = ["trio (>=0.26.1)"] [[package]] name = "appnope" @@ -191,33 +191,33 @@ dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"] [[package]] name = "black" -version = "24.8.0" +version = "24.10.0" description = "The uncompromising code formatter." optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - 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a/vision_agent/agent/__init__.py b/vision_agent/agent/__init__.py index 65d20c3f..13341a4d 100644 --- a/vision_agent/agent/__init__.py +++ b/vision_agent/agent/__init__.py @@ -1,4 +1,4 @@ -from .agent import Agent +from .agent import Agent, AgentCoder, AgentPlanner from .vision_agent import VisionAgent from .vision_agent_coder import ( AnthropicVisionAgentCoder, @@ -17,3 +17,4 @@ VisionAgentPlanner, ) from .vision_agent_planner_v2 import VisionAgentPlannerV2 +from .vision_agent_v2 import VisionAgentV2 diff --git a/vision_agent/agent/agent.py b/vision_agent/agent/agent.py index ca2cf181..8617afe7 100644 --- a/vision_agent/agent/agent.py +++ b/vision_agent/agent/agent.py @@ -2,7 +2,9 @@ from pathlib import Path from typing import Any, Dict, List, Optional, Union +from vision_agent.agent.types import AgentMessage, CodeContext, PlanContext from vision_agent.lmm.types import Message +from vision_agent.utils.execute import CodeInterpreter class Agent(ABC): @@ -20,3 +22,34 @@ def log_progress(self, data: Dict[str, Any]) -> None: This is a hook that is intended for reporting the progress of the agent. """ pass + + +class AgentCoder(Agent): + @abstractmethod + def generate_code( + self, + chat: List[AgentMessage], + max_steps: Optional[int] = None, + code_interpreter: Optional[CodeInterpreter] = None, + ) -> CodeContext: + pass + + @abstractmethod + def generate_code_from_plan( + self, + chat: List[AgentMessage], + plan_context: PlanContext, + code_interpreter: Optional[CodeInterpreter] = None, + ) -> CodeContext: + pass + + +class AgentPlanner(Agent): + @abstractmethod + def generate_plan( + self, + chat: List[AgentMessage], + max_steps: Optional[int] = None, + code_interpreter: Optional[CodeInterpreter] = None, + ) -> PlanContext: + pass diff --git a/vision_agent/agent/agent_utils.py b/vision_agent/agent/agent_utils.py index 78687a58..2684a588 100644 --- a/vision_agent/agent/agent_utils.py +++ b/vision_agent/agent/agent_utils.py @@ -4,16 +4,17 @@ import re import sys import tempfile -from typing import Any, Dict, List, Optional, Tuple, cast +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union, cast import libcst as cst -from pydantic import BaseModel from rich.console import Console from rich.style import Style from rich.syntax import Syntax from rich.table import Table import vision_agent.tools as T +from vision_agent.agent.types import AgentMessage, PlanContext from vision_agent.lmm.types import Message from vision_agent.utils.execute import CodeInterpreter, Execution from vision_agent.utils.image_utils import b64_to_pil, convert_to_b64 @@ -24,19 +25,6 @@ _MAX_TABULATE_COL_WIDTH = 80 -class PlanContext(BaseModel): - plan: str - instructions: List[str] - code: str - - -class CodeContext(BaseModel): - code: str - test: str - success: bool - test_result: Execution - - def _extract_sub_json(json_str: str) -> Optional[Dict[str, Any]]: json_pattern = r"\{.*\}" match = re.search(json_pattern, json_str, re.DOTALL) @@ -228,15 +216,15 @@ def print_table(title: str, columns: List[str], rows: List[List[str]]) -> None: def add_media_to_chat( - chat: List[Message], code_interpreter: CodeInterpreter -) -> Tuple[List[Message], List[Message], List[str]]: + chat: List[AgentMessage], code_interpreter: Optional[CodeInterpreter] = None +) -> Tuple[List[AgentMessage], List[AgentMessage], List[Union[str, Path]]]: orig_chat = copy.deepcopy(chat) int_chat = copy.deepcopy(chat) - media_list = [] + media_list: List[Union[str, Path]] = [] for chat_i in int_chat: - if "media" in chat_i: - media_list_i = [] - for media in chat_i["media"]: + if chat_i.media is not None: + media_list_i: List[Union[str, Path]] = [] + for media in chat_i.media: if isinstance(media, str) and media.startswith("data:image/"): media_pil = b64_to_pil(media) with tempfile.NamedTemporaryFile( @@ -244,25 +232,29 @@ def add_media_to_chat( ) as temp_file: media_pil.save(temp_file, format="PNG") media = str(temp_file.name) - media = str(code_interpreter.upload_file(media)) # type: ignore + if code_interpreter is not None: + media = str(code_interpreter.upload_file(media)) media_list_i.append(media) - # don't duplicate appending media name - if not str(chat_i["content"]).endswith(f" Media name {media}"): - chat_i["content"] += f" Media name {media}" # type: ignore - chat_i["media"] = media_list_i + # don't duplicate appending media name and only add them for user messages + if ( + not str(chat_i.content).endswith(f" Media name {media}") + and chat_i.role == "user" + ): + chat_i.content += f" Media name {media}" + chat_i.media = media_list_i if len(media_list_i) > 0 else None media_list.extend(media_list_i) int_chat = cast( - List[Message], + List[AgentMessage], [ ( - { - "role": c["role"], - "content": c["content"], - "media": c["media"], - } - if "media" in c - else {"role": c["role"], "content": c["content"]} + AgentMessage( + role=c.role, + content=c.content, + media=c.media, + ) + if c.media is not None + else AgentMessage(role=c.role, content=c.content, media=None) ) for c in int_chat ], @@ -283,6 +275,27 @@ def capture_media_from_exec(execution: Execution) -> List[str]: return images +def convert_message_to_agentmessage( + input: Union[str, List[Message]], + media: Optional[Union[str, Path]] = None, +) -> List[AgentMessage]: + if isinstance(input, str): + input_msg = [ + AgentMessage( + role="user", + content=input, + media=([media] if media is not None else None), + ) + ] + else: + input_msg = [ + AgentMessage(role=msg["role"], content=msg["content"], media=None) + for msg in input + ] + input_msg[0].media = [media] if media is not None else None + return input_msg + + def strip_function_calls( # noqa: C901 code: str, exclusions: Optional[List[str]] = None ) -> str: diff --git a/vision_agent/agent/types.py b/vision_agent/agent/types.py new file mode 100644 index 00000000..f87afbdf --- /dev/null +++ b/vision_agent/agent/types.py @@ -0,0 +1,51 @@ +from pathlib import Path +from typing import List, Literal, Optional, Union + +from pydantic import BaseModel + +from vision_agent.utils.execute import Execution + + +class AgentMessage(BaseModel): + """AgentMessage encompases messages sent to the entire Agentic system, which includes + both LMMs and sub-agents. + + user: The user's message. + assistant: The assistant's message. + observation: An observation made after conducting an action, either by the user or + assistant. + interaction: An interaction between the user and the assistant. For example if the + assistant wants to ask the user for help on a task, it could send an + interaction message. + conversation: Messages coming from the conversation agent, this is a type of + assistant messages. + planner: Messages coming from the planner agent, this is a type of assistant + messages. + coder: Messages coming from the coder agent, this is a type of assistant messages. + + """ + + role: Union[ + Literal["user"], + Literal["assistant"], # planner, coder and conversation are of type assistant + Literal["observation"], + Literal["interaction"], + Literal["conversation"], + Literal["planner"], + Literal["coder"], + ] + content: str + media: Optional[List[Union[str, Path]]] = None + + +class PlanContext(BaseModel): + plan: str + instructions: List[str] + code: str + + +class CodeContext(BaseModel): + code: str + test: str + success: bool + test_result: Execution diff --git a/vision_agent/agent/vision_agent_coder_v2.py b/vision_agent/agent/vision_agent_coder_v2.py index 9f1150eb..6e463d51 100644 --- a/vision_agent/agent/vision_agent_coder_v2.py +++ b/vision_agent/agent/vision_agent_coder_v2.py @@ -6,19 +6,19 @@ from rich.markup import escape import vision_agent.tools as T -from vision_agent.agent import Agent +from vision_agent.agent import AgentCoder, AgentPlanner from vision_agent.agent.agent_utils import ( - CodeContext, DefaultImports, - PlanContext, add_media_to_chat, capture_media_from_exec, + convert_message_to_agentmessage, extract_tag, format_feedback, format_plan_v2, print_code, strip_function_calls, ) +from vision_agent.agent.types import AgentMessage, CodeContext, PlanContext from vision_agent.agent.vision_agent_coder_prompts_v2 import CODE, FIX_BUG, TEST from vision_agent.agent.vision_agent_planner_v2 import VisionAgentPlannerV2 from vision_agent.lmm import LMM, AnthropicLMM @@ -34,6 +34,12 @@ _CONSOLE = Console() +def format_code_context( + code_context: CodeContext, +) -> str: + return f"{code_context.code}\n{code_context.test}" + + def retrieve_tools( plan: List[str], tool_recommender: Sim, @@ -49,46 +55,54 @@ def retrieve_tools( def write_code( coder: LMM, - chat: List[Message], + chat: List[AgentMessage], tool_docs: str, plan: str, ) -> str: chat = copy.deepcopy(chat) - if chat[-1]["role"] != "user": + if chat[-1].role != "user": raise ValueError("Last chat message must be from the user.") - user_request = chat[-1]["content"] + user_request = chat[-1].content prompt = CODE.format( docstring=tool_docs, question=user_request, plan=plan, ) - chat[-1]["content"] = prompt - response = coder(chat, stream=False) - return extract_tag(response, "code") # type: ignore + response = cast(str, coder([{"role": "user", "content": prompt}], stream=False)) + maybe_code = extract_tag(response, "code") + + # if the response wasn't properly formatted with the code tags just retrun the response + if maybe_code is None: + return response + return maybe_code def write_test( tester: LMM, - chat: List[Message], + chat: List[AgentMessage], tool_util_docs: str, code: str, media_list: Optional[Sequence[Union[str, Path]]] = None, ) -> str: chat = copy.deepcopy(chat) - if chat[-1]["role"] != "user": + if chat[-1].role != "user": raise ValueError("Last chat message must be from the user.") - user_request = chat[-1]["content"] + user_request = chat[-1].content prompt = TEST.format( docstring=tool_util_docs, question=user_request, code=code, media=media_list, ) - chat[-1]["content"] = prompt - response = tester(chat, stream=False) - return extract_tag(response, "code") # type: ignore + response = cast(str, tester([{"role": "user", "content": prompt}], stream=False)) + maybe_code = extract_tag(response, "code") + + # if the response wasn't properly formatted with the code tags just retrun the response + if maybe_code is None: + return response + return maybe_code def debug_code( @@ -170,12 +184,11 @@ def write_and_test_code( coder: LMM, tester: LMM, debugger: LMM, - chat: List[Message], + chat: List[AgentMessage], plan: str, tool_docs: str, code_interpreter: CodeInterpreter, media_list: List[Union[str, Path]], - update_callback: Callable[[Dict[str, Any]], None], verbose: bool, ) -> CodeContext: code = write_code( @@ -226,14 +239,6 @@ def write_and_test_code( f"[bold cyan]Code execution result after attempted fix:[/bold cyan] [yellow]{escape(result.text(include_logs=True))}[/yellow]" ) - update_callback( - { - "role": "assistant", - "content": f"{DefaultImports.to_code_string()}\n{code}\n{DefaultImports.to_code_string()}\n{test}", - "media": capture_media_from_exec(result), - } - ) - return CodeContext( code=f"{DefaultImports.to_code_string()}\n{code}", test=f"{DefaultImports.to_code_string()}\n{test}", @@ -242,10 +247,12 @@ def write_and_test_code( ) -class VisionAgentCoderV2(Agent): +class VisionAgentCoderV2(AgentCoder): + """VisionAgentCoderV2 is an agent that will write vision code for you.""" + def __init__( self, - planner: Optional[Agent] = None, + planner: Optional[AgentPlanner] = None, coder: Optional[LMM] = None, tester: Optional[LMM] = None, debugger: Optional[LMM] = None, @@ -254,6 +261,25 @@ def __init__( code_sandbox_runtime: Optional[str] = None, update_callback: Callable[[Dict[str, Any]], None] = lambda _: None, ) -> None: + """Initialize the VisionAgentCoderV2. + + Parameters: + planner (Optional[AgentPlanner]): The planner agent to use for generating + vision plans. If None, a default VisionAgentPlannerV2 will be used. + coder (Optional[LMM]): The language model to use for the coder agent. If + None, a default AnthropicLMM will be used. + tester (Optional[LMM]): The language model to use for the tester agent. If + None, a default AnthropicLMM will be used. + debugger (Optional[LMM]): The language model to use for the debugger agent. + tool_recommender (Optional[Union[str, Sim]]): The tool recommender to use. + verbose (bool): Whether to print out debug information. + code_sandbox_runtime (Optional[str]): The code sandbox runtime to use, can + be one of: None, "local" or "e2b". If None, it will read from the + environment variable CODE_SANDBOX_RUNTIME. + update_callback (Callable[[Dict[str, Any]], None]): The callback function + that will send back intermediate conversation messages. + """ + self.planner = ( planner if planner is not None @@ -290,20 +316,52 @@ def __call__( self, input: Union[str, List[Message]], media: Optional[Union[str, Path]] = None, - ) -> Union[str, List[Message]]: - if isinstance(input, str): - input = [{"role": "user", "content": input}] - if media is not None: - input[0]["media"] = [media] - return self.generate_code(input).code - - def generate_code(self, chat: List[Message]) -> CodeContext: + ) -> str: + """Generate vision code from a conversation. + + Parameters: + input (Union[str, List[Message]]): The input to the agent. This can be a + string or a list of messages in the format of [{"role": "user", + "content": "describe your task here..."}, ...]. + media (Optional[Union[str, Path]]): The path to the media file to use with + the input. This can be an image or video file. + + Returns: + str: The generated code as a string. + """ + + input_msg = convert_message_to_agentmessage(input, media) + return self.generate_code(input_msg).code + + def generate_code( + self, + chat: List[AgentMessage], + max_steps: Optional[int] = None, + code_interpreter: Optional[CodeInterpreter] = None, + ) -> CodeContext: + """Generate vision code from a conversation. + + Parameters: + chat (List[AgentMessage]): The input to the agent. This should be a list of + AgentMessage objects. + code_interpreter (Optional[CodeInterpreter]): The code interpreter to use. + + Returns: + CodeContext: The generated code as a CodeContext object which includes the + code, test code, whether or not it was exceuted successfully, and the + execution result. + """ + chat = copy.deepcopy(chat) - with CodeInterpreterFactory.new_instance( - self.code_sandbox_runtime + with ( + CodeInterpreterFactory.new_instance(self.code_sandbox_runtime) + if code_interpreter is None + else code_interpreter ) as code_interpreter: int_chat, orig_chat, _ = add_media_to_chat(chat, code_interpreter) - plan_context = self.planner.generate_plan(int_chat, code_interpreter) # type: ignore + plan_context = self.planner.generate_plan( + int_chat, max_steps=max_steps, code_interpreter=code_interpreter + ) code_context = self.generate_code_from_plan( orig_chat, plan_context, @@ -313,13 +371,30 @@ def generate_code(self, chat: List[Message]) -> CodeContext: def generate_code_from_plan( self, - chat: List[Message], + chat: List[AgentMessage], plan_context: PlanContext, code_interpreter: Optional[CodeInterpreter] = None, ) -> CodeContext: + """Generate vision code from a conversation and a previously made plan. This + will skip the planning step and go straight to generating code. + + Parameters: + chat (List[AgentMessage]): The input to the agent. This should be a list of + AgentMessage objects. + plan_context (PlanContext): The plan context that was previously generated. + code_interpreter (Optional[CodeInterpreter]): The code interpreter to use. + + Returns: + CodeContext: The generated code as a CodeContext object which includes the + code, test code, whether or not it was exceuted successfully, and the + execution result. + """ + chat = copy.deepcopy(chat) - with CodeInterpreterFactory.new_instance( - self.code_sandbox_runtime + with ( + CodeInterpreterFactory.new_instance(self.code_sandbox_runtime) + if code_interpreter is None + else code_interpreter ) as code_interpreter: int_chat, _, media_list = add_media_to_chat(chat, code_interpreter) tool_docs = retrieve_tools(plan_context.instructions, self.tool_recommender) @@ -331,10 +406,23 @@ def generate_code_from_plan( plan=format_plan_v2(plan_context), tool_docs=tool_docs, code_interpreter=code_interpreter, - media_list=media_list, # type: ignore - update_callback=self.update_callback, + media_list=media_list, verbose=self.verbose, ) + + self.update_callback( + { + "role": "coder", + "content": format_code_context(code_context), + "media": capture_media_from_exec(code_context.test_result), + } + ) + self.update_callback( + { + "role": "observation", + "content": code_context.test_result.text(), + } + ) return code_context def log_progress(self, data: Dict[str, Any]) -> None: diff --git a/vision_agent/agent/vision_agent_planner_prompts_v2.py b/vision_agent/agent/vision_agent_planner_prompts_v2.py index c56ec5d0..42676167 100644 --- a/vision_agent/agent/vision_agent_planner_prompts_v2.py +++ b/vision_agent/agent/vision_agent_planner_prompts_v2.py @@ -389,7 +389,7 @@ def merge_bounding_box_list(bboxes): print(f"{len(objects_with_tape)} boxes with tape found") -OBJERVATION: +OBSERVATION: 3 boxes were tracked 2 boxes with tape found 6 diff --git a/vision_agent/agent/vision_agent_planner_v2.py b/vision_agent/agent/vision_agent_planner_v2.py index 77fe5633..02a4f8e0 100644 --- a/vision_agent/agent/vision_agent_planner_v2.py +++ b/vision_agent/agent/vision_agent_planner_v2.py @@ -1,5 +1,6 @@ import copy import logging +import time from concurrent.futures import ThreadPoolExecutor, as_completed from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast @@ -10,16 +11,17 @@ import vision_agent.tools as T import vision_agent.tools.planner_tools as pt -from vision_agent.agent import Agent +from vision_agent.agent import AgentPlanner from vision_agent.agent.agent_utils import ( - PlanContext, add_media_to_chat, capture_media_from_exec, + convert_message_to_agentmessage, extract_json, extract_tag, print_code, print_table, ) +from vision_agent.agent.types import AgentMessage, PlanContext from vision_agent.agent.vision_agent_planner_prompts_v2 import ( CRITIQUE_PLAN, EXAMPLE_PLAN1, @@ -70,26 +72,24 @@ def prepend_imports(code: str) -> str: def get_planning( - chat: List[Message], + chat: List[AgentMessage], ) -> str: chat = copy.deepcopy(chat) planning = "" for chat_i in chat: - if chat_i["role"] == "user": - planning += f"USER: {chat_i['content']}\n\n" - elif chat_i["role"] == "observation": - planning += f"OBSERVATION: {chat_i['content']}\n\n" - elif chat_i["role"] == "assistant": - planning += f"ASSISTANT: {chat_i['content']}\n\n" - else: - raise ValueError(f"Unknown role: {chat_i['role']}") + if chat_i.role == "user": + planning += f"USER: {chat_i.content}\n\n" + elif chat_i.role == "observation": + planning += f"OBSERVATION: {chat_i.content}\n\n" + elif chat_i.role == "planner": + planning += f"AGENT: {chat_i.content}\n\n" return planning def run_planning( - chat: List[Message], - media_list: List[str], + chat: List[AgentMessage], + media_list: List[Union[str, Path]], model: LMM, ) -> str: # only keep last 10 messages for planning @@ -102,16 +102,16 @@ def run_planning( ) message: Message = {"role": "user", "content": prompt} - if chat[-1]["role"] == "observation" and "media" in chat[-1]: - message["media"] = chat[-1]["media"] + if chat[-1].role == "observation" and chat[-1].media is not None: + message["media"] = chat[-1].media response = model.chat([message]) return cast(str, response) def run_multi_trial_planning( - chat: List[Message], - media_list: List[str], + chat: List[AgentMessage], + media_list: List[Union[str, Path]], model: LMM, ) -> str: planning = get_planning(chat) @@ -123,8 +123,8 @@ def run_multi_trial_planning( ) message: Message = {"role": "user", "content": prompt} - if chat[-1]["role"] == "observation" and "media" in chat[-1]: - message["media"] = chat[-1]["media"] + if chat[-1].role == "observation" and chat[-1].media is not None: + message["media"] = chat[-1].media responses = [] with ThreadPoolExecutor() as executor: @@ -151,7 +151,9 @@ def run_multi_trial_planning( return cast(str, responses[0]) -def run_critic(chat: List[Message], media_list: List[str], model: LMM) -> Optional[str]: +def run_critic( + chat: List[AgentMessage], media_list: List[Union[str, Path]], model: LMM +) -> Optional[str]: planning = get_planning(chat) prompt = CRITIQUE_PLAN.format( planning=planning, @@ -196,17 +198,19 @@ def response_safeguards(response: str) -> str: def execute_code_action( code: str, code_interpreter: CodeInterpreter, - chat: List[Message], + chat: List[AgentMessage], model: LMM, verbose: bool = False, ) -> Tuple[Execution, str, str]: if verbose: print_code("Code to Execute:", code) + start = time.time() execution = code_interpreter.exec_cell(DefaultPlanningImports.prepend_imports(code)) + end = time.time() obs = execution.text(include_results=False).strip() if verbose: _CONSOLE.print( - f"[bold cyan]Code Execution Output:[/bold cyan] [yellow]{escape(obs)}[/yellow]" + f"[bold cyan]Code Execution Output ({end - start:.2f} sec):[/bold cyan] [yellow]{escape(obs)}[/yellow]" ) count = 1 @@ -246,13 +250,13 @@ def find_and_replace_code(response: str, code: str) -> str: def maybe_run_code( code: Optional[str], response: str, - chat: List[Message], - media_list: List[str], + chat: List[AgentMessage], + media_list: List[Union[str, Path]], model: LMM, code_interpreter: CodeInterpreter, verbose: bool = False, -) -> List[Message]: - return_chat: List[Message] = [] +) -> List[AgentMessage]: + return_chat: List[AgentMessage] = [] if code is not None: code = code_safeguards(code) execution, obs, code = execute_code_action( @@ -262,30 +266,32 @@ def maybe_run_code( # if we had to debug the code to fix an issue, replace the old code # with the fixed code in the response fixed_response = find_and_replace_code(response, code) - return_chat.append({"role": "assistant", "content": fixed_response}) + return_chat.append( + AgentMessage(role="planner", content=fixed_response, media=None) + ) media_data = capture_media_from_exec(execution) - int_chat_elt: Message = {"role": "observation", "content": obs} + int_chat_elt = AgentMessage(role="observation", content=obs, media=None) if media_list: - int_chat_elt["media"] = media_data + int_chat_elt.media = cast(List[Union[str, Path]], media_data) return_chat.append(int_chat_elt) else: - return_chat.append({"role": "assistant", "content": response}) + return_chat.append(AgentMessage(role="planner", content=response, media=None)) return return_chat def create_finalize_plan( - chat: List[Message], + chat: List[AgentMessage], model: LMM, verbose: bool = False, -) -> Tuple[List[Message], PlanContext]: +) -> Tuple[List[AgentMessage], PlanContext]: prompt = FINALIZE_PLAN.format( planning=get_planning(chat), excluded_tools=str([t.__name__ for t in pt.PLANNER_TOOLS]), ) response = model.chat([{"role": "user", "content": prompt}]) plan_str = cast(str, response) - return_chat: List[Message] = [{"role": "assistant", "content": plan_str}] + return_chat = [AgentMessage(role="planner", content=plan_str, media=None)] plan_json = extract_tag(plan_str, "json") plan = ( @@ -305,7 +311,16 @@ def create_finalize_plan( return return_chat, PlanContext(**plan) -class VisionAgentPlannerV2(Agent): +def get_steps(chat: List[AgentMessage], max_steps: int) -> int: + for chat_elt in reversed(chat): + if "" in chat_elt.content: + return int(extract_tag(chat_elt.content, "count")) # type: ignore + return max_steps + + +class VisionAgentPlannerV2(AgentPlanner): + """VisionAgentPlannerV2 is a class that generates a plan to solve a vision task.""" + def __init__( self, planner: Optional[LMM] = None, @@ -317,6 +332,25 @@ def __init__( code_sandbox_runtime: Optional[str] = None, update_callback: Callable[[Dict[str, Any]], None] = lambda _: None, ) -> None: + """Initialize the VisionAgentPlannerV2. + + Parameters: + planner (Optional[LMM]): The language model to use for planning. If None, a + default AnthropicLMM will be used. + critic (Optional[LMM]): The language model to use for critiquing the plan. + If None, a default AnthropicLMM will be used. + max_steps (int): The maximum number of steps to plan. + use_multi_trial_planning (bool): Whether to use multi-trial planning. + critique_steps (int): The number of steps between critiques. If critic steps + is larger than max_steps no critiques will be made. + verbose (bool): Whether to print out debug information. + code_sandbox_runtime (Optional[str]): The code sandbox runtime to use, can + be one of: None, "local" or "e2b". If None, it will read from the + environment variable CODE_SANDBOX_RUNTIME. + update_callback (Callable[[Dict[str, Any]], None]): The callback function + that will send back intermediate conversation messages. + """ + self.planner = ( planner if planner is not None @@ -339,20 +373,42 @@ def __call__( self, input: Union[str, List[Message]], media: Optional[Union[str, Path]] = None, - ) -> Union[str, List[Message]]: - if isinstance(input, str): - if media is not None: - input = [{"role": "user", "content": input, "media": [media]}] - else: - input = [{"role": "user", "content": input}] - plan = self.generate_plan(input) - return str(plan) + ) -> str: + """Generate a plan to solve a vision task. + + Parameters: + input (Union[str, List[Message]]): The input to the agent. This can be a + string or a list of messages in the format of [{"role": "user", + "content": "describe your task here..."}, ...]. + media (Optional[Union[str, Path]]): The path to the media file to use with + the input. This can be an image or video file. + + Returns: + str: The generated plan as a string. + """ + + input_msg = convert_message_to_agentmessage(input, media) + plan = self.generate_plan(input_msg) + return plan.plan def generate_plan( self, - chat: List[Message], + chat: List[AgentMessage], + max_steps: Optional[int] = None, code_interpreter: Optional[CodeInterpreter] = None, ) -> PlanContext: + """Generate a plan to solve a vision task. + + Parameters: + chat (List[AgentMessage]): The conversation messages to generate a plan for. + max_steps (Optional[int]): The maximum number of steps to plan. + code_interpreter (Optional[CodeInterpreter]): The code interpreter to use. + + Returns: + PlanContext: The generated plan including the instructions and code snippets + needed to solve the task. + """ + if not chat: raise ValueError("Chat cannot be empty") @@ -360,13 +416,16 @@ def generate_plan( code_interpreter = code_interpreter or CodeInterpreterFactory.new_instance( self.code_sandbox_runtime ) + max_steps = max_steps or self.max_steps with code_interpreter: critque_steps = 1 - step = self.max_steps finished = False int_chat, _, media_list = add_media_to_chat(chat, code_interpreter) - int_chat[-1]["content"] += f"\n{step}\n" # type: ignore + + step = get_steps(int_chat, max_steps) + if "" not in int_chat[-1].content and step == max_steps: + int_chat[-1].content += f"\n{step}\n" while step > 0 and not finished: if self.use_multi_trial_planning: response = run_multi_trial_planning( @@ -402,29 +461,29 @@ def generate_plan( if critque_steps % self.critique_steps == 0: critique = run_critic(int_chat, media_list, self.critic) - if critique is not None and int_chat[-1]["role"] == "observation": + if critique is not None and int_chat[-1].role == "observation": _CONSOLE.print( f"[bold cyan]Critique:[/bold cyan] [red]{critique}[/red]" ) critique_str = f"\n[critique]\n{critique}\n[end of critique]" - updated_chat[-1]["content"] += critique_str # type: ignore + updated_chat[-1].content += critique_str # if plan was critiqued, ensure we don't finish so we can # respond to the critique finished = False critque_steps += 1 step -= 1 - updated_chat[-1]["content"] += f"\n{step}\n" # type: ignore + updated_chat[-1].content += f"\n{step}\n" int_chat.extend(updated_chat) for chat_elt in updated_chat: - self.update_callback(chat_elt) + self.update_callback(chat_elt.model_dump()) updated_chat, plan_context = create_finalize_plan( int_chat, self.planner, self.verbose ) int_chat.extend(updated_chat) for chat_elt in updated_chat: - self.update_callback(chat_elt) + self.update_callback(chat_elt.model_dump()) return plan_context diff --git a/vision_agent/agent/vision_agent_prompts.py b/vision_agent/agent/vision_agent_prompts.py index 62338df0..bc0bb13f 100644 --- a/vision_agent/agent/vision_agent_prompts.py +++ b/vision_agent/agent/vision_agent_prompts.py @@ -55,10 +55,10 @@ OBSERVATION: [Artifact dog_detector.py (5 lines total)] -0|from vision_agent.tools import load_image, owl_v2 +0|from vision_agent.tools import load_image, owl_v2_image 1|def detect_dogs(image_path: str): 2| image = load_image(image_path) -3| dogs = owl_v2("dog", image) +3| dogs = owl_v2_image("dog", image) 4| return dogs [End of artifact] @@ -96,10 +96,10 @@ OBSERVATION: [Artifact dog_detector.py (5 lines total)] -0|from vision_agent.tools import load_image, owl_v2 +0|from vision_agent.tools import load_image, owl_v2_image 1|def detect_dogs(image_path: str): 2| image = load_image(image_path) -3| dogs = owl_v2("dog", image, threshold=0.24) +3| dogs = owl_v2_image("dog", image, threshold=0.24) 4| return dogs [End of artifact] diff --git a/vision_agent/agent/vision_agent_prompts_v2.py b/vision_agent/agent/vision_agent_prompts_v2.py new file mode 100644 index 00000000..faa16d03 --- /dev/null +++ b/vision_agent/agent/vision_agent_prompts_v2.py @@ -0,0 +1,46 @@ +CONVERSATION = """ +**Role**: You are a help agent that assists users with their vision-related questions. + +**Actions**: +`generate_or_edit_vision_code` - This action will generate code for the user to solve a vision task. It will also edit vision code for the user, this is useful if the user wants to modify vision-related aspects of the code such as changing the vision model or the image pre-processing steps. + +`edit_code` - If the user already has code for a vision task, from a previous call to `generate_or_edit_vision_code` and wants to quickly modify the code, you can use this action to edit the code. This is good for non-vision related changes such as formatting the response as a json or removing print statements. + +**Exampels**: +Here is an example of how you can interact with a user and Actions: +--- START EXAMPLES --- +USER: Can you help me write code to detect dogs in this image? Media name images/dogs.jpg + +AGENT: Yes, I can help you with that. I will write the code to detect dogs in the image.generate_or_edit_vision_code + +OBSERVATION: + +from vision_agent.tools import load_image, owl_v2_image +def detect_dogs(image_path: str): + image = load_image(image_path) + dogs = owl_v2_image(image) + return dogs + + +def test_detect_dogs(): + dogs = detect_dogs("images/dogs.jpg") + assert len(dogs) > 0 + + +AGENT: Here is the code to detect dogs in the image. + +--- END EXAMPLES --- + +**Conversation**: +Here is the current conversation so far: +--- START CONVERSATION --- +{conversation} +--- END CONVERSATION --- + +**Instructions**: +1. Only respond with a single tag and a single tag. +2. Respond in the following format, the tag is optional and can be excluded if you do not want to take any action: + +Your response to the user's message +The action you want to take from **Actions** +""" diff --git a/vision_agent/agent/vision_agent_v2.py b/vision_agent/agent/vision_agent_v2.py new file mode 100644 index 00000000..523a77b4 --- /dev/null +++ b/vision_agent/agent/vision_agent_v2.py @@ -0,0 +1,215 @@ +import copy +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Union, cast + +from vision_agent.agent import Agent, AgentCoder, VisionAgentCoderV2 +from vision_agent.agent.agent_utils import ( + add_media_to_chat, + convert_message_to_agentmessage, + extract_tag, +) +from vision_agent.agent.types import AgentMessage, PlanContext +from vision_agent.agent.vision_agent_coder_v2 import format_code_context +from vision_agent.agent.vision_agent_prompts_v2 import CONVERSATION +from vision_agent.lmm import LMM, AnthropicLMM +from vision_agent.lmm.types import Message +from vision_agent.utils.execute import CodeInterpreter, CodeInterpreterFactory + + +def format_conversation(chat: List[AgentMessage]) -> str: + chat = copy.deepcopy(chat) + prompt = "" + for chat_i in chat: + if chat_i.role == "user": + prompt += f"USER: {chat_i.content}\n\n" + elif chat_i.role == "observation" or chat_i.role == "coder": + prompt += f"OBSERVATION: {chat_i.content}\n\n" + elif chat_i.role == "conversation": + prompt += f"AGENT: {chat_i.content}\n\n" + return prompt + + +def run_conversation(agent: LMM, chat: List[AgentMessage]) -> str: + # only keep last 10 messages + conv = format_conversation(chat[-10:]) + prompt = CONVERSATION.format( + conversation=conv, + ) + response = agent([{"role": "user", "content": prompt}], stream=False) + return cast(str, response) + + +def extract_conversation_for_generate_code( + chat: List[AgentMessage], +) -> List[AgentMessage]: + chat = copy.deepcopy(chat) + extracted_chat = [] + for chat_i in chat: + if chat_i.role == "user": + extracted_chat.append(chat_i) + elif chat_i.role == "coder": + if "" in chat_i.content and "" in chat_i.content: + extracted_chat.append(chat_i) + + return extracted_chat + + +def maybe_run_action( + coder: AgentCoder, + action: Optional[str], + chat: List[AgentMessage], + code_interpreter: Optional[CodeInterpreter] = None, +) -> Optional[List[AgentMessage]]: + if action == "generate_or_edit_vision_code": + extracted_chat = extract_conversation_for_generate_code(chat) + # there's an issue here because coder.generate_code will send it's code_context + # to the outside user via it's update_callback, but we don't necessarily have + # access to that update_callback here, so we re-create the message using + # format_code_context. + code_context = coder.generate_code( + extracted_chat, code_interpreter=code_interpreter + ) + return [ + AgentMessage(role="coder", content=format_code_context(code_context)), + AgentMessage(role="observation", content=code_context.test_result.text()), + ] + elif action == "edit_code": + extracted_chat = extract_conversation_for_generate_code(chat) + plan_context = PlanContext( + plan="Edit the latest code observed in the fewest steps possible according to the user's feedback.", + instructions=[], + code="", + ) + code_context = coder.generate_code_from_plan( + extracted_chat, plan_context, code_interpreter=code_interpreter + ) + return [ + AgentMessage(role="coder", content=format_code_context(code_context)), + AgentMessage(role="observation", content=code_context.test_result.text()), + ] + elif action == "view_image": + pass + + return None + + +class VisionAgentV2(Agent): + """VisionAgentV2 is a conversational agent that allows you to more easily use a + coder agent such as VisionAgentCoderV2 to write vision code for you. + """ + + def __init__( + self, + agent: Optional[LMM] = None, + coder: Optional[AgentCoder] = None, + verbose: bool = False, + code_sandbox_runtime: Optional[str] = None, + update_callback: Callable[[Dict[str, Any]], None] = lambda x: None, + ) -> None: + """Initialize the VisionAgentV2. + + Parameters: + agent (Optional[LMM]): The language model to use for the agent. If None, a + default AnthropicLMM will be used. + coder (Optional[AgentCoder]): The coder agent to use for generating vision + code. If None, a default VisionAgentCoderV2 will be used. + verbose (bool): Whether to print out debug information. + code_sandbox_runtime (Optional[str]): The code sandbox runtime to use, can + be one of: None, "local" or "e2b". If None, it will read from the + environment variable CODE_SANDBOX_RUNTIME. + update_callback (Callable[[Dict[str, Any]], None]): The callback function + that will send back intermediate conversation messages. + """ + + self.agent = ( + agent + if agent is not None + else AnthropicLMM( + model_name="claude-3-5-sonnet-20241022", + temperature=0.0, + ) + ) + self.coder = ( + coder + if coder is not None + else VisionAgentCoderV2(verbose=verbose, update_callback=update_callback) + ) + + self.verbose = verbose + self.code_sandbox_runtime = code_sandbox_runtime + self.update_callback = update_callback + + # force coder to use the same update_callback + if hasattr(self.coder, "update_callback"): + self.coder.update_callback = update_callback + + def __call__( + self, + input: Union[str, List[Message]], + media: Optional[Union[str, Path]] = None, + ) -> str: + """Conversational interface to the agent. This is the main method to use to + interact with the agent. It takes in a string or list of messages and returns + the agent's response as a string. + + Parameters: + input (Union[str, List[Message]]): The input to the agent. This can be a + string or a list of messages in the format of [{"role": "user", + "content": "describe your task here..."}, ...]. + media (Optional[Union[str, Path]]): The path to the media file to use with + the input. This can be an image or video file. + + Returns: + str: The agent's response as a string. + """ + + input_msg = convert_message_to_agentmessage(input, media) + return self.chat(input_msg)[-1].content + + def chat( + self, + chat: List[AgentMessage], + ) -> List[AgentMessage]: + """Conversational interface to the agent. This is the main method to use to + interact with the agent. It takes in a list of messages and returns the agent's + response as a list of messages. + + Parameters: + chat (List[AgentMessage]): The input to the agent. This should be a list of + AgentMessage objects. + + Returns: + List[AgentMessage]: The agent's response as a list of AgentMessage objects. + """ + + return_chat = [] + with CodeInterpreterFactory.new_instance( + self.code_sandbox_runtime + ) as code_interpreter: + int_chat, _, _ = add_media_to_chat(chat, code_interpreter) + response_context = run_conversation(self.agent, int_chat) + return_chat.append( + AgentMessage(role="conversation", content=response_context) + ) + self.update_callback(return_chat[-1].model_dump()) + + action = extract_tag(response_context, "action") + + updated_chat = maybe_run_action( + self.coder, action, int_chat, code_interpreter=code_interpreter + ) + if updated_chat is not None: + # do not append updated_chat to return_chat becuase the observation + # from running the action will have already been added via the callbacks + obs_response_context = run_conversation( + self.agent, return_chat + updated_chat + ) + return_chat.append( + AgentMessage(role="conversation", content=obs_response_context) + ) + self.update_callback(return_chat[-1].model_dump()) + + return return_chat + + def log_progress(self, data: Dict[str, Any]) -> None: + pass diff --git a/vision_agent/utils/execute.py b/vision_agent/utils/execute.py index 043e0f2d..17dff0e0 100644 --- a/vision_agent/utils/execute.py +++ b/vision_agent/utils/execute.py @@ -38,7 +38,7 @@ load_dotenv() _LOGGER = logging.getLogger(__name__) -_SESSION_TIMEOUT = 600 # 10 minutes +_SESSION_TIMEOUT = 180 # 3 minutes WORKSPACE = Path(os.getenv("WORKSPACE", ""))

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