Source code for langchain_experimental.autonomous_agents.hugginggpt.task_planner

import json
import re
from abc import abstractmethod
from typing import Any, Dict, List, Optional, Union

from langchain.base_language import BaseLanguageModel
from langchain.chains import LLMChain
from langchain.tools.base import BaseTool
from langchain_core.callbacks.manager import Callbacks
from langchain_core.prompts.chat import (
    AIMessagePromptTemplate,
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)

from langchain_experimental.pydantic_v1 import BaseModel

DEMONSTRATIONS = [
    {
        "role": "user",
        "content": "please show me a video and an image of (based on the text) 'a boy is running' and dub it",  # noqa: E501
    },
    {
        "role": "assistant",
        "content": '[{{"task": "video_generator", "id": 0, "dep": [-1], "args": {{"prompt": "a boy is running" }}}}, {{"task": "text_reader", "id": 1, "dep": [-1], "args": {{"text": "a boy is running" }}}}, {{"task": "image_generator", "id": 2, "dep": [-1], "args": {{"prompt": "a boy is running" }}}}]',  # noqa: E501
    },
    {
        "role": "user",
        "content": "Give you some pictures e1.jpg, e2.png, e3.jpg, help me count the number of sheep?",  # noqa: E501
    },
    {
        "role": "assistant",
        "content": '[ {{"task": "image_qa", "id": 0, "dep": [-1], "args": {{"image": "e1.jpg", "question": "How many sheep in the picture"}}}}, {{"task": "image_qa", "id": 1, "dep": [-1], "args": {{"image": "e2.jpg", "question": "How many sheep in the picture"}}}}, {{"task": "image_qa", "id": 2, "dep": [-1], "args": {{"image": "e3.jpg", "question": "How many sheep in the picture"}}}}]',  # noqa: E501
    },
]


[docs]class TaskPlaningChain(LLMChain): """Chain to execute tasks."""
[docs] @classmethod def from_llm( cls, llm: BaseLanguageModel, demos: List[Dict] = DEMONSTRATIONS, verbose: bool = True, ) -> LLMChain: """Get the response parser.""" system_template = """#1 Task Planning Stage: The AI assistant can parse user input to several tasks: [{{"task": task, "id": task_id, "dep": dependency_task_id, "args": {{"input name": text may contain <resource-dep_id>}}}}]. The special tag "dep_id" refer to the one generated text/image/audio in the dependency task (Please consider whether the dependency task generates resources of this type.) and "dep_id" must be in "dep" list. The "dep" field denotes the ids of the previous prerequisite tasks which generate a new resource that the current task relies on. The task MUST be selected from the following tools (along with tool description, input name and output type): {tools}. There may be multiple tasks of the same type. Think step by step about all the tasks needed to resolve the user's request. Parse out as few tasks as possible while ensuring that the user request can be resolved. Pay attention to the dependencies and order among tasks. If the user input can't be parsed, you need to reply empty JSON [].""" # noqa: E501 human_template = """Now I input: {input}.""" system_message_prompt = SystemMessagePromptTemplate.from_template( system_template ) human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) demo_messages: List[ Union[HumanMessagePromptTemplate, AIMessagePromptTemplate] ] = [] for demo in demos: if demo["role"] == "user": demo_messages.append( HumanMessagePromptTemplate.from_template(demo["content"]) ) else: demo_messages.append( AIMessagePromptTemplate.from_template(demo["content"]) ) # demo_messages.append(message) prompt = ChatPromptTemplate.from_messages( [system_message_prompt, *demo_messages, human_message_prompt] ) return cls(prompt=prompt, llm=llm, verbose=verbose)
[docs]class Step: """A step in the plan."""
[docs] def __init__( self, task: str, id: int, dep: List[int], args: Dict[str, str], tool: BaseTool ): self.task = task self.id = id self.dep = dep self.args = args self.tool = tool
[docs]class Plan: """A plan to execute."""
[docs] def __init__(self, steps: List[Step]): self.steps = steps
def __str__(self) -> str: return str([str(step) for step in self.steps]) def __repr__(self) -> str: return str(self)
[docs]class BasePlanner(BaseModel): """Base class for a planner."""
[docs] @abstractmethod def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan: """Given input, decide what to do."""
[docs] @abstractmethod async def aplan( self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any ) -> Plan: """Asynchronous Given input, decide what to do."""
[docs]class PlanningOutputParser(BaseModel): """Parses the output of the planning stage."""
[docs] def parse(self, text: str, hf_tools: List[BaseTool]) -> Plan: """Parse the output of the planning stage. Args: text: The output of the planning stage. hf_tools: The tools available. Returns: The plan. """ steps = [] for v in json.loads(re.findall(r"\[.*\]", text)[0]): choose_tool = None for tool in hf_tools: if tool.name == v["task"]: choose_tool = tool break if choose_tool: steps.append(Step(v["task"], v["id"], v["dep"], v["args"], tool)) return Plan(steps=steps)
[docs]class TaskPlanner(BasePlanner): """Planner for tasks.""" llm_chain: LLMChain output_parser: PlanningOutputParser stop: Optional[List] = None
[docs] def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan: """Given input, decided what to do.""" inputs["tools"] = [ f"{tool.name}: {tool.description}" for tool in inputs["hf_tools"] ] llm_response = self.llm_chain.run(**inputs, stop=self.stop, callbacks=callbacks) return self.output_parser.parse(llm_response, inputs["hf_tools"])
[docs] async def aplan( self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any ) -> Plan: """Asynchronous Given input, decided what to do.""" inputs["hf_tools"] = [ f"{tool.name}: {tool.description}" for tool in inputs["hf_tools"] ] llm_response = await self.llm_chain.arun( **inputs, stop=self.stop, callbacks=callbacks ) return self.output_parser.parse(llm_response, inputs["hf_tools"])
[docs]def load_chat_planner(llm: BaseLanguageModel) -> TaskPlanner: """Load the chat planner.""" llm_chain = TaskPlaningChain.from_llm(llm) return TaskPlanner(llm_chain=llm_chain, output_parser=PlanningOutputParser())