mirror of
https://github.com/PostHog/posthog.git
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115 lines
3.5 KiB
Python
115 lines
3.5 KiB
Python
import operator
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from abc import ABC, abstractmethod
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from collections.abc import Sequence
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from enum import StrEnum
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from typing import Annotated, Optional, TypedDict, Union
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from jsonref import replace_refs
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from langchain_core.agents import AgentAction
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from langchain_core.messages import (
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HumanMessage as LangchainHumanMessage,
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merge_message_runs,
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)
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from langchain_core.runnables import RunnableConfig
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from langgraph.graph import END, START
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from pydantic import BaseModel, Field
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from posthog.models.team.team import Team
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from posthog.schema import (
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AssistantMessage,
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FailureMessage,
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HumanMessage,
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RootAssistantMessage,
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RouterMessage,
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VisualizationMessage,
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)
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AssistantMessageUnion = Union[AssistantMessage, HumanMessage, VisualizationMessage, FailureMessage, RouterMessage]
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class Conversation(BaseModel):
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messages: list[RootAssistantMessage] = Field(..., min_length=1, max_length=20)
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session_id: str
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class AssistantState(TypedDict, total=False):
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messages: Annotated[Sequence[AssistantMessageUnion], operator.add]
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intermediate_steps: Optional[list[tuple[AgentAction, Optional[str]]]]
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plan: Optional[str]
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class AssistantNodeName(StrEnum):
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START = START
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END = END
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ROUTER = "router"
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TRENDS_PLANNER = "trends_planner"
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TRENDS_PLANNER_TOOLS = "trends_planner_tools"
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TRENDS_GENERATOR = "trends_generator"
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TRENDS_GENERATOR_TOOLS = "trends_generator_tools"
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FUNNEL_PLANNER = "funnel_planner"
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FUNNEL_PLANNER_TOOLS = "funnel_planner_tools"
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FUNNEL_GENERATOR = "funnel_generator"
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FUNNEL_GENERATOR_TOOLS = "funnel_generator_tools"
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SUMMARIZER = "summarizer"
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class AssistantNode(ABC):
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_team: Team
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def __init__(self, team: Team):
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self._team = team
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@abstractmethod
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def run(cls, state: AssistantState, config: RunnableConfig) -> AssistantState:
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raise NotImplementedError
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def remove_line_breaks(line: str) -> str:
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return line.replace("\n", " ")
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def merge_human_messages(messages: list[LangchainHumanMessage]) -> list[LangchainHumanMessage]:
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"""
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Filters out duplicated human messages and merges them into one message.
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"""
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contents = set()
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filtered_messages = []
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for message in messages:
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if message.content in contents:
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continue
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contents.add(message.content)
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filtered_messages.append(message)
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return merge_message_runs(filtered_messages)
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def filter_visualization_conversation(
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messages: Sequence[AssistantMessageUnion],
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) -> tuple[list[LangchainHumanMessage], list[VisualizationMessage]]:
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"""
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Splits, filters and merges the message history to be consumable by agents. Returns human and visualization messages.
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"""
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stack: list[LangchainHumanMessage] = []
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human_messages: list[LangchainHumanMessage] = []
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visualization_messages: list[VisualizationMessage] = []
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for message in messages:
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if isinstance(message, HumanMessage):
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stack.append(LangchainHumanMessage(content=message.content))
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elif isinstance(message, VisualizationMessage) and message.answer:
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if stack:
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human_messages += merge_human_messages(stack)
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stack = []
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visualization_messages.append(message)
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if stack:
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human_messages += merge_human_messages(stack)
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return human_messages, visualization_messages
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def dereference_schema(schema: dict) -> dict:
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new_schema: dict = replace_refs(schema, proxies=False, lazy_load=False)
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if "$defs" in new_schema:
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new_schema.pop("$defs")
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return new_schema
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