mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
c010ec8b71
- `.get_relevant_documents(query)` -> `.invoke(query)`
- `.get_relevant_documents(query=query)` -> `.invoke(query)`
- `.get_relevant_documents(query, callbacks=callbacks)` ->
`.invoke(query, config={"callbacks": callbacks})`
- `.get_relevant_documents(query, **kwargs)` -> `.invoke(query,
**kwargs)`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
107 lines
4.6 KiB
Python
107 lines
4.6 KiB
Python
import time
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from typing import Any, Callable, List, cast
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from langchain.tools.base import BaseTool
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from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
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from langchain_core.prompts.chat import (
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BaseChatPromptTemplate,
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)
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from langchain_core.vectorstores import VectorStoreRetriever
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from langchain_experimental.autonomous_agents.autogpt.prompt_generator import get_prompt
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from langchain_experimental.pydantic_v1 import BaseModel
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# This class has a metaclass conflict: both `BaseChatPromptTemplate` and `BaseModel`
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# define a metaclass to use, and the two metaclasses attempt to define
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# the same functions but in mutually-incompatible ways.
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# It isn't clear how to resolve this, and this code predates mypy
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# beginning to perform that check.
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#
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# Mypy errors:
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# ```
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# Definition of "__private_attributes__" in base class "BaseModel" is
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# incompatible with definition in base class "BaseModel" [misc]
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# Definition of "__repr_name__" in base class "Representation" is
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# incompatible with definition in base class "BaseModel" [misc]
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# Definition of "__pretty__" in base class "Representation" is
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# incompatible with definition in base class "BaseModel" [misc]
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# Definition of "__repr_str__" in base class "Representation" is
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# incompatible with definition in base class "BaseModel" [misc]
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# Definition of "__rich_repr__" in base class "Representation" is
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# incompatible with definition in base class "BaseModel" [misc]
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# Metaclass conflict: the metaclass of a derived class must be
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# a (non-strict) subclass of the metaclasses of all its bases [misc]
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# ```
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#
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# TODO: look into refactoring this class in a way that avoids the mypy type errors
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class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel): # type: ignore[misc]
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"""Prompt for AutoGPT."""
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ai_name: str
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ai_role: str
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tools: List[BaseTool]
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token_counter: Callable[[str], int]
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send_token_limit: int = 4196
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def construct_full_prompt(self, goals: List[str]) -> str:
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prompt_start = (
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"Your decisions must always be made independently "
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"without seeking user assistance.\n"
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"Play to your strengths as an LLM and pursue simple "
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"strategies with no legal complications.\n"
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"If you have completed all your tasks, make sure to "
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'use the "finish" command.'
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)
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# Construct full prompt
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full_prompt = (
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f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
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)
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for i, goal in enumerate(goals):
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full_prompt += f"{i+1}. {goal}\n"
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full_prompt += f"\n\n{get_prompt(self.tools)}"
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return full_prompt
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def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
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base_prompt = SystemMessage(content=self.construct_full_prompt(kwargs["goals"]))
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time_prompt = SystemMessage(
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content=f"The current time and date is {time.strftime('%c')}"
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)
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used_tokens = self.token_counter(
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cast(str, base_prompt.content)
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) + self.token_counter(cast(str, time_prompt.content))
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memory: VectorStoreRetriever = kwargs["memory"]
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previous_messages = kwargs["messages"]
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relevant_docs = memory.invoke(str(previous_messages[-10:]))
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relevant_memory = [d.page_content for d in relevant_docs]
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relevant_memory_tokens = sum(
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[self.token_counter(doc) for doc in relevant_memory]
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)
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while used_tokens + relevant_memory_tokens > 2500:
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relevant_memory = relevant_memory[:-1]
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relevant_memory_tokens = sum(
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[self.token_counter(doc) for doc in relevant_memory]
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)
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content_format = (
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f"This reminds you of these events "
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f"from your past:\n{relevant_memory}\n\n"
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)
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memory_message = SystemMessage(content=content_format)
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used_tokens += self.token_counter(cast(str, memory_message.content))
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historical_messages: List[BaseMessage] = []
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for message in previous_messages[-10:][::-1]:
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message_tokens = self.token_counter(message.content)
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if used_tokens + message_tokens > self.send_token_limit - 1000:
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break
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historical_messages = [message] + historical_messages
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used_tokens += message_tokens
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input_message = HumanMessage(content=kwargs["user_input"])
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messages: List[BaseMessage] = [base_prompt, time_prompt, memory_message]
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messages += historical_messages
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messages.append(input_message)
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return messages
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def pretty_repr(self, html: bool = False) -> str:
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raise NotImplementedError
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