id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
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4554bcce218a-0 | Source code for langchain.experimental.generative_agents.generative_agent
import re
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.experimental.gen... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
4554bcce218a-1 | arbitrary_types_allowed = True
# LLM-related methods
@staticmethod
def _parse_list(text: str) -> List[str]:
"""Parse a newline-separated string into a list of strings."""
lines = re.split(r"\n", text.strip())
return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines]
de... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
4554bcce218a-2 | entity_action = self._get_entity_action(observation, entity_name)
q1 = f"What is the relationship between {self.name} and {entity_name}"
q2 = f"{entity_name} is {entity_action}"
return self.chain(prompt=prompt).run(q1=q1, queries=[q1, q2]).strip()
def _generate_reaction(
self, observ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
4554bcce218a-3 | )
consumed_tokens = self.llm.get_num_tokens(
prompt.format(most_recent_memories="", **kwargs)
)
kwargs[self.memory.most_recent_memories_token_key] = consumed_tokens
return self.chain(prompt=prompt).run(**kwargs).strip()
def _clean_response(self, text: str) -> str:
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
4554bcce218a-4 | if "SAY:" in result:
said_value = self._clean_response(result.split("SAY:")[-1])
return True, f"{self.name} said {said_value}"
else:
return False, result
[docs] def generate_dialogue_response(
self, observation: str, now: Optional[datetime] = None
) -> Tuple[bo... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
4554bcce218a-5 | },
)
return True, f"{self.name} said {response_text}"
else:
return False, result
######################################################
# Agent stateful' summary methods. #
# Each dialog or response prompt includes a header #
# summarizing ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
4554bcce218a-6 | + f"\nInnate traits: {self.traits}"
+ f"\n{self.summary}"
)
[docs] def get_full_header(
self, force_refresh: bool = False, now: Optional[datetime] = None
) -> str:
"""Return a full header of the agent's status, summary, and current time."""
now = datetime.now() if now ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
2e6db2ec1bdc-0 | Source code for langchain.experimental.generative_agents.memory
import logging
import re
from datetime import datetime
from typing import Any, Dict, List, Optional
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.prompts import PromptTemplate
from langchain.retrievers ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2e6db2ec1bdc-1 | # output keys
relevant_memories_key: str = "relevant_memories"
relevant_memories_simple_key: str = "relevant_memories_simple"
most_recent_memories_key: str = "most_recent_memories"
now_key: str = "now"
reflecting: bool = False
def chain(self, prompt: PromptTemplate) -> LLMChain:
return L... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2e6db2ec1bdc-2 | ) -> List[str]:
"""Generate 'insights' on a topic of reflection, based on pertinent memories."""
prompt = PromptTemplate.from_template(
"Statements about {topic}\n"
+ "{related_statements}\n\n"
+ "What 5 high-level insights can you infer from the above statements?"
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2e6db2ec1bdc-3 | "On the scale of 1 to 10, where 1 is purely mundane"
+ " (e.g., brushing teeth, making bed) and 10 is"
+ " extremely poignant (e.g., a break up, college"
+ " acceptance), rate the likely poignancy of the"
+ " following piece of memory. Respond with a single integer."
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2e6db2ec1bdc-4 | and not self.reflecting
):
self.reflecting = True
self.pause_to_reflect(now=now)
# Hack to clear the importance from reflection
self.aggregate_importance = 0.0
self.reflecting = False
return result
[docs] def fetch_memories(
self, ob... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2e6db2ec1bdc-5 | break
consumed_tokens += self.llm.get_num_tokens(doc.page_content)
if consumed_tokens < self.max_tokens_limit:
result.append(doc)
return self.format_memories_simple(result)
@property
def memory_variables(self) -> List[str]:
"""Input keys this memory class ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2e6db2ec1bdc-6 | [docs] def clear(self) -> None:
"""Clear memory contents."""
# TODO
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
838b4ca5c756-0 | Source code for langchain.experimental.autonomous_agents.baby_agi.baby_agi
"""BabyAGI agent."""
from collections import deque
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerFo... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
838b4ca5c756-1 | print(str(t["task_id"]) + ": " + t["task_name"])
def print_next_task(self, task: Dict) -> None:
print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
print(str(task["task_id"]) + ": " + task["task_name"])
def print_task_result(self, result: str) -> None:
print("\033[93m... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
838b4ca5c756-2 | next_task_id = int(this_task_id) + 1
response = self.task_prioritization_chain.run(
task_names=", ".join(task_names),
next_task_id=str(next_task_id),
objective=objective,
)
new_tasks = response.split("\n")
prioritized_task_list = []
for task_st... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
838b4ca5c756-3 | """Run the agent."""
objective = inputs["objective"]
first_task = inputs.get("first_task", "Make a todo list")
self.add_task({"task_id": 1, "task_name": first_task})
num_iters = 0
while True:
if self.task_list:
self.print_task_list()
# ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
838b4ca5c756-4 | break
return {}
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
verbose: bool = False,
task_execution_chain: Optional[Chain] = None,
**kwargs: Dict[str, Any],
) -> "BabyAGI":
"""Initialize the BabyAGI Con... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
efab84a4f806-0 | Source code for langchain.experimental.autonomous_agents.autogpt.agent
from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.au... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
efab84a4f806-1 | ai_role: str,
memory: VectorStoreRetriever,
tools: List[BaseTool],
llm: BaseChatModel,
human_in_the_loop: bool = False,
output_parser: Optional[BaseAutoGPTOutputParser] = None,
) -> AutoGPT:
prompt = AutoGPTPrompt(
ai_name=ai_name,
ai_role=ai_r... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
efab84a4f806-2 | # Get command name and arguments
action = self.output_parser.parse(assistant_reply)
tools = {t.name: t for t in self.tools}
if action.name == FINISH_NAME:
return action.args["response"]
if action.name in tools:
tool = tools[action.name]
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
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